According to the data from 2025, some 16 percent of respondents said that rising health care costs were the most important health issue facing the United States. Cancer ranked second on the list with 15 percent. Issues with healthcare costsCurrently, the most urgent problem facing American healthcare is the high costs of care. The high expense of healthcare may deter people from getting the appropriate treatment when they need medical care or cause them to completely forego preventative care visits. Many Americans reported that they may skip prescription doses or refrain from taking medication as prescribed due to financial concerns. Such health-related behavior can result in major health problems, which may raise the long-term cost of care. Inflation, medical debt, and unforeseen medical expenses have all added to the burden that health costs are placing on household income. Gun violence issueThe gun violence epidemic has plagued the United States over the past few years, yet very little has been done to address the issue. In recent years, gun violence has become the leading cause of death among American children and teens. Even though more than half of Americans are in favor of tougher gun control regulations, there is little political will to strongly reform the current gun law. Gun violence has a deep traumatic impact on survivors and society, it is developing into a major public health crisis in the United States.

We asked U.S. consumers about "Prevalence of health conditions" and found that *************************************************************** takes the top spot, while ************************** is at the other end of the ranking.These results are based on a representative online survey conducted in 2025 among 13,690 consumers in the United States.

A 2024 survey found that over half of U.S. individuals indicated the cost of accessing treatment was the biggest problem facing the national healthcare system. This is much higher than the global average of 32 percent and is in line with the high cost of health care in the U.S. compared to other high-income countries. Bureaucracy along with a lack of staff were also considered to be pressing issues. This statistic reveals the share of individuals who said select problems were the biggest facing the health care system in the United States in 2024.

As of June 2025, some 15 percent of Americans surveyed were of the opinion that opioids and fentanyl addiction is the number one public health threat in the U.S., a slight increase from 12 percent in February 2025. Furthermore, obesity was viewed as a top public health issue by 22 percent of Americans in June 2025.
U.S. healthcare issues The United States has the highest healthcare spending globally. The majority of Americans considered rising healthcare costs as the most important healthcare problem facing the U.S. in January 2023. While COVID-19 and cancer were ranked second on the list. Due to unfortunate mass shooting incidents in the country, gun violence is typically related to homicide and is not considered a healthcare issue. Although the most popular method of suicide among Americans who attempted it was a gun or firearm, the link between gun access and suicide is frequently ignored as a public health crisis.

Changelog:

Update September 20, 2021: Data and overview updated to reflect data used in the September 15 story Over Half of States Have Rolled Back Public Health Powers in Pandemic. It includes 303 state or local public health leaders who resigned, retired or were fired between April 1, 2020 and Sept. 12, 2021. Previous versions of this dataset reflected data used in the Dec. 2020 and April 2021 stories.

Overview

Across the U.S., state and local public health officials have found themselves at the center of a political storm as they combat the worst pandemic in a century. Amid a fractured federal response, the usually invisible army of workers charged with preventing the spread of infectious disease has become a public punching bag.

In the midst of the coronavirus pandemic, at least 303 state or local public health leaders in 41 states have resigned, retired or been fired since April 1, 2020, according to an ongoing investigation by The Associated Press and KHN.

According to experts, that is the largest exodus of public health leaders in American history.

Many left due to political blowback or pandemic pressure, as they became the target of groups that have coalesced around a common goal — fighting and even threatening officials over mask orders and well-established public health activities like quarantines and contact tracing. Some left to take higher profile positions, or due to health concerns. Others were fired for poor performance. Dozens retired. An untold number of lower level staffers have also left.

The result is a further erosion of the nation’s already fragile public health infrastructure, which KHN and the AP documented beginning in 2020 in the Underfunded and Under Threat project.

Findings

The AP and KHN found that:

• One in five Americans live in a community that has lost its local public health department leader during the pandemic
• Top public health officials in 28 states have left state-level departments ## Using this data To filter for data specific to your state, use this query

To get total numbers of exits by state, broken down by state and local departments, use this query

Methodology

KHN and AP counted how many state and local public health leaders have left their jobs between April 1, 2020 and Sept. 12, 2021.

The government tasks public health workers with improving the health of the general population, through their work to encourage healthy living and prevent infectious disease. To that end, public health officials do everything from inspecting water and food safety to testing the nation’s babies for metabolic diseases and contact tracing cases of syphilis.

