Targeting the right interventions to the right people and places: the role of geospatial analysis in HIV program planning

Using GPS-defined venue-based affiliation networks among Black sexually minoritized men and transgender women to identify locations for HIV prevention interventions

PURPOSE

HIV biomedical intervention uptake is suboptimal among Black sexually minoritized men (SMM) and transgender women (TW). Venues where people meet and interact shape HIV-related risk and prevention behaviors. We aimed to construct GPS-defined venue-based affiliation networks and identify the unique set of venues that could maximize reach of HIV biomedical interventions among Black SMM and TW.

METHODS

We used baseline survey and GPS data from 272 Black SMM and TW in the Neighborhoods and Networks (N2) Cohort Study in Chicago, Illinois (2018-2019). We mapped participants' GPS data to the nearest pre-identified SMM- and TW-friendly venue (n = 222) to construct affiliation networks. Network analyses were performed to identify influential venues that can yield high reach to intervention candidates.

RESULTS

Participants were affiliated with 75.5 % of all pre-identified venues based on GPS data. Two influential venues were identified in the non-PrEP use network, which when combined, could reach 52.5 % of participants not taking PrEP. Participants that could be reached through these two influential venues reported more non-main sex partners than participants not affiliated with either venue (p = 0.049).

CONCLUSION

We demonstrate a potential for GPS-defined venue-based affiliation networks to identify unique combinations of venues that could maximize the impact of HIV prevention interventions.

Moving beyond hotspots of HIV prevalence to geospatial hotspots of UNAIDS 95-95-95 targets in sub-Saharan Africa

The HIV epidemic in sub-Saharan Africa displays a varied geographical distribution, with particular regions termed as HIV hotspots due to a higher prevalence of infection. Addressing these hotspots is essential for controlling the epidemic. However, these regions, influenced by historical factors, challenge standard interventions. Legacy effects-the lasting impact of past events-play a substantial role in the persistence of these hotspots. To address this challenge of the standard interventions, we propose a shift towards the UNAIDS 95-95-95 targets. Spatial analysis of HIV viral load and antiretroviral therapy coverage can provide a more comprehensive perspective on the epidemic's dynamics. Studies in Zambia and Zimbabwe, using this approach, have revealed disparities in HIV care metrics across regions. By focusing on the UNAIDS 95-95-95 targets, more effective control strategies can be designed, with consideration of both historical and current factors. This approach would offer a solution-oriented strategy, emphasising tailored interventions based on specific regional needs.

Geospatial and temporal mapping of detectable HIV-1 viral loads amid dolutegravir rollout in KwaZulu-Natal, South Africa

South Africa rolled out dolutegravir (DTG) as first-line antiretroviral therapy (ART) in December 2019 to overcome high rates of pretreatment non-nucleoside reverse transcriptase inhibitor drug resistance. In the context of transition to DTG-based ART, this study spatiotemporally analysed detectable HIV viral loads (VLs) prior to- and following DTG rollout in public-sector healthcare facilities in KwaZulu-Natal (KZN) province, the epicentre of the HIV epidemic in South Africa. We retrospectively curated a HIV VL database using de-identified routine VL data obtained from the National Health Laboratory Service for the period January 2018 to June 2022. We analysed trends in HIV viraemia and mapped median log10 HIV VLs per facility on inverse distance weighted interpolation maps. We used Getis-Ord Gi* hotspot analysis to identify geospatial HIV hotspots. We obtained 7,639,978 HIV VL records from 736 healthcare facilities across KZN, of which 1,031,171 (13.5%) had detectable VLs (i.e., VLs ≥400 copies/millilitre (mL)). Of those with detectable VLs, we observed an overall decrease in HIV VLs between 2018 and 2022 (median 4.093 log10 copies/mL; 95% confidence interval (CI) 4.087-4.100 to median 3.563 log10 copies/mL; CI 3.553-3.572), p<0.01 (median test). The downward trend in proportion of HIV VLs ≥1000 copies/mL over time was accompanied by an inverse upward trend in the proportion of HIV VLs between 400 and 999 copies/mL. Moreover, specific coastal and northern districts of KZN had persistently higher VLs, with emergent hotspots demonstrating spatial clustering of high median log10 HIV VLs. The overall decrease in HIV VLs over time shows good progress towards achieving UNAIDS 95-95-95 targets in KZN, South Africa. The DTG-transition has been associated with a reduction in VLs, however, there is a need for pre-emptive monitoring of low-level viraemia. Furthermore, our findings highlight that specific districts will need intensified HIV care despite DTG rollout.

Geographic variation in 5-year mortality following HIV diagnosis: implications for clinical interventions

Characterizing spatial distribution of HIV outcomes is vital for targeting interventions to areas most at risk. We performed spatial analysis to identify geographic clusters and factors associated with mortality in KwaZulu-Natal, South Africa. We utilized Sizanani trial (NCT01188941) data, which enrolled participants August 2010-January 2013 and obtained vital status at 5.8 (IQR 5.0-6.4) years of follow-up. We mapped geocoded addresses to 2011 Census-defined small area layer (SAL) centroids, used Kulldorff's spatial scan statistic to identify mortality clusters, and compared socio-demographic factors for SALs within and outside mortality clusters. We assigned 1,143 participants living with HIV (260 [23%] of whom died during follow-up) to 677 SALs. One lower mortality cluster (n = 90, RR = 0.23, p = 0.022) was identified near a hospital outside Durban. SALs in the cluster were younger (24y vs 25y, p < 0.001); had fewer bedrooms/household (3 vs 4, p < 0.001); had more females (52% vs 51%, p = 0.013) and residents with no schooling past age 20 (4% vs 3%, p < 0.001) or no education at all (4% vs 3%, p < 0.001); had fewer residents with income >3,200 ZAR/month (5% vs 9%, p < 0.001); and had reduced access to piped water (p < 0.001), refuse disposal (p < 0.001), and toilets (p < 0.001). Targeted interventions may improve outcomes in areas with similar characteristics.

