Blog & News
Charting Two Decades of the Evolving Opioid Crisis
November 18, 2022:
In more than 20 years, the overdose crisis has shifted and grown from prescription opioids to a range of illicitly trafficked drugs |
Just over a decade after the U.S. Centers for Disease Control and Prevention (CDC) declared an “epidemic” of overdoses from prescription painkillers in 2011, the opioid crisis is worse than ever—yet it only vaguely resembles those earlier days. While people are still dying of overdoses tied to prescription opioids, the problem has largely shifted to illicitly trafficked opioids, such as fentanyl, and is now deeply intertwined with other non-opioid substances, such as methamphetamine and cocaine.
This blog examines the history and evolution of the opioid crisis through several charts based on data from SHADAC’s State Health Compare.
Changing attitudes in opioid prescribing
Today, it is broadly accepted that the over-prescribing of opioid painkillers—such as the blockbuster drug Oxycontin (a brand-name version of oxycodone produced by Purdue Pharma) and related semi-synthetic opioid hydrocodone—sparked what became an epidemic of overdoses and deaths. Over more than 10 years, deaths from overdoses involving those medications gradually crept upward until they finally capture widespread attention. When the toll of overdose deaths from prescription opioids was fully recognized and the U.S. healthcare system started to grapple with the problem, efforts to curb prescribing began.
In the first chart, data from the U.S. Drug Enforcement Agency show how legal sales of oxycodone and hydrocodone began the early days of the crisis at about 5 kilograms per 100,000 people in 2000. But within a decade, oxycodone sales roughly quadrupled and hydrocodone sales almost tripled (Figure 1). Following growing awareness of the problem, legal sales of both drugs—driven by prescriptions written by healthcare providers—have declined substantially. In 2021, hydrocodone sales had fallen to about 5 kilograms per 100,000 people, and oxycodone sales to less than 10 kilograms per 100,000 people.
Figure 1: Prescription opioid painkiller sales, 2000-2021
Source: SHADAC analysis of U.S. Drug Enforcement Agency's Automated Reports and Consolidated Ordering System (ARCOS) Retail Drug Summary Reports, obtained from statehealthcompare.shadac.org.
These data demonstrate that legal sales of prescription opioid painkillers are down dramatically from their heights when the CDC first rang alarm bells over the issue. However, some experts and advocates argue that the abrupt pivot in opioid prescribing practices was not done carefully enough, with many people who were already addicted to opioids suddenly cut off without sufficient screening for addiction or access to treatment. And data show that although deaths from prescription opioids have essentially plateaued since prescribing of opioid painkillers dropped, opioid overdose deaths have still continued to climb.
Persistently growing rates of opioid overdose deaths
Because overdose deaths were driven largely by prescription opioid painkillers in the early days of the epidemic, the hope and expectation was that death rates would drop as healthcare providers curtailed their prescribing of the risky medications. But this next set of charts shows that this hope was not fully realized.
In fact, the enhanced scrutiny on opioid prescribing and declining sales of prescription opioid painkillers appears to have marked a turning point in the epidemic. While deaths from prescription opioid painkillers have not declined dramatically, their growth trend leveled off and has remained relatively steady, around 4 deaths per 100,000 people, for almost a decade (Figure 2).
Figure 2: Prescription opioid painkiller death rates, 1999-2020
Source: SHADAC analysis of National Center for Health Statistics mortality files, obtained from statehealthcompare.shadac.org.
But to the shock and horror of many, overall overdose death rates—including prescription opioid painkillers as well as all other kinds of opioids—only continued to accelerate.
Figure 3: Death rates from all opioid types, 1999-2020
Source: SHADAC analysis of National Center for Health Statistics mortality files, obtained from statehealthcompare.shadac.org.
In less than a decade, the overdose death rate from all opioids roughly tripled, from 7.3 deaths per 100,000 people in 2011 to 21.4 deaths per 100,000 people in 2020 (Figure 3). But this time, the growth came from different types of opioids. First came heroin—an opioid that is without legal medical uses in the U.S. and is only available through the illicit drug trade. This was followed by fentanyl (and other closely related synthetic opioids), which does have legal medical uses but has been adopted by traffickers as a new drug of choice.
Figure 4: Death rates from fentanyl and other synthetic opioids, 1999-2020
Source: SHADAC analysis of National Center for Health Statistics mortality files, obtained from statehealthcompare.shadac.org.
In just a few years, the opioid crisis had transformed. What began as a problem rooted in widespread availability of prescription opioid painkillers shifted to illicitly trafficked opioids as legal opioid sales started to fall. By 2020, heroin overdose death rates had more than tripled since the CDC declared an opioid epidemic. And overdose death rates from fentanyl and similar synthetic opioids had grown more than 20 times (Figure 4).
