Evaluating the Effects of Large Scale Health Interventions in
                   Developing Countries:
              The Z...
The Zambia Malaria Initiative


  Starting 2001, Zambia committed to large scale-up of malaria
   control and treatment

...
Why Zambia, Why Now?


  History of malaria control: big successes in post-World War II
  period using DDT
  WHO etc. vi...
Figure 1:  Malaria Deaths  

 6,000

 5,000

 4,000
                                                   malaria inpatient 
...
A Big Success
  Malaria deaths fell by half (2000-08) while population rose by 30%

  Similar decline for inpatient mala...
Our Paper


  Organize, clean, cross-check data
    o Apply our skills to help understand what is going on

  Study rela...
Data


  DHS 2001 and 2007. Standard data. Great timing!

  NMCC data on nets, spraying, anti-malarial drugs, etc.
    o...
The HMIS
  1995-2008, quarterly data
  Disease data (diagnosis, death, inpatient and outpatient), service
   delivery
 ...
Improvement of the HMIS

  Re-collect data that never made it into the national dataset

  systematically scanned for ou...
Changes in the HMIS

  Fill in of missing observations (about 4%)
  Corrections of errors (see table 1)
  Biggest examp...
Remaining Issues in the HMIS Data: Diagnosis and Access
  Mis diagnosis due to
    o Treating all fevers as malaria
     ...
Remaining Issues in HMIS Data: Extent of HMIS Coverage


 Not all cases (or even all deaths) enter the government system
...
HMIS vs. DHS: Under 5 Deaths
             HMIS under- 5 times          DHS under- HMIS
             five deaths column 1  ...
Figure 3: Deaths by Province in DHS vs. HMIS
                         180           50




                               ...
Figure 4: Mortality Changes: HMIS vs. DHS
                        0
                                                      ...
Remaining Issues in HMIS Data: Non-Reporting Facilities


 Many zero values may be non-reports

 Two ways to deal with t...
Figure 2: Deaths per 1,000 Children Under 5, HMIS
               10.00                                                    ...
Figure 5: Malaria Cases and Deaths, Chained Index
 160

 140

 120

                                                      ...
Figure 6: Ratio of Malaria to Non-Malaria Mortality
         0.7


         0.6


         0.5


         0.4             ...
Seasonality in Deaths – full period
                                  0

                                ‐0.1
 deaths per ...
Seasonal Malaria Mortality

                         0


                       ‐0.1
 Deaths per Thousand




            ...
Seasonal in All-Cause Mortality
                         0

                       ‐0.1

                       ‐0.2

    ...
Elements of program
  Treated bednets (more than half of 2008 budget)
  Indoor Residual Spraying
  artemisinin-based co...
Number of
                       Population covered
          bednets                               RDT Distributed
      ...
Nets distributed per  Percentage of children   Percentage of 
                 person between 2001  in households owning  ...
Fraction of
                                     Percentage of
                   population                          Urba...
Assessing the Link from Rollout to Incidence


   Want to learn the structural effect of inputs (nets, spraying, etc.) on...
Table 7: Bednets, child fever and child diarrhea, DHS 
 

Dependent variable            Child had fever over last two week...
Table 9:  Control for baseline level in micro‐level regression, DHS 

Dependent variable           Child had fever over la...
Table 11: Bednets and Death of Child in last 5 years 
                                    (1)          (2)         (3)
HH ...
Table 13 B: ITN Distribution and Malaria Relative to Population

                      Malaria      Malaria      Other    ...
Table 14: IRS Results, DHS 

Dependent variable                      Child had fever over last two weeks
                 ...
IRS in the HMIS (Table 15A)
                     Malaria Malaria     Other     Malaria     Malaria     Other
             ...
Table 15 B (Nets and Spraying Adjusted by Population)
                   Malaria     Malaria   Other   Malaria      Malari...
Figure 8: Health Facilities and Spraying in the Chingola District 2008




Green crosses represent health facilities, blac...
Conclusions



   Anti-malaria campaign has been a huge success

   Other dimensions of health push also huge success

 ...
Future direction for research
   How does malaria (or health more generally) affect economic outcomes?
     o Macarthur a...
Sustainability and Further Progress



      This is not eradication (yet?)

