Human Mobility & HIV Workshop - May 24, 2021
Welcome - Dianne Rausch, Ph.D., NIMH
Plenary Talk: Human Mobility and the HIV Pandemic: Challenges, Questions, Solutions - Carol Camlin, Ph.D.
Session I: Human Mobility Across the Globe
Moderator: Kate Clouse, PhD, MPH
Characterizing the magnitude and scope of mobility among PLWH in Tennessee and its impact on HIV Care Outcomes - Aimalohi Ahonkhai MD, MPH
HIV, structural inequalities, and social drivers of migration in the Hispanic Caribbean - Mark Padilla, MD, MPH
Working with mobile women engaged in sex work in Uganda: Implications for access to care, retention and HIV treatment outcomes -
Fred Ssewamala, PhD, MSW
Joshua Kiyingi, MSTAT
Proscovia Nabunva, PhD, MSW
Conflict, Displacement, and HIV in Ukraine - Tetyana Vasylyeva, DPhil
Session 2: New Methodologies and Approaches to Understanding Mobility
Moderator: Larry Chang, MD, MPH
Modelling countrywide mobility networks & HIV Epidemics - Sally Blower, PhD
Elucidating the link between migration and HIV through epidemiology and phylogenetics - Kate Grabowski, PhD, ScM
Development of high-dimensional multivariate spatiotemporal models - Frank Tanser, PhD, MSc, FRGS, Adrian Dobra, PhD
Session 3: Personal Perspective on Mobility
Moderator: Mark Padilla, MD, MPH
Advocacy and Points of Consideration
Bernal Cruz, MSW
Pari Mazhar, MSW, LCSW
Day 1 Wrap Up - Holly Campbell-Rosen, PhD
DR. CAMPBELL: Good afternoon, good evening, welcome to today's workshop. I am Holly Campbell, program officer of the Human Mobility and HIV Research Program within the Division of AIDS Research at the National Institute of Mental Health. We are excited about this workshop and are so happy that you have joined us today.
I will turn this over to my Director, Dr. Dianne Rausch, who will make some introductory comments. And then I will review some housekeeping items. Thank you, Dianne?
Agenda Item: Welcome
DR. RAUSCH: Thank you, Holly, good morning everyone. I also want to welcome you to this NIMH Workshop on Human Mobility in HIV in the Global Environment. It is a pleasure that so many of you have joined us today.
Population mobility has long been recognized as an important factor in managing both prevention and treatment of HIV. Providing the resources to accommodate mobility is critically important to be able to facilitate continuous knowledge and availability of interventions to prevent acquisition and maintain successful treatment and requires attention in creatives strategies.
The NIMH has long recognized this as an important issue, we recently expanded our focus on the impact of human mobility can have on the lives of people with HIV and those from high incidence communities. Mobility, and its many forms, plays a significant role in forward transmission and can derail testing, prevention and treatment efforts.
This workshop was convened to stimulate research on human mobility in HIV and to identify gaps in knowledge and opportunities for research. Today and tomorrow, we will hear from prominent researchers about the diversity of mobile populations affected by HIV, methods for understanding the mobility patterns, and interventions developed from mobile populations. We will also hear about lessons learned from researchers with expertise in humanitarian settings. The perspective of advocates for mobile populations will be shared during a special panel discussion.
I would like to close my introduction by saying that understanding and addressing the determinants of health associated with human mobility is key to accelerating the fight against HIV/AIDS. It is our hope that this workshop will shed light on this issue and move the field forward and contribute to ending of the epidemic. Welcome, and enjoy the workshop.
Next, Holly Campbell will make some housekeeping remarks.
DR. CAMPBELL: Thank you, as I said previously, we are very excited about this workshop and its potential to stimulate the field and contribute to our understanding of HIV transmission, prevention and treatment in the context of mobility. Before I review the housekeeping items, I would like to thank Dr. Carol Camlin for serving as a scientific co-chair with me, in developing the agenda, and workshop theme. Carol, you're an amazing scientist and a collaborator, it has been a privilege to work with you.
I would also like to thank my colleagues from the Division of the AIDS Research, Drs. Greg Greenwood and Colleen Lawhorn, for dedicating their time and guidance to this event as co-organizers.
Now I have the pleasure of introducing Dr. Kate Clouse, our moderator for the plenary talk. Dr. Clouse of Vanderbilt University is an infectious disease epidemiologist who designs and implements HIV studies primarily of patient engagement in South Africa. We will have the opportunity to hear about her work during her presentation tomorrow. Dr. Clouse take it away.
DR. CLOUSE: Our plenary speaker today is Dr. Carol Camlin. Dr. Camlin is a social demographer and behavioral scientist at the University of California San Francisco in the Department of Gynecology and Reproductive Sciences. With a joint appointment in the Department of Medicine, Center for AIDS Prevention Studies. Her research program crosses the disciplines of population studies, sociology, and behavioral sciences and is focused on the study of population mobility and HIV prevention and care outcomes.
She is the co-PI of two NIMH funded RO1s, exploring mobility in East Africa. As well as a K24 midcareer mentoring award. Along with Holly Campbell, Dr. Camlin is the scientific cochair of this workshop. We would like to thank them both for their hard work in organizing this event. The title of Dr. Camlin’s talk today is Human Mobility and the HIV Pandemic: Challenges, questions, solutions. Welcome, Carol Camlin.
Agenda Item: Human Mobility and the HIV Pandemic: Challenges, Questions, Solutions
DR. CAMLIN: Thank you Dr. Clouse, thank you Dr. Campbell, and distinguished guests and panelists. I am so honored to speak with you today on this topic, Human Mobility and the HIV Pandemic: Challenges, Questions, Solutions, a topic I became interested in almost 20 years ago, when I was working as a demographer at a research center in South Africa. As a scientific co-chair of the meeting, I want to extend my welcome to all of you as well. We have a really exciting agenda over the next two days with a range of disciplinary perspectives on this topic. I'm looking forward to all of it. Before I get started, I just want to double check, do see my screen? Everything appearing okay?
DR. CAMPBELL: Yes, looks great.
DR. CAMLIN: In my talk today, I will be addressing these questions: what does the term human mobility encompass? What are the magnitude and trends in human mobility across the globe and by region? What are the links between human mobility and HIV? Why are sex differences in forms of mobility important for understanding HIV transmission dynamics? What are the potential challenges posed by mobility for HIV eradication efforts? What potential interventions might help to address the challenges that are posed by mobility for achieving HIV epidemic control?
I would first like to set the stage, both for my talk and for the two-day workshop, by talking about some of these terms that we’ll all be referring to.
To this first question, what does the term human mobility encompass, well, it has multiple dimensions. The one that we might be most familiar with are the spatial dimensions of human mobility. In the field of demography, there's a strong emphasis on international migration, which is defined as a change of residence over a national boundary. Secondarily, internal migration, which are changes of residence over geopolitical boundaries within countries, like provinces or states.
We also examine flows as part of this spatial dimension. There is the circular mobility flow, which are movements back and forth between origins and destinations. There is the paradigmatic rural to urban flow, which is encompassed in the concept of urbanization. But then there are also alternative flows, like rural to rural flows or rural to peri urban, and other alternative and more complex flows. Somewhat lesser focus has been put on the really important temporal dimensions of mobility. There are migrations that are permanent or assumed to be permanent or planned to be permanent that might end up being temporary.
There are dimensions of seasonality in certain settings. We measure frequency and duration of moves. And then there are the social dimensions of mobility. There is forced mobility in the event of natural disasters, climate change, and civil unrest that causes the creation of refugee populations, and we will be hearing a lot about that tomorrow. And then there is voluntary migration for labor related and other purposes. Sometimes the distinctions between these two major forms, forced versus voluntary, can be blurry.
There are the purposes of migration for labor, nuptiality and other reasons. And then there are individual dyadic and network characteristics of ability. We will be learning more about these later on this afternoon.
All of these dimensions are important for understanding the links between mobility and HIV. What do we know about the magnitude and trends in human mobility? The embedded figured that you see to the left from the international migration, shows that there was a bit of a flattening between 1990 and 2000, but thereafter, the trend towards increasing international migration was resumed from what had been seen in the decades prior to 1990.
Last year the estimated migrant population was 3.6 percent of the world’s population. That international migrant stock is generally defined as the proportion of people that are foreign-born who are resident in countries. However, while overall, international migration is increasing, it is not doing so uniformly across regions. There's been increasing skewness in the distribution of migration destinations compared with migration origin. Which implies an increasing concentration of the global migrant population and a shrinking number of prime destinations.
You can see, in the map, the rise of new migration hubs in Europe, the Gulf and Asia.This reflects how human resources and economic activities have been increasingly concentrated in a relatively low number of countries, or more precisely, metropolitan areas within a few countries which reflects processes of urbanization.
These changes are reflective of the fact that really globalization is not uniform and flat, but rather, it is asymmetrical, and it does not necessarily manifest itself as a change in the volume, but rather, in the underlying patterns of migration.
What do we know about the gendered patterns of human mobility? What we have seen globally, is a broader feminization of migration, both international migration and internal migration in the last three decades or so. Since the year 1970, at least, female migration has been on the rise. By the year 2000, about half of the worlds international migrants were women. That proportion shrunk slightly in the last decade, so that you now we see 48.1 percent female versus 51.9 percent male, among the international migrants stock. But women now predominate migration flows to more developed countries, in several of the world’s regions.
As we've been starkly reminded with the COVID-19 pandemic, viruses do not recognize borders and they are not concerned only with international migration. So, while the field of demography principally is focused on international migration and its macrolevel demographic and socioeconomic impacts, in HIV research and public health more broadly, we have needed to better understand and measure other forms of mobility, circular movements, between origin and destinations,migrations that are temporary as well as permanent, and complex mobility flows in order to understand the impacts for mobility for local and regional HIV epidemics.
In HIV research, we know that these more localized and complex forms of mobility also have contributed to the persistence of HIV in specific settings so many decades after the links between local epidemics formed to become a global epidemic. And how they have done so, how do we think about the links between mobility and the spread of HIV? We know that in sub-Saharan Africa, for example, as in the map that you see, HIV spread by the quarters of human mobility along major transit routes and spread from urban areas to rural areas. And it continues to play a role in sustaining of HIV pandemic, by linking geographically separate HIV epidemics, but intensifies HIV transmission by enabling prior sexual behavior and it disrupts care engagement.
What do we know about trends in internal migration and these more complex forms of mobility? Well, they are harder to measure, data sources tend to be limited, and the measures are highly varied. There is a wide variability in trends both within and across countries and regions in measures that we use such as crude migration and intensity. But we can draw some conclusions and what is clear, especially in low and middle income countries, is that there has been a rapid urbanization and the growth of urban mega-cities and urban slums, and the shrinkage of smaller cities and regional cities. We have growth of peri-urban areas, often outside of the legal jurisdictions of cities, with slums that are even less formal than urban slums and with even fewer services and resources. And across Africa and the global South, women’s rates of internal migration have now met or exceeded those of men.
My work has been focused on Sub Saharan Africa, and the rest of this talks focuses on my work in the region. My early work focused on female migration in Sub Saharan Africa, where the magnitude of women’s participation in migration was often not fully measured because conventional approaches to the measurement of mobility has tended to better capture men's patterns of movement. We know that from the existing data, in southern and eastern Africa, men's and women's movement patterns differ. Men have been more likely to engage in permanent or circular migration, and women engage in temporary migration to maintain multiple residence to make more frequent returns to households of origin and to travel over shorter distance.
We also know that men’s and women’s destinations have differed. Men have tended to migrate to cities where they have had more opportunities in the formal employment sector. Women have continued to migrate to informal settlement areas, peri-urban slums, and regional towns. We know that in some settings, in sub-Saharan Africa, men's flows have been becoming more like women's flows as living conditions have worsened in urban slums, and some areas.
Even we have seen some evidence of counter urbanization in some areas and the growth of peri-urban areas. Let me just say that female migration, as well as migration in general, is very important for economic development. Female migration is as important to development. In Africa, migrants retain links to rural households as a form of risk insurance in volatile labor markets to maintain land and lineage. Female migration has been a key livelihood strategy, especially for poor households, because women return more frequently, and they tend to remit more money to households of origin, even though they are more likely to be unemployed, or in unstable or informal sector employment.
What is driving this feminization of migration at least in southern and eastern Africa? It is not primarily associational, as in accompanying a husband to a new setting, as had been assumed previously. Because of women's greater labor force participation, which has risen as a result of loss of traditional income supports, because of high rates of male unemployment and declining marriage rates. We have seen transformations in gender norms, linked to these changes in material conditions. So while there are traditional constraints to female migrations have lifted, in turn, women’s migration has resulted in women holding more aspiration of egalitarian relationships in seeking and attaining more equality in labor.
So the implications for HIV research in Africa, the role of migration and the spread of HIV was well established. But earlier research largely focused on male migration or did not examine sex differences in migration. In southern Africa, women migrated to destinations with higher HIV prevalence compared to those of men. We found that women had more risky sexual behavior in context of migration compared to men and to women that did not migrate.
All of this for me, led to a need to better understand women's migration experiences, in the context of their migration. To better understand why risks for women in the context of migration were particularly elevated. With the support of a K01 award a few years ago, I began to conduct research to pursue these questions. What are the unique forms of mobility that women were engaging in? What were the spatial, and social features of their migration destinations? What factors facilitated their HIV risks both in origins, and in destinations?
We found that in Western Kenya, women engaged in diverse mobility flows. There was a lot of migration to and from Kisumu and Nairobi. This was also related to postelection violence. We saw migrations from rural areas outside of Kisumu into Kisumu, which is the third largest city in Kenya. We saw lots of temporary movements back and forth between Kisumu and Nairobi with secondhand goods sourcing and movements over the border into Uganda, especially around the agricultural trade and over the border with Tanzania, around textiles and other goods.
We found that women – that multiple migrations were common over the women's lifetimes. The circulation between multiple residences were also common. So in our qualitative research we found there were aspects of women's migration experiences that might have facilitated their HIV acquisition risk at origin because of widowhood, separation or divorce, and gender-based violence. All of which could have led to them being infected with HIV before they migrated because of potential exposure to HIV, from a spouse or inheritor, because of the loss of property, housing, land or livelihood, or because of social isolation and vulnerability. We found there were aspects of women's migration experiences that might have facilitated their acquisition and transmission risks at destination.
We found that the engagement in transactional commercial sex among female informal sector traders was common enough that it was said that women mixed her business, she mixes her business between sex trade, and other trade. We also described the transactional sex for fish economy around the Lake Victoria Lake shore communities that was common enough that it was said she pays twice, with money and her body to access fish to sell on the internal market.
Women engaged in a range of covert to overt forms of transactional or commercial sex, sometimes used to cover of trading as a way of protecting social identities, while their main income was from commercial sex work. Versus people who are more traditionally more comfortable with the social identity of being a commercial sex worker in brothel based venues.
In the sex for fish economy at Lake Victoria which was said to be something that might have really arrived in beach communities when the AIDS epidemic began to peek in Kenya. There was a high mortality, and a lot of migration of women to beaches who had been widowed, where previously, women would've had access to fish through their kin relationships, like being married to a fisherman for example, women formed relationships that were transactional with fishermen in order to get access to fish to sell it on the local markets.
The system that is called the Jaboya system is very controversial, locally very controversial, and communities are actively engaged in their own responses to those. With many communities banning the Jaboya system in many places, perhaps it is a bit more underground but nevertheless, this has contributed to the spread of HIV in this region.
We felt that women's mobility is an underrecognized social antecedent to the sustained high HIV epidemic in Eastern Africa and that highly mobile women in Western Kenya were at high risk of transmitting HIV because of the circumstances that drove their migration, like widowhood, also increased their HIV risk at origin.
Also at risk of HIV acquisition – because migration contexts facilitated multiple main partners in different places and engaging in transactional sex and commercial sex work So our work provided some evidence for some of the pathways that had been theorized to link mobility to HIV acquisition.
This is a schema developed by Susan Cassels and her colleagues, that I thought was really helpful, that proposed specific pathways to which mobility contributes to HIV acquisition. One was the intrinsic risk pathway where immobile persons are at higher risk of HIV due to a mechanism that independently causes both disease exposure and outcomes. For example, a predisposition to risk-taking could influence both high-risk sexual behavior and mobility. I described how really, the structural drivers of women's mobility such as unequal labor market opportunities can drive their engagement in both mobility and higher sexual behavior.
There is this bridging pathway where migrants link otherwise distinct subpopulations, diffusing higher risk behavioral norms across networks. Mobility is known to increase interactions and dispose individuals to partners from hire prevalent eras in sub-Saharan Africa, and our research has provided that evidence for that pathway.
And then they proposed a community displacement pathway which posits that sexual network structures of sending communities can change as a result of significant out-migration. Numerous dyadic studies of mobility in risk behavior in couples revealed inconsistent and contradictory findings, which really underscores the dynamic nature of mobility. The varied nature of mobility at different context, and populations. The need to understand mobility in specific populations and settings, in order to craft effective responses.
To that end, I conducted a five-year study, again with support from the National Institute of Mental Health, to investigate how mobility affects HIV transmission dynamics in HIV cure cascade outcomes in the context of a large test and treat trial, the sustainable East Africa Research Community Health Trial. It was a six-year study in 32 communities of about 10,000 persons, each, in Uganda, and Kenya, that tested the effectiveness of its model of a test and treat strategy for reducing HIV incidents and improving community health outcomes. Our mobility and search study aimed to measure the mobility of individuals in these communities participating in this trial and to estimate the impact that that mobility in HIV incidence in HIV care cascade outcomes in these communities. We also undertook community engaged research to develop options for intervention strategies to improve care engagement for mobile individuals. We conducted the study in 12 of the 32 communities, and our sample was 2715 members of a cohort. We had a HIV incidence cohort within that of cohabiting couples stratified by mobility status of 240 couples. This is a map of the communities in the three different regions of western Kenya, Eastern Uganda, and southwestern Uganda.
We developed new instruments and high-resolution measures of mobility that were designed to be gender inclusive, and encapsulate – to the best of our ability, the complex and dynamic nature of mobility. We conducted a biannual survey every six months, to measure these aspects of mobility, both forms, tempura city and geography.
We conducted annual sample collections to measure for STIs, chlamydia and gonorrhea specifically. And to measure ARV hair levels. We collected information on sexual behavior using relationship history calendars, and this is a calendar I adapted from one developed by Nancy Luke and Shelly Clark. We were able to collect personal partner monthly data, and a lot of information on the partnership characteristics for people within the study.
