Abstract
Sub-Saharan African countries have some of the world’s highest rates of maternal mortality. Most research on maternal mortality focuses on factors during pregnancy and delivery. However, consistent with the fetal programming hypothesis, a woman’s maternal survival may also be related to conditions she experienced while in utero. I examine this hypothesis in 14 African countries by relating rainfall when a woman was in utero with her maternal survival later in her life. High levels of rainfall, representing better in utero conditions, decrease the probability of maternal death by 1.1 percentage points, a 58 % decrease from a mean of 1.9 %. Higher rainfall while in utero reduces the probability of anemia during pregnancy, a risk factor for postpartum hemorrhage. Another plausible pathway is through a reduction in body mass index, a predictor of pregnancy-induced hypertension. Improving conditions for pregnant women will have inter-generational effects, benefiting pregnant women today and improving their daughters’ maternal survival.
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Notes
The final dataset includes data from the following DHS: Cameroon 2004, Congo DRC 2007, Cote d’Ivoire 1994, Ethiopia 2000 and 2005, Guinea 1999 and 2005, Kenya 1998 and 2003, Lesotho 2004/05, Malawi 2000 and 2004/2005, Mali 2001 and 2006, Niger 2006, Senegal 2005, Swaziland 2006/2007, Zambia 2007, and Zimbabwe 1999 and 2005/2006.
In the main specification, the sample is not restricted based on the available migration variables because this restriction would significantly reduce the sample size and the ability to detect an effect, given the low probability of the outcome variable. Robustness checks validate the inclusion of the entire sample in the main analysis.
The rainfall data are available through the National Data Climatic Center at http://www.ncdc.noaa.gov/oa/climate/ghcn-monthly/index.php and the Demographic and Health Survey data are available through MEASURE DHS at www.measuredhs.com.
Location-year fixed effects could not be included in the model because of limitations in capacity to include right-side variables.
In the main analyses, the coefficients on these two variables are not statistically significant which means that imputation of missing rainfall data is not affecting the main results. When the sample is restricted to observations with no missing rainfall data, the main results are not meaningfully affected.
The level of rainfall during the pre-conception period may have affected these women’s mothers’ ability to conceive, affecting in turn the size of the birth cohort. Similarly, the level of rainfall during the in utero period may affect fetal survival, thereby potentially affecting the number of births and the characteristics of this birth cohort.
This identification strategy to assess selective fertility and in utero survival was suggested by an anonymous reviewer and improves on my prior approach which used individual-level analyses even though the outcome is at the birth cohort/weather station level. The individual-level analyses gave greater weight to observations from larger cohorts since they would be included more frequently in the data.
The reported results are assumed to be statistically significant at the 5 % level, unless otherwise noted. The effect of both positive and negative rainfall shocks are calculated as the linear combination of the coefficient on the z-score and the dummy variable, as reported in the last rows of the referenced tables.
It is worthy to note that rainfall during the pre-conception period also appears to affect maternal survival later in life. A positive rainfall shock during pre-conception period decreases the probability of maternal death by 0.58 percentage points (p < 0.05), while a negative rainfall shock during the in utero period decreases the probability of maternal death by 0.93 percentage points (p < 0.01).
The rainy season dummies are defined according to data from “BBC–World Weather–Country guides” (http://www.bbc.co.uk/weather/world/country_guides/).
The sibling weights are defined as B/S where B is the original number of siblings in a household and S is the number of surviving siblings in a household. More weight will be given to households with fewer surviving siblings (higher mortality rates or lower S).
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Acknowledgments
This research was conducted while I received support from Harvard Graduate Society Dissertation Completion Fellowship, Institute for Quantitative Social Science, and Abt Associates, including the Development and Dissemination Grants and the Journal Authors Support Group. I would like to thank my dissertation committee, David Cutler, Esther Duflo, and Joseph Newhouse, for their invaluable guidance, review of drafts, and consistent availability throughout the study and Paul Krezanoski for many helpful discussions regarding this study. I would also like to thank colleagues at Abt Associates as well as former classmates for their comments and feedback and acknowledge the Harvard’s Center for Geographical Analysis for technical assistance. Finally, I would like to thank the anonymous referees for their comments and suggestions which helped improve this paper.
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Appendix
Appendix
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1.1 Text A1
1.1.1 Simple random sample of each generation of sisters, adjusted for double counting
The DHS interview all women of reproductive age within the household. For DHS that include sisterhood data, the survey respondents also provide data on their sisters, including both those who are in the household and those who have moved out of the household or who have died. There is potential double counting from the sisterhood data because multiple respondents in the same household could be sisters who each report on (1) the same sister who died and (2) the same sister who moved out of the household. Double counting is only a problem for respondents in a household who are sisters to each other. The dataset does not identify whether respondents are sisters, because respondents only identify themselves in relation to the household head (HH). Respondents who are most likely to have the same mother are women who are identified as “daughter of HH head” or “sister of HH head” and “HH head.” To adjust for double counting of sisters who have moved away and sisters who have died, the responses from the eldest sister respondent are kept as long as the information she provides matches other sister respondents’ information for the year of birth and year of death of their sisters within a range of ±2 years. The sibling history from the eldest sister respondent is prioritized because she will have been present for the majority of her mother’s births and should have the best recall of siblings and their histories. The final dataset, which accounts for double counting, represents each generation of sisters within the household, including the survey respondents themselves, and the sisters she reports on, both those who have moved out of the household and those who have died.
1.2 Text A2
1.2.1 Description of rainfall data
The rainfall data come from the Global Historical Climatology Network (GHCN) precipitation data. There is significant variation by countries in terms of yearly average rainfall. Niger has both the lowest average yearly rainfall (412 mm/year) and the least variation in yearly rainfall (1 standard deviation equals 221 mm), while Guinea has both the highest average yearly rainfall (2080 mm/year) and the greatest variation in yearly rainfall (1 standard deviation equals 808 mm/year). On average, there are 60 weather stations per country but the number of weather stations varies from as low as 13 in Guinea to as high as 126 in Zimbabwe.
1.2.2 Imputation of missing rainfall
The number of missing rainfall readings varies by observation. On average, observations are missing 13 months of rainfall readings out of 60, where 60 equals the total number of months from the pre-conception year up to age 3. There is considerable variation in missing rainfall data by country. Observations in Mali are missing an average of 1 month of rainfall data, while observations in Congo, the outlier country in terms of missing rainfall data, are missing an average of 43 months of rainfall data.
The main model used to estimate missing rainfall uses, as predictors, the two previous months of rainfall data in the same weather station for the same year as well as rainfall for the same month and year from the next two closest weather stations. The model is fitted using all available years of rainfall data by country. A year-season trend is included to account for yearly trends in rainfall as well as variations by country between the rainy and dry season. Other models were also estimated using only some of the arguments (a second model uses only the two previous months of rainfall in the same weather station and from the same year, a third model uses rainfall from the same month and year from the next two closest weather stations, and a fourth model uses rainfall from the same month in any of the next five closest weather stations). While these models yield a higher number of imputed rainfall readings relative to the main model because they have fewer non-missing data requirements, these models also have a higher root mean square error (RMSE), meaning that they are not as good of a fit for predicting missing data. As a result, the main analysis uses the imputation model with the most arguments because it represents the best fit for the data.
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Comfort, A.B. Long-term effect of in utero conditions on maternal survival later in life: evidence from Sub-Saharan Africa. J Popul Econ 29, 493–527 (2016). https://doi.org/10.1007/s00148-015-0581-9
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DOI: https://doi.org/10.1007/s00148-015-0581-9