Improving estimates of district HIV prevalence and burden in South Africa using small area estimation techniques

A small area model to assess temporal trends and sub-national disparities in healthcare quality

Monitoring subnational healthcare quality is important for identifying and addressing geographic inequities. Yet, health facility surveys are rarely powered to support the generation of estimates at more local levels. With this study, we propose an analytical approach for estimating both temporal and subnational patterns of healthcare quality indicators from health facility survey data. This method uses random effects to account for differences between survey instruments; space-time processes to leverage correlations in space and time; and covariates to incorporate auxiliary information. We applied this method for three countries in which at least four health facility surveys had been conducted since 1999 - Kenya, Senegal, and Tanzania - and estimated measures of sick-child care quality per WHO Service Availability and Readiness Assessment (SARA) guidelines at programmatic subnational level, between 1999 and 2020. Model performance metrics indicated good out-of-sample predictive validity, illustrating the potential utility of geospatial statistical models for health facility data. This method offers a way to jointly estimate indicators of healthcare quality over space and time, which could then provide insights to decision-makers and health service program managers.

Accelerating HIV epidemic control in Benue state, Nigeria, 2019-2021: the APIN program experience

Introduction

As at 2019, Nigeria was ranked the fourth highest HIV burden in the world. There is varied geographical HIV prevalence in Nigeria. The progress made is inequitable across geographical locations and sub-populations (18). Benue state has the second highest HIV prevalence in Nigeria. In 2018, about 35,623 people living with HIV (PLHIV) were yet to commence antiretroviral treatment (ART) in the state, accounting for an estimated ART coverage gap of 11% out of the combined gap of 320,921 in the country. To close this gap, the Benue ART surge (BAS) was implemented. The aim of this study was to describe the BAS strategic approaches and demonstrate progress in expanding ART access for PLHIV in Benue State, Nigeria.

Methods

BAS was implemented in 252 health facilities from May 2019 to September 2021. Data were collected and reported using an Excel-based dashboard and electronic medical records. The trend of HIV case identification, ART initiation, viral load suppression rate, and rate of interruption in treatment during the BAS period was then described and analyzed.

Results

Out of 893,462 clients reached, 6.7% (n = 60,297) were diagnosed with HIV and 99.8% (n = 60,236) were initiated on ART. HIV case identification per month increased by 467% from 650 at baseline to a peak of 3685 in August 2020, and then declined by 35% to 2380 in September 2021. All new HIV-infected patients (100%) were linked to ART. Viral load testing coverage and viral load suppression rate increased from 30% (43,185/126,004) and 84% (n = 36,165/43,185) at baseline to 95% (n = 193,890/204,095) and 96% (185,785/193,890), respectively.

Conclusion

Implementation of the BAS improved access to comprehensive HIV services in Benue State. The increase in HIV case identification and ART initiation significantly reduced the HIV treatment gap in the state. To fast track the attainment of UNAIDS 95-95-95 goals, lessons learnt from the BAS should be adapted and scaled up in the national HIV program in Nigeria.

High prevalence of SARS-CoV-2 antibodies in pregnant women after the second wave of infections in the inner-city of Johannesburg, Gauteng Province, South Africa

OBJECTIVES

After South Africa's second wave of COVID-19, this study estimated the SARS-CoV-2 seroprevalence among pregnant women in inner-city Johannesburg, South Africa.

METHODS

In this cross-sectional survey, 500 pregnant women who were non-COVID-19-vaccinated (aged ≥12 years) were enrolled, and demographic and clinical data were collected. Serum samples were tested using the Wantai SARS-CoV-2 spike antibody enzyme-linked immunosorbent assay and Roche Elecsys® anti-SARS-CoV-2 nucleocapsid antibody assays. Seropositivity was defined as SARS-CoV-2 antibodies on either (primary) or both (secondary) assays. Univariate Poisson regression assessed risk factors associated with seropositivity.