What did we find? Well, migration was common. Seventeen percent had changed the residence over either the national or international boundary within the past five years. Seven percent in the past one year. Most migration – by the way, this was in the baseline year, most migration was internal and varied a lot by region, with higher levels in Kenya, followed by southwestern Uganda, followed by Eastern Uganda. Men were more likely in these very rural communities, to have migrated at all. For example, 20 percent versus 15 percent women in the past five years. Women only predominated in the most localized forms of migration and mobility. We found that 9 percent travelled for the purposes of work in the past six months, this was travelling away from the home of residence that required sleeping overnight, at least, one night in the past month over any of the past six months. That was 17 percent versus 2 percent of women in the past six months at baseline. Forty-four percent travelled for other purposes, including caregiving or care seeking, funerals, visiting families, holidays, schooling, and women predominated in these forms of mobility, 54 percent versus 31 percent of men in the baseline. Those figures carried forward, year after year. It was a lot of consistency.
These plots show the magnitude of the sex specific short-term mobility by origin and destination pairs, at the county and district level in our baseline.
The countries are color coded, with Kenya in yellow, southwestern Uganda in green, eastern Uganda in blue. As you can see, short-term mobility occurred predominantly within counties or districts of origin, or also nearby counties. In women, the travel was commonly originating in the Migori County and Homa Bay County in Kenya, and the Bushenyi District in Uganda. Men travelled most commonly originated in Migori in Kenya or Mitooma District in Uganda or Homa Bay in Kenya. We learn from this a lot about the purposes in the context of this mobility. The communities with the highest levels of mobility were primarily rural, for search. But also contained corridors to regional capitals and bordering countries like DRC, Rwanda and Tanzania.
In the high mobility areas, without major truck routes, there were other factors that drew mobility. For example, Mitooma, contained no major truck routes, but it does draw traders to its coffee, tea and banana plantations. In Kenya, Homa Bay contributes the largest sources of fish catches, and all of the five Kenyan Counties bordering Lake Victoria. It is a high area of high circulation of fishermen and traders.
We also saw associations between sexual partnership to currency, both in the past two years and in the past two months, and this was defined as any overlapping of sexual partners within any given month over the period. We saw that that was quite striking, especially for women, the differences between those who had migrated or verses not migrated. Those who migrated were much more likely to have a sexual partnership concurrency within the two year period. Similarly, when we only looked at mobility in the past six months, that also was associated with sexual partnership concurrency in the past six months, and that difference was particularly pronounced for women.
This chart shows the age and sex patterns of concurrency, which gives a snapshot of the age, sex mixing patterns of the population with women. Younger ages, more likely, than of older ages to be engaged in concurrent partnerships and the reverse for men. Older men more than younger men, were more likely to have concurrent partners.
Here are the results of multi-varied analysis, with sex stratified models, where we examined association of different metrics of mobility with partnership concurrency with a temporarily of exposures and outcomes, again, aligned. As you can see, migration in the past two years was associated with elevated relative risks of concurrency in the same time period, but affect sizes were larger for women than for men. For example, the adjusted risk ratio being was almost two, compared to 1.47 for men who had engaged in any past two-year migration.
Again, labor related travel in the past six months was associated with concurrency in the same time period, with the effect especially pronounced for women and mobility for other purposes was associated with concurrency in men only.
Using multivariate analysis, with sex stratified models, adjusted for age and region, we find higher probability of work-related mobility among HIV-positive men and women, compared to HIV negative men and women. The amount of time spent away from home was associated with HIV-positive in men, adjusting for age and region. There was no evidence that recent migration was associated with HIV for either men or women in adjusted analyses.
We also examined mobility and its associations with STI because investigation of STI epidemiology can also shed light on HIV risk and STI’s themselves pose increased HIV acquisition transmission and impaired fertility risks. So we wanted to examine what is the prevalence and incidents of STIs across measures of mobility over time? What are the sexual behaviors in context that mediate between mobility and STI prevalence? So we conducted, again, annual testing for gonorrhea and Chlamydia, using gene expert machine in our full sample. Eighty-five percent of everyone in the sample was tested for all four years, with 94 percent of the sample having engaged in the testing at least three of those time periods. We did provide treatment for all participants and their sexual partners throughout the years.
Here are the results of annual urine testing for CT and NG at baseline, and follow up years, one, two and three. Overall at baseline, we found 3.1 percent of testing persons testing positive for chlamydia, gonorrhea or both. In general, women had higher rates of STI positivity than men and we saw an increase in trend in positivity rates rising up to 4.8 percent at the end of year three. We are investigating the potential causes of that increasing trend in STI diagnosis. This is reflective of global increases seen in STI's over these years.
Over the duration of observation, ever having been positive at annual testing was 12.7 percent, with a diagnosis of chlamydia or gonorrhea being about evenly split, 6.1 percent versus 5.9 percent, and very few individuals testing positive for both. One of our questions was, what happens with STIs in individuals over time? Is it the same people who are getting repeat infections? This chart shows the answer. Its clearly, no. A majority persons who were observed to have an STI only had one prevalent episode, the top bar, on annual testing. We look at the pattern of prevalence over the four years and the frequency distribution. You can see those top four bars for presenting those with a single year positive account. And that those account for the significant majority of our observed STI patterns.
One of the questions was do we see associations between measures of mobility and a risk of prevalent STI overtime? We see the odds ratios for risk of prevalent STI over time for measures of mobility, sexual risk behaviors and selected demographics from repeated measures, logistic regression, adjusted for clustering within individuals. These are taken from bivariate analyses without adjustment for other factors.
We found increased risk for STIs with recent migration history and some suggestion for increased risk for recent travel for work and non-work purposes. Likewise, a number of significant behavioral risk factors for prevalent STIs and differences by important demographic groups. We are currently examining whether the relationships between STI and mobility are mediated by differences between sexual behaviors reported by individuals who are mobile versus non-mobile, and how those things vary by sex and other demographic characteristics.
We also examined risk of HIV acquisition in the total search population over three years. We looked at mobility incidents by mobility, pre-baseline and over three years and evaluated those differences by sex. And used regression to estimate incident ratios of HIV acquisition among mobile relative to non-mobile in adults and sex stratified multi-variant models adjusted for various characteristics in clustering by community.
The main takeaway is that the risk of HIV acquisition by year three was higher in adults who reported mobility prior to the baseline. It was almost 50 percent higher in those who lived more than one month outside of the community in the past 12 months. With similar effects for men and women at baseline. The risk for HIV acquisition was over 40 percent higher among men who spent some nights away in the past month at baseline.
So the temporal ordering of exposure and outcome can’t be ascertained definitively in this analysis. But the magnitude of the association was highest for those who had lived greater than 12 months in the past three years outside of the community, and those who changed residence in the past year. So both permanent forms of mobility and temporary forms of mobility were associated with higher risk HIV acquisition and in these rural communities.
The mobility study, we also looked at how care engagement was impacted by forms of mobility. We did find that the odds of care engagement were significantly low and those that have migrated in the past year. And in qualitative research in the same population, we are examining and analyzing this data now and find that people do not always plan and anticipate their travel. Patients might leave without informing clinics or carrying medications. The duration of travel varies from days to months. Women can have less control over their schedules and more vulnerability to transactional sex for livelihoods. Also we found that mobile patients shared drugs with one another while traveling.
The main take away from our research so far is that there are major differences in the forms and magnitude of mobility across regions, by sex and by HIV status, with a greater proportion by men migrating, and a greater proportion of women travelling more recently. Men and women living with HIV tended to be more mobile and tended to be more likely to recently have migrated. The purposes of mobility differed by sex with men composing a greater proportion of those who travel for work, and woman for non-labor related reasons. Communities with higher proportions of mobile residents tended to have higher prevalence and even very recent mobility was associated with higher prevalence.
Mobility was significantly associated with the risk of HIV acquisition and was influenced by both sex and the type of mobility. And not only conventional measures of mobility, but also shorter, localized forms of mobility were associated with both prevalent and incident infection. Short-term mobility related to livelihood was particularly associated with HIV acquisition risk, particularly for women.
Regardless of the mechanism, the higher-level mobility among HIV-infected individuals is a major implication for the global HIV response, so understanding the drivers of mobility, knowing where people are moving, when they move, and who these mobile people are, can enable us to identify and strategically target our efforts.
We see that in the context of large-scale universal testing and treatment efforts, similar findings have been reported in the ANRS TasP trial in KZN, in the article by Larmarange and colleagues. The circulation newly infected individuals in and out of communities slowed down the TasP efforts to increase ERT coverage in population viral suppression. And Rakai, in the context of prolonged scale of combination prevention interventions. HIV incidents did not decline among recent migrants, in contrast to declining. – excuse me, HIV incidents among permanent residence and non-recent migrants in the population. In search, as I have reviewed mobility, was significantly associated with HIV acquisition. We know that new models of differentiated care are needed to simplify and adapt services to better meet the needs of mobile people living with HIV.
New therapeutic technologies that permit mobile individuals to visit clinics less often, including longer acting formulations of ART and PrEP, will be especially beneficial for mobile populations. We need to think about expanding service delivery beyond clinical settings, into key destinations and transit hubs for mobile populations. And we need to think about ways to strengthen the connectivity between health facilities, both through electronic and other means.
We need to think about economic interventions that reduce financial pressures and facilitate individual’s ability to prioritize health. So migration is very important for development and economic well-being, we need to find a way to preserve it, to permit it, but also to allow people to protect their health as they engage in it.
I am particularly excited about social network based interventions, rather than fixed location clinic and community-based approaches that can be used, given that mobile individuals are embedded within social networks of other mobile individuals. And you will learn about one intervention tomorrow in a presentation by my colleague, on one such study.
I would just like to close by thanking all of the participants in the studies that I presented today. My colleagues, migration studies, mobility and search and search studies, the funders, advisors and collaborators, and especially the National Institutes of Health. I just want to welcome you again, and now I will end for questions. Thank you.
DR. CLOUSE: Thank you Dr. Camlin, that was a great plenary to get us started today. I welcome everyone's questions into the Q&A box. We have one there. I also have a few others that we have two, already, great. I will go ahead and start with the ones that are in the Q&A box.
Do you have any hypothesis for the trend in increased STIs over time? Could the lower incidence in year one and two, be secondary to a drop in risk behavior related to being on the protocol which waned over time?
DR. CAMLIN: I know that outside of our context, STIs have also been increasing in the region, and I know that – and I do not fully have an explanation for it. I think that at the same time that we have seen the benefits of all of our approaches to controlling HIV, yield dividends. We have not seen the same levels of STI control in the setting in Western Kenya and in Uganda, as elsewhere in the world. It is possible that other panelists here could have insights on this question.
DR. CLOUSE: For that we would love for people to bring it up in their talks if they want to mention it or put it in the chat, too. Another question, are current research designs, or data collection tools, better suited to capture men's internal mobility than women? If so, why? What are some ideas on how to improve that?
DR. CAMLIN: Yes, I think that what we have tended to see are the measures that have been most prominent in the field of demography. We know that measures of migration have tended to focus on international migration, men are more likely to have the resources to be able to migrate internationally and women have not because they have less opportunities to do so. Measures of internal migration vary so much from country to country, since this is reliance, since this is another population-based surveys, sometimes there are surveys of labor, migration and women’s mobility is not captured and women are not captured in the population’s defined a labor migrants because their mobility is less likely to be in – their livelihoods are less likely to be in the formal labor sector. So expanding definitions of labor to include non-formal sector labor would result in women being better included in populations of labor migrants and improving the collection within country forms of mobility will also better capture women’s participation in mobility.
In terms of surveys, it is very important for us to be thinking about not only long-distance, permanent, and informal, but also think about localized shorter-term and complex forms of mobility, in order to capture men's and women's movements.
Also, thinking about purposes of mobility is important. For many years, we had simply – you know, was the person in the household on the night before the visit? Was the main metric used to measure short-term mobility? And there, you are also more likely to capture man's absence because they are away for longer periods of time and women circulate to, and from. Expanding a bit, and using more than one measure, will allow us to have more inclusivity to our measurement of mobility across both sexes.
DR. CLOUSE: Thank you. Another question that starts with a compliment, impressive work with very informative results. Could you elaborate on the migration detail? Such as the balance of circulation versus permanent relocation? The duration of residence – one night, weeks, months, and the destination among those who returned to their origin. Did you get that?
DR. CAMLIN: Yes. I think that those are data that I would not be able to quantify, right off the top of my head in this talk, at this moment, but I will say these are quantifiable metrics. We do see differences by region, and by sex. They do have to do with employment opportunities, they have to do with labor markets, and also with the environment.
For example, just take the case of beach communities in Kenya. We know that men follow fish across the lake, we cannot predict the patterns of movement of fish in Lake Victoria, but we know that men will travel across the lake to multiple different settings and offload fish, and women would tend to follow the men who are following the fish. Female fish traders might maintain links to markets where they can sell that fish, once they find it. They will travel back and forth between beach areas, and regional towns. Whereas, men are more likely to be located at beaches or out on the water.
There is seasonality where there are migrants that just travelled to Elderet, when the wheat harvest is coming in. We know that the sex workers who are then travelling, the workers will find out where workers are gathering. They will be following those workers to predict the movement of commercial sex workers, along with the movement of labor migrants. All of these things require attention to the context of migration. In specific settings.
DR. CLOUSE: Thank you. Let's see, how do you see social networks being critical to future interventions in this population? I know you talked about that, in one of your last slides. What additional research is needed to consider how to design the most effective interventions using social networks?
DR. CAMLIN: We need to be thinking about ways of measuring social networks that allow us to approximate full network studies, which are quite complex to carry out. Those are absolutely necessary, and important, full network studies are crucial. Once we have the knowledge of social networks, and how the social networks - what the characteristics are of social networks and mobile populations in specific settings, we can think about how to use that information for intervention.
For example, in our study, the study we will talk about with my colleague Dr. Thirumurthy, will talk about tomorrow, we are trying to figure out, well, can you identify network central people who can then distribute HIV self-test kits to others and help to link others in their social networks to HIV prevention, to PrEP, or to treatment if they are not engaged? Those people who are traveling together in communicating with each other, can help one another. Since they are already helping one another, are there ways for us to find out about people's own indigenous social networks, and who are the most influence people in those networks who can be facilitators of care engagement for everyone else.
DR. CLOUSE: Another question that starts with the compliment, thank you for this excellent talk. You mentioned that longer acting medications, that is long-acting PrEP, can be helpful for mobile populations. Can you speak to rates of PrEP uptake among mobile populations that you have worked with and discuss any PrEP or any other HIV prevention interventions targeting mobile populations?
DR. CAMLIN: I think PrEP uptake is a work in progress, for many populations. I think that there are particularly difficult mobile populations – mobile populations are particularly difficult, in terms of reaching these kinds of populations with services such as PrEP. With ART, there are several interesting intervention studies, including yours, Dr. Clouse’s and others, that will be presented tomorrow to actually describe some of these exciting approaches.
There are approaches that are being tested in the field right now, such as the search sapphire follow on study. Where we are testing out something called 'dynamic choice treatment' for mobile populations that have mobility coordinator, and mobile populations are offered a travel pack, or longer drug refills, phone engagement with providers. Those kinds of options to help facilitate their ability to stay adherent to HIV medications while they are travelling. There are many things being tested in the field right now that are particularly exciting and promising. Hopefully we will know soon which of these approaches might be most promising.
DR. CLOUSE: We have one more question that was very similar. It was about interventions aimed at using access to treatment for mobile populations, which I think you just answered. We will also be talking more about it in tomorrow session. We have one more minute for the last one. LA Biomedical Solutions are solutions when mobility can be predicted, departure and return dates. Does your research give any insight on this?
DR. CAMLIN: Yes, I think that – there is a certain proportion of people who can predict their mobility, really well. For example, I gave you the wheat harvest example, where people are going to plantations, or other kinds of formal sector destinations that have an anticipated period of work time.
There are other kinds of – especially more informal livelihoods, that are harder to predict. So many times I would interview, and then there were traders who would talk about how, "I went to Keisi(?) to source my goods, and everything was stolen and I lost everything and I had to go back to this other place to source. Or people are not able to sell and then they have to stay longer because they did not get the income that they needed to pay rent or school fees. Women's mobility – again, it is less predictable than men's, by virtue of the kinds of livelihood opportunities that are available to them.
Session I: Human Mobility Across the Globe
DR. CLOUSE: Thank you. With that, we have reached our time allotment for the plenary. Thank you again, Dr. Camlin, for a great talk. Thank you to the audience for your questions. We will now move on to session I: Human Mobility Across The Globe.
As a reminder to please put your questions in the Q&A. We are not going to stop for questions after each speaker. We will get to questions at the end. I will be taking not a question and if you could please mention which speaker you would like for me to direct your question to, that will help me out.
I will introduce all of our speakers right now and then they will proceed to give their talks without delay. Our first speaker today, is Dr. Aimalohi Ahonkhai. She is an infectious diseases clinician with focus training in HIV medicine, epidemiology, implementation and outcomes research. Dr. Ahonkhai is on faulty at Vanderbilt University Medical Center, where she is joint appointments in the Division of Infectious Diseases and the Institute for Global Health. She is committed to optimizing clinical outcomes for marginalized patients living with HIV, and has focused her efforts on implementation research in sub-Saharan Africa, and more recently, in Tennessee, which she will talk about today.
Dr. Mark Padilila is a professor of anthropology in the Department of Global and Sociocultural Studies at Florida International University. He is a medical anthropologist with research and teaching interests of gender, sexuality, race, migration, political economy, commercial or . transactional sex, theories of tourism, and critical HIV-AIDS and drug research.
Next we have a team from the Brown School at Washington University in St. Louis. Dr. Fred Ssewamala is the William E Gordon Distinguished Professor of Social Work and Public Health. He is the founding director of the International Center for Child Research and Development and also directs SMART, strengthening mental health research and training Africa.
Dr. Joshua Kiyingi is a first year public health science doctoral student where he has coordinated several international studies. His research interests are child behavioral health, HIV and STI risk reduction, and youth and women livelihood in low resource communities in Sub Saharan Africa.
Dr. Proscovia Nabunva is a research assistant professor and serves as the codirector for the International Center for Child Health and Development.
