Mediation is used to separate direct and indirect effects of an exposure variable on anoutcome variable. In this thesis, a mediation model is extended to account for censoredmediator and outcome variable. The two-part framework is used to account for thecensoring. The counterfactual based causal effects of this model are derived. A MonteCarlo study is performed to evaluate the behaviour of the causal effects accounting forcensoring, together with a comparison with methods for estimating the causal effectswithout accounting for censoring. The results of the Monte Carlo study show that theeffects accounting for censoring have substantially smaller bias when censoring is present.The proposed effects also seem to have a low cost with unbiased estimates for samplesizes as small as 100 for the two-part mediator model. In the case of limited mediatorand outcome, sample sizes larger than 300 is required for reliable improvements. A smallsensitivity analysis stresses the need of further development of the two-part models.

Indiana University-Purdue University Indianapolis (IUPUI) / The two-part model (2PM) is the most widely applied modeling and estimation framework in empirical health economics. By design, the two-part model allows the process governing observation at zero to systematically differ from that which determines non-zero observations. The former is commonly referred to as the extensive margin (EM) and the latter is called the intensive margin (IM). The analytic focus of my dissertation is on the development of a general framework for specifying, estimating and drawing inference regarding causally interpretable (CI) effect parameters in the 2PM context. Our proposed fully parametric 2PM (FP2PM) framework comprises very flexible versions of the EM and IM for both continuous and count-valued outcome models and encompasses all implementations of the 2PM found in the literature. Because our modeling approach is potential outcomes (PO) based, it provides a context for clear definition of targeted counterfactual CI parameters of interest. This PO basis also provides a context for identifying the conditions under which such parameters can be consistently estimated using the observable data (via the appropriately specified data generating process). These conditions also ensure that the estimation results are CI. There is substantial literature on statistical testing for model selection in the 2PM context, yet there has been virtually no attention paid to testing the “one-part” null hypothesis. Within our general modeling and estimation framework, we devise a relatively simple test of that null for both continuous and count-valued outcomes. We illustrate our proposed model, method and testing protocol in the context of estimating price effects on the demand for alcohol.

Indiana University-Purdue University Indianapolis (IUPUI) / The general potential outcomes framework (GPOF) is an essential structure that facilitates clear and coherent specification, identification, and estimation of causal effects. This dissertation utilizes and extends the GPOF, to specify, identify, and estimate causally interpretable (CI) effect parameter (EP) for an outcome of interest that manifests as either a value in a specified subset of the real line or a qualitative event -- a partially qualitative outcome (PQO). The limitations of the conventional GPOF for casting a regression model for a PQO is discussed. The GPOF is only capable of delivering an EP that is subject to a bias due to bad control. The dissertation proposes an outcome measure that maintains all of the essential features of a PQO that is entirely real-valued and is not subject to the bad control critique; the P-weighted outcome – the outcome weighted by the probability that it manifests as a quantitative (real) value. I detail a regression-based estimation method for such EP and, using simulated data, demonstrate its implementation and validate its consistency for the targeted EP. The practicality of the proposed approach is demonstrated by estimating the causal effect of a fully effective policy that bans pregnant women from smoking during pregnancy on a new measure of birth weight. The dissertation also proposes a Generalized Control Function (GCF) approach for modeling and estimating a CI parameter in the context of a fully parametric two-part model (2PM) for a continuous outcome in which the causal variable of interest is continuous and endogenous. The proposed approach is cast within the GPOF. Given a fully parametric specification for the causal variable and under regular Instrumental Variables (IV) assumptions, the approach is shown to satisfy the conditional independence assumption that is often difficult to hold under alternative approaches. Using simulated data, a full information maximum likelihood (FIML) estimator is derived for estimating the “deep” parameters of the model. The Average Incremental Effect (AIE) estimator based on these deep parameter estimates is shown to outperform other conventional estimators. I apply the method for estimating the medical care cost of obesity in youth in the US.

