Abstract

BackgroundEvaluations of health systems strengthening (HSS) interventions using observational data are rarely used for causal inference due to limited data availability. Routinely collected national data allow use of quasi-experimental designs such as interrupted time series (ITS). Rwanda has invested in a robust electronic health management information system (HMIS) that captures monthly healthcare utilization data. We used ITS to evaluate impact of an HSS intervention to improve primary health care facility readiness on health service utilization in two rural districts of Rwanda.MethodsWe used controlled ITS analysis to compare changes in healthcare utilization at health centers (HC) that received the intervention (n = 13) to propensity score matched non-intervention health centers in Rwanda (n = 86) from January 2008 to December 2012. HC support included infrastructure renovation, salary support, medical equipment, referral network strengthening, and clinical training. Baseline quarterly mean outpatient visit rates and population density were used to model propensity scores. The intervention began in May 2010 and was implemented over a twelve-month period. We used monthly healthcare utilization data from the national Rwandan HMIS to study changes in the (1) number of facility deliveries per 10,000 women, (2) number of referrals for high risk pregnancy per 100,000 women, and (3) the number of outpatient visits performed per 1,000 catchment population.ResultsPHIT HC experienced significantly higher monthly delivery rates post-HSS during the April-June season than comparison (3.19/10,000, 95% CI: [0.27, 6.10]). In 2010, this represented a 13% relative increase, and in 2011, this represented a 23% relative increase. The post-HSS change in monthly rate of high-risk pregnancies referred increased slightly in intervention compared to control HC (0.03/10,000, 95% CI: [-0.007, 0.06]). There was a small immediate post-HSS increase in outpatient visit rates in intervention compared to control HC (6.64/1,000, 95% CI: [-13.52, 26.81]).ConclusionWe failed to find strong evidence of post-HSS increases in outpatient visit rates or referral rates at health centers, which could be explained by small sample size and high baseline nation-wide health service coverage. However, our findings demonstrate that high quality routinely collected health facility data combined with ITS can be used for rigorous policy evaluation in resource-limited settings.

Highlights

  • Health systems strengthening (HSS) interventions have become popular strategies to advance population health gains in low income countries [1,2,3,4,5]

  • Population Health Implementation and Training (PHIT) health centers (HC) experienced significantly higher monthly delivery rates post-health systems strengthening (HSS) during the AprilJune season than comparison (3.19/10,000, 95% confidence intervals (95% CI): [0.27, 6.10])

  • We failed to find strong evidence of post-HSS increases in outpatient visit rates or referral rates at health centers, which could be explained by small sample size and high baseline nation-wide health service coverage

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Summary

Introduction

Health systems strengthening (HSS) interventions have become popular strategies to advance population health gains in low income countries [1,2,3,4,5]. An underutilized resource that could be used to address this issue is the wealth of routinely collected service utilization data produced by national health management information systems (HMIS) in low income countries [8, 10]. Analysis using HMIS leverages health systems time series data that are already being used for management and improvement, while allowing for evaluation designs informed by principles of causal inference [11]. A potential reason for limited use of HMIS for HSS evaluations is the perception that data quality is poor [12, 13], despite several positive results following data quality assessments of these systems in low income countries [14,15,16].

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