Our final speaker for this session is Dr. Tetyana Vasylyeva, who is an assistant professor in the Division of Infectious Diseases and Global Public Health at UC San Diego. Her research relies on viral phylodynamics, a branch of evolutionary biology that uses viral genetic sequences to study phylogenic trees and virus transmission dynamics. Applying a combination of phylodynamics and epidemiological modelling, Dr. Vasyleva studies how major political, social, or economic crises, so-called “big events”, can affect HIV spread worldwide.
To start, please welcome Dr. Aimalohi Ahonkhai.
Agenda Item: Characterizing the magnitude and scope of mobility among PLWH in Tennessee and its impact on HIV care outcomes
DR. AHONKHAI: Thank you so much. Let me share my screen. Good afternoon everybody, my title of my talk today is Characterizing the Magnitude and Scope of Mobility among People Living with HIV in Tennessee and Its Impact on HIV Care Outcomes.
So, Dr. Camlin provided a really nice overview of this but mobility is associated with increased risk of HIV infection, and likely also impacts the entire HIV care continuum. But there is really a substantial research gap on this topic in the United States. According to U.S. Census Bureau data, the US population experiences regular mobility but key populations at risk of poor HIV outcomes seem to be more frequently mobile. For example, 10 percent of the US population reports a change in residence from the prior year, but higher mobility is seen among non-Hispanic blacks, Hispanic individuals, young adult it just 18 to 34 years, and individuals who are unemployed. These of course, as we know are overlapping risk groups for individuals at increased risk acquisition and for poor HIV outcomes.
We know that mobility may impact HIV care continuum outcomes in a number of ways. This can happen through disrupting access to care and treatment services. As this figure illustrates on the right, that can happen in a number of ways. Interrupting medication supply, having lack of privacy to take ART when travelling, during periods of mobility, fear of disclosure of HIV status and unfamiliar settings, disruptions of schedule, loss of valuable social support, and also, poor knowledge of new help systems in the context of mobility.
In addition to this, mobility can impact care continuum outcomes by hampering outcome ascertainment. In the US, that is particularly important when that mobility crosses state borders. As sometimes we will see, surveillance data is not routinely and consistently linked across state borders to allow for accurate ascertainment of these care continuum outcomes. For this, border communities are had high risk for either type of these disruptions and outcomes.
In Tennessee, where this analysis is focused, focused efforts in the state largely are driven by the Department of Health, have improved care continuum outcomes, but they continue to lag below national targets. In this figure on the right, we can see linkage to cure of the state from 2018, it shows about 64 percent of individuals linked to cure within 30 days. This is lower than the national target of 85 percent.
About 57 percent of individuals are retained by care, and 61 percent achieve viral suppression, again, that is much lower than the national target of 85 percent.
In addition, Tennessee is home to the one of the countries priority counties ending the epidemic activities. The County of seat of Memphis, or Shelby County. Finally, Tennessee is in a particularly unique situation, that it is bordered by more state than any other state in the country. Specifically, Kentucky, Virginia, North Carolina, Mississippi, Alabama, Georgia, Arkansas and Missouri. A lot of opportunity to cross state borders.
With that background, the objectives of this analysis for the first is to determine the prevalence and spatial variation of across and within state mobility among people living with HIV in Tennessee between 2016 and 2017. And to assess the impact of within state mobility between 2016 and 2017 on engagement and care and virologic control in the subsequent calendar year.
To accomplish these objectives, we identified people living with HIV in the state between 2016 and 2017, using surveillance data from the Tennessee Department of Health. Then, we merged that data with national database called Accurint. And Accurint is a national Lexis-Nexis service that is used by a lot of institutions, including law enforcement. It contains unique individual information, like addresses and phone numbers, associated with individuals residing in the United States. So the Tennessee Department of Health merges their surveillance data with this national database on a quarterly basis to update addresses. We use this data to conduct spatial analyses to first characterize spatial variation in mobility and to identify areas of unexpectedly high mobility. And then, via statistical analyses, to first identify how a mobile patient subgroup, and to assess relationships between mobility and retention, viral suppression, and loss of follow-up,that, we did using Poisson Regression.
This is a schematic of the statistical analysis. You can see here, from the figure that we had measured mobility in two ways. First, using the total number of moves, again, that was based on unique addresses from that Accurint database and the total linear distance moved among people living with HIV in 2016 and 2017. And then we looked at the association between that and retention, lost to follow-up of viral suppression in the subsequent calendar year.
The definitions for the Care Continuum outcomes are in the blue box below. Retention was defined as having greater than or equal to 2 CD4 or viral load lab values, during the year that we are at least three months apart. For the loss to follow-up definition, for 2017, these were individuals who had a CD4 viral load lab in the prior year, were alive and were not retained but had no labs in 2018. Similarly, for lost to follow-up for 2018, and viral suppression was defined as a viral load less than 200 copies per ml.
For the spatial analysis, patient addresses, as I mentioned, were obtained using the Accurint database, those were deidentified to the Census track. As this image on the left shows, a Census tract is a relatively permanent statistical subdivision of a county. You have the whole state, the county, and then these county subdivisions. They are generally in size of about 1200 to 8000 people, ideally, about 4000 people. So, at the individual addresses, were deidentified to the Census tract in all of our analysis were at the level of the Census tract. We created patient movement paths by summarizing movement across the Census tracts by patient ID, and we visualized the density of these movements using a line density tool, which I will share with you.
We then calculated the movement rate per Census tract by summarizing total moves by people living with HIV into a Census tract, normalized by the total number of people living with HIV in that tract.
And then finally, we conducted a cluster and outlier analysis using what is called the Local Moran’s I spatial statistic to identify high-low spatial outliers, and I will describe that for you.
This is our cohort, some baseline characteristics. We were looking at 17,489 people living with HIV in Tennessee between 2015 and 2017. The mean age was 44 yes. 56 percent of this cohort were non-Hispanic blacks, and 36.8 percent were non-Hispanic whites. Then we had a variable with gender and transmission risk factor, which was predominated by a cisgender males who risk factor was MSM. That was 50.7 percent of the cohort. That was followed by cisgender female whose transmission risk factor was heterosexual sex, and they made up 16.5 percent of the cohort.
This slide summarizes one-year outcomes for the cohort. You can see here, in 2017, 33.6 percent were retained, 11 percent lost to follow-up, 82 percent virally suppressed, and there were no deaths. And then in 2018, 56.3 percent retained, 10 percent lost to follow-up, 85 percent virally suppressed, and -- 2 percent of the cohort died in 2018.
This is the summary of the mobility data. As you recall, we measured mobility in two ways, number of moves and average total miles moved. This was in linear distance. You can see here that nearly one third, 32 percent of individuals had at least one move during this follow-up period. The total average number of miles moved was 51.
Then we went on to look at which patient subgroups were most mobile. Again, we had these two types of mobility, in the blue is the total number of moves. You can see here that the groups were at higher risk, young persons, non-Hispanic blacks, and people who inject drugs. The reference groups for each of these categories as listed across the bottom of the slide. In summary, you can see that individuals who were greater than 35 years were 60 percent less likely to have more moves than younger individuals. Non-Hispanic blacks were 10 percent more likely to have more moves than non-Hispanic whites, and then people who inject drugs, whether cisgender female or male's, were 15 to 38 percent more likely to have more moves than cisgender females whose risk factor for HIV was heterosexual sex. When we look at total miles moved, the groups that were increased risks were just young persons and people who inject drugs, you can see the odds ratio there.
In this figure, from the Spatial Analysis, you can see the US broken into Census tracts. The tan and camel color is highlighted, if at least one person living with HIV in Tennessee between 2016 and 2017, lived in that tract. This image really highlights wide spatial variability in the places where people living with HIV in Tennessee have actually lived across the country.
This next image is called a dot density map. This looks at addresses lived at by people living with HIV in Tennessee, again, during the study period. One dot represents a person, but it is offset randomly, so that a person's exact location cannot be identified. Importantly, this figure merges two data sources: the purple dots are from that Accurint database, that national data source that I mentioned, and then, the green dots are from local data sources within the state, so these are any time a lab is sent to the health department, which is required for reporting. The patient address that is associated with that lab or other local sources are used here.
This really highlights that if we do not have these national data sources, we really have a very limited scope on the amount of mobility that is actually occurring. So, that will be just the green dot, which look localized to the state, but once we add the Accurint data, in those purple dots, we can see the scope of mobility of our patients.
In this figure, we were presented straight line distance movement of our cohort during this study period. Each person's movement path is equivalent to one line feature. This analysis incorporates multiple moves, out-of-state or in-state by a person, including, circular movements, so, back and forth to the same address. The density of the line reflects how common that movement is. The image on the left is of the entire US, and you can see movement paths in every direction to the west, to Texas, to Arizona, to California. North, toward Illinois, to northeast towards Delaware, South towards Florida.
When we look at the more regional map of the Southeast, you can see this really dense movement path between Memphis and Nashville. Others outside of the state, between Memphis and Nashville and Atlanta, and others within the state between Nashville and Chattanooga, Knoxville, Johnson City.
For this figure, we calculated move rates as the total number of moves for people living with HIV within a census tract within a study period. The darker colors here, the black, purple, light purple and dark purple, represent higher move rates in a particular census tract. The important thing here is you can see here that these areas of high movement rates are both within major metropolitan areas and outside of them. So, the ones illustrated here, for instance, in Memphis, in Nashville, and in Knoxville, you can see that these darker colors are concentrated both within these regions, but also, outside of them.
This next figure highlights further that same concept. This is our spatial clustering outlier analysis. Where, what we did is we look at groups of census tracts and compare mobility in one census tract to the tracts around it. For this way, we can identify areas of high mobility, so, that is a high mobility census tract surrounded by other high mobility, or high low outliers, high mobility census tract surrounded by a relatively lower areas of mobility. Since we were interested in mobility, this is what we focused on – these high/low outliers, and these dark red census tracts. Again, we can see here that these are concentrated on the outskirts or outside of the major metropolitan areas that I have identified here. We have observed 42 census tracts with these unexpectedly high movement rates.
The next couple of slides that I will show you are some forest plots summarizing our statistical data looking at the association between mobility and care continuum outcomes. They all have the same format where the mobility measure is here, this is Poisson Regression data, with the risk ratio of one at the dotted dark line. And the other covariates we have adjusted for age, race and combined gender and transmission risk factor variable.
So in this slide you can see the reference of zero moves. So whether you have one, two or three moves, there is a 7 to 10 percent decreased risk of retention associated with those moves. When we look at total number of miles moves, you can see that there is a nice dose response between the total number of miles moved and decreased risks of retention. So much so that really long distance total accumulative mobility here of 1000 miles is associated with a 51 percent reduction in the risk of retention in the subsequent year.
Here, we've moved to the outcome to the loss of follow-up, we have referenced the number of moves, whether that was one, two or three, compared to zero, associated with the 17 to 20 percent increased risk of lost to follow-up in the subsequent year. We can see the same dose-response relationship with the total number of miles moved. Interestingly, we looked at viral suppression, we saw no significant association between either number of moves or total miles moved on the outcome of viral suppression. This is also in adjusted analysis.
In conclusion, we found that one third of people living with HIV in Tennessee experienced mobility, and this is much higher than what we saw for the general population. Mobility is both regional and national and much of the mobility experience is not captured by local data sources. We saw that mobility, especially across cumulative long distances is associated with decreased risk of retention and increased risk of long-term follow-up. And that higher than expected rates of mobility are seen outside of and on the outskirts of major metropolitan areas. Young people, non-Hispanic black individuals, and people who inject drugs are among the most mobile demographic populations.
Our next steps for this analysis are to identify geospatial correlates of mobility through additional geospatial analyses. And to explore perceived drivers and implications of this mobility through focus group discussions with local health departments, surveillance coordinators and case managers.
Some of the implications include the idea that policies to ensure care access among these highly mobile populations may be critical for meeting the epidemic goals. Also, tailored interventions may be necessary to do that. The importance of data sharing across state borders to facilitate better outcome ascertainment is critical as well.
I’d like to acknowledge all the partners on this analysis, the Tennessee Center for AIDS Research, the Department of Health, the Vanderbilt Institute for Interdisciplinary Geospatial Research, and other colleagues at Vanderbilt University and Vanderbilt Institute for Global Health. And then our funding source from the NIH through a supplement to our Center for AIDS Research. Thank you so much.
Agenda Item: HIV, structural inequalities, and social drivers of migration in the Hispanic Caribbean
DR. PADILLA: Okay, I believe I am next. I am Mark Padilla. I'm a medical anthropologist, as was mentioned in the introduction. I would like to share my slides here.
So, the title of the talk is HIV, Structural Inequalities, and Social Drivers of Migration in the Hispanic Caribbean. I am going to try to do something slightly perhaps unique, which is to talk about more about the ethnographic research. I am an anthropologist, so when I talk about ethnographic research, I am talking about long-term community engagement, participant observation, qualitative interviewing, and place-based observations, which is what I will discuss today.
I have two goals for this discussion, which is to use a couple of ethnographic case studies, one from the Dominican Republic and the other from Puerto Rico, to illustrate the importance of social and structural context for understanding human mobility and HIV. I hope to do this from a humanistic kind of perspective.
So, the first example is on the Syndemic of deportation, tourism labor, and HIV and substance use in the Dominican Republic, where I have done research for about 20 years now. New research in Puerto Rico that is on the problem of physician outmigration to the mainland US, what we referred to as 'colonial health care', and I will mention that further enclosing, also, chronic disease management in Puerto Rico.
The second goal is to demonstrate the utility of participatory visual methodologies for contextualizing HIV and human mobility within community experiences.
So the first comment is really just one that is to situate my work and the work that I hoped to represent today within a broader literature from the social sciences and humanities, which deal with migration from a perspective that I describe as the phenomenological ethnography. It is a focus on the body, and embodiment. What it means to be in the shoes of migrants, and this is very much in course, in line with the anthropological tradition, but it is also engaged with from other disciplines like sociology and social geography.
That work and identify social structures that have specific impacts on migrant health phenomenon such as isolation, stigma discrimination, gender issues, and I think the keynote really addressed well, healthcare access barriers. It can also inform policies and structural interventions through community collaborations and the incorporation of voices. That is something that animates my work to try to include voices, and I will illustrate that event.
Between 2013 and 2018, I was PI of a NIDA funded RO1 entitled, Migration, Tourism, and the HIV Drug Use Syndemic in the Dominican Republic. It used mixed method research on substance use and HIV's and the Syndemic relationships amongst those, with male tourism employees in two tourism cities (Santo Domingo and Boca Chica). It incorporated ethnographic mapping, which is essentially a way to look at space both qualitatively and in terms of its physical characteristics, as well as its symbolic or its social characteristics.
We also had a geographer involved in that project and did some GIS applications. It aims to describe the structural factors that contribute to these HIV substance use Syndemic in the countries leading industry of tourism. Tourism continues to dominate the Dominican economy, and of course now in the post COVID era, the devastation of the tourism industry is something we are dealing with.
Syndemics, the idea for those of you who may not have heard the term, but Syndemic goes beyond comorbidity to make deposit that social and structural factors that are primary drivers of health outcomes. In this case of migrants, the migration related health outcomes so that it is more than just comorbid conditions. It is about larger, structural and social conditions that facilitate kind of clustering of different health related outcomes on a broad scale.
This is just a map to illustrate some of the prior background in the study area of the Dominican Republic that demonstrated the level of labor related migration of men from rural areas to the two tourism destinations. This became a very important preliminary finding that motivated the R01, subsequently, these are the migratory trajectories internally in the country, among men, 200 men who participated in the survey portion of that were primarily ethnographic study, that then we published in AJPH and then became the foundation for the larger study.
It is hard to traverse all the dimensions of the project, but there is kind of this phenomenon of what I sometimes describe as the strange bedfellows of how does deportation actually connect to the tourism industry and connect to HIV and substance use? We have an article on this connection, but essentially there are a number of structural factors that emanate from the US. So amongst Dominican populations that have migrated, many of the participants we recruited in the Dominican Republic had grown up in New York or other areas of the US. They had emigrated as undocumented, often they did not know their documentation status. But as of the 1990s, it became much easier for the US illegal immigration reform and immigrant responsibility act, to deport noncriminal offensives. Since then, the Dominican Republic has seen a large increase in male deportees that are deported back to the Dominican Republic. On the right, you see one part of the ethnographic map for one Dominican deportee whom we interviewed and who described each leg of his journey, both to Puerto Rico and to the continental US. His repeated deportation back to Santo Domingo. We were surprised by the number of multiple trips and deportations that these men described. It was not just one. It was multiple often, in many cases. And without specifically recruiting deportees, it turned out that 50 percent of the study participants were actually reported a deportation history.
So to try and summarize some of the key findings from this project, I did mention this exponential increase in male deportations from the US back to the Dominican Republic, and in many cases those individuals actually were already battling a history of addiction. So they were struggling to overcome their addiction. One of the tragedies that we kind of uncovered in some of the publications, is the fact that Dominican law, there are two laws in particular, facilitate the stigmatization and recriminalization of deportees when they get back, 'home'. Which, for many, is not a place that they actually knew well.
But when they were returned, forcibly repatriated, they also confronted these laws. One of them is a highly criminalizing approach to drug control policies and law 1588. That actually is based on a Reaganesque ype approach to drug control and the Dominican Republic, and it basically puts these deportees in a very straight line to potential incarceration in the Dominican Republic as well, after often experiencing a long period of incarceration in the United States.
There is another law that – or kind of a political practice, that creates a registry of deportees, when they arrive. So in the bottom picture on the right, it is a picture we were allowed to actually to observe some deportees arriving from the United States. And they immediately, upon getting off the plane, every deportee – regardless of what the crime is – they are processed as criminals. There entered into a registry in the Dominican Republic which they call, La Ficia(?) and basically La Ficia prevents any deportee finding a formal sector job. So they are basically relegated after spending their time in the US, they are relegated to informal sector jobs.
This combines structural with problems in relation to the lack of evidence-based drug programs available in the Dominican Republic. Also, many deportees are fluent in English. So that tends to foster their attraction to sex work and informal drug sales in the tourism areas since those are the things that are in high demand in those environments.
So, we argue in multiple publications that this combination of the sex tourism market, and tourist demand for sex and drugs, they generate geographies of extreme vulnerability, or we can say in Syndemic language, they create a Syndemic use of HIV and substance use in these environments.
This is just a visualization of some of the factors I'm describing that our Syndemic. They are factors that contribute to these problems. Sort of the social and legal production of deportability. This is a lot of terms used in deportation studies, including a volume I recommend called, 'The Deportation Regime'. The resulting trauma and isolation that results from deportation, the restigmatization of deportees, which I mentioned, which is very real in the Dominican context. There they are often perceived as failed migrants.
