Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning

Objective: There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV. Design/methods: Six candidate methods – including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases – were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. Results: Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. Conclusions: Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates

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

Global, regional and country-level 90-90-90 estimates for 2018: assessing progress towards the 2020 target.

BACKGROUND: In 2014, the Joint United Nations Programme on HIV/AIDS (UNAIDS) and partners set the 90-90-90 target for the year 2020: diagnose 90% of all people living with HIV (PLHIV); treat 90% of people who know their status; and suppress the virus in 90% of people on treatment. In 2015, countries began reporting to UNAIDS on progress against 90-90-90 using standard definitions and methods. METHODS: We used data submitted to UNAIDS from 170 countries to assess country-specific progress towards 90-90-90 through 2018. To assess global and regional progress, overall and by sex for adults 15 years and older, we combined country-reported data with estimates generated with a Bayesian hierarchical model. RESULTS: A total of 60 countries reported on all three 90s in 2018, up from 23 in 2015. Among all PLHIV worldwide, 79% (67-92%) knew their HIV status. Of these, 78% (69-82%) were accessing treatment and 86% (72-92%) of people accessing treatment had suppressed viral loads. Of the 37.9 million (32.7-44.0 million) PLHIV worldwide, 53% (43-63%) had suppressed viral loads. The gap to fully achieving 73% of PLHIV with suppressed viral load was 7.7 million; 15 countries had already achieved this target by 2018. CONCLUSION: Increased data availability has led to improved measures of country and global progress towards the 90-90-90 target. Although gains in access to testing and treatment continue, many countries and regions are unlikely to reach 90-90-90 by 2020

Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning

Objective: There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV. Design/methods: Six candidate methods – including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases – were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. Results: Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. Conclusions: Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates.</p

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 to 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 were among ages 35-39 for both women and men, while the most untreated PLHIV were among ages 25-29 for women and 30-34 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 61–79%) resided in Lilongwe City, 20% (14–27%) in Lilongwe district outside the metropolis, and 9% (6–12%) in neighbouring Dowa district. Thirty-eight percent (26–50%) of Lilongwe Rural residents and 39% (27–50%) of Dowa residents received treatment at facilities in Lilongwe City. Conclusions: The Naomi model synthesises 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

Experienced and Perceived Risks of Mycobacterial Diseases: A Cross Sectional Study among Agropastoral Communities in Northern Tanzania

Objective The current study was conducted to assess experienced risk factors and perceptions of mycobacterial diseases in communities in northern Tanzania. Methods We conducted a cross-sectional study in Arusha and Manyara regions in Northern Tanzania. We enrolled tuberculosis (TB) patients attending Mount Meru Hospital, Enduleni Hospital and Haydom Lutheran Hospitals in Arusha municipality, Ngorongoro and Mbulu districts, respectively. Patient addresses were recorded during their first visit to the hospitals. Patients with confirmed diagnosis of TB by sputum smear microscopy and/or culture at central laboratory were followed up and interviewed using pre-tested questionnaires, and selected relatives and neighbors were also interviewed. The study was conducted between June 2011 and May 2013. Results The study involved 164 respondents: 41(25%) were TB patients, 68(41.5%) were their relatives and 55(33.5%) their neighbors. Sixty four (39%) knew a risk factor for mycobacterial disease. Overall, 64(39%) perceived to be at risk of mycobacterial diseases. Exposure to potential risks of mycobacterial diseases were: keeping livestock, not boiling drinking water, large family, smoking and sharing dwelling with TB patients. Rural dwellers were more often livestock keepers (p<0.01), more often shared dwelling with livestock (p<0.01) than urban dwellers. More primary school leavers reported sharing dwelling with TB patients than participants with secondary and higher education (p = 0.01). Conclusion Livestock keeping, sharing dwelling with livestock, sharing household with a TB patient were perceived risk factors for mycobacterial diseases and the participants were exposed to some of these risk factors. Improving knowledge about the risk factors may protect them from these serious diseases

Association between socioeconomic position and tuberculosis in a large population-based study in rural Malawi.

SETTING: There is increasing interest in social structural interventions for tuberculosis. The association between poverty and tuberculosis is well established in many settings, but less clear in rural Africa. In Karonga District, Malawi, we found an association between higher socioeconomic status and tuberculosis from 1986-1996, independent of HIV status and other factors. OBJECTIVE: To investigate the relationship in the same area in 1997-2010. DESIGN: All adults in the district with new laboratory-confirmed tuberculosis were included. They were compared with community controls, selected concurrently and frequency-matched for age, sex and area. RESULTS: 1707 cases and 2678 controls were interviewed (response rates >95%). The odds of TB were increased in those working in the cash compared to subsistence economy (p<0.001), and with better housing (p-trend=0.006), but decreased with increased asset ownership (p-trend=0.003). The associations with occupation and housing were partly mediated by HIV status, but remained significant. CONCLUSION: Different socioeconomic measures capture different pathways of the association between socioeconomic status and tuberculosis. Subsistence farmers may be relatively unexposed whereas those in the cash economy travel more, and may be more likely to come forward for diagnosis. In this setting "better houses" may be less well ventilated and residents may spend more time indoors