Synthesis, Consolidation and Building Consensus on Key and Priority Population Size Estimation Numbers in Uganda

FINAL REPORT October 2019

TABLE OF CONTENTS

TABLE OF CONTENTS 2 Operational Definitions 3 List of Acronyms and Abbreviations 5 Acknowledgement 6 Executive Summary 7

1.0 INTRODUCTION 7 1.1 Background and Rationale 7 1.2 Purpose 8 1.3 Specific Objectives 9

2.0 METHODOLOGY 9 2.1 Overview 9 2.2 Literature Review Methodology 10 2.3 Selection of Studies for Meta-Analysis 11 2.4 Modelling KPs Size Estimates 12 2.5 Methods for Reaching Consensus on the Estimates 15

3.0 FINDINGS 1: HIV/AIDS IN UGANDA 17 3.1 Epidemiology and Trends of HIV in Uganda 17

3.2 The Role of Key Populations in the HIV Epidemic in Uganda 18

4.0 FINDINGS 2: KEY POPULATIONS 20 4.1 Key Populations: Definitions, Operational Typology and Structure 20 4.2 Eligible Studies for Meta-Analysis on Population Size of key populations in Uganda 27 4.3 Estimates of Sizes of the Different Types of Key Populations 33 4.4 Mapping of predicted estimates in Uganda 44 4.5 Strengths and Weaknesses of Size Estimation Methodologies 46

5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS 47 BIBLIOGRAPHY 48 APPENDICES 53

Operational Definitions

Bisexual: A person who is attracted to and /or has sex with both men and women, and who identifies with this as a cultural identity. The terms men who have sex with both men and women or women who have sex with both women and men should be used unless individuals or groups self-identify as bisexual.

Concentrated epidemic: HIV has spread rapidly in one or more populations but is not well established in the general population. Typically, the prevalence is over 5% in subpopulations while remaining under 1% in the general population, although these thresholds must be interpreted with caution. In a concentrated HIV epidemic, there is still the opportunity to focus HIV prevention, treatment, care, and support efforts on the most affected subpopulations, while recognizing that no subpopulation is fully self-contained.

Fisher folk/fishing Community: A community that is substantially dependent on, or substantially engaged in the harvest or processing of fishery resources to meet social and economic needs.

Gender: “The social attributes and opportunities associated with being male and female and the relationships between women and men and girls and boys, as well as the relations between women and those between men. These attributes, opportunities and relationships are socially constructed and are learned through socialization processes. They are context/time-specific and changeable. Gender determines what is expected, allowed and valued in a woman or a man in a given context. In most societies, there are differences and inequalities between women and men in responsibilities assigned, activities undertaken, access to and control over resources, as well as decision-making opportunities”.

Generalized epidemic: An epidemic that is self-sustaining through heterosexual transmission. In a generalized epidemic, HIV prevalence usually exceeds 1% among pregnant women attending antenatal clinics.

Injecting drug user: The term ‘injecting drug users’ is preferable to ‘drug addicts’ or ‘drug abusers’, which are derogatory terms that are not conducive to fostering the trust and respect required when engaging with people who use drugs. Note that the term intravenous drug users’ is incorrect because subcutaneous and intramuscular routes may be involved. A preferable term that places the emphasis on people first is ‘person who injects drugs’. A broader term that may apply in some situations is a person who uses drugs.

Key populations: UNAIDS considers gay men and other men who have sex with men, sex workers and their clients, transgender people, people who inject drugs and prisoners and other incarcerated people as the main key population groups. These populations often suffer from punitive laws or stigmatizing policies, and they are among the most likely to be exposed to HIV. Their engagement is critical to a successful HIV response everywhere—they are key to the epidemic and key to the response. Countries should define the specific populations that are key to their epidemic and response based on the epidemiological and social context. The term key populations at higher risk also may be used more broadly, referring to additional populations that are most at risk of acquiring or transmitting HIV, regardless of the legal and policy environment.

*Each country should define the specific populations that are key to their epidemic and response based on the epidemiological and social context.

Lesbian: A woman attracted to other women. She may or may not be having sex with women, and a woman having sex with women may or may not be a lesbian. The term women who have sex with women should be used unless individuals or groups self-identify as lesbians.

Men who have sex with men: Males who have sex with males, regardless of whether or not they also have sex with women or have a personal or social gay or bisexual identity. This concept is useful because it also includes men who self-identify as heterosexual but who have sex with other men.

People who inject drugs: People who take in psychoactive drugs intravenously for non-medical purposes.

People who use drugs: People who consume psychoactive drugs in non-injecting forms such as smoking, chewing or sniffing.

Priority populations: Populations which by virtue of demographic factors (age, gender, ethnicity, income level, education attainment or grade level, marital status) or behavioral factors or health care coverage status or geography are at increased risk of HIV.

People in prisons and other closed settings: There are many different terms used to denote places of detention, which hold people who are awaiting trial, who have been convicted or who are subject to other conditions of security. Similarly, different terms are used for those who are detained. In this guidance document, the term “prisons and other closed settings” refers to all places of detention within a country, and the terms “prisoners” and “detainees” refer to all those detained in criminal justice and prison facilities, including adult and juvenile males and females, during the investigation of a crime, while awaiting trial, after conviction, before sentencing and after sentencing. This term does not formally include people detained for reasons relating to immigration or refugee status, those detained without charge, and those sentenced to compulsory treatment and to rehabilitation centers.

Risk: Risk is defined as the risk of exposure to HIV or the likelihood that a person may acquire HIV. Behaviors, not membership of a group, place individuals in situations in which they may be exposed to HIV, and certain behaviors create, increase or perpetuate risk.

Risk factor: An aspect of personal behavior or lifestyle or an environmental exposure which based on epidemiological evidence is known to be associated with HIV transmission or acquisition.

Sex worker: Sex work is the provision of sexual services for money or goods. Sex workers are women, men and transgendered people who receive money or goods in exchange for sexual services, and who consciously define those activities as income generating even if they do not consider sex work as their occupation.

Transgender: An umbrella term to describe people whose gender identity and expression does not conform to the norms and expectations traditionally associated with their sex at birth. Transgender people include individuals who have received gender reassignment surgery, individuals who have received gender-related medical interventions other than surgery (e.g. hormone therapy) and individuals who identify as having no gender, multiple genders or alternative genders.

Vulnerability: Vulnerability refers to unequal opportunities, social exclusion, unemployment, or precarious employment and other social, cultural, political, and economic factors that make a person more susceptible to HIV infection and to developing AIDS. The factors underlying vulnerability may reduce the ability of individuals and communities to avoid HIV risk and may be outside the control of individuals.

Women who have sex with women: The term women who have sex with women (including adolescents and young women) includes not only women who self-identify as lesbian or homosexual and have sex only with other women, but also bisexual women and women who self-identify as heterosexual, but who have sex with other women.

List of Acronyms and Abbreviations

ADPG AIDS Development Partner Group AIDS Acquired Immune Deficiency Syndrome AMICAALL Alliance of Mayors and Municipal Leaders on HIV/AIDS in Africa BBS Bio-behavioural survey CHAU Community Health Alliance Uganda CIs Confidence Intervals CRC Capture-Recapture Method DHS Demographic and Health Survey DRC Democratic Republic of the Congo FSWs Female sex workers HIV Human Immuno-deficiency Virus IBBS Integrated Biological and Behavioral Surveillance IDUs Injecting drug users KP Key Populations KPSE Key Populations Size Estimation Study KP-TWG Key Population Technical Working Group MakSPH Makerere University School of Public Health MARPs Most at risk populations MCMC Markov Chain Monte Carlo MoH Ministry of Health MoT Modes of Transmission Study MSM Men who have sex with men PEPFAR Presidential Emergency Fund for AIDS Relief PLACE Priorities for Local AIDS Control Efforts PMSE Programmatic Mapping and Size Estimation Study PWDs Persons with disabilities PWIDs People who inject drugs RDS Response Driven Sampling SE Standard Error TWG Technical Working Group UAC Uganda AIDS Commission UKPC Uganda Key Population Consortium UNAIDS The Joint United Nations Programme on HIV/AIDS UPS Uganda Prisons Service UPHIA Uganda Population-based HIV Impact Assessment survey WHO World Health Organization WOC Wisdom of the Crowds

Acknowledgement The synthesis, consolidation and building consensus on key and priority population size estimation study was commissioned by the Uganda AIDS Commission (UAC) and the Ministry of Health (MoH) with technical and financial support from the UNAIDS Uganda Country Office and implemented by a team of consultants from the Makerere University School of Public Health. We would like to acknowledge the Technical Working Group for key populations (KP-TWG) and the National Key Population Size Estimation dialogue that conceptualized and initiated this important exercise. Secondly, we would like to thank UNAIDS and its partners for mobilizing the necessary resources to ensure the successful completion of the study without which it would not have been possible.

The KP-TWG is composed of MoH, UAC, academia, implementing partners and Civil Society Organizations active in planning and programming for key and priority populations. It also consists of key population organizations or their representatives. They are appreciated for their contribution to the design, implementation and interpretation of the findings from this study.

A key population size estimation (KPSE) technical team consisting of UAC, MoH, MARPI, CDC and UNAIDS was constituted to oversee the implementation of the study. We are indebted to this team for the technical input and the oversight provided to the team of consultants. Through this technical team, the consultants received feedback from the initial drafts of the report from the UNAIDS Country Office, the Regional Support Team and UNAIDS Headquarters in Geneva. We are grateful to the UNAIDS Country Director for making this happen.

A number of organizations and individuals shared published literature, program reports and other materials that we used for the desk review. We would particularly wish to single out the CRANE Survey, PLACE, MARPs Network, CHAU and their study managers for recognition. We also wish to thank the Uganda Key Population Consortium and their members for providing their program reports and the enriching interaction that we had in meetings held at the UNAIDS Country Office. We also wish to thank all those organizations and individuals that attended the Stakeholder Validation meeting at the UAC and provided useful comments that were used to enrich the report.

Finally, this study would not have been possible without the untiring effort of Prof. Fred Wabwire-Mangen, Dr. Julius Ssempiira and Ms. Maria Kwesiga. We would like to appreciate them for their technical competence in this field, their commitment and resolve to contribute this study to the narrow field of studies on key population size estimation.

Executive Summary Background and Rationale

Despite marked advancement in prevention, care and treatment, HIV in Uganda continues to exert severe constraints on the public health and economic well-being of a sizeable proportion of the population. The type of HIV epidemic in Uganda is described as severe, mature, and generalized but with sub-epidemics in different key, priority and other populations. Key populations are described by WHO and UNAIDS as those populations which are at higher risk for HIV irrespective of the epidemic type or local context and which face social and legal challenges that increase their vulnerability. According to global guidance, key populations include; that increase their vulnerability to HIV. These guidelines focus on five key populations: 1) men who have sex with men, 2) people who inject drugs, 3) people in prisons and other closed settings, 4) sex workers and 5) transgender people while priority populations include uniformed services, truck drivers, migrant workers, adolescent girls and young women and sero-discordant couples.

There is a paucity of data on the sizes of key populations in Uganda. In many cases, the available estimates are based on localized groups or unrepresentative non-national samples. Furthermore, there is a lack of consensus on the most accurate method to use to estimate key population sizes which are crucial for planning HIV programs. Therefore, this study was commissioned by the Uganda AIDS Commission and its partners to fill this void in information on a population that is underserved. The purpose of this study was to conduct a detailed desk review of existing published and grey literature on key populations sizes in Uganda and estimate sizes for each key population using statistical modelling and to reach consensus on key populations sizes that can be used for planning and programming.

Methodology

We conducted a desk review of both published and grey literature written within the last 5 to 15 years. Peer reviewed published journal articles were sourced from Google Scholar and PubMed as well as other internet-based databases using relevant key search words. Websites of organizations working on HIV/AIDS in Uganda were also browsed for literature on HIV in Uganda generally and key populations specifically. A detailed annotated bibliography was generated from this component of the study. Studies which provided proportions or absolute numbers of key populations with their confidence intervals, had a description of the context and populations as well as the design and sampling of the study population were included in the KP size estimations at national and subnational levels using meta-analysis and geo-spatial models, respectively.

A random effects meta-analysis model for surveys was used to estimate KP sizes at national level, whereas a geospatial model was used to produce district level estimates and their distributions in the country. Frequent consultations were held with members of the KP technical team and individual experts in the field. Peer review was sought from UNAIDS Regional Support Team and National Headquarters.

A national stakeholders meeting consisting of members of the KP-TWG, National Prevention Committee for HIV/AIDS of the UAC and other key stakeholders was convened to discuss, validate and reach consensus on the key population size estimates.

Key Findings

Epidemiology of HIV and the Role of Key Populations

In the 2016-2017 Uganda Population-based HIV Impact Assessment (UPHIA), HIV prevalence was estimated at 6.2% (5.8 – 6.7) among adults aged 15 – 63 years of age and at 0.5% among children aged 0 – 14 years (MoH, 2019). There was marked geographic heterogeneity with the prevalence was lowest in West Nile at 3.1% and highest in Central 1 at 8.0%. Regarding HIV incidence, a study from the Rakai Health Sciences Program showed a 42% decline in HIV incidence over a period of 10 years following combination prevention (Grabowski et. al., 2017). EPP and Spectrum modelling indicates that in 2018 there were a total of 1,318,736 PLWH compared to 1,500,000 in 2016 and 1,486,642 as of December 2014.

Key populations have a disproportionately higher HIV prevalence, riskier sexual behaviors and therefore higher incidence of HIV. According to the 2014 MoT, female sex, their clients and their partners contributed 20% of new infections; men who have sex with men and their female partners 0.6% while PWIDs contributed 0.4%. Key populations thus play a central role in the dynamics of the HIV epidemic

Definition and Typology of Key, Priority and Other Populations

The operational typology and structures of key, priority and other populations are described. This was developed using pertinent literature, program reports and stakeholder consultations. Information includes definitions provided from these varying sources and further descriptives such as socio-demographics characteristics, settings/ workplace, behavioral risk factors, legal implications and stigmatization, where applicable.

The key populations described include female sex workers, men who have sex with men, people who inject drugs, people in prisons and other closed settings and transgender individuals. Priority populations include fisher folk, uniformed personnel and truckers and their assistants. The other population described is women who have sex with women. It is important to understand the dynamics of these populations when planning programming.

Estimates of Sizes of Key and Priority Populations

The most prevalent KP in Uganda is fisher folks (>730,000) and the least is PWID (>7,500). FSW and men who have sex with men sizes were estimated at over 130,000 (50,744-210,849) and over 22,000 (12,692-32,635), respectively. The estimated men who have sex with men size is lower than FSW contrary to the demographic expectation - this estimate reflects the overt rather than the hidden men who have sex with men populations perhaps as a result of the current legal environment that criminalizes men who have sex with men activities. In all districts, a higher proportion of fisher folks was predicted whereas a lower size of PWIDs was predicted. Kampala has the highest number of men who have sex with men and FSW predicted. Bukwo district has the least number of female sex workers. men who have sex with men are heterogeneously distributed - the central and mid-eastern districts have the highest concentration and the districts in the northern part of the country have the lowest proportion. The female sex workers are more evenly distributed in the country but with a higher than average concentration of female sex workers in the districts where the major highways traverse and districts with emerging economic activities such as oil and gold mining are taking place, respectively, and districts located at the national boundaries. The PWIDs are predominantly distributed in Kampala and the eastern parts of the country.

Conclusions and Recommendations

● No single method of size estimation of key populations can be considered as standard

● Each method has unique strengths and weaknesses which explains the apparent differences in estimates

● The meta-analysis and geostatistical models give slightly different estimates for KPs at national level, though their CIs overlap

● Periodical updating of the model estimates whenever more outcome and/or covariate data becomes available is recommended

● An Integrated Bio-behavioral Survey complimented with modelling approaches for small area estimations is recommended

Study Limitations

● A smaller number of estimation studies have been conducted at national level which has affected the precision of our model estimates.

● Due to the paucity mentioned above we had to include secondary studies in the meta- analysis such as PEPFAR COP report estimates and modes of transmission studies, whose estimates had been derived based on secondary data sources. This would amount to ‘double-counting’ in the models, but this decision was informed by sensitivity analyses that indicated better precision when these studies were included.

● It was also not possible to conduct a meta-analysis per each estimation technique employed as recommended in literature to ensure less heterogeneity of our estimates. This resulted in high heterogeneity of our modeled pooled estimates

● The subnational sizes were estimated at a lower resolution i.e., district because covariates data we used in the geospatial models was not available at smaller geographical units such as sub-county and parish ● Most studies did not report standard errors making their addition into the meta-analysis problematic – we had to assume a simple random design to enable the calculation of standard errors. This assumption may have affected the precision of our estimates

1.0 INTRODUCTION

1.1 Background and Rationale AIDS in Uganda was first described approximately 35 years ago in two fish landing sites, Kasensero and Lukunya in Rakai District, as a disease of unknown aetiology characterized by chronic wasting (Serwadda et al. 1985). Despite marked advancement in prevention, care and treatment, the epidemic rages on, exerting a high burden on the public health and economic well- being of a sizeable proportion of the population. The Uganda Population-based HIV Impact Assessment survey (UPHIA) 2016-2017 estimated that 72.5% of adults 15-64 knew their status; of those who knew their status, 90.4% were receiving life-saving ART and 83.7% of those on treatment were virally suppressed (MoH, 2019). However, despite this progress towards the 90- 90-90 goal, the burden of HIV is still high with the national HIV prevalence estimated at 6.2% (5.8-6.7) among adult population aged 15 – 49 years translating to a total burden of HIV in Uganda of 1,299,391in 2018 down from 1,500,000 in 2016 (MoH Spectrum Estimates, 2017). Spectrum further estimated that the number of new infections in 2017 was approximately 52,813.

