maternal mortality sri lanka estimating maternal mortality i_lozano_110110_ihme


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maternal mortality sri lanka estimating maternal mortality i_lozano_110110_ihme

  1. 1. Estimating maternal mortality I November 1, 2010 Rafael Lozano Professor of Global Health
  2. 2. Outline <ul><li>Dependent variable </li></ul><ul><ul><li>PMDF to maternal mortality rates </li></ul></ul><ul><li>Model form and covariates </li></ul>
  3. 3. Challenges in modeling causes of death <ul><li>Data are missing for many years </li></ul><ul><li>Non-sampling error can be large in some settings </li></ul><ul><li>There is marked variation in temporal trends across countries </li></ul><ul><li>The available covariates explain only a moderate component of the variance </li></ul>
  4. 4. What we have
  5. 5. What we want
  6. 6. How can we get there? <ul><li>Statistical models can be used to help explore relationships: </li></ul><ul><ul><li>Identify factors (from the literature) that are likely related to maternal mortality </li></ul></ul><ul><ul><li>Estimate the empirical relationship between those factors and the outcome of interest using a regression model </li></ul></ul><ul><ul><li>Use those empirical relationships to inform our estimates of maternal mortality </li></ul></ul>
  7. 7. Outline <ul><li>Dependent variable </li></ul><ul><ul><li>PMDF to maternal mortality rates </li></ul></ul><ul><li>Model form and covariates </li></ul>
  8. 8. Dependent variable selection <ul><li>Dependent variable choices: </li></ul><ul><ul><li>Maternal mortality ratio (MMR) </li></ul></ul><ul><ul><li>Proportion of all deaths due to maternal causes (PMDF) </li></ul></ul><ul><ul><li>Maternal mortality rates </li></ul></ul><ul><li>Dependent variable can either be a summary measure or an age-specific measure </li></ul><ul><li>We model the log of the age-specific maternal mortality rates </li></ul>
  9. 9. Choice of dependent variable <ul><li>Why model rates rather than the PMDF? </li></ul><ul><ul><li>The PMDF is particularly sensitive to other causes of death </li></ul></ul><ul><ul><ul><li>For example, in the event of an earthquake, epidemic (such as HIV), or increase in RTIs, the PMDF will be influenced </li></ul></ul></ul><ul><ul><ul><li>This requires not only modeling maternal mortality, but modeling everything else that explains variation in the PMDF </li></ul></ul></ul><ul><ul><li>At the extremes of the PMDF (close to zero, close to one), models can behave unpredictably </li></ul></ul>
  10. 10. Why Model Age-Specific Rates? <ul><li>Maternal death rate varies by age, we choose to model age-specific rates to allow for different countries to have different levels and time trends in the maternal death rate. </li></ul><ul><li>Shifts in fertility for example to older ages in some countries influences the age-pattern of maternal mortality. </li></ul><ul><li>Modeling all ages combined forces all countries to have identical patterns over time which is undesirable. </li></ul>
  11. 11. Processing input data
  12. 12. Why use the PMDF to get to rates? <ul><li>We do not calculate the MMR or maternal mortality rate directly from the raw data, but calculate the age-specific (i.e., 15-19, 20-24…45-49) fraction of all deaths in women due to maternal causes (PMDF) </li></ul><ul><li>We then multiply these PMDFs by the all-cause adult mortality estimates discussed earlier, to arrive at the number of maternal deaths </li></ul><ul><li>This allows for the correction of underreporting in the level of maternal mortality as the all-causes mortality “envelopes” </li></ul><ul><li>It also may reduce the effect of recall bias in survey data </li></ul>
  13. 13. PMDF to population maternal rates From input data source
  14. 14. PMDF to population maternal deaths, by age Input data source Age group # of maternal deaths # of all-cause deaths PMDF National mortality envelopes Population maternal deaths 15-19 80 1,000 8.0% 1,355 108 20-24 132 1,100 12.0% 1,990 239 25-29 132 1,100 12.0% 3,775 453 30-34 143 1,300 11.0% 4,935 543 35-39 126 1,400 9.0% 5,700 513 40-44 90 1,500 6.0% 6,575 395 45-49 61 1,900 3.2% 7,725 247
  15. 15. Outline <ul><li>Dependent variable </li></ul><ul><ul><li>PMDF to maternal mortality rates </li></ul></ul><ul><li>Model form and covariates </li></ul>
  16. 16. Form of the regression model <ul><li>Rates can be modeled directly, as with OLS or robust linear approaches (such as Huber-White, Tukey, or median regression) </li></ul><ul><ul><li>Robust approaches are important because of the presence of outliers, zeros, and other extreme observations </li></ul></ul><ul><li>Rates can also be modeled using count models such as Poisson or negative binomial models </li></ul><ul><ul><li>The data does not meet the Poisson assumption that the mean equals the variance, in other words, the data is over-dispersed </li></ul></ul><ul><ul><li>The degree of over-dispersion is related to age, which can be allowed for using the generalized negative binomial model </li></ul></ul>
  17. 17. So what is related to maternal mortality? <ul><li>Fertility </li></ul><ul><li>GDP </li></ul><ul><li>Education </li></ul><ul><li>Neonatal mortality </li></ul><ul><li>HIV prevalence </li></ul><ul><li>Coverage of skilled birth attendance or in-facility birth </li></ul><ul><li>Others? </li></ul><ul><li>And what do we have in a complete time series for all countries? </li></ul>
  18. 18. Transforming the covariates <ul><li>Examine the relationship between each of these covariates and the log of the maternal mortality rate (dependent variable) </li></ul><ul><li>Transformations for model: </li></ul><ul><ul><li>log of the total fertility rate </li></ul></ul><ul><ul><li>log of the distributed lag of GDP per capita </li></ul></ul><ul><ul><li>HIV-squared as well as HIV sero-prevalence </li></ul></ul><ul><li>Look for co-linearity </li></ul><ul><ul><li>SBA highly co-linear with neonatal mortality and GDP per capita, and was also only available for 1986-2008 </li></ul></ul>
  19. 19. TFR vs. ln(maternal mortality rate)
  20. 20. First stage regression model   Robust Regression   Coefficient Std. Error Intercept 4.715 0.100 ln(TFR) 1.903 0.022 ln(GDP per capita) -0.511 0.010 Neonatal mortality 13.662 0.721 Education -0.086 0.003 HIV 0.108 0.005 HIV² -0.001 0.000 Age 15-19 -1.176 0.021 Age 20-24 -0.374 0.020 Age 25-29 -0.077 0.020 Age 35-39 -0.165 0.020 Age 40-44 -0.633 0.021 Age 45-49 -1.390 0.025
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  32. 32. HIV counterfactual <ul><li>HIV is believed to be a major contributor to maternal deaths </li></ul><ul><li>What would happen to maternal mortality if we “turned off” the effect of HIV at the population level? </li></ul><ul><ul><li>Develop a counterfactual scenario: what would have happened with maternal mortality if there had been no HIV </li></ul></ul><ul><li>In the linear model, switch HIV prevalence to zero, rather than its observed value </li></ul>