Essays in Cluster Sampling and Causal Inference

This thesis consists of three papers in applied statistics, specifically in cluster sampling, causal inference, and measurement error. The first paper studies the problem of estimating the finite population mean from a two-stage sample with unequal selection probabilies in a Bayesian framework. Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. In a two-stage cluster sampling design, clusters are first selected with probability proportional to cluster size, and units are then randomly sampled within selected clusters. Methodological challenges arise when the sizes of nonsampled cluster are unknown. We propose both nonparametric and parametric Bayesian approaches for predicting the cluster size, and we implement inference for the unknown cluster sizes simultaneously with inference for survey outcome. We implement this method in Stan and use simulation studies to compare the performance of an integrated Bayesian approach to classical methods on their frequentist properties. We then apply our propsed method to the Fragile Families and Child Wellbeing study as an illustration of complex survey inference. The second paper focuses on the problem of weak instrumental variables, motivated by estimating the causal effect of incarceration on recidivism. An instrument is weak when it is only weakly predictive of the treatment of interest. Given the well-known pitfalls of weak instrumental variables, we propose a method for strengthening a weak instrument. We use a matching strategy that pairs observations to be close on observed covariates but far on the instrument. This strategy strengthens the instrument, but with the tradeoff of reduced sample size. To help guide the applied researcher in selecting a match, we propose simulating the power of a sensitivity analysis and design sensitivity and using graphical methods to examine the results. We also demonstrate the use of recently developed methods for identifying effect modification, which is an interaction between a pretreatment covariate and the treatment. Larger and less variable treatment effects are less sensitive to unobserved bias, so identifying when effect modification is present and which covariates may be the source is important. We undertake our study in the context of studying the causal effect of incarceration on recividism via a natural experiment in the state of Pennsylvania, a motivating example that illustrates each component of our analysis. The third paper considers the issue of measurement error in the context of survey sampling and hierarchical models. Researchers are often interested in studying the relationship between community-levels variables and individual outcomes. This approach often requires estimating the neighborhood-level variable of interest from the sampled households, which induces measurement error in the neighborhood-level covariate since not all households are sampled. Other times, neighborhood-level variables are not observed directly, and only a noisy proxy is available. In both cases, the observed variables may contain measurement error. Measurement error is known to attenuate the coefficient of the mismeasured variable, but it can also affect other coefficients in the model, and ignoring measurement error can lead to misleading inference. We propose a Bayesian hierarchical model that integrates an explicit model for the measurement error process along with a model for the outcome of interest for both sampling-induced measurement error and classical measurement error. Advances in Bayesian computation, specifically the development of the Stan probabilistic programming language, make the implementation of such models easy and straightforward

Statistical Graphics for Survey Weights

Survey weights are used for correcting known differences between the sample and the population due to sampling design, nonresponse, undercoverage, and other factors. However, practical considerations often result in weights that are not constructed in a systematic fashion. Graphical methods can be useful in understanding complex survey weights and their relations with other variables in the dataset, particularly when little to no information on the construction of the weights is available. Graphical tools can also assist in diagnostics, including detection of outliers and extreme weights. We apply our methods to the Fragile Families and Child Wellbeing Study, an ongoing longitudinal survey.Los pesos de muestreo se utilizan para corregir las diferencias conocidas entre la muestra y la población debido al diseño muestral, la falta de respuesta, subcobertura, y otros factores. Sin embargo, consideraciones prácticas a menudo resultan en pesos que no se han construido de una manera sistemática. Los métodos gráficos pueden ser útiles en la comprensión de ponderaciones complejas de la encuesta y sus relaciones con otras variables del conjunto de datos, sobre todo cuando se dispone de poca información sobre la construcción de los pesos. Las herramientas gráficas también pueden ayudar en el diagnóstico, incluyendo la detección de valores atípicos y pesos extremos. Aplicamos nuestros métodos en el estudio de Familias Frágiles y Bienestar Infantil, un estudio longitudinal en curso

