Susan stata.project-village midwives

163 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
163
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Susan stata.project-village midwives

  1. 1. Susan Chen03/25/2011 Bidan Desa: Did It Work?I. Research Design Do government efforts to provide health care have an impact on the populations that theprograms target? This study explores the question in the context of Indonesia, analyzing the effects ofthe Village Midwife (or Bidan Desa) program that began in the early 1990s. Analysis is conducted todetermine if provision of midwife services to reproductive age women would affect health outcomes. The Village Midwife program places trained midwives in villages and townships in an effortto increase women’s access to reproductive health care. Village midwives have a number of duties,including provision of health and family planning services, promoting community participation inhealth, working with traditional birth attendants, and referring complicated cases to health clinics andhospitals (Government of India, 1989). The quasi-experiment focuses on rural regions and uses data from the government-sponsoredlongitudinal Indonesia Family Life Survey (IFLS). The IFLS is a panel survey of individuals, andhouseholds. The first round of data (IFLS1) was collected in 1993 and included interviews with 7,224households and with 22,347 individuals within those households (Frankenberg, 2001). The analysislooks at a sample of 8824 individuals in 13 provinces. This large sample size should allow forsignificant and robust analysis. Thirty-nine percent of participants (2926) were women between ages19-45 (reproductive age), 22 percent (2017) were women over age 45, 25 percent (2132) were menbetween ages 19-45, and 20 percent (1749) were men over age 45. In 1997, a survey (IFLS2) wasconducted to re-interview all IFLS1 individuals (International Household Survey Network, 2009). The dependent variable in these analyses is health status, or the difference between the BodyMass Index in 1997 and BMI in 1993. Positive values of the dependent variable represent a positive 1  
  2. 2. change in health status between 1993 and 1997.II. Effect of Having Midwife on Average BMI From 1993 to 1997, all villages—with and without midwives—showed statistically significantoverall gains in BMI. Thus, there is an overall improvement in health from 1993 to 1997. Surprisingly, villages without midwives showed a significantly higher BMI than villages withmidwives. This appears to indicate that villages with midwives reduce BMI, and therefore aredetrimental to improving health outcomes. However, this conclusion neglects to consider the fact thatmidwives were assigned to rural areas far away from health centers so the health of the populationtended to be worse than that of the urban population. As a result, these areas may already have a lowBMI to begin with (selection bias). The communities that gained a Village Midwife may be morelikely to have residents with low income and education or low levels of socioeconomic development.The baseline characteristics (individual and community) may vary. The ability to make a causal claim from this data alone is weak. First, while BMI is oneimportant health statistic, it is not adequate in itself as an overall health indicator. Statistics shouldalso take into account disease prevalence, for example. Thus, there is a construct validity problemwith using BMI to indicate health status. The study analysis also fails to consider omitted variablesthat can affect BMI; for example, the changing economic environment and shift in food prices. Because the Village Midwife program is not the only aspect of the health environment thatmay have changed during the 1990s, it is important to control for other dimensions of the healthservice environment, and for levels of infrastructure more generally. Therefore, the study hasconstructed measures for health access. Access to the Village Midwife program is measured aswhether or not a Village Midwife was available in each year of the survey. Access to public clinics ismeasured as the distance from the community to the nearest health center. Access to outreach efforts 2  
  3. 3. was measured by a variable indicating whether or not the community receives monthly visits fromhealth center staff. Physical infrastructure is measured by whether the community’s main road ispaved and whether a public phone is located in the community.III. Changes in BMI 1993-1997 In considering how access to services affects behaviors or outcomes, one must consider thatservices may be systematically placed to increase the likelihood that they reach people with particularcharacteristics. For example, health centers may be targeted toward areas where the population ispoor. To address the issue of non-random placement of midwives, this study examines change inhealth status as a function of whether the community gained a Village Midwife. A difference in difference analysis shows no significant difference between villages thatgained a midwife and those that did not. Both villages increased BMI over the years. A difference indifference analysis, by measuring