Epidemiology of diabetes and related mortality: early screening, socioecological determinants, and the value of prevention

The focus of my dissertation is prediction, prevention, and economic valuation of type 2 diabetes. I studied individual level type 2 diabetes risk factors, spatial spillover effect of diabetes-related mortality ecological risk factors, and individuals’ loss of well-being due to diabetes. In the first essay, titled “Diabetes Risk Prediction: Multivariate Nonlinear Interaction Approach,” I argue that the success in preventing or delaying the incidence of type 2 diabetes and subsequent complications depend on the early detection of undiagnosed cases and identifying people at high-risk. However, early detection of type 2 diabetes is seldom feasible because the symptoms show up late, and screening the entire population is very costly. Individuals who are prone (e.g., due to family history) to developing type 2 diabetes and those with undiagnosed diabetes need to be targeted for early screening. Thus, it is imperative to continue designing assessment mechanisms that help to identify individuals at high-risk based on simple, non-invasive, inexpensive, and routine clinical measurements. In this paper, I build a model that helps to predict type 2 diabetes with readily available, inexpensive, non-invasive, and easy-to-collect information. National Health and Nutrition Examination Survey (NHANES) data is analyzed to build this risk model. A non-parametric regression method, Multivariate Adaptive Regression Splines (MARS), is used to allow for interactions and non-linearity in the model. A risk prediction model using the MARS approach achieved a performance level of 87% accuracy with area under receiver operating character curve (AUROC) of 0.86, which is higher than similar models based on invasive and non-invasive measurements. Moreover, this model requires few measurements and limited information that may be obtained in settings such as community-based chronic disease prevention programs and workplace well-being programs. Therefore, this risk prediction model can be translated into a usable risk-scoring tool in community chronic prevention and employee wellness programs. The second essay, titled “Spatial Spillover Effect from Socio-Ecological Determinants of Diabetes-Related Mortality in the US,” explores the spatial spillover effect from socio-ecological risk factors that are associated with type 2 diabetes-related mortality. I studied the spatial spillover effect of change in socioeconomic gradients (education, employment, and household income), retail food environments, and access to health-care on diabetes-related mortality rates (DRMR) across the United States. To examine mortality clusters and factors associated with the clusters and spatial spillover effect, seven-year aggregates of multiple-cause mortality data from CDC WONDER compressed mortality database was merged with several sources of county-level data. The results show that high DRMR cluster counties are located throughout the Southern Plains, Southeastern, and Appalachian regions. High DRMR clusters are characterized by lower socioeconomic status, high density of fast food restaurants, lack of access to grocery stores, high proportion of African Americans, and low physical activity. Moreover, the impacts from change in socioeconomic gradients and the retail food environment in a particular county spill over to neighboring counties. The implication is that improvement in socioeconomic status and access to healthy food would significantly reduce DRMR in contiguous US counties. The third essay, titled “What is the Value in Diabetes Prevention? A Subjective Well-Being Valuation Approach,” uses loss of well-being due to diabetes to quantify the monetary value of diabetes prevention in the US population. In this paper, I argue that the current preference-based health valuation approach is not appropriate for prevention-based programs valuation because they do not capture the social and economic value that an individual puts on a health condition. I utilize a recently developed subjective well-being valuation approach to quantify the monetary value of loss in well-being due to diabetes in the US population. This approach assumes that individuals derive overall life satisfaction from well-being, which is a function of health and income. Health, in turn, is produced by the combined input of an individual’s behaviors and medical technology. Thus, a marginal trade-off between health and income is used to derive the monetary value of health. The Panel Study of Income Dynamics (PSID) data was utilized for this study. The result shows that the monetary value for diabetes prevention is about $37,000, which is less than the current implicit threshold for program implementation. The resulting monetary value will help to quantify the societal value of diabetes prevention, which can be used to estimate the benefit side of the cost-benefit analysis

Evaluating Land Use/Land Cover Change and Its Socioeconomic Implications in Agarfa District of Bale Zone, Southeastern Ethiopia

A systematic analysis of land use/cover change is so decisive to exactly understand the extent of change and take essential measures to curb down the rate of changes and protect the land cover resources sustainably. This land use/land cover change study was conducted in Agarfa district of Bale zone, Oromia Regional State, Southeastern Ethiopia. The objectives of this study were to evaluate the trends, drivers and its socio-economic and environmental implication in study area. A descriptive research method was employed to achieve the intended objectives of the study. In the three years (1976, 1995, and 2014) Landsat Satellite images and socio-economic survey were the main data sources for this study. ERDAS Imagine and Arch-GIS tools were used to classify and generate land use/land cover maps of the study area. Survey questionnaires, key informant interviews, and field observation were employed to obtain information on drivers and its socio-economic and environmental implication in the district. The results show that the land use/land cover of the study area had changed dramatically during the period of 38 years. A rapid loss of forest land and shrub land cover in the landscape took place between 1976 and 2014. Conversely, agriculture and grazing lands were increased by 30% and 42% respectively at the expense of the lost land use/land cover types. Forest land is the most converted cover type during the entire study period. In the 38 years, forest lands diminished by over 65% of the original forest cover that was existed at the base year (1976). Local climate change, declining agricultural productivity and livestock quantity and quality and scarcity of fuel wood and constructional materials were some of the socio-economic and livelihood impacts of land use and land cover change of the study area. Thus, this finding affords information to land users and policy makers on extent of the change and social forces leading to this changes and its subsequent implication on local socio-economic and environmental conditions of the study area