The HIV epidemic in Uganda is described as severe, mature, and generalized but with concentrated sub-epidemics in different key and priority populations. The World Health Organization defines key populations as populations who are at higher risk for HIV irrespective of the epidemic type or local context and who face social and legal challenges that increase their vulnerability. They include sex workers, men who have sex with men, people who inject drugs, people in prison and other closed settings and transgender people. In addition to experiencing elevated HIV risk and burden and facing legal and social issues, these populations historically have not received adequate priority in the response to the HIV epidemic, especially in countries with generalized HIV epidemics. Priority populations are those populations which by virtue of demographic factors (age, gender, ethnicity, income level, education attainment or grade level, marital status) or behavioral factors or health care coverage status or geography are at increased risk of HIV. In Uganda, for the context of this paper, priority populations include fisher folk, uniformed services and truck drivers and women who have sex with women are included as other populations.

Recent studies indicate that HIV prevalence is 15-40% in fishing communities, 31.3 % among female sex workers, 18% in the partners of female sex workers, 12.7% in men who have sex with men and 18.2% among men in uniformed services (PEPFAR 2018; Opio, Muyonga, & Mulumba, 2013; Seeley, Nakiyingi-Miiro, et al., 2012; Asiki et al., 2011). The 2014 Uganda Modes of Transmission study estimated that 11% of new infections in the previous 12 months were attributed to female sex workers, their clients, and their clients’ partners. In addition to the high prevalence and incidence of HIV, key populations also face major challenges in accessing prevention, care and treatment services due to stigma, discrimination, criminalization and legal barriers. At the policy level, it is difficult to provide quality and equitable services to key populations without information on their size, operational typology and location. KPs thus play a significant role in driving the HIV epidemic. The Uganda AIDS Commission, in collaboration with UNAIDS and partners, thus undertook this synthesis, triangulation and harmonization of the several key population mapping studies, population size estimations in order to generate sufficient data that is required for planning and service delivery in Uganda.

For purposes of this synthesis we have adopted the definition of key populations to include; men who have sex with men (MSM), female sex workers (FSW), persons who inject drugs (PWID), people in prisons and other closed settings and transgender. Under priority populations we have included fisher folk, uniformed services and truck drivers and for other populations, we have included women who have sex with women. We did not include adolescent girls and young women or sero-discordant couples. The depth and breadth of review of these populations varied markedly due to data availability constraints. Our study definition of key and priority populations varies only slightly if at all from the global definition.

1.2 Purpose The purpose of this synthesis was to support stakeholders to gain consensus on the available data for key populations, make an estimation of national key population sizes that will inform assessment of service coverage for key populations.

1.3 Specific Objectives 1. Examine the relevant background literature about HIV/AIDS in Uganda, its epidemiology, trends and the role of key populations in driving the epidemic a) A descriptive epidemiology of the HIV/AIDS epidemic in Uganda b) A description of the role played by key populations in the Uganda epidemic 2. Review existing study reports, gray literature and peer-reviewed publications on key populations in Uganda including: a) A description of the definition, operational typology and structures of key populations b) A review of the available mapping of key populations and their locations in Uganda 3. Analyze and synthesize available size estimation data and refine size estimates for each key population a) A list of eligible studies for meta-analysis on population sizes for the different key populations b) Estimates of sizes of the different types of key populations and their location c) Mapping of predicted estimates of key populations in Uganda d) Strengths and weaknesses of size estimation methodologies for the different key populations 4. Facilitate a national stakeholder size estimation validation meeting for key populations 5. Produce a synthesis report with recommendations for future size estimation exercises

2.0 METHODOLOGY

2.1 Overview The primary methodology used was a desk review of published and grey literature. Peer reviewed journal articles were sourced electronically using search engines (Google, PubMed and Medline) as well as other internet-based databases, and websites of major organizations working on HIV/AIDS were browsed for literature relevant to the epidemic in Uganda. Where data from Uganda were not available, regional data were used. A detailed annotated bibliography was generated and is annexed to this report. Meta-analysis and geospatial models were used to calculate pooled estimates of KP proportions and sizes at the national and sub-national level, respectively.

The preferred source of research information was peer-reviewed journal articles, authoritative national reports and policy documents as well as sector and program reports from reputable and credible organizations. Data were also validated through consultations with knowledgeable key informants, scientific peers, AIDS Development Partners and discussions with technical working group members.

2.2 Literature Review Methodology

a) The Search Strategy We searched multiple databases of scientific literature, reference lists in program and consultancy reports and had individual consultations with experts in the field of HIV and key populations. A review of computerized databases such as PubMed and MEDLINE yielded a substantial number of titles and abstracts for screening. We also used Google and Google Scholar Search engines to access the articles as well as available published reports. Key search words used included, but were not limited to: HIV/AIDS, key populations, MARPs, size estimation and mapping of men who have sex with men, people who inject drugs, female sex workers, fisher folks, truckers and prisoners among others, and size estimation methodologies and sampling techniques. Specific stakeholder websites were explored, including those of WHO, UNAIDS, the United States President's Emergency Plan for AIDS Relief (PEPFAR), UAC and MoH. Other sites that provided important leads and resources included the specific key population websites where available. A substantial body of evidence was obtained from grey literature (reports) complemented by a small number of observational studies.

Additionally, national level policy documents were reviewed including but not limited to the following: ● The 2011-2015 National HIV Preventions Strategy ● The 2011 Uganda AIDS Indicator Survey 2011 ● 2014 Uganda HIV Modes of Transmission Study: Analysis of HIV Prevention Response and Modes of Transmission in Uganda ● The 2016 Uganda Demographic and Health Survey ● Uganda Population-based HIV Impact Assessment, 2016-2017

Regional/National Studies Reports reviewed included but not limited to the following: ● STAR EC. Population Estimation, HIV KAP Study and Hotspots Mapping among Most at-risk Populations in the East Central Region of Uganda January 2012 ● Makerere University School of Public Health, CDC and Ministry of Health Crane Survey, August 2013 ● AMICAALL/KCCA: Mapping and size estimation of the key affected population in Kampala, 2013 ● Priorities for Local AIDS Control Efforts (PLACE) in 40 districts of Uganda Draft Report, 2018

b) Eligibility Criteria for research articles Studies or articles were eligible for inclusion in this literature review if: (1) they were published within the last 15 years, (2) they were primary studies, guidelines or reviews; (3) they were published in English; (4) they were relevant to key population size estimation and in line with the objectives to be addressed in the review.

c) Articles Reviewed After screening titles and abstracts, full papers from PubMed, MEDLINE and Scopus meeting the key search words were selected for further screening respectively. Those papers that met the inclusion criteria were reviewed and an annotated bibliography generated. The literature review process is summarized in figure 1 while all articles reviewed are captured in the references section. A separated annotated bibliography report is appended to the main report.

Figure 1: The Literature Review Process

Step II Step III Step I Titles and abstracts of Full papers meeting key Inclusion and exclusion papers on KPs search words with a focus criteria were applied to on Key Population Size select papers for review retrieved from PubMed and Scopus Estimation selected for and bibliography further screening generated

d) Writing the Annotated Bibliography Using papers collected during the literature review, an annotated bibliography was generated. Each citation from the literature review was followed by a descriptive and evaluative paragraph, emphasizing its relevance to the study objectives.

The annotated bibliography is divided into sections including Operational typology, Mapping and structures of key populations, Methodology of size estimation (Census, Enumeration and Mapping, Population Survey Methods eg. Bio-behavioral Surveys, Capture-Recapture, Multiplier-Based Method, Network Scale-up and Wisdom of the Crowds) and Size Estimation Sampling Techniques (Geographical Mapping, Respondent Driven Sampling and Snowballing).

2.3 Selection of Studies for Meta-Analysis For generating the database of size estimates of the different key populations, the research team abstracted studies that provided proportions or absolute numbers of key populations with their confidence intervals. We also noted the descriptions of the context and populations used for the study as well as the design and sampling of the study population.

2.4 Modelling KPs Size Estimates We employed two modelling approaches to estimate the population sizes of three KPs namely, (men who have sex with men, female sex workers, PWIDs) and one priority population (Fisher folks). These groups were the ones with a significant number of estimation studies conducted in the past 15 years. We used a meta –analysis random effects model to estimate a pooled national estimate of the proportion of KPs at national level.

Whereas the national KP estimates are useful for mobilizing resources at the country level, however, they do not reveal the geographical distribution of KPs at subnational scale which is critical for guiding resource allocation and priority setting. To address this necessity, we used a geospatial model fitted with data collected from subnational studies to predict the proportion of KPs in non-surveyed districts and produce a district-level distribution of KPs. This will enable stakeholders including donors, government and programs to target initiatives to the most affected geographical areas.

The details of the models are explained below.

Model 1: Meta-Analysis random effects model In the first modelling framework we fitted a meta-analysis random effects model on the data from studies at national level to estimate sizes of KPs using a meta-analysis method that was extended to survey data by Rao et al. (2008).

Researchers often use meta-analysis to integrate results of several independent studies when information from each study lacks conclusive evidence. Traditionally two approaches are used in pooling estimates, namely, the fixed effects and the random effects models. In the fixed effects model, effects are assumed constant across the set of studies, whereas in the random effects meta- analysis model, the effects are assumed to come from a common probability distribution. We used the latter approach in which between-survey variability in addition to within-study variability was incorporated in estimation of the variance of the pooled estimate. Our choice of a random effects model was informed by the heterogeneity in the methods and designs employed in the individual studies as well as the different settings in which the studies were conducted. The random effects model also provides a more conservative estimate of variance and hence results in more accurate inferences about population estimates unlike the fixed effects model.

The major criteria used for inclusion of the study in the meta-analysis were; population coverage (national scale) and time period. Only estimation studies conducted at national level and not more than 10 years were included in the meta-analysis. Studies conducted at Subnational scale were not included as this would pose a problem of disparate population coverage. Thus, only four studies (female sex workers), eight (men who have sex with men), three (fisher folks), and four (IDUs) were eligible for inclusion in the meta-analysis. Also, we did not include in the meta- analysis studies that attempted a census. The choice of 10 years was premised on the hypothesis that this period was not long enough for major changes to happen in behavioral trends and legal conditions that would strongly affect the existence of KPs were minimal. For each study we obtained the reported size estimate and used it to estimate the proportion of KPs in the study population by dividing it by the age-appropriate population denominator. These population denominators were available from the Uganda Population and Household Census (UPHC 2014) and the appropriate age groups for each KP was determined based on literature. The denominators used were; Men age 15+ years for men who have sex with men, Women 15-49 years for FSW, and All people age 15+ years for PWIDs and fisher folks. Combining estimates from different studies requires standard errors but most studies did not report standard errors. For studies that did report standard errors, an assumption of a simple random sampling design was made - using and the formula for a binomial proportion were able to estimate missing standard errors. The pooled proportion for each KP at country level was estimated by multiplying the proportions by a weight inversely proportional to the variance, summed the weighted proportion estimates across studies, and then divided by the sum of the weights.

푘 ∑푗=1 푤푗 휃푗 Thus, the pooled proportion, 휃푝 = 푘 , where 푤푗 is the weight of study 푗 and 휃푗 is the ∑푗=1 푤푗 proportion of KPs in the study j. 푤푗 is the study-level weight for the 푗 th study, and equals to the sum of the within- and between-study variances. This pooled estimate is therefore a weighted combination of the estimates from the studies included in the meta-analysis.

Prior to estimating the pooled estimate, we tested for heterogeneity of the studies using the Q 2 2 푄푤−푑푓 statistic (Rao 2008) and Higgins’ 퐼 index using this formula , 퐼 = 100푥( ), where 푄푤 = 푄푤 2 2 ∑푗 푤푗(휃푗 − 휃푤) , 퐼 is the proportion of total variation in the observed results of the studies explained by heterogeneity, 푄푤 is the statistic for testing homogeneity among the studies, df is the number of degrees of freedom, 휃푗 is the estimate from study j, and 휃푤 is the overall pooled proportion. Under the null hypothesis, Q has approximately 휒2 distribution with (k-1) degrees of freedom. Values of the 퐼2 index around 25, 50, and 75 typically correspond to low, medium, and high heterogeneity, respectively.

The population estimates for each KP was calculated by multiplying the pooled proportion with population denominators obtained from the UPHC 2014. The population data were adjusted for the population growth rate and appropriate age groups from which the KPs come typically come from. The meta-analysis estimations were calculated in Excel and validated in STATA (Stata Corp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP).

Model 2: Geostatistical model A geostatistical model was used to estimate the size of KPs at district level. For each KP, a geostatistical model was developed in the Bayesian framework utilizing data from the surveyed districts to predict the proportion of KPs in all non-surveyed districts. The models were fitted with seven predictors that were confirmed from a variable selection procedure to explain maximal variation in the existence of KPs. These predictors were; HIV prevalence, population density, number of people living with HIV, number of new HIV infections, HIV/AIDS mortality, ART coverage, and PMTCT coverage. The district-specific HIV prevalence estimates were obtained from a UNAIDS report (Developing subnational estimates of HIV prevalence and the number of people living with HIV, 2014) and were estimated using the prevR routine. The population density data for each district was available in the district census reports (UPHC 2014). The rest of the covariates were obtained from the Uganda PEPFAR data.

The key inputs in the model were the size estimate of KPs from the previous studies, the population denominators in each district, and the predictor variables at district level. We denoted

푌(푑푖) to be the number of KPs who were reported in a study conducted from district 푑푖, and we let 푁(푑푖) be the population denominator for the appropriate age group for each KP in the district.

According to the principles of probability theory, 푁(푑푖) follows a Binomial distribution, i.e.,

푌(푑푖)|푁(푑푖), 푝(푑푖)~퐵푖푛(푁(푑푖), 푝(푑푖)) ∀i ∈ 1,…,l, where d={d1, d2,…,dl} is the set of districts where 2 estimation studies were conducted, 푑푖 ⊂ 푅 and 푝(. ) indicates the proportion of a given KP in the district. A Bayesian geostatistical model to estimate this probability was formulated as follows:

푇 푙표푔푖푡(푝(푑푖)) = 훽 푋(푑푖) + 휔(푑푖),

T where 푋(푑푖) are the predictors (HIV prevalence and population density) in district 푑푖, 훽 = (훽1, 훽7) is the vector of regression coefficients that quantify the relationship between the proportion of 푇 KP e and the predictors and 휔 = (휔(푑1), 휔(푑2), … , 휔(푑푙)) is a zero-mean latent spatial process that follows a multivariate normal distribution, i.e., 휔~푀푉푁(0, 𝜎2푅). R is the correlation matrix defined by an exponential parametric function of the distance 푘푖푗 between two districts 푑푖 and 푑푗 2 i.e., 푅(푑푖, 푑푗) = 푒푥푝 (−푘푖푗𝜌). The parameter 𝜎 is the spatial variance and 𝜌 is a smoothing parameter that controls the rate of correlation decrease as distance increases.

Bayesian inference requires assignment of prior distributions to model parameters. We assumed the following vague priors for the following model parameters - an inverse-gamma for 𝜎2, a gamma for 𝜌, and non-informative Gaussian distributions with mean 휇 0 and variance 103 for the 2 3 regression coefficients. Thus, 𝜎 ~IG(0.01,0.01), 𝜌~Gamma(2.01,1.01), 훽푝~N(0, 10 ), p=1,2.

Parameter estimation was based on the joint posterior distribution below; p(훽,휔, 𝜌, 𝜎2|푌) ∝L(훽,휔, 𝜌, 𝜎2; 푌) p(훽) p(휔|𝜎2, 𝜌) p(𝜎2) p(𝜌), where L(훽,휔, 𝜌, 𝜎2; 푌) is the likelihood, p(𝜎2), p(휔|𝜎2, 𝜌), p(𝜎2) and p(𝜌) are prior distributions of regression parameters, spatial random effects, variance and correlation parameters, respectively.

Prediction of proportion of KPs in non-surveyed districts Parameter estimates from the geostatistical models fitted above were added into the Bayesian Kriging to predict the proportion of KPs in districts where no studies were conducted using the following predictive posterior distribution;

2 2 p(Yn| Y) = ∫ 푝(훽, 휔푁)푝(휔, 𝜎 , 𝜌)푝(푌(푑푖))푑훽푑휔푛푑휔푑𝜎 푑𝜌, where T Yn=(푌(푑1), 푌(푑2), … , 푌(푑푛) ) is the size estimates of KPs in districts where no estimation studies were conducted 푑푛 = {푑1, 푑2, … , 푑2}, and 휔푛 is the spatial random effect at 푑푛. The distribution of 2 −1 2 −1 휔푛 given 휔 is multivariate normal, ie., 푝(휔, 𝜎 , 𝜌) =MVN (푅01푅11 푈, 𝜎 (푅01 − 푅01푅11 푅10)), with 푇 푅11 = cor(휔, 휔), 푅01 = 푅10 = cor(휔푛, 휔) and p(푌(푑푛)|훽, 휔(푑푛) )~ 퐵푖푛(푌(푑푛), 푝푑(푑푛)) , and therefore the proportion of KPs in each non-surveyed district was estimated using the model below;

푇 (푙표푔푖푡(푝푑(푑푛))= 훽 푋(푠푑) + 휔(푠푑).