A Bayesian Evidence Synthesis Approach to Estimate Disease Prevalence in Hard-To-Reach Populations: Hepatitis C in New York City

Existing methods to estimate the prevalence of chronic hepatitis C (HCV) in New York City (NYC) are limited in scope and fail to assess hard-to-reach subpopulations with highest risk such as injecting drug users (IDUs). To address these limitations, we employ a Bayesian multi-parameter evidence synthesis model to systematically combine multiple sources of data, account for bias in certain data sources, and provide unbiased HCV prevalence estimates with associated uncertainty. Our approach improves on previous estimates by explicitly accounting for injecting drug use and including data from high-risk subpopulations such as the incarcerated, and is more inclusive, utilizing ten NYC data sources. In addition, we derive two new equations to allow age at first injecting drug use data for former and current IDUs to be incorporated into the Bayesian evidence synthesis, a first for this type of model. Our estimated overall HCV prevalence as of 2012 among NYC adults aged 20-59 years is 2.78% (95% CI 2.61-2.94%), which represents between 124,900 and 140,000 chronic HCV cases. These estimates suggest that HCV prevalence in NYC is higher than previously indicated from household surveys (2.2%) and the surveillance system (2.37%), and that HCV transmission is increasing among young injecting adults in NYC. An ancillary benefit from our results is an estimate of current IDUs aged 20-59 in NYC: 0.58% or 27,600 individuals

Pre-hospital management protocols and perceived difficulty in diagnosing acute heart failure

Aim To illustrate the pre-hospital management arsenals and protocols in different EMS units, and to estimate the perceived difficulty of diagnosing suspected acute heart failure (AHF) compared with other common pre-hospital conditions. Methods and results A multinational survey included 104 emergency medical service (EMS) regions from 18 countries. Diagnostic and therapeutic arsenals related to AHF management were reported for each type of EMS unit. The prevalence and contents of management protocols for common medical conditions treated pre-hospitally was collected. The perceived difficulty of diagnosing AHF and other medical conditions by emergency medical dispatchers and EMS personnel was interrogated. Ultrasound devices and point-of-care testing were available in advanced life support and helicopter EMS units in fewer than 25% of EMS regions. AHF protocols were present in 80.8% of regions. Protocols for ST-elevation myocardial infarction, chest pain, and dyspnoea were present in 95.2, 80.8, and 76.0% of EMS regions, respectively. Protocolized diagnostic actions for AHF management included 12-lead electrocardiogram (92.1% of regions), ultrasound examination (16.0%), and point-of-care testings for troponin and BNP (6.0 and 3.5%). Therapeutic actions included supplementary oxygen (93.2%), non-invasive ventilation (80.7%), intravenous furosemide, opiates, nitroglycerine (69.0, 68.6, and 57.0%), and intubation 71.5%. Diagnosing suspected AHF was considered easy to moderate by EMS personnel and moderate to difficult by emergency medical dispatchers (without significant differences between de novo and decompensated heart failure). In both settings, diagnosis of suspected AHF was considered easier than pulmonary embolism and more difficult than ST-elevation myocardial infarction, asthma, and stroke. Conclusions The prevalence of AHF protocols is rather high but the contents seem to vary. Difficulty of diagnosing suspected AHF seems to be moderate compared with other pre-hospital conditions.Peer reviewe

Pre-hospital management protocols and perceived difficulty in diagnosing acute heart failure