Exclusion from formal wage labor through these mechanisms in policies which lead people into informal tourism labor, often drug involvement and sex work related. Again, the criminalization of drug use, so you get involved in drug sales and then you are much more likely to end up in a local jail. The resulting mental health sequela, and increased HIV risk. These arguments and the ethnography that I cannot expand upon right now, due to time, is described in this article, 'Tourism, Labor, and Body Suffering, and the Deportation Regime in the Dominican Republic' in the Anthropology Quarterly, if you are interested in more details on that.
Now I want to switch a little bit to a subcomponent of that same R01, in which we engaged in a Photo Voice project with community members who were facing addiction and describe a little bit that methodology, since it has actually turned out to be something that has been very impactful.
This kind of approach incorporates a Freirean approach. This is a Brazilian philosopher who described this notion of praxis, which is the combination of the collective community analysis and an action orientation. It is very much embedded in the methodological approach of Photo Voice, which aims to amplify community voices through caption images that are taken by community members themselves, creating new opportunities to reflect and potentially transform conditions of health and wellbeing in the local setting. I think it is worth mentioning this a potential methodology for addressing migrant health.
These are the seven individuals who became involved in our project through connections with a local harm reduction initiative that they were trying to get off the ground. There was no harm reduction to speak of in Santo Domingo at the time, and they were trying to advocate for that. And when we met them they said, "we are ready to show our faces. We are ready to not be invisible. We are tired of being incarcerated. We are tired of not having services for us. We are tired of suffering from a lack of clean needles from opioid replacement therapy." Many of them were heroin addicted.
So, when our team heard that and having a background in Photo Voice methodology, we proposed this approach to them and they were very, very engaged. I can’t go through all of the different methodological dimensions of photo voice, but Photo Voice does involve one of the main components to it, it is an ongoing project that does involve this Freirean practice, in the sense you have multiple meetings, multiple workshops, and there are training involved, reflection involved. The participants themselves very much guide the analysis. This is very centrally CBPR. In this case, because people are often Street involved are homeless, or unstably housed, it actually added to the timeline because we had a really difficult time in our community facilitators. A difficult time to track down everybody for repeated meetings and to get their photos and to make sure that they were safe while taking photos. There is a lot I can go through here in terms of methodology, but in our case, it took nine months. It can be done more quickly than that. I have implemented photo voice in eight sessions over the course of four months. So it is possible to do it more quickly.
Now I just want to show in the spirit of voices, I want to show some of the productions by the team members and I am just going to let them be and just pass the slides. They wrote the captions and they took the pictures and we just facilitated the process.
So, we have published on the results of this visual ethnography, this visual component through the adaptation of photo voice in Journal Arts and Health. If you are not familiar with it, it is a wonderful journal that includes a lot of images. So this kind of methodology is very much publishable. In peer review venues, and we have had about five more photo voice projects over the years. So, I'm happy to talk about that more if that is of interest. One of the great benefits of photo voice, I think is the community engaged component where the photographers themselves or as we talk about them, as activist photographers, because they are very much engaged in the critical reflection component of their community organizing and involvement. They are often present at these exhibitions and we have done over 20 exhibitions in the Dominican Republic and some in Miami on this project. And I continue to present on it, it became quite an impactful event. It had a wide distribution in school settings, in universities, among their drug commission locally, and it helped to sort of push some ongoing thought and dialogue about the policies that were affecting people.
I know I am getting close to the end and I do not have too much more to say here, I just wanted to mention that we are now embarking on a very exciting new RO1 called physician migration and its effects on Puerto Rico health care system. Some people are not even aware of the crisis of physician loss from Puerto Rico, it has been escalating in recent years. There are a number of factors including the colonial relationship to the United States. Economic crisis, the lack of disaster preparedness and investment. Medicare reimbursements for doctors are capped in Puerto Rico so they cannot make nearly as much money, so there are economic motivations. And healthcare sector investment in general. Hurricane Maria is another factor, of course. And we are using both community ethnography and physician survey to identify the causes and consequences of physician migration in the context of Puerto Rico.
I am showing this very briefly here, this is the conceptual approach from the grant and one of the innovations of this project kind of reflects my interest in social context, is the issue of spatial stigma, which I can talk about further. But this idea that spaces can become degraded symbolically and socially by the narratives that we hear about them. Puerto Rico has become something that we hypothesize might function as a source of spatial stigma for Puerto Ricans and physicians, and it might motivate them to migrate. We are so far, in our qualitative phase, we are just in phase 1, but we have already found some of the findings from or interviews with physicians that suggest that this may in fact be the case.
Since I know I am out of time, let me think my collaborators from both Puerto Rico and Dominican Republic and the associate organizations, and also the funders of the National Institutes of Health and NIDA and National Institute for Minority Health Disparities. I believe the next speaker is Fred Ssewamala, and colleagues.
Agenda Item: Working with mobile women engaged in sex work in Uganda: Implications for access to care, retention and HIV treatment outcomes
DR. SSEWAMALA: Thank you so much, Mark for that excellent presentation and thanks to each of the presenters who came before us. Thank you so much. You laid the groundwork for us. My name is Fred Ssewamala, I am presenting this work on behalf of my colleagues Susan Witte, a colleague at Columbia University, with whom we are and MPIs.I am also going to be a bit unusual in that how our presentation, I tried to put in the investigate part of the presenters. I also put our collaborator to be part of the presenters, so we will divide the 20 minutes that we have among a few of us.
So with that, in case we run out of time, we want to acknowledge I think you have, if you want to put it in view mode please. Okay, so with that, we also want to thank our funders and people we have worked with.
I do not define my work as work for mobile populations but mobile population, are just part of the group that we work with. I work with young people, I work with women, I work with young girls, so, we work with different kinds of people. So when we are invited to be able to present our work around women who engage in sex work, as a mobile population that we are working with in Uganda, we feel this was an opportunity for us to be able to share. This is work still in progress, and has been affected by COVID. What we are going to present is present a conceptual model, we will present some of the descriptive that we see but we have not completed really our entire sample size.
So what we are going to do, I will try to look at how does poverty impact the use and engagement with care and treatment. So I think that the keynote presented it so well. The photo voice by Mark Padilla, who is a colleague of mine, there is clear manifestation of issues related to poverty.
Aimalohi Ahonkhai, my apologies, I may mispronounce her name, she also highlighted issues related to poverty. So what we do here is try to figure out how does poverty impact these mobile populations.
I want to always to start with this quote from Amartya Sen. "We live in a world of unprecedented opulence, of the kind that would have been hard even to imagine a century or two ago. And yet, we also live in a world with remarkable deprivation. There are many new problems as well as old ones, including persistence of poverty and unfulfilled elementary needs."
So this connects us back to why we are interested in this particular population. In the next slide, I want to highlight this issue whereby the region where I work, and where our keynote speaker spoke on, is the poorest region on earth. It is a region where out of the 736 million people, 413 million in poverty lived in Sub-Saharan Africa. That is where we work.
In the next slide, if you go to the next slide, proceed. The next slide, we know that poverty does not only impact us as individuals, it also increases our respective behaviors. It also impacts us in where we engage in care and treatment, it does a direct relationship between physical health and mental health. And fear exists, both if you want material things (indiscernible) or you want to highlight (indiscernible) which is when people only associate, they will think and deal differently.
From the presentation so far, we know that poverty is playing a key in making most of these populations be mobile. People move because of climate change. Because their areas are dry. People move because of unstable income so they are trying to figure out where can I earn from. People move because of seasonality and that kind of stuff. So we always approach this in terms of what can we do to enrich empowerment, to address our issues of inequity.
In the next slide, the other thing that we should really highlight here, is that the region with dark red or whatever, that is a region which is also heavily affected by HIV and AIDS. It is the poorest. You have lots of regional migrations going on, but you also have a lot of HIV and AIDS going on. So that is where we work here. In the next side, what happens is that when you look at the population, women who engage in sex work, people who use women who engage in sex work, and people who inject drugs, among others, are what you call our key populations. If they are mobile, and given what we have already been told, if they are mobile then chances are less likely to engage with services very well, and yet we need for them to engage in services.
What women engaged in sex work tell us is they engage in sex work because they want to generate income to support themselves. Yet, even with that, they face economic hardships, seasonality, lack of control over their working situation, disruption of income due to you know, policing and in most cases, especially where we work, it is illegal – engaging in sex work is illegal. Then they are also engaged in consumption of alcohol and substance abuse.
So, the project we're going to talk to you about, is really about how we address the structural causes of being mobile. So that these women, that we work with, can engage with treatment and care and support. So our next slides, are going to be presented by my colleague Proscovia Nabunva, and then we also have one of our colleagues, she will also present.
DR. NABUNYA: Thank you so much Dr. Fred. So to provide a brief overview of our study. He study we call Kyaterekera Project, literally meaning a safe or preparing for tomorrow. So this is a five year study funded by the National Institute of Mental Health, and the overall goal of the study is to examine the impact of adding economic strength and competence on traditional HIV risk reduction efforts.
From the previous slides, we know that women do not engage in sex work because they are interested in the lifestyle, but it is because of the poverty that is driving them into sex work. This study is being implemented in Uganda. It was designed to target 990 women who were self-identified as women engaged in sex work. These are located in about 33 HIV hotspots in the southern and region of Uganda.
These hotspots are small towns about the highways that are connecting Kenya, Nairobi, Mombasa, and going through Uganda and then going over to (indiscernible) and also Rwanda. These are small towns where women do engage in sex work around those highways. But also around fishing villages and some of the neighboring rural areas.
For our study, the inclusion criteria – we engaged young women 18 years and above, who are reported in engaging in transactional sex. This is the engaging of sex for money, goods or services, so not just sex alone, and also, if they hade reported engagement in one or more episodes of unprotected sex in the past 30 days. Putting them at risk of acquiring and transmitting HIV.
This is our study region in Uganda. It is the greater Masaka region. As you can see, it is along Lake Victoria. The second map shows you exactly where those HIV hotspots are located. Again, we also have sites along the fishing villages on Lake Victoria.
So, as Dr. Fred just touched upon, in the theoretical framework that is guiding our work, so our theories and capability theories, also our social cognitive theory. We have incorporated or adapted that the core tenants of social cognitive theory, especially self-efficacy around condom use, condom negotiations skills in our intervention. But also in our financial sessions were part of the financial and economic empowerment that we are providing to our study participants.
So the Kyaterekera study is a randomized control trial with three arms. So all recruited women receive HIV risk reduction. This is where they talk about reducing risk around HIV transmission and condom negotiation, issues around violence, all participants receive those sessions. We added a session that is receiving financial empowerment in the terms of savings accounts. Women stay there for a period of time. This is many of the incentives they get from the several sessions they attend as part of the study. So they get incentives and we encourage them to save that money and we match it by ratio of 2:1. They can use this money to do anything they want, and their goal is to be able to reduce the number of times they are engaging in sex work or even the income they're getting from sex work that might expose them to HIV transmission.
So in addition to financial savings, we also provide financial literacy sessions. There are six sessions where they talk about saving, banking, budgeting, debt management and saving for emergencies. So that is treatment group 1. Then our treatment group 2, that received all of these components plus a vocational skills training. So it could be weaving, papermaking, and they receive additional mentorship to support their transition from vocation skills training into employment or business development.
So we have heard from previous speakers that this is a population that is very hard to engage with but also to access. So how have we have been able to access them? We have collaboratively worked with community stakeholders. So we formed a community collaborative board that has been advising us from the get-go. Where to recruit, who to engage, what services will be more appropriate, what time to meet with the women. Some of these representatives are organizations who are already working with women engaged in sex work. But they also include women themselves. So for each district you have seven regions. We have a representative of women engaged in sex work. We have a research organization that are based in the region. We have the government of Uganda who is or presented by the Ministry of Health. So the district health officers. We also have law enforcement because in Uganda, sex work is illegal. So for us to be able to engage with them, we had to make sure that our law enforcement or the police were involved. So they are all part of our community and collaborative board and they are informed every step of the way in recruitment, and in delivering our interventions.
In the next slide, Joshua is going to present some of the baseline findings. This is out of the sample we recruited before COVID.
DR. KIYINGI: Thank you Dr. Proscovia. By the time that Uganda went into a lockdown, which was March 2020, was conducting field activities that is recruitment. So we had to suspend a few activities. By the time we suspended our field activities, we recruited a total of 542 participants across 19 sites. These were 18 to 55 years and they reported to engage in sex work five days a week.
All of the participants we recruited were tested for HIV and STIs. From that, 41 percent are HIV-positive, and 11 percent of these tested positive for STIs we were testing. Of those that were HIV positive, we found out that 87 percent of these were already enrolled in ART, and 23 percent were – sorry this is supposed to be 13 percent. Thirteen percent were initiated on ART during our recruitment process. I also found out that 72 percent of those were HIV-positive and their viral suppressed.
At these 19 sites we divided into three so we had to categorize them. So we had the fishing villages located along Lake Victoria Shores, then small towns located along the highways in Uganda in the regions we are working in, and the rural areas.
We found that most of the participants were HIV-positive. From the fishing villages, that is 56 percent. They also had the highest number of positive for STIs with 18 percent. But also when you look at the viral suppression, the lowest number were participants with suppression rate which was at 66 percent.
Next slide. So the participants who tested HIV-negative were encouraged to enroll in on PrEP. So we had a total of 320 participants who were HIV negative. Of these 11 percent were already enrolled in PrEP by the time we recruited them into our study. We also had 53 percent of these enrolled on PrEP during the recruitment process and other field activities we conducted. We had 35 percent who refused the PrEP services. The reason that was given was that they were on medication. Also those who were enrolled in PrEP thought that their friends would think they were HIV-positive if they saw them with the tins of the ART.
After baseline and recruitment of participants, we give them four sessions of HIV risk reduction. So when looking at the attendance HIV reduction across the 19 sites, change from 71 to 74 percent. But when we look at the categories in the fishing villages, small towns and rural areas, we see the fishing villages are at the higher attendance compared to small towns and rural areas.
So we have five collection points. We have conducted 3 time points, that are baseline, six month follow-up and 12 months follow-up. So it is an interval of six months. From the graph we can see that fishing villages had the lowest retention rate at 12 months follow-up interviews, which was 87 percent. Next slide.
So what have we done to manage or to retain these participants into the study. So we have a detailed future contact form. So with this form we capture the participants telephone number, and other numbers, which where we can reach them in case we don’t get them on the original number or their personal numbers. We also capture their place of residence and other alternative places where we can find them in case we don’t find them at their original place of residence. We also have a strong collaboration with our community stakeholders, as Dr. Proscovia mentioned. We have a community board where we have ladies who also engage in sex work, who help us to trace these participants. We also have regular check ins with our participants to invite them for our activities. But we also have our established office in the study region, which has over 50 staff, who help us to trace these participants within the region and also outside the region.
I invite Dr. Fred to give the conclusion.
DR. SSEWAMALA: Thank you, so much Joshua. This is what we see preliminarily, and this is what Joshua, he may have misspoken on the line graph, but this is what the results really indicate.
We find that women in fishing villages tend to have high prevalence for HIV and STIs I don't think this is a new finding. It is not a new finding when you look at the work of most including our colleagues with the prerequisite science program, this is in line with that. think what we have to think about so what then? Why is it that these numbers are high? I think it has to do with this is an analogy of work, and the mobility of the population around the fishing villages.
We also see that woman in fishing villages were really less likely to be suppressed. Okay? So, we have to think about ways and how we engage women, who are very mobile, and they are engaged in sex work around the fishing villages, but also they are not all the time around the fishing villages, so they are very mobile in that they travel a lot.
For example, some of our women will tell us they will go over to Tanzania, and when there is no fish, they will figure out where else to go. So what can we do to make sure that this very mobile population is also engaged in this treatment?
We also finding that women in fishing villages were less likely, more likely to refuse taking on PrEP. And I think Joshua told us very well that many of them think that the packaging is not right, there is stigma still attached with PrEP, and like our keynote said, the PrEP optic is a very completed issue. So some women think that when they are with their customers, I do not want them to think I am HIV-positive when I am not.
Overall, when you look at their HIV attendance over time, at six months – at about six months or less – we keep them engaged. But with time, and it could've been COVID, it could have been several issues, it is reduced. So such issues definitely need to be highlighted.
Then we also need to think about if we are delivering an intervention, and these are prolonged HIV risk reduction interventions. How can we package it in such a way that in a short time, and when you get them during the system that they are definitely engaged. What we found in the area is that increasing the collaboration with stakeholders is truly important. We know that women engaging in sex work are listing the region where they work is illegal. But when you engage with stakeholders, and they know exactly what is going on, and they know why you are intervening to get these women in a safe home and eventually getting them out of this trade, then they tend to be able to engage with you.
So these are some of the implications that we saw. At least in our work, based on the preliminary findings, which was stated, which was extremely descriptive, but that is where we are right now.
So, in the next slide, I want to just say that if we are going to work with mobile populations it will take a village. You know, I am from Africa. It will take a village. It will not be one researcher. It won’t be a couple of researchers, but it will need a real engagement. It is not one group of people. It is not just women engaging in sex work. There are also men moving because of labor. There are women moving because of several reasons. So I think it will take a village. But it will also take this combination intervention, thinking about what works and what doesn't work.
So I want in closing, I want to end up with a team of people who work with us because as I said, it takes a village. So it takes a lot of people for us to do this work. We are truly grateful to NIH, specifically, NIMH, for funding this work that I just presented. I will end there.
Agenda Item: Conflict, Displacement, and HIV in Ukraine
DR. VASYLVEVA: Thank you for your presentation and I will take over for now. My name is Tetyana Vasylyeva, I will be presenting on the conflict displacement and HIV in Ukraine.
So, I will start with a very brief introduction into Ukraine and the Ukrainian HIV epidemic. I will talk but how we tried to study the effect of the current and ongoing war in the Ukraine on the HIV epidemic in the country. And I will finish with just a few slides on the very recent project called STREAM, where we studied HIV in internally-displaced people who inject drugs.
Ukraine is an Eastern European country. It has a population of 44 million people and about one quarter of a million of them are living with HIV. IV prevalence has been estimated around 1 percent for the last probably 10 years, and the epidemic actually started in the mid-90s in southern Ukraine.
Suddenly all of these reports were coming in about the rapidly growing number of new HIV cases in people who inject drugs, in Modesa and other southern Ukrainian regions.
