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 maternal mortality sri lanka estimating maternal mortality i_lozano_110110_ihme Presentation Transcript

  • Estimating maternal mortality I November 1, 2010 Rafael Lozano Professor of Global Health
  • Outline
    • Dependent variable
      • PMDF to maternal mortality rates
    • Model form and covariates
  • Challenges in modeling causes of death
    • Data are missing for many years
    • Non-sampling error can be large in some settings
    • There is marked variation in temporal trends across countries
    • The available covariates explain only a moderate component of the variance
  • What we have
  • What we want
  • How can we get there?
    • Statistical models can be used to help explore relationships:
      • Identify factors (from the literature) that are likely related to maternal mortality
      • Estimate the empirical relationship between those factors and the outcome of interest using a regression model
      • Use those empirical relationships to inform our estimates of maternal mortality
  • Outline
    • Dependent variable
      • PMDF to maternal mortality rates
    • Model form and covariates
  • Dependent variable selection
    • Dependent variable choices:
      • Maternal mortality ratio (MMR)
      • Proportion of all deaths due to maternal causes (PMDF)
      • Maternal mortality rates
    • Dependent variable can either be a summary measure or an age-specific measure
    • We model the log of the age-specific maternal mortality rates
  • Choice of dependent variable
    • Why model rates rather than the PMDF?
      • The PMDF is particularly sensitive to other causes of death
        • For example, in the event of an earthquake, epidemic (such as HIV), or increase in RTIs, the PMDF will be influenced
        • This requires not only modeling maternal mortality, but modeling everything else that explains variation in the PMDF
      • At the extremes of the PMDF (close to zero, close to one), models can behave unpredictably
  • Why Model Age-Specific Rates?
    • 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.
    • Shifts in fertility for example to older ages in some countries influences the age-pattern of maternal mortality.
    • Modeling all ages combined forces all countries to have identical patterns over time which is undesirable.
  • Processing input data
  • Why use the PMDF to get to rates?
    • 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)
    • We then multiply these PMDFs by the all-cause adult mortality estimates discussed earlier, to arrive at the number of maternal deaths
    • This allows for the correction of underreporting in the level of maternal mortality as the all-causes mortality “envelopes”
    • It also may reduce the effect of recall bias in survey data
  • PMDF to population maternal rates From input data source
  • 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
  • Outline
    • Dependent variable
      • PMDF to maternal mortality rates
    • Model form and covariates
  • Form of the regression model
    • Rates can be modeled directly, as with OLS or robust linear approaches (such as Huber-White, Tukey, or median regression)
      • Robust approaches are important because of the presence of outliers, zeros, and other extreme observations
    • Rates can also be modeled using count models such as Poisson or negative binomial models
      • The data does not meet the Poisson assumption that the mean equals the variance, in other words, the data is over-dispersed
      • The degree of over-dispersion is related to age, which can be allowed for using the generalized negative binomial model
  • So what is related to maternal mortality?
    • Fertility
    • GDP
    • Education
    • Neonatal mortality
    • HIV prevalence
    • Coverage of skilled birth attendance or in-facility birth
    • Others?
    • And what do we have in a complete time series for all countries?
  • Transforming the covariates
    • Examine the relationship between each of these covariates and the log of the maternal mortality rate (dependent variable)
    • Transformations for model:
      • log of the total fertility rate
      • log of the distributed lag of GDP per capita
      • HIV-squared as well as HIV sero-prevalence
    • Look for co-linearity
      • SBA highly co-linear with neonatal mortality and GDP per capita, and was also only available for 1986-2008
  • TFR vs. ln(maternal mortality rate)
  • 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
  • HIV counterfactual
    • HIV is believed to be a major contributor to maternal deaths
    • What would happen to maternal mortality if we “turned off” the effect of HIV at the population level?
      • Develop a counterfactual scenario: what would have happened with maternal mortality if there had been no HIV
    • In the linear model, switch HIV prevalence to zero, rather than its observed value