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maternal mortality sri lanka estimating maternal mortality ii_lozano_110210_ihme

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  • Each of these weights have parameters that allow us to narrow or widen the windows, as well as alter the decay functions.
  • Transcript

    • 1. Estimating maternal mortality II November 2, 2010 Christopher J.L. Murray Institute Director
    • 2. Outline
      • Outlier detection
      • Modeling approaches II: space-time regression
    • 3. Outliers: a reality in this dataset
      • Maternal mortality is extremely rare, even where MMRs are very high
      • This can result in substantial sampling error and stochastic variation
      • Measurement error is also always possible
      • Together, these factors can result in the presence of outliers
    • 4. What’s the problem with outliers?
      • What IS an outlier?
        • An outlier can be understood as an atypical observation that appears to be derived from some distribution other than the one of interest
        • An outlier is an observation that is numerically distant from the rest of the data , or appears to deviate markedly from other members of the sample in which it occurs
        • Naive interpretation of statistics derived from data sets that include outliers may be misleading
        • Outliers can:
          • Distort estimates
          • Increase standard errors
          • Reduce the accuracy of fits
    • 5. What is an outlier in this dataset?
      • Outliers relative to other measurements in the same country
      • Outliers relative to what would be expected on the basis of the linear model predictions
      • Outliers relative to MMRs observed in countries with similar levels of development and health-system access
    • 6. Outlier detection
      • Numerous methods have been proposed to identify outliers
      • However, most agree that they should not be used as a blanket approach to delete outliers from a dataset
      • Some degree of judgment and expert review is needed to decide how to treat those outliers
    • 7. Approach to outlier detection
      • Identify and remove extreme outliers, in three ways:
        • Examine relationship of residuals from first stage regression with covariates
        • Examine the above relationship with particular attention towards non-VR sources
        • Examine the summary MMR measure
    • 8.
    • 9. Approach to outlier detection
      • Identify and remove extreme outliers, in three ways:
        • Examine relationship of residuals from first stage regression with covariates
        • Examine the relationship between the outcome and various covariates, with special attention towards non-VR data
          • Blurosphere plots
        • Examine the summary MMR measure
    • 10.
    • 11. Approach to outlier detection
      • Identify and remove extreme outliers, in three ways:
        • Examine relationship of residuals from first stage regression with covariates
        • Examine the above relationship with particular attention towards non-VR sources
        • Examine the summary MMR measure
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    • 23. Outline
      • Outlier detection
      • Modeling approaches II: space-time regression
    • 24. Recall the steps in the first stage:
    • 25. First stage linear 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
    • 26. But, the linear predictions don’t track the data very well
    • 27. The linear regression isn’t enough
      • The covariates available (TFR, GDP, neonatal mortality, HIV prevalence, education) can not explain all of the variation in the dependent variable
      • There may be other determinants of maternal mortality, not included in the model, that vary systematically across space and time
      • So, some of the residual variation in the error term may vary systematically across space and time
      • How can we take advantage of that systematic variation to improve the predictions?
    • 28. General Modeling Strategy (Two stages) Linear model estimation Spatial-temporal local regression
    • 29. Spatial-temporal regression
      • Spatial-temporal regression methods are used in geospatial analysis, meteorology, soil chemistry, and other fields to capture this systematic variation
      • Use the residuals from the first stage regression
        • Take advantage of spatial and temporal patterns in the residuals from the first stage regression
        • Run a local fixed effects regression with weights on the data for each country-year regression
      • Smooth the residual differences over countries and across time
      • Add in these smoothed differences to the predicted trend from step 1
    • 30. Weights for spatial-temporal regression
      • Space weight
        • Countries within the same GBD region will be more related
        • 21 GBD regions defined based on epidemiology
      • Time weight
        • Think that time points closer together will be more related
        • Use the tricubic weighting function
      • Age weight
        • Think that ages closer together will be more related
        • Use an exponential decay weighting function
      • Final weights the product of the space, time and age weights
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    • 42. HIV counterfactual estimates
      • What would have happened in the absence of HIV?
      • In most countries of the region, HIV has had a negligible impact on maternal mortality