Many parts of the country have a health officer and a health director/administrator by statute. The analysis counted both of those positions if they existed. For state-level departments, the count tracks people in the top and second-highest-ranking job.

The analysis includes exits of top department officials regardless of reason, because no matter the reason, each left a vacancy at the top of a health agency during the pandemic. Reasons for departures include political pressure, health concerns and poor performance. Others left to take higher profile positions or to retire. Some departments had multiple top officials exit over the course of the pandemic; each is included in the analysis.

Reporters compiled the exit list by reaching out to public health associations and experts in every state and interviewing hundreds of public health employees. They also received information from the National Association of City and County Health Officials, and combed news reports and records.

Public health departments can be found at multiple levels of government. Each state has a department that handles these tasks, but most states also have local departments that either operate under local or state control. The population served by each local health department is calculated using the U.S. Census Bureau 2019 Population Estimates based on each department’s jurisdiction.

KHN and the AP have worked since the spring on a series of stories documenting the funding, staffing and problems around public health. A previous data distribution detailed a decade's worth of cuts to state and local spending and staffing on public health. That data can be found here.

Attribution

Findings and the data should be cited as: "According to a KHN and Associated Press report."

Is Data Missing?

If you know of a public health official in your state or area who has left that position between April 1, 2020 and Sept. 12, 2021 and isn't currently in our dataset, please contact authors Anna Maria Barry-Jester annab@kff.org, Hannah Recht hrecht@kff.org, Michelle Smith mrsmith@ap.org and Lauren Weber laurenw@kff.org.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Leading Causes of Death US

1980–2009 by Sex, Race, and Hispanic Origin

By Health [source]

About this dataset

This fascinating dataset takes a look at the leading causes of death in the United States from 1980-2009, broken down by sex, race, and Hispanic origin. This data sheds light on how mortality in the US has changed over time among these categories. Accounting for everything from heart disease to cancer to suicide, this insight can be used by health researchers and policy makers to gain a better understanding of disparities in healthcare and deaths across different groups. Whether studying questions related to public health or more targeted population issues such as gender biases in death rates, this dataset provides an important resource for anyone interested in examining mortality across demographic lines

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How to use the dataset

This dataset can be used to explore some of the leading causes of death in the United States from 1980 to 2009, broken down by sex, race, and Hispanic origin. This data can be used to better understand mortality trends and risk factors associated with different populations in America.

By using this dataset you can compare and contrast mortality rates across different gender, racial, and ethnic groups during this time period. You can also compare different causes of death within these demographic categories to see if there are any patterns over time or notable differences between groups.

You could even use this data to track changes across population groups as a whole or look at details for specific years or types of causes of death in particular groups. With this information one may gain insight into health disparities across population segments in America— aiding advocates for social change & public policy shifts toward improved health outcomes for all Americans!

Research Ideas

• Analyzing regional or state-level differences in mortality rates over time.
• Examining the beahvioral factors or risk factors associated with each cause of death for different genders and populations.
• Examining the prevalence of each cause of death as a proportion to an overall population trend in different socio-economic categories such as race or income level

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Selected_Trend_Table_from_Health_United_States_2011._Leading_causes_of_death_and_numbers_of_deaths_by_sex_race_and_Hispanic_origin_United_States_1980_and_2009.csv | Column name | Description | |:-------------------|:---------------------------------------------------------------------------------------------------------| | Group | The group of people the cause of death applies to (e.g. men, women, whites, blacks, hispanics). (String) | | Year | The year the cause of death was recorded. (Integer) | | Cause of death | The cause of death. (String) | | Flag | A flag indicating whether the cause of death is considered a leading cause. (Boolean) | | Deaths | The number of deaths attributed to the cause of death. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

A survey of adults in the United States found that around ** percent of respondents identified mobility issues, cancer, and cognitive decline as their top aging-related health concerns. This statistic depicts the percentage of adults in the United States who had select health concerns related to the aging process as of 2024.