      Maintaining 75% reduction much harder...
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Project 3 Final Slides Ashraf Fink Weil

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Project 3 Final Slides Ashraf Fink Weil

  1. 1. Evaluating the Effects of Large Scale Health Interventions in Developing Countries: The Zambian Malaria Initiative Nava Ashraf, Harvard University and NBER Günther Fink, Harvard University David N. Weil, Brown University and NBER December 2009
  2. 2. The Zambia Malaria Initiative  Starting 2001, Zambia committed to large scale-up of malaria control and treatment  Large commitment of domestic and donor resources  Goal: 75% reduction in malaria incidence, 20% reduction in under-five mortality
  3. 3. Why Zambia, Why Now?  History of malaria control: big successes in post-World War II period using DDT  WHO etc. viewed Africa as too difficult  Within Zambia: Success against malaria in post-independence, following by massive backsliding  Maturation of new technologies (treated nets, ACT, RDT)  Donor focus  Desire for a big win as demonstration  Institutional capacity, political commitment, favorable climate
  4. 4. Figure 1:  Malaria Deaths   6,000 5,000 4,000 malaria inpatient  3,000 deaths under 5 malaria inpatient  2,000 deaths 5 and over 1,000 0 2000 2002 2004 2006 2008 Source: HMIS
  5. 5. A Big Success  Malaria deaths fell by half (2000-08) while population rose by 30%  Similar decline for inpatient malaria visits  DHS 2001-2007: o fever previous two weeks (under 5) fell from 45% to 18% o under five mortality fell from 168 to 119 (not all from malaria)  25,000 children’s lives saved per year  HDI equivalent: 25% growth of income per capita
  6. 6. Our Paper  Organize, clean, cross-check data o Apply our skills to help understand what is going on  Study relation of inputs (nets distributed, houses sprayed, etc.) and outputs (health outcomes) o “bang for buck” o Need for caution in doing this!  Use Zambian experiment to understand economic effects of malaria and its control
  7. 7. Data  DHS 2001 and 2007. Standard data. Great timing!  NMCC data on nets, spraying, anti-malarial drugs, etc. o NMCC takes strong hand in centralizing and coordinating NGO activities  Health Management Information System (HMIS)
  8. 8. The HMIS  1995-2008, quarterly data  Disease data (diagnosis, death, inpatient and outpatient), service delivery  All MOH facilities from hospitals to health posts (except level 3 referral hospitals).  Data passed from facility (1,554)  district (72)  province (9)  Lusaka  Cleaned/checked at district and province levels  Opportunities for error: o Varying quality of record keeping at facility level o Data entry (only once, no consistency checks) o Only most recent quarter appended to central data set; updates, corrections missed
  9. 9. Improvement of the HMIS  Re-collect data that never made it into the national dataset  systematically scanned for outliers and suspicious data points (duplicate figures, significant variance between quarters or years, reporting inconsistencies)  District health officials were asked to find missing reports and justify all irregular data  9 provincial data workshops, total cost $200,000; 250 total attendees  Not only (or mostly) data improvement: also capacity building, analysis of impact of health interventions.
  10. 10. Changes in the HMIS  Fill in of missing observations (about 4%)  Corrections of errors (see table 1)  Biggest example: change in under-five malaria deaths 2006-2007 o Initial: rose by 13% o Corrected: fell by 18%
  11. 11. Remaining Issues in the HMIS Data: Diagnosis and Access  Mis diagnosis due to o Treating all fevers as malaria  Fell with introduction of RDTs – bias in trend o Stigma leads to HIV deaths reported as malaria – bias in level or trend  Abolition of user fees for adults in rural facilities: spike in outpatient visits that year  To minimize all these biases: we look at inpatient cases, malaria deaths, total deaths
  12. 12. Remaining Issues in HMIS Data: Extent of HMIS Coverage  Not all cases (or even all deaths) enter the government system  What if this is non-representative or changes over time? o HMIS better in urban than rural? Miss much malaria mortality. o Program rolled out best near HMIS reporting facilities?