The models were implemented in OpenBUGS version 3.2.3 (Imperial College and Medical Research Council, London, UK). Parameters were estimated using Markov chain Monte Carlo (MCMC) simulation. A two-chain algorithm for 200,000 iterations with an initial burn-in period of 5,000 iterations was ran. Convergence was assessed by visual inspection of trace and density plots and analytically by the Gelman and Rubin diagnostic. Parameters were summarized by their posterior medians and 95% Bayesian Credible Intervals (BCIs). The Bayesian Kriging was implemented in the R statistical software.

The maps of the distribution of KPs in the country were produced in ESRI’s ArcGIS 10.2.1 (http://www.esri.com/).

2.5 Methods for Reaching Consensus on the Estimates This study was commissioned by the Uganda AIDS Commission and the Ministry of Health with technical and financial support from the UNAIDS Uganda Country Office. The consultants were responsible to the Head of Prevention at the Uganda AIDS Commission. The Director of Monitoring and Evaluation was responsible for coordination and the routine management of the assignment. A KPSE technical team consisting of UAC, MoH and UNAIDS was constituted to oversee the implementation of the study. This team met multiple times at the UNAIDS Country Office.

Every effort was made to ensure consensus throughout the implementation of this study starting with the drafting of the terms of reference and the contracting of the study team. The inception report was presented to the Key Populations Technical Working Group (KP-TWG) of the Ministry of Health which discussed it and provided feedback to the consultants especially on the key populations to study and the source of data to be reviewed. The consultants generated a list of key stakeholders who were identified during the KP-TWG meeting to provide research and program reports, size estimation publications and other materials deemed useful to the consultants. Where necessary, these stakeholders were visited and discussions held to gain consensus on emerging information on the sizes and typology of key populations.

A draft technical report was submitted to the KPSE technical team for review and comments. The comments were discussed with the consultants and a second meeting of an expanded KP-TWG proposed to be held after the comments had been incorporated. The KP-TWG was to be expanded by co-opting more technical experts in size estimation of key populations and HIV modelling. This process was combined with the stakeholder validation and consultation meeting. stage of consensus building was a key population stakeholder validation and consultation meeting involving KP-TWG members, stakeholders who were knowledgeable in the field of size estimation and HIV modelling as well as implementing partners who were responsible for the planning and provision of health services for key populations. This meeting made suggestions which were used to further enrich the report. Two additional consensus building meetings involved the AIDS Development Partners Group (ADPG) and the Uganda Key Populations Consortium (UKPC). Both these fora had very useful suggestions that were incorporated into the final report.

3.0 FINDINGS 1: HIV/AIDS in Uganda

3.1 Epidemiology and Trends of HIV in Uganda The epidemiology and trends of HIV in Uganda have been well characterized through a series of regular national sero-behavioral surveys conducted at intervals of approximately five years apart in 1988-89, 2004-05, 2011 and more recently in 2016. Additional sources of data on the status of the HIV epidemic include demographic and health surveys, longitudinal studies, mathematical models’ estimations and projections as well as modes of transmission studies. A series of demographic and health surveys with an AIDS module were conducted in Uganda in 1989, 1995, 2000, 2006, 2011 and 2016. These surveys are conducted every five years and provide very useful data on HIV/AIDS indicators as well as HIV/AIDS-related behavioral indicators. Ante-Natal Care sentinel surveillance sites have provided data on HIV trends among pregnant women since 1989. These sites are fairly distributed around the country and until recently provided data that were compiled into epidemiological sentinel surveillance reports released annually by the Ministry of Health.

According to the 2016-2017 UPHIA, HIV prevalence was estimated at 6.2% (5.8 – 6.7) among adults aged 15 – 63 years of age. In the age group 15 – 49 years, HIV prevalence was 6% (4.7% males and 7.6% in females). In children aged 0 – 14 years, prevalence was 0.5% (0.4% males and 0.7% females). HIV prevalence was lowest in West Nile at 3.1% and highest in Central 1 at 8.0%. Central 1 consists of the districts of Kalangala, Masaka, Mpigi, Rakai, Lyantonde, Sembabule, and Wakiso districts (MoH, 2019). The 2016-2017 UPHIA was the first sero-behavioral survey to estimate incidence in Uganda using the lag avidity assay, however the results are not yet available publicly (MoH, 2019). Though not nationally representative, a study from the Rakai Health Sciences Program showed a 42% decline in HIV incidence over a period of 10 years following HIV combination prevention (Grabowski et. al., 2017). Ministry of Health Estimation Projection Package and Spectrum modelling indicates that there were a total of 1,318,736 people living with HIV in 2018. This is a decrease from the 2016 estimate of 1.5 million and less than the 1,486,642 of people living with HIV as of December 2014. This decline is likely due to a number of success factors including the impact of increasing ART coverage as evidenced by the 90.4% of those who know their status receiving life-saving ART and 83.7% of those on treatment being virally suppressed (MoH, 2019). However, a large proportion of the population is still not accessing prevention programming. It is estimated that 40.7% of men and 33.3% of women aged 15 – 49 years are still unaware of their HIV status while 53.4% of men are uncircumcised (58.4% rural and 44.4% urban).

Since 1985 when the first cases of AIDS were described in Uganda, the HIV/AIDS epidemic evolved into a heterogeneous epidemic affecting different population sub-groups and resulting into multiple and diverse micro epidemics with different transmission dynamics. As previously mentioned, the HIV epidemic in Uganda is described as severe, mature, and generalized but with concentrated sub-epidemics in different key and priority populations who are at higher risk for HIV and who face social and legal challenges that increase their vulnerability.

3.2 The Role of Key Populations in the HIV Epidemic in Uganda According to the Modes of Transmission Study of 2014 (UAC, 2016), female sex workers, their clients and their partners contributed a total of 20% of new infections; men who have sex with men and their female partners contributed 0.6% while people who inject drugs contributed 0.4%. Due to behavioral practices paired with limited access to prevention and care services, key populations are more vulnerable in the risk of acquiring and transmitting HIV. These sexual and health access behaviors, when combined with the changes in underlying contextual and structural factors such as changes in social norms, poverty, gender inequities and stigma further enhance transmission.

Figure 1: Estimated Distribution and Incidence of New Infections

Source: 2014 Modes of Transmission Study

While all populations have a level of vulnerability and are therefore at risk of HIV infection, certain population groups are at a higher risk of infection. For instance, it has been observed that any kind of profession which involves migration and being away from home makes people more vulnerable to getting infected (Seeley et al., 2005). Long-distance truck drivers and fishing communities are some examples of mobile populations who often work away from home. For time immemorial, migration has been an important factor in the spread of infectious diseases and increasing vulnerability and susceptibility to infection of those who migrant. Truck drivers spend prolonged periods of time away from home and often resort to casual sex or develop regular, non-marital sexual relationships while in transit. Several factors contribute to this practice, such as loneliness, peer pressure, alcohol use, and the desire to satisfy their sexual needs. Without family and community support systems, migrant workers are more likely to engage in risky behaviors and potentially pass those risks on to their own family and community members when they return home. Moreover, the truck routes have many roadside truck stops, where sex workers create the opportunity for transactional sex with multiple sex partners and often with minimal condom use (UAC, 2011; UAC, 2016). Surveys that have been conducted in fishing communities located in a number of countries have revealed that HIV infection among people in fishing communities is high, ranging between 4 and 14 times higher than the national average prevalence for adults aged 15-49 years (Opio et al., 2011).

Key populations can play a central role in the dynamics of the HIV epidemic and therefore size estimates and appropriate access to health services is required.

4.0 FINDINGS 2: KEY POPULATIONS 4.1 Key Populations: Definitions, Operational Typology and Structure Understanding the social organization of key populations including the typology, structure and context is very important in planning HIV prevention, care and treatment programs that will ensure access and equity of service delivery.

Female sex workers Key populations have a specific social organization that influences where they congregate and solicit for their sexual partners. This is better described for female sex workers than for other KPs. While sex work has been long established, its extent, typology and organization varies considerably across different countries, cultures and contexts. Knowledge of the operational typologies and structure of female sex workers is an output of mapping exercises that are useful in providing services to these mostly hidden populations. They can also be used to evaluate risks and vulnerabilities associated with the different typologies and thus contribute to HIV prevention programs.

UNAIDS defines the term sex work as the provision of sexual services for money or goods. Sex workers are women, men and transgendered people who receive money or goods in exchange for sexual services, and who consciously define those activities as income generating even if they do not consider sex work as their occupation (UNAIDS, 2015). The AMICAALL Uganda Chapter defines female sex workers (female sex workers) as any female, who undertakes sexual activity with a man for money or other items/benefits as an occupation or as a primary source of livelihood irrespective of site of operation (AMICAALL, 2013). The CRANE survey further defines female sex workers in their study population as females who are aged 15 years and above and who are actively selling sex to men/women (MakSPH, 2017). The PLACE II study defines female sex workers as women who reported they received cash for sex in the past 12 months or who indicated that they participated in sex work in the past 12 months (Ssengooba, Atuyambe & Kasasa, 2018).

As shown by these varying definitions, “sex work” is largely a designation of interpretation and this must be considered when planning programming. For size estimation, we have focused specifically on female sex workers.

Female sex workers are very diverse and comprise members of various socio-economic classes, education levels, ethnicities, marital and fertility statuses, geographic locations and many other characteristics (Tamale, 2010; Zalwango et al., 2010; Mbonye et al., 2012). Despite this great variation, some common traits as well as key differences have been identified. Tamale (2010) presents types of sex work according to workplace – outdoor (street-based), indoor (brothel, bar, lodge, or private home-based) and export (outside of Uganda’s national borders). These divisions are cross-cut by differences in socio-economic status, ethnicity and other characteristics that differentially impact female sex workers’ abilities to ameliorate HIV risk factors. In a study of female sex workers in Uganda, the majority of female sex workers were relatively young between 15 years and 40 years of age. They reported poverty, lack of employment, and means of supplementing income as key influencing factors in joining commercial sex profession (Muyonga et al., 2012).

Literature on sex wok within sub-Saharan Africa tends to group the practice along an axis of motivation at the one end – disenfranchised women engage in “survival sex” in order to provide for their basic needs (Luke, 2005). These women include young, single parents, the poorly educated and the low-skilled occupying poorly paying jobs (Mbonye et al., 2012). At the other end of the continuum are, affluent young women pursuing relationships with older men, potentially in pursuit of luxury goods and the frills of modern society (Leclerc-Madlala, 2003; Cole, 2007).

Unprotected sexual intercourse with multiple partners increases the risk of exposure and transmission of HIV. Female sex workers operating on an economic incentive to have more sexual partners, are a critical population to address with HIV prevention program. In addition, a connection to large numbers of men within the general population could potentially increase the impact for others to the virus, less-at-risk individuals, and further highlights the importance of prevention with female sex workers (UNAIDS, 2008).

Operational typologies for female sex workers are defined according to the primary place of solicitation of clients, the place of sexual encounter, earnings, mode of operation whether establishment-based or non-establishment-based, nature of the sex work network and level of autonomy from pimps, madams or brothel owners if establishment-based. In establishment- based sex work, solicitation generally occurs indirectly through pimps and brokers while in non- establishment-based sex solicitation is done directly by the female sex workers themselves (Emmanuel et al., 2013).

Female sex workers in Uganda are not a homogenous group, as they come from a diverse range of backgrounds. Hence, the levels of risk that they face in terms of HIV infection varies greatly depending on their background, whether they work from their house, a lodge or ‘on the street’, and whether they have access to condoms, among other factors. A wealthy female sex worker supplying services to businessmen in the higher-class hotels of Kampala, for instance, may face a very different level of risk to that of an impoverished girl who is being forced to sell sex in a bar at a landing site on the shores of Lake Victoria. Despite this diversity, female sex workers often share several common experiences in their lives, regardless of their background. Some of these factors and experiences can increase their exposure to HIV. These factors include the following:

● Multiple, non-regular partners and more frequent sexual intercourse; ● Inconsistent condom use; ● Social, legal factors and stigma; ● Access to care and treatment; ● Alcohol and drug use and; ● Violence (KMCC, 2014).

Sex work settings may cater to local communities or primarily involve transient, migrant and mobile populations of both female sex workers and clients. A STAR EC study found that in Eastern Central Uganda, female sex workers were mainly located in urban areas and fish landing sites. These were categorized in three main profiles as bar based, lodge based and those disguisedly employed (Muyonga et al., 2012), yet the settings in which sex work may occur can be broadened to range from venue based including brothels or other dedicated establishments to roadsides, markets, petrol stations, truck stops, parks, hotels, bars, restaurants and private homes, and may be recognizable or hidden, or street based (UAC and MoH, 2014). With modernization of the industry a few other typologies of female sex workers have evolved to include massage parlor, night clubs and casinos (AMICAALL, 2013).

Street-based female sex workers tend to work alone in nonspecific locations and solicit clients at various ‘pick up points’ in public places such as streets, marketplaces and other locations. They may perform sex work in hotels, houses or abandoned houses or cars. Some will contact their clients through mobile phones and wait on the streets to be picked up. Public or street-based female sex workers are highly vulnerable to assault and crime.

Home-based female sex workers operate from their homes where they may live alone or with families and rely mostly on network operators to seek clients. In some cases, the family may be unaware of the sex work and though the solicitation can take place at home, the sex work may take place elsewhere. In other cases, the family is aware and both the solicitation and sex work takes place in the home. Whereas home-based sex work takes place in both urban and rural areas, it is the most common type of typology in the rural settings. In urban settings home-based sex work takes place predominantly in slum areas. Home based female sex workers are considered the most hidden and may be the most difficult to reach for mapping or service provision.

Another consideration is how female sex workers and clients connect whether through brokers, through open solicitation, through network operators or by cell phone and who controls the work conditions of the female sex worker whether they enjoy individual autonomy or are controlled by madams and pimps (Emmanuel et al., 2013). Network operators such as pimps, madams, taxi drivers and waiters play a significant role in linking female sex workers to clients in exchange for a proportion of the female sex workers’ income. Network operators may also provide protection to female sex workers and places for sexual activity. In smaller cities, sex work is extremely hidden and most female sex workers connect with clients through network operators, while in larger, less conservative cities, a substantial number of female sex workers still operate independently through particular street locations known to clients.

With the heterogeneity found among female sex workers, the complexity of this group must be taken into consideration in terms of prevention and care programming.

Men Who Have Sex with Men AMICAALL defines the term “men who have sex with men” to denote all men who have sex with other men as a matter of preference or practice, regardless of their sexual identity or sexual orientation, and irrespective of whether they also have sex with women or not. This concept therefore includes men who self‐identify as heterosexual but have sex with other men. The term does not refer to those men who might have had sex with other men as part of sexual experimentations or very occasionally depending on special circumstances (AMICAALL, 2013).

The PLACE II study defines men who have sex with men as men who indicated that they had sex with a man in the past 12 months. To further this, men who have sex with men must also indicate i) that they had sex either with only men or with men and women in the past 12 months, and ii) that they had anal sex in the past 12 months (Ssengooba, Atuyambe & Kasasa, 2018).

The CRANE survey defines men who have sex with men in their study population as men who are age 18+ years and have had anal sex with a male (MakSPH, 2017). Findings of the CRANE Study offer more information regarding men who have sex with men in Uganda. The survey reaffirms some facts, including that almost all men who have sex with men in Kampala are Ugandan nationals, dispelling claims that there are no local men who have sex with men or that they only have sex with foreigners. Further, most men who have sex with men had or still have sex with women, many were or are married, co-habiting with women, and/or have biological children. Thus, men who have sex with men in Kampala appear firmly embedded in the general population. The large proportion of men who have sex with men that report buying or selling sex and the less frequent reporting of life-time injection drug serve as a reminder that Kampala men who have sex with men share risk factors beyond their defining behavioral characteristic.

Anti-homosexual and ant-gay sentiment is a common attitude among Uganda’s general population where an Anti-Homosexuality Act criminalizing and heavily penalizing this sexual orientation was signed into law in 2014, only to be nullified later for procedural reasons (Parliament of Uganda, 2014). Transgender women are often perceived by the general population and health care workers to be men who have sex with men and are therefore subject to severe stigmatization and discrimination (Britton, 2017).

As men having sex with men in Uganda is highly illegal with many sensitivities, criminalization has created barriers in services delivery that drive this group underground, therefore there are few studies that describe their typology or behaviors. Additional information is needed in order to plan for this key population.

People who Inject Drugs International experiences and national anecdotes suggest that the use of illicit drugs or illegal narcotic substances is a critical causative cum vulnerability factor to HIV and AIDS and the association is closely related with multiple sexual partners and inconsistent use of condoms among users (Choudhry, et al., 2014). Alcohol and drug use is highlighted as one of the risk factors driving the HIV epidemic in Uganda as reflected through the Modes of Transmission and Prevention Response Analysis (UNAIDS Country Report March, 2014) though there has been limited focus on this sub-population in national programming seen through the HIV National Strategic and Priority Action Plans that do not include specific strategies for targeting them (MARPs Network and UHRN, 2016).