RESULTS

The median age was 27.4 years, and HIV prevalence was 26.7%. SARS-CoV-2 seroprevalence was 64.0% (95% confidence interval [CI]: 59.6-68.2%) on the primary and 54% (95% CI: 49.5-58.4%) on the secondary measure. Most (96.6%) women who were SARS-CoV-2-seropositive reported no symptoms. On the Roche assay, we detected lower seroprevalence among women living with HIV than women without HIV (48.9% vs 61.7%, P-value = 0.018), and especially low levels among women living with HIV with a clusters of differentiation 4 <350 cells/ml compared with women without immune suppression (22.2% vs 56.4%, prevalence rate ratio = 0.4; 95% CI: 0.2-0.9; P-value = 0.046).

CONCLUSION

Pregnant women attending routine antenatal care had a high SARS-CoV-2 seroprevalence after the second wave in South Africa, and most had asymptomatic infections. Seroprevalence surveys in pregnant women present a feasible method of monitoring the course of the pandemic over time.

"Acute Kidney Injury predictive models: advanced yet far from application in resource-constrained settings."

Acute kidney injury (AKI) remains a significant cause of morbidity and mortality in hospitalized patients, particularly critically ill patients. It poses a public health challenge in resource-constrained settings due to high administrative costs. AKI is commonly misdiagnosed due to its painless onset and late disruption of serum creatinine, which is the gold standard biomarker for AKI diagnosis. There is increasing research into the use of early biomarkers and the development of predictive models for early AKI diagnosis using clinical, laboratory, and imaging data. This field note provides insight into the challenges of using available AKI prediction models in resource-constrained environments, as well as perspectives that practitioners in these settings may find useful

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.

Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)

Background

The HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation.

Methods

We estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay–Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts.

Results

The results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts (n = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates.

Conclusions

HIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility.

When Pregnancy Coincides with Positive Diagnosis of HIV: Accounts of the Process of Acceptance of Self and Motherhood among Women in South Africa

Literature has highlighted the unique period of vulnerability following an HIV diagnosis during pregnancy. Despite the high burden of HIV among pregnant women in South Africa, the experiences of women diagnosed with HIV during pregnancy have rarely been explored in isolation from those diagnosed at different times. This paper explored the experiences of women who were diagnosed with HIV when pregnant and assessed their emotional recovery beyond diagnosis. The study used a qualitative descriptive phenomenological approach to conduct interviews with women recruited from ART clinics in a health district in South Africa. Participants included 19 women sampled purposively. The interviews were transcribed verbatim and analysed following the thematic approach. Testing positive during pregnancy and being free of symptoms increased the shock, disbelief, and strong emotions exhibited. For the women, the diagnosis of HIV coincided with pregnancy and transformed pregnancy from excitement to anxiety. Although the transition from being HIV negative to becoming HIV positive and pregnant was overwhelming, with the passage of time, the women transitioned to feelings of acceptance. However, the process of acceptance was slow and varied, with some experiencing non-acceptance for extended periods. Non-acceptance of HIV diagnosis has serious adverse public health consequences for the individual. Integrating continuous HIV counselling and culturally appropriate psychosocial care into practice could foster acceptance for pregnant women with HIV diagnosis.

A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa

Despite making significant progress in tackling its HIV epidemic, South Africa, with 7.7 million people living with HIV, still has the biggest HIV epidemic in the world. The Government, in collaboration with developmental partners and agencies, has been strengthening its responses to the HIV epidemic to better target the delivery of HIV care, treatment strategies and prevention services. Population-based household HIV surveys have, over time, contributed to the country’s efforts in monitoring and understanding the magnitude and heterogeneity of the HIV epidemic. Local-level monitoring of progress made against HIV and AIDS is increasingly needed for decision making. Previous studies have provided evidence of substantial subnational variation in the HIV epidemic. Using HIV prevalence data from the 2016 South African Demographic and Health Survey, we compare three spatial smoothing models, namely, the intrinsically conditionally autoregressive normal, Laplace and skew-t (ICAR-normal, ICAR-Laplace and ICAR-skew-t) in the estimation of the HIV prevalence across 52 districts in South Africa. The parameters of the resulting models are estimated using Bayesian approaches. The skewness parameter for the ICAR-skew-t model was not statistically significant, suggesting the absence of skewness in the HIV prevalence data. Based on the deviance information criterion (DIC) model selection, the ICAR-normal and ICAR-Laplace had DIC values of 291.3 and 315, respectively, which were lower than that of the ICAR-skewed t (348.1). However, based on the model adequacy criterion using the conditional predictive ordinates (CPO), the ICAR-skew-t distribution had the lowest CPO value. Thus, the ICAR-skew-t was the best spatial smoothing model for the estimation of HIV prevalence in our study.

