Environmental Health Nexus: Designing Predictive Models for Improving Public Health Interventions

University of Minnesota Ph.D. dissertation. May 2018. Major: Environmental Health. Advisor: Matteo Convertino. 1 computer file (PDF); xx, 197 pages.The environment embodies all surroundings of humans, including natural (e.g., climate, rivers, and animals) and built (e.g., roads and buildings) components. The environment is closely related to population health both directly and indirectly. Ambient temperature exposure and air pollution, for example, can directly affect population health through its direct impacts on human cardiovascular and respiratory functions. Rainfall, on the other hand, can indirectly affect population health through its impacts on disease-transmitting vectors, such as mosquitoes. The U.S. Global Change Research Group and the Intergovernmental Panel on Climate Change both highlight the importance of the environment on population health. Environmental health is a challenging research topic for a variety of reasons. First, it is difficult to select the appropriate environmental indicators. Thanks to technological advancements in instrument precision, remote sensing, and many other fields, there is now an unprecedented amount of environmental information available to researchers. Although data availability issues still exist, the bigger question now is how to select information that is most relevant and appropriate to answer research questions. For example, when studying the epidemiological link between ambient temperature and population health, the most fundamental task is to select the appropriate indicator for ambient temperature. Because there are over 60 potential indicators that are all designed to approximate temperature perceived by the human body, this task can be a challenge. Second, along with the wide range of indicators comes a large volume of environmental data that is now available. Some ambient environmental indicators, such as air temperature, are available on a three-hour basis globally with high resolution since the 1980s. Technologies such as geographic information systems (GIS) have empowered public health to access this information. However, extracting this information for public health purpose is not always easy and may involve specialized technical expertise. Furthermore, incorporating this high-granularity data with traditionally scarcer public health data also entails technical difficulties. Last but not least, from a computational standpoint, it is challenging to work with high-dimensional data, especially given different research objectives. Environmental health issues do not usually deal with only a pair of exposure and response factors because no environmental factor exists independently. When studying dengue fever, for example, the link between temperature and disease occurrences is not two-dimensional because climate (e.g., rainfall), environmental (e.g., river network, non-human primates), and societal factors (e.g., human mobility network) are also involved. Reducing high-dimensional data to the essentials in order to meet research objectives is easier said than done. It involves sophisticated quantitative methods such as complexity science. It also largely depends on the specific research questions, e.g., if the model used to study the environmental health issue is for risk assessment, risk comparison, or disease forecast. Despite the technical challenges, environmental information has tremendous potential in terms of ecosystem service for population health research. Existing research has already generated many valuable outcomes with great real-life implications. However, the uptake rates of such knowledge among public health policy- and decision-makers remain low. An important underlying reason is that current knowledge contains little necessary details needed to influence policies and decisions. Moreover, policy- and decision-makers often lack the technical expertise to translate the results from research articles into valuable information to their specific context. The relationship between research and policy is predominantly driven by the research, i.e., the supply-side of epidemiological knowledge. Such supply-driven model has already been proven to be suboptimal in terms of maximizing the impact of research. Targeting the challenges discussed above, this dissertation focuses on designing quantitative predictive models for improving environmental health policy and decisions. More specifically, it generates evidence-based science to improve policies and decisions with respect to risk communication, impact assessment, and intervention planning. Although the end-users of specific studies included are environmental health managers and practitioners, the knowledge generated is also valuable to environmental health and methods researchers. Within the overarching theme, two projects were completed over the course of this dissertation. The first project used environmental information to forecast infectious disease outbreaks. Infectious diseases that rely on vector-borne, water-borne, air-borne, and zoonotic transmissions are all considered environmentally sensitive infectious diseases. Two studies were completed for influenza outbreaks in the U.S. and dengue fever outbreaks in San Juan, Puerto Rico and Iquitos, Peru. The research objective was to design statistical models that maximize forecast accuracy in terms of future outbreak timing and magnitude. Meteorological factors such as temperature, humidity, and precipitation were considered. The end-users in these projects were the U.S. Centers for Disease Control and Prevention, the National Oceanic and Atmospheric Administration, and local public health agencies. The end-goal was to reduce disease burden through preventative intervention planning. The forecasting methods used in these disease forecast models were uniquely designed for environmentally sensitive infectious diseases. Based on the nature of the transmission mechanisms involved, the models considered substantial temporal delays between the environmental exposure and population health responses. Traditionally, researchers have relied on measurements such as auto-correlation and partial auto-correlation coefficients to assess these temporal delays. However, these coefficients are constrained by linear assumptions. In this project, mutual information (a concept in information theory) was adopted as an alternative measure that quantifies the delayed relationship between environmental exposure and health response. The second component was to design evidence-based and policy-oriented models for managing population health risks associated with ambient temperature exposure. This component was a collaborative effort with the Minnesota Department of Health and the U.S. Centers for Disease Control and Prevention. The study site is the Minneapolis-St. Paul Twin Cities Metropolitan Area. The environmental indicator to measure ambient temperature exposure was selected using a data-driven approach. The risk assessment models aim at improving the quality of public health policy and decision-making. This project expands on the existing risk assessment methods by developing various modifications and extensions to meet the needs of risk communication, impact assessment, and intervention planning. In this dissertation, three studies from this second project (ambient temperature) are presented. The work described above has important epidemiological, methodological, and policy implications. It also contributes to a bigger picture, which is to design decision support tools for environmental health management. An ideal decision support tool should combine general and universal patterns in epidemiology with the local public health context to optimize policies and decisions under uncertain scenarios. This type of tool has been developed for ecosystem management (e.g., wetland restoration) and for chronic care. However, for environmental health issues, this type of tool does not yet exist. Currently, environmental health management still largely relies on past experiences of policy and decision makers. Essential knowledge needed for creating such decision support tools is not yet fully available. This dissertation provides some of the missing answers, with the ultimate goal of rationalizing and optimizing public health policies and decisions regarding environmental health intervention