In some ways, those developments may not be surprising. Economics tells us that when a good becomes scarce—such as prescription opioids after recognition of the crisis—people tend to cut back their consumption. But when people can’t simply stop their consumption, as in the case of addiction, they often turn to substitutes. With opioids, that unfortunately left many people to seek out substances such as heroin on the illicit market, where the purity and potency is unreliable, making them even riskier than prescription opioids. And once drug traffickers embraced the potent opioid fentanyl, it pervaded the illegal drug trade and became entangled with non-opioid substances, such as cocaine and methamphetamine.
A metastatic phase of the crisis
The opioid crisis is complex, and definitive evidence of how the epidemic evolved is hard to find. That is particularly the case in understanding the role of the illicit drug trade. However, it is widely accepted that some people shifted to heroin as prescription opioid painkillers became harder to obtain. Afterward, drug traffickers incorporated fentanyl into their supplies, sometimes to cheaply boost the potency of their heroin or simply to pass fentanyl off as heroin. Another approach was manufacturing counterfeit prescription medications, such as fake Oxycontin pills that actually contained fentanyl rather than the less-potent opioid oxycodone.
Over time, fentanyl became ubiquitous in the U.S. illicit drug market. Non-opioid illicitly trafficked substances, particularly cocaine and methamphetamine, are now often contaminated with fentanyl and related powerful opioids. And various kinds of illicitly trafficked counterfeit medications are found to contain fentanyl. In some cases, those counterfeit pills may mimic prescription opioid painkillers, but other counterfeit pills may contain fentanyl, even if the legitimate version does not. For instance, law enforcement agencies have reported interdicting fake stimulants (e.g., Ritalin and Adderall pills) that are mixtures of methamphetamine and fentanyl, even though the genuine medications do not contain opioids.
Ultimately, the pernicious impact of fentanyl and related synthetic opioids have transformed the opioid crisis. The prescription opioid painkillers that sparked this epidemic now only account for a fraction of drug overdose deaths in the U.S. (Figure 5), dwarfed by the toll of synthetic opioids such as fentanyl. And largely because of the pervasiveness of fentanyl in the market for illicit substances, deaths involving non-opioids also have grown to historic levels.
Figure 5: National Death rates for opioid and non-opioid substances, 2020
Source: SHADAC analysis of National Center for Health Statistics mortality files, obtained from statehealthcompare.shadac.org.
Lessons from opioids data
More than two decades into the opioid crisis, the main consistency is that the problem is continually evolving. When the role of prescription opioids in overdose deaths led the health care system to curtail dangerously generous prescribing of those medications, some people seemingly transitioned to illicitly trafficked opioids. When drug traffickers saw an opportunity to enhance their profits on heroin sales by using fentanyl and by manufacturing counterfeit pills, the epidemic became even deadlier. And as fentanyl became more pervasive, it also became intertwined with non-opioid substances, such as cocaine and methamphetamine—further extending the deadly reach of opioids.
To better understand and address the opioid crisis, it is important to use data to identify evolving patterns and trends and to anticipate new developments before they snowball into larger public health threats. Examining the history of the opioid crisis may prove useful to avoid recurrences of similar situations in the future, but it is also important to recognize the limitations of retrospective data for designing solutions to a dynamic situation. For instance, an overemphasis on prescribing of opioid painkillers now, when the vast majority of drug overdose deaths are caused by illicitly trafficked substances, could be considered akin to driving down a busy interstate highway while fixed on the rearview mirror.
Related Reading:
Blog: 2020 U.S. alcohol-involved deaths climbed by 26.6%, and drug overdose deaths by 30.6%
Resource Page: The Opioid Epidemic in the United States
Blog & News
Pandemic drinking may exacerbate upward-trending alcohol deaths
June 14, 2021:Even before 2020, alcohol-involved deaths reached a modern record
Considering the well-deserved attention paid to the opioid crisis in recent years, few people might guess that rates of alcohol-involved deaths were as high as or higher than opioid overdose death rates in nearly half of states (Figure 1).1 Like the opioid crisis, the trend in alcohol-involved deaths is also worsening, having grown by roughly 50 percent in just over a decade. All this was before the coronavirus crisis had even begun.
