Person:

Iyer, Hari

Background: Performance-based financing (PBF) has demonstrated a range of successes and failures in improving health outcomes across low- and middle-income countries. Evidence indicates that the success of PBF depends, in large part, on the model selected, in relation to a variety of contextual factors. Objective: Partners In Health∣Inshuti Mu Buzima aimed to evaluate health outcomes associated with a novel capacity-building model of PBF at health centers throughout Kirehe district, Rwanda. Design: Thirteen health centers in Kirehe district, which provide healthcare to a population of over 300,000 people, agreed to participate in a PBF initiative scheme that integrated data feedback, quality improvement coaching, peer-to-peer learning, and district-level priority setting. Health centers’ progress toward collectively agreed upon site-specific health targets was assessed every 6 months for 18 months. Incentives were awarded only when health centers met goals on all three priorities health centers had collectively agreed upon: 90% coverage of community-based health insurance, 70% contraceptive prevalence rate, and zero acute severe malnutrition cases. Improvement across all four time points and facilities was measured using mixed-effects linear regression. Findings: At 6-month follow-up, 4 of 13 health centers had met 1 target. At 12-month follow-up, 7 centers had met 1 target, and by 18-month follow-up, 6 centers had met 2 targets and 2 centers had met all 3. Average health center performance had improved significantly across the district for all three targets: mean insurance coverage increased from 68% at baseline to 93% (p<0.001); mean number of acute malnutrition cases in the previous 6 months declined from 24 to 5 per facility (p<0.001); and contraceptive prevalence increased from 42 to 59% (p<0.001). A number of innovative improvement initiatives were identified. Conclusion: The combining of PBF, district engagement/support, and peer-to-peer learning resulted in significant improvements despite resource constraints and is now being considered as a model for scale-up in other districts of Rwanda.

Background: Nearly a quarter of a million children have acquired HIV, prompting the implementation of new protocols—Option B and B+—for treating HIV+ pregnant women. While efficacy has been demonstrated in randomized trials, there is limited real-world evidence on the impact of these changes. Using longitudinal, routinely collected data we assessed the impact of the adoption of WHO Option B in Rwanda on mother to infant transmission. Methods: We used interrupted time series analysis to evaluate the impact of Option B on mother-to-child HIV transmission in Rwanda. Our primary outcome was the proportion of HIV tests in infants with positive results at six weeks of age. We included data for 20 months before and 22 months after the 2010 policy change. Results: Of the 15,830 HIV tests conducted during our study period, 392 tested positive. We found a significant decrease in both the level (-2.08 positive tests per 100 tests conducted, 95% CI: -2.71 to -1.45, p < 0.001) and trend (-0.11 positive tests per 100 tests conducted per month, 95% CI: -0.16 to -0.07, p < 0.001) of test positivity. This represents an estimated 297 fewer children born without HIV in the post-policy period or a 46% reduction in HIV transmission from mother to child. Conclusions: The adoption of Option B in Rwanda contributed to an immediate decrease in the rate of HIV transmission from mother to child. This suggests other countries may benefit from adopting these WHO guidelines.

Background: Integrated management of childhood illness (IMCI) can reduce under-5 morbidity and mortality in low-income settings. A program to strengthen IMCI practices through Mentorship and Enhanced Supervision at Health centers (MESH) was implemented in two rural districts in eastern Rwanda in 2010. Methods: We estimated cost per improvement in quality of care as measured by the difference in correct diagnosis and correct treatment at baseline and 12 months of MESH. Costs of developing and implementing MESH were estimated in 2011 United States Dollars (USD) from the provider perspective using both top-down and bottom-up approaches, from programmatic financial records and site-level data. Improvement in quality of care attributed to MESH was measured through case management observations (n = 292 cases at baseline, 413 cases at 12 months), with outcomes from the intervention already published. Sensitivity analyses were conducted to assess uncertainty under different assumptions of quality of care and patient volume. Results: The total annual cost of MESH was US$ 27,955.74 and the average cost added by MESH per IMCI patient was US$1.06. Salary and benefits accounted for the majority of total annual costs (US$22,400 /year). Improvements in quality of care after 12 months of MESH implementation cost US$2.95 per additional child correctly diagnosed and $5.30 per additional child correctly treated. Conclusions: The incremental costs per additional child correctly diagnosed and child correctly treated suggest that MESH could be an affordable method for improving IMCI quality of care elsewhere in Rwanda and similar settings. Integrating MESH into existing supervision systems would further reduce costs, increasing potential for spread.

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.

Strong primary health care systems are essential for implementing universal health coverage and fulfilling health rights entitlements, but disagreement exists over how best to create them. Comparing countries with similar histories, lifestyle practices, and geography but divergent health outcomes can yield insights into possible mechanisms for improvement. Rwanda and Burundi are two such countries. Both faced protracted periods of violence in the 1990s, leading to significant societal upheaval. In subsequent years, Rwanda’s improvement in health has been far greater than Burundi’s. To understand how this divergence occurred, we studied trends in life expectancy following the periods of instability in both countries, as well as the health policies implemented after these conflicts. We used World Bank Development Indicators to assess trends in life expectancy in the two countries and then evaluated health policy reforms using Walt and Gilson’s framework. Following both countries’ implementation of health sector policies in 2005, we found a statistically significant increase in life expectancy in Rwanda after adjusting for GDP per capita (14.7 years, 95% CI: 11.4–18.0), relative to Burundi (4.6 years, 95% CI: 1.8–7.5). Strong public sector leadership, investments in health information systems, equity-driven policies, and the use of foreign aid to invest in local capacity helped Rwanda achieve greater health gains compared to Burundi.