Most symptom measures either implicitly or explicitly distinguish between symptom presence and symptom severity. For example, item 2 on the PHQ-9, a commonly used measure of depressive symptoms, asks respondents to rate how much they have been 'feeling down, depressed, or hopeless.' The response options are 0 (Not at all), 1 (Several Days), 2 (More than half the days), and 3 (Nearly every day). Answering 0 indicates that the symptom is not present, and any response greater than 0 suggests the symptom is present. Higher values indicate higher severity of the symptom. Although the response options distinguish between symptom presence and severity, most users of the PHQ-9 score it by assuming that a 0 (i.e., no symptom), lack of symptoms, is the low end of the severity spectrum. However, clinically, there is often a distinction between experiencing symptoms and how severe any one of those symptoms is. Baldwin and Olsen (2023) developed a sequential item-response theory model that can be used to evaluate whether symptom presence and symptom severity should be separated or considered part of the same construct. We applied the sequential model to 3 datasets, a sample of 6242 participants, containing a variety of measures (e.g., Beck Depression Inventory- Second Edition, State Trait Anxiety Inventory, Penn State Worry Questionnaire). The results indicate that the Two-Part model has best overall fit out of the three models (Two-part, Extreme Response, Unique relationship), suggesting that symptom presence and severity should typically be considered distinct constructs. We discuss the implications for scoring and clinical use of symptom measures in light of our results.

Background: The consequences of poor nutrition are well known and of wide concern. Governments and public health agencies utilise food and diet surveillance data to make decisions that lead to improvements in nutrition. These surveys often utilise complex sample designs for efficient data collection. There are several challenges in the statistical analysis of dietary intake data collected using complex survey designs, which have not been fully addressed by current methods. Firstly, the shape of the distribution of intake can be highly skewed due to the presence of outlier observations and a large proportion of zero observations arising from the inability of the food diary to capture consumption within the period of observation. Secondly, dietary data is subject to variability arising from day-to-day individual variation in food consumption and measurement error, to be accounted for in the estimation procedure for correct inferences. Thirdly, the complex sample design needs to be incorporated into the estimation procedure to allow extrapolation of results into the target population. This thesis aims to develop novel statistical methods to address these challenges, applied to the analysis of iron intake data from the UK National Diet and Nutrition Survey Rolling Programme (NDNS RP) and UK national prescription data of iron deficiency medication. Methods: 1) To assess the nutritional status of particular population groups a two-part model with a generalised gamma (GG) distribution was developed for intakes that show high frequencies of zero observations. The two-part model accommodated the sources of data variation of dietary intake with a random intercept in each component, which could be correlated to allow a correlation between the probability of consuming and the amount consumed. 2) To identify population groups at risk of low nutrient intakes, a linear quantile mixed-effects model was developed to model quantiles of the distribution of intake as a function of explanatory variables. The proposed approach was illustrated by comparing the quantiles of iron intake with Lower Reference Nutrient Intakes (LRNI) recommendations using NDNS RP. This thesis extended the estimation procedures of both the two-part model with GG distribution and the linear quantile mixed-effects model to incorporate the complex sample design in three steps: the likelihood function was multiplied by the sample weightings; bootstrap methods for the estimation of the variance and finally, the variance estimation of the model parameters was stratified by the survey strata. 3) To evaluate the allocation of resources to alleviate nutritional deficiencies, a quantile linear mixed-effects model was used to analyse the distribution of expenditure on iron deficiency medication across health boards in the UK. Expenditure is likely to depend on the iron status of the region; therefore, for a fair comparison among health boards, iron status was estimated using the method developed in objective 2) and used in the specification of the median amount spent. Each health board is formed by a set of general practices (GPs), therefore, a random intercept was used to induce correlation between expenditure from two GPs from the same health board. Finally, the approaches in objectives 1) and 2) were compared with the traditional approach based on weighted linear regression modelling used in the NDNS RP reports. All analyses were implemented using SAS and R. Results: The two-part model with GG distribution fitted to amount of iron consumed from selected episodically food, showed that females tended to have greater odds of consuming iron from foods but consumed smaller amounts. As age groups increased, consumption tended to increase relative to the reference group though odds of consumption varied. Iron consumption also appeared to be dependent on National Statistics Socio-Economic Classification (NSSEC) group with lower social groups consuming less, in general. The quantiles of iron intake estimated using the linear quantile mixed-effects model showed that more than 25% of females aged 11-50y are below the LRNI, and that 11-18y girls are the group at highest of deficiency in the UK. Predictions of spending on iron medication in the UK based on the linear quantile mixed-effects model showed areas of higher iron intake resulted in lower spending on treating iron deficiency. In a geographical display of expenditure, Northern Ireland featured the lowest amount spent. Comparing the results from the methods proposed here showed that using the traditional approach based on weighted regression analysis could result in spurious associations. Discussion: This thesis developed novel approaches to the analysis of dietary complex survey data to address three important objectives of diet surveillance, namely the mean estimation of food intake by population groups, identification of groups at high risk of nutrient deficiency and allocation of resources to alleviate nutrient deficiencies. The methods provided models of good fit to dietary data, accounted for the sources of data variability and extended the estimation procedures to incorporate the complex sample survey design. The use of a GG distribution for modelling intake is an important improvement over existing methods, as it includes many distributions with different shapes and its domain takes non-negative values. The two-part model accommodated the sources of data variation of dietary intake with a random intercept in each component, which could be correlated to allow a correlation between the probability of consuming and the amount consumed. This also improves existing approaches that assume a zero correlation. The linear quantile mixed-effects model utilises the asymmetric Laplace distribution which can also accommodate many different distributional shapes, and likelihood-based estimation is robust to model misspecification. This method is an important improvement over existing methods used in nutritional research as it explicitly models the quantiles in terms of explanatory variables using a novel quantile regression model with random effects. The application of these models to UK national data confirmed the association of poorer diets and lower social class, identified the group of 11-50y females as a group at high risk of iron deficiency, and highlighted Northern Ireland as the region with the lowest expenditure on iron prescriptions.

