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Author ORCID Identifier
https://orcid.org/ 0000-0002-3540-8255
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Public Health
Year Degree Awarded
2022
Month Degree Awarded
September
First Advisor
Leontine Alkema
Second Advisor
Antoine Chambaz
Third Advisor
Laura Balzer
Subject Categories
Biostatistics
Abstract
The international community via the United Nations Sustainable Development Goals has set the target of universal access to reproductive health-care services, including family planning, by 2030. Progress towards reaching this goal is assessed by tracking appropriate demographic and health indicators at national and subnational levels. This task is challenging, however, in populations where relevant data are limited or of low quality. Statistical models are then needed to estimate and project demographic and health indicators in populations based on the available data. Our first contribution, in Chapter 1, is to unify many existing demographic and health indicator models by proposing an overarching model class, Temporal Models for Multiple Populations. In Chapter 2, we focus on the Modern Contraceptive Prevalence Rate (mCPR) indicator, which we model at the national level with a novel Bayesian method based on B-splines. Finally, in Chapter 3 we turn to the problem of defining and estimating the effect of interventions on family planning behavior using a novel targeted Bayesian estimator for Marginal Structural Models. TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back
DOI
https://doi.org/10.7275/30431180
Recommended Citation
Susmann, Herbert P., "Bayesian Hierarchical Temporal Modeling and Targeted Learning with Application to Reproductive Health" (2022). Doctoral Dissertations. 2668.
https://doi.org/10.7275/30431180
https://scholarworks.umass.edu/dissertations_2/2668
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.