changes in BMI, helps control for the fact that the villages selectedto receive midwives may have different initial health characteristics than those that did not. This pre-post non-equivalent group design reduces, but does not eliminate, threats to internalvalidity. For example, suppose national events affected urban areas differently than rural areas, BMIdifferences can still reflect more than just the treatment effect. Furthermore, the construct validityproblem with using BMI to indicate health status lingers. Table 1: Changes in BMI and Changes in Midwife Status Midwife in Village No Midwife in Village Differences in BMIMean BMI 1993 20.83 (0.09) 21.37 (0.04) -.55** (0.1)(SE in parenthesis) 1997 21.10 (0.05) 22.41 (0.06) -1.31** (0.08) Gained Midwife Did Not Gain Midwife Difference in DifferencesChange in BMI +0.33** +0.37** -0.04from 1993-1997 (0.03) (0.02) (0.03)**Significant at 5% level 3  
  4. 4. IV. Effects of Gaining a Midwife on BMI Changes Among Different Demographic Groups Indonesia’s midwivery program targets women of reproductive age. Therefore, healthoutcomes (positive BMI changes) are expected to have largest effect on this subgroup with theintroduction of midwives. After conducting an OLS regression of 4 subgroups (women age 45 andyounger, older than 45, men age 45 and younger, older than 45), the results suggest that the VillageMidwife program has positively affected the health status of the group toward whom it is targeted:women of reproductive age (age 45 and younger). The fact that the positive effects of the program are limited to reproductive-age women addsstrength to the argument that the association is causal, rather than arising from some other factor thathas changed concurrently with changes in access to Village Midwives (if such a factor were drivingthe improvements in health status, it would likely affect both women and men, rather than onlyreproductive-age women). Significant tests confirm that among subgroups gaining a midwife, the only significantdifferences appear between young reproductive-age women and all the other subgroups. For womenolder than 45, there is no impact on BMI of gaining a midwife. Nor is there any impact on BMI ofgaining a midwife for men. The interaction between gaining a Village Midwife and age is negative,which implies that the health-enhancing effects of Village Midwives decline as women get older.This finding in intuitive, since the services that Village Midwives typically offer (prenatal, delivery,family planning services), are particularly relevant for younger women. This pre-post non-equivalent group design reduces threats to internal validity and makes amore confident statement of causality. However, some variables like disease may affect differentgroups differently so causation claims are not unassailable. 4  
  5. 5. Table 2: Changes In BMI Among Different Subgroups (SE in parenthesis) BMI Change BMI Change BMI Change Adjusted Adjusted for for Individual and Individual Community Characteristics Characteristics Did Not Gain Midwife Women Age Over 45 -0.59** -0.55** -0.59** N=2017 (0.06) (0.06) (0.06) Men Age 45 and Below -0.31** -0.32** -0.31** N=2132 (0.06) (0.06) (0.06) Men Age Over 45 -0.63** -0.63** -0.65** N=1749 (0.07) (0.07) (0.07) Gained a Midwife Women Age 45 and Below +0.11*** +0.15** +0.13** N=1308 (0.06) (0.06) (0.06) Women Age Over 45 -0.20** -0.20** -0.19** N=929 (0.09) (0.09) (0.09) Men Age 45 and Below -0.20** -0.21** -0.19** N=964 (0.09) (0.09) (0.09) Men Age Over 45 -0.26** -0.26** -0.26** N=820 (0.10) (0.10) (0.10) Reference Group Women Age 45 and Below 0.70 0.56 0.56 Who Did Not Gain A (0.04) (0.05) (0.10) Midwife, N=2926**BMI change is significant at 0.05 level, ***BMI change is significant at 0.10 levelThe dependent variable is specified as BMI(97)- BMI(93).V. Midwife Gains and BMI Changes, Controlled for Individual Characteristics Correlations at a point in time between characteristics of the health service environment andhealth outcomes will be biased by failure to address the individual characteristics of the midwives.By examining changes in health at the individual level as a function of changes in health programs,the researchers hold constant aspects of the community in which an individual lives that may affectboth access to services and health status. I further explored whether the effect of gaining a Village Midwife varies across subgroups,adjusting for individual effects. In this analysis, after controlling for individual effects such as 5  