TB STIGMA – MEASUREMENT GUIDANCE

TB is the most deadly infectious disease in the world, and stigma continues to play a significant role in worsening the epidemic. Stigma and discrimination not only stop people from seeking care but also make it more difficult for those on treatment to continue, both of which make the disease more difficult to treat in the long-term and mean those infected are more likely to transmit the disease to those around them. TB Stigma – Measurement Guidance is a manual to help generate enough information about stigma issues to design and monitor and evaluate efforts to reduce TB stigma. It can help in planning TB stigma baseline measurements and monitoring trends to capture the outcomes of TB stigma reduction efforts. This manual is designed for health workers, professional or management staff, people who advocate for those with TB, and all who need to understand and respond to TB stigma

Priorities and Public Safety: Reentry and the Rising Costs of our Corrections System

Examines trends in the state's corrections budgets, prison population, and recidivism and the impact of the economic crisis on corrections policies nationwide. Argues for more cost-efficient evidence-based policies, with examples from other states

A Methodological Approach for Measuring the Impact of HTA

There is a lack of evidence concerning the link between HTA and outcomes in terms of health improvements. This work proposes a framework for assessing the impact of HTA. This impact assessment is a necessary step in then better understanding the value for money of HTA bodies. We emphasis that this is still a work in progress. iDSI has developed a theory of change-based framework in order to evaluate the impact the iDSI has on institutional strengthening – leading to ‘better decisions’ for ‘better health’. This framework recognises that there is a complex translation process between better decisions and better health dependent on many assumptions about local factors and systems, including linkage between decisions and budgets, delivery, implementation, and data accuracy. Work has been undertaken over the last 6 months developing a methodological approach for measuring the impact of health technology assessment (HTA). Two case studies are used to illustrate the approach. At the core of impact assessment is a requirement to link causes and effects, to explain ‘how’ and ‘why’ and to identify – and thus improve or adapt – mechanisms leading to impact. Policy makers also want to know ‘to what extent’ or ‘the magnitude of impact’. The framework developed adopts an economic approach nested in theory of change as a means of both quantifying the magnitude of impact (utilising economic models) as well as explaining why and how impact happens (drawing on theory based approaches) in order to reinforce learning as to how to improve our response and optimise the use of HTA to have the greatest impact in a given context. This should also enable us to capture and explain wider impact – perhaps more intangible aspects which cannot be easily quantified. This may also possibly increase policy-makers’ ‘buy-in’

Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies

On Regulatory and Organizational Constraints in Visualization Design and Evaluation

