Associations with HIV testing in Uganda: an analysis of the Lot Quality Assurance Sampling database 2003-2012

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.

Making Smarter Decisions Faster: Systems Engineering to Improve the Global Public Health Response to HIV

PURPOSE OF REVIEW

This review offers an operational definition of systems engineering (SE) as applied to public health, reviews applications of SE in the field of HIV, and identifies opportunities and challenges of broader application of SE in global health.

RECENT FINDINGS

SE involves the deliberate sequencing of three steps: diagnosing a problem, evaluating options using modeling or optimization, and providing actionable recommendations. SE includes diverse tools (from process improvement to mathematical modeling) applied to decisions at various levels (from local staffing decisions to planning national-level roll-out of new interventions). Contextual factors are crucial to effective decision-making, but there are gaps in understanding global decision-making processes. Integrating SE into pre-service training and translating SE tools to be more accessible could increase utilization of SE approaches in global health. SE is a promising, but under-recognized approach to improve public health response to HIV globally.

Institutionalizing and sustaining social change in health systems: the case of Uganda

The key to high impact health services is institutionalizing and sustaining programme evaluation. Uganda represents a success story in the use of a specific programme evaluation method: Lot Quality Assurance Sampling (LQAS). Institutionalization is defined by two C’s: competent programme evaluators and control mechanisms that effectively use evaluation data to improve health services. Sustainability means continued training and funding for the evaluation approach. Social science literature that researches institutionalization has emphasized ‘stability’, whereas in global health, the issue is determining how to improve the impact of services by ‘changing’ programmes. In Uganda, we measured the extent of the institutionalization and sustainability of evaluating programmes that produce change in nine districts sampled to represent three largely rural regions and varying levels of effective health programmes. We used the proportion of mothers with children aged 0–11 months who delivered in a health facility as the principal indicator to measure programme effectiveness. Interviews and focus groups were conducted among directors, evaluation supervisors, data collectors in the district health offices, and informant interviews conducted individually at the central government level. Seven of the nine districts demonstrated a high level of institutionalization of evaluation. The two others had only conducted one round of programme evaluation. When we control for the availability of health facilities, we find that the degree of institutionalization is moderately related to the prevalence of the delivery of a baby in a health facility. Evaluation was institutionalized at the central government level. Sustainability existed at both levels. Several measures indicate that lessons from the nine district case studies may be relevant to the 74 districts that had at least two rounds of programme evaluation. We note that there is an association between the evaluation data being used to change health services, and the four separate indicators being used to measure women's health and child survival services. We conclude that the two C’s (competent evaluators and control mechanisms) have been critical for sustaining programme evaluation in Uganda.