Small-area estimation and analysis of HIV/AIDS indicators for precise geographical targeting of health interventions in Nigeria. a spatial microsimulation approach

BACKGROUND

Precise geographical targeting is well recognised as an indispensable intervention strategy for achieving many Sustainable Development Goals (SDGs). This is more cogent for health-related goals such as the reduction of the HIV/AIDS pandemic, which exhibits substantial spatial heterogeneity at various spatial scales (including at microscale levels). Despite the dire data limitations in Low and Middle Income Countries (LMICs), it is essential to produce fine-scale estimates of health-related indicators such as HIV/AIDS. Existing small-area estimates (SAEs) incorporate limited synthesis of the spatial and socio-behavioural aspects of the HIV/AIDS pandemic and/or are not adequately grounded in international indicator frameworks for sustainable development initiatives. They are, therefore, of limited policy-relevance, not least because of their inability to provide necessary fine-scale socio-spatial disaggregation of relevant indicators.

METHODS

The current study attempts to overcome these challenges through innovative utilisation of gridded demographic datasets for SAEs as well as the mapping of standard HIV/AIDS indicators in LMICs using spatial microsimulation (SMS).

RESULTS

The result is a spatially enriched synthetic individual-level population of the study area as well as microscale estimates of four standard HIV/AIDS and sexual behaviour indicators. The analysis of these indicators follows similar studies with the added advantage of mapping fine-grained spatial patterns to facilitate precise geographical targeting of relevant interventions. In doing so, the need to explicate socio-spatial variations through proper socioeconomic disaggregation of data is reiterated.

CONCLUSIONS

In addition to creating SAEs of standard health-related indicators from disparate multivariate data, the outputs make it possible to establish more robust links (even at individual levels) with other mesoscale models, thereby enabling spatial analytics to be more responsive to evidence-based policymaking in LMICs. It is hoped that international organisations concerned with producing SDG-related indicators for LMICs move towards SAEs of such metrics using methods like SMS.

Progress Towards UNAIDS's 95-95-95 Targets in Zimbabwe: Sociodemographic Constraints and Geospatial Heterogeneity

The HIV/AIDS epidemic remains critical in sub-Saharan Africa, with UNAIDS establishing "95-95-95" targets to optimize HIV care. Using the Zimbabwe Population-based HIV Impact Assessment (ZIMPHIA) geospatial data, this study aimed to identify patterns in these targets and determinants impacting the HIV care continuum in underserved Zimbabwean communities. Analysis techniques, including Gaussian kernel interpolation, optimized hotspot, and multivariate geospatial k-means clustering, were utilized to establish spatial patterns and cluster regional HIV care continuum needs. Further, we investigated healthcare availability, access, and social determinants and scrutinized the association between socio-demographic and behavioral covariates with HIV care outcomes. Disparities in progress toward the "95-95-95" targets were noted across different regions, with each target demonstrating unique geographic patterns, resulting in four distinct clusters with specific HIV care needs. Key factors associated with gaps in achieving targets included younger age, male sex, employment, and minority or no religious affiliation. Our study uncovers significant spatial heterogeneity in the HIV care continuum in Zimbabwe, with unique regional patterns in "95-95-95" targets. The spatial analysis of the UNAIDS targets presented here could prove instrumental in designing effective control strategies by identifying vulnerable communities that are falling short of these targets and require intensified efforts. Our result provides insights for designing region-specific interventions and enhancing community-level factors, emphasizing the need to address regional gaps and improve HIV care outcomes in vulnerable communities lagging behind.

Does adherence to antiretroviral therapy differ by place of residence in Nigeria? A systematic review and meta-analysis protocol

INTRODUCTION

Retention in care is still a significant challenge in the HIV treatment cascade and varies extensively across regions, leading to poor adherence to antiretroviral therapy (ART). Several factors across different socioecological levels, such as health and community-level factors, inhibit ART adherence among people living with HIV (PLWH) in Nigeria. This review seeks to ask whether adherence to ART differs by place of residence in Nigeria.

METHOD AND ANALYSIS

This systematic review and meta-analysis will follow the updated Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines. We will search PubMed, SCOPUS, CINAHL, Web of Science, MEDLINE (Ovid) and Google Scholar from onset to 30 April 2023. We will include only quantitative observational studies or mixed-method studies that measured the association between place of residence (urban and rural) and adherence to ART among PLWH aged ≥15 years. Title and abstract screening, full-text screening, data extraction and quality assessment will be done by two reviewers independently. A third reviewer will resolve disagreements. We will extract the author's name and year of publication, study aim(s), participant's characteristics, sample size, sampling method, region and state, adherence definition and adherence level in urban and rural areas. Data will be analysed with Review Manager V.5.4. The Q statistical test will be used to assess between-study heterogeneity, while Higgins and Thompson's I² will be used to quantify the heterogeneity level. Several subgroups and sensitivity analyses will be conducted.

ETHICS AND DISSEMINATION

Ethical approval is not warranted for this study since primary published data will be utilised. The findings of this review will be published in a high-impact peer-reviewed journal and presented at a conference.

PROSPERO REGISTRATION NUMBER

CRD42022371965.

