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Unraveling the causes of health inequalities

Unraveling the causes of health inequalities. Adam Wagstaff. What’s it all about?. Having measured inequalities, natural next step is to seek to account for them TN#15 and TN#14 present methods aimed at decomposing causes of inequality

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Unraveling the causes of health inequalities

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  1. Unraveling the causes of health inequalities Adam Wagstaff

  2. What’s it all about? • Having measured inequalities, natural next step is to seek to account for them • TN#15 and TN#14 present methods aimed at decomposing causes of inequality • Core idea is that outcome variable is caused by a set of determinants, which vary systematically with SES • E.g. poor have lower income but also less knowledge, worse access to drinking water, lack insurance coverage, etc. • Want to know extent to which inequalities in health status are due to (a) inequalities in income, (b) inequalities in knowledge, (c) inequalities in access to drinking water, etc.

  3. Oaxaca • Oaxaca decomposes gap in outcome vbl between two groups • Attraction of Oaxaca over decomposition in TN#14 is that it allows for the possibility that inequalities caused in part by differences in effects of determinants • For example, health of the poor may be less responsive to changes in insurance coverage, or to changes in access to drinking water, etc.

  4. equation for non-poor y ynon-poor equation for poor ypoor xpoor xnon-poor x

  5. equation for non-poor y ynon-poor equation for poor ypoor xpoor xnon-poor x

  6. But how far due to diffs in b’s rather than diffs in x’s? equation for non-poor y ynon-poor equation for poor ypoor xpoor xnon-poor x

  7. Oaxaca #1: eqn (4) equation for non-poor y ynon-poor Dbxnon-poor equation for poor Dxb poor ypoor xpoor xnon-poor x

  8. Oaxaca #2: eqn (5) equation for non-poor y ynon-poor Dxbnon-poor Dbxnon-poor equation for poor Dbxpoor Dxb poor ypoor xpoor xnon-poor x

  9. Seeing how to do it …through an example from Vietnam Av. HAZ z-score kids<10 yrs: Poor = -1.86 Non-poor = -1.44 Diff = 0.42 U.S. reference group = 0.00

  10. The regression equation • y is the HAZ malnutrition score • Same regression model as Wagstaff et al. [8] • x includes • log of the child’s age in months (lnage) • sex = 1 if male • safewtr = 1 if drinking water is safe • oksan = 1 if satisfactory sanitation, • years of schooling of the child’s mother (schmom) • log of HH per capita consumption (lnpcexp) • poor = 1 if child’s HH is poor (if pcexp<Dong 1,790,000

  11. Differences in means between non-poor and poor

  12. Testing for significant differences in b’s in Stata xi: reg haz i.poor*lnage i.poor*sex i.poor*safwtr i.poor*oksan i.poor*schmom i.poor*lnpcexp [aw=wt] testparm _I*

  13. Stata regression output

  14. Stata F-test output—sign. diffs.  use separate eqns . testparm _I* ( 1) _Ipoor_1 = 0.0 ( 2) _IpooXlnage_1 = 0.0 ( 3) _IpooXsex_1 = 0.0 ( 4) _IpooXsafwt_1 = 0.0 ( 5) _IpooXoksan_1 = 0.0 ( 6) _IpooXschmo_1 = 0.0 ( 7) _IpooXlnpce_1 = 0.0 F( 7, 5154) = 2.03 Prob > F = 0.0472

  15. Oaxaca in numbers

  16. Oaxaca in a chart Oaxaca #1 Oaxaca #2

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