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1 Health and Health Care Delivery in Developing Countries Michael Kremer Economics 1386 Fall 2006.

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Presentation on theme: "1 Health and Health Care Delivery in Developing Countries Michael Kremer Economics 1386 Fall 2006."— Presentation transcript:

1 1 Health and Health Care Delivery in Developing Countries Michael Kremer Economics 1386 Fall 2006

2 2 The Determinants of Mortality Cutler, Deaton, Llenas-Muney (2005) Life expectancy increased 30% last century Hunter-gatherers: 25 years England 1700: 37 years England : 41 years UK today: 77 years South Asia 2004: 63 years (WB WDI, 2006) Vietnam today: higher life expectancy (69 years) than US in 1900 (47 years) but US in 1900 had 10 times higher per capita income than Vietnam today (Kremer 2002) Sub-Saharan Africa 2004: 46 (WB WDI, 2006) Why? Possible explanations: Improved nutrition Public health Urbanization Vaccination Medical treatments Early life factors

3 3 The Determinants of Mortality (II) Still - differences between rich and poor countries Progress in health until HIV/AIDS Correlation between per capita income and mortality Correlation within countries: richer, better educated people live longer. Why? Unclear Access to medical care Resources for non-health care Differences in health-related behaviors Social structures, stress and health Authors: Scientific advance and technical progress Less on direct causality of income

4 4 Health Care Delivery in Rural Rajasthan Banerjee, Deaton and Duflo (2004) Survey of 100 hamlets in Udaipur, collaboration with Seva Mandir (NGO) and Vidhya Bhavan (schools group) Village survey, facility/traditional healer survey, weekly visit to facilities, and household/individual survey Very poor region Adult literacy: men 46%, women 11% 21% of households have electricity

5 5 Health Care Delivery in Rural Rajasthan (II) Poor health status >50% of men and women anemic 93% men and 88% women below US BMI cut-off for low nutrition High self-reported disease symptoms Health facility use Adults visit 0.51 times per month More for wealthier households 0.12 to public facilities, 0.11 to bhopas, 0.28 to private facilities 7% of household budget spent on health

6 6 Health Care Delivery in Rural Rajasthan (III) Public Health Care Facilities Official policy: one subcentre with one nurse per 3000 people, open 6 days per week, 6 hours per day, free care 3600 people per subcentre. One primary health centre (PHC) with 5.8 medical personnel and 1.5 doctors per 48,000 people 45% absenteeism in subcentre, 35% in larger PHCs Subcentres closed 56% of time, unpredictable 32% given injection, 6% given drip, 3% given test 75% report that visit made them feel better

7 7 Health Care Delivery in Rural Rajasthan (IV) Private Care Poor training as doctors 41% have no medical degree, 18% have no medical training, 17% did not graduate from high school 68% given injection, 12% given drip, 3% test 81% report that visit made them feel better Almost comparable costs even though public supposed to be free: Public: Rs 71 Private: Rs 84 Bhopa: Rs 61 Reported satisfaction compared with poor health outcomes suggests need for state involvement

8 8 Do Conditional Cash Transfers Improve Child Health? Gertler (2004) on PROGRESA Requirements for cash transfer: Clinic visits for infants, young children, pregnant/lactating women Vaccinations for infants Yearly clinic checkups for all family members Meetings on health, hygiene, nutrition for adults Illness rate of treatment children 39.5% lower than control group Treatment children almost 1 cm taller Treatment children 25.5% less likely to be anemic

9 9 Contracting for Health: Evidence from Cambodia Indu Bhushan Erik Bloom David Clingingsmith Rathavuth Hong Elizabeth King Michael Kremer Benjamin Loevinsohn Brad Schwartz

10 10 Contracting in Cambodia Many developing countries have mix of salaried government and private fee for service provision. Lots of problems: Weak incentives of government providers (high absence rates) Glucose drip problem: misalignment of private providers’ incentives with patients’ interest (asymmetric information) public health (inadequate attention to externalities from infectious disease, drug resistance) Lack of risk sharing In 1999, Cambodia tried experiment of tendering contracts for management of health services in certain districts Monitored performance against 8 targeted outcomes Increased public funding (offset by decreased private expenditure) District level contracting could potentially: Tie incentives to public health objectives Allow benchmark competititon Pool risks with limited adverse selection (in rural areas) But could also lead to diversion of effort from important non-targeted activities.

11 11 Estimating program impact: Call for bids in 8 districts, randomly chosen from 12 candidates Acceptable bids in only some districts IV with random assignment Small number of districts Clustering Randomization inference Average effects

12 12 Main results Delivery of targeted services improved Non-targeted services were unchanged Some evidence of health improvement Management of health centers improved. Patients shifted to public providers Effect on total spending (public and private) was neutral to negative.