In Uganda, there is a variety of drugs used by respondents ranging from those locally produced to those imported, injected and non-injected. Some of these were documented and included: Sisha, Miraaji, Marichan, Cannabis (Marijuana), and a cocktail of tobacco and marijuana locally known as ‘kagolo’, Thina, Brown and white sugar, Kuber, Cocaine, Heroin, Kut (Miraa) and Mandrax among others. We, however, will focus on people who inject drugs. A 2016 study by MARPs Network and Uganda Harm Reduction Network found that several drugs are injected and include Heroine, Ox-cordon, Pethidine, Heroin, lysergic acid diethylamide (LSD) and cocaine. The major sources of these injecting drugs were from Kenya, Mombasa, Somalia and Kampala. The suppliers include truck drivers all the way from Mombasa, Kenyans, Indians and Somalis. The cost of these drugs range between 50,000 to 250,000 UGX.

A study conducted in Kampala by MARPs Network in 2012-2013 highlighted HIV prevalence at 17% among injecting drug users. However little attention is being given to them and yet they may have a major contribution to the 7.3% (Uganda AIDS Indicator Survey 2011) prevalence of the total population of Uganda (MARPs Network and UHRN, 2016).

In 2017, the Community Health Alliance of Uganda (CHAU) published a report titled “Population estimation and rapid assessment on harm reduction and HIV prevention among people who inject drugs in Kampala City and Mbale Town, Uganda” This report sets out many findings as well as the typology, knowledge and behaviors of this key population. Some findings are highlighted below:

Socio-demographic characteristics of PWID cut across all socio-demographic characteristics and include male and female, youths and adults, the rich and the poor, the educated and the uneducated, among others. The PWID who participated in the survey were mainly young adults with an average age of 27.4 (Females, 25.9; and Males, 27.8). PWID were mostly involved in artisanal semi-skilled work (18.4%), informal casual work (17.6%), sex work (13.6%), transport services (12%) and petty trade 8.8%), however, many were also unemployed and sustain their injecting practice by hanging out with friends who can afford to buy drugs and share with them, while others steal or perform other illegal activities to raise money to purchase drugs. A number of PWID also belonged to another key population category, especially female sex workers (11.2%), fisher folk (6.4%), truckers (4%), and men who have sex with men (CHAU, 2017).

Ghettos or hideouts, such as an abandoned house or an alley, often poorly kept and unsanitary, were the most popular venue for injecting used in the past three months, followed by homes and the supplier’s premises. Others included dedicated rooms and friend’s homes. Heroin was the most injected drug in both Kampala and Mbale (CHAU, 2017).

Most PWID reported that they inject twice a day, i.e. in the morning and in the evening. Those who have more money to spend on drugs or have a steady access to drugs inject at any time and more frequently. A big proportion of the PWID reported that they used a needle two to four times before they disposed it off. Only 30.4% reported that they used a needle once, while 11.2% reported that they used it ten times or more before disposal, while some (3.2%) could not tell how long a needle was used before disposal. About one quarter of the PWID (25.8%) reported that they used a syringe once and disposed it off, while the biggest proportion (38.7%) reported that they used it two to four times. Up to 17.7% reported that they used it ten or more times before disposal. About three quarters of the PWID (75.8%) reported that they used only one needle per day, while about one fifth reported that they used two to four needles in a day (CHAU, 2017).

Majority of the PWID who participated in the survey (57.6%) reported that in the preceding three months, they had used a syringe/needle previously used by someone else. Another practice which was revealed in this study is that of drawing one’s blood and using it to mix the drug, and then the solution is shared by several people and even the needle used to draw the blood is used by others to inject. Sharing of needles and syringes was found to be out of both necessity - because users cannot afford to buy their own drugs all the time - and as cultural norm of friendship and brotherhood (“one blood”) among injectors (CHAU, 2017).

While alcohol and drug use were highlighted as one of the risk factors driving the HIV epidemic in Uganda (HIV Modes of Transmission and Prevention Response Analysis Synthesis Report 2014, Draft), there has not been detailed social studies in Uganda to explore this relationship to inform national programming. Evidence is scanty on the effects of specific types of drug use, levels of addiction and other predisposing factors while responses to why people take drugs are related to poverty, peer pressure, limited awareness, and normalization of drug use as sub- culture and effects of drugs on users (MARPs Network and UHRN, 2016). More efforts must be taken to better inform programming for this group.

People in Prisons and other Closed Settings There are many different terms used to denote places of detention, which hold people who are awaiting trial, who have been convicted or who are subject to other conditions of security. Similarly, different terms are used for those who are detained. In this guidance document, the term “prisons and other closed settings” refers to all places of detention within a country, and the terms “prisoners” and “detainees” refer to all those detained in criminal justice and prison facilities, including adult and juvenile males and females, during the investigation of a crime, while awaiting trial, after conviction, before sentencing and after sentencing. This term does not formally include people detained for reasons relating to immigration or refugee status, those detained without charge, and those sentenced to compulsory treatment and to rehabilitation centers (WHO, 2016).

Prisoners also known as inmates or detainees are persons who are confined to a facility, jail or penitentiary in which they are deprived of their liberty or a variety of other freedoms following conviction for a crime or felony under the authority of the state. Globally, more than 11 million people were incarcerated in prisons all over the world in 2016. Among these people are populations at especially high risk of HIV infection as a consequence of risk factors that are at play both before incarceration and once in prison where there are frequent opportunities for further transmission. Over-represented among this population are key and vulnerable populations including people who use drugs, sex workers, men who have sex with men, transgender people, and others who are most marginalized in communities. The prison environment predominantly of overcrowding, stress, malnutrition, poor sanitation, risk behavior including drugs and violence makes people living with HIV more susceptible to getting ill and yet the prisoner’s wellbeing is often neglected and overlooked. Consequently, prisoners experience high HIV disease burdens and have little or no access to HIV treatment, prevention, and care. In addition, prisoners are disproportionately affected by tuberculosis, viral hepatitis and mental health issues. Despite this fact, comprehensive health services are rarely available within prisons.

UNAIDS estimates that people in prison are on average five times more likely to be living with HIV compared with adults who are not incarcerated (UNAIDS, 2017). It is estimated that around 3.8% of the global prison population is living with HIV and 2.8% have active tuberculosis (TB). The regions where prisoners are most affected by HIV are East and Southern Africa and West and Central Africa both of which have high HIV prevalence in the general population.

In Uganda, the Prisons Act 2006 mandates Uganda Prison Service (UPS) to provide custodial and rehabilitative services including health and welfare to offenders in Uganda (Uganda Prisons Service, 2013). In 2013, UPS had 241 (223 male and 91 female) prison units, 38,158 prisoners and 6,182 staff. Only about 20% of prison units had health-care units, and less than 5% of these units provided comprehensive HIV services (including HIV testing, diagnosis, CD4 monitoring and onsite ART). According to the UPS 2013 Sero-Behavioral Survey, HIV prevalence was 12.0% among staff and 15.0% among prisoners compared to 7.3% among the general population aged 15-59 years (UPS, 2017). HIV was less prevalent among male than female participants among staff (10.5% vs.14.5%) and similarly less prevalent among male than female prisoners (14.4% vs.24.2%).

Among male staff, 2.9% reported having had sex with other men and 38.8% of these had at least 2 male sexual partners in the past three months. The proportion of male prisoners who reported having had sex with men was 6.5%, and 50.3% of those had at least 2 sexual partners. Overall, 2.5% of all men reported being coerced to have sex in prison reportedly either by staff or other prisoners; half of them within the first year in prison. Among female prisoners who had ever had sex in their lifetime, 3 (1.4%) of 223 reported being forced to have sex whilst in prison. 2.3% (13/566) female prisoners reported having had consensual sex with men while in prison; 2 (15.4%) of them had used a condom. Among male prisoners, 38% reported having engaged in transactional sex but only 14.7% of these used condoms all the time. Among female prisoners, 24.7% had engaged in transactional sex but half (50%) of them used condoms all the time. The unadjusted incidence of recent HIV infections among the staff was 1.2 per 1,000 individuals, while that among prisoners was 5.3 per 1,000 prisoners. Incidence of recent HIV-1 infections was estimated by applying the Limiting Antigen Avidity assay (LAg) to HIV positive samples.

Transgender UNAIDS defines a transgender person as one who has a gender identity that is different from his or her sex at birth. Transgender people may be male to female (female appearance) or female to male (male appearance). It is preferable to describe them as ‘he’ or ‘she’ according to their gender identity, i.e. the gender that they are presenting, not their sex at birth (UNAIDS, 2011). Estimates suggest that the transgender population could be between 0.1% and 1.1% of reproductive age adults (UNAIDS, 2014).

Many transgender individuals experience social exclusion and marginalization because of the way in which they express their gender identity. Many transgender people lack legal recognition of their affirmed gender and therefore are without identity papers that reflect who they are. Without appropriate identity papers, transgender people are excluded from education and employment. Transgender people often face discrimination, violence and lack of access to appropriate health care. All of these factors contribute to increasing the vulnerability of transgender people to HIV. There is an HIV burden among transgender individuals with transgender women among the populations most heavily affected by HIV and are 49 times more likely to acquire HIV than all adults of reproductive age (Baral et al., 2013) and an estimated 19% of transgender women are living with HIV. Transgender women are often perceived by the general population and health care workers to be men who have sex with men and are therefore subject to severe stigmatization and discrimination (Britton, 2017 retrieved from King et al., 2019). The impact of HIV on transgender men has yet to be established (UNAIDS, 2014). However, in sub-Saharan Africa, where many countries face generalized HIV epidemics, the burden of HIV disease among transgender women is largely unknown, as HIV programs rarely report specifically on transgender populations, or because transgender women may not identify themselves in health care settings due to discrimination and stigma (Jobson et al., 2012). In the Uganda National Strategic Plan, though female sex workers, men who have sex with men, people who inject drugs and people in prisons and closed settings are named, transgender people are not (WHO, 2018).

In 2019, Tranz Network Uganda conducted a needs assessment to establish and document challenges and experience of transgender people in accessing health and other social services in Uganda. The assessment also sought to establish the challenges faced by civil society organizations and community-based organizations, providing health and other social services to transgender people. The needs assessment found generally, the greatest need of transgender persons was the need for employment reported by over 67% of respondents. Although the assessment found a significant number of the respondents had attained secondary and tertiary education, the need for education support was expressed by approximately 56% of the transgender persons who participated in the assessment. On the other hand, the assessment found about 62% expressing needs for health services or support to obtain a health service. With the assessment, lack of money to buy medicine was reported by 22% and for transportation to a health facility by 43% of the respondents. This stood out as major barriers in access to services (Tranz Network Uganda, 2019).

Among the main healthcare needs were: the need for human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) treatment and care expressed by 28.1%, treatment for sexually transmitted infections/diseases 21.0%, assistance to access Gender Affirming Healthcare- surgery (22.3%) and the need for Hormones expressed by 17.0% of the transgender persons in the assessment (Tranz Network Uganda, 2019).

UNAIDS suggests quantitative and qualitative research on transgender women and men must be expanded as there is very limited research or data related to transgender people particularly transgender men. A research agenda should be developed that includes the vulnerabilities experienced by transgender people and that seeks to improve understanding of the best HIV prevention, treatment and care options (UNAIDS, 2014). With the limited information regarding transgender individuals in Uganda, this continues to hold true.

Though we were unable to estimate the number of transgender individuals in our modelling due to the limited number of studies, it is estimated that transgender population is between 0.1% and 1.1% of the population. Tranz Network Uganda used baseline data from multiple sources and a mix of Quadrat and proportion distribution to develop a size estimation of the transgender community. Overall, the estimated population of transgender is approximately 0.03% (95% confidence). This translates to approximately 1.2 million people in Uganda, based on population total estimate of 40 million. However, this only applies based on assumption of normal distribution of transgender population in Uganda (Tranz Network Uganda, 2019).

4.2 Priority Populations

Fisher folk Fisher folk have varying definitions which makes this key population difficult to measure. AMICAALL defines fisher folk as any individual who is engaged in or as the main occupation of fishing or related fishing sector activities such as transportation of fish, fish‐processing and fish‐ trading at the fish landing sites- not including female sex workers operating at the landing sites (AMICAALL, 2013).

The term “fishing community” is also fold in the literature. A fishing community is a group of people that substantially depends on or engages in harvesting fish to meet social and economic needs, and includes the owners of fishing vessels, operators, crew and fish processors (Lake Victoria Basin Commission, 2010). A fishing community has also been defined as a socioeconomic group of persons living together in a locality and deriving their livelihood directly or indirectly from fishing activities. Members of fishing communities include men and women who fish (boat crew), boat owners, fish processors, boat makers, local fishing gear makers or repairers, fishing equipment dealers and managers, as well as fishmongers and traders (Opio, Muyonga, & Mulumba, 2013).

The UAC Multi-Sectoral HIV Programming for MARPS in Uganda: Review of Profiles, Sizes and Program Coverage defines the two terms together stating the fisher folk/fishing community as a community that is substantially dependent on, or substantially engaged in, the harvest or processing of fishery resources to meet social and economic needs (UAC, 2014). In the MARPs Estimates and Behavioral Study in East Central Region of Uganda, fisher folks were found to be mainly located at landing sites on Lake Victoria, Kyoga and River Nile distributed in six districts namely: Bugiri, Kamuli, Kaliro, Buyende, Mayuge and Namayingo districts. Their population was estimated at about 63,640 people mainly males and highly mobile. Sharing of women, multiple sexual partnerships and unprotected sex was common. These risky behaviors were attributed to alcohol consumption, redundancy during daytime with disposable income and limited access to condoms (Muyonga et al., 2012).

The prevalence of HIV is assumed to be higher in fishing communities than in the general population because of sexual behaviors at landing sites. Fishing landing sites are hubs of economic and social activity which support a burgeoning commercial sex industry (Mazzeo, 2004). A high proportion of the study population in five Lake Victoria communities reported risky sexual behavior, including multiple sex partners, failure to use condoms even with partners known to be HIV-infected, transactional sex and sex under the influence of alcohol or drugs (Asiki et al., 2011).

Many factors make fishing communities vulnerable to HIV. Several lifestyle factors suggest that heterosexual sex is the main way in which HIV spreads in fishing communities. Complex combinations of biological, social, cultural and economic factors determine susceptibility to HIV. Several known HIV risk factors converge around fishing activities, though not all of these factors are present in all fishing communities. Vulnerability stems from the nature and dynamics of the fish trade and fishing lifestyle in which a number of known or hypothesized ‘risk factors’ converge:

● Many people in fishing communities are mobile or migratory and the social structures that constrain sexual behavior in more stable communities may not apply; ● Cash income, poverty, irregular working hours and being away from home mean that fishermen have disposable income and leisure time (when not fishing) – factors that favor the consumption of alcohol and use of prostitutes; ● Alcohol and drugs are widely used in fishing communities; ● Health services for fishing communities are limited due to both geographical remoteness and inaccessibility, as well as low health-seeking behavior; ● Fishing is a high-risk occupation which contributes to a culture of risk denial or risk confrontation, and extends to displays of bravado and risk-taking in social and sexual arenas; ● Fishing communities are often socially marginalized and potentially seen to have a low status, which can cause exaggerated or ‘oppositional’ forms of masculinity in men; and ● Fishing communities are vulnerable to HIV due to inadequate prevention, treatment and mitigation measures (KMCC Uganda, 2014; Great Lakes Initiative on AIDS and Global AIDS Monitoring & Evaluation Team, 2008; Kissling et al., 2005; Allison & Seeley, 2004; Seeley & Allison, 2005).

Other priority populations reviewed included uniformed personnel and truckers though the information is scantier for these groups.

Uniformed Personnel The uniformed personnel typically include police, prison guards and the army. Risky behaviors including multiple sexual relations and unprotected sex are common and these are influenced by sharing accommodation, enticement of women staff, night duty and alcohol consumption. The rescue of accident victims without protective gear especially among the traffic police is also associated with risk of HIV infection (Muyonga et al., 2012).

Truckers and their assistants AMICAALL defines a trucker as a person employed to work as a long-distance driver or turn‐ boy on a heavy truck carrying freight, usually over a long distance, often requiring him/her to spend several nights away from home during a single round trip. This category does not include drivers and turn‐boys of small trucks operating within the country or agents/pimps and managers of parking lots (AMICAALL, 2013).

On the other hand, The UAC Multi-Sectoral HIV Programming for MARPS in Uganda: Review of Profiles, Sizes and Programme Coverage defines truckers and their assistants as individuals who earn living transporting goods across major transport corridors within the country and across national boundaries (UAC, 2014). In a synthesis report by the Knowledge Management and Communications Capacity, factors that contributed to multiple sexual relationships and limited condom use included the long periods away from marital homes, peer pressure, alcohol consumption and drug abuse (KMCC, 2014).

4.3 Other Populations

Women who have sex with women Though not currently listed as a key or vulnerable population lesbian women are another marginalized population. A lesbian is defined as woman whose primary sexual and affectional orientation is toward people of the same gender while the term ‘women who have sex with women’ includes not only women who self-identify as lesbian or homosexual and have sex only with other women but also bisexual women as well as women who self-identify as heterosexual but have sex with other women.

