This document discusses outlier detection and modeling approaches for estimating maternal mortality rates. It outlines detecting and removing outliers by examining residuals from regression models, relationships between outcomes and covariates, and summary maternal mortality measures. It then discusses using spatial-temporal regression to capture systematic variation not explained by linear models, by running local regressions on residuals from the first stage weighted by geographic, time, and age proximity. This allows smoothing residual differences over countries and time to improve predictions.

1. Estimating maternal mortality II November 2, 2010 Christopher J.L. Murray Institute Director

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

28. General Modeling Strategy (Two stages) Linear model estimation Spatial-temporal local regression