Subnational HIV incidence trends in Malawi: large, heterogeneous declines across space

The rate of new HIV infections globally has decreased substantially from its peak in the late 1990s, but the epidemic persists and remains highest in many countries in eastern and southern Africa. Previous research hypothesised that, as the epidemic recedes, it will become increasingly concentrated among sub-populations and geographic areas where transmission is the highest and that are least effectively reached by treatment and prevention services. However, empirical data on subnational HIV incidence trends is sparse, and the local transmission rates in the context of effective treatment scale-up are unknown. In this work, we developed a novel Bayesian spatio-temporal epidemic model to estimate adult HIV prevalence, incidence and treatment coverage at the district level in Malawi from 2010 through the end of 2021. We found that HIV incidence decreased in every district of Malawi between 2010 and 2021 but the rate of decline varied by area. National-level treatment coverage more than tripled between 2010 and 2021 and more than doubled in every district. Large increases in treatment coverage were associated with declines in HIV transmission, with 12 districts having incidence-prevalence ratios of 0.03 or less (a previously suggested threshold for epidemic control). Across districts, incidence varied more than HIV prevalence and ART coverage, suggesting that the epidemic is becoming increasingly spatially concentrated. Our results highlight the success of the Malawi HIV treatment programme over the past decade, with large improvements in treatment coverage leading to commensurate declines in incidence. More broadly, we demonstrate the utility of spatially resolved HIV modelling in generalized epidemic settings. By estimating temporal changes in key epidemic indicators at a relatively fine spatial resolution, we were able to directly assess, for the first time, whether the ART scaleup in Malawi resulted in spatial gaps or hotspots. Regular use of this type of analysis will allow HIV program managers to monitor the equity of their treatment and prevention programmes and their subnational progress towards epidemic control.

Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000-2018

BACKGROUND

Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is still among the leading causes of disease burden and mortality in sub-Saharan Africa (SSA), and the world is not on track to meet targets set for ending the epidemic by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United Nations Sustainable Development Goals (SDGs). Precise HIV burden information is critical for effective geographic and epidemiological targeting of prevention and treatment interventions. Age- and sex-specific HIV prevalence estimates are widely available at the national level, and region-wide local estimates were recently published for adults overall. We add further dimensionality to previous analyses by estimating HIV prevalence at local scales, stratified into sex-specific 5-year age groups for adults ages 15-59 years across SSA.

METHODS

We analyzed data from 91 seroprevalence surveys and sentinel surveillance among antenatal care clinic (ANC) attendees using model-based geostatistical methods to produce estimates of HIV prevalence across 43 countries in SSA, from years 2000 to 2018, at a 5 × 5-km resolution and presented among second administrative level (typically districts or counties) units.

RESULTS

We found substantial variation in HIV prevalence across localities, ages, and sexes that have been masked in earlier analyses. Within-country variation in prevalence in 2018 was a median 3.5 times greater across ages and sexes, compared to for all adults combined. We note large within-district prevalence differences between age groups: for men, 50% of districts displayed at least a 14-fold difference between age groups with the highest and lowest prevalence, and at least a 9-fold difference for women. Prevalence trends also varied over time; between 2000 and 2018, 70% of all districts saw a reduction in prevalence greater than five percentage points in at least one sex and age group. Meanwhile, over 30% of all districts saw at least a five percentage point prevalence increase in one or more sex and age group.

CONCLUSIONS

As the HIV epidemic persists and evolves in SSA, geographic and demographic shifts in prevention and treatment efforts are necessary. These estimates offer epidemiologically informative detail to better guide more targeted interventions, vital for combating HIV in SSA.

Spatial distribution and determinants of HIV prevalence among adults in urban Ethiopia: Findings from the Ethiopia Population-based HIV Impact Assessment Survey (2017–2018)

The design and evaluation of national HIV programs often rely on aggregated national data, which may obscure localized HIV epidemics. In Ethiopia, even though the national adult HIV prevalence has decreased, little information is available about local areas and subpopulations. To inform HIV prevention efforts for specific populations, we identified geographic locations and drivers of HIV transmission. We used data from adults aged 15–64 years who participated in the Ethiopian Population-based HIV Impact Assessment survey (October 2017–April 2018). Location-related information for the survey clusters was obtained from the 2007 Ethiopia population census. Spatial autocorrelation of HIV prevalence data were analyzed via a Global Moran’s I test. Geographically weighted regression analysis was used to show the relationship of covariates. The finding indicated that uncircumcised men in certain hotspot towns and divorced or widowed individuals in hotspot woredas/towns might have contributed to the average increase in HIV prevalence in the hotspot areas. Hotspot analysis findings indicated that, localized, context-specific intervention efforts tailored to at-risk populations, such as divorced or widowed women or uncircumcised men, could decrease HIV transmission and prevalence in urban Ethiopia.

Mapping HIV prevalence in Nigeria using small area estimates to develop a targeted HIV intervention strategy

Objective

Although geographically specific data can help target HIV prevention and treatment strategies, Nigeria relies on national- and state-level estimates for policymaking and intervention planning. We calculated sub-state estimates along the HIV continuum of care in Nigeria.

Design

Using data from the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) (July–December 2018), we conducted a geospatial analysis estimating three key programmatic indicators: prevalence of HIV infection among adults (aged 15–64 years); antiretroviral therapy (ART) coverage among adults living with HIV; and viral load suppression (VLS) rate among adults living with HIV.

Methods

We used an ensemble modeling method called stacked generalization to analyze available covariates and a geostatistical model to incorporate the output from stacking as well as spatial autocorrelation in the modeled outcomes. Separate models were fitted for each indicator. Finally, we produced raster estimates of each indicator on an approximately 5×5-km grid and estimates at the sub-state/local government area (LGA) and state level.