Association of citrulline concentration at birth with lower respiratory tract infection in infancy: Findings from a multi-site birth cohort study

Assessing the association of the newborn metabolic state with severity of subsequent respiratory tract infection may provide important insights on infection pathogenesis. In this multi-site birth cohort study, we identified newborn metabolites associated with lower respiratory tract infection (LRTI) in the first year of life in a discovery cohort and assessed for replication in two independent cohorts. Increased citrulline concentration was associated with decreased odds of LRTI (discovery cohort: aOR 0.83 [95% CI 0.70-0.99], p = 0.04; replication cohorts: aOR 0.58 [95% CI 0.28-1.22], p = 0.15). While our findings require further replication and investigation of mechanisms of action, they identify a novel target for LRTI prevention and treatment

Growth Dynamics of Dairy Processing Firms in the European Union

The structure of the dairy processing industry in the European Union has changed enormously in recent decades. In many countries the industry is characterized by a few large companies with a big market share accompanied by many small processors that often produce for niche markets. This paper investigates which factors relate to growth of dairy processing firms. Using a unique ten-year panel data set and recently developed dynamic panel data estimators, the growth process of dairy processors is investigated for six rather diverse European countries. The data structure and the estimation method allow for dealing with endogeneity issues in an appropriate way. Firm size growth measured in total assets is found to be affected by firm size, firm age and financial variables. Growth in number of employees is only affected by firm age and lagged labour productivity. Implications for these results are given in the final section of the paper

Spatial Distribution of Underweight, Overweight and Obesity among Women and Children: Results from the 2011 Uganda Demographic and Health Survey

While undernutrition and infectious diseases are still persistent in developing countries, overweight, obesity, and associated comorbidities have become more prevalent. Uganda, a developing sub-Saharan African country, is currently experiencing the public health paradox of undernutrition and overnutrition. We utilized the 2011 Uganda Demographic and Health Survey (DHS) to examine risk factors and hot spots for underweight, overweight, and obesity among adult females (N = 2,420) and their children (N = 1,099) using ordinary least squares and multinomial logit regression and the ArcGIS Getis-Ord Gi* statistic. Overweight and obese women were significantly more likely to have overweight children, and overweight was correlated with being in the highest wealth class (OR = 2.94, 95% CI = 1.99–4.35), and residing in an urban (OR = 1.76, 95% CI = 1.34–2.29) but not a conflict prone (OR = 0.48, 95% CI = 0.29–0.78) area. Underweight clustered significantly in the Northern and Northeastern regions, while overweight females and children clustered in the Southeast. We demonstrate that the DHS can be used to assess geographic clustering and burden of disease, thereby allowing for targeted programs and policies. Further, we pinpoint specific regions and population groups in Uganda for targeted preventive measures and treatment to reduce the burden of overweight and chronic diseases in Uganda

From differential abundance to mtGWAS: accurate and scalable methodology for metabolomics data with non-ignorable missing observations and latent factors

Metabolomics is the high-throughput study of small molecule metabolites. Besides offering novel biological insights, these data contain unique statistical challenges, the most glaring of which is the many non-ignorable missing metabolite observations. To address this issue, nearly all analysis pipelines first impute missing observations, and subsequently perform analyses with methods designed for complete data. While clearly erroneous, these pipelines provide key practical advantages not present in existing statistically rigorous methods, including using both observed and missing data to increase power, fast computation to support phenome- and genome-wide analyses, and streamlined estimates for factor models. To bridge this gap between statistical fidelity and practical utility, we developed MS-NIMBLE, a statistically rigorous and powerful suite of methods that offers all the practical benefits of imputation pipelines to perform phenome-wide differential abundance analyses, metabolite genome-wide association studies (mtGWAS), and factor analysis with non-ignorable missing data. Critically, we tailor MS-NIMBLE to perform differential abundance and mtGWAS in the presence of latent factors, which reduces biases and improves power. In addition to proving its statistical and computational efficiency, we demonstrate its superior performance using three real metabolomic datasets.Comment: 19 pages of main text; 89 pages with supplement; 3 figures and 2 table

A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts

Background: The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness. Methods: We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0–2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling. Results: In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders Conclusion: We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with asthma morbidity, as demonstrated in two ECHO birth cohort studies, and provides a more statistically powerful method of testing etiologic hypotheses of childhood wheezing illness and asthma

Descriptive statistics for ISAAC-wheezing module severity items.

<p>Descriptive statistics for ISAAC-wheezing module severity items.</p

Participant characteristics for all cohorts.

<p>Participant characteristics for all cohorts.</p

Latent variable model of wheezing illness severity.

<p>Panel <i>a</i> shows the latent wheezing severity model used in cohorts 1 and 3. The severity of wheezing illness is estimated as a unidimensional latent variable (η₁) with four reflective ordinal indicators: wheezing episode frequency (y₁), frequency of wheeze-related sleep disturbance (y₂), wheeze-related speech disturbance (y₃), and exercise-induced wheeze (y₄). The ordinal indicators are presumed to be coarse measurements of underlying continuous variables (y₁*-y₄*). Panel <i>b</i> shows the multilevel wheezing severity model used in the Cohort 2 analyses. The within-schools level of the model is identical to panel <i>a</i>. The between-schools level of the model accounts for non-independence due to clustering within schools and study sites. Estimated parameters are depicted in red.</p