  6. 6. income and education, young women gaining midwives are still the subgroup that gains significantlymore than any other subgroup. These results further strengthen our causal claim because individualdifferences are accounted for.VI. Midwife Gains and BMI Changes, Controlled for Individual & Community Characteristics A skeptic might argue that close proximity to a health care center or living in a communitythat has a monthly visit from health center staff might affect health outcomes positively. Levels ofsocioeconomic development vary by whether the community has a public phone and whether themain road in the community is paved. The next analysis controls for both individual and the broadercommunity effects. The results from this analysis further strengthen our causal claim in that young womengaining midwives are still the subgroup that gains significantly more than any other subgroup, evenwhen adjusted for village-by-village differences in infrastructure. In combination, the results from Parts IV, V, and VI indicate a strong causal claim betweengaining a midwife and increases in BMI among young women. The Village Midwife program wasimplemented to address concerns about maternal health. To this extent, the study achieved itsintended affect. Accounting for individual and community characteristics, the study mitigates biasedestimates of the impact of services. The results suggest that efforts of the Ministry of Health torapidly expand access to midwifery services has had a pay off in terms of the health status of womenof reproductive age. Nevertheless, there are still three threats to validity that might be problematic whengeneralizing the results above. 1) Construct Validity: BMI is not the only indicator of overall health, although it is an important one. Other outcomes such as infant mortality or disease rates might be relevant. 6  
  7. 7. 2) External Validity: This program targets poor, rural areas and cannot be generalized to urban contexts without recognizing cultural differences in maternal attitudes and behaviors. 3) Internal Validity: The BMI variation among various subgroups might be due to differences in collection times. People’s BMIs change at different times of the year. Young men might have low BMIs if they have been farming in the Summer; young women might have low BMIs when they are taking exams in the Spring.VII. Hypothetical Time Series Analysis To further strengthen the causal claim, we can control for time effects. A time series analysisrequires different observations for every individual. Consequently, each individual will have multipleobservations in the study, corresponding to the number of times they were surveyed. To conduct atime-series analysis, I would collect data from a non-equivalent group design that observed manycomparison groups while considering the various arrival times of midwives. The data would becollected 3-5 years before the arrival of midwives, to isolate health trends existing pre-arrival, and 3-5 years after their arrival, to adjust for seasonal changes within one year (internal validity problem). In addition, I can collect data on other health indicators such as infant mortality and diseaserates to remedy the construct validity concern with using solely the BMI measure. This extra controlwill provide a fuller picture of health outcomes. With these controls, I expect the results of the regression to be more confidently reflective ofthe effects of the intervention. The effects from pre-existing health trends and seasonal changes willbe excluded. Thus, the study would increase the ability to make a causal claim about the intervention.Hypothetical Regression Discontinuity Analysis In regression discontinuity designs, participants are assigned to comparison groups solely onthe basis of a cutoff score. In this study, the Indonesian Ministry of Health would have to set a cutoff 7  
  8. 8. BMI average to determine whether or not a village would gain a midwife. For example, the Ministryof Health can allocate a midwife to villages with an average BMI of below 19, and those villageswith an average BMI above 19 would not receive a midwife. Assuming this analysis is rigorously andfully implemented by those assigning midwives to villages, this study can then compare villages thatreceived midwives and were within a short range of the baseline BMI cutoff. As an example, villageswith an average BMI of 18-19 can be compared with those with an average BMI of 19-20. This type of analysis is beneficial for comparing groups that are just above the cutoff (controlgroup) with those that are just below the cutoff (treatment group). If we discover that villages onopposing sides of the cutoff yielded different effects, this finding will strengthen the internal validityof our study design and make a strong case that the intervention is the main causal driver. However,there are a number of problems with this type of analysis. While the results of the study wouldsuggest a strong causal claim, the claim can only be extended to programs with an average cutoffBMI of 19. The causal claim would be very strong for the given cutoff. However, the results cannotbe generalized to other cutoff levels (e.g. 18 or 20). Other studies may show significantly variedeffects for different subgroups. For example, one might predict that a similar village midwifeprogram will have more drastic results for those with a lower average BMI at baseline.ReferencesFrankenberg, Elizabeth and Duncan Thomas. "Womens Health and Pregnancy Outcomes: Do Services Make a Difference?." Demography 38.2 (2001): 253-265.Government of Indonesia. 1989. Panduan Bidan di Tingkat Desa. Jakarta: Direktorat Jenderal Pembinaan Kesehatan Masyarakat.International Household Survey Network (2009). IHSN - Indonesian Family Life Survey. Retrieved March 20, 2011 from http://surveynetwork.org 8  

×