This thesis consists of three essays that address various aspects of the economics of maternal health care. The first two essays examine the determinants of utilization of maternal health care services in low-income countries, while the third essay examines the determinants of utilization of prenatal ultrasonography in Canada. The first essay examines the influence of prenatal attendance (as well as a wide array of observed individual-, household- and community-level characteristics) on a woman’s decision to give birth at a health facility or at home for thirty-two low-income countries (across Asia, Sub-Saharan Africa and Latin America). This empirical investigation employs the Demographic and Health Surveys (DHS) data and a two-level random intercept model. The results show that prenatal attendance has a substantial influence on the use of facility delivery in all three geographical regions. Women having four prenatal visits were 7.3 times more likely to deliver at a health facility than those with no prenatal care. The second essay addresses two related questions: what factors determine a woman’s decision to seek prenatal care; and are those the same factors that determine the frequency of care? This investigation also utilizes Demographic and Health Surveys (DHS) data for thirty-two low-income countries (across Asia, Sub-Saharan Africa and Latin America) and applies a two-part and multi-level model to that data. The results suggest that, though a wide range of factors influence both decisions, that influence varies in magnitude across the two decisions, as well as across the three geographical regions. The third essay examines the influence of various socioeconomic and demographic factors on the frequency of prenatal ultrasounds in Canada, while controlling for maternal risk profiles. This investigation utilizes data from the Maternity Experience Survey (MES) of the Canadian Perinatal Surveillance System and employs a count data regression model (the Poisson distribution) to estimate the effect of various factors on the number of prenatal ultrasounds. The results of this investigation suggest that, even after controlling for maternal risk factors, the type of health-care provider, province of prenatal care, and timings of first ultrasound are the strongest predictors of number of ultrasounds.

This thesis consists of three essays that address various aspects of the economics of maternal health care. The first two essays examine the determinants of utilization of maternal health care services in low-income countries, while the third essay examines the determinants of utilization of prenatal ultrasonography in Canada. The first essay examines the influence of prenatal attendance (as well as a wide array of observed individual-, household- and community-level characteristics) on a woman’s decision to give birth at a health facility or at home for thirty-two low-income countries (across Asia, Sub-Saharan Africa and Latin America). This empirical investigation employs the Demographic and Health Surveys (DHS) data and a two-level random intercept model. The results show that prenatal attendance has a substantial influence on the use of facility delivery in all three geographical regions. Women having four prenatal visits were 7.3 times more likely to deliver at a health facility than those with no prenatal care. The second essay addresses two related questions: what factors determine a woman’s decision to seek prenatal care; and are those the same factors that determine the frequency of care? This investigation also utilizes Demographic and Health Surveys (DHS) data for thirty-two low-income countries (across Asia, Sub-Saharan Africa and Latin America) and applies a two-part and multi-level model to that data. The results suggest that, though a wide range of factors influence both decisions, that influence varies in magnitude across the two decisions, as well as across the three geographical regions. The third essay examines the influence of various socioeconomic and demographic factors on the frequency of prenatal ultrasounds in Canada, while controlling for maternal risk profiles. This investigation utilizes data from the Maternity Experience Survey (MES) of the Canadian Perinatal Surveillance System and employs a count data regression model (the Poisson distribution) to estimate the effect of various factors on the number of prenatal ultrasounds. The results of this investigation suggest that, even after controlling for maternal risk factors, the type of health-care provider, province of prenatal care, and timings of first ultrasound are the strongest predictors of number of ultrasounds.

