1 Influence of PBF Indicators on Health Coverage Kathy Kantengwa M.D, MPA; PBF advisor, MSH Montreux, November 2010 Rwanda IHSS Project

2 Authors Ndizeye, Cedric, USAID/IHSSP, Rwanda De Naeyer, Ludwig, USAID/IHSSP, Rwanda Kantengwa, Kathy, USAID/IHSSP, Rwanda Collins, David, USAID/IHSSP, Rwanda Karengera, Steven, MOH, Rwanda

3 Outline  Background  Challenges/opportunities  Data analysis objective  What indicators defined  Results

4 Opportunities Global agenda:  MDG goals: addressing the needs of the poor and for specific health problems National Agenda: Vision 2020/PRSP-EDPRS goals (Poverty reduction papers)  Universal Health Coverage: reduce financial barriers to quality essential health services (minimum package) at all levels  Expand the offer of preventive health services: Exploit positive externalities  Health providers motivated

5 Data analysis objective Has PBF contributed to improvement in quantity and quality indicators related to MDG 4 – 5 goals? Methodology: Analysis of time-series and before-after evaluation of 3 different datasets (impact evaluation data, DHS data, Routine PBF indicators data)

6 What indicators are defined?  Health center output indicators: usually less than 15  14 maternal and child health PBF output indicators ­ A set of indicators consists of the number of visits to the facility ­ A set of indicators of the clinical content  Many quality indicators (checklist of )  Each indicator associated with a specific price, but quality indicators are modifiers  Eligibility for premium on HIV indicators subject to maintaining our improving primary care services (PEFAR to improve Health systems)

7 Focus on getting health impact through PBF By setting indicators which will increase the productivity and quality of care:  Identifying the few most critical interventions which, if delivered at the right time and place to the right people, will have the greatest impact  Pre-determine system requirements for achieving adequate scale  Identify bottlenecks across the system building blocks  target PBF efforts through indicators against those bottlenecks  Validate reported data including quality

8 PBF on MCH health coverage: Services data (PBF database, Rwanda)

9 Increase in Volume of Services (after 39 months) PBF IndicatorJanuary 2006 average/month/ health center ( 258 health centers on average) March 2009 average/month/ health center (297 health centers on average) Percentage increase (linear/log R2) Institutional Deliveries % (log 0.77) New Curative Consultations % (log 0.28) ANC new cases % (log 0.05) Family Planning new users % (linear 0.79) Family Planning users at the end of the month % (linear 0.98)

10

11

12 PBF on MCH health coverage: Impact evaluation WB (P. Basinga, P Getner & al, 2010: Paying Primary Health care centers for performance in Rwanda)

13 Estimated impact of PBF on maternal and child health care services

14 Trend of institutional delivery for treatment and control facilities

15 Trend of prenatal care quality between treatment and control facility ( ) 15

16 Rwanda health sector performance status: (Rwanda DHS data)

17 Progress based on DHS Source: Rwanda DHS 2005 and 2008 IndicatorsDHS-2000DHS- 2005DHS-2008 Contraceptive prevalence: All methods 17%36% Contraceptive prevalence: Modern methods4%10%27% Antenatal Care 94%96% Delivery in Health Centers26%39%52% Infant Mortality rate10786/1000 live births62/1000 live births Under-Five Mortality rate196152/1000 live births103/1000 live births Maternal Mortality rate1071 Anemia Prevalence : Children 56%48% Anemia Prevalence : Women 33%27% Malaria prevalence: Children -2.10% Malaria prevalence: Women -1.10% Vaccination : All 75%80.40% Vaccination : Measles 86%90% Fertility 6.1 children5.5 Children

18 Infant mortality rate (deaths per 1,000) % decrease over 2 years

19 Under 5 children mortality rate (deaths per 1,000) % decline over 2 years

20 Comparison of Maternal Mortality Ratio and Facility-Based Deliveries

21 Modern contraception prevalence (% year-old women) % increase over two years

22 Births attended by skilled health personnel (% of births) % increase over two years

23 Conclusion  PBF positively affects health coverage of preventive services  PBF is a systems strengthening reform that can help accelerate the achievement of MDGs 4 &5  Target for choice of indicators: few most critical interventions which, if delivered at the right time and place to the right people, will have the greatest impact

24 Lessons learnt  PBF can lead to: A significant increase in service production A significant increase in quality of services.  PBF service data are reliable for systems analysis or programs impact analysis  With PBF, Health facilities reports are complete, timely and accurate : improved HMIS  Clearly defined and agreed upon measurable goals must be linked to routine and transparent reporting with an effective system for validating data.  For services that depend more on patient behavior (4 ANC visits), need community interventions

25 Limitations  Denominator definition for routine data analysis (Change of the health pyramid?)  Routine data underestimated, private for profits sector health facilities are excluded

26 Data restrictions The data shown in this presentation should not be quoted without permission of the authors.

27 Click to edit Master title style Thank you