Basically, before 1994, first of all there were very few infections of HIV cases in the Ukraine, but also, most of them were attributed to sexual transmission. Within four years, by 1998, more than three quarters of new HIV cases were attributed to injecting drugs. With years, this proportion changed and the ratio of sexual to drug injection transmissions was changing and now the majority of cases are again, attributed to sexual transmissions. However, it is still considered that people who inject drugs actually play a driving role in this epidemic because a lot of those cases, a lot of those sexually attributed cases are in sexual partners of people who inject drugs.
So, the number of cases grew rapidly in the Ukraine, and it escalated so quickly that it was one of the fastest growing HIV epidemics at the time. And what made it so, was a number of social, economical, and political changes in the country. Obviously, the collapse of the Soviet Union, which was associated with the rise of criminal groups and also a rise of drug use in the late 80s and early 1990s.
So those political changes pretty much ignited the HIV epidemic in the Ukraine, while just seven years ago in 2013 to 2014, a number of other political changes have affected the existing HIV epidemic in the country. It all started in late 2013, with what was called, Maidan movement. It started in Kyiv, which is the capital of Ukraine, people went to the streets to protest the recently adopted policies by the president at the time. And the first people to protest were students and then the protests were violently suppressed, and more people showed up to protest this violence. This all escalated quickly and left Kyiv, as you can see in this picture, the center of Kyiv burned down, but also this conflict spread to other parts of the country. And most importantly, they resulted in the annexation of Crimea in early 2014, and started the conflict in the eastern most regions of Ukraine on the border was Russia.
Up to this day, those two regions are separated in the areas that are controlled by the Ukrainian government and areas that are controlled by Rebel. These two regions, Donetsk and Lugansk.
Interestingly, as demonstrated by this figure, those two regions in the East, were also the region's most heavily affected by the HIV epidemic in the country. These two regions are very densely populated, and they have very high number of people who inject drugs and a very high HIV prevalence. In fact, as of 2013, right before the conflict started, a quarter of all people living with HIV in the Ukraine were residing in these two areas. So historically, these regions have accounted for a large proportion of the Ukrainian HIV epidemic.
Since the conflict started in 2014, even though before that we started seeing some decline in the number of new HIV cases each year, actually in 2012 it was the first year that the decline had been registered in the Ukraine since the beginning of the epidemic in the early 90s. But in the last years, the upward trend started again. This graph needs to be read with a grain of salt because of course, since 2014, we did not have the complete information because we do not have data from Crimea, and we do not have data from parts of the occupied parts of the Donetsk and Lugansk region.
Some of the public health consequences of the annexation of Crimea and of the conflict in eastern parts the Ukraine, were pretty obvious immediately. So Ukraine actually has a very strong harm reduction program in opioid situation treatment, in particular, and is easily accessible to people who inject drugs, but in Russia, methadone is prohibited so these programs are not allowed. Which means that once the Crimea has been annexed by Russia, a number of people lost their access to opioid institute treatment, which resulted in a number of overdoses and also, a lot of people were forced to move to continental Ukraine to be able to continue to access the services that they needed.
Another major consequence has been the massive internal migration within the country. It is estimated that between, since 2014, between 1.5 to 2 million people have been internally displaced in Ukraine.
Interestingly, majority of those people did not move far away from their previous place of residence. If you look at these numbers, the areas with the highest number of internally displaced people are actually the same areas that are affected by conflict. Because most people moved from the more affected areas from the capital cities of Donetsk and Lugansk to other parts of the Donetsk and Lugansk region where it was more you know, stable or more peaceful for them. So a lot of them had to move but stayed within the same regions of the Ukraine.
So to investigate the effect of this migration on the HIV dynamic within the country, we have used molecular epidemiology methods, and I will very briefly describe how they work and what they allowed us to do.
So we used phylogenetics, which is basically the study that describes the evolutionary history and relatedness between different species. And this relatedness it is usually depicted as a phylogenetic tree, which shows how related to each other we are, in our case, these viral organisms. Phylogenetics can then be extended into phylodynamic which basically studies how these phylogenetic trees were shaped by certain evolutionary or epidemiological processes. So it allows us to describe how these trees were formed in time.
These methods are often applied to measurably evolving populations such as viruses, meaning that viruses evolve so fast that we can pretty much track their evolution in real time, which has been demonstrated by the recent use of molecular epidemiology methods to confront the COVID pandemic.
These methods rely on molecular clock techniques which assume genetic sequences evolve at the relatively constant rate at the time. And on coalescence theory, that assumes that size of the study population is proportional to the time with which you can find a common ancestor to any two lineages in your population.
And, specifically in our case, we used a geographic method to answer our questions. Basically, phylogeography allows us to estimate the time and location of common ancestors of a number of viruses that we observe. So for example, if we sample HIV genetic sequences from different points of time in different locations like in Odessa Crimea, let us say, phylogeography allows us to estimate where and when was the most recent common ancestor of all of these sequences that we have observed.
So, in terms of data we used all of the HIV-1 subtype A sequences available from the Ukrainian Drug Resistance database. We looked at A sequences because they constituted more than 90 percent of all Ukrainian HIV sequences, it is really dominant subtype in the country. The Drug Resistance database basically includes all of the HIV sequences sampled as part of the efforts to monitor resistance to treatment drugs.
So, we used this data set and we ran phylogeographical analysis to estimate the direction of virus migration events between the regions. What we were able to show, was that the eastern regions, Donetsk and Lugansk, were the regions of origin of absolute majority of more than 90 percent of virus migration events within the Ukraine.
What that means, is that in our analysis, most of the virus migration events originated in the East and ended up in one of the other parts of Ukraine. First, we thought okay, this might not be too suppressant, it might have nothing to do with the war because the East was already the most HIV, the most heavily HIV affected region in the country. So maybe that was the pattern we observed even before. And then to investigate this further, we ran an analysis where we basically split the time into before and after the beginning of war. And what we observed, was that this pattern of East contributing infections to other parts of the country or originated after the war started.
So there was, significant support to the model that assumed that this was the newly established pattern. And we ran this analysis on a number of subsets, we ran a number of times, but our results were robust to this different sensitivity analysis that we are trying to test.
What we then did, we looked at whether this importation and exportation events - so the virus movement events, were correlated with a number of internal displaced people. And what we found was that the higher number of exportation events, so the higher number of events when the region was exporting the virus to other parts of the country was correlated with a larger number of internally displaced people within that region. It might sound counterintuitive at first, but as you might remember in one of the maps that I showed before, I spoke about how the majority of people did not actually move outside of Donetsk and Lugansk region. They stayed within those regions.
So a large number of IDP's residing in those regions that also resulted in the highest number of exportation events was actually what we should have expected. It also correlated with a higher number with high HIV prevalence in general population, which also we know is the case for Donetsk Lugansk region. However, here comes the big discrepancy, the higher number of importation events, so that is when the virus was introduced to the region, was correlated with the higher number of HIV-positive internally displaced people relocated to these regions.
So this means that because we have a large number of internal displaced people in Donetsk and Lugansk, but we have a higher number of HIV-positive internally displaced people in other regions. So this either means that HIV-positive displaced people choose to go to other regions because this is where it is easier for them to access care, and to continue with their treatment, or there are people who are staying in the regions a number of them are living with HIV, but they are not being linked to care. They are not accessing HIV treatment post migration.
Now, I will very briefly talk about our most recent work. This project called STREAM, which was supposed to be Spatial and Temporal Rapid Epidemiological Analysis and Migrants, but of course COVID forced us to cross out the rapid part. But this was a pilot project that allowed us to study HIV in internally displaced people who inject drugs in Odessa. Odessa is a region in southern Ukraine. It has a high number of internally displaced people, particularly of younger age between 20-45 years. So what we have done with this project was that we conducted a survey of internally displaced PWID. The survey included questions about their migration experiences. But we also conducted training of staff of Odessa Regional Virological Laboratory in genetic sequence and techniques. This is because unfortunately, sequence and capacity was very low in Ukraine and his has been particularly evident in, again, in the recent COVID epidemic. But this limits our ability to use molecular epidemiology techniques to answer some of the public health questions in Ukraine.
So we want to expand the access to the genetic sequencing training and techniques in the country. Our plan is to sequence HIV and hepatitis C, and then apply molecular epidemiology methods to the obtained sequences to answer some of the questions of the timing of the transmission events, and also the virus migration movement between the IDPWID community and the local community.
So the training, which was conducted in September 2020, was very popular with our colleagues in Ukraine. It was conducted by my colleagues, Anna Kovalenko from the University of Cambridge and the University of Oxford. They have been excellent in showing up to use this tiny device called a MinION to our colleagues in Ukraine. As I said, the training has been very well attended and very popular, and we plan to extend this work to other research laboratories this time in Kyiv, Ukraine.
The survey we conducted as a respondent driven sampling of internally displaced people who inject drugs. We started with HIV-positive seeds, and then we asked them to invite their peers into the study. In this manner, we have recruited over 160 people into our study. This was within just 2.5 months. About 40 percent of them were HIV-positive, which was a much higher proportion than what was observed in other surveys of PWID in Odessa.
But the absolute majority of these people living with HIV in our sample, were newly diagnosed so they could not know about their stages before their participation in our study. They were, of course, linked to care, immediately. Also, highly proportion of people were HCV positive by rapid test.
Just a very brief description of our sample: about 20 percent of our participants were female. A lot of them were from Donetsk, and a large of them were from Crimea, more than 20 percent, which was much higher than the proportion of displaced people from Crimea in the number of displaced people in Ukraine.
Interestingly, most of them have migrated to Odessa straight away – about 70 percent. So right after the conflict started in 2014-15, but about 30 percent of them have migrated to other regions, and then they migrated to Odessa.
The last point I want to make is that with the housing situation, is particularly difficult situation for this group because only 5 percent of them were homeless immediately after they migrated. Then at the time of the survey, 20 percent of them became homeless. I know I'm out of time, but my main conclusion is that this effect of the work on the Ukraine HIV epidemic that was described, I want to make it clear that this is not necessarily the new infections that we are observing. This is most likely just the redistribution of the existing HIV infections within the country that we are able to capture within our methods. With that, I want to thank my funders, fellowship and my colleagues in Ukraine, especially, and my Oxford team. Thank you.
DR. CLOUSE: Thank you all for those great talks. We have six minutes for a very brief Q&A. So if everyone can please keep their answers very brief. We will start with Dr. Sswamala and the group from Wash U. They say, "Thank you for a great presentation. You mentioned the complexity surrounding HIV risk among those highly mobile populations around fishing communities. I would like to know if you have any data on how these women self-identify with regards to their HIV risk.
In your data among the high-risk women how many admit to being involved in sex work versus buying and selling of fish, and if you have any areas where do the two groups may intersect? Also, how do you believe participants self-identification of risk may impact their acceptance of risk reduction methods?
DR. SSEWAMALA: Thank you so much for that question. So the women are to be screened fast. So because of this population since they are mobile, the women are supposed to be screened before they engage in the studies.
So they have to certify and identify as women engaged in sex work exchanging for money, goods, services in the last 30 days. So that was our definition of a woman who is engaged in sex work. So they self-identified, - so that is one.
Then 2, you asked whether how different would this group be? No, how was there self-identification exposes them to risk. Was that question? Was it something like that? Anyway, we do ask both the self-report, but we also have questions. So we run real test. As many of you know, for the self-reporting tests associated with sexually exposed behavior, most of them say they're not taking risks, I am using a condom. In most cases they say, "I am using condoms all the time." But when you test them and find their HIV-positive, some of them, and Joshua can answer this better than me, some of them will say, I do not know I was HIV-positive. So there were those who do not know there HIV-positive. They thought they were doing the right thing and using the condom and that kind of stuff. So we have self-report, but we also have draw blood, we have real biomarkers. I hope that answers your question.
DR. CLOUSE: Thank you very much. Question for DR. Ahonkai. Have you looked at the move rates and how they map on the southern states and counties of the national effort to address the HIV epidemic?
DR. AHONKAI: That is a great question. We are still in the early stages of this analysis, and it has really been focused on Tennessee. The population of people living within Tennessee. I think that is an excellent question and can collaborate with others to extend this work, which is like I said, not really highlighted as much domestically, but we can see from all of these talks that there are a lot of analogous drivers of mobility despite being in different communities and different countries that are contextually really important.
So I think that is an extension of this work that we need to do.
DR. CLOUSE: Thank you. A question, sort of two questions that are related to Dr. Vasylyeva. Can you comment on any unique challenges of conducting research on populations affected by HIV and the conflict and associated trauma? And also can you speak to the ethical, legal and social implications of conducting phylogenetic studies in Ukraine?
DR. VASYLYEVA: With the first question, obviously, mental health in these populations is a topic that has been understudied, and it is not a focus of my current research. But we are planning to include this into our further studies in Ukraine.
The challenge is that the migration, with the specific community, has happened six, seven years ago. There is a serious recall bias, and the trauma experienced by the population that had to migrate versus the local population, makes the recall bias very different in those communities, and difficult to assess any mental health input, six or seven years after this is happened.
But as for the legal challenges, this is been a hot topic for debate, in general, in the molecular epidemiology field in the last few years, it is important to say that we are making sure that people are not identifiable in any case, on any of the reports that have been produced by the study.
The benefits of this analysis, and my case, outweighed the risk. But this is, as I said, an ongoing discussion. I assume many people can have a different opinion.
DR. CLOUSE: Thank you. I think that takes us to the end of the session. Thank you to all of our speakers for excellent presentations. I believe Dr. Campbell will come in now.
DR. CAMPBELL: Thank you Dr. Clouse and all of the participants of the session. We are now going to a five minute break and there will be a slide projected in a moment that counts down or counts up, to the time we need to return. Thanks, and have a good break.
Session 2: New Methodologies and Approaches to Understanding Mobility
DR. CHANG: We are going to get started, we have a great group of speakers lined up. I'm going to quickly introduce them. First, we have Sally Blower who is a professor and director at the Center for Biomedical Modeling in the David Geffen School of Medicine at UCLA.
Our second speaker will be Kate Grabowski, who is a assistant professor in the Department of Pathology and Epidemiology at Johns Hopkins University, and a senior epidemiologist with the Rakai Health Science Program.
Then we are going to have a tag team of Frank Tanser, who is an infectious diseases epidemiologist and a professor at the University of Lincoln. Along with Adrian Dobra, who is a professor at the University of Washington and director of the Masters of Science and Data Science Program.
Dr. Blower, would you like to lead us off?
Agenda Item: Modelling countrywide mobility networks & HIV epidemics
DR. BLOWER: Thank you. Good morning, good afternoon and good evening, wherever in the world you are. So, the title of my talk is modelling countrywide mobility networks and HIV epidemics in sub-Saharan Africa.
The first thing I'm going to say is what everyone who is obviously listening in, participating in this workshop. When do we care about mobility, it changes everything we know, who is at risk, who is transmitting and where transmission occurs.
What I'm going to concentrate on and focus my group for the past few years, is thinking about migration of the entire general population. The scale I'm going to talk about is countrywide and the type of mobility, circular mobility, and by circular mobility this is from an overnight stay to being away from home for a year.
To give an overview of the material I'm going to cover, I'm going to go through some of the approaches that are used, some methodology to talk about geostatistical modeling, HIV epidemics and prevalence maps, gets to infection. I am going to talk a bit about modeling mobility in terms of mobility maps, connectivity maps and countrywide mobility networks using phone data.
Then I am going to put that together, talking about modeling mobility and HIV epidemics. To say something about current transmission models which will probably be a bit controversial, and then talk about what I am calling the next generation transmission models, and then spatial networks, this is very different from sexual networks or social networks.
MS. BOLLING: Sally, I am sorry to interrupt, are you sharing your screen?
DR. BLOWER: Yes.
MS. BOLLING: We are not seeing your screen, can you start the share over for us?
DR. BLOWER: The two problems I am going to briefly talk about at the end, talking about mobility and access to treatment and mobility and the spatial scale of interventions. Next?
This is a surface prevalence map, a geostatistical model using statistics and HIV testing data and basically use spatial interpolation and smoothing of this geo-referenced HIV-testing data. This is something now that is relatively common in HIV modelling.
And this shows Malawi, the national prevalence there is 11 percent. You can see the color scan is prevalence, and that goes from a low of about zero, up to a high of 27 percent. You can say obviously the average, thinking about the average prevalence, is not so useful when you are thinking about country level, the whole epidemic. That it is really high in urban areas.
The Isoclines, it is part of the methodology, the amount of smoothing you do when you smooth the data. And this is showing smoothing that involves moving over 300 individuals. So that is the prevalence map. As you can see, it goes from 0 to 27 percent.
Next please. This then, shows the density of infection and the density of infection is the number of people living with HIV, per square kilometer. Again, Malawi. And this shows, the geographic location of all people living with HIV, 15 to 49 years old and how the methodology for getting these maps, again this is sort of standard research now in our field. You take the prevalence map on the previous page, or the previous slide, and then you multiply it with population density map. Since you are multiplying maps, it is called raster multiplication. You can see now the prevalence gave us one picture, but now looking at the density of infection, we can see looking at the color scale, the density of infection goes from about 0 to 1 percent, sorry, 0 to 1 people living with HIV per square kilometer, up to over a thousand. These kinds of maps and the thousands of the red areas are where cities are, you can see also there are some areas along Lake Malawi, where there are quite a few HIV-infected people. These kinds of maps give you a view of prevalence.
Next please. Okay, this is a way of modeling mobility. What I am showing you here are connectivity maps and mobility maps. Anything you want greater details, you can find in these papers by Brian Coburn, in my group. And this again, you use spatial interpolation and smoothing. So you have geo-referenced data of particular sample sites and you can smooth it over the country. This is Lesotho, for those of you who do not know, and it is completely surrounded by South Africa.
Figure A shows the connectivity map, what is shown there is the percentage of households with adult members living elsewhere in Lesotho. So there's obviously a connection between people moving between these areas.
You can see it in the internal parts of Lesotho, the rural areas, the mountainous areas, up to 60 percent of households have members living elsewhere. So obviously quite a lot of connectivity.
In figure B it shows a connectivity map with the percentage of households with adult members living in South Africa. Not surprisingly, people who have a household member living in South Africa, they tend to live around the borders, and again, it is up to 60 percent of households. So people will be coming to and fro, who are in long term partnership.
