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

Provision of HIV testing services and its impact on the HIV positivity rate in the public health sector in KwaZulu-Natal: a ten-year review

South Africa has been rated as having the most severe HIV epidemic in the world since it has one of the largest populations of people living with HIV (PLHIV). KwaZulu-Natal (KZN) is the epicentre of the HIV epidemic. The HIV test and treat services in the public health sector are critical to managing the epidemic and responding to the increase in HIV infections. The KwaZulu-Natal Department of Health (DOH) commissioned a review of the provision of HIV testing services in the province and aimed to investigate its impact on the HIV positivity rate over a ten-year period. The study was an ecological study design using data extracted from the Department's District Health Information System (DHIS). Descriptive analysis was conducted in addition to ANOVA and multiple regression analysis. The results of this study have shown that the total number of HIV tests conducted over the ten-year period in the province has increased with the highest number of HIV tests being conducted in the 2018/2019 MTEF year. ANOVA analysis indicates that there was a statistically significant difference in the total number of HIV tests conducted and the number of HIV tests per 100 000 population across the province's 11 districts (p < 0.001). Statistically significant differences were observed in the HIV testing rate and in the HIV positivity rate over the period (p < 0.001). Results from multiple regression analysis showed that the HIV testing rate per 100 000 population was the strongest predictor of the HIV positivity rate. HIV positivity among clients correlated negatively with the number of HIV tests conducted per 100 000 population (r = -0.823; p < 0.001) and the HIV testing rate (r = -0.324; p < 0.01). This study has found that HIV testing could have an impact on reducing the positivity rate of HIV in the province and is therefore an effective strategy in curbing the HIV epidemic. The KwaZulu-Natal Department of Health should ensure that strategies for implementing and maintaining HIV testing and treating services should continue at an accelerated rate in order to achieve the first 95 of the UNAIDS 2025 SDG target.

Profile of the cohort of people being treated for HIV infection in the SUS, Brazil, 2015-2018

OBJECTIVE

To build an integrated database of individual and service data from the cohort of people who started antiretroviral therapy (ART), from 2015 to 2018, in Brazil.

METHODS

Open cohort study that includes people aged 15 years or older who started ART from 2015 to 2018, with follow-up in services of the Brazilian Unified Health System (SUS), and who responded to the 2016/2017 Qualiaids national survey. The source of individual data was the related HIV database, derived from the probabilistic linkage between data from the SUS systems of diagnostic information, medication, tests, and deaths. The data source for the services was the services' response database to the Qualiaids survey. After analysis of consistency and exclusions, the database of individuals was deterministically related to the database of services.

RESULTS

The cohort comprised 132,540 people monitored in 941 SUS services. Of these services, 59% are located in the Southeast region and 49% followed 51 to 500 cohort participants. The average performance of organization and management of patient care ranged from 29% to 75%. Most of the cohort participants are male, black and mixed, aged between 20 and 39 years old, and have between 4 and 11 years of schooling. Median baseline T-CD4 was 419 cells/mm3, 6% had an episode of tuberculosis, and 2% died of HIV disease.

CONCLUSION

For the first time in Brazil, this cohort provides the opportunity for a joint analysis of individual factors and services in the production of positive and negative clinical outcomes of HIV treatment.

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.