South Africa District HIV Estimates combine district-level data about HIV from several sources in a statistical model:
District HIV estimates are developed using the Naomi model, a small-area estimation model for estimating HIV prevalence and PLHIV, ART coverage, and new HIV infections at district level by sex and five-year age group. The model combines district-level data about multiple outcomes from several sources in a Bayesian statistical model to produce consistent estimates of multiple outcomes of interest.
Download Naomi estimates calibrated to Thembisa 4.6 outputs (by sex and 5-year age group)
For raw (uncalibrated) Naomi estimates or previous versions, see the tab Previous versions.
South Africa District Estimates are developed through a technical collaboration of:
in partnership with the South Africa Department of Health, South Africa National AIDS Council, UNAIDS, US Centers for Disease Control (CDC), and the Human Sciences Research Council (HSRC).
Estimates are reviewed by the South Africa Estimates and Modelling Technical Working Group, chaired by the National Department of Health and South Afria National AIDS Council.
For further information, questions, or feedback about South Africa District HIV Estimates, please contact:
Full estimates in spreadsheet format:
Naomi estimates calibrated to Thembisa 4.6 outputs (by sex and 5-year age group) Naomi estimates calibrated to Thembisa 4.6 outputs (by sex and coarse <15/15+ age groups)
Raw Naomi estimates (Not adjusted to Thembisa 4.5 outputs)
For planning purposes the version of estimates calibrated to Thembisa 4.6 outputs by sex and 5-year age group are recommended.
Within the zipped folder, the spreadsheet indicators.csv
contains estimates of all indicators stratified by:
For each estimate, the mean estimate and 95% uncertainty range are reported.
The file boundaries.geojson
contains geographic boundaries, which can be used producing maps of estimates.
Thembisa is a mathematical model of the South African HIV epidemic at national and province level. The Thembisa model represents the behaviours and processes that determine HIV transmission, disease progression, and effects of interventions. Thembisa reconstructs the full trajectory of the HIV epidemic in South Africa with medium range projections (1985-2040).
The Naomi model is a statistical model that balances evidence from multiple data sources at district level, but does not represent mechanisms of HIV transmission. Naomi is focused on current estimates (2022-23) for district-level planning purposes.
For some policy and planning purposes, it is useful for central values for district, provincial, and national estimates to exactly align. After fitting the model, a second version of district estimates are produced that are adjusted such that aggregated district estimates match to Thembisa provincial estimates, interpolated to the relevant quarter for district estimates. Before adjusting district estimates, Thembisa provincial estimates are first adjusted to match Thembisa national estimates.
Thembisa | Naomi | |
---|---|---|
Geographical level | National, provincial | District |
Demographic inputs | CARe model | District estimates by Statistics SA, adjusted to Thembisa |
Age stratification | Single year (0, 1, …, 89, 90+) | Five-year age groups 0 to 80+ |
Migration | Migration between provinces contributes to changes in HIV prevalence | Migration is not explicitly modelled |
HIV incidence | Population is divided into risk groups, key populations. Assumptions about sexual risk behaviour and HIV transmission drive incidence. | By sex, age, and district (10+ population only). Depends on transmission rate, district prevalence, and district ART coverage |
HIV disease progression | Acute HIV + 4 CD4 stages | Not explicitly modelled |
Diagnosis, testing | Testing rates depend on age, sex, CD4 stage, year | Not included |
ART uptake | Depends on CD4 stage, sex, age, year | Depends on district, sex, age, year |
Adjustment for cross-border ART | None | Persons have a small probability of receiving ART at facilities in a neighbouring district to their residence |
Mortality | Depends on age, sex, current CD4 (if untreated), time since ART start and baseline CD4 (if treated) | HIV survival based on survival probabilities from Thembisa by sex, age, province (not a model output) |
Calibration to HIV prevalence data | HSRC & DHS surveys; Antenatal prevalence surveys; Key population surveys | HSRC 2017 & DHS surveys; Routine antenatal HIV testing prevalence |
Inclusion of programme data | HIV testing data, ART totals, viral suppression, VMMCs | ART totals |
Most recent programme data | March 2022 | September 2022 |
Other calibration data | Recorded deaths (national); Self-reported testing history | ART coverage among women attending first ANC |
South Africa District HIV Estimates are developed using the Naomi model. The Naomi model is a small-area estimation model for estimating HIV prevalence and PLHIV, ART coverage, and new HIV infections at district level by sex and five-year age group. The model combines district-level data about multiple outcomes from several sources in a Bayesian statistical model to produce consistent estimates of multiple outcomes of interest.
The model focuses on current estimates and short-term one-year ahead projections for HIV programme planning purposes.
Population
District-level population estimates by sex and age group are drawn from the Statistics South Africa Mid-Year Population Estimates1 and adjusted to match the Thembisa 4.6 province-level population by sex and age group.
HIV prevalence
The first step is to produce cross-sectional estimates for HIV prevalence, ART coverage, and HIV incidence in June 2017, the mid-point of the most recent nationally-representative household surveys.
For HIV prevalence, the model is calibrated to survey data about HIV prevalence by district, sex, and five-year age group from the National HIV Prevalence, Incidence, Behaviour and Communication Survey (SABSSM) 20172 and Demographic and Health Survey (DHS) 20163. Since the survey sample size in each district is relatively small, routinely reported data about HIV prevalence among pregnant women attending their first ANC visit, extracted from the national District Health Management Information System (DHIS), are used to improve estimates of the spatial pattern of HIV.