Ensuring Optimal Community HIV Testing Services in Nigeria Using an Enhanced Community Case-Finding Package (ECCP), October 2019-March 2020: Acceleration to HIV Epidemic Control

Purpose

The 2018 Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) showed Nigeria’s progress toward the UNAIDS 90-90-90 targets: 47% of HIV-positive individuals knew their status; of these, 96% were receiving antiretroviral therapy (ART); and of these, 81% were virally suppressed. To improve identification of HIV-positive individuals, Nigeria developed an Enhanced Community Case-Finding Package (ECCP). We describe ECCP implementation in nine states and assess its effect.

Methods

ECCP included four core strategies (small area estimation [SAE] of people living with HIV [PLHIV], map of HIV-positive patients by residence, HIV risk-screening tool [HRST], and index testing [IT]) and four supportive strategies (alternative healthcare outlets, performance-based incentives for field testers, Project Extension for Community Healthcare Outcomes, and interactive dashboards). ECCP was deployed in nine of 10 states prioritized for ART scale-up. Weekly program data (October 2019–March 2020) were tracked and analyzed.

Results

Of the total 774 LGAs in Nigeria, using SAE, 103 (13.3%) high-burden LGAs were identified, in which 2605 (28.0%) out of 9,294 hotspots were prioritized by mapping newly identified PLHIV by residential addresses. Over 22 weeks, among 882,449 individuals screened using HRST, 723,993 (82.0%) were eligible and tested for HIV (state range, 43.7–90.4%), out of which 20,616 were positive. Through IT, an additional 3,724 PLHIV were identified. In total, 24,340 PLHIV were identified and 97.4% were linked to life-saving antiretroviral therapy. The number of newly identified PLHIV increased 17-fold over 22 weeks (week 1: 89; week 22: 1,632). Overall mean HIV positivity rate by state was 3.3% (range, 1.8–6.4%).

Conclusion

Using ECCP in nine states in Nigeria increased the number of PLHIV in the community who knew their status, allowing them to access life-saving care and decreasing the risk of HIV transmission.

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.

Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda

Background

Model-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for districts in Uganda.

Methods

Our analysis used direct survey and model-based estimation methods, including Fay-Herriot (area-level) and Battese-Harter-Fuller (unit-level) small area models. We used regression analysis to assess for consistency in estimating HIV prevalence. We use a ratio analysis of the mean square error and the coefficient of variation of the estimates to evaluate precision. The models were applied to Uganda Population-Based HIV Impact Assessment 2016/2017 data with auxiliary information from the 2016 Lot Quality Assurance Sampling survey and antenatal care data from district health information system datasets for unit-level and area-level models, respectively.

Results

Estimates from the model-based and the direct survey methods were similar. However, direct survey estimates were unstable compared with the model-based estimates. Area-level model estimates were more stable than unit-level model estimates. The correlation between unit-level and direct survey estimates was (β₁ = 0.66, r² = 0.862), and correlation between area-level model and direct survey estimates was (β₁ = 0.44, r² = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level models, respectively, compared to the direct survey estimates.

Conclusions

Although the unit-level model estimates were less precise than the area-level model estimates, they were highly correlated with the direct survey estimates and had less standard error associated with estimates than the area-level model. Unit-level models provide more accurate and reliable data to support local decision-making when unit-level auxiliary information is available.

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.

