Connecting the Dots: The Promise of Integrated Data Systems for Policy Analysis and Systems Reform

This article explores the use of integrated administrative data systems in support of policy reform through interagency collaboration and research. The legal, ethical, scientific and economic challenges of interagency data sharing are examined. A survey of eight integrated data systems, including states, local governments and university-based efforts, explores how the developers have addressed these challenges. Some exemplary uses of the systems are provided to illustrate the range, usefulness and import of these systems for policy and program reform. Recommendations are offered for the broader adoption of these systems and for their expanded use by various stakeholders

Using Predictive Models and Linked Datasets to Understand Risk of Fatal Opioid Overdose

Problem Statement The opioid crisis has had a devastating impact on the United States that will span generations. Public health agencies are increasingly looking toward data-driven solutions to understand risk factors, identify high-risk individuals, and direct interventions. Leveraging data captured by public health, healthcare, and other social and human service agencies will be increasingly common, as will applying sophisticated risk modeling to predict outcomes. This dissertation examines risk factors and models in the literature, compares multivariate predictive models with existing threshold-based risk identification, and measures the impact patient matching algorithms have on understanding risk when linking disparate patient-level datasets together. Methods A comprehensive review of the literature from 2008-2018 examined predictive model variables and performance related to opioid overdose using prescription history and other data sources. Using 2015 Maryland Prescription Drug Monitoring Program (PDMP) data and 2015-2016 death data, multiple risk identification methods for fatal opioid overdose were quantified and compared, including a multivariate risk model and common prescription-based thresholds. Finally, criminal justice data from 2013-2015 were matched with PDMP data at the patient-level using three matching algorithms to understand the impact on risk indicator prevalence and performance of a risk model. Results Risk models are increasingly being explored in the literature in recent years, although most use a payer-specific cohort and risk factor and measure definitions were inconsistent. Generally, risk models identified more individuals at risk of a fatal opioid overdose than simple risk thresholds, however, there may be value in combining the risk model with simple thresholds to identify high-risk individuals. Finally, the probabilistically matched population resulted in the highest degree of matching with arrest and death data, although risk model performance was comparable across all algorithms. Conclusions These results illustrate the ways predictive models based on PDMP data can assist with identifying high-risk individuals as a standalone tool or in combination with other risk stratification methods. The matching technique used to link person-level data across disparate data together affects the risk prevalence and factors, although model performance indicates a basic deterministic matching algorithm may be a suitable approach depending on resource constraints and scope of analysis

Effect of early preventive dental care on dental treatment, expenditures, and oral health among Medicaid enrolled children

Dental decay is the most prevalent chronic disease of childhood and dental care is the number one unmet healthcare need. Professional organizations are aggressively promoting a preventive dental visit by age one, but there is not strong evidence on effectiveness of early preventive dental care. The three studies in this dissertation examine the effects of the timing of a first preventive visit. The first two studies relied on data from NC Medicaid claims (1999 - 2006) and an oral health surveillance dataset to compare dental treatment, expenditures and disease status of children who had an early preventive dental visit to children who had preventive visits at older ages. The third study used a simulation model to examine the effects of alternative Medicaid policy options for the timing of the first preventive visit. We found that children who had a preventive visit by age 18 months had fewer treatments and lower expenditures than children who had a first preventive visit at age 25-36 months, but children who had a first preventive visit at age 49-60 months had less treatment than children with a visit by age 18 months. Our results indicated that children who had early preventive visits were at higher risk for disease at a very young age than children who had visits at older ages; however, they had no difference in their disease status at age five years. Further, when we expanded the definition of a preventive visit to include children who had worse oral health, children with an early preventive visit had fewer treatments and lower expenditures than children who had preventive visits at older ages. Taken together, these findings indicate that early preventive visits may be effective among children at an elevated risk for disease. The third study also found that targeting children at high risk for the age one visit was the optimal policy. These findings support the policy to promote early visits among children at higher risk for disease and allow other children to delay first visit until age three years, particularly when the supply of dentists is limited

Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation

A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment opens the door to credible quasi-experimental research designs for the evaluation of school effectiveness. Yet the question of how best to separate the lottery-generated variation integral to such designs from non-random preferences and priorities remains open. This paper develops easily-implemented empirical strategies that fully exploit the random assignment embedded in a wide class of mechanisms, while also revealing why seats are randomized at one school but not another. We use these methods to evaluate charter schools in Denver, one of a growing number of districts that combine charter and traditional public schools in a unified assignment system. The resulting estimates show large achievement gains from charter school attendance. Our approach generates efficiency gains over ad hoc methods, such as those that focus on schools ranked first, while also identifying a more representative average causal effect. We also show how to use centralized assignment mechanisms to identify causal effects in models with multiple school sectors

Acute inflammation and infection: the effects on recovery following moderate-to-severe traumatic brain injury