Problem-based visualization research provides explicit guidance toward identifying and designing for the needs of users, but absent is more concrete guidance toward factors external to a user's needs that also have implications for visualization design and evaluation. This lack of more explicit guidance can leave visualization researchers and practitioners vulnerable to unforeseen constraints beyond the user's needs that can affect the validity of evaluations, or even lead to the premature termination of a project. Here we explore two types of external constraints in depth, regulatory and organizational constraints, and describe how these constraints impact visualization design and evaluation. By borrowing from techniques in software development, project management, and visualization research we recommend strategies for identifying, mitigating, and evaluating these external constraints through a design study methodology. Finally, we present an application of those recommendations in a healthcare case study. We argue that by explicitly incorporating external constraints into visualization design and evaluation, researchers and practitioners can improve the utility and validity of their visualization solution and improve the likelihood of successful collaborations with industries where external constraints are more present.Comment: 9 pages, 2 figures, presented at BELIV workshop associated with IEEE VIS 201

A Data-informed Public Health Policy-Makers Platform

Hearing loss is a disease exhibiting a growing trend due to the number of factors, including but not limited to the mundane exposure to the noise and ever-increasing amount of older population. In the framework of a public health policymaking process, modeling of the hearing loss disease based on data is a key factor in alleviating the issues related to the disease issuing effective public health policies. First, the paper describes the steps of the data-driven policymaking process. Afterward, a scenario along with the part of the proposed platform, responsible for supporting policymaking are presented. With the aim of demonstrating the capabilities and usability of the platform for the policy-makers, some initial results of preliminary analytics are presented in a framework of a policy-making process. Ultimately, the utility of the approach is validated throughout the results of the survey which was presented to the health system policy-makers professionals involved in the policy development process in Croatia

Management Capacity Assessment for National Health Programs: A study of RCH Program in Gujarat State

The Ministry of Health and Family Welfare, Government of India administers a large number of national health programs such as Malaria control program, Blindness control program, National AIDS control program, Reproductive and Child Health (RCH) Program and so on. However, effective management of these programs has always come under scrutiny, as these programs consume a large amount of resources. As health is a state government subject in India, it is necessary to assess the management capacity of the department of Health and Family Welfare (H & FW) in each state. In this paper, we focus on the management capacity assessment for RCH program. Based on extensive literature survey, and discussions with senior officers in charge of RCH program at the centre and several states, we have developed a conceptual framework for management capacity assessment. Central to our conceptual framework are the following determinants of management capacity at the state dept of H & FW: (1) Capacity to formulate a clear statement of the state’s RCH Policy, Goals, and a Strategic Plan to achieve the Objectives, consistent with the resources available, (2) A well designed organizational structure for the H&FW department to provide the necessary support for achieving the policy goals, (3) Capacity of the H & FW department for effective management of RCH program, (4) Clear documentation of HR policies (qualifications, transfer, promotions, training etc) for RCH managers, (5) Role of External Stakeholders (6) Management Systems for Planning, Implementation and Monitoring RCH program, and (7) Institutional Processes and procedures For each of the above determinants, we have identified a set of indicators to assess the management capacity and designed a management capacity assessment tool to estimate these indicators. A pilot survey of our management capacity assessment tool in a few states helped us to refine certain instruments in our tool and finalize the same. Our management tool has been accepted by the Ministry of H & FW, Government of India and it has asked all the states and union territories to carry out a self assessment of their management capacity for RCH program. We have also recommended a suitable structure for effective management of RCH program for each state based on its population, the number of people in the reproductive age group, expected number of childbirths, and the current status of its H&FW department in delivering RCH services. This recommended structure can be used as a guideline by each state to identify its capacity gaps and take the necessary steps to augment its management capacity.

"Positive Youth Justice Initiative Phase I Implementation Evaluation"

Sierra Health Foundation launched the Positive Youth Justice Initiative (PYJI) in 2012 with the goal of supporting California counties to change the way they approach and work with justice-involved youth. Through an integrated model that invests in youth, treats trauma, provides wraparound service delivery, and strengthens local infrastructure, PYJI seeks to reduce barriers to crossover youths' successful transition to adulthood, including structural biases that exacerbate the over-representation of youth of color in the juvenile justice system. The two-year external evaluation of the implementation of systems change reforms in Phase I of PYJI— which included interviews, focus groups, and surveys with staff, youth, and caregivers in participating counties—explored the successes and challenges of the four counties (Alameda, San Diego, San Joaquin, and Solano) who have been implementing this far-reaching and ambitious initiative. This brief summarizes the key areas of progress and areas of challenge in PYJI implementation; facilitators of and hurdles to successful implementation; notable impacts of PYJI thus far; and areas for consideration as counties move forward in their efforts to achieve reforms that are both impactful and sustainable