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

Since 2013, the Robert Wood Johnson Foundation (RWJF) has led the development of a pioneering national action framework to advance a "culture that enables all in our diverse society to lead healthier lives now and for generations to come." Accomplishing these principles requires a national paradigm shift from a traditionally disease and health care-centric view of health toward one that focuses on well-being. Recognizing that paradigm shifts require intentional actions, RWJF worked with RAND researchers to design an actionable path to fulfill the Culture of Health (CoH) vision. A central piece of this work is the development of measures to assess constructs underlying a CoH. The National Survey of Health Attitudes (NSHA) is a survey that RWJF and RAND analysts developed and conducted as part of the foundation's CoH strategic framework. The foundation undertook this survey to measure key constructs that could not be measured in other data sources. Thus, the survey was not meant to capture the full action framework that informs CoH, but rather just selected measure areas. The questions in this survey primarily addressed the action area: making health a shared value. The survey covers a variety of topics, including views regarding what factors influence health, such as the notion of health interdependence (peer, family, neighborhood, and workplace drivers of health), values related to national and community investment for health and well-being; behaviors around health and well-being, including civic engagement on behalf of health, and the role of community engagement and sense of community in relation to health attitudes and values. This study includes the results from the 2023 RWJF National Survey of Health Attitudes. The 2023 survey is the third wave of the NSHA. The first wave was conducted in 2015 (ICPSR 37405) and the second wave in 2018 (ICPSR 37633). The 2023 report complements the overview of the 2015 survey described in the RAND report Development of the Robert Wood Johnson Foundation National Survey of Health Attitudes (Carman et al., 2016), and its subsequent topline 2018 Survey of National Health Attitudes: Description and Top-Line Summary (Carman et al., 2019) and is organized similarly for consistency. A companion set of longitudinal surveys during the COVID-19 pandemic was fielded between 2020 and 2021 and is further described in four top-line reports, COVID-19 and the Experiences of Populations at Greater Risk (Carman et al., 2020-2021). The questions in the 2023 survey uniquely capture aspects of American mindset about health, health equity, structural racism, and wellbeing in ways that are not present in other surveys. This version of the NSHA can be viewed in three main sections: (1) individual health experiences, perspectives, and knowledge (making health a shared value); (2) health equity perspectives; and (3) community wellbeing, including climate views and barriers to community engagement. Insights from the surveys referenced above, including this one, have established a baseline and set of cross-sectional pulse checks on where the American public is regarding their recognition of social determinants of health, their understanding of health inequities including structural racism, their willingness to address those inequities and their indication of who in society should be responsible for solving health inequities.

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US Population Health Management (PHM) Market Size 2025-2029

The us population health management (phm) market size is forecast to increase by USD 6.04 billion at a CAGR of 7.4% between 2024 and 2029.

The Population Health Management (PHM) market in the US is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and analytics. These technologies enable healthcare providers to collect, analyze, and act on patient data to improve health outcomes and reduce costs. However, the high perceived costs associated with PHM solutions pose a challenge for some organizations, limiting their ability to fully implement and optimize these technologies. Despite this obstacle, the potential benefits of PHM, including improved patient care and population health, make it a strategic priority for many healthcare organizations. To capitalize on this opportunity, companies must focus on cost-effective solutions and innovative approaches to addressing the challenges of PHM implementation and optimization. By leveraging advanced analytics, cloud technologies, and strategic partnerships, organizations can overcome cost barriers and deliver better care to their patient populations.

What will be the size of the US Population Health Management (PHM) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The Population Health Management (PHM) market in the US is experiencing significant advancements, integrating various elements to improve patient outcomes and reduce healthcare costs. Public health surveillance and data governance ensure accurate population health data, enabling healthcare leaders to identify health disparities and target interventions. Quality measures and health literacy initiatives promote transparency and patient activation, while data visualization and business intelligence facilitate data-driven decision-making. Behavioral health integration, substance abuse treatment, and mental health services address the growing need for holistic care, and outcome-based contracts incentivize providers to focus on patient outcomes. Health communication, community health workers, and patient portals enhance patient engagement, while wearable devices and mHealth technologies provide real-time data for personalized care plans. Precision medicine and predictive modeling leverage advanced analytics to tailor treatment approaches, and social service integration addresses the social determinants of health. Health data management, data storytelling, and healthcare innovation continue to drive market growth, transforming the industry and improving overall population health.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareServicesDeploymentCloudOn-premisesEnd-userHealthcare providersHealthcare payersEmployers and government bodiesGeographyNorth AmericaUS

