TY - JOUR
T1 - Impact of a district-wide health center strengthening intervention on healthcare utilization in rural Rwanda
T2 - Use of interrupted time series analysis
AU - Iyer, Hari S.
AU - Hirschhorn, Lisa R.
AU - Nisingizwe, Marie Paul
AU - Kamanzi, Emmanuel
AU - Drobac, Peter C.
AU - Rwabukwisi, Felix C.
AU - Law, Michael R.
AU - Muhire, Andrew
AU - Rusanganwa, Vincent
AU - Basinga, Paulin
N1 - Funding Information:
This study was completed as part of training in interrupted time series analysis developed and led by MRL and sponsored by funds from the Doris Duke Charitable Foundation’s African Health Initiative. The authors thank the Rwanda Ministry of Health for providing expertise in using their national health management information system data. We also thank Partners In Health, the Brigham and Women’s Hospital and Harvard Medical School for their logistical and technical support with this research study. We gratefully acknowledge the financial support from the Doris Duke Charitable Foundation’s African Health Initiative, through PHIT Partnership funding for implementation of this study (Grant number: 2009P001941). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. HSI was supported in part by the National Institutes of Health research training grant (NIH, T32 CA 009001). Dr. Law received salary support through a Canada Research Chair and a Michael Smith Foundation for Health Research Scholar Award.
Publisher Copyright:
© 2017 Iyer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/8
Y1 - 2017/8
N2 - Background: Evaluations 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. Methods: We 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. Results: PHIT 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]). Conclusion: 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. 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.
AB - Background: Evaluations 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. Methods: We 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. Results: PHIT 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]). Conclusion: 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. 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.
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U2 - 10.1371/journal.pone.0182418
DO - 10.1371/journal.pone.0182418
M3 - Article
C2 - 28763505
AN - SCOPUS:85026528478
VL - 12
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 8
M1 - e0182418
ER -