Finally, a mobility map, showing the percentage of adults who made at least one overnight trip in the past 12 months. You can see here, this is up to 70 percent. So both the connectivity between households having members living elsewhere, generally a spouse, and people just moving around, there is an awful lot of mobility.
Next please. Okay, how can you model mobility at the countrywide level? You can do it with phone data and you have probably heard a lot about phone data, we have used it a lot for COVID. Here we have mobile phone data for the entire country of Namibia, how phone data, you can tell movement patterns is when you make a call, your call bounces off the nearest cell tower, you move somewhere else and it bounces off of that cell tower. So if you can get the cell records from phone companies, which do not really like to give out these data, but they do it, a few phone companies do, then you can work out these large-scale mobility patterns. If you look at the graph, it shows constituencies are areas and districts in Namibia. There are over 100 of them. This shows that over 25, 23 percent of people spent time outside of their home constituency in the previous year. So this is for the entire population.
And here, this is modelling a mobility network. So here, the nodes are the areas, the constituencies and the links are people moving between places. So there is nothing about sexual behavior or social behavior here, it is just moving in space.
So, to talk about the modelling mobility and HIV epidemics, how could he put that altogether? The problem with current transmission models – here's what I'm going to say, it is controversial to people who work in transmission models, is that the basic structure of most transmission models has not changed in the last 30 years. These models were originally developed to apply to concentrate epidemics in resource rich countries.
So basically for communities of men who have sex with men. They have got very complex in behavioral terms. These models are now being used for Africa generalized epidemics. The problem is they have no mobility in them. So, all of the transmission in these transmission models is localized transmission. The control strategies that you get out of these models, and basically all they can give is that you should target the high prevalence areas. So this is hotspot targeting. This is essentially because there is no mobility.
The next generation transmission models is I think we need a totally different structure, and some people are developing these. We published one in 2020, but, as I said, some of the groups that are developing them, these are specifically designed for generalized HIV epidemics in resource constrained countries. Most of the models are not complex yet in behavioral terms, and it is not clear if it is the first priority, but they mobility in them, and therefore you can have localized and mobility driven transmission. Thank you, next.
Okay, what you get when you put mobility and HIV epidemics together online with traditional transmission models, next transmission transition models can show you dynamic behaviors. These models can show that they are sources and sinks. So the sources are providing viral introductions, infections into other areas to term sinks. R0 – probably everyone now knows about this because of COVID, even my ninety-year-old mother knows about it, is the basic reproduction number, and the number of secondary infections that one person causes.
So in a source, the R0 is greater than one. In a sink, it is less than one. So on its own, if they are not linked then you would have transmission of the sub epidemic in the source, but he would not have one in the sink.
When they are joined together by mobility you then have 1 overall R0 for the entire system. If together, the source plus the sink, and the R0 is greater than one, which is above the dotted diagonal you can see the color figure, you can see that transmission on the X axis is the R0 in the sink, when there is no mobility. Then on the Y excess for the R0 on the source when there is no mobility. And you can see that if R0 is above 0.9, so it is less than one, it can be maintained by the sink. But if the transmission is very low in the sink, say .5, even if the R0 in the source is greater than 1, it is going to drag it down and there will be neither sub-epidemic won’t exist in either the source or the sink. This is because the people from the source are going into the sink and the transmission is too low in the two places together.
This shows a very much more complex model, next generation model, that my group, was just published in Nat Communication a few months ago. This is a spatial network again, and there was actually no sexual behavior and those social behavior. The constituencies were modeling Namibia. So a node in this network is a constituency, a spatial area, and what we are modeling are risk lows. People moving around. So risk can be exported through two mechanisms: people living with HIV in constituency in node X can go out and visit another node and potentially cause a transmission event. People from other nodes, uninfected people, can come into node X and potentially become infected. So node X can export a risk in two ways.
Node X can also import risk in two ways, people living with HIV and other nodes can come in and cause transmission events in node X. Then uninfected people in node X can go to other nodes and potentially get infected there. So node X can import risk into those two mechanisms.
So, here, we have modeled all of these two constituencies in the entire country. So you have over 100 nodes and people going back and forth between them based on the mobility network that I showed a few slides ago, that we got from the phone data.
So what you have then is inflow risk hubs. So some areas are incredibly susceptible to infections coming in, viral introductions from other areas, and some of the risk hubs that outflow risk hubs, and they are the most risky hubs due to people visiting and getting infected, or they are residents going out and causing infections. In this, we now have ability driven risk hubs.
Can I have the next slide? This is a risk network, and I do not expect you to read it. If you want to read all of the names of the constituencies, you can go to the paper. But this is basically showing you all of the connections between all of the constituencies due to people moving between them. This is based on actual data, as I said, the mobile phone data that tells us where people move. The prevalence is very different constituencies. It is 8 percent, and it goes around, anti-clockwise, and gets higher, and then increases to 39 percent.
So basically, you can see there are multiple potential transition corridors throughout the country. There is huge connectivity. There is the general population moving around and there is great complexity. Thank you, next.
You could analyze that spatial risk network using networks from physics and you can analyze that and identify the mobility driven risk hubs and identify them. The most important risk hub, the export of risk is the capital. I am sorry I am probably going to mangle the name, Windhoek West. This is the top out-flow risk hub and you can see all the places here linked to the capital, which is in the center, due to people moving between the capital and returning home. This is all secular transmission.
So it is connected to many places throughout the country and it exports risk to them. Next slide.
This shows the ego network of Windhoek West. What a ego-network is is it is showing very complex network, that I showed a few slides back. But just showing the part of it that is coming out of the capital. The most important thing to see here, Windhoek West, the prevalence there is 12 percent.
So the most important mobility risk hub that is exporting risk throughout the country is not a hotspot. In fact, it is basically the average prevalence, which is 11 percent. The reason it is a risk hub is because of the mobility. So much mobility of people going in and out. Next Slide.
So, that then just concludes that bit, and to show that we need to think about new control strategies that are not based on targeting hotspots but mobility driven risk hubs because they might well be, and in our analysis, they are not high prevalence areas.
This then is just to show new problems thinking about mobility and access to treatment, again, a paper that my group published in 2020, if people want to look at more details.
What you can do with countries, and this is any country, is a good workout a friction surface. This is showing a friction surface from Malawi. What you do here is you get a lot of a publicly available data on the topography, the road network, quality of the roads, land cover, and you can put this all together then work out this friction surface map. It will enable you to see how long it will take to cross any square kilometer in Malawi. So you can see it ranges from 60 seconds to 55 minutes. It obviously depends on mode of transport.
You can then put this together with an infrastructure map showing you where the healthcare systems are and you can workout geographic accessibility, how long it would take anyone anywhere in the country to reach their nearest healthcare facility that provides art. This you can work out depending upon their mode of transportation, whether they are driving, biking or walking. Next slide.
Now, this is just a threat a new idea of super communities about mobility and spatial scale of interventions. Basically, using methods from physics and the mobile phone network, you can work out that there is actually large areas of spatial communities that people spend their time in. So maybe the community where your relatives live, your friends live, where you work and where you live. You spend a lot of time going between that. So, that is essentially your super community. Thinking about interventions then, these are probably the scale that we should be doing when we are thinking about doing an intervention. Next slide.
So I just want to thank my collaborators: Vittoria, Justin, Eugenio. Also, the other collaborators whom I do not have pictured, Brian, Laurence Katie and Luckson. And finally, and certainly not least, I also want to thank NIAID for my funding.
Now, Kate Grabowski is going to speak.
Agenda Item: Elucidating the link between migration and HIV through epidemiology and phylogenetics
DR. GABROWSKI: Thank you for the wonderful opportunity for having me here today. The last talk was a wonderful lead in to what I am going to be discussion in the next 20 minutes. The work today that I will be presenting is work that has been done by hundreds of people at the Rakai Health Scientist Program, the Pangaea HIV consortium, folks at John Hopkins University and at Oxford. Really, without funding from NIH, and the Bill and Melinda Gates Foundation, none of this work would be possible. Thank you to our funders.
So, I think as we have seen from a number of talks throughout this webinar today, migration is really essential to understanding transmission dynamics in Sub-Saharan Africa. Early in the pandemic it was found to be a major factor driving the dissemination of the virus, and we continue to see here today that it is critical in modern HIV epidemiology as well.
The figure to the right is a phylogenetic illustration of viral dissemination out of the Democratic Republic of Congo that was reconstructed using viral genomes from the NIH public databases.
So, I really think that perhaps nowhere in the world is this issue of migration more important than in Sub- Sahara Africa, where the burden of HIV infections and AIDS-related mortality are concentrated.
We have seen us figure today a couple of times. This is showing the HIV prevalence per 5 km² in sub-Saharan Africa. You can see from this figure that this epidemic is very spatially heterogeneous. We have geographic foci of high HIV incidence in prevalence communities. Sometimes we refer to these areas as hotspots. As Sally pointed out in the prior talk, these areas can be concentrated – these high prevalence areas can either be areas of low or high population density. As Sally also noted, modelling has founded the geographic targeting of interventions and these hotspots can be cost-efficient, and can potentially ameliorate the broader epidemic, but really under certain conditions and assumptions within these models. So we can think talk a little bit about more in these discussions.
So, within Africa, Lake Victoria fishing communities are probably among the most well-known HIV hotspots. The areas that we particularly do surveillance in along the coast of Lake Victoria in Uganda, have adult HIV prevalence of anywhere between 25-40 percent. So it is among the highest burdens of the world. They also have among the highest HIV incidence rates in the world. They have been well profiled in the global media. So these are just two articles from The Guardian about how AIDS was unleashed upon Africa from these communities. These are all in the last couple of years, and how really these communities are fueling deadly HIV surges throughout the region, in the second article as well.
Because these communities have drawn so much attention, because of their high HIV burden, they were strategically targeted CHP, by the Ministry of Health, and they were labelled as priority communities for targeted control. If you read into the guidelines from the time, the recommendation explicitly states that fishing communities were not only being targeted because they had a high burden, but also because they were believed to be major sources of new HIV infections to inland population areas
So we started wondering, where does that information come from? Are there other examples within Sub-Saharan Africa where we can see these geographic hotspots that are driving transmission in lower prevalence areas? Do we have any empirical data to suggest that this assumption is rooted in reality? When we started delving into literature, we actually could not find any.
We sought to test this hypothesis that these high prevalence fishing communities along Lake Victoria were drawing the inland epidemic in the East Africa region. To do that, we did data from the Rakai Community Cohort Study, one of the largest population-based surveillance HIV cohorts in the entire world, ongoing since 1994. It is conducted in Rakai and surrounding districts. You can see it highlighted in that red box on the map. It is situated along Lake Victoria.
We currently have 40 communities that are under surveillance in the RCCS. Including four fishing sites along Lake Victoria. The Rakai survey itself, consists of a census, and an actual behavioral survey that includes HIV testing for all qualifying adults between the ages of 15 and 49 years. To qualify for the study, you have to meet the age criteria and be a resident of that community and be able to provide consent. It is a general population study.
The surveys are conducted every 1.5 to two years. During the census, we do a household enumeration. We document migrations in and out of households, including places of origin, destination, and the reasons for movement. We are able to look at patterns of migration both between our surveyed communities, but to elsewhere within Uganda and the rest of the region.
This is the HIV epidemic in Uganda. The top left-hand panel, you can see our different survey communities. The 36 different inland communities, and you can see an orange along the coast of Lake Victoria, the four fish landing sites. It was really important, that we understood population density, and case burden. Here in panel B – you are looking at the population density in the era. You can see that these fishing communities have very low population density, compared to the population density on the interior.
In figure C, we see that these fishing communities have externally high HIV prevalence. If you look at the overall case burden, most of it is concentrated within our inland areas. I think understanding hotspots in terms of total case burden, and HIV prevalence is really essential to understanding whether or not hotspots can drive transmission, and how important they are for targeting control.
Before we get into the phylogenetics portion of the talk, I want to talk about some of the analysis we have done looking at migration dynamics within the RCCS – particularly pertaining to the different types of communities that we survey.
If you look across our different 40 communities, which can roughly be classified as either rural, trading communities, these Victoria fishing communities, we see that it in any given survey, new migrants moving into our populations account for between nine to 49 percent of the population. Figure A here – this panel on the left, it shows the trading communities in yellow and fishing communities in blue, and the rural grand communities in green. You can see these semi urban trading centers, and in particular that have extraordinarily high rates of in and out migration which tend to be correlated with one another.
If you break this down in terms of HIV status, we find that approximately 25 percent of our total HIV burden in any given year, is due to new migrations. There is a lot of turnover of the HIV sero positive in these communities, some more so than others.
We saw from Carol that a lot of the plenary today that a lot of the migration within sub-Saharan African is hyper local, which we see here. Median place from place of origin is approximately 28 km across all of these communities. It is nearly twice as far as the fishing community. In these like Victoria fishing communities, we are getting people moving from all over the place. Whereas, in urban centers and trading communities, we see a lot more local migration – particularly around urban centers, there is hyper-local migration, like inside, and outside of the communities right outside where the urban centers are located.
If we look at sources of new diagnosis in the Rakai Community cohort study, we find that a little over 13 percent of our new diagnosis are actually from incident cases. So people we have followed over time that have been negative and turned positive. The vast majority of new cases that we are identifying as our surveillance efforts, are actually from individuals who are moving into our community and being newly diagnosed for the first time.
You can see that most of these in migrant diagnoses are among women. We have a substantial set of new diagnoses among people who are newly recruiting into our new cohort, because they are newly age eligible, or they were in the communities beforehand but had never participated. The bulk of our new diagnoses are coming from migration events into the community.
Like others, we see higher HIV prevalence on average overall, so these are figures – I think they will be hard to follow right now, but trust me, when I tell you that these figures show higher age-specific HIV prevalence among women, and agrarian communities, which are all the way to the left, trading communities to the right, and fishing communities in the middle. We see higher prevalence, particularly in these agrarian communities among migrants.
Men we do not see any elevated HIV prevalence among male migrants. Even when stratified by age, and community type. We do see however though that there is elevated HIV incidence among recent migrants, both men and women.
This was a study done by one of my master students, she found that within the first two years following a migrant event, women had nearly 2 folds the HIV incidence compared to permanent residence. This was also similar to male migrant. If we look past this two-year timeframe, we do not see any increase in HIV incidence. The other thing she found that was highlighted earlier, despite seeing overall declines, very substantial declines in HIV incidence in our study communities as a whole, we are not seeing declines in incidence among these mobile populations. Recent migrants continue to maintain very, very high HIV incidence while everybody else's incidence is declining with a combination of prevention treatment, underscoring the need to tailor new interventions to these groups.
One of the other critical things we found using census migration data was that with respect to these like Victoria fishing communities, we find what appears to be an assortative HIV mixing on the scale where HIV-positive persons, irrespective of where they are moving from, tend to move to areas of higher HIV prevalence.
If you look at these areas among the coast, you can see all of these red arrows – which indicates the prevalence of the migrating populations moving into these fishing areas. It does not matter where you are coming from, there is always read arrows going into these fishing communities. Where we see much lighter arrows going into the inland communities. It appears that these fishing communities are drawing very high prevalence of populations, and there is probably a lot of introduction of HIV virus into the communities themselves.
With all that epidemiological data in mind, we started doing some phylogenetic analyses to quantify how much transmission is flowing from these high prevalence communities to these inland areas. This is a lot of work that has been done in collaboration with Ratmann at Imperial College and with the Pangea HIV Consortium, which is a large Bill & Melinda Gates funded consortium, that is generating deep sequence data throughout multiple sites in Africa.
In this Nature Communication paper cited here, we reconstructed HIV transmission networks from individuals who were virally unsuppressed in our cohort communities. We took that deep sequence data and took a tool called phylo scanner to redirected HIV transmission networks, and when we reconstruct these transmission networks, what we identify are source recipient pairs of transmission.
We can use that to look at flow of infection across communities.
We took that phylogenomic data, and integrated it with individual level migration information to actually assess transmission flows across communities. There is a lot of sort of methodological details here, which for the sake of time, I will not get into too much detail.
Let me show you the results of these phylogenetic analysis. These show the geographic reconstruction transmission events. In the top left-hand panel, what we are looking at are transmission events that are occurring from inland communities to inland communities. The bottom right panel are the endogenous transmissions within fishing communities. The top right and the bottom left are the transmissions occurring across communities.
In the bottom left hand panel, we are seeing the number of transmissions going from inland communities to fishing sites, and on the top right, from fishing sites to inland sites. What you can see, there is actually more transmissions – more actual directly observed transmissions that we are observing, occurring from these inland areas to these fishing sites.
Our initial hypothesis going in, was that these hotspots are driving transmission and the inland areas.
In fact, we found an estimated flow ratio of more than 2.5 times greater from inland to fishing than vice versa. Kind of turning our hypothesis on our heads and really finding these lower prevalence higher burden communities are driving transmission in fishing communities more so than vice versa.
One of the critical things that we did as part of this analysis is just for sampling biases. I think this is an often ignored, or mostly ignored area. In phylogenetic studies we rarely have good sampling fractions from our populations of interest. If you have uneven sampling fractions, you can find places that look like sources when they really are not. So, we did a lot of work to adjust for the underlying burden, case burden, within the population, the sequencing rates within populations.
What we find when we do that, all of these adjustments on the right, is that more than 88 percent of all transmissions are occurring within the inland communities, just 5 percent within the fishing communities themselves, and there's relatively few cross-community transmissions, 4.3 percent from inland to fishing, and 1.7 percent from fishing to inland. Again, these cross-community transmissions are really being driven in the opposite direction from what we initially hypothesized.
This is a cautionary tale that you need to do these adjustments, particularly if you work with phylogenetic data.
And then, just wanted to show a couple more figures before I conclude. Here, we are looking at sources of transmission by migration, status and sex. One of the big questions we always get asked is are these migrants driving transmission in the population? We start to look at that. We did this separately for fishing communities and inland communities.
Here, what we are seeing is that in these fishing communities, resident men who lived there. tend to be the source of most transmissions, following by resident woman. And then in migrating men and women contribute to substantially less transmission.
When we actually stacked this up to what their overall prevalence of these population's, it is very similar. What we do not find is that in these in migrating individuals are driving transmission in excess of their overall prevalence in the population.
If we look at inland communities, you can really sort of see this outsized role for resident men in driving transmission, compared to resident women, and either in migrating man or in migrating woman. This is a group, this resident male population, is really the group we really want to be thinking about targeting.