ART coverage
ART coverage by district, age, and sex is estimated from household survey data about the presence of ARV biomarkers in HIV-positive survey respondents. Routinely reported ART coverage among pregnant women prior to first ANC visit is used as a covariate for the spatial pattern of ART coverage. The ART coverage and HIV prevalence are also calibrated so that total number on ART matches the number of adults and children accessing treatment in each district through public sector and private sector treatment provision, reported through the national DHIS and Council for Medical Schemes, respectively.
ART attendance in neighbouring districts
A challenge for estimating treatment coverage at district level is that persons may access ART services in a different district than their residence, for example if facilities are closer or perceived to provide better services. The model allows for a probability that resident PLHIV access ART in a neighbouring district. The prior assumption is that the large majority of PLHIV will access ART in their district of residence, but this probability can vary based on district data about the number receiving ART compared to HIV prevalence, ART coverage and population.
HIV incidence
HIV incidence in 2017 is estimated from the proportion recently infected from the SABSSM 2017 survey. HIV incidence is determined by the district-level HIV prevalence and ART coverage such that the estimated HIV incidence is related to the prevalence of unsuppressed HIV in the population. The sex and age distribution of new infections is estimated using incidence rate ratios from Thembisa.
Projection to September 2022 and 2023
The next step of the model is to conduct a one-step projection of the population to September 2022. Population estimates are updated with Statistics South Africa district projections. The number of PLHIV is projected from 2017 to 2022 based on survival estimates by province, sex, and age group from Thembisa over the same period (which accounts for HIV disease progression and effects of ART scale up on reducing AIDS mortality). ART coverage is updated based on the number on ART in September 2022 from DHIS and medical scheme reporting. Finally, for programme planning purposes, a short term projection is calculated to September 2023 using the same methods. The number on ART is projected based on Thembisa model projection for rates of further scale up.
Paediatric estimates
For paediatric estimates, where household survey data have low numbers of observed cases, the model uses the ratio of female adult HIV prevalence to child HIV prevalence from Thembisa provincial results to estimate the average district-level prevalence among children and the new paediatric infections between 2017 and 2022. Paediatric ART coverage is determined by the number of children receiving ART in each district and SABBSM survey data about ART coverage among children.
Versions
A preliminary version of the Naomi model was first developed in South Africa in 2019 and district estimate were publicly released at www.hivdata.org.za in November 2020.
The model has been used in UNAIDS-supported national HIV estimates in 40 other sub-Saharan African countries. Future development of Naomi will extend the model for additional data sources and indicators including the HIV care cascade and prevention indicators.
Prevalence is the estimated number of people (aged 15-49 years) living with HIV in the district divided by the total resident population aged 15-49 years.
ART coverage is the estimated number of residents in the district currently on ART (aged 15+ years) divided by the number of residents aged 15+ years living with HIV.
Hovering over each district shows the estimated number of residents on ART and the number of clients receiving ART at health facilities in the district. These numbers may be different because some residents obtain treatment at health facilities in neighbouring districts.
HIV incidence rate is the number of new HIV infections occurring during the previous year among residents in the district (aged 15-49 years) divided by the number of HIV negative residents aged 15-49 years.
This page contains previously released versions of Naomi district estimates for archival purposes.
Each version of Naomi district estimates involves re-estimation of historical and current results for all districts with updated data inputs about each component of the model Therefore, changes in successive releases of Naomi estimates should not be used to estimate time trends in indicators. For uses cases where time trends are required, please contact developers (jeffrey.eaton@imperial.ac.uk) to discuss.
Naomi estimates for September 2022 were reviewed and adopted by the South Africa HIV Estimates Techincal Working Group in March 2023.
Estimates were produced using the Naomi model version 2.9.9 (code here).
Key updates in the September 2022 district estimates were:
plhiv_attend
representing the number of PLHIV in the health facility catchment for each district, accounting for estimated patterns of ART care seeking in neighbouring districts. This output should be used as a denominator for catchment ART coverage and target setting calculated using TROA as the numerator (public and private sector combined).untreated_plhiv_attend
representing the number of untreated PLHIV in the health facility catchment for each district. This is a direct estimates for the ‘treatment gap’ at health facilities.The following data were updated in the September 2022 estimates:
Download Naomi estimates for September 2022:
Naomi estimates for September 2021 were reviewed and adopted by the South Africa HIV Estimates Techincal Working Group in April 2022.
Key updates in the September 2021 district estimates were:
plhiv_attend
estimate for the number of PLHIV in the catchment for health facilities in each district.
Download Naomi estimates for September 2021:
Naomi district HIV estimates for September 2020 were reviewed and adopted by the South Africa HIV Estimates Technical Working Group in March 2021.
Key updates in the September 2020 district estimates were:
Download Naomi estimates for September 2020:
District HIV estimates for March 2020 were first released via hivdata.org.za in November 2020 coinciding with a national HIV estimates launch webinar hosted by the South Africa National AIDS Control Council (SANAC).
Download March 2020 estimates: zaf_district_naomi-output_march-2020.zip
Statistics South Africa. Mid-year population estimates 2022. 2022. Available: https://www.statssa.gov.za/publications/P0302/P03022022.pdf. Accessed 3 May 2023↩︎
Simbayi LC, Zuma K, Zungu N, Moyo S, Marinda E, Jooste S, et al. South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2017. Cape Town: Human Sciences Research Council; 2019. Available: https://www.hsrcpress.ac.za/books/south-african-national-hiv-prevalence-incidence-behaviour-and-communication-survey-2017. Accessed 6 Nov 2019↩︎
Department of Health, Statistics South Africa, South African Medical Research Council, ICF. South Africa Demographic and Health Survey 2016. Pretoria; 2019. Available: https://www.dhsprogram.com/pubs/pdf/FR337/FR337.pdf. Accessed 19 March 2019↩︎