Results

Estimates for all three indicators varied both within and between states. While state-level HIV prevalence ranged from 0.3% (95% uncertainty interval [UI]: 0.3%–0.5%]) to 4.3% (95% UI: 3.7%–4.9%), LGA prevalence ranged from 0.2% (95% UI: 0.1%–0.5%) to 8.5% (95% UI: 5.8%–12.2%). Although the range in ART coverage did not substantially differ at state level (25.6%–76.9%) and LGA level (21.9%–81.9%), the mean absolute difference in ART coverage between LGAs within states was 16.7 percentage points (range, 3.5–38.5 percentage points). States with large differences in ART coverage between LGAs also showed large differences in VLS—regardless of level of effective treatment coverage—indicating that state-level geographic targeting may be insufficient to address coverage gaps.

Conclusion

Geospatial analysis across the HIV continuum of care can effectively highlight sub-state variation and identify areas that require further attention in order to achieve epidemic control. By generating local estimates, governments, donors, and other implementing partners will be better positioned to conduct targeted interventions and prioritize resource distribution.

The geography and inter-community configuration of new sexual partnership formation in a rural South African population over fourteen years (2003-2016)

Understanding spatial configuration of sexual network structure is critical for effective use of HIV preventative interventions in a community. However, this has never been described at the population level for any setting in sub-Saharan Africa. We constructed the comprehensive geospatial sexual network among new heterosexual partnerships in rural KwaZulu-Natal, South Africa. In the Africa Health Research Institute (AHRI)'s population-based surveillance, we identified stable sexual partnerships among individuals (≥15 years) from 2003 to 2016. Sexual partnerships and residency were recorded via household surveys (every 4-6 months). We geolocated residents and migration events and mapped the geospatial linkages of sexual partners at the start of sexual partnerships. In a grid composed by 108 cells (nodes; 3kmx3km per cell) covering the surveillance area (438km2), we calculated the degree of connectivity and centrality of the nodes and examined their association with HIV prevalence and incidence per cell. Of 2401 new sexual partnerships, 21% (n = 495) had both partners living within the surveillance area at the start of sexual partnerships, and 76% (376/495) were linked to the geographic HIV cluster with high HIV prevalence identified in a peri-urban community. Overall, 57 nodes had at least one connection to another node. The nodes in the peri-urban cluster had higher connectivity (mean = 19, range: 9-32), compared to outside the cluster (6, range: 1-16). The node's degree of connectivity was positively associated with HIV prevalence of the cell (Pearson correlation coefficient = 0.67; p <0.005). The peri-urban cluster contained nine of the 10 nodes that composed of a single large central module in the community. About 17% of sexual partnerships (n = 421) were formed between a resident and a non-resident partner who out-migrated. Most of these non-resident partners lived in KwaZulu-Natal (86.7%), followed by Gauteng (9.7%), and the median distance between a resident and a non-resident partner was 50.1km (IQR: 23.2-177.2). We found that the peri-urban HIV cluster served as the highly connected central node of the network for sexual partnership formation. The network was also connected beyond the surveillance area across South Africa. Understanding spatial sexual network can improve the provision of spatially targeted and effective interventions.

Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa

INTRODUCTION

HIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small-area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five-year age groups.

METHODS

Small-area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district-level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016-2018.

RESULTS

Adult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty-eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city.

CONCLUSIONS

The Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data.

Role of geospatial mapping in the planning of HIV programs: A case study from Southern India

ABSTRACT

Geographic information systems (GIS) tools can be used to understand the spatial distribution of local HIV epidemics but are often underutilized, especially in low-middle income countries. We present characteristics of an HIV epidemic within Hyderabad, a large city in southern India, as a case study to highlight the utility of such data in program planning.Cross-sectional sample recruited using respondent-driven sampling in a cluster-randomized trial.We analyzed data from 2 cross-sectional respondent-driven sampling surveys of MSM in Hyderabad, which were conducted as part of a cluster-randomized trial. All participants were tested for HIV and those positive underwent viral load quantification. ArcGIS was used to create heat maps of MSM distribution using self-reported postal code of residence and combined into larger zones containing at least 200 MSM.Postal code data was available for 661 MSM (66.2%) in the baseline and 978 MSM (97.8%) in the follow-up survey. The proportion of HIV-positive MSM (12.7-15.7%) and prevalence of virally suppressed persons (2.6-8.2%) increased between the 2 surveys. The distribution of all MSM, HIV-positive MSM, and HIV-viremic MSM differed significantly by geographic zone with several zones having higher numbers of HIV-positive and viremic individuals than would be expected based on the distribution of all MSM.The prevalence of HIV and HIV viremia among MSM differed by geographic zones within a city and evolved over time. Such data could be critical to improving program implementation efficiency by accurately targeting resources to population characteristics.

Beyond HIV prevalence: identifying people living with HIV within underserved areas in South Africa

INTRODUCTION

Despite progress towards the Joint United Nations Programme on HIV/AIDS 95-95-95 targets, South Africa is still suffering from one of the largest HIV epidemics globally. In this study, we generated high-resolution HIV prevalence maps and identified people living with HIV (PLHIV) in underserved areas to provide essential information for the optimal allocation of HIV-related services.

METHODS

The data come from the South Africa Demographic and Health Survey conducted in 2016 and spatial variables from other published literature. We produced high-resolution maps of HIV prevalence and underserved areas, defined as a greater than 30 min travel time to the nearest healthcare facility. Using these maps and the population density, we mapped PLHIV and the PLHIV within underserved areas for 30, 60 and 120 min thresholds.