An open-label, randomized crossover study to evaluate the acceptability and preference for contraceptive options in female adolescents, 15 to 19 years of age in Cape Town, as a proxy for HIV prevention methods (UChoose)

Introduction

Young women in Southern Africa have extremely high HIV incidence rates necessitating the availability of female‐controlled prevention methods. Understanding adolescent preference for seeking contraception would improve our understanding of acceptability, feasibility and adherence to similar modes of delivery for HIV prevention.

Methods

UChoose was an open‐label randomized crossover study over 32 weeks which aimed to evaluate the acceptability and preference for contraceptive options in healthy, HIV‐uninfected, female adolescents aged 15 to 19 years, as a proxy for similar HIV prevention methods. Participants were assigned to a contraceptive method for a period of 16 weeks in the form of a bi‐monthly injectable contraceptive, monthly vaginal Nuvaring^® or daily combined oral contraceptive (COC) and then asked to state their preference. At 16 weeks, participants crossed over to another contraceptive method, to ensure that all participants tried the Nuvaring^® (least familiar modality) and additionally, either the injection or COC. Primary outcomes were contraceptive acceptability and preference. At the end of the 32 weeks they were also asked to imagine their preference for an HIV prevention modality. Secondary endpoints included changes in sexual behaviour, contraceptive adherence and preference for biomedical and behavioural HIV prevention methods.

Results

Of the 180 participants screened, 130 were enrolled and randomized to the Nuvaring^® (n = 45), injection (n = 45) or COC (n = 40). Significantly more Nuvaring^® users (24/116; 20.7%) requested to change to another contraceptive option compared to injection (1/73; 1.4% p = 0.0002) and COC users (4/49; 8% p = 0.074). Of those that remained on the Nuvaring^®, adherence was significantly higher than to COC (p < 0.0001). Significantly more injection users (77/80; 96.3%) thought this delivery mode was convenient to use compared to Nuvaring^® (74/89; 83.1%; p = 0.0409) or COC (38/50; 76.0%; p = 0.0034). Overall, the preferred contraceptive choice was injection, followed by the ring and lastly the pill.

Conclusions

Adherence to daily COC was difficult for adolescents in this cohort and the least favoured potential HIV prevention option. While some preferred vaginal ring use, these data suggest that long‐acting injectables would be the preferred prevention method for adolescent girls and young women. This study highlights the need for additional options for HIV prevention in youth.

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.

Parenting the child with HIV in limited resource communities in South Africa: mothers with HIV's emotional vulnerability and hope for the future

Introduction:

A diagnosis of HIV does not affect the well-being of mothers alone but also affects how they care for their children. The aim of this study was to explore how mothers who were diagnosed with HIV when pregnant or when their children became ill experience raising children living with HIV. The purpose was to understand how a diagnosis of HIV impacts mothering their children at different points on the mothering journey.

Methods:

Using descriptive phenomenological enquiry, interviews were conducted with 28 mothers recruited via purposeful sampling from clinics in health district in South Africa. The interviews were audiotaped, transcribed verbatim, and analysed following the thematic approach.

Results:

The mothers found mothering a child living with HIV stressful and associated with constant thoughts of death. The burden of mothering was increased for mothers who had to confront emotions of self-blame and guilt for unintentionally infecting the child. They used secrecy to protect their children from the social consequences of a diagnosis of HIV. The thought of living with HIV weighed on them every day and they expressed their experience of intense feelings of chronic worry, anxiety, and sadness. The findings identified high levels of stress, with the mothers expressing emotions suggestive of depression. With time, they accepted living with HIV and embraced motherhood, and became better mothers.

Conclusion:

The negative coping strategies used to deal with the child’s HIV diagnosis and high levels of stress and anxiety identified in the study underscore the need to address the psychosocial needs of mothers living with HIV. There is need to provide psychosocial support and continuous counselling for these mothers post diagnosis and upon a positive HIV diagnosis of the child to women enrolled in the prevention of mother to child transmission of HIV programme.

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed.

Updates to the Spectrum/AIM model for estimating key HIV indicators at national and subnational levels

Supplemental Digital Content is available in the text

Background:

The Spectrum/AIM model is used by national programs and UNAIDS to prepare annual estimates of the status of the HIV epidemic in 170 countries. The model and assumptions are updated regularly under the guidance of the UNAIDS Reference Group on Estimates, Modelling and Projections in response to new data, studies and program needs. This article describes the most recent updates for the 2018 round of estimates.