In this last panel, what we are looking at our the proportion of cross community transmissions that are being driven by people who are migrating between communities versus people who have external partnerships and then come back to those communities. We see migration contributes much more substantial role here, but again, a lot of it has been driven by residents who have external partnerships and are introducing HIV by that mechanism.
Just a summary of our phylogenetic findings; we find that the lakeside and inland epidemic is largely distinct. We do not see a ton of cross community transmission between them. But, when we did, we found that the net flow of infections was actually from low to high prevalence populations, rather than vice versa.
And then, the last major messages that we did not say that these migrant populations were accounting for transmissions in excess of their numbers in the population.
So, all of these findings tie nicely with the mobility -mobile phone information that Sally presented in the last talk. We need to start thinking about what we are talking about with hotspots. Is it geographic hotspot targeting really makes sense in certain situations?
In conclusion, we find that migration is common in positively associated with HIV prevalence and incidence. We find that migration of positive person from low to high prevalence areas may underpin hotspot dynamics in some cases, like we see in these Victoria fishing communities.
While geographic targeting of high prevalence areas is essential for local populations, under some conditions, it would really have a limited effect in neighboring lower prevalence populations. Lastly, I think, when we think about those Guardian articles, the results should really caution us against equating and stigmatizing people who live in these areas as population groups that drive diseases spread.
There are a large number of people to acknowledge, and I will just leave this slide up so that you can see their names. Thank you very much.
Frank Tanser will share some information about South Africa.
Agenda Item: Development of high-dimensional multivariate spatiotemporal models
DR. TANSER: Thank you, Kate, I think you for that excellent presentation. Good afternoon everybody. Pleasure for me to present this afternoon on human mobility and HIV acquisition in rural South Africa. I wanted to start with a introduction to the epidemic South Africa. In South Africa the exceptionally high rates of internal migration are rooted in apartheid era policies aimed at ensuring the supply of rural African male laborers, especially to the mines.
Presently, high rates of circular migration continue and are high for both men and women. It is no longer a dominantly male characteristic. From the onset, migration HIV has been closely intertwined.
The work that I am speaking on this afternoon takes place in the Northeast of the country of South Africa, in the province of KwaZulu-Natal. KwaZulu-Natal is widely regarded by many to beat the global epicenter of the epidemic. In this population, about 30 percent of the entire population are infected with HIV.
The map on the right shows the HIV surveillance where this work is taking place. In that area that you see there, there are 90,000 people and every adult of that population is eligible for population-based HIV testing. That is taking place since 2004.
This is one of the largest incidence cohorts in the world. And now, about 3500 incidence infections observed to date, about 105,000 person years of observation. Those large numbers give powerful statistical inference was not that is what I will be able to tell you now. Can we go to the next slide please?
This is the predominantly rural population. It is a very poor population, it is the second-most deprived district in South Africa. Although it is dominantly rural, there are pockets of urban populations.
The largest of burden of new HIV infection has fallen on young females. This slide is our very first cohort of fieldworkers in about 2000. Carol Camlin would have been at the center at the same time. I don’t have a picture of her so I could not put it in.
Of his original cohort of fieldworkers, four succumbed to disease that they were helping a study. Really tragically underlying the setting in which we were operating. But the good news is that things are changing and I will show that now. Next slide please.
The incidence landscape is shifting, and it is shifting quite radically. This publication published in Nature Communication showing a 43 percent drop in new HIV infections, and reductions in both males and female, which is encouraging. Although, drop in females started slacking later, it has had a radical drop.
This has been coming out in PNAS shortly. It shows that not only are incidence reducing radically, but the age patterns of incidence are changing dramatically. There has been a seismic shift. If you look at the red and compare them with the green curves - so the figure on the left is men and the figure on the right is women. You can see how the burden of HIV has reduced, but it is also shifted to the older age groups. Men between 2004 and 2019, the median age of sero conversion has increased by 7.5 years. In females, it is increased by 3.5 years. Prevention packages are going to need to take cognizant to the fact that things are changing rapidly. The reasons behind this are explored in this paper, which will be coming out shortly.
I guess the question is, if we think about migration and we know that it is a dynamic concept, and we superimposing it on a dynamic epidemic context, what is it going to look like for the future?
This is work done by PhD student, who does a systematic review looking at all migration studies in South Africa that took HIV incidents as an outcome. And showed that risk of infection among migrants is about 70 percent greater than a non-migrant population. Substantial additional risk.
Then we looked at migrant women and compared risk of HIV acquisition before and after the roll out of ART, taking 2010 as a cutoff date when ART was scaled up. What it showed was that there was a substantial drop in risk of HIV acquisition amongst migrant women. However, there was full substantially greater risk compared to the non-migrant population. So, in a sense, although all risks of HIV infection are coming down, there still substantially higher among the migrant population. That is not changing.
So, what I want to do now is given that migration is an inherently spatial phenomenon, I want to quickly, the time that is available, and Adrian Dobra is going to be sharing this session with me, so he will be speaking about the high dimensional models that we are going to be doing. But, I wanted to look at HIV risk of space as far as mobility is concerned. The macro, meso and the macro level, just taking snapshots of different works that we have done recently.
This is work led by Adrian Dobra, published in AIDS in 2017. This shows all of the migrant destinations with the tiny study area that I showed you before, which is about 20 km x 20 km. I was quite staggered when I saw this map, because one thinks of the tiny community in the northeast of the country, to some extent interacting with the rest of the country. Just underlines the mobility, how mobile these populations still are.
One of the things that I wanted to investigate with this work is the extent to which distance migrated increases risk of HIV acquisition. Because our hypothesis is that distance is a crude proxy for separation from one's family, and the further the distance migrated, the more separation, the greater the risk.
What we saw is a very clear relationship between distance migrated and risk of HIV acquisition. At about 40 km distance, HIV acquisition increased by 50 percent. A little bit further, to Durban, 169 km, increased 75 percent. And then after that, gradual and clear increase in risk of acquisition of infection. Really showing how clear that relationship in risk is. If I go back to the previous slide, please. You might be able to see Richards Bay, which is about 50 percent risk, Durban is about 75 percent risk and then increasing thereafter.
Go forward two slides please. That was some work we did at the country level, now we will speak about the meso level. We have spoken about these high-risk communities. This work published in International Journal of Epidemiology, showing clear clustering of new HIV infections in those two ellipses you see there. The blues are the lows, less than 2, one new infection per hundred person years. The reds are greater four, up to six infections per hundred person years. In those two ellipses there, there is a significantly higher number of new HIV infections, then we would be expected given the underlying population distribution.
The key question is then to what is the extent these high-risk communities, are they interacting with other parts of the study area? I think as Kate and Sally alluded to, there is sometimes been an over simplistic thinking about these sort of communities, that they just continually seeding epidemics elsewhere. I think there is a more complicated story than that and I will demonstrate that now.
This is some of the phylogenetic analysis we've done with poll sequences. Each line represents two individual linked by the genetics of the virus. What one can start to see is a substantial proportion of these linked infections seem to walk back to these so-called hotspot communities, which are kind of the peri-urban communities. Providing some support for hypothesis, these are involved in the transmission dynamics. Not necessarily as a sort of engine just seeding migrant epidemics because some of the infections are going in, some of the are coming out.
These phylogenetic analyses are sometimes subject to limitations. They need to be triangulated with other data, and if we can go to the next slide please.
This is work done by Hae-Young, where we took 2000 new sexual partnerships and looked at the spatial dynamics. We broke the study area up into a grid, you can see on the left – the red squares are the squares that are contained within those hotspot communities. What we see is that these hotspot communities are highly connected nodes. They have the new partnerships of people coming in, and people inland making partners without. 76 percent of those new sexual partnerships – almost 2000 sexual partnerships, involve those hotspot communities. So far more than would be expected given the amount of people there. Essentially, these are highly connected nodes. Therefore they could be substantial prevention dividends gained in those communities.
Really, three lines of inquiry; the geographic phylogenetic, sexual behavior, all essentially showing the same picture.
The next is the microlevel. This is really the research frontier. It presents a lot of opportunity for preventions, and treatment. Understanding migration and mobility at the micro level. Next slide please.
This is work we will be doing called the Sesikhona Cohort. The Seisikhona which means "We are here." It will enroll a thousand young adults that will be will e given Android smart phones, will use those smart phones to study the link between different mobility patterns and risky behaviors. They will also use the smart phone as an intervention device through precision messaging.
There is some pilot data you can see on the left, showing that it works really well. Participants will have their location recorded every hour, those will be fed into algorithms, and they will receive precision messaging based on the location. If they are in a high prevalence location for example, they will have appropriate messaging. Adrian will be speaking on how we analyse the data, and how we will be analyzing this data. Watch this space. It is in the early days, but I'm very excited about that. Next slide please. Next slide.
Conclusion before we move on to Adrian, next slide please. The HIV epidemic and Human Mobility remain inextricably intertwined in South Africa. In the context of a rapidly evolving HIV epidemic, and declining incidence rates, migrants continue to be at substantially high risk of acquisition of infection. About 70 percent.
To drive the epidemic to low levels of endemicity, and be sure that no one is left behind, that means tailored interventions to reach the most vulnerable groups and communities, of which migrants are particularly large and important group.
Understanding short-term mobility patterns and relationship to HIV risk presents clear opportunities for reaching and intervening successfully in migrant populations. One might hypothesize that as the epidemic continues to retreat, migrants will become proportionately more important to make sure we reach these kinds of vulnerable groups.
Many acknowledgements, lots of people helped with this. Thanks to the funders. Thanks to the NIH and other funders as well.
Here is a list of papers I have briefly referred to but you are welcome to read those, if interested. Thank you very much! I will hand it over to Adrian.
DR. DOBRA: I will be talking about a couple of technical aspects related to mobility, and that relates to how we actually think conceptualize mobility from the data that is available. I will try to highlight two questions that are hidden in the research, that we are doing. However, I do not feel that these questions have been explicitly addressed. Although they are crucial.
For example, one question that is currently very little studied, is the question of; how long should we look at people before we can say we know about their mobility patterns?
Some of the data that the studies are currently publishing, are looking at data that comes from surveys, so people being asked where they have been, so on. However, that means that we are basically treating everybody in the study respective of – say their demographics, or age group, in a similar fashion. Is this the right thing to do? Should we differentiate, with respect to demographic characteristics and known risk factors?
The second part, I would like to highlight how we should see the movement of an individual, and propose a model that will help us with this question.
For the first part, figuring out how long we should look at the mobility of an individual with data from an individual, before we can say that we know that they have been studied like this in some sense. I worked on the framework that identified activity distributions over space and time. This activity distributions showed where people spend their time conditional on a graded structure. This activity distribution takes into consideration, movement between grid cells, and time spent in each grid cell. Conditional on this then, we can obtain maps that we can use to figure out how much change we have in those maps across time.
What these maps show, for example, there is not clear difference between stability of mobility patterns between men and women, which is something we would already guess. However, when it comes to looking across age groups, there are key differences. For example, for older people, it takes less time – significantly less time to identify the key locations where they visit. However, for younger people, it takes longer. We should look at them longer. In order to figure out the key locations where they are, the location they visit most. That is because their mobility patterns change more across time.
If we are also interested in identifying those locations that are visited less often, then we should look at increased periods of time across age groups. It comes down to studies in their design, making this difference between demographics, age groups and risk factors of the people that are involved. We have not seen much discussion of that in the current literature.
The second question relates to how do we model location data? There are approaches for doing this in the literature. What I want to think about here is a person who spends most of their time in that red square, right?
They have occasional visits, longer trips outside of that. Right? If we blow up that area, that triangle where people spend most of their time, we see there is a pattern there. This pattern follows locations that are important. They are important hotspots of mobility, for this person. We also see a lot of movement on roads, where people are using road structures. It is important to figure out that you have models that clearly capture those hotspots of mobility, for one person and then movement between these hotspots on the road structures.
In the past, activity spaces have been modelled and determined by imposing a predetermining structure for them. The idea in the structure was to capture anchor locations – those important locations that are most visited, in addition to those other locations that are less important. Of course, map them and so on. However, imposing a predetermined geometry such as ellipses or polygons is not the right thing to do because the shape of these activity spaces varies quite a bit because of the way individuals are moving.
For that reason, I worked on developing a model that is a mixture model that has specific components for the hotspots, another component for movement on the roads. As well as another component for movement around the anchor locations.
What I want you to think about is an example of an individual who has three hot spots of mobility, three anchor locations. Three locations they visit most. Those are the sides of the strangle. This individual travels on the edges of the triangle. Between the locations. They spend around two or three anchor locations. This gives a mixture model with three components. One component for each type of movement. What I want you to see, geometrically, movement takes place in spaces of different dimensions. That is something that has to be explicitly taken into account.
Using these specifications, it is possible to identify with better precision, the shape and geometry of the activity spaces because these three components are explicitly taken into account. It turns out, this makes a big difference when it comes to identifying relevant measures of mobility based on activity space. Just as an example, these are 8 individuals that happen to live in the same area So, those are there raw GPS measurements.
Without going into any details, these are how these three component mixture models capture their mobility patterns as you can see. These modules are able to capture the key anchor locations, those are the red points. Also, mobility or the road network structure between these anchor locations, as movement around the anchor location.
Why is this important? This is important because of everything we want to do in mobility studies. So, when it comes down to designing GPS studies, designing human mobility studies, we need to take a differentiated approach in the duration of time that is needed to observe individuals, depending on their sociodemographic characteristics. We need informed ways to figure out the length of the study and the frequency of the observation that we collect.
Then, based on these three component way to represent activity spaces, we can then figure out overlaps between activity spaces so that we can look at networks. Of course, the question of how to embed data into GPS studies is very much open. There is a lot of work that is left to do in this area. With this, I know that we are over time. So, I will stop here. Thank you.
Agenda Item: Q&A
DR. CHANG: Thank you very much Dr. Dobra, and all the speakers for those excellent presentations. We are running a little bit behind, but I think we have a couple of time for a couple of questions. And maybe I will start with Dr. Blower. Dr. Blower, the cell phone seems like an incredibly powerful tool. But, I was wondering if there are any pitfalls or issues that researchers should think about when working with or interpreting cell phone data and making inferences about mobility?
DR. BLOWER: I think mobile phone data, the biggest problem is that we do not have enough. I think, what has been absolutely one of the most amazing things with lots of the COVID models that have been published to show how mobility is so important to understanding transmission. And also, understanding the introduction of COVID into different countries.
So, I think HIV, we have to get to get our hands on these kind of data and we can do some really interesting novel things. The one thing that is very different or problematic with HIV, is that it is not possible to know the gender with the mobile phone data. There are different patterns. So, that is one big issue. But, I think, that is also something methodologically that people can start to think about.
DR. CHANG: This is a question for Dr. Grabowski. Would you consider members of the military as part of resident men or mobile men? I'm curious about what HIV risk they may contribute?
DR. GRABOWSKI: I think it is important to distinguish between people who are mobile from migrating populations. Migrating populations are among those who are mobile, but mobile people are also consist of people who may briefly leave the communities and come back. We have not specifically looked at military populations who maybe periodically move away and then come back.
I will say that they comprise of a very small proportion of our overall population. So, in terms of their occupational prevalence in the general population, that is less than 5 percent. So I think it would be pretty difficult to look at their role, just given the size of that population. I do think it is an excellent question, it highlights some of the limitations over our research, in that it is mostly focused on people who move and stay at a location with intention to stay there, rather than people who are briefly moving back and forth.
DR. CHANG: Great. I have one final question for Dr. Tanser. The distance migrated also correlate with infection risk for women also, or was it nonsignificant for women?
DR. TANSER: It did correlate with infections of women, to attain the 50 percent of additional risk group, required a further migration of it. So, I think men sort of hit the 50 percent additional risk by 40 km. Woman hit that just over 100. It was not quite as noticeable, but it was there.
DR. CHANG: One final question, this is for either Dr. Dobra or Dr. Tanser. How are you measuring GPS in your new study via cell phones? What app are you using?
DR. TANSER: it is an app called, “Ithica”. It is used by a lot of different researchers across the globe. It has built-in capacity for questionnaires and targeted messaging, already built in it.
DR. CHANG: Thank you again for all of the speakers. I think that was an excellent session. We will move on to the next one.
Session 3: Personal Perspectives on Mobility
DR. PADILLA: It is a huge pleasure for me to be moderating a discussion with two incredible professionals and activists who have deep experience at multiple levels with migrant populations and migration research.
I would like to welcome Bernard Cruz and Pari Mazhar. Despite their different migratory trajectories, I think both of them illustrate the power and the resilience of migrants, their contributions to research and advocacy in multiple levels and the wisdom and humanity of their first-hand stories of migration.
Bernal Cruz migrated from Guatemala in 1990 in the context of the Civil War in his country of origin. And now works directly with migrant communities in multiple positions that range from policy to activism to research.
One quality that is near and dear to my heart, as an anthropologist, is the self-description as a storyteller. Which I believe provides great power inhumanity to his work.
Pari Mazhar is a licensed clinical social worker who was born and raised in Iran and who has experienced many of the social and legal challenges that migrants often face while also showing great resilience in making her way through the asylum process and now dedicating her life's work to issues that deeply affect the health and well-being of migrants and other marginalized communities.
So, Bernal and Pari, you both bring such important perspective into the workshop stop and wonder, the beginning, if you can summarize some of the work that you do, for whom the advocate, and how do you envision your role in that work? How does this work inform your research and collaborations with researchers? So maybe we can start with Bernal, approximately five minutes each. Bernal, when you're ready, go ahead.
Agenda Item: Advocacy and Points of Consideration
MR. CRUZ: Thank you for that wonderful introduction, I blushed a little bit, that was very kind. I do want to also recognize the fact that Pari and I both consider ourselves storytellers, and of course, identify with the powerful experience and the power of healing that storytelling can help. Thank you for including that bit.
As stated, my name is Bernal Cruz. I am from Guatemala, originally. I currently live just outside of Portland, Oregon. I identify myself as a migrant and an asylum individual. I came to the U.S. right in the middle of the Civil War in Guatemala. I was a 13-year-old at the time. My current practice is that I am a social worker. My foundation is in community-based social work. Not very coincidentally, I work with unaccompanied children were currently in the custody of the federal government.
That has been my paying job for the past six years. But, I spent a lot of my time in other endeavors. I work currently is the president of Rain International, we try and do a lot of assistance for programs throughout the world trying to help migrants integrate and help communities develop an environment where migrants can be welcomed.