RESULTS

There was substantial geographic variation in HIV prevalence, ranging from 1.4% to 24.2%, with a median of 11.5% for men, and from 2.1% to 48.1%, with a median of 20.6% for women. Gauteng province showed the highest density for both HIV prevalence and PLHIV. 80% of all areas in the country were identified as underserved areas (30 min threshold), which contained more than 16% and 20% of the total men and women living with HIV, respectively. KwaZulu-Natal province had the largest number of PLHIV in underserved areas (30 min threshold) and showed less than one healthcare facility per 1000 PLHIV.

CONCLUSION

Our study showed extensive spatial variation of HIV prevalence and significant numbers of PLHIV in underserved areas in South Africa. Moreover, we identified locations where HIV-related services need to be intensified to reach the ~1.5 million PLHIV in underserved areas, particularly in KwaZulu-Natal province, with less than one healthcare facility per 1000 PLHIV.

Spatiotemporal Analysis of COVID-19 Pandemic and Predictive Models based on Artificial Intelligence for different States of India

Geographical and spatial diversities play important roles in dynamics of spread of COVID-19 virus. These phenomena are not properly addressed in the literature yet. In this paper, COVID data of various states of India are collected. The data had been processed and analysed using an open-source software. A framework based on Susceptible, Infectious, Hospitalised, Recovered and Deaths model to determine the effects of geographical diversities of Indian states on COVID-19 pandemic has been developed. The confirmed, cured and death cases due to the virus have been analysed for different state. Reasons behind the differences in number of cases in different states are identified. An improved Long-Short-Term-Memory algorithm has been developed to forecast the virus spread and recovery of patients for the next one month. Numerical results along with discussions are given.

Modelling of HIV prevention and treatment progress in five South African metropolitan districts

Globally, large proportions of HIV-positive populations live in cities. The Fast-Track cities project aims to advance progress toward elimination of HIV as a public health threat by accelerating the response in cities across the world. This study applies a well-established HIV transmission model to provide key HIV estimates for the five largest metropolitan districts in South Africa (SA): Cape Town, Ekurhuleni, eThekwini, Johannesburg and Tshwane. We calibrate the model to metro-specific data sources and estimate progress toward the 90-90-90 targets set by UNAIDS (90% of people living with HIV (PLHIV) diagnosed, 90% of those diagnosed on antiretroviral therapy (ART) and viral suppression in 90% of those on ART). We use the model to predict progress towards similarly defined 95-95-95 targets in 2030. In SA, 90.5% of PLHIV were diagnosed in 2018, with metro estimates ranging from 86% in Johannesburg to 92% in eThekwini. However, only 68.4% of HIV-diagnosed individuals nationally were on ART in 2018, with the proportion ranging from 56% in Tshwane to 73% in eThekwini. Fractions of ART users who were virally suppressed ranged from 77% in Ekurhuleni to 91% in eThekwini, compared to 86% in the whole country. All five metros are making good progress to reach diagnosis targets and all (with the exception of Ekurhuleni) are expected to reach viral suppression targets in 2020. However, the metros and South Africa face severe challenges in reaching the 90% ART treatment target.

Is It the Timing? Short-Term Mobility and Coital Frequency in Agbogbloshie, Ghana

Short-term mobility is often associated with increased sexual risk behavior. Mobile individuals often have higher rates of sexual risk behavior compared to non-mobile individuals, but the reasons why are not clear. Using monthly retrospective panel data from 202 men and 282 women in Agbogbloshie, Ghana, we tested whether short-term mobility was associated with changes in coital frequency, and whether the association was due to the act of travel in the given month (e.g., enabling higher risk behavior), the reason for travel, or an individual's travel propensity at other times in the year. Overnight travel specifically to visit family or friends, or for education, health, or other reasons, was associated with increased coital frequency for men. However, men with higher travel propensities had lower overall coital frequency and the act of traveling enabled more sex only for the most frequent male travelers. Men who seldom traveled had much higher coital frequency, but the act of traveling was not associated with additional sex acts. For women, travel for education, health, or other reasons increased coital frequency. Occasional female travelers had slightly more sex acts compared to non-mobile women, and the act of traveling for these women was associated with slight increases in coital frequency, supporting the enabling hypothesis. Highly mobile women had fewer sex acts per month on average. Our findings suggest that mobility characteristics measured on a broad temporal scale, as well as the reason for mobility, are important to understand the relationship between short-term mobility and sexual behavior.

Challenges in estimating HIV prevalence trends and geographical variation in HIV prevalence using antenatal data: Insights from mathematical modelling

BACKGROUND

HIV prevalence data among pregnant women have been critical to estimating HIV trends and geographical patterns of HIV in many African countries. Although antenatal HIV prevalence data are known to be biased representations of HIV prevalence in the general population, mathematical models have made various adjustments to control for known sources of bias, including the effect of HIV on fertility, the age profile of pregnant women and sexual experience.

METHODS AND FINDINGS

We assessed whether assumptions about antenatal bias affect conclusions about trends and geographical variation in HIV prevalence, using simulated datasets generated by an agent-based model of HIV and fertility in South Africa. Results suggest that even when controlling for age and other previously-considered sources of bias, antenatal bias in South Africa has not been constant over time, and trends in bias differ substantially by age. Differences in the average duration of infection explain much of this variation. We propose an HIV duration-adjusted measure of antenatal bias that is more stable, which yields higher estimates of HIV incidence in recent years and at older ages. Simpler measures of antenatal bias, which are not age-adjusted, yield estimates of HIV prevalence and incidence that are too high in the early stages of the HIV epidemic, and that are less precise. Antenatal bias in South Africa is substantially greater in urban areas than in rural areas.

CONCLUSIONS

Age-standardized approaches to defining antenatal bias are likely to improve precision in model-based estimates, and further recency adjustments increase estimates of HIV incidence in recent years and at older ages. Incompletely adjusting for changing antenatal bias may explain why previous model estimates overstated the early HIV burden in South Africa. New assays to estimate the fraction of HIV-positive pregnant women who are recently infected could play an important role in better estimating antenatal bias.