Methods:

New data on AIDS-related mortality from Europe and Brazil have been used to update mortality rates of those not on antiretroviral therapy (ART). Household survey data and new studies of pregnant women, mothers, and children have been used to improve estimates of the number of HIV-positive pregnant and breastfeeding women and pediatric ART initiation. New tools to estimate geographic variation in HIV prevalence have been used to prepare district estimates of key indicators.

Results:

The 2018 version of Spectrum includes: new estimates of non-ART AIDS-related mortality by CD4⁺ count that depend on ART coverage; a procedure to estimate country-specific patterns of HIV incidence by age by fitting to prevalence by age from household surveys; an updated estimate of postpartum transmission with ART started before pregnancy of 0.023% per month; an updated estimate of retention on treatment at delivery of 80% for all women on ART; a somewhat older pattern of ART initiation by age that has 26% of new pediatric patients initiating ART at 10–14 years of age, 18% at 2–4 years of age, and 26% at 5–9 years of age; and a new tool for estimating key HIV indicators at the district level.

Conclusion:

The new methods and data implemented in the 2018 version of Spectrum allow national programs more flexibility in describing their programs and are intended to improve the estimates of adult mortality and pediatric HIV indicators.

HIV estimates through 2018: data for decision-making

BACKGROUND

Global targets call for a 75% reduction in new HIV infections and AIDS deaths between 2010 and 2020. UNAIDS supports countries to measure progress towards these targets. In 2019, this effort resulted in revised national, regional and global estimates reflecting the best available data.

METHODS

Spectrum software was used to develop estimates for 170 countries. Country teams from 151 countries developed HIV estimates directly and estimates for an additional 19 country were developed by UNAIDS based on available evidence. 107 countries employed models using HIV prevalence data from sentinel surveillance, routinely collected HIV testing and household surveys while the remaining 63 countries applied models using HIV case surveillance and/or reported AIDS deaths. Model parameters were informed by the UNAIDS Reference Group on Estimates, Modeling and Projections.

RESULTS

HIV estimates were available for 170 countries representing 99% of the global population. An estimated 37.9 million (uncertainty bounds 32.7-44.0 million) people were living with HIV in 2018. There were 1.7 million (1.4-2.3 million) new infections and 770 000 (570 000-1.1 million) AIDS-related deaths. New HIV infections declined in five of eight regions and AIDS deaths were declining in six of eight regions between 2010 and 2018.

CONCLUSION

The estimates demonstrate progress towards ending the AIDS epidemic by 2030, however, through 2018 declines in new HIV infections and AIDS-related deaths were not sufficient to meet global interim targets. The UNAIDS estimates have made important contributions to guide decisions about the HIV response at global, regional and country level.

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.

Generating statistics from health facility data: the state of routine health information systems in Eastern and Southern Africa

Health facility data are a critical source of local and continuous health statistics. Countries have introduced web-based information systems that facilitate data management, analysis, use and visualisation of health facility data. Working with teams of Ministry of Health and country public health institutions analysts from 14 countries in Eastern and Southern Africa, we explored data quality using national-level and subnational-level (mostly district) data for the period 2013-2017. The focus was on endline analysis where reported health facility and other data are compiled, assessed and adjusted for data quality, primarily to inform planning and assessments of progress and performance. The analyses showed that although completeness of reporting was generally high, there were persistent data quality issues that were common across the 14 countries, especially at the subnational level. These included the presence of extreme outliers, lack of consistency of the reported data over time and between indicators (such as vaccination and antenatal care), and challenges related to projected target populations, which are used as denominators in the computation of coverage statistics. Continuous efforts to improve recording and reporting of events by health facilities, systematic examination and reporting of data quality issues, feedback and communication mechanisms between programme managers, care providers and data officers, and transparent corrections and adjustments will be critical to improve the quality of health statistics generated from health facility data.