And I spent a lot of my time also in local endeavors. I've worked with local counties public health advisory council. I currently serve on city commission that is really pressed with trying to understand how policies that are being rolled out may affect new arriving members of our community. It is a thoughtful process that is involved. It was actually sanctioned by the city. So, Portland can be really progressive city. I've recently done consultant work with HIAS(?), specifically in the dissemination of education materials and training for fieldworkers. Working with arriving Venezuelan migrants in Guyana, mostly around the subject of mental health and psychosocial support. And, I also spent my time, locally, doing some guest lecturing, storytelling, currently, I am a Ph.D. student with the University of Lisbon on their migration studies and social psychology.
I feel that the practice that I really stem from is, like I said, the foundation is community social work. So, all of my endeavors, whether they are actual practice, advocacy, activism, or research oriented, I sort of have that focus in the end that is really where I stem from and the space that I served claim to be the origin of my practice. So, thank you for allowing the introduction.
DR. PADILLA: Terrific. Pari, should we turn to you, whenever you are ready?
MS. MAZHAR: Yes, hello. I am sorry that I have to have the mask and remind you that we are in COVID. What would you like me to say? Talk about me, who I am?
My background is currently, as Mark introduced me, I am a clinical social worker. I was born and raised in Iran, lived through the revolution of 1978, and the war between Iran and Iraq. Due to my activism and human rights activism, my activism for women's rights. I was having a real problem and I was arrested a few times. In my third arrest, at a very young age, we realized that my life was not safe in Iran. Myself, I went to Turkey in 1995, in 1986, I came to the United States from Turkey and I obtained an asylum.
When I was granted asylum based on the regular process, six months later, you will get your paper and you will start the process of getting a green card. But that alien number, think about that, think about the alien number that they give to us. It stays with us forever, and we are an alien with a number. That is our welcome.
It took 14 years, the six-month process took me 14 years, that overall process was 20 years. That is why and how, but I lived with many other people in the limbo of 20 years of wondering what will be. In case of the risk of deportation, -- but I have to imagine about all of those things.
Finally, during this time, I worked 2 to 3 jobs and put myself through the school because after banning from the University in Iran, after my arrest, or any University, that was my termination. If I escaped, I have to do my studying and graduate school. In 2001, I was able to get my naturalization. In 2002, I was graduated. And finally, I became a citizen.
I graduated. I have been working over 25 years as a social worker, in different capacities. The majority of it I have been directing, and designing a program for children, families, LGBT youth. In the last several years, I have been very focused from the beginning. It was part of my identity. I work with (indiscernible) behavioral health, a large community based program or health center. I worked for nine years here, and other community centers with vulnerable population, including LGBT and refugees, and immigrants, and I worked in developing and improving the service for them.
I was a director for child and families for years, I saw at least a few clients for many reasons. It was a cultural relevance, the services to provide. Some was the language, some was intersectionality issues so hospitals sought me.
The last two years, all of my work, I'm a senior director of equity and diversity inclusion. All of my clinical work I work from a framework with equity, I am an activist, I'm a social justice activist. They are all connected. They are integrated for myself. Mark, allow me to tell my participants. These are all forms of my identity I connected to. I want to tell you – there is one aspect to me that is a true self. You may raise your eyebrows. That's okay, too, but I have to be transparent. All of those things, they're all my relations. That is who I am. I am the community connector, I am the relational person, and this is for all my relations to date. I am also an infinite potential – like many of you sitting in this line, and infinite possibilities. Why my consciousness – my real identity and through self, I believe everybody. I cannot impose on others. It is the awareness at this moment that I'm connected to you. Connected through language, intellect, to really connect my soul – some call it the spirit, some call it the soul. I call it an awareness of consciousness. My conscious being with you. I may go in and out of the space, but that is who I am. That is the true identity of who I am.
DR. PADILLA: Pari, I really appreciate that. I love the humanity that you bring to this discussion, and I think one of the things as I listen to both of you, speaking, and as an anthropologist – when you mentioned the alien number, and what it feels like to be labelled with a number.
As an anthropologist I love those morsels of richness of what it's like to live in the shoes of someone who has gone through what you have gone through. I want to focus us on some of those human aspects. I think when we discussed previously, this idea about scientific objectivity, and I think it would be interesting to talk about it to the group. Sometimes scientific approaches strive to achieve this mythical objectivity, a term that is sometimes synonymous with the idea of a well-designed study, in which you do not have investigator biases. You do not unduly shape the findings of your research project, because you are burdened by other voices in your head.
When taken to the extreme, a researcher might believe that greater distance from the populations that he, or she studies, is a sign of a well-constructed research project. And unfortunate results of that could be that it can distance the researcher from real lives, and people and the individuals that have the greatest stake in what we are doing with this work.
I would love to hear both of you reflect on how scientists can remain 'objective' in their research, while also staying attuned to the voices and experiences of migrant communities. What practices can they engage in to ensure that the human element remains within their scientific products? Either of you can go right ahead.
MR. CRUZ: I'll go first, if you do not mind. Thank you for that. What a rich discussion. There is a lot of philosophical undertones implicated. Mainly, the questions of what do we know and how do we know these things? I think this delicate balance that we strive for in trying to uphold humanity in the work that we do, while trying to remain objective and try to ensure that our work reflects quality work that is useful and that can advance the field – that is in important thing to observe. I borrow here from my own clinical practice, and I want to remind folks that I started – my trajectory was a very clinically oriented practice. I ended up becoming a community-based social worker. I daresay that I have become a better community social worker, because I feel like I have a good grasp on critical aspects.
The clinical development of what I call my clinical chops, and my ability to assess - I don’t want to focus on just diagnosis, but really understand how it had a lot to do with my own reflection. Seeking supervision and trying to understand – what I'm trying to still understand, right, of this ongoing trajectory of learning. My positionality, and what may appear to who just gets to know me, this apparent coincidence that I came to the US as a teenager, and I now work with teenagers who are entering the US. That is not a coincidence that is lost on me. There is something profound about that.
I did not require years of therapy to get there, but I required years of supervision to understand my positionality because we are able to detach ourselves completely from any subject that we undertake as a matter of study. I think that would be a principal thing. I think all of us need to be able to recognize that it is a factor to detach yourself from that, by asking yourself the question that you are asking by virtue that you are designing the study and the way you are designing the study. You are in that way unable to detach completely.
Do we work harder to establish a distance between us and them being our subjects? Or do we uphold humanity, and recognize the importance of a relationship that can exist between research, and subjects, to the end of, ultimately, to the point of actually developing knowledge that can be collaborated? It can be a collaborative process of knowledge that can ultimately benefit the communities that you are actually working for.
To me, it comes down to the three aspects of reflexivity, recognizing your position, and the ability to be deliberate, and determined to understand the ways in which your interpretation is being shaped by your lived experiences and by the real cultural reference frame that you come from. That would be my two cents. It is an ongoing process that requires a lot more reflexivity and a lot more understanding of oneself, and position, I believe.
MS. MAZHAR: Very well said, Bernal I do not know how to answer this. Let me put it in this way. I have to go through at least three ways that are interconnected to answer this question. If we look, as I will not repeat what Bernal said, power dynamic positionality and relationship - if we are looking at certain population that I am working, and I have been working with, the immigrants that are displaced from their country of origin, no matter how many places they've lived. We need to look from a trauma informed approach, and lens. Resiliency and equity lens. Today at least, we talked about this today, quickly. In the words of trauma, there are certain questions we do not ask. We do not ask why, but we ask how. Right? In behavioral health. How certain experiences are impacted in your life, that it manifests itself in your behavior, shaping your life pattern. That how is important.
Certain data marks can be collected, I apologize, I am not a researcher, but I understand some a little bit about the research. So certain data is okay. You can collect it and it is useful. If you talk about the trauma of the people and backgrounds with displacement, gender gaps, poverty, economic stability, or instability. Those sort of things in a different methodology, we can use to get to that data.
If we ask about the 'how', we need to know the story of people. We need to put the face on the humanity of people. Not just because so you can feel bad, no. Trauma to trauma, person-to-person, family to family, nation to nation, impact is different. Resilience is different.
How do we know that how? How did you cross the border? Did you come through the mountain? Did you come through the sea? You lost multiple losses. That compounded trauma. We are not talking about one traumatic event. We are talking about the chronic and compounded trauma. That has a very specific impact with variation.
The first specific impact we know is under the health of individual, and psyche of the community and health of the community. We talk about the resiliency, but let me tell you something, resiliency and trauma, and equity has been thrown as things. Resiliency costs us. For some of us, resiliency is the matter of survival. For others, it is a luxury and another skill.
Resiliency, when it is a matter of survival, is not enough. It does not promote health. It does not promote the drive. We need to look beyond that. When we look beyond trauma and the impact of it, we know scientifically what happens with that. When traumatic stress is continuously, the first thing it does is physical and biological, I was talking to the other day epigenetic concept, it impacts the biology of our emotions. Some scientists, maybe they do not believe in the biology of it. We know that the brain is everywhere. It is not just here. Everywhere there is a brain, and memory. Trauma, as well. In order to understand that, it does not mean that they have to tell the detail of their story, but we need to know the spirit of how it is being impacted.
DR. PADILLA: I appreciate that. I think what you are emphasizing about the incorporating of stories, people's personal histories, is something very practical and to link it to some questions because I'm getting some questions from the group here. There is a question that is potentially for Bernal to reflect on what we are discussing about with the human element. If you could recommend to researchers, one human aspect that could improve a research study, perhaps something involving the training of researchers. What would that be?
MR. CRUZ: I am not sure if I understand the question correctly, but I will give you my go to answer. Any opportunity that we have to uphold our own humanity, it is really an interest of all of the people that we are trying to help. I think it is really easy for us to lose sight of that sometimes. I think it is easy to fall into the practice of upholding this apparent need to develop this distance between our subjects or the participants, and ourselves, as researchers, when, rather, I believe the opposite is more important.
I think that when you look at complex problems like the topic that we are discussing today, mobility, what that entails, the spatial implications of that, the utter loss that is sometimes experienced by folks, I think it is important for me to make a clarification. My experience is purely with migrants and refugees. I understand that mobility is a much more complicated subject. As a side note, we have had to deal with some of that here in Oregon, with some of the fires that took place last year where people were displaced just within the state, and experienced a great deal. of loss.
Continuing to explore and uphold our own dignity and recognizing how it permeates the work that we do is important. I do wish, I've always said this, I've always wished that researchers and scientists had an expectation, if not for themselves, but more as a ubiquitous standard, to do that necessary reflection. In the clinical world, we call it "supervision", which is really therapy for therapist. There's a lot to unpack. And there's a lot that we might discover about ourselves if we decided to dive fully dive into a question that would ultimately reveal to us much more about, not just the subject that we are trying to study, but the reasons why we are trying to study it and how we are trying to implement that knowledge. I do not know what that would be called. I guess, for the purposes of today's discussion, I would describe it as "supervision." I do not know really that within the academic and scientific world, folks feel equipped to supervise others in that aspect of research. What would really be a trajectory of self-development and understanding my position today, why am I asking the question that I am asking, and what extent am willing to go to discover more about myself, which is ultimately the discovery of all of us?
DR. PADILLA: It makes me think, back to your prior comments about the need for reflexivity and a sense of positionality in the researchers. These are all things that emerge out of the social sciences and the humanities. I think that, to me, and hearing what you are saying and reflecting on it for this panel, it seems to me that some cross-disciplinary or multidisciplinary training that allows researchers from different traditions to talk about issues of objectivity and subjectivity and researcher positionality. Those are not necessarily are brought into every kind of scientific discussion at the highest levels.
I think you are saying some things that are really important and that they connect to the question that we received about potential training opportunities as well. Pari, did you have something to add?
MS. MAZHAR: Yes. We need to know why we are in this field. Each of us, as an advocate, social worker, researcher, why are we doing the research that we are doing and who is going to benefit and how? Within that, that is why we need to have a lens of equity. This is what I think researchers would be good to explore in. Are we hearing from everyone that we need to hear from? Are we providing or making room for people who do not have a voice or do not have the ability to participate? Or whatever else is going on.
Have we heard from everyone that we need to hear in this research, and this design of a program? Are we integrating their feedback? And how are we integrating their feedback These are all simple questions of equity - health equity.
And, are there things that we should be considering that we have not considered. And what is all of our individual and collective value? Does our research align with ever value? Is our research data driven are data directed? There is a difference between that, and I know that you know all of that. But how does the result of our research and work communicate with the community about the impact, potential impact, positive or negative, and how do we follow up? Over and over, we hear from people of color, from refugees, that we get invited. We provide our stories come over and over, and then we do not know what happens. That is a given a feeling of exploitation to people. It re-triggering all of the humiliation, everything that based on their experience, they had, and we need to just think about that. That lens of equity and health equity.
DR. PADILLA: That is very important. It seems that we have a few more minutes for Q&A, so if anyone has any additional questions, feel free to message me in the chat. In the meantime, to follow-up with that, it makes me think of more community collaborative approaches and community-based participatory research, at least in philosophy, if not always in practice. The idea behind CDPR being that you have community involvement from the very moment of conception of a project to the very end, analysis and dissemination. I know that in reality, sometimes it does not actually work that way as much as we would like it to. I am wondering if either of you have any experiences, positive experiences, with researchers that have served as a model for you in that kind of approach, where communities really taking into consideration? And if there are any best practices, observations, that you would come up with on those positive experiences? Do either of you have any thoughts on that?
MR. CRUZ: I can comment on that. Just having entered the University of Lisbon for this PhD program, I have been exposed to a great number of social scientist who are really mindful of their interactions. I think it goes without saying that certainly many anthropologist, for years, and decades, have been aware of their presence in communities. There is this ongoing joke about what is the composition of a Guatemalan village. I've heard of a Mexican village. This percentage men, this percentage women. The men are usually less because they are all up north working. This percentage of children and this percentage anthropologist. It is like 2 percent. There is always this token anthropologist in the village, trying to observe and write notes and so on.
So, I have been exposed to a number of these. In fact, I've been exposed most recently to really cool visual methods of understanding migration and root causes and that sort of thing. Most of these are very participatory focused. The conclusions, it is very cool when the conclusions and the findings are shared with the members of the community that you were studying, prior to actually finalizing the product. It really gives a sense and feel that this is really for us, by us. I almost like the concept that this is more like by you, for you, and I am here as a humble facilitator of the exchange of knowledge and the production of knowledge that we are trying to build.
I think there is the gamut. There's a point in time where scientists may come to villages and talk to folks, or urban areas, or whatever and talk to folks about concepts that they do not really understand about because it has not been part of their basis of knowledge. But, I think, wherever there is the possibility to collaborate, I think that is the richest kind of knowledge. I think that is the kind of knowledge that really stands a chance to create a revolution. And ultimately, I go back to this concept, that we must always reconsider what we know. But equally important, how we know these things. Because these things are constantly changing. In collaboration with - I go back to my own roots and background, in collaboration with my own folks, and people weapon on this land for centuries. Our understanding of things might actually shift. We are so quick and easy to dismiss ancestral knowledge. I think we need to open our minds just a little bit to those kinds of understandings of knowledge and we might have a spark go off.
I will always invite the possibility and potential to practice what I call "a true collaboration with participants." I know it is difficult and it is not always optimal, in our scenarios. There has been a lot of discussion where that might not be applicable. I am not diluted in that sense, but I think it is something that we can practice and keep in mind, we might be surprised with the results that we get.
MS. MAZHAR: I will just say it quickly, so that people can research about it. Is it coming through or sharing? Here it is. PCR. Check this out. This is based on the pair aces(?). Just focusing on adverse childhood experience, there's this model coming from the George Washington University and Wendy Alice is the director of this program. Many states are involved. It is the focusing on the community poverty, discrimination. There are basically three, that in a collective way, to build community resiliency in older people or a community is traumatized, they can bounce back. They do a lot of policy work, a lot of community and neighborhood work. A lot more, but I will just let you guys check it out. This new framework that is pair of aces., I am sure that everybody knows about the Aces.
DR. PADILLA: Thank you. It seems like there is one more question here for Bernal. Try to make it brief because we are in our last couple of minutes here. Are there unique challenges that we should be aware of for unaccompanied minors as it relates to provision of care? Are the risk concerns for youth seeking asylum magnified?
MR. CRUZ: Yes. We could spend an hour talking about this, I will be very brief. I think we are not entirely been made aware of the challenges are. As you may know, the unaccompanied children population is a population that for all intents and purposes, is a secret population. There is not really access to the data. I happened to be in a very privileged position in that way. I have very strict directives do not talk about that today.
But what I can say is that unaccompanied children will exit the programs under the custody of the federal government currently. They will enter our communities and there is where actually I think a lot of the problems begin. They begin after a certain period of honeymoon, if you will. Their adjustment period is rather difficult, especially for this population. I think there's a stigma attached to an unaccompanied child, even within the population of migrant and other migrant children.
I think the other important think that anybody working with this population needs to understand is this continuum that exists on this idea of trauma. Where we need to reconsider the definition. When we talk about trauma, we are really talking about the propensity or the potential to experience trauma after exposure to an event. That is to say, an event in itself, does not constitute that a person is traumatized. When we do that, we are looking at a person from the place of deficit, rather than from a place of open-mindedness. People can have post-traumatic growth, people can have enhanced resilience as a result of their experiences. I think to simply assume that a person who is underage and traveled to this country by themselves, is now damaged by virtue of the traumatizing things that happened to them, is a disservice entirely.
So just trying to keep an open mind about what that really means, ultimately. Thank you.
DR. PADILLA: Thank you, that was a great note to end on. Thank you both so much for your insights and wisdom in this discussion. I think we are moving on now to day one wrap up with Holly, if you are ready.
Agenda Item: Day 1 Wrap Up
DR. CAMPBELL: Thank you very much, that was a wonderful discussion. I thought I would thank everyone who is still here with us. I really appreciate you staying until the end. I think we had a real treat in the last 45 minutes.
It was a wonderful day, we had a wonderful plenary opening, that set the stage for the whole meeting. We've a series of talks highlighting various forms of mobility and the drivers of different forms of health. And three wonderful talks describing methods to model and understand mobility and all of its dimensions. Finally, this wonderful discussion about our humanness and ensuring that we hear and include the voices of all, to ensure equity and inclusion in research and information exchange. I learned so much from the last panel discussion, thank you.
I hope you can join us tomorrow. We start an hour earlier, 11 AM, Eastern time. See you then, thank you.
(Whereupon, the meeting adjourned.)