  13. 13. HMIS vs. DHS: Under 5 Deaths HMIS under- 5 times DHS under- HMIS five deaths column 1 five mortality deaths as % per 1,000 per 1,000 of DHS deaths 2001 8.63 43.2 168 25.7% 2007 5.08 25.4 119 21.3% % change 41% 29%  HMIS gets only 20-25% of total deaths!  DHS mortality measured in 2007 is for 2003-2007: so too high for 2007  HMIS decline in mortality 2001 to average 2003-07 is exactly 29%
  14. 14. Figure 3: Deaths by Province in DHS vs. HMIS 180 50 HMIS Under 5 Deaths per 1000 times 5 160 45 40 DHS Under 5 Mortality 140 35 120 30 100 25 80 20 60 DHS 2007 15 HMIS 2007 40 10 20 5 0 0
  15. 15. Figure 4: Mortality Changes: HMIS vs. DHS 0 Copperbelt Lusaka Change in child mortality DHS 2001 - 2007 Eastern -.1 Northern North-Western -.3 -.2 Western Southern Luapula Central -.4 -.6 -.5 -.4 -.3 -.2 -.1 Change in child mortality HMIS 2001 - 2007
  16. 16. Remaining Issues in HMIS Data: Non-Reporting Facilities  Many zero values may be non-reports  Two ways to deal with this: o Sample of “always reporting facilities” o Construct chain-index
  17. 17. Figure 2: Deaths per 1,000 Children Under 5, HMIS 10.00 2.50 9.00 8.00 2.00 7.00 High Quality Sample 6.00 1.50 Full Sample 5.00 4.00 1.00 3.00 full sample 2.00 0.50 high quality sample 1.00 0.00 0.00 2000 2001 2002 2003 2004 2005 2006 2007 2008
  18. 18. Figure 5: Malaria Cases and Deaths, Chained Index 160 140 120 Outpatients Under 5 100 Outpatients 5+ 80 Inpatients Under 5 Inpatients Over 5 60 Deaths_O5 40 Deaths Over 5 20 0 2000 2001 2002 2003 2004 2005 2006 2007 2008
  19. 19. Figure 6: Ratio of Malaria to Non-Malaria Mortality 0.7 0.6 0.5 0.4 under 5, all facilities Ratio under 5, always reporting 0.3 5+, all facilities 5+, always reporting 0.2 0.1 0 2000 2002 2004 2006 2008
  20. 20. Seasonality in Deaths – full period 0 ‐0.1 deaths per thousand children ‐0.2 ‐0.3 ‐0.4 all under 5 deaths ‐0.5 malaria deaths ‐0.6 ‐0.7 ‐0.8 1 2 3 4 Quarter
  21. 21. Seasonal Malaria Mortality 0 ‐0.1 Deaths per Thousand ‐0.2 ‐0.3 post pre ‐0.4 ‐0.5 ‐0.6 1 2 3 4 Quarter
  22. 22. Seasonal in All-Cause Mortality 0 ‐0.1 ‐0.2 ‐0.3 Deaths per Thousand ‐0.4 ‐0.5 post pre ‐0.6 ‐0.7 ‐0.8 ‐0.9 ‐1 1 2 3 4
  23. 23. Elements of program  Treated bednets (more than half of 2008 budget)  Indoor Residual Spraying  artemisinin-based combination therapy (ACT)  Rapid Diagnostic Testing  IPT in pregnancy  Big contemporaneous push on HIV, tuberculosis, and child health!
  24. 24. Number of Population covered bednets RDT Distributed by spraying distributed 2002 112,020 - 0 2003 557,071 324,137 0 2004 176,082 679,582 0 2005 516,999 1,163,802 172,257 2006 1,163,113 2,836,778 25,700 2007 2,446,102 3,286,514 243,600 2008 964,748 5,558,822 2,015,500
  25. 25. Nets distributed per  Percentage of children  Percentage of  person between 2001  in households owning  children sleeping  and 2007 DHS  at least one net 2007  under net 2007  Central  0.15 0.68  0.37 Copperbelt  0.12 0.74  0.43 Eastern  0.12 0.71  0.37 Luapula  0.43 0.86  0.74 Lusaka  0.16 0.68  0.30 Northern  0.15 0.57  0.41 North‐Western  0.39 0.73  0.43 Southern  0.22 0.60  0.25 Western  0.64 0.87  0.55 Total  0.26 0.72  0.43
  26. 26. Fraction of Percentage of population Urbanization children in 2007 officially covered (2000) DHS living in Province by spraying in sprayed households 2006 Central 0.12 0.12 .24 Copperbelt 0.63 0.41 .78 Eastern 0.00 0.02 .09 Luapula 0.00 0.01 .13 Lusaka 0.73 0.29 .82 Northern 0.00 0.04 .14 North- Western 0.09 0.14 .12 Southern 0.16 0.13 .21 Western 0.00 0.02 .12    
  27. 27. Assessing the Link from Rollout to Incidence  Want to learn the structural effect of inputs (nets, spraying, etc.) on outputs (disease, death)  Treatment is not randomly applied o Resources pushed to areas in need (or forecast need) o modalities chosen in optimizing fashion o Efficacy of local staff important omitted variable (field works says)  Can we sign the biases? (current conditions, health staff efficacy, forecast conditions)  Identifying variation comes from o Deviation from optimal plan, random events o Discontinuities in response function (e.g. IRS rollout; ACT stockouts; bednets in 2008?)
  28. 28. Table 7: Bednets, child fever and child diarrhea, DHS    Dependent variable Child had fever over last two weeks (1) (2) (3) (4) HH owns bednet -0.0213* -0.921*** (0.0111) (0.267) slept under net -0.0106 (0.0110) Bednet distribution pc -0.209*** (0.0487) Observations 11193 11027 11193 11193 R-squared 0.129 0.128 0.131 -0.513    Placebo test with diarrhea  