Costs of providing HIV care and optimal allocation of HIV resources in Guyana

INTRODUCTION

Great strides in responding to the HIV epidemic have led to improved access to and uptake of HIV services in Guyana, a lower-middle-income country with a generalized HIV epidemic. Despite efforts to scale up HIV treatment and adopt the test and start strategy, little is known about costs of HIV services across the care cascade.

METHODS

We collected cost data from the national laboratory and nine selected treatment facilities in five of the country's ten Regions, and estimated the costs associated with HIV testing and services (HTS) and antiretroviral therapy (ART) from a provider perspective from January 1, 2016 to December 31, 2016. We then used the unit costs to construct four resource allocation scenarios. In the first two scenarios, we calculated how close Guyana would currently be to its 2020 targets if the allocation of funding across programs and regions over 2017-2020 had (a) remained unchanged from latest-reported levels, or (b) been optimally distributed to minimize incidence and deaths. In the next two, we estimated the resources that would have been required to meet the 2020 targets if those resources had been distributed (a) according to latest-reported patterns, or (b) optimally to minimize incidence and deaths.

RESULTS

The mean cost per test was US$15 and the mean cost per person tested positive was US$796. The mean annual cost per of maintaining established adult and pediatric patients on ART were US$428 and US$410, respectively. The mean annual cost of maintaining virally suppressed patients was US$648. Cost variation across sites may suggest opportunities for improvements in efficiency, or may reflect variation in facility type and patient volume. There may also be scope for improvements in allocative efficiency; we estimated a 28% reduction in the total resources required to meet Guyana's 2020 targets if funds had been optimally distributed to minimize infections and deaths.

CONCLUSIONS

We provide the first estimates of costs along the HIV cascade in the Caribbean and assessed efficiencies using novel context-specific data on the costs associated with diagnostic, treatment, and viral suppression. The findings call for better targeting of services, and efficient service delivery models and resource allocation, while scaling up HIV services to maximize investment impact.

Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps

BACKGROUND

In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in 'single-aggregation disease maps' whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated.

RESULTS

We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016.

CONCLUSIONS

The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

A Geographic Hotspot and Emerging Transmission Cluster of the HIV-1 Epidemic Among Older Adults in a Rural Area of Eastern China

Despite the implementation of health education and free condom distribution for decades, the HIV/AIDS epidemic among older adults in China shows no sign of declining. This study aims to identify HIV transmission patterns and pathways in a rural county area and provide insight for developing effective HIV prevention strategies among older adults. Epidemiological field surveys combined with phylogenetic analysis were used to identify potential HIV transmission linkage in one rural county with a rapidly increasing HIV epidemic among older adults. A total of 160 HIV-positive individuals and their HIV-positive sexual partners diagnosed between 2015 and 2018 were recruited. Among them, 69.4% (n = 111) were diagnosed at age 50 or older, 68.1% (n = 109) were men, and 55.0% (n = 88) were married. Ninety-six participants self-reported as having been infected with HIV through commercial heterosexual behavior and 24 were infected from their HIV-positive spouse. CRF08_BC (67.0%, n = 73) and CRF07_AE (19.3%, n = 21) were the prevalent HIV stains and formed 15 clusters at distance cutoff of 0.0025. Participants diagnosed with HIV at age ≥50, those who were divorced or bereft their spouse, or infected with HIV locally were more prevalent within the 15 molecular clusters. We identified specific venues as the primary hotspot and found that commercial heterosexual contact between older men and commercial sex workers contributed to the local HIV epidemic. In rural areas with an increasing HIV epidemic, detecting HIV transmission clusters through epidemiological and phylogenetic analysis as well as designing localized HIV prevention strategies should be prioritized.

Spatiotemporal transmission dynamics of the COVID-19 pandemic and its impact on critical healthcare capacity

The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic.

Evolving HIV epidemics: the urgent need to refocus on populations with risk

Purpose of review

To explore the comparative importance of HIV infections among key populations and their intimate partners as HIV epidemics evolve, and to review implications for guiding responses.

Recent findings

Even as concentrated epidemics evolve, new infections among current and former key population members and their intimate partners dominate new infections. Prevalent infections in the general population grow primarily because of key population turnover and infections among their intimate partners. In generalized epidemic settings, data and analysis on key populations are often inadequate to assess the impact of key population-focused responses, so they remain limited in coverage and under resourced. Models must incorporate downstream infections in comparing impacts of alternative responses.

Summary

Recognize that every epidemic is unique, moving beyond the overly simplistic concentrated/generalized epidemic paradigm that can misdirect resources. Guide HIV responses by gathering and using locally relevant data, understanding risk heterogeneity, and applying modeling at both national and sub-national levels to optimize resource allocations among different populations for greatest impact. Translate this improved understanding into clear, unequivocal advice for policymakers on where to focus for impact, breaking them free of the generalized/concentrated paradigm limiting their thinking and affecting their decisions.