4.4 Eligible Studies for Meta-Analysis on Population Size of key populations in Uganda

In the past decade, a number of estimation studies have been conducted in Uganda both at national and subnational level using a variety of estimation methods ranging from extrapolation, mapping and enumeration, capture-recapture, service multiplier methods, modelling, gate- keepers, to respondent driven sampling approaches such as snow-balling. Tables 1-8 provide a brief enumeration of these studies per group, the area where the study was conducted and the estimates and their confidence intervals if the information was available. Most of these studies are documented in unpublished reports and were mainly conducted at subnational level. It should be noted that no study was based on a population-based nationally representative sample. This has been attributed to the current legal framework which criminalizes membership to most of these groups. The apparent differences in estimates from studies conducted in the same area by different agencies may partly be explained by the hidden nature of some groups due to high levels of stigma. A fair level of information was available for the four groups, female sex workers, men who have sex with men, PWIDs and fisher folks, but information was scanty for other groups such as prisoners, transgenders and truckers. men who have sex with men had eleven estimation studies, of which eight were at national level. Fisher folks and PWIDs had seven estimation studies of which three and four, respectively were at national scale.

Due to the limited number of KP estimation studies conducted at national level, we included estimates from secondary studies such as the PEPFAR Uganda Country Operational Plan reports and the Uganda AIDS commision modes of transmission studies in order to improve on the precision the pooled estimates.

Table 1: List of estimation studies for Female Sex Workers.

Study Setting Denominator KP estimates (95%CI) Proportion (%) Female Sex Workers (Women 15-49 years) AMICAALL Uganda Programme Kawempe 29,691 721 (649,787) 2.7% (2013). Mapping and Size Estimation Rubaga 49,806 492 (369, 615) 1.0% of the Key Populations in Kampala Central 19,623 515 (399, 630) 2.6% Capital City Authority Final Report Makindye 40,293 347 (235, 460) 0.9% Nakawa 82,762 200 (159, 242) 0.2% MakSPH (2017) Crane Survey Report: Kampala 221175 8,848 (6337, 17470) 2.1% Population Size Estimation among Mbale 127,653 692 (473, 912) 0.5% Female sex workers, People Who Inject Drugs and Men Who Have Sex Jinja 36,272 801 (533, 1069) 2.2% with Men in 12 Locations, Uganda. Wakiso 32,199 827 (502, 1152) 2.2%

Mbarara 99,679 1,903 (1057, 2749) 2.6%

Gulu 78,244 1,425 (892, 1958) 1.9%

Kabarole 27,795 397 (325, 469) 1.4%

Busia 29,817 960 (591, 1330) 3.2%

Malaba/ Tororo 9,566 538 (325, 751) 5.6%

Masaka 54,854 511 (383, 639) 0.9%

Kabale 30,640 376 (246, 506) 1.2%

PEPFAR. (2018). Uganda Country National 9,041,773 198,376 0.5% Operational Plan 2018: Strategic Directional Summary UAC. (2009). Uganda Modes of National 6521236 32,652 0.5% Transmission Study. UAC. (2014). Uganda Modes of National 7520048 54,549 0.7% Transmission Study. Place II (2018) National 9,041,773 230,000 2.18%-3.09 2.2%

MARPs Network. (2018). Profile of Kampala (Central, 221175 13,127 9,516- 16,738 5.9% Key and Priority Population Hotspots Kawempe, in Kampala and Wakiso. Makindye, Rubaga, Nakawa)

Table 2: List of estimation studies for Men who have Sex with Men

Study Setting Denominator KP estimates (95%CI) Proportion (%) Men who have sex with men (Men age 15+ years) PLACE II (2018) National 9,538,574 45,000 (0.46%, 0.56%) 0.5%

MakSPH (2017). Crane Survey Report Kampala 222701 14,019 (4995, 40949) 6.3% Mbale 58,334 380 (298, 462) 0.7% Jinja 32,260 1,099 (350, 1,849) 3.4% Wakiso 25,267 376 (280, 455) 1.5% Mbarara 75,974 295 (234, 356) 0.4% Gulu 65,149 179 (169, 189) 0.3% Kabarole 23,305 334 (258, 411) 1.4% Mukono 66,189 263 (227, 300) 0.4% PEPFAR. (2017). Uganda Country National 9'538'574 41,948 0.4% Operational Plan 2017 PEPFAR. (2018). Uganda Country National 9'538'574 46,679 0.5% Operational Plan 2018 Multi-Sectoral HIV Programming for Kampala 222701 253 0.1% MARPs in Uganda Draft. (2014). AMICAALL/ KCCA Study Report 2013 Crane Survey 2013 findings National 9'538'574 7790 0.1% Crane Survey 2010 findings National 9'538'574 10,533 0.1% AMICAALL Uganda Programme. Kawempe 29'691 106 (96,116) 0.4% (2013). Mapping and Size Estimation Rubaga 49'806 70 (53,88) 0.14% of the Key Populations in Kampala Central 19'623 76 (47,105) 0.4% Capital City Authority Final Report. Makindye 40'293 51 (34,68) 0.13%

Nakawa 82'762 18 (15,20) 0.02% Modes of Transmission Study (2009). National. 7'566'415 3,976 0.05% Population 15-49 Modes of Transmission Study (2014) National. 9'041'773 10,533 0.12% Population 15-49 MARPs Network. (2018). Profile of Kampala (Central, 222,701 2904 (1,994,3813) 1.3% Key and Priority Population Hotspots Kawempe, in Kampala and Wakiso. Makindye, Rubaga, Nakawa) Wakiso (Kira, 521'449 765 0.15% Entebbe, Sabagabo, Nansana, Kyengera, Busunju, Wakiso, Kakiri) Wakiso (Kira, 1,025,312 1868 (1316, 2419) 0.2% Entebbe, Sabagabo, Nansana, Kyengera, Busunju, Wakiso, Kakiri)

Table 3: List of estimation studies for Persons Who Inject Drugs

Study Setting Denominator KP estimates (95%CI) Proportion (%) Person Who Inject Drugs (PWIDs) PLACE II (2018) National 18,000,000 18,000 (0.07%, 0.27%) 0.1% (people age 15+) MakSPH (2017). Crane Survey All age 15+ and 472,902 3,837 (2952,4722) 0.8% Report: Population Size Estimation currently injected among Female sex workers, People drugs - Kampala Who Inject Drugs and Men Who Have Sex with Men in 12 Locations, Uganda.

CHAU (2017). Population Estimation Mbale 244,480 621 (432– 1,439) 0.3% and Rapid Assessment on Harm Reduction and HIV Prevention among People who Inject Drugs in Kampala City and Mbale Town PEPFAR. (2018). Uganda Country National 17,706,686 3,837 0.01% Operational Plan 2018: Strategic Directional Summary UAC. (2009). Uganda Modes of National - 13,060,787 994 0.01% Transmission Study Population aged 15- 49 UAC. (2014). Uganda Modes of National - 15,972,917 1,597 0.01% Transmission Study. Population aged 15- 49 MARPs Network. (2018). Profile of Kampala (Central, 472,902 6,013 (4109,7916) 1.3% Key and Priority Population Hotspots Kawempe, In Kampala And Wakiso. Makindye, Rubaga, Nakawa) Wakiso (Kira, 1,025,312 1868 (1316, 2419) 0.2% Entebbe, Sabagabo, Nansana, Kyengera, Busunju, Wakiso, Kakiri)

Table 4: List of estimation studies for Fisher folks

Study Setting Denominator KP estimates (95%CI) Proportion (%) FISHER FOLK (Uganda population age 18+) IOM (2013). HIV KAP and Population Arua 776,700 5,839 0.75% Size Estimates for Fisher Folk in Six Apac 349,000 5,210 1.5% Districts in Uganda. Kasese 747,800 16,334 2.2% Masaka 830,000 16,414 2.0% Rakai 518,000 12,300 2.4% Wakiso 2,007,700 5,448 0.3% PLACE II (2018). National 19,872,029 36,000 0.1% – 0.3% 0.2%

GLIA and GAMET. (2008). Rapid National 30,262,610 118,786 0.4% analysis of HIV epidemiology and (2007) HIV response data on vulnerable populations in the Great Lakes Region of Africa Star-EC (2012). Population Estimates, Subnational- 2,892,300 63,640 2.2% HIV Knowledge, Attitudes and Buyende, Kamuli, Practices Study, Hotspots Mapping Kaliro, Luuka, among Most at Risk Populations in Namutumba, East Central Region of Uganda Iganga, Bugiri, Mayuge, Namayingo MARPs Network. (2018). Profile of Kampala (Central, 1,500,700 1428 (946,1910) 0.1% Key and Priority Population Hotspots Kawempe, in Kampala and Wakiso. Makindye, Rubaga, Nakawa) Wakiso (Kira, 2,007,700 4457 (3256, 5658) 0.2% Entebbe, Sabagabo, Nansana, Kyengera, Busunju, Wakiso, Kakiri) Wakiso (Kira, 1,025,312 1868 (1316, 2419) 0.2% Entebbe, Sabagabo, Nansana, Kyengera, Busunju, Wakiso, Kakiri)

Table 5: List of estimation studies for Truckers

Study Setting KP estimates (95%CI)

Multi-Sectoral HIV Programming for MARPs in National - men aged 18+ years 31,588 Uganda. (2014). GLIA and GAMET. (2008). Rapid analysis of HIV National - men aged 18+ years 47,385 epidemiology and HIV response data on vulnerable populations in the Great Lakes Region of Africa. Star-EC. (2012). Population Estimates, HIV Knowledge, Sub-national - Buyende, Kamuli, 321 Attitudes and Practices Study, Hotspots Mapping Kaliro, Liika, Namutumba, Iganga, among Most at Risk Populations in East Central Region Bugiri, Mayuge, Namayingo of Uganda MARPs Network. (2018). Profile of Key and Priority Kampala (Central, Kawempe, 1,771 – 3,896 Population Hotspots In Kampala And Wakiso. Makindye, Rubaga, Nakawa Wakiso (Kira, Entebbe, Sabagabo, 797 – 1,819 Nansana, Kyengera, Busunju, Wakiso, Kakiri

Table 6: List of estimation studies for uniformed services personnel Study Setting KP estimates (95%CI)

Multi-Sectoral HIV Programming for MARPs in National 650,000 Uganda Draft. (2014). Star-EC (2012). Population Estimates, HIV Knowledge, Subnational - Buyende, Kamuli, 2000 Attitudes and Practices Study, Hotspots Mapping Kaliro, Liika, Namutumba, Iganga, among Most at Risk Populations in East Central Region Bugiri, Mayuge, Namayingo of Uganda MARPs Network. (2018). Profile of Key and Priority Kampala (Central, Kawempe, 932 – 2,035 Population Hotspots In Kampala And Wakiso. Makindye, Rubaga, Nakawa) Wakiso (Kira, Entebbe, Sabagabo, 981 – 1,853 Nansana, Kyengera, Busunju, Wakiso, Kakiri)

Table 7: List of estimation studies for Prisoners

Study Setting KP estimates (95%CI)

AMICAALL Uganda Programme. (2013). Mapping and Subnational – central 25,000 Size Estimation of the Key Populations in Kampala Capital City Authority Final Report.

PEPFAR. (2018). Uganda Country Operational Plan National - From Uganda Prisons 42,000 2018: Strategic Directional Summary Services. Uganda Prisons Service Sero-Behavioral Survey, 2017) GLIA and GAMET. (2008). Rapid analysis of HIV National 26,126 epidemiology and HIV response data on vulnerable populations in the Great Lakes Region of Africa. Uganda Prisons Service. (2013). Sero-Behavioural National - From Uganda Prisons 38,158 (1,606 Survey Report Services. female and 36,552 male)

4.5 KP sizes estimation 4.5.1 Estimates from the Meta-analysis random effects model

In Table 9, the pooled estimates of the proportion of KPs in Uganda and their 95% confidence intervals (CIs) are presented. The size estimates are shown in Table 9. Figures 4a-4d are forest plots depicting the individual study estimates and the pooled proportion estimate with their 95% CIs.

Results show that the most prevalent KP in Uganda was fisher folks (>130,000) and the least was PWID (>7,500). FSW and men who have sex with men sizes were estimated at over 130,000 (50,744-210,849) and over 22,000 (12,692-32,635), respectively. The estimated men who have sex with men size is lower than female sex workers contrary to the demographic expectation. These estimates more likely reflect the overt rather than the hidden populations due to the current legal environment in the country that criminalizes men who have sex with men. This could have contributed to under estimation of men who have sex with men in the primary studies. These estimates are in line with size estimates of the most weighted individual studies that were included in the meta-analysis i.e., CRANE, PLACE, AMICAALL, Modes of Transmission and MARPs Study estimations. Similarly, the female sex worker estimate is higher than expected because the pooled estimate derived most weight from the PLACE study (2.54%) and the PEPFAR COP reports (2.2%) which had the highest weights.

The 퐼2 statistics for each of the four groups indicate a great deal of heterogeneity in the studies which perhaps is due to the disparity in the study designs and estimation methods employed by each study.

Table 9: Pooled proportion estimates of KPs in Uganda

Key population Estimated Lower 95% Upper 95% CI 퐼2 (P-value) proportion (%) CI (%) (%) MSM 0.25 0.14 0.36 99.9% (P<0.0001) FSW 1.49 0.58 2.41 99.9% (P<0.0001) PWID 0.04 0.01 0.06 99% (P<0.0001 Fisher Folks 3.98 0.96 7.01 99.9% (P<0.0001)

Table 10: Population size estimates

Key population Age group Population Population Lower Upper denominator estimate bound bound MSM Men age 15+ years 9,065,192 22,663 12,692 32,635

FSW Women 15-49 years 8,748,881 130,359 50,744 210,849

PWID All people age 15+ 18,388,692 7,356 1,839 11,034 years Fisher Folk All people age 15+ 18,388,692 731,870 176,532 1,289,048 years

Figure 4a: MSM forest plot (The black line indicates a line of zero proportion, the red line and diamond represent the pooled estimate and the CIs respectively)

Figure 4b: FSW forest plot (The black line indicates a line of zero proportion, the red line and diamond represent the pooled estimate and the CIs respectively)

Figure 4c: IDUs forest plot (The black line indicates a line of zero proportion, the red line and diamond represent the pooled estimate and the CIs respectively)

Figure 4d: Fisher folks forest plot (The black line indicates a line of zero proportion, the red line and diamond represent the pooled estimate and the CIs respectively)

4.5.2 KP subnational estimates and distribution

The subnational (district) KP size estimates from the Geostatistical prediction model are presented in Tables 11-14. In all districts, a higher proportion of fisher folks was predicted whereas a lower size of PWIDs was predicted. Kampala had the highest number of men who have sex with men and female sex workers predicted. Bukwo district was estimated with the lowest numbers of female sex workers.

