نبذة مختصرة : Thesis (Master's)--University of Washington, 2018 ; Introduction Accurate and unbiased estimates of fertility are critical in creating population policy and evaluating maternal mortality ratio. In settings without the routine collection of vital statistics, fertility data often comes from complete birth histories, which can suffer from biases due to the passage of time, including recall bias—women forgetting their births—and selection bias—women who die having systematically different fertility than those who live to be surveyed. Comprehensive estimates of time-related bias using nationally representative surveys in multiple locations do not exist. Methods This paper develops a method to measure and correct for these biases using 299 complete birth history surveys. I calculate cohort fertility rates of the same cohort from different surveys with different recall periods, find the ratio of a reference cohort fertility rate from the survey with the lowest recall to each other estimated cohort fertility rate, and model the log ratio as a function of recall length, reference recall length, age, maternal mortality ratio, female adult age-specific mortality, and region. These ratios are then predicted as if the reference recall was 0 and applied to the raw cohort Age Specific Fertility Rate (ASFR) to get a value that is adjusted for bias. Results The cohort-recall specific estimates for each country of analysis showed considerable patterning across different recall periods. Predicted ratios of reference ASFR, with the lowest possible recall within a country, cohort, and age, to survey specific ASFR ranged between 0.66 for a three year recall of the 1948 birth cohort in Timor-Leste and 1.40 for a 26 year recall of the 1965 birth cohort also in Timor-Leste, with a mean and median of 0.98. The relationship between recall and the ratio varied by age. For younger ages, including 15-19 and 20-24 years, the model found an underestimation of fertility in relation to the reference with an increasing recall period; for older ...
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