The Arts and Tools for Using Routine Health Data to Establish HIV High Burden Areas: The Pilot Case of KwaZulu-Natal South Africa

Background: To optimally allocate limited health resources in responding to the HIV epidemic, South Africa has undertaken to generate local epidemiological profiles identifying high disease burden areas. Central to achieving this, is the need for readily available quality health data linked to both large and small geographic areas. South Africa has relied on national population-based surveys: the Household HIV Survey and the National Antenatal Sentinel HIV and Syphilis Prevalence Survey (ANC) amongst others for such data for informing policy decisions. However, these surveys are conducted approximately every 2 and 3 years creating a gap in data and evidence required for policy. At subnational levels, timely decisions are required with frequent course corrections in the interim. Routinely collected HIV testing data at public health facilities have the potential to provide this much needed information, as a proxy measure of HIV prevalence in the population, when survey data is not available. The South African District health information system (DHIS) contains aggregated routine health data from public health facilities which is used in this article. Methods: Using spatial interpolation methods we combine three "types" of data: (1) 2015 gridded high-resolution population data, (2) age-structure data as defined in South Africa mid-year population estimates, 2015; and (3) georeferenced health facilities HIV-testing data from DHIS for individuals (15-49 years old) who tested in health care facilities in the district in 2015 to delineate high HIV disease burden areas using density surface of either HIV positivity and/or number of people living with HIV (PLHIV). For validation, we extracted interpolated values at the facility locations and compared with the real observed values calculating the residuals. Lower residuals means the Inverse Weighted Distance (IDW) interpolator provided reliable prediction at unknown locations. Results were adjusted to provincial published HIV estimates and aggregated to municipalities. Uncertainty measures map at municipalities is provided. Data on major cities and roads networks was only included for orientation and better visualization of the high burden areas. Results: Results shows the HIV burden at local municipality level, with high disease burden in municipalities in eThekwini, iLembe and uMngundgudlovu; and around major cities and national routes. Conclusion: The methods provide accurate estimates of the local HIV burden at the municipality level. Areas with high population density have high numbers of PLHIV. The analysis puts into the hand of decision makers a tool that they can use to generate evidence for HIV programming. The method allows decision makers to routinely update and use facility level data in understanding the local epidemic.

Optimal allocation of HIV resources among geographical regions

BACKGROUND

Health resources are limited, which means spending should be focused on the people, places and programs that matter most. Choosing the mix of programs to maximize a health outcome is termed allocative efficiency. Here, we extend the methodology of allocative efficiency to answer the question of how resources should be distributed among different geographic regions.

METHODS

We describe a novel geographical optimization algorithm, which has been implemented as an extension to the Optima HIV model. This algorithm identifies an optimal funding of services and programs across regions, such as multiple countries or multiple districts within a country. The algorithm consists of three steps: (1) calibrating the model to each region, (2) determining the optimal allocation for each region across a range of different budget levels, and (3) finding the budget level in each region that minimizes the outcome (such as reducing new HIV infections and/or HIV-related deaths), subject to the constraint of fixed total budget across all regions. As a case study, we applied this method to determine an illustrative allocation of HIV program funding across three representative oblasts (regions) in Ukraine (Mykolayiv, Poltava, and Zhytomyr) to minimize the number of new HIV infections.

RESULTS

Geographical optimization was found to identify solutions with better outcomes than would be possible by considering region-specific allocations alone. In the case of Ukraine, prior to optimization (i.e. with status quo spending), a total of 244,000 HIV-related disability-adjusted life years (DALYs) were estimated to occur from 2016 to 2030 across the three oblasts. With optimization within (but not between) oblasts, this was estimated to be reduced to 181,000. With geographical optimization (i.e., allowing reallocation of funds between oblasts), this was estimated to be further reduced to 173,000.

CONCLUSIONS

With the increasing availability of region- and even facility-level data, geographical optimization is likely to play an increasingly important role in health economic decision making. Although the largest gains are typically due to reallocating resources to the most effective interventions, especially treatment, further gains can be achieved by optimally reallocating resources between regions. Finally, the methods described here are not restricted to geographical optimization, and can be applied to other problems where competing resources need to be allocated with constraints, such as between diseases.

Spatial analysis of HIV-TB co-clustering in Uganda

BACKGROUND

Tuberculosis (TB) is the leading cause of death for individuals infected with Human immunodeficiency virus (HIV). Conversely, HIV is the most important risk factor in the progression of TB from the latent to the active status. In order to manage this double epidemic situation, an integrated approach that includes HIV management in TB patients was proposed by the World Health Organization and was implemented in Uganda (one of the countries endemic with both diseases). To enable targeted intervention using the integrated approach, areas with high disease prevalence rates for TB and HIV need to be identified first. However, there is no such study in Uganda, addressing the joint spatial patterns of these two diseases.

METHODS

This study uses global Moran's index, spatial scan statistics and bivariate global and local Moran's indices to investigate the geographical clustering patterns of both diseases, as individuals and as combined. The data used are TB and HIV case data for 2015, 2016 and 2017 obtained from the District Health Information Software 2 system, housed and maintained by the Ministry of Health, Uganda.

RESULTS

Results from this analysis show that while TB and HIV diseases are highly correlated (55-76%), they exhibit relatively different spatial clustering patterns across Uganda. The joint TB/HIV prevalence shows consistent hotspot clusters around districts surrounding Lake Victoria as well as northern Uganda. These two clusters could be linked to the presence of high HIV prevalence among the fishing communities of Lake Victoria and the presence of refugees and internally displaced people camps, respectively. The consistent cold spot observed in eastern Uganda and around Kasese could be explained by low HIV prevalence in communities with circumcision tradition.

CONCLUSIONS

This study makes a significant contribution to TB/HIV public health bodies around Uganda by identifying areas with high joint disease burden, in the light of TB/HIV co-infection. It, thus, provides a valuable starting point for an informed and targeted intervention, as a positive step towards a TB and HIV-AIDS free community.

Contextual drivers of HIV risk among young African women

INTRODUCTION

Significant progress has been made in the African HIV pandemic; however, the pace of incidence decline has slowed or stalled in many East and Southern African countries, especially among young women. This stall is worrying because many countries have burgeoning youth populations. There is an important window of opportunity to halt the epidemic as well as the potential for millions more infections if primary prevention efforts are not strengthened.