Current thinking by Traumatic Brain Injury (TBI) researchers and clinicians has devolved from the idea that TBI is an event with a finite recovery period, and have shifted to considering TBI a chronic disease with long-term implications for health. Therefore, there is great interest in determining acute biological and clinical factors that influence long-term health and function after injury. This interest drives the two central themes of this dissertation, to better understand: 1) the continuum of TBI disability from acute to chronic recovery; 2) the effects of non-neurological factors on recovery from TBI. Notably, the availability of data that spans the TBI disability continuum—from early stages post-injury to death—is sparse. Aim 1 of this dissertation explains a probabilistic marching procedure used to merge two databases, the National Trauma Databank and TBI Model Systems, which creates an infrastructure to examine the long-term effects of relevant acute care variables. In aim 2, the merged dataset is leveraged to assess the negative effects of acute care hospital-acquired pneumonia (HAP) on long-term global disability and health care utilization. HAP is one example of a non-neurological factor that impacts TBI recovery. Aim 3 focuses on two systemic markers of inflammation and hormone dysfunction, tumor necrosis factor-alpha (TNFα) and estradiol (E2), and assesses their inter-relationship acutely after injury, and their temporal relationship to mortality. The public health implications of the work herein provide observational data to better understand the continuum of TBI disability, and major non-neurological contributors to recovery from injury

THE OFF-LABEL USE OF ATYPICAL ANTIPSYCHOTICS AND ITS IMPACT ON ATTENTION DEFICIT/HYPERACTIVITY DISORDER (ADHD)

Atypical antipsychotics (AAPs) (also known as second-generation antipsychotics) are the US Food and Drug Administration (FDA) approved medications for schizophrenia, bipolar I disorder, depression and autism. Compared to the typical antipsychotics, AAPs were marketed as reducing adverse side effects such as extrapyramidal symptoms. This resulted in extensive use of AAPs for not only the FDA approved indications but also other conditions that are not approved. However, several post-marketing clinical trials evaluated the use of AAPs and reported serious adverse side effects, including metabolic syndrome, cardiovascular events, or death. The extensive use of AAPs by pediatrics is an important policy problem that imposes serious concerns on public health and economy in the US. A large proportion of total pediatric AAP use is off-label in which the safety and effectiveness are not yet established. Moreover, among the off-label conditions for which AAPs were used, ADHD was the most common primary mental diagnosis. From public health perspective, the risk of type II diabetes in pediatric AAP users was estimated. A retrospective cohort study was conducted and a twice higher risk of developing type II diabetes was estimated for AAP users compared to non-users in pediatrics. From economic efficiency perspective, the cost-effectiveness of AAPs compared to other ADHD medications in pediatric ADHD patients was estimated. Among non-stimulant ADHD medication treatment strategies, AAPs resulted in the lower expected health outcome than other ADHD medications. Also, AAPs were not a favored choice with respect to cost-effectiveness. A comparative effectiveness study that compares resource utilization and costs between atypical antipsychotic (AAP) users and non-AAP users in ADHD revealed that AAP users were likely to visit a healthcare facility for outpatient and inpatient services more frequently than non-AAP users. Total health care costs were significantly higher for AAP users with additional costs of $1,393 (2012 dollars) during six months and$2,784 (2012 dollars) during a year after initiating the AAP treatment

Using Microanalytical Simulation Methods in Educational Evaluation: An Exploratory Study

Scientifically based research used to inform evidence based school reform efforts has been required by the federal government in order to receive grant funding since the reenactment of No Child Left Behind (2002). Educational evaluators are thus faced with the challenge to use rigorous research designs to establish causal relationships. However, access to student-level longitudinal or comparison group data is often scarce, which significantly restricts researchers’ choice of research design. Although most state departments of education have school- and district-level data available to the public, the individual student-level data that are often needed to perform appropriate statistical analyses are unavailable. This exploratory study demonstrates the process of and provides evidence that microanalytical simulation methods, conducted using Microsoft Excel, may be a useful research tool in the field of education if adequate school-level modeling information is available. These simulation methods may assist in providing greater opportunities to execute more rigorous methodological designs

Privacy in context : the costs and benefits of a new deidentification method

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (leaves 131-150).The American public continues to be concerned about medical privacy. Policy research continues to show people's demand for health organizations to protect patient-specific data. Health organizations need personally identifiable data for unhampered decision making; however, identifiable data are often the basis of information abuse if such data are improperly disclosed. This thesis shows that health organizations may use deidentified data for key routine organizational operations. I construct a technology adoption model and investigate if a for-profit health insurer could use deidentified data for key internal software quality management applications. If privacy-related data are analyzed without rigor, little support is found to incorporate more privacy protections into such applications. Legal and financial motivations appear lacking. Adding privacy safeguards to such software programs apparently doesn't improve policy-holder care quality. Existing technical approaches do not readily allow for data deidentification while permitting key computations within the applications. A closer analysis of data reaches different conclusions. I describe the bills that are currently passing through Congress to mitigate abuses of identifiable data that exist within organizations.(cont.) I create a cost and medical benefits model demonstrating the financial losses to the insurer and medical losses to its policy-holders due to less privacy protection within the routine software applications. One of the model components describes the Predictive Modeling application (PMA), used to identify an insurer's chronically-ill policy-holders. Disease management programs can enhance the care and reduce the costs of such individuals because improving such people's health can reduce costs to the paying organization. The model quantifies the decrease in care and rise in the insurer's claim costs as the PMA must work with suboptimal data due to policy-holders' privacy concerns regarding the routine software applications. I create a model for selecting variables to improve data linkage in software applications in general. An encryption-based approach, which allows for the secure linkage of records despite errors in linkage variables, is subsequently constructed. I test this approach as part of a general data deidentification method on an actual PMA used by health insurers. The PMA's performance is found to be the same as if executing on identifiable data.by Stanley Trepetin.Ph.D