Aim To illustrate the pre-hospital management arsenals and protocols in different EMS units, and to estimate the perceived difficulty of diagnosing suspected acute heart failure (AHF) compared with other common pre-hospital conditions. Methods and results A multinational survey included 104 emergency medical service (EMS) regions from 18 countries. Diagnostic and therapeutic arsenals related to AHF management were reported for each type of EMS unit. The prevalence and contents of management protocols for common medical conditions treated pre-hospitally was collected. The perceived difficulty of diagnosing AHF and other medical conditions by emergency medical dispatchers and EMS personnel was interrogated. Ultrasound devices and point-of-care testing were available in advanced life support and helicopter EMS units in fewer than 25% of EMS regions. AHF protocols were present in 80.8% of regions. Protocols for ST-elevation myocardial infarction, chest pain, and dyspnoea were present in 95.2, 80.8, and 76.0% of EMS regions, respectively. Protocolized diagnostic actions for AHF management included 12-lead electrocardiogram (92.1% of regions), ultrasound examination (16.0%), and point-of-care testings for troponin and BNP (6.0 and 3.5%). Therapeutic actions included supplementary oxygen (93.2%), non-invasive ventilation (80.7%), intravenous furosemide, opiates, nitroglycerine (69.0, 68.6, and 57.0%), and intubation 71.5%. Diagnosing suspected AHF was considered easy to moderate by EMS personnel and moderate to difficult by emergency medical dispatchers (without significant differences between de novo and decompensated heart failure). In both settings, diagnosis of suspected AHF was considered easier than pulmonary embolism and more difficult than ST-elevation myocardial infarction, asthma, and stroke. Conclusions The prevalence of AHF protocols is rather high but the contents seem to vary. Difficulty of diagnosing suspected AHF seems to be moderate compared with other pre-hospital conditions

Algorithms for enhancing public health utility of national causes-of-death data

<p>Abstract</p> <p>Background</p> <p>Coverage and quality of cause-of-death (CoD) data varies across countries and time. Valid, reliable, and comparable assessments of trends in causes of death from even the best systems are limited by three problems: a) changes in the <it>International Statistical Classification of Diseases and Related Health Problems </it>(ICD) over time; b) the use of tabulation lists where substantial detail on causes of death is lost; and c) many deaths assigned to causes that cannot or should not be considered underlying causes of death, often called garbage codes (GCs). The Global Burden of Disease Study and the World Health Organization have developed various methods to enhance comparability of CoD data. In this study, we attempt to build on these approaches to enhance the utility of national cause-of-death data for public health analysis.</p> <p>Methods</p> <p>Based on careful consideration of 4,434 country-years of CoD data from 145 countries from 1901 to 2008, encompassing 743 million deaths in ICD versions 1 to 10 as well as country-specific cause lists, we have developed a public health-oriented cause-of-death list. These 56 causes are organized hierarchically and encompass all deaths. Each cause has been mapped from ICD-6 to ICD-10 and, where possible, they have also been mapped to the <it>International List of Causes of Death </it>1-5. We developed a typology of different classes of GCs. In each ICD revision, GCs have been identified. Target causes to which these GCs should be redistributed have been identified based on certification practice and/or pathophysiology. Proportionate redistribution, statistical models, and expert algorithms have been developed to redistribute GCs to target codes for each age-sex group.</p> <p>Results</p> <p>The fraction of all deaths assigned to GCs varies tremendously across countries and revisions of the ICD. In general, across all country-years of data available, GCs have declined from more than 43% in ICD-7 to 24% in ICD-10. In some regions, such as Australasia, GCs in 2005 are as low as 11%, while in some developing countries, such as Thailand, they are greater than 50%. Across different age groups, the composition of GCs varies tremendously - three classes of GCs steadily increase with age, but ambiguous codes within a particular disease chapter are also common for injuries at younger ages. The impact of redistribution is to change the number of deaths assigned to particular causes for a given age-sex group. These changes alter ranks across countries for any given year by a number of different causes, change time trends, and alter the rank order of causes within a country.</p> <p>Conclusions</p> <p>By mapping CoD through different ICD versions and redistributing GCs, we believe the public health utility of CoD data can be substantially enhanced, leading to an increased demand for higher quality CoD data from health sector decision-makers.</p

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe