Adjusted Income and Poverty in the US

Changes in People and Health Insurance

By U.S. Census Bureau [source]

About this dataset

The U.S. Bureau of the Census' Current Population Survey, Annual Social and Economic Supplements, presents an insightful look into American society at any given time period. Through it's annual data, one can understand the makeup of a nation across a multitude of parameters--including income level distribution measures, poverty status characteristics and health insurance coverage broken down by age, race/ethnicity and gender.

This chart series is like a snapshot into America's past--allowing us to monitor both current progress made in regards to economic stability while also reflecting on the growth (or lack thereof) achieved over different decades in terms of racial discrepancies in poverty levels as well as an individual's ability present etc to maintain financial health. The series looks at data collected from 1959-2015; providing information on number/percentage all noninstitutionalized population (15+ years old) who are below or above poverty thresholds as well as median earnings for male/female earners adjusted for real inflation values (based on current dollars). Insights such as these enable us to gain key information about how economic disparities have fared during each year throughout this half century time span and how policy changes have impacted the overall wellbeing on a national level since then

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How to use the dataset

Introduction

This dataset contains information on the equivalence-adjusted income and poverty in the US from 1967 to 2015. It includes information on the population without health insurance coverage by state, total workers and full-time, year-round workers by sex and female-to-male earnings ratio, selected measures of equivalence-adjusted income dispersion, people in poverty by selected characteristics, and measures of income inequality. This guide will explain how to use this dataset effectively for analysis.

Data Overview

The datasets contain both summary statistics and detailed breakdowns for different categories throughout the years 1967 to 2015. In Table A1 you can find data on population without health insurance coverage by state during that time period. Table A4 contains total numbers of workers as well as real median earning details organized by sex and male/female earning ratios over time period in question. The tables A3 through 5 include more specific details related to measurements of Equivalence Adjusted Income Dispersion such as Gini Coefficient values.. Both table 2 & 3 provides detail breakdowns relating to Income distribution measurements between 2014 & 2015 along with other related statistical figures regarding individuals below poverty line during this time period based upon age , race , Hispanic Origin factors.

Data Cleaning/Preparation Specifics

This dataset follows a similar notation used throughout each table so it shouldn't be difficult understand what is being represented .However representing individual components like Gini Coefficient (TableA3) or Female ratio Vs Male earnings remains abstract in comparison especially when attempting visualization techniques (Charting). In order for users not familiar with certain terms like “Equivalence -Adjusted Income Dispersion” it would need explaining thoroughly or these terms should at least be highlighted & avoid confusing readers . Level out Missing Data that is within range statistically makes sense according “Census Technical Docs” . For example missing value data pertaining Individual Poverty estimates have based upon qualification requirements where numbers are rounded up after exchange calculations ( See official Raw Data column Notes available under Sources ).

Visualization Strategies

For effective visualization there needs be understanding between what counts supplied are actually representing For example: Column such as Difference Between Female & Male Earnings shown TableA4 helps gauge pay gap but difference between % Measures significantly important when charting any changes overtime diagrams or identifying movements visually from various bar /line graphs dealing this type data set . Other numerical aspects such Gender Ratio

Research Ideas

• Tracking changes in poverty levels over time by state and ethnicity
• Examining the impact of government programs like the EITC and CTC on pov...

In 2024, around ********* of U.S. adults with a family income of less than 100% Federal Poverty Level (FPL) did not have health insurance, the lowest in the provided time interval. This statistic shows the percentage of adults aged 18-64 years without health insurance in the United States from 2019 to 2024, by family income as a percentage of FPL.

This dataset contains estimates of health insured and uninsured population for 2020 at county and state level based on US Census Bureau program, The Small Area Health Insurance Estimates (SAHIE) program. For every state and county for each demographic group, defined by age, gender, race/ethnicity and income relative to poverty, the estimated number of persons insured and uninsured is given along with the margin of error.

BackgroundIn 2016, the Chinese government introduced an integration reform of the health insurance system with the aim to enhance equity in healthcare coverage and reduce disparities between urban and rural sectors. The gradual introduction of the policy integrating urban and rural medical insurance in pilot cities provides an opportunity to evaluate the policy impact. This study attempts to assess the policy impact of urban–rural health insurance integration on the chronic poverty of rural residents and to analyze the mechanisms.MethodBased on the four waves of data from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011, 2013, 2015, and 2018, we employed a staggered difference-in-differences (staggered DID) model to assess the impact of integrating urban–rural health insurance on poverty vulnerability among rural inhabitants and a mediation model to analyze the mechanism channel of the policy impact.Results(1) Baseline regression analysis revealed that the urban–rural health insurance integration significantly reduced the poverty vulnerability of rural residents by 6.32% (p < 0.01). The one health insurance system with one unified scheme of contributions and benefits package (OSOS, 6.27%, p < 0.01) is more effective than the transitional one health insurance system with multiple schemes (OSMS, 3.25%, p < 0.01). (2) The heterogeneity analysis results showed that the urban–rural health insurance integration had a more significant impact on vulnerable groups with relatively poor health (7.84%, p < 0.1) than those with fairly good health (6.07%, p < 0.01), and it also significantly reduced the poverty vulnerability of the group with chronic diseases by 9.59% (p < 0.01). The integration policy can significantly reduce the poverty vulnerability of the low consumption and low medical expenditure groups by 8.6% (p < 0.01) and 7.64% (p < 0.01), respectively, compared to their counterparts. (3) The mechanism analysis results showed that the urban–rural health insurance integration can partially enhance labor supply (14.23%, p < 0.01) and physical examinations (6.28%, p < 0.01). The indirect effects of labor supply and physical examination in reducing poverty vulnerability are 0.14%, 0.13% respectively.ConclusionThe urban–rural health insurance integration policy significantly reduced poverty vulnerability, and the OSOS is more effective than the OSMS. The urban–rural health insurance integration policy can significantly reduce poverty vulnerability for low consumption and poor health groups. Labor supply and physical examination are indirect channels of the impact. Both channels potentially increase rural household income and expectations of investment in human health capital to achieve the policy objective of eliminating chronic poverty.

Abstract (en): In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Presence of Common Scales: Patient Health Questionnaire-9 (PHQ-9) Total Severity Score SF-8 Health Survey Physical Component Score SF-8 Health Survey Mental Component Score Framingham Risk Score Response Rates: For the mail surveys, the response rates were 45 percent for the initial survey, 49 percent for the six month survey, and 41 percent for the 12 month survey. For the in-person survey the response rate was 59 percent. The individu...

In 2024, *** percent of all people in the United States didn't have health insurance. The share of Americans without health insurance saw a steady increase from 2015 to 2019 before starting to decline from 2020 to 2023. Factors like the implementation of Medicaid expansion in additional states and growth in private health insurance coverage led to the decline in the uninsured population, despite the economic challenges due to the pandemic in 2020. More coverage after Obamacare The groups who saw the biggest improvement in health insurance coverage after the ACA was enacted were Hispanic and Black Americans. Meanwhile, the share of White Americans without health insurance also fell due to Obamacare, but the drop in that group wasn’t as dramatic as in other ethnic groups. This is primarily due to the fact that the uninsured rate among White Americans was much lower pre-ACA than among any other group, so there was less room for improvement. ACA was especially significant for those with low income Although the ACA was signed into law in 2010, many of its major provisions didn’t come into force until 2014, which accounts for the sharp drop in Americans without health insurance in 2014. Adults with a family income lower than 200% of Federal Poverty Level (FPL) were especially impacted by the law, as the share of uninsured adults in this income group dropped ** percent between 2013 and 2015.

This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions

The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties.

For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64

•3 sex categories: both sexes, male, and female

•6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold

•4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race).

In addition, estimates for age category 0-18 by the income categories listed above are published.

Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured.

This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges.

We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response.

The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010

Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.

The survey represents employer-based health insurance coverage among U.S. women in 2008, by poverty level. The results show that 13 percent of women who fell under the "poor" category had employer-sponsored health insurance coverage.

This paper identifies a “health premium” of insurance coverage: insured individuals are more likely to maintain good health or recover from poor health. We introduce this feature into a prototypical macro-health model and estimate the baseline economy by matching the observed joint distribution of health insurance, health, and income over the life cycle. Quantitative analysis reveals that an individual’s insurance status has a substantial and persistent impact on health. Providing universal health coverage would narrow health and life expectancy gaps, with a mixed effect on the income distribution in the absence of any additional redistribution of income or wealth.

In 2023, around ** percent of all uninsured were from the <100% poverty level group. Moreover, of those in the lowest poverty level, roughly one in five were uninsured. This statistic shows the percentage and rate of non-elderly people without health insurance in the U.S. in 2023, by family poverty level.

In District of Columbia, the poverty rate is 14.5% and the uninsured rate is 3.4%. Percent of people below the federal poverty line and the share without health insurance. Source: ACS 5-year estimates (derived).

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The 2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

This dataset offers census tract level estimates for the number of uninsured noninstitutionalized civilians, number of persons below poverty line, unemployed population, number of persons with no high school diploma, which are socioeconomic characteristics with a negative impact on the access to healthcare services.

ObjectiveTo determine the association of Medicaid expansion with health insurance coverage by marital status and sex.MethodsA population-based, quasi-experimental policy analysis was undertaken of the implementation of the Patient Protection and Affordable Care Act’s (ACA) Medicaid expansion provision on or after January 1, 2014. The 2010–16 American Community Survey provided data on 3,874,432 Medicaid-eligible adults aged 19–64 with incomes up to 138% of the federal poverty level. The outcome measures were no health insurance coverage and Medicaid coverage. The predictor variables were marital status and sex, with controls for family size, poverty status, race/ethnicity, education, employment status, immigration status, and metropolitan residence.ResultsIn 2016, the uninsured rate for married men and women in a Medicaid expansion state was 21.2% and 17.1%, respectively, compared to 37.4% for married men and 35.8% for married women in a non-expansion state. The Medicaid coverage rate grew between 14.8% to 19.3% in Medicaid expansion states, which contrasts with less than a 5% growth in non-expansion states. Triple differences analysis suggests that, for women of all age groups, Medicaid expansion resulted in a 1.6 percentage point lower uninsured rate for married women compared to unmarried women. For men, there was not a statistically significant difference in the uninsured rate for married compared to unmarried men. For women of all age groups, there was a 2.6 percentage point higher Medicaid coverage rate for married compared to unmarried women. For men, there was a 1.8 percentage point higher Medicaid coverage rate for married compared to unmarried men.ConclusionMedicaid expansion under the ACA differentially lowered uninsurance and improved Medicaid coverage for married persons, especially married women, more than unmarried persons.

Health Outcomes and Socioeconomic Factors

A Study of US County Data

By Data Exercises [source]

About this dataset

This dataset contains a wealth of health-related information and socio-economic data aggregated from multiple sources such as the American Community Survey, clinicaltrials.gov, and cancer.gov, covering a variety of US counties. Your task is to use this collection of data to build an Ordinary Least Squares (OLS) regression model that predicts the target death rate in each county. The model should incorporate variables related to population size, health insurance coverage, educational attainment levels, median incomes and poverty rates. Additionally you will need to assess linearity between your model parameters; measure serial independence among errors; test for heteroskedasticity; evaluate normality in the residual distribution; identify any outliers or missing values and determine how categories variables are handled; compare models through implementation with k=10 cross validation within linear regressions as well as assessing multicollinearity among model parameters. Examine your results by utilizing statistical agreements such as R-squared values and Root Mean Square Error (RMSE) while also interpreting implications uncovered by your analysis based on health outcomes compared to correlates among demographics surrounding those effected most closely by land structure along geographic boundaries throughout the United States

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How to use the dataset

This dataset provides data on health outcomes, demographics, and socio-economic factors for various US counties from 2010-2016. It can be used to uncover trends in health outcomes and socioeconomic factors across different counties in the US over a six year period.