Table 11: MSM district KP probability of existence, and size estimates

District Population Probability (%) Size male age Estimate Lower CI Upper CI Estimate Lower CI Upper CI 15+ ABIM 29814 0.21 0.19 0.25 63 57 75 ADJUMANI 60978 0.13 0.10 0.19 78 62 119 AGAGO 59887 0.24 0.21 0.30 144 128 182 ALEBTONG 59783 0.25 0.23 0.29 152 137 172 AMOLATAR 39241 0.28 0.25 0.31 110 100 120 AMUDAT 30151 0.15 0.12 0.18 46 37 54 AMURIA 72223 0.36 0.32 0.40 259 231 288 AMURU 50616 0.16 0.13 0.22 79 66 111 APAC 98323 0.25 0.23 0.28 242 221 275 ARUA 208591 0.19 0.15 0.29 389 312 596 BUDAKA 55706 0.35 0.29 0.39 193 160 218 BUDUDA 57030 0.69 0.60 0.78 391 340 445 BUGIRI 104763 0.58 0.43 0.65 610 455 682 BUIKWE 115463 0.75 0.67 0.84 872 774 975 BUKEDEA 50512 0.32 0.28 0.35 163 142 177 BUKOMANSIMBI 39423 0.38 0.36 0.50 152 141 198 BUKWO 24152 0.17 0.14 0.20 41 33 48 BULAMBULI 47993 0.27 0.24 0.30 131 113 144 BULIISA 30697 0.17 0.15 0.23 52 45 71 BUNDIBUGYO 59575 0.31 0.26 0.37 183 157 219 BUSHENYI 61705 0.30 0.26 0.36 185 160 224 BUSIA 86636 0.29 0.21 0.36 252 181 315 BUTALEJA 65808 0.38 0.31 0.43 253 203 284 BUTAMBALA 26334 0.41 0.38 0.49 109 101 129 BUVUMA 24594 0.49 0.44 0.57 121 109 140 BUYENDE 86195 0.36 0.28 0.38 310 240 330 DOKOLO 48512 0.26 0.23 0.29 127 114 141 GOMBA 42072 0.36 0.33 0.47 153 140 196 GULU 118346 0.20 0.17 0.23 237 203 269 HOIMA 154314 0.23 0.21 0.31 356 317 478 IBANDA 65367 0.23 0.20 0.28 154 131 185 IGANGA 134655 0.82 0.62 0.92 1101 830 1235 ISINGIRO 131694 0.26 0.23 0.32 348 301 427 JINJA 123072 2.13 1.95 2.32 2622 2403 2858 KAABONG 44824 0.15 0.13 0.20 67 57 90 KABALE 140056 0.22 0.18 0.30 313 251 421 KABAROLE 125487 0.40 0.36 0.45 504 453 563 KABERAMAIDO 57238 0.27 0.23 0.30 157 133 173 KALANGALA 14258 0.73 0.58 1.03 105 83 147 KALIRO 63315 0.39 0.29 0.44 250 183 277 KALUNGU 48227 0.44 0.41 0.57 212 195 275 KAMPALA 400198 3.50 3.45 3.56 14019 13791 14247 KAMULI 129902 0.52 0.40 0.57 682 524 742 KAMWENGE 112996 0.21 0.18 0.25 237 207 283 KANUNGU 66354 0.26 0.22 0.33 170 144 222 KAPCHORWA 27762 0.20 0.16 0.23 55 46 65 KASESE 185894 0.23 0.20 0.29 434 370 537 KATAKWI 43941 0.13 0.11 0.16 59 48 71 KAYUNGA 97596 0.40 0.33 0.43 393 320 415 KIBAALE 214331 0.24 0.21 0.31 513 460 657 KIBOGA 39397 0.36 0.32 0.42 142 127 166 KIBUKU 54278 0.35 0.27 0.41 190 146 220 KIRUHURA 87857 0.22 0.20 0.27 197 176 239 KIRYANDONGO 71340 0.21 0.18 0.26 147 131 187 KISORO 75885 0.25 0.21 0.34 193 156 260 KITGUM 53654 0.16 0.13 0.22 85 72 117 KOBOKO 55836 0.18 0.14 0.28 100 76 157 KOLE 64432 0.27 0.24 0.32 176 157 207 KOTIDO 47655 0.25 0.22 0.32 121 106 152 KUMI 69054 0.19 0.15 0.22 130 102 153 KWEEN 25425 0.19 0.16 0.22 49 41 56 KYANKWANZI 57784 0.32 0.29 0.40 184 168 232 KYENJOJO 113619 0.30 0.28 0.36 344 313 410 LAMWO 35138 0.19 0.17 0.28 68 58 98 LIRA 109100 0.34 0.31 0.40 376 339 432 LUUKA 63809 0.68 0.52 0.76 431 332 487 LUWERO 121332 0.39 0.35 0.42 472 425 512 LWENGO 72093 0.41 0.38 0.54 295 271 391 LYANTONDE 25139 0.28 0.25 0.35 70 64 89 MANAFWA 93440 0.49 0.42 0.55 456 391 516 MARACHA 49135 0.14 0.11 0.22 68 53 107 MASAKA 78378 0.40 0.37 0.55 313 287 430 MASINDI 77832 0.22 0.20 0.28 172 156 221 MAYUGE 127747 0.58 0.49 0.63 736 632 809 MBALE 131382 1.00 0.88 1.16 1313 1158 1521 MBARARA 125357 0.35 0.31 0.40 445 393 496 MITOOMA 48590 0.28 0.24 0.36 138 116 173 MITYANA 87259 0.41 0.37 0.47 356 321 413 MOROTO 27684 0.20 0.18 0.24 56 49 66 MOYO 36358 0.15 0.12 0.24 55 42 86 MPIGI 66587 0.45 0.40 0.54 300 269 359 MUBENDE 184881 0.31 0.29 0.39 578 542 725 MUKONO 159430 0.27 0.24 0.30 431 380 484 NAKAPIRIPIRIT 45993 0.16 0.14 0.19 76 63 87 NAKASEKE 55835 0.28 0.26 0.31 156 144 176 NAKASONGOLA 52615 0.36 0.32 0.41 190 170 214 NAMIYANGO 48356 0.63 0.55 0.69 306 264 336 NAMUTUMBA 58900 0.40 0.30 0.46 238 176 268 NAPAK 67625 0.23 0.20 0.26 155 137 177 NEBBI 38332 0.18 0.15 0.26 67 56 100 NGORA 102556 0.27 0.22 0.30 278 222 306 NTOROKO 37839 0.29 0.26 0.36 110 99 136 NTUNGAMO 17556 0.24 0.20 0.29 41 34 52 NWOYA 129201 0.21 0.18 0.29 271 237 376 OTUKE 35995 0.23 0.21 0.27 82 75 97 OYAM 28437 0.21 0.18 0.25 60 52 72 PADER 103283 0.26 0.23 0.33 273 236 343 PALLISA 48512 0.30 0.23 0.35 148 112 171 RAKAI 103075 0.27 0.25 0.37 283 253 381 BUHWEJU 33086 0.23 0.19 0.28 75 64 93 RUKUNGIRI 34099 0.25 0.21 0.32 84 71 108 SSEMBABULE 79442 0.27 0.25 0.35 216 200 280 SERERE 84039 0.30 0.24 0.33 254 199 275 SIRONKO 55550 0.65 0.55 0.75 361 306 417 KYEGEGWA 76716 0.27 0.24 0.30 211 188 231 SOROTI 65263 0.34 0.28 0.37 223 186 244 TORORO 67211 0.54 0.44 0.59 362 297 396 WAKISO 139693 0.63 0.56 0.71 880 784 993 YUMBE 550590 0.14 0.10 0.22 746 560 1197 ZOMBO 131902 0.16 0.13 0.25 216 169 331

Table 12: FSWs district KP probability of existence, and size estimates

Probability (%) Size Population male Estimate Lower CI Upper CI Estimate Lower CI Upper CI District age 15+ ABIM 27649 1.20 1.09 1.62 333 301 449 ADJUMANI 59034 2.15 1.59 3.38 1271 937 1997 AGAGO 57683 1.33 1.23 1.67 770 708 964 ALEBTONG 57136 1.43 1.29 1.71 816 736 976 AMOLATAR 37250 1.88 1.65 2.06 701 616 768 AMUDAT 28338 0.96 0.73 1.56 271 208 443 AMURIA 68616 1.03 0.89 1.35 707 611 926 AMURU 48309 1.97 1.68 2.44 952 813 1178 APAC 93512 1.90 1.66 2.14 1775 1553 2005 ARUA 199098 2.07 1.59 3.11 4128 3159 6190 BUDAKA 52853 1.21 1.00 1.52 638 530 801 BUDUDA 53676 0.76 0.65 0.89 407 347 476 BUGIRI 98911 3.15 2.92 3.47 3111 2885 3430 BUHWEJU 31454 1.69 1.48 2.04 532 466 641 BUIKWE 110658 2.34 1.50 3.45 2590 1659 3819 BUKEDEA 47903 0.69 0.62 0.83 328 299 398 BUKOMANSIMBI 38308 1.35 1.21 1.51 515 462 577 BUKWO 22632 0.88 0.70 1.28 199 159 289 BULAMBULI 44963 0.66 0.60 0.84 295 269 379 BULIISA 28797 2.08 1.64 2.77 599 471 798 BUNDIBUGYO 56836 1.63 1.33 2.16 928 755 1230 BUSHENYI 59746 1.69 1.53 1.91 1011 912 1142 BUSIA 82543 3.29 2.96 3.61 2715 2441 2981 BUTALEJA 62345 2.47 2.08 3.03 1538 1298 1888 BUTAMBALA 25476 1.78 1.64 1.93 452 417 491 BUVUMA 22811 1.89 1.21 2.87 430 276 654 BUYENDE 81260 1.94 1.65 2.28 1578 1341 1849 DOKOLO 46296 1.63 1.40 1.97 754 647 911 GOMBA 40590 1.52 1.38 1.65 618 560 672 GULU 112516 1.84 1.72 1.95 2068 1935 2199 HOIMA 145523 1.93 1.57 2.51 2816 2289 3656 IBANDA 62906 1.94 1.43 2.83 1217 900 1778 IGANGA 128403 2.63 2.28 3.02 3373 2925 3873 ISINGIRO 124785 1.58 1.43 1.80 1973 1789 2241 JINJA 118734 2.29 1.95 2.60 2722 2318 3089 KAABONG 42922 1.49 1.14 2.23 638 491 958 KABALE 135446 1.25 1.11 1.40 1693 1503 1897 KABAROLE 120246 1.45 1.29 1.62 1743 1552 1946 KABERAMAIDO 54105 1.57 1.34 1.89 847 723 1023 KALANGALA 13542 1.06 0.53 1.89 143 72 256 KALIRO 60077 1.98 1.63 2.51 1192 982 1507 KALUNGU 46690 1.31 1.22 1.42 610 568 661 KAMPALA 384461 5.80 1.50 14.17 22314 5774 54490 KAMULI 124313 2.37 1.94 2.99 2952 2416 3719 KAMWENGE 106871 1.81 1.38 2.56 1936 1471 2735 KANUNGU 63918 1.38 1.27 1.51 881 813 966 KAPCHORWA 26518 0.78 0.59 1.19 208 155 315 KASESE 178012 1.75 1.38 2.44 3123 2456 4339 KATAKWI 41979 1.12 0.77 1.83 469 324 766 KAYUNGA 93873 2.49 1.99 2.95 2334 1870 2771 KIBAALE 199992 1.89 1.52 2.50 3777 3030 4991 KIBOGA 37682 1.97 1.72 2.29 744 647 862 KIBUKU 51380 1.76 1.34 2.43 902 690 1249 KIRUHURA 83308 1.75 1.47 2.19 1462 1226 1820 KIRYANDONGO 68004 2.03 1.72 2.42 1377 1168 1648 KISORO 72819 1.33 1.18 1.51 965 856 1102 KITGUM 51731 1.62 1.34 2.16 839 692 1117 KOBOKO 52783 2.12 1.54 3.53 1118 812 1865 KOLE 61332 1.73 1.55 2.00 1062 948 1227 KOTIDO 45365 1.31 1.08 1.90 595 490 862 KUMI 65439 1.06 0.74 1.68 693 482 1098 KWEEN 24247 0.77 0.65 1.09 188 157 263 KYANKWANZI 54278 1.84 1.64 2.03 1000 892 1102 KYEGEGWA 70334 0.61 0.56 0.71 426 391 496 KYENJOJO 107510 1.63 1.43 1.90 1752 1534 2046 LAMWO 33991 1.63 1.48 1.89 555 502 643 LIRA 104093 1.55 1.45 1.72 1612 1510 1792 LUUKA 61250 2.43 1.95 3.17 1491 1197 1943 LUWERO 116174 2.51 2.14 3.04 2912 2487 3536 LWENGO 69845 1.27 1.14 1.42 890 795 994 LYANTONDE 23981 1.51 1.32 1.79 363 317 429 MANAFWA 89475 1.20 1.06 1.37 1077 949 1225 MARACHA 47208 2.14 1.55 3.45 1011 730 1631 MASAKA 75220 0.94 0.85 1.04 710 643 782 MASINDI 74283 2.11 1.72 2.67 1570 1278 1982 MAYUGE 121502 2.34 1.84 2.96 2847 2232 3602 MBALE 124959 0.58 0.46 0.69 721 575 857 MBARARA 120227 1.94 1.80 2.07 2333 2159 2489 MITOOMA 47042 1.65 1.42 1.99 776 669 937 MITYANA 83998 2.00 1.75 2.25 1680 1470 1890 MOROTO 26508 1.01 0.86 1.59 267 227 421 MOYO 34863 2.12 1.56 3.45 740 544 1202 MPIGI 63775 1.65 1.33 1.98 1054 851 1264 MUBENDE 174662 1.77 1.63 1.91 3094 2854 3337 MUKONO 152094 2.47 1.96 3.21 3754 2988 4884 NAKAPIRIPIRIT 43028 0.89 0.71 1.37 383 306 590 NAKASEKE 50131 2.20 1.89 2.48 1101 949 1244 NAKASONGOLA 46115 1.90 1.62 2.28 877 746 1053 NAMIYANGO 56604 2.07 1.42 2.98 1170 804 1687 NAMUTUMBA 64218 2.49 2.05 3.14 1599 1320 2014 NAPAK 36823 0.95 0.85 1.42 352 313 523 NEBBI 97679 2.10 1.60 3.09 2052 1562 3018 NGORA 36130 1.16 0.97 1.46 419 349 529 NTOROKO 16842 1.54 1.39 1.69 259 235 285 NTUNGAMO 124076 1.56 1.40 1.79 1941 1731 2217 NWOYA 32480 1.88 1.68 2.01 610 547 654 OTUKE 26781 1.33 1.23 1.63 356 331 437 OYAM 98387 1.91 1.64 2.30 1874 1616 2260 PADER 46586 1.47 1.26 1.75 686 589 815 PALLISA 97896 1.49 1.11 2.14 1459 1086 2096 RAKAI 131350 1.23 1.07 1.48 1616 1403 1945 RUKUNGIRI 81285 1.53 1.39 1.72 1244 1126 1402 SEMBABULE 64151 1.52 1.34 1.78 975 857 1140 SERERE 71919 1.57 1.32 1.91 1128 949 1376 SIRONKO 62539 0.49 0.31 0.73 307 194 454 SOROTI 75349 1.18 1.08 1.42 887 814 1074 TORORO 133472 5.72 5.08 6.35 7632 6784 8472 WAKISO 509087 2.66 2.29 3.02 13558 11645 15365 YUMBE 123128 2.22 1.52 4.00 2732 1876 4924 ZOMBO 60949 2.18 1.51 3.73 1328 923 2272

Table 13: PWID district KP probability of existence, and size estimates

District Population age 15+ Probability (%) Size Estimate Lower CI Upper CI Estimate Lower CI Upper CI

ABIM 59627 0.02 0.01 0.05 15 8 31 ADJUMANI 121955 0.02 0.01 0.03 30 16 40 AGAGO 119774 0.02 0.01 0.05 30 16 56 ALEBTONG 119566 0.03 0.01 0.05 31 18 58 AMOLATAR 74481 0.03 0.01 0.04 20 11 32 AMUDAT 60302 0.03 0.01 0.07 15 7 42 AMURIA 144445 0.03 0.01 0.06 38 21 86 AMURU 101231 0.02 0.01 0.03 25 13 34 APAC 196645 0.03 0.01 0.04 51 29 76 ARUA 417182 0.02 0.01 0.03 102 61 140 BUDAKA 111411 0.03 0.01 0.09 35 14 103 BUDUDA 114060 0.04 0.01 0.12 43 12 137 BUGIRI 209526 0.03 0.02 0.06 65 37 132 BUIKWE 230925 0.03 0.02 0.05 73 41 121 BUKEDEA 101023 0.03 0.01 0.09 28 12 91 BUKOMANSIMBI 78845 0.02 0.01 0.03 16 8 26 BUKWO 48304 0.03 0.01 0.08 13 6 40 BULAMBULI 95985 0.03 0.01 0.09 27 11 87 BULIISA 61393 0.02 0.01 0.03 14 8 19 BUNDIBUGYO 119150 0.02 0.01 0.03 26 17 41 BUSHENYI 123409 0.02 0.01 0.03 26 15 42 BUSIA 173272 0.03 0.02 0.06 53 29 110 BUTALEJA 131616 0.03 0.01 0.08 40 18 112 BUTAMBALA 52667 0.02 0.01 0.03 11 5 18 BUVUMA 49187 0.03 0.02 0.03 13 9 15 BUYENDE 172389 0.03 0.02 0.05 48 29 91 DOKOLO 97024 0.03 0.02 0.05 26 15 45 GOMBA 84143 0.02 0.01 0.03 16 9 23 GULU 236691 0.03 0.01 0.04 59 32 87 HOIMA 308627 0.02 0.01 0.03 68 44 94 IBANDA 130733 0.02 0.01 0.03 27 16 44 IGANGA 269309 0.03 0.02 0.06 88 55 157 ISINGIRO 263388 0.02 0.01 0.03 52 31 80 JINJA 246144 0.04 0.03 0.05 91 68 115 KAABONG 89648 0.02 0.01 0.04 22 11 40 KABALE 280112 0.02 0.01 0.04 58 35 99 KABAROLE 250974 0.02 0.01 0.03 53 35 82 KABERAMAIDO 114476 0.03 0.02 0.05 30 18 58 KALANGALA 28515 0.02 0.01 0.03 6 4 8 KALIRO 126630 0.03 0.02 0.06 36 22 78 KALUNGU 96453 0.02 0.01 0.03 19 9 31 KAMPALA 800395 0.35 0.00 0.55 2774 3 4375 KAMULI 259804 0.03 0.02 0.05 80 53 130 KAMWENGE 225991 0.02 0.01 0.03 45 28 69 KANUNGU 132707 0.02 0.01 0.03 27 17 43 KAPCHORWA 55524 0.03 0.01 0.09 16 7 50 KASESE 371787 0.02 0.01 0.03 77 48 123 KATAKWI 87882 0.03 0.01 0.06 22 12 56 KAYUNGA 195191 0.03 0.02 0.05 58 35 93 KIBAALE 428661 0.02 0.01 0.03 90 61 127 KIBOGA 78793 0.02 0.02 0.03 17 12 23 KIBUKU 108555 0.03 0.01 0.08 34 16 84 KIRUHURA 175713 0.02 0.01 0.03 33 20 49 KIRYANDONGO 142679 0.02 0.01 0.03 35 20 47 KISORO 151769 0.02 0.01 0.04 32 20 57 KITGUM 107308 0.02 0.01 0.04 26 14 45 KOBOKO 111671 0.02 0.02 0.04 27 17 40 KOLE 128863 0.03 0.02 0.04 35 21 51 KOTIDO 95310 0.03 0.01 0.05 26 14 48 KUMI 138108 0.03 0.01 0.08 39 19 104 KWEEN 50849 0.03 0.01 0.08 13 6 42 KYANKWANZI 115567 0.02 0.01 0.03 25 17 34 KYENJOJO 227238 0.02 0.01 0.03 47 31 69 LAMWO 70275 0.02 0.01 0.04 17 9 27 LIRA 218200 0.03 0.02 0.04 61 36 96 LUUKA 127617 0.03 0.02 0.05 40 27 67 LUWERO 242664 0.03 0.02 0.04 69 41 106 LWENGO 144185 0.02 0.01 0.03 30 15 49 LYANTONDE 50278 0.02 0.01 0.03 10 6 14 MANAFWA 186880 0.03 0.01 0.11 65 21 200 MARACHA 98270 0.02 0.01 0.03 24 13 32 MASAKA 156664 0.02 0.01 0.03 31 17 47 MASINDI 155664 0.02 0.01 0.03 37 23 48 MAYUGE 255493 0.03 0.02 0.04 76 53 101 MBALE 262764 0.04 0.01 0.16 106 25 418 MBARARA 250714 0.02 0.01 0.03 51 30 82 MITOOMA 97180 0.02 0.01 0.04 21 12 36 MITYANA 174518 0.02 0.01 0.03 37 21 52 MOROTO 55368 0.02 0.01 0.06 14 7 32 MOYO 72716 0.03 0.01 0.03 18 10 25 MPIGI 133174 0.02 0.01 0.03 26 12 42 MUBENDE 369761 0.02 0.01 0.03 75 46 105 MUKONO 318860 0.03 0.02 0.04 85 62 115 NAKAPIRIPIRIT 91986 0.02 0.01 0.07 23 11 65 NAKASEKE 105230 0.02 0.01 0.03 25 15 36 NAKASONGOLA 96712 0.03 0.01 0.04 25 14 38 NAMIYANGO 117800 0.03 0.02 0.04 34 22 49 NAMUTUMBA 135252 0.03 0.02 0.07 39 22 91 NAPAK 76663 0.02 0.01 0.06 19 10 46 NEBBI 205111 0.02 0.01 0.03 49 29 67 NGORA 75677 0.03 0.01 0.07 20 11 52 NTOROKO 35111 0.02 0.01 0.03 7 4 11 NTUNGAMO 258402 0.02 0.01 0.03 52 32 86 NWOYA 71989 0.02 0.01 0.03 18 9 24 OTUKE 56874 0.02 0.01 0.05 14 8 28 OYAM 206565 0.03 0.02 0.04 53 31 74 PADER 97024 0.02 0.01 0.04 24 13 41 PALLISA 206150 0.03 0.02 0.07 62 31 148 RAKAI 273464 0.02 0.01 0.03 53 30 81 BUHWEJU 66172 0.02 0.01 0.03 13 8 21 RUKUNGIRI 168078 0.02 0.01 0.03 35 21 56 SSEMBABULE 134421 0.02 0.01 0.03 26 15 38 SERERE 152132 0.03 0.02 0.06 41 24 87 SIRONKO 130525 0.03 0.01 0.11 39 13 145 KYEGEGWA 153431 0.03 0.01 0.10 43 16 154 SOROTI 158884 0.03 0.02 0.06 44 24 96 TORORO 279385 0.03 0.01 0.09 88 36 246 WAKISO 1101180 0.02 0.01 0.06 252 63 704 YUMBE 263803 0.02 0.01 0.03 65 38 90 ZOMBO 127824 0.02 0.01 0.03 30 19 43