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

Population Health Management (PHM) software in the US gathers patient data from healthcare systems and utilizes advanced analytics tools, including data visualization and business intelligence, to predict health conditions and improve patient care. PHM software aims to enhance healthcare efficiency, reduce costs, and ensure quality patient care. By analyzing accurate patient data, PHM software enables the identification of community health risks, leading to proactive interventions and better health outcomes. The adoption of PHM software is on the rise in the US due to the growing emphasis on value-based care and the increasing prevalence of chronic diseases. Machine learning, artificial intelligence, and predictive analytics are integral components of PHM software, enabling healthcare payers to develop personalized care plans and improve care coordination. Data integration and interoperability facilitate seamless data sharing among various healthcare stakeholders, while data visualization tools help in making informed decisions. Public health agencies and healthcare providers leverage PHM software for population health research, disease management programs, and quality improvement initiatives. Cloud computing and data warehousing provide the necessary infrastructure for storing and managing large volumes of population health data. Healthcare regulations mandate the adoption of PHM software to ensure compliance with data privacy and security standards. PHM software also supports care management services, patient engagement platforms, and remote patient monitoring, empowering patients to take charge of their health. Welln

Women's Health Market Overview

Women's health encompasses various medical and wellness concerns unique to females throughout their lives.

This includes matters related to reproductive health, pregnancy, and childbirth, and the management of conditions such as breast cancer, gynecological issues, menopause, and hormonal imbalances.

Additionally, women's health well-being encompasses mental health, dietary considerations, and overall physical health.

Key components involve regular health check-ups, ensuring access to contraception, and providing family planning services.

The promotion of women's health involves the dissemination of knowledge, early identification of health problems, and tailoring of healthcare interventions to empower women to lead healthy lives while enabling them to make well-informed decisions concerning their bodies and reproductive choices.

This Data set is from the Behavioral Risk Factor Surveillance System survey of the United States. "The Behavioral Risk Factor Surveillance System (BRFSS) is the worlds largest, on-going telephone health survey system, tracking health conditions and risk behaviors in the United States yearly since 1984. Conducted by the 50 state health departments as well as those in the District of Columbia, Puerto Rico, Guam, and the U.S. Virgin Islands with support from the CDC, BRFSS provides state-specific information about issues such as asthma, diabetes, health care access, alcohol use, hypertension, obesity, cancer screening, nutrition and physical activity, tobacco use, and more." (http://www.cdc.gov/brfss/index.htm) Data URL: http://www.cdc.gov/brfss/maps/gis_data.htm All values a percentage from 0-100

From the New York Times GITHUB source: CSV US counties "The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository. United States Data

Data on cumulative coronavirus cases and deaths can be found in two files for states and counties.

Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information."

The specific data here, is the data PER US COUNTY.

The CSV link for counties is: https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv

Introduction

EHR Industry Statistics: Electronic Health Records (EHRs) are digital versions of patient paper charts, revolutionizing healthcare by providing instant, secure access to comprehensive medical information.

They include details like medical history, diagnoses, medications, and test results, consolidating data from various sources into one accessible record.

EHRs enhance patient care by supporting better coordination among healthcare providers, improving efficiency through reduced paperwork, and enabling patient engagement via access to their records.

Challenges include high implementation costs, interoperability issues between different systems, and concerns about data privacy.

Looking ahead, advancements aim to improve interoperability, enhance data analytics, and integrate with telemedicine for more efficient and personalized healthcare delivery.

This dataset explores Foster Care FY2000 - FY2005 Entries, Exits, and Numbers of Children In Care on the Last Day of Each Federal Fiscal Year. NOTE: This table reflects State data submitted to the Children's Bureau as of March 2007. The table does not include any estimates for individual States. Jurisdictions with insufficient data ("NA") are not included in the total for that year. Pre-2003 Nevada data were generated from various sources, rather than from a statewide child welfare system. NOTE: Ideally, if the number of children in the "in care" count declines, as it did during this period, the number of exits should consistently be greater than the number of entries in that year. However, this does not occur with these data. Underreporting of foster care exits by some States is the major reason for this data quality issue.

Digital Health And Wellness Market Outlook

The global digital health and wellness market size was valued at approximately USD 150 billion in 2023 and is projected to reach nearly USD 600 billion by 2032, growing at a compound annual growth rate (CAGR) of 16.5% during the forecast period. This substantial growth is primarily driven by the increasing adoption of digital health technologies, the rising prevalence of chronic diseases, and the continuous advancements in healthcare IT infrastructure.

One of the major growth factors contributing to the expansion of the digital health and wellness market is the rising prevalence of chronic diseases such as diabetes, cardiovascular diseases, and respiratory disorders. With an aging global population and changing lifestyles, the burden of chronic diseases is escalating, necessitating efficient and innovative healthcare solutions. Digital health technologies, such as telehealth and remote monitoring, offer significant advantages in managing these conditions by facilitating timely interventions and continuous monitoring, thereby improving patient outcomes and reducing healthcare costs.

Another significant factor driving the market growth is the increasing penetration of smartphones and the internet, coupled with the growing awareness and acceptance of digital health solutions among consumers and healthcare providers. Mobile health (mHealth) applications are becoming essential tools for health management, providing users with easy access to health information, remote consultations, and personalized health tracking. The convenience and accessibility offered by these digital health tools are encouraging more individuals to adopt them, leading to a surge in demand and market expansion.

The continuous advancements in healthcare IT infrastructure and the integration of advanced technologies such as artificial intelligence (AI), big data analytics, and the Internet of Things (IoT) are further propelling the digital health and wellness market. These technologies enable more precise diagnostics, personalized treatment plans, and efficient healthcare delivery. For instance, AI-powered algorithms can analyze vast amounts of health data to identify patterns and predict potential health issues, while IoT devices facilitate seamless data exchange between patients and healthcare providers, enhancing the overall efficiency of healthcare services.

Regionally, North America is leading the digital health and wellness market, driven by the high adoption rate of advanced healthcare technologies, supportive government policies, and significant investments in healthcare IT infrastructure. The United States, in particular, is at the forefront, with numerous digital health startups and established firms actively innovating in this space. However, other regions such as Europe and Asia Pacific are also witnessing substantial growth, fueled by increasing healthcare expenditures, rising awareness about digital health solutions, and the expansion of telehealth services.

Connected Health Solutions are becoming increasingly pivotal in the digital health and wellness market, as they offer a seamless integration of various healthcare services through digital platforms. These solutions enable real-time communication and data sharing between patients and healthcare providers, enhancing the quality and efficiency of care. By leveraging technologies such as IoT and AI, Connected Health Solutions facilitate continuous monitoring and personalized healthcare, which is particularly beneficial for managing chronic conditions. The ability to connect different healthcare systems and devices ensures that patients receive comprehensive and coordinated care, reducing the risk of errors and improving overall health outcomes. As the demand for integrated healthcare solutions grows, Connected Health Solutions are set to play a crucial role in transforming the healthcare landscape.

Component Analysis

The digital health and wellness market is segmented by component into software, hardware, and services. Each component plays a crucial role in the functionality and efficiency of digital health solutions, and their interplay determines the overall effectiveness of digital health systems. Software, as a component, encompasses a wide array of applications, including electronic health records (EHRs), telehealth platforms, a

This dissertation argues for a new understanding of the politics of health. Health politics is usually seen as merely another redistributive issue, no different than questions of taxation or other social policies. Scholars and policy-makers assume that the demand for government-funded health care is driven chiefly by the needs of the economically worst-off, and that Left governments are the best partisan prescription for solving the health care crisis. However, my dissertation argues that health politics is not a traditionally redistributive issue, and that the institutions of American representative democracy present virtually insurmountable obstacles to translating voters' preferences on health into government policy. Focusing on the US as the most dramati c failure of democracy to improve the health of its citizens, I explain how volatility in public opinion, the dynamics of representation, and institutional determinants of policy choice have all conspired to obstruct the rise of a modern health care system in the United States. Placing the American case into a comparative perspective, I also demonstrate how politics and inequality explain why the US lags in health and health care provision behind other industrialized democracies, and, consequently, how Americans bear disproportionate human costs in infant lives during economic crises. The dissertation consists of four empirical papers that study politics and health, from the microfoundations of preference formation to voter choice and comparative macro-level outcomes over time. The first paper reports the results of a nationally-representative survey experiment I designed to measure how cueing Americans to income inequality and racial disparities changes their willingness to back the reform of the health care system. It finds that cueing Americans to income ineq uality and especially to racial disparities causes them to shift their support from health care to the alternate social policy goal of education reform, which is viewed as a more effective tool for achieving wealth redistribution or upward social mobility. The second paper estimates the role of health care demand and policy preferences in Americans' vote choice in five national elections over the past two decades, combining for the first time Medicare usage data from over 48.8 million individual hospital records for elderly Americans with district-level Congressional election outcomes, as well as estimating vote choice models using the Congressional Cooperative Election Survey and the Harvard School of Public Health/Kaiser Family Foundation poll during the Obama-McCain 2008 Presidential election. The results show health care preferences are multi-dimensional and their effect on vote choice is heterogeneous; depending on the local political context --- health and socio-economic disparities in particular --- the demand for lower medical costs and greater health care coverage can lead voters to vote for Democratic or Republican candidates. The third paper places the US in the comparative context, showing that partisanship and income inequality have both a short- and long-term impact on infant mortality and life expectancy in advanced industrialized democracies. Finally, the fourth paper (with Matthew C. Harding and Carlos Lamarche) quantifies the effect that prolonged economic recessions have on infant mortality across all industrialized democracies, using new quantile regression models with dynamic country effects. The results show that when crises strike, American infants are at a comparatively greater risk than infants in other industrialized democracies, with the US experiencing a greater than proportional increase in infant mortality in the short-run, requiring substantial public spending on healthcare in order to offset these human costs. This dissertation pinpoints when and why Americans refuse to support expansion of government-funded health care; why, even when there is demand for a government intervention, voters still elect candidates who obstruct heath care reform; and how politics and inequality hurt the health of the US public, exposing some of the most vulnerable Americans to even greater risks during economic downturns. The dissertation suggests that the electoral institutions of American democracy cannot serve the voters' interests when it comes to health, the most basic component of human welfare. Improving Americans' health, therefore, first requires diagnosing and successfully treating the pathologies in American representative democracy.