13 13 Previous work Keller and Schwartz (2001) Bhushan, Keller, and Schwartz (2002) Schwartz and Bhushan (2004) Based on 2001 survey of districts where program actually implemented

14 14 Overview Background on project and context Model of health care provision Empirical methods Health services results Targeted outcomes Non-targeted outcomes Health outcomes Choice of provider Health facility management Consumer perception of care quality Public and private health spending Conclusion

15 15 Cambodian health context Political background Khmer Rouge Vietnamese-backed regime 1993 Elections; adoption of market economy 1998 End of fighting Health care system Government health worker salary ~85% GDP/cap; politicization of civil service, governance issues Boom in private medical practice after ‘93: government staff moonlighting drug sellers get about 33% of curative visits in 1997 Traditional understanding of health, disease Spending high; health service coverage poor Project runs Huge improvements in health over study period. Health center construction.

16 16 Contracting Project I Covered 11% of population Responsible for Minimum Package of Activities Targets on improvement of child and maternal health service coverage. Prevention oriented

17 17 Two program variants Contracting in (CI) Management authority, but can’t hire/fire, procure outside government Operating budget through government Contracting out (CO) Full control of staffing--hire and fire Full control of procurement Operating budget through ADB/WB loans

18 18 Bidding Fixed price per capita bids; increased public spending Technical criteria and price 3 districts got no technically acceptable bids

19 19 HR practices under contracting I Example: Peareng district, contracting in (CI) Facilities signed annual contracts with NGO, workers 3-mo subcontracts. Private practice banned. Staff motivation viewed as key problem Additional payment on top of government salary Fixed supplement, attendance, facility performance Staff incentives based on targeted outcomes, patient satisfaction, quality of care, and no fraud

20 20 HR practices under contracting II All contractors chose to use some expatriates. Between 0.5 and 3.0 expatriate staff per contracted district. Local staff between 90 and 150 per district.

21 21 HR practices under contracting III Additional compensation for workers in all treated districts Two officially banned private practice, three allowed it Contractor compensation choices Contracting in (CI): Base salary plus performance bonus, no provision for firing Contracting out (CO): high fixed salaries, with possibility of firing non-performers CO attracted some staff from outside district, outside government service

22 22 Randomization procedure Three provinces with 3, 4, and 5 treatment-eligible districts respectively Randomly assign CI, CO, and comparison district within each province. Remaining 3 districts randomized in capital Each district had equal probability of being CI, CO, comparison

23 23 Data Baseline household survey in 1997, follow-up in 2003; facility survey in randomly selected villages in each of 12 districts; 7-14 households per village randomly chosen in each survey year Household census, recent illnesses and treatment, program outcomes Follow-up included health service quality module

24 24 Targeted outcomes at baseline Baseline Percentage by Random Assignment Comp. Contracting In Contracting OutGoal Fully immunized child34%28%31%70% Children get Vitamin A42%46%41%70% Antenatal care9%11%13%50% Delivery by trained personnel24%27%32%50% Delivery in a health facility3%6%5%10% Use modern birth control method13%12%18%30% Knowledge of birth control22%27%20%70% Use of public health care facilities4% 3%Increase

25 25 Targeted outcomes at baseline Baseline Percentage by Random Assignment Comp. Contracting In Contracting OutGoal Fully immunized child34%28%31%70% Children get Vitamin A42%46%41%70% Antenatal care9%11%13%50% Delivery by trained personnel24%27%32%50% Delivery in a health facility3%6%5%10% Use modern birth control method13%12%18%30% Knowledge of birth control22%27%20%70% Use of public health care facilities4% 3%Increase

26 26 Randomization quality Will see baselines as go through each outcome Of baseline levels for 22 outcomes: three significant for each of CI and CO under clustering one significant for each of CI and CO under randomization inference

27 27 Holmstrom and Milgrom (1991) Model of health service provision Suppose targeted and non-targeted outcomes produced by exerting various kinds of costly effort Suppose only targeted outcome T is contractable; linear compensation contract in T Increasing incentives for T may lead to more or less NT, depending on whether effort types relevant for T and NT are complements or substitutes Either plausible

28 28 Econometric Issues Selection into treatment CO: 4 districts tendered, 3 awarded CI: 4 districts tendered, 2 awarded Previous analysis based on actual treatment status, not initial assignment Perhaps NGOs focused bids on districts where gains would be easiest Cluster-level intervention Clustering, randomization inference Family-level effects

29 29 Econometric Issues Selection into treatment CO: 4 districts tendered, 3 awarded CI: 4 districts tendered, 2 awarded Previous analysis based on actual treatment status, not initial assignment Perhaps NGOs focused bids on districts where gains would be easiest Cluster-level intervention Clustering, randomization inference Family-level effects

30 30 Empirical method I District-level intervention with individual outcomes Randomly-assigned eligibility an instrument for actual treatment. TOT for outcome k: Instruments:

31 31 Empirical method I District-level intervention with individual outcomes Randomly-assigned eligibility an instrument for actual treatment. TOT for outcome k: Instruments:

32 32 Empirical method II District-level intervention with individual outcomes Need to account for district level shocks Clustering may over-reject null with small number of clusters Randomization inference Create full set placebo random assignments using actual randomization process. (Rosenbaum 2002) Generate placebo treatment effect for each member of the set. Use distribution of placebo treatment effects as test distribution. Low power: imposes no structure on error

33 33 Randomization Inference

34 34 Empirical method III Average effect size (AES) for family of K outcomes Kling, Katz, Leibman, and Sonbanmatsu (2003), O’Brien (1984) Joint estimation of TOT for K outcomes Aggregate to get common unit of observation v VCM estimates cross-equation correlation of effects AES is the average treatment effect measured in standard deviation units. We use the standard deviation of the change in outcome for comparison group.

35 35 Empirical method III Average effect size (AES) for family of K outcomes Kling, Katz, Leibman, and Sonbanmatsu (2003), O’Brien (1984) Joint estimation of TOT for K outcomes Aggregate to get common unit of observation v VCM estimates cross-equation correlation of effects AES is the average treatment effect measured in standard deviation units. We use the standard deviation of the change in outcome for comparison group.

36 36 Change in District Averages I

37 37 Change in District Averages II

38 38 TOT for changes in targeted outcomes Notes: IV regressions including province X year fixed effects. Average effects are differential increases caused by treatment in units of baseline comparison-group standard deviations. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

39 39 TOT for changes in targeted outcomes Notes: IV regressions including province X year fixed effects. Average effects are differential increases caused by treatment in units of baseline comparison-group standard deviations. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

40 40 Robustness check: wealth controls Notes: All IV regressions in Panel B include province X year fixed effects and wealth controls. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

41 41 TOT for non-contracted outcomes Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

42 42 TOT for non-contracted outcomes Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

43 43 TOT for non-contracted outcomes Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

44 44 TOT for final health outcomes Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

45 45 TOT for final health outcomes Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

46 46 TOT for changes in care-seeking outcomes Notes: IV regressions with provinceXyear effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Average effect codes drug seller and traditional healer visits as negative and qualified private and public provider visits as positive.

47 47 TOT for health center management I Notes: All columns are IV regressions in levels with province fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

48 48 TOT for health center management II Notes: All columns are IV regressions in levels with province fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

49 49 AES for 11 health center management outcomes Health center open with patients All scheduled staff present Child delivery service available User fees clearly posted Number of supervisor visits Number of outreach trips Index of equipment installed and functional Index of drugs and other supplies available All childhood immunizations available Notes: Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

50 50 TOT for consumer perception of quality Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P- values for treatment effects computed by randomization inference. Treatment effects are in bold.

51 51 TOT for health care spending I Notes: All outcomes in 2003 USD per capita. All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

52 52 TOT for health care spending I Notes: All outcomes in 2003 USD per capita. All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

53 53 TOT for health care spending II Notes: All outcomes in 2003 USD per capita. All regressions include province fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

54 54 Effects of spending on health outcomes I

55 55 Effects of spending on health outcomes II

56 56 Conclusion I Increase in targeted outcomes of about one-half standard deviation. Improved management, particularly in contracting out. No evidence non-targeted outcomes decreased. Total spending neutral or decreased.

57 57 Conclusion II Institutional change plus increased public spending Only one of set of possible interventions Interesting for policy because feasible External validity difficult to assess Additional trials in post-conflict environments and others with serious health care delivery problems.

58 58 Intradistrict correlation and wealth controls in 1997

59 59 ITT effects on quantiles of individual spending Notes: Mean and quantile regressions with provinceXyear effects. Standard errors presented in parentheses are corrected for clustering at the district level using bootstrap. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

60 60 Results in a nutshell Contracting improved facility management: availability of 24h care, staff presence, supervision Large, positive and significant effects on targeted outcomes, no effects were significantly negative Non-targeted outcomes showed gains or no effect. No average effect. Increased use of public facilities, mostly at expense of drug sellers Contracted quality of care perceived as worse than comparison Effect on total spending neutral to negative

61 61 Public spending breakdown


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