Table 14: Fisher folks district KP probability of existence, and size estimates

District Population age Probability (%) Size 15+ Estimate Lower CI Upper CI Estimate Lower CI Upper CI ABIM 59627.00 2.98 2.69 3.61 1779 1602 2150 ADJUMANI 121955.00 1.80 1.45 2.36 2198 1764 2875 AGAGO 119774.00 2.67 2.33 3.31 3192 2786 3967 ALEBTONG 119566.00 2.43 2.19 2.81 2900 2614 3363 AMOLATAR 74481.00 2.54 2.37 2.74 1892 1762 2043 AMUDAT 60302.00 3.17 2.80 3.67 1913 1688 2214 AMURIA 144445.00 2.55 2.35 2.95 3687 3397 4259 AMURU 101231.00 2.09 1.70 2.69 2116 1721 2725 APAC 196645.00 2.38 2.12 2.63 4671 4169 5175 ARUA 417182.00 1.25 0.96 1.55 5229 4003 6451 BUDAKA 111411.00 2.06 1.83 2.37 2299 2036 2642 BUDUDA 114060.00 1.08 1.03 1.28 1238 1177 1462 BUGIRI 209526.00 1.99 1.69 2.41 4175 3540 5041 BUIKWE 230925.00 2.31 1.73 3.17 5325 3985 7310 BUKEDEA 101023.00 2.71 2.39 3.16 2741 2410 3196 BUKOMANSIMBI 78845.00 2.70 2.48 3.10 2127 1952 2445 BUKWO 48304.00 2.93 2.55 3.42 1414 1234 1653 BULAMBULI 95985.00 2.68 2.36 3.10 2568 2270 2975 BULIISA 61393.00 2.15 1.82 2.68 1317 1119 1643 BUNDIBUGYO 119150.00 2.57 2.16 3.02 3058 2579 3604 BUSHENYI 123409.00 3.15 2.86 3.57 3884 3524 4401 BUSIA 173272.00 2.01 1.72 2.40 3489 2985 4163 BUTALEJA 131616.00 2.18 1.88 2.58 2873 2480 3394 BUTAMBALA 52667.00 1.08 1.01 1.22 569 534 642 BUVUMA 49187.00 2.31 1.91 2.80 1134 940 1375 BUYENDE 172389.00 2.56 2.23 3.03 4417 3847 5226 DOKOLO 97024.00 2.34 2.14 2.58 2269 2077 2507 GOMBA 84143.00 2.19 2.10 2.55 1846 1764 2144 GULU 236691.00 2.12 1.78 2.70 5028 4202 6383 HOIMA 308627.00 2.03 1.75 2.57 6279 5404 7938 IBANDA 130733.00 3.00 2.76 3.34 3924 3606 4366 IGANGA 269309.00 1.74 1.49 2.08 4681 4008 5614 ISINGIRO 263388.00 3.48 3.32 3.63 9159 8750 9551 JINJA 246144.00 1.22 1.05 1.45 3013 2594 3581 KAABONG 89648.00 2.88 2.53 3.61 2583 2272 3237 KABALE 280112.00 3.01 2.73 3.38 8428 7651 9456 KABAROLE 250974.00 2.75 2.36 3.17 6908 5922 7963 KABERAMAIDO 114476.00 2.48 2.32 2.75 2841 2652 3147 KALANGALA 28515.00 3.04 2.68 3.66 866 764 1044 KALIRO 126630.00 2.46 2.09 2.98 3120 2641 3774 KALUNGU 96453.00 2.39 2.19 2.78 2305 2114 2680 KAMPALA 800395.00 0.00 0.00 0.00 0 0 1 KAMULI 259804.00 2.04 1.71 2.50 5299 4449 6495 KAMWENGE 225991.00 3.38 3.09 3.67 7633 6975 8304 KANUNGU 132707.00 3.53 3.21 3.95 4685 4256 5244 KAPCHORWA 55524.00 2.27 2.04 2.56 1259 1130 1424 KASESE 371787.00 3.53 3.03 4.05 13109 11266 15056 KATAKWI 87882.00 2.97 2.66 3.44 2609 2340 3022 KAYUNGA 195191.00 2.18 1.82 2.70 4265 3553 5269 KIBAALE 428661.00 2.19 1.98 2.69 9405 8469 11534 KIBOGA 78793.00 1.74 1.65 2.09 1370 1303 1645 KIBUKU 108555.00 2.04 1.80 2.37 2212 1949 2569 KIRUHURA 175713.00 3.79 3.67 3.88 6651 6449 6813 KIRYANDONGO 142679.00 2.13 1.83 2.51 3033 2609 3587 KISORO 151769.00 2.79 2.50 3.20 4229 3791 4855 KITGUM 107308.00 2.61 2.24 3.36 2804 2407 3610 KOBOKO 111671.00 1.36 1.04 1.87 1522 1163 2083 KOLE 128863.00 1.92 1.66 2.28 2477 2139 2937 KOTIDO 95310.00 2.01 1.77 2.54 1919 1684 2416 KUMI 138108.00 2.41 2.16 2.77 3331 2978 3820 KWEEN 50849.00 2.97 2.60 3.47 1512 1323 1766 KYANKWANZI 115567.00 2.06 1.89 2.55 2378 2190 2943 KYENJOJO 227238.00 2.73 2.43 3.09 6202 5527 7030 LAMWO 70275.00 2.53 2.10 3.38 1781 1478 2373 LIRA 218200.00 1.86 1.62 2.22 4069 3540 4843 LUUKA 127617.00 1.92 1.61 2.35 2451 2060 2999 LUWERO 242664.00 1.92 1.59 2.37 4662 3849 5748 LWENGO 144185.00 3.04 2.79 3.37 4390 4024 4861 LYANTONDE 50278.00 3.45 3.28 3.67 1733 1650 1846 MANAFWA 186880.00 1.51 1.41 1.70 2826 2634 3172 MARACHA 98270.00 1.61 1.25 1.99 1579 1231 1959 MASAKA 156664.00 5.96 5.06 7.96 9331 7924 12478 MASINDI 155664.00 1.92 1.69 2.36 2988 2624 3673 MAYUGE 255493.00 1.90 1.60 2.30 4864 4091 5883 MBALE 262764.00 0.74 0.69 0.91 1939 1815 2400 MBARARA 250714.00 3.13 2.94 3.44 7855 7363 8616 MITOOMA 97180.00 2.80 2.53 3.25 2722 2454 3158 MITYANA 174518.00 1.16 1.13 1.36 2032 1968 2372 MOROTO 55368.00 3.21 2.90 3.78 1778 1605 2093 MOYO 72716.00 1.59 1.25 2.17 1153 906 1580 MPIGI 133174.00 1.13 1.07 1.25 1499 1424 1665 MUBENDE 369761.00 2.01 1.94 2.41 7432 7187 8913 MUKONO 318860.00 1.49 1.21 1.82 4745 3857 5810 NAKAPIRIPIRIT 91986.00 3.26 2.88 3.80 3002 2647 3491 NAKASEKE 105230.00 2.03 1.87 2.40 2136 1969 2525 NAKASONGOLA 96712.00 2.38 2.21 2.69 2298 2135 2605 NAMIYANGO 117800.00 1.96 1.70 2.30 2310 2004 2708 NAMUTUMBA 135252.00 2.37 1.99 2.89 3203 2687 3913 NAPAK 76663.00 3.11 2.83 3.65 2381 2167 2800 NEBBI 205111.00 1.57 1.25 1.93 3221 2556 3955 NGORA 75677.00 2.60 2.31 2.99 1965 1750 2266 NTOROKO 35111.00 3.09 2.68 3.56 1086 940 1250 NTUNGAMO 258402.00 3.27 3.02 3.59 8457 7809 9287 NWOYA 71989.00 2.08 1.71 2.56 1495 1232 1842 OTUKE 56874.00 2.77 2.48 3.27 1574 1413 1861 OYAM 206565.00 2.03 1.73 2.43 4198 3579 5026 PADER 97024.00 2.47 2.10 3.11 2400 2036 3019 PALLISA 206150.00 2.12 1.89 2.43 4366 3887 5014 RAKAI 273464.00 3.26 3.09 3.52 8901 8462 9619 BUHWEJU 66172.00 3.93 3.53 4.34 2599 2339 2869 RUKUNGIRI 168078.00 3.32 3.03 3.75 5585 5085 6305 SSEMBABULE 134421.00 3.19 3.05 3.48 4294 4099 4682 SERERE 152132.00 2.57 2.31 2.95 3907 3517 4488 SIRONKO 130525.00 2.29 2.04 2.61 2988 2669 3413 KYEGEGWA 153431.00 2.77 2.41 3.26 4245 3696 5001 SOROTI 158884.00 2.36 2.19 2.68 3752 3483 4256 TORORO 279385.00 2.01 1.76 2.35 5628 4930 6574 WAKISO 1101180.00 0.22 0.19 0.26 2397 2078 2842 YUMBE 263803.00 1.51 1.18 2.05 3996 3112 5399 ZOMBO 127824.00 1.44 1.12 1.79 1835 1433 2291

4.6 Mapping of predicted estimates in Uganda

We mapped the district-level predicted median size estimates and their 95% Bayesian Credible Intervals in Figures 1-3 using the district shapefiles of 2012 that consisted of 112 districts by then. We did not use a recent shapefile because the most important predictor - HIC prevalence- was only available at district level for the year 2012. Figure 1 shows the distribution of the proportion of men who have sex with men in Uganda. Results from this distribution indicate a heterogeneous distribution of this group with the central and mid-eastern districts having the highest concentration and the northern-based districts having the lowest proportion. The female sex workers are more evenly distributed in the country but with a higher than average concentration of female sex workers in the districts where the major highways traverse and districts with emerging economic activities such as oil and gold mining are taking place, respectively, and districts located at the national boundaries. The PWIDs are predominantly distributed in Kampala and the eastern parts of the country.

Figure 1: Distribution of model-based estimates and confidence intervals of proportion of MSM in Uganda

Figure 2: Distribution of model-based estimates and confidence intervals of proportion of FSW in Uganda

Figure 3: Distribution of model-based estimates and confidence intervals of proportion of PWID in Uganda

4.7 Strengths and Weaknesses of Size Estimation Methodologies

Establishing the sizes of key populations is crucial to epidemiologists and modelers to estimate and project HIV prevalence, incidence and distribution of KPs within their country as well as estimate the future course of the HIV epidemic. It also enables policy makers, programmers and development partners to plan for the provision of services to these populations, advocate for resource allocation, program planning, monitoring and evaluation. Therefore, the value of good and replicable estimates of sizes of at-risk populations based on sound methods cannot be over- emphasized. Estimating the size of these populations and their burden of HIV disease is extremely challenging given the existing legal barriers and stigma making them hidden and hard to reach. Furthermore, there is no widely recognized single gold standard for estimating the population sizes of these key populations. The lack of a gold standard for size estimation of hidden populations makes it difficult to assess which among these methods is most accurate. In this situation, epidemiologist, modelers and public health practitioners are faced with assessing the strengths and weaknesses of each method and considering which combination of methods can be used to provide the best usable estimates.

Below we describe methods that are commonly used either alone or in combination, to arrive at estimates of the size of populations at high risk for HIV providing examples of in which situations they have been employed. We follow this description with an assessment of the strengths and weaknesses of each method and leave it to the reader to decide on the best method or combination of methods to use in key population size estimation.

Census, enumeration and mapping Census-taking is a method used to directly count members of a key population group by observing or interacting with them in locations/venues where they engage in risk behaviors. This is a good method for estimating the size of the visible portion of a key population, i.e. the subset that can be found at physical locations and that can be identified as comprising persons who engage in the behavior that defines the key population (WHO & UNAIDS, 2016). Mapping using the census method involves visiting all known sites or venues in a defined geographic area, developing estimates for each individual site and then summing those estimates to create a total across all the sites. This may involve conducting a head count of key population members at each site, as observed by the team conducting the mapping, or it may involve estimates given by key informants (people who are found at the site itself or who are familiar with the network of people associated with the site), or ideally some combination of the two (WHO & UNAIDS, 2016).

The geographical mapping approach focuses on identifying the locations frequented by high-risk populations and characterizes specific spots in terms of operational typologies and the sexual networks present. Preliminary steps of the geographical mapping approach involve developing or acquiring maps of the targeted area, segmentation of the target area into smaller geographic zones to facilitate field work planning and data collection and identifying and enlisting the support of key stakeholders and gatekeepers linked to the spots and the target areas (Odek et al., 2014).

In Uganda, this method has been used to estimate key population sizes by AMICAALL in the 2013 Mapping and Size Estimation of Key Populations in Kampala Capital City Authority whose aim was to determine the location, estimate the size and understand the operational typology of the key populations at higher risk of HIV infection in Kampala. The study adopted the Geographical Mapping Approach proposed by Emmanuel et al., (2012) with a few modifications and adaptations to suit the local context, needs and realities.

Bio-behavioral Surveys Bio-behavioural surveys (BBS) provide specific population level estimates for the burden of HIV disease and HIV-related risk factors and estimates for the coverage of prevention and treatment services for populations at increased risk for HIV (WHO, 2017). These populations do not have conventional sampling frames, meaning that complex sampling designs can be required. In Uganda, Makerere University School of Public Health conducted a bio-behavioral survey among fishing communities of the Lake Kyoga region using this methodology to estimate the prevalence of HIV, syphilis and schistosomiasis, and assess the level of access to HIV prevention, care and treatment and maternal and child health services in these communities.