  29. 29. Table 9:  Control for baseline level in micro‐level regression, DHS  Dependent variable Child had fever over last two weeks (1) (2) (3) (4) HH owns bednet -0.0141 -0.695 (0.0105) (0.496) Child slept under net -0.00428 (0.00895) Bednet distribution -0.104*** (0.0364) Baseline fever 0.867*** 0.888*** 0.806*** 0.393 prevalence (0.0944) (0.0933) (0.0973) (0.400) Observations 11193 11027 11193 11193 R-squared 0.136 0.135 0.136 -0.229  
  30. 30. Table 11: Bednets and Death of Child in last 5 years  (1) (2) (3) HH owns bednet -0.00968 (0.00690) Kids in HH slept with -0.0486*** (0.00608) ITN district coverage -0.0443* (0.0255) Female -0.0199*** -0.0199*** -0.0199*** (0.00538) (0.00535) (0.00539) Observations 13201 13201 13201 R-squared 0.032 0.036 0.032    Full coverage reduces deaths by 4.4 percentage points
  31. 31. Table 13 B: ITN Distribution and Malaria Relative to Population Malaria Malaria Other Malaria Malaria Other inpatients deaths per deaths inpatients deaths per deaths per per 1000 1000 per 1000 per 1000 1000 1000 children children children children children children under 5 under 5 under 5 under 5 under 5 under 5 (1) (2) (3) (4) (5) (6) Nets per capita 6.088 -0.121 -1.543 (9.872) (0.309) (1.102) L1 nets per capita -26.25*** -0.778*** -0.709 -30.14** -0.852** -1.797* (9.279) (0.271) (0.769) (12.74) (0.382) (1.077) L2 nets per capita -33.50 -0.0370 -3.839** (36.40) (0.817) (1.557) Observations 573 573 573 501 501 501 R-squared 0.811 0.634 0.744 0.824 0.637 0.771
  32. 32. Table 14: IRS Results, DHS  Dependent variable Child had fever over last two weeks (1) (2) (3) (4) Percentage of district population 0.102*** sprayed (0.0192) Household sprayed (self-report) 0.0482** -0.0162 (0.0195) (0.0199) Fraction of households sprayed in -0.00778 Cluster (0.0394) 2nd wave dummy -0.283*** -0.257*** (0.0122) (0.0108) Observations 11524 11523 5671 5672 R-squared 0.123 0.121 0.047 0.046
  33. 33. IRS in the HMIS (Table 15A) Malaria Malaria Other Malaria Malaria Other inpatient deaths deaths inpatients deaths deaths s under under 5 under 5 under 5 under 5 under 5 5 (1) (2) (3) (4) (5) (6) Spraying target -241.5 -22.57* 0.539 -308.9* -24.72** -0.278 Dummy (189.1) (12.15) (17.62) (176.4) (12.12) (17.28) Lag 1 Bed nets in -9.351*** -0.298*** -0.113 thousands (2.324) (0.0702) (0.147) Observations 573 573 573 573 573 573 R-squared 0.866 0.760 0.905 0.873 0.766 0.905
  34. 34. Table 15 B (Nets and Spraying Adjusted by Population) Malaria Malaria Other Malaria Malaria Other inpatients deaths deaths inpatients deaths deaths per 1000 per 1000 per 1000 per 1000 per 1000 per 1000 children children children children children children under 5 under 5 under 5 under 5 under 5 under 5 (1) (2) (3) (4) (5) (6) Fraction 6.199 -0.416 0.792 2.526 -0.558 0.722 Sprayed (9.660) (0.370) (0.559) (9.760) (0.372) (0.543) Nets per capita -25.38*** -0.984*** -0.484 (9.548) (0.257) (0.704) Observations 573 573 573 573 573 573 R-squared 0.809 0.656 0.787 0.811 0.661 0.787
  35. 35. Figure 8: Health Facilities and Spraying in the Chingola District 2008 Green crosses represent health facilities, black dots sprayed structures. Grey lines are district boundaries.
  36. 36. Conclusions  Anti-malaria campaign has been a huge success  Other dimensions of health push also huge success  Cleaned up HMIS useful tool for tracking rollout and impact  Input->outcome results: very tentative evidence that we see nets working better than spraying
  37. 37. Future direction for research  How does malaria (or health more generally) affect economic outcomes? o Macarthur and Sachs o Acemoglu and Johnson o Ashraf, Lester, and Weil  Zambia provides good identifying variation because o Impetus for campaign was (largely) exogenous o Regional variations in rollout partly random o Possible to identify other random shocks  Issues to study o Fertility (rural TFR rose from 6.9 to 7.5, urban flat at 4.0) o Labor productivity o education
  38. 38. Sustainability and Further Progress  This is not eradication (yet?)  Maintaining 75% reduction much harder than maintaining 100%  Resource demands will remain high  Always danger of relapse

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