On December 26th, 2004, 280,000 people lost their lives. A massive earthquake struck Indonesia, triggering a tsunami that affected several countries, including Thailand. The disaster had important implications for health status of Thai citizens, as well as health system planning, and thus underscores the need to study its long-term effect. This dissertation examined the patterns, determinants, and spatial analysis of health service utilization following the tsunami in Thailand. The primary aim was to determine whether tsunami-affected status (personal injury or property loss) and distance to a health facility (public health center or hospital) influenced health service utilization. The study population included Thai citizens (aged 14+), living in the tsunami-affected Thai provinces: Phuket, Phang Nga, Krabi, and Ranong. Study participants were randomly selected from the ‘affected’ and ‘unaffected’ populations. One and two years after the tsunami, participants were interviewed in-person on demographic and socio-economic factors, disaster impact, health status, and health service utilization. Five types of health services were examined: outpatient services, inpatient services, home visits, medications, and informal (unpaid) care. Distance to a health facility was calculated using Geographic Information System’s Network Analyst. The Grossman model of the demand for health care and a distance decay concept provided the foundation for this study. A propensity score method and a two-part model were used to examine the study objectives. There were 1,889 participants. One year after the tsunami, individuals affected by property loss were more likely to use medications than unaffected participants. Two years after the tsunami, individuals with personal injury were more likely to use outpatient services, medications, and informal care than unaffected participants. Distance to a health facility was associated with the use of medications and informal care. The results confirmed the long-term effect of a tsunami. This dissertation may assist the decision- and policy-makers in the identification of those most likely to use health services and in the request of health resources to the affected areas. The patterns, determinants, and spatial analysis of health service utilization found in this study may not be specific to a tsunami and may provide insights on post-disaster contexts of other natural disasters.

On December 26th, 2004, 280,000 people lost their lives. A massive earthquake struck Indonesia, triggering a tsunami that affected several countries, including Thailand. The disaster had important implications for health status of Thai citizens, as well as health system planning, and thus underscores the need to study its long-term effect. This dissertation examined the patterns, determinants, and spatial analysis of health service utilization following the tsunami in Thailand. The primary aim was to determine whether tsunami-affected status (personal injury or property loss) and distance to a health facility (public health center or hospital) influenced health service utilization. The study population included Thai citizens (aged 14+), living in the tsunami-affected Thai provinces: Phuket, Phang Nga, Krabi, and Ranong. Study participants were randomly selected from the ‘affected’ and ‘unaffected’ populations. One and two years after the tsunami, participants were interviewed in-person on demographic and socio-economic factors, disaster impact, health status, and health service utilization. Five types of health services were examined: outpatient services, inpatient services, home visits, medications, and informal (unpaid) care. Distance to a health facility was calculated using Geographic Information System’s Network Analyst. The Grossman model of the demand for health care and a distance decay concept provided the foundation for this study. A propensity score method and a two-part model were used to examine the study objectives. There were 1,889 participants. One year after the tsunami, individuals affected by property loss were more likely to use medications than unaffected participants. Two years after the tsunami, individuals with personal injury were more likely to use outpatient services, medications, and informal care than unaffected participants. Distance to a health facility was associated with the use of medications and informal care. The results confirmed the long-term effect of a tsunami. This dissertation may assist the decision- and policy-makers in the identification of those most likely to use health services and in the request of health resources to the affected areas. The patterns, determinants, and spatial analysis of health service utilization found in this study may not be specific to a tsunami and may provide insights on post-disaster contexts of other natural disasters.

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