DISCUSSION

Many hyper-endemic settings have been exposed to numerous interventions; however, HIV incidence among young women has remained high. In this paper, we characterize the intervention context and examine how it can be strategically utilized to maximize HIV prevention interventions among young women. We begin by examining how contextual dynamics drive HIV risk. We illustrate how epidemiological contexts, gendered normative and economic contexts, and environmental contexts work synergistically to make young women especially vulnerable to HIV infection. We then examine how these contexts can undermine HIV prevention interventions. Finally, we discuss the importance of fully mapping out the intervention context to enhance the effectiveness of HIV prevention interventions.

CONCLUSIONS

Understanding an intervention context, and how its features work together to amplify young women's risk in hyper-endemic settings can contribute to sustained momentum in reducing HIV incidence among young women and help to limit the reach of the HIV pandemic into new generations of Africans.

Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017

HIV/AIDS is a leading cause of disease burden in sub-Saharan Africa. Existing evidence has demonstrated that there is substantial local variation in the prevalence of HIV; however, subnational variation has not been investigated at a high spatial resolution across the continent. Here we explore within-country variation at a 5 × 5-km resolution in sub-Saharan Africa by estimating the prevalence of HIV among adults (aged 15-49 years) and the corresponding number of people living with HIV from 2000 to 2017. Our analysis reveals substantial within-country variation in the prevalence of HIV throughout sub-Saharan Africa and local differences in both the direction and rate of change in HIV prevalence between 2000 and 2017, highlighting the degree to which important local differences are masked when examining trends at the country level. These fine-scale estimates of HIV prevalence across space and time provide an important tool for precisely targeting the interventions that are necessary to bringing HIV infections under control in sub-Saharan Africa.

Most new HIV infections, vertical transmissions and AIDS-related deaths occur in lower-prevalence countries

OBJECTIVES

The Joint United Nations Programme on HIV/AIDS (UNAIDS) targets aim to reduce new HIV infections below 500,000 per year by 2020. Despite targeted prevention programmes, total new infections remained in 2016 and 2017 at 1,800,000 cases. We have aimed to analyse data from 2017 and to compare HIV incidence, AIDS-related deaths and provision of antiretroviral therapy (ART) to adults, pregnant women and children living with HIV in lower- and higher-prevalence countries. Vertical or mother-to-child transmission (MTCT) and early infant diagnosis (EID) rates were also investigated.

METHODS

UNAIDSinfo data use the Spectrum model to represent country-level HIV data. Countries with epidemics over 40,000 HIV cases were separated into higher prevalence (≥4.5%) and lower prevalence (<4.5%). Least squares linear regression, weighted by epidemic size and controlled for gross domestic product/capita, was used to compare HIV prevalence with estimated ART coverage in adults (≥15 years), children (0-14 years), pregnant women, and EID rates and MTCT rates. Data were then compared between higher- and lower-prevalence groups, including numbers of new HIV infections and AIDS-related deaths.

RESULTS

Data were available for 56 countries. Twelve higher-prevalence countries accounted for 16.7 million and 44 lower-prevalence ones for 15.1 million people living with HIV, altogether making up 87.5% of the global estimate. Lower-prevalence countries had less ART coverage for adults, pregnant women and children, lower EID rates and higher AIDS-related death levels. There were more new HIV infections in adults and children in lower- than higher-prevalence countries.

CONCLUSIONS

Most new HIV infections, MTCTs and AIDS-related deaths occurred in countries with an HIV prevalence rate below 4.5%. Many of these countries are not targeted by access programmes, such as the President' Emergency Plan for AIDS Relief. More intensive programmes of diagnosis and treatment are needed in these countries in the effort to reduce global new HIV infections below 500,000 per year by 2020.

Towards UNAIDS Fast-Track goals: targeting priority geographic areas for HIV prevention and care in Zimbabwe

INTRODUCTION

Zimbabwe has made substantial progress towards the Joint United Nations Programme on HIV/AIDS (UNAIDS) targets of 90-90-90 by 2020, with 73% of people living with HIV diagnosed, 87% of those diagnosed on antiretroviral therapy (ART) and 86% of those on ART virally suppressed. Despite this exceptional response, more effort is needed to completely achieve the UNAIDS targets. Here, we conducted a detailed spatial analysis of the geographical structure of the HIV epidemic in Zimbabwe to include geographical prioritization as a key component of their overall HIV intervention strategy.

METHODS

Data were obtained from Zimbabwe Demographic and Health Survey (ZDHS) conducted in 2015 as well as estimations from the Zimbabwe Population-Based HIV Impact Assessment (ZIMPHIA) 2016 report, and other published literature. Data were used to produce high-resolution maps of HIV prevalence. Using these maps combined with the population density maps, we mapped the HIV-infected population lacking ART coverage and viral suppression.

RESULTS

HIV maps for both sexes illustrated similar geographical variation of HIV prevalence within the country. HIV-infected populations lacking ART coverage and viral suppression were concentrated in the main cities and urban settlements such as Bulawayo, Harare, Ruwa and Chitungwiza.

CONCLUSION

Our study showed extensive local variation in HIV disease burden across Zimbabwe for both women and men. The high-resolution maps generated here identified areas wherein high density of HIV-infected individuals are lacking ART coverage and viral suppression. These results suggest that there is need to tailor HIV programmes to address specific local needs to efficiently achieve epidemic control in Zimbabwe.

Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data

BACKGROUND

Large geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV 'hotspots' is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania.

METHODS

Population-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation.

RESULTS

Routinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV 'hotspots' in > 50% of the high HIV burden areas.

CONCLUSION

Clinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV 'hotspots'). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation.