Did PEPFAR investments result in health system strengthening? A retrospective longitudinal study measuring non-HIV health service utilization at the district level

Abstract

Objectives PEPFAR’s initial rapid scale-up approach was largely a vertical effort focused fairly exclusively on AIDS. The purpose of our research was to identify spill-over health system effects, if any, of investments intended to stem the HIV epidemic over a 6-year period with evidence from Uganda. The test of whether there were health system expansions (aside from direct HIV programming) was evidence of increases in utilization of non-HIV services—such as outpatient visits, in-facility births or immunizations—that could be associated with varying levels of PEPFAR investments at the district level.Methods Uganda’s Health Management Information System article-based records were available from mid-2005 onwards. We visited all 112 District Health offices to collect routine monthly reports (which contain data aggregated from monthly facility reports) and annual reports (which contain data aggregated from annual facility reports). Counts of individuals on anti-retroviral therapy (ART) at year-end served as our primary predictor variable. We grouped district-months into tertiles of high, medium or low PEPFAR investment based on their total reported number of patients on ART at the end of the year. We generated incidence-rate ratios, interpreted as the relative rate of the outcome measure in relation to the lowest investment PEPFAR tertile, holding constant control variables in the model.Results We found PEPFAR investment overall was associated with small declines in service volumes in several key areas of non-HIV care (outpatient care for young children, TB tests and in-facility deliveries), after adjusting for sanitation, elementary education and HIV prevalence. For example, districts with medium and high ART investment had 11% fewer outpatient visits for children aged 4 and younger compared with low investment districts, incidence rate ratio (IRR) of 0.89 for high investment compared with low (95% CI, 0.85–0.94) and IRR of 0.93 for medium compared with low (0.90–0.96). Similarly, 22% fewer TB sputum tests were performed in high investment districts compared with low investment, [IRR 0.78 (0.72–0.85)] and 13% fewer in medium compared with low, [IRR 0.88 (0.83–0.94)]. Districts with medium and high ART investment had 5% fewer in-facility deliveries compared with low investment districts [IRR 0.95 for high compared with low, (91–1.00) and 0.96 for medium compared with low (0.93–0.99)]. Although not statistically significant, the rate of maternal deaths in high investment district-months was 13% lower than observed in low investment districts.Conclusions This study sought to understand whether PEPFAR, as a vertical programme, may have had a spill-over effect on the health system generally, as measured by utilization. Our conclusion is that it did not, at least not in Uganda.

Using Routine Health Information Data for Research in Low- and Middle-Income Countries: A Systematic Review

“…Many of the studies utilized the highly disaggregated nature of the data by using either facility or district level data, with the exception of two studies which modelled national trends 33,74 . Studies commonly applied strategies to account for temporal autocorrelation and the correlation between geographical units, including generalized linear models 58 , multi-level analysis 75,76 , and ordinary least-squares regression with adjustment for seasonality and lag 34,37,77 . Among studies that modelled multiple facilities or administrative regions, random effects were commonly applied to account for heterogeneity.…”

“…Studies of malaria, for example, commonly included climate data from satellites in their models to control for important temporal factors, for example precipitation, humidity, and temperature 77,78 . Other studies incorporated information from other national community surveys, health facility surveys, and program data as covariates 34,75 . While most studies controlled for potential confounders by including covariates in analytic models, one study on maternal health service applied propensity score matching to further remove biases from differences in covariate distribution 37 .…”

“…These strategies consisted of exclusion, imputation, interpolation, veri cation, and accounting for missing data in modeling. Exclusion of missing data was the most common practice, and among studies that used this technique, they excluded facilities from the analytic samples 38,41,45,52,65,91,[93][94][95][96][97][98] , restricted the study period based on explicit criteria 54,99 , or applied sensitivity analysis to compare various exclusion criteria 41,100,101 . Imputation methods varied from assigning speci c values to the missing observation 42,86,91,[102][103][104] , to various modeling strategies such as conditional autoregressive model 87 , generalized linear regression 103 , and iterative singular value decomposition 103 .…”

“…Some studies assumed data were missing at random, which was accounted for in speci c modeling methods such as mixed-effect models 65,103 . When the source of data could be reached, some studies also described verifying the missing information using registries where the original data were recorded 39,78,99,105,106 .…”

Health Management Information System (HMIS); Whose Data is it Anyway? Contextual Challenges

“…The emergence of bilateral agencies like global fund and Bill-gates with specific interest in HIV, Malaria and TB has almost seen most new infrastructure development in health care in Uganda focus on these three diseases. Most of the HIV/AIDS interventions are implemented in form of projects with a lot of data/ information demands most probably as donor requirement that a number of parallel reporting systems emerged [40,41].…”