These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues. The National Health Interview Survey (NHIS) is conducted annually and sponsored by the National Center for Health Statistics (NCHS), which is part of the U.S. Public Health Service. The purpose of the NHIS is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive across the United States population through the collection and analysis of data on a broad range of health topics. The redesigned NHIS questionnaire introduced in 1997 (see National Health Interview Survey, 1997 [ICPSR 2954]) consists of a core that remains largely unchanged from year to year, plus an assortment of supplements varying from year to year. The 2010 NHIS Core consists of three modules: Family, Sample Adult, and Sample Child. The datasets derived from these modules include Household Level, Family Level, Person Level, Injury/Poison Episode Level, Injury/Poison Verbatim Level, Sample Adult Level, and Sample Child level. The 2010 NHIS supplements consist of stand alone datasets for Cancer Level and Quality of Life data derived from the Sample Adult core and Disability Questions Tests 2010 Level derived from the Family core questionnaire. Additional supplementary questions can be found in the Sample Child dataset on the topics of cancer, immunization, mental health, and mental health services and in the Sample Adult dataset on the topics of epilepsy, immunization, and occupational health. Part 1, Household Level, contains data on type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each sampling unit. Parts 2-5 are based on the Family Core questionnaire. Part 2, Family Level, provides information on all family members with respect to family size, family structure, health status, limitation of daily activities, cognitive impairment, health conditions, doctor visits, hospital stays, health care access and utilization, employment, income, participation in government assistance programs, and basic demographic information. Part 3, Person Level, includes information on sex, age, race, marital status, education, family income, major activities, health status, health care costs, activity limits, and employment status. Parts 4 and 5, Injury/Poisoning Episode Level and Injury/Poisoning Verbatim Level, consist of questions about injuries and poisonings that resulted in medical consultations for any family members and contains information about the external cause and nature of the injury or poisoning episode and what the person was doing at the time of the injury or poisoning episode, in addition to the date and place of occurrence. A randomly-selected adult in each family was interviewed for Part 6, Sample Adult Level, regarding specific health issues, the relation between employment and health, health status, health care and doctor visits, limitation of daily activities, immunizations, and behaviors such as smoking, alcohol consumption, and physical activity. Demographic information, including occupation and industry, also was collected. The respondents to Part 6 also completed Part 7, Cancer Level, which consists of a set of supplemental questions about diet and nutrition, physical activity, tobacco, cancer screening, genetic testing, family history, and survivorship. Part 8, Sample Child Level, provides information from an adult in the household on medical conditions of one child in the household, such as developmental or intellectual disabilities, respiratory problems, seizures, allergies, and use of special equipment like hearing aids, braces, or wheelchairs. Parts 9 through 13 comprise the additional Supplements and Paradata for the 2010 NHIS. Part 9, Disability Questions Tests 2010 Level