The dataset contains a variety of information including statefips (a two digit code that identifies the state), countyfips (a three digit code that identifies the county), avg household size, avg annual count of cancer cases, average deaths per year, target death rate, median household income, population estimate for 2015, poverty percent study per capita binned income as well as demographic information such as median age of male and female population percent married households adults with no high school diploma adults with high school diploma percentage with some college education bachelor's degree holders among adults over 25 years old employed persons 16 and over unemployed persons 16 and over private coverage available private coverage available alone temporary private coverage available public coverage available public coverage available alone percentages of white black Asian other race married households and birth rate.

Using this dataset you can build a multivariate ordinary least squares regression model to predict “target_deathrate”. You will also need to implement k-fold (k=10) cross validation to best select your model parameters. Model diagnostics should be performed in order to assess linearity serial independence heteroskedasticity normality multicollinearity etc., while outliers missing values or categorical variables will also have an effect your model selection process. Finally it is important to interpret the resulting models within their context based upon all given factors associated with it such as outliers missing values demographic changes etc., before arriving at a meaningful conclusion which may explain trends in health outcomes and socioeconomic factors found within this dataset

Research Ideas

• Analysis of factors influencing target deathrates in different US counties.
• Prediction of the effects of varying poverty levels on health outcomes in different US counties.
• In-depth analysis of how various socio-economic factors (e.g., median income, educational attainment, etc.) contribute to overall public health outcomes in US counties

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. -...

Table from the American Community Survey (ACS) 5-year series on disabilities and health insurance related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes C21007 Age by Veteran Status by Poverty Status in the Past 12 Months by Disability Status, B27010 Types of Health Insurance Coverage by Age, B22010 Receipt of Food Stamps/SNAP by Disability Status for Households. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): C21007, B27010, B22010Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within Arc

This statistic shows the estimated impacts of the Affordable Care Act (ACA) on health insurance coverage in the United States in 2014 and 2023. For 2023, it is estimated that there will be ** million more individuals covered under Medicaid and CHIP. Medicaid is an optional plan for the United States. Enrollment for this plan is expected to increase after provisions from the Affordable Care Act that established an minimum eligibility threshold at *** percent of the federal poverty level.

Universal Health Coverage (UHC) is a global objective aimed at providing equitable access to essential and cost-effective healthcare services, irrespective of individuals’ financial circumstances. Despite efforts to promote UHC through health insurance programs, the uptake in Kenya remains low. This study aimed to explore the factors influencing health insurance uptake and offer insights for effective policy development and outreach programs. The study utilized machine learning techniques on data from the 2021 FinAccess Survey. Among the models examined, the Random Forest model demonstrated the highest performance with notable metrics, including a high Kappa score of 0.9273, Recall score of 0.9640, F1 score of 0.9636, and Accuracy of 0.9636. The study identified several crucial predictors of health insurance uptake, ranked in ascending order of importance by the optimal model, including poverty vulnerability, social security usage, income, education, and marital status. The results suggest that affordability is a significant barrier to health insurance uptake. The study highlights the need to address affordability challenges and implement targeted interventions to improve health insurance uptake in Kenya, thereby advancing progress towards achieving Universal Health Coverage (UHC) and ensuring universal access to quality healthcare services.

In New York, the poverty rate is 13.7% and the uninsured rate is 5.1%. Percent of people below the federal poverty line and the share without health insurance. Source: ACS 5-year estimates (derived).

In Oregon, the poverty rate is 11.9% and the uninsured rate is 6.2%. Percent of people below the federal poverty line and the share without health insurance. Source: ACS 5-year estimates (derived).