A bio-behavioural survey (BBS), in conjunction with other studies, can also provide an opportunity to estimate the size of the surveyed population. In Kenya, an Integrated Biological and Behavioral Surveillance (IBBS) survey was conducted on HIV and STIs among key populations including men who have sex with men, female sex workers and people who inject drugs, conducted in 2010 and 2011 with a secondary objective to estimate the population sizes of these KPs in Nairobi and Kisumu. The methodology used to determine these estimates were mixed methods including service multiplier, wisdom of the crowds and literature review.

In 2014, the Tanzania Ministry of Health published a national HIV and STI Biological and Behavioral Survey among female sex workers in seven high prevalence regions across Tanzania: Dar es Salaam, Iringa, Mbeya, Mwanza, Shinyanga, Tabora and Mara. The primary objective of this study was to assess the prevalence of HIV and other sexually transmitted infections and related risk factors among female sex workers in Tanzania to provide an evidence base for targeted prevention and services for this group but also provided population estimates and corresponding 95% confidence intervals using respondent driven sampling (RDS) for these regions.

Capture Recapture method of key population size estimation Field-based capture–recapture is a survey-based method of population size estimation. It involves visiting sites where key population members are known to congregate on two separate occasions, tagging all key population members found at the sites on each occasion, calculating the degree of overlap between key populations, and deriving a size estimate. It is well suited for key populations that are highly mobile. For this method to be used, a number of key criteria must be met (FHI, 2003):

1) Samples must be independent from one another, and not correlated 2) Each member of the population should have an equal, non-zero probability of being “captured” 3) The individuals identified in both captures must be correctly identified as recaptures, and no-one else should be identified as a recapture and 4) There should be no major in-migration or out-migration from the population between the initial and the second captures

This method was used in the Crane Survey conducted by the Makerere University School of Public Health in the first survey in 2010 and in subsequent surveys in 2017 and 2018 (Crane Survey 2010; 2017 and 2018). In the first survey, individuals in a fixed geographical area were sampled (captured) and tagged (marked), by providing a “gift/unique object”. A second sample of individuals was then captured independently, of which some number will have been marked previously. The number of previously marked, ‘recaptured’ individuals facilitates an estimate of the whole population. After a short-time interval, a second capture (recapture) was initiated; in this second sample, each individual was assessed if they had been tagged (during the first capture) or not. The total number of individuals in each capture as well as the number of individuals common to both captures were used to estimate the total target population size.

Multiplier methods The multiplier method involves using two overlapping sources of data from the same target population to derive population size estimates. The overlap is a marker or identifier that matches the members of the group to each other, either at the individual or the group level. A mathematical formula is applied using the matching information to estimate the size of the total population of interest (WHO & UNAIDS, 2016). Multiplier methods generally rely on having information from two sources that overlap in a known way: the first is usually an institution or service with which the population to be estimated is in contact, and the second is the population at risk itself. Estimates are derived by multiplying the number of people who attend the institution or service over a certain period by the inverse of the proportion of the population who say they attended over the same period (FHI, 2003).

To use program/institutional and survey data together to estimate the size of a population, the members of the population all have to have a chance of being included in both the survey and in the institutional or program data (Medhi et al., 2012). Multiplier methods can also be used with information from two separate population-based survey samples that intersect in some way, as long as the size of one of the groups is relatively well known. The most common example is sex workers and clients (FHI, 2003). When surveys among key populations are planned for other purposes (e.g. surveillance or monitoring and evaluation), a multiplier can be integrated with little added cost (Grasso et al., 2018).

Network Scale-up Network scale up is a method for estimating hidden and hard to reach populations by recognizing that an individual’s social network is representative of the whole population. That is, one person’s group of friends reflects the characteristics of the network as a whole. Therefore, by asking the respondent questions about an acquaintance, rather than the respondent themselves, one introduces a level of anonymity allowing the responses to be honest without fear of stigma or other negative consequences (UNAIDS 2012; WHO 2013). Key advantages of this method over other methods include the use of a random sample, results that can be compared over time, validation checks, and importantly, network scale-up does not require the key populations to report on their own highly stigmatised and sometimes illegal behaviours (UNAIDS, 2012).

The estimate requires three pieces of information: the number of key population groups known (collected in the survey), the personal network size (PNS) of the respondent (estimated from the survey) and the total number of people in the general population. The PNS of the respondent can be identified through the back estimation of populations of known sizes or through summation.

It is most useful when the conditions required to work directly with key populations (good rapport, willingness of key populations to participate, assurances that data collection will not harm the population. It is also useful when identifying and accessing key population members are expected to be difficult.

This method was used in Rwanda in 2011 to estimate the population sizes of female sex workers, their clients, men who have sex with men and PWIDs. The study found that overall, it was feasible to use these methods in sub-Saharan Africa although there were challenges of an appropriate definition for sex workers and a lack of data to use for adjustments to reduce bias and error (UNAIDS, 2012).

Wisdom of the Crowd Wisdom of the crowd is the collective opinion of a group of individuals rather than that of a single expert. It is premised on the idea that large groups of people are collectively smarter than even individual experts when it comes to problem solving, decision making, innovating and predicting. This method is based on the assumption that, in aggregate, the responses of sufficient number of key populations on their numbers will provide a good estimate of the actual number of their population. The wisdom of the crowd method of size estimation relies on the knowledge of members of the population and asks them to estimate how many people are in their group. The median of their responses is taken to be the population size estimate for this method.

Key to this approach are several assumptions: (1) persons in a large sample tend to have unique information or perspectives about the population in question and (2) when individuals in the sample are asked the same question, individual responses are not influenced by others in the sample and in aggregate, any extreme outliers in responses tend to cancel each other out.

The CHAU Survey which used wisdom of the crowd as one of its methods was conducted in Kampala City Council Authority and Mbale Town in Uganda to describe the epidemiological and social situation regarding injecting and drug use as well as estimate the size of PWIDs in the two towns. Using the “wisdom of the crowd” approach, PWIDs and key informants were interviewed and asked about the average, minimum and maximum number of PWIDs who operate in the locations that they knew of (CHAU, 2017). Their responses were used to estimate the number of PWID in the 2 locations.

This method was also used by Okal et al. (2013) in Kenya to estimate the size of key populations at risk for HIV infection in conjunction with the multiplier method, literature review and stakeholder consensus. The method entailed adding questions to the interviewer-administered bio-behavioural survey instrument; for example, participants were asked about how many men who have sex with men they believe to be present in a particular location. Median, range and quartile descriptive statistics were then calculated.

Strengths and Weaknesses

Table 17: Strengths and Weaknesses of Methodologies

Method Strengths Weaknesses Size Estimation Methods Census, enumeration and mapping ● Census method is easy to explain as it simply ● Most-at-risk populations are often hidden. Both attempts to count all members of the population methods will miss members of the population not ● Enumeration method maps then covers just a visible to the public. fraction of the population. ● Community guides are necessary to improve access. ● Census is time-consuming and expensive to conduct ● Enumeration method requires a reliable sample frame of venues. ● Overestimate if population is mobile and double counted ● Underestimate if populations are well hidden

Population Survey Methods eg Bio- ● Surveys are common and familiar. ● Difficult to use when the behaviours are rare or behavioral Surveys ● Straightforward to analyse and easy to explain to stigmatized. data ● Only reaches people residing in households, schools ● users. or other institutions used to create the sampling frame. ● Respondents are unlikely to admit to high risk or stigmatized behaviours if interview is not confidential or if interviewer is not skilled at establishing trust and rapport

Capture Recapture (2 Source; ● A simple two sample capture recapture ● Relies on assumptions that are hard to meet in Petersen) ● method is relatively easy to use. normal field conditions ● Does not require much data. ● Two samples are independent and not correlated ● Does not require statistical expertise. ● Each population member has an equal chance of selection. ● Each member is correctly identified as ‘capture’ or ‘recapture’. ● No major in/out migration is occurring. ● Sample size is large enough to be meaningful

Multiplier methods ● Straightforward if data sources are available. ● The two data sources must be independent. ● Flexible method, useful in many circumstances. ● The data sources must define the population in the same way. ● Time periods, age ranges and geographic areas from the two data sources are not always aligned. ● Data collected from existing sources may be inaccurate.

Network Scale-up ● Can generate estimates from general population ● Average personal network size difficult to estimate. rather than hard-to reach populations. ● Subgroups may not associate with members of the ● Individuals are often more likely to report on the general population. behaviour of others instead of their own behaviour ● Respondent may be unaware someone in his/her ● A single survey can be used to create size estimates network engages in behaviour of interest. for multiple hidden populations ● Respondents may be hesitant to admit to knowing individuals with the specified behaviour.

Wisdom of the Crowd ● Possibility of bias in reporting

Sampling Methods Respondent-Driven Sampling ● For many hard-to-reach RDS can be an effective ● Method leaves possibility for missed separate, isolated sampling tool that facilitates population-based, key population networks in Kampala weighted estimates ● It is possible that the confidence intervals generated by RDS may be too narrow ● The RDS approach reduces masking, because it gives respondents the opportunity to allow peers to decide for themselves whether they wish to participate

Snowball Sampling ● The technique is often more efficient and sometimes ● Potential problems of representativeness and sampling less expensive than using traditional recruitment principles strategies to gather participants in proportion to the ● There is no statistically reliable way to estimate focus community whether “saturation” of the sample has been reached ● Trust may be developed as referrals are made by acquaintances or peers rather than other more formal methods of identification ● Economical, efficient and effective in various studies

Adapted from Table A-11.1 Summary of methods for estimating the size of key populations on page 110 – 111 In WHO, CDC, UNAIDS, FHI 360. Biobehavioral survey guidelines for Populations at Risk for HIV. Geneva: World Health Organization; 2017.

5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS

5.1 Discussion We estimated key population sizes in Uganda at national and subnational levels using a meta- analysis model and a geostatistical model, respectively.

Traditionally meta-analysis has been used in research to estimate quantitatively the pooled effect of a treatment effect or risk factor for the disease from several independent clinical trials as well as observational studies (Haidich, 2012). We used a meta-analysis methodology for survey data that was first proposed by Rao (Rao at al., 2008). This approach obtains a single summary estimate from several survey estimates and has since been applied by other researchers in health services research to estimate KP sizes in other countries. This approach was used by Purcell et al., 2012 to estimate the population size of men who have sex with men in the United States to obtain HIV and Syphilis rates (Purcell et al., 2012).

The practical problems of combining results in a meta-analysis from disparate studies is the absence of standard errors of estimates from individual studies that are necessary for computation of a single pooled estimate. However, most of the KP estimation studies that have been conducted in Uganda used purposive sampling such as snow balling to reach the groups due to their hidden nature - meaning that standard error estimates were not available. It is a common practice in meta-analysis to make assumptions of a simple random sample design when standard error information is missing for individual studies to facilitate the combination of survey-specific estimates to arrive at the pooled estimate (Rao et al., 2012). This of course has its drawbacks but assuming a complex sampling design would have required the inter cluster correlation parameter that has not been established.

Dealing with heterogeneity is straight forward for meta-analysis for clinical trials due to the availability of standard guidelines. However, it is not the case with meta-analysis for surveys which is still relatively new. In our study each study employed a different estimation technique which introduces heterogeneity. The ideal estimation scenario (zero heterogeneity) would have been achieved by only combining studies that used the same estimation technique. However, this was not possible as few studies have been conducted at national level and each used a different estimation technique. We used a random effects type of meta-analysis and not fixed effects model. The former produces more conservative estimates that are more reliable where there are variations in estimation methods, sampling designs, and tools employed by individual studies unlike the latter that assumes homogeneity.

The geostatistical model we used for district-level estimates belongs to a family of geospatial models that are a unique state-of-the-art approach that are generally used for local area estimations to identify the most affected geographical areas and population groups at local scales (Gemperli, 2003). This approach was used in the estimation of men who have sex with men population sizes in Cote d’Ivoire (Datta, 2018) using HIV prevalence in district as the covariate. In our case we have used seven covariates that were identified by a variable selection algorithm as having a strong correlation with the existence of KPs in a given district. The motivation for producing district-level estimates was informed by the need for aiding planners and policy makers in decision making. Whereas the national KP estimates are useful for mobilizing resources at the country level, however, they do not reveal the geographical distribution of KPs at subnational scale which is critical for guiding resource allocation and priority setting (Giardina, 2014). To address this necessity, we performed district level size estimations to enable stakeholders including donors, government and programs to target activities to the most affected geographical areas.

The men who have sex with men estimates derived from our models are lower than female sex workers contrary to the demographic expectation. This can be explained by the fact that our estimates more likely reflect the overt rather than the hidden populations due to the current legal situation in the country that criminalizes men who have sex with men activities. This could have contributed to under estimation of men who have sex with men in the primary studies. Nonetheless, our estimates are in line with most of the primary studies included in the models.

Also, female sex workers’ estimates are three times higher than this year’s Global Acute Malnutrition submission. The high female sex worker estimate was as a result of the stronger influence from the high weighted studies (PLACE and PEPFAR COP) which also reported higher estimates of female sex workers. Their stronger weights skewed the pooled estimates in the same direction.

Although we have included the PEPFAR COP and modes of transmission study estimates in the estimation of national level KP estimates, it should be noted, however, that these estimates were not derived from independent studies but extrapolations from other studies motivated by the need for estimating national level KP sizes. The decision to have these secondary studies included in the estimation was informed by the sensitivity analyses that showed that excluding their estimates from the meta-analysis resulted in a pooled estimate will low precision unlike estimates in which they were included.

In the revised report we have included the methods used by different estimation studies in the respective tables as well as provided an appraisal of each method’s pros and cons in the discussion. It is quite apparent from the presentation that each method has unique methodological flaws which explains the apparent differences in estimates from these methods in the same geographical locations.

Results of comparison of our estimates with those from PLACE and Crane studies in the appendix showed that in some districts the confidence intervals overlap implying similarity of estimates but were dissimilar for others. Policy makers will be confident in designing and implementing activities in districts where these estimates are similar, but this is not clear in the districts where this is not the case. It goes without saying that the population-based survey will provide the best estimates of KP sizes. Even then, this survey will most likely produce regional level estimates but may not be powered to produce district-level estimates. This calls for more support towards developing capacity in modelling approaches both statistical and dynamic as well as investing in the compilation and updating of covariates databases to make small area estimations lower than the domains possible in population-based surveys.

We have included in the models not only data collected from studies but also program data from stakeholders such as MARPs and the Ministry of Health. These estimates can even be improved by adding more data from other partners and covariates that have a strong correlation with the existence and/or behavior of KPs. Most districts with high KP estimates are those with a high economic activity such as major cities and towns, regional economic hubs and those located along the national borders which experience a high influx of people from other countries. This will require a unique programming approach to take into consideration mobile populations that are not indigenous in the country or locality to address the HIV problem in these groups.

The two models we have employed in this study i.e., meta-analysis and geostatistical give slightly different estimates for KPs at national level, though their confidence intervals overlap. The discrepancy in estimates is due to the difficulty to get all covariates for the geostatistical model for estimating district level estimates that explain a big proportion of variation in the outcome. Compiling covariates for socioeconomic outcomes at small areas for prediction is a daunting task as it is not easy to get exposure data for variables that covary with the outcome and at the same time readily accessible at all districts to enable prediction at small areas across the country. We recommend periodical updating of the model estimates by running these models whenever more outcome and/or covariate data becomes available.

Our KP estimates differ from those from neighboring countries such as Kenya and Tanzania. This can be attributed to several reasons but majorly is the approach used. Whereas we have used quantitative modelling approaches based on empirical data, a qualitative approach involving consensus building in which stakeholders met and came up with what they thought was a good estimate of each KP in the country. The two approaches can be complimentary to each other, but we have high confidence in our approach since it can be replicated by other researchers to produce estimates similar to what we obtained. Nevertheless, our estimates should also be subjected to the stakeholders’ appraisal to ascertain if they make sense based on experiences in the field. This is critical because even the primary studies on which we based our models also suffer from bias in study designs since most relied on purposive sampling techniques which undermines the estimation of how many subjects in the population are represented by each subject in the sample. This may lead to under or overestimation of population sizes in the studies.

5.2 Conclusions and Recommendations

● No single method of size estimation of key populations can be considered as standard ● Each method has unique strengths and weaknesses which explains the apparent differences in estimates

● The meta-analysis and geostatistical models give slightly different estimates for KPs at national level, though their CIs overlap

● Periodical updating of the model estimates whenever more outcome and/or covariate data becomes available is recommended

● An Integrated Bio-behavioral Survey complimented with modelling approaches for small area estimations is recommended

Study Limitations

● A smaller number of estimation studies have been conducted at national level which has affected the precision of our model estimates.

● Due to the paucity mentioned above we had to include secondary studies in the meta- analysis such as PEPFAR COP report estimates and modes of transmission studies, whose estimates had been derived based on secondary data sources. This would amount to ‘double-counting’ in the models, but this decision was informed by sensitivity analyses that indicated better precision when these studies were included.

● It was also not possible to conduct a meta-analysis per each estimation technique employed as recommended in literature to ensure less heterogeneity of our estimates. This resulted in high heterogeneity of our modeled pooled estimates

● The subnational sizes were estimated at a lower resolution i.e., district because covariates data we used in the geospatial models was not available at smaller geographical units such as sub-county and parish

● Most studies did not report standard errors making their addition into the meta-analysis problematic – we had to assume a simple random design to enable the calculation of standard errors. This assumption may have affected the precision of our estimates

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