Figure 1. State alcohol death rates vs. opioid death rates, 2019
Data and analysis on alcohol-involved deaths Read more about growing alcohol-involved death |
Now, evidence is accumulating that the pandemic precipitated dangerous changes in the way people consume alcohol in the United States. For instance, research has found increased alcohol sales since the crisis began, a finding illustrated by data showing that liquor taxes represented a rare instance of increased revenues for some states, such as Minnesota, during the COVID pandemic.2,3 Other studies have found that U.S. adults report consuming more alcohol in order to deal with pandemic-related stress, and that they are drinking more frequently and engaging in more high-risk drinking behaviors, such as heavy drinking and binge drinking.4,5,6
As we climb our way out of the immediate crisis, the U.S. will need to shift attention back to long-running public health threats. Beyond the obvious toll of the virus itself, another legacy of the pandemic may be the exacerbation of existing problems, including alcohol-related deaths and the opioid crisis. The opioid crisis was commonly recognized before 2020, but the upward trend in alcohol deaths was still occurring largely under the radar (Figure 2). But recent attention to risky pandemic-related alcohol consumption can sharpen our focus on this emerging concern.
Figure 2. U.S. alcohol-involved death rates, 2000-2019
With alcohol especially, the U.S. has a window of opportunity to intervene before many people’s pandemic-era risky drinking habits result in deaths, since the bulk of alcohol-involved deaths result from years of excessive drinking. In the coming years, it will be vital for states to monitor and study these issues and to consider doubling down on policy initiatives to curb the tide through efforts such as enhancing access to treatment of substance use disorder and by persuading and assisting people in recalibrating their alcohol consumption to healthier levels.
Visit State Health Compare to explore state-level data on alcohol death and opioid death rates.
1 SHADAC Staff. U.S. alcohol-related deaths grew nearly 50% in two decades: SHADAC briefs examine the number among subgroups and states. https://www.shadac.org/news/us-alcohol-related-deaths-grew-nearly-50-two-decades. Published April 19, 2021. Accessed May 12, 2021.
2 Rebalancing the ‘COVID-19 effect’ on alcohol sales. Nielseniq.com. https://nielseniq.com/global/en/insights/2020/rebalancing-the-covid-19-effect-on-alcohol-sales/. Published May 7, 2020. Accessed May 12, 2021.
3 Ewoldt J. Liquor stores neared sales records for 2020 as bars, restaurants closed. Startribune.com. https://www.startribune.com/liquor-stores-neared-sales-records-for-2020-as-bars-restaurants-closed/573469221/. Published December 26, 2020. Accessed May, 12, 2021.
4 American Psychological Association. Stress in America: One year later, a new wave of pandemic health concerns. https://www.apa.org/news/press/releases/stress/2021/sia-pandemic-report.pdf. Published March 2021. Accessed May 12, 2021.
5 Pollard MS, Tucker JS, Green HD. Changes in adult alcohol use and consequences during the COVID-19 pandemic in the US. JAMA Netw Open. 2020; 3(9): e2022942. doi: 10.1001/jamanetworkopen.2020.22942.
6 Grossman ER, Benjamin-Neelon SE, Sonnenschein S. Alcohol Consumption during the COVID-19 Pandemic: A Cross-Sectional Survey of US Adults. Int J Environ Res Public Health. 2020;17(24): 9189. doi: 10.3390/ijerph17249189
Blog & News
Expert Perspective: State COVID-19 Data Dashboards (State Health & Value Strategies Cross-Post)
April 10, 2020:The following content is cross-posted from State Health and Value Strategies. It was first published on April 9, 2020.
Authors: Emily Zylla and Lacey Hartman, SHADAC
Accurate, timely data is a key tool in states’ efforts to understand and respond to the impact of the coronavirus (COVID-19) outbreak at the local level. There have also been increasing calls to further break down COVID-19 data into subcategories (such as by gender, age, and race and ethnicity) in order to track the impact of the disease on specific populations. As of April 6, all 50 states and DC are publicly reporting some type of data related to COVID-19, such as the number of positive tests and/or the number of deaths. Furthermore, some states have recently begun to utilize innovative dashboards in order to visualize and track reported cases of coronavirus disease as well as monitor additional related key indicators. These dashboards are designed to organize complex data in an easy-to-digest visual format, allowing the audience to easily interpret key trends and patterns at a glance (e.g., see SHADAC’s COVID-19 dashboard template, which is currently under development using mock data).
Source: SHADAC COVID-19 dashboard template under development using mock data.
This expert perspective reviews the key indicators currently being tracked by states via their COVID-19 dashboards and also provides an overview of “best practices” states can consider when developing or modifying these same COVID-19 dashboards.
States that currently publish COVID-19 dashboards include: |
|
Alabama | Montana |
Alaska | Nebraska |
Arizona | Nevada |
Arkansas | New Jersey |
California | North Carolina |
Colorado | North Dakota |
Delaware | Ohio |
Florida | Oregon |
Idaho | South Carolina |
Indiana | Texas |
Iowa | Utah |
Kansas | Virginia |
Louisiana | Washington |
Maryland | West Virginia |
Minnesota | Wyoming |
Missouri |
Current Status of COVID-19 Dashboards
As of April 6, we identified 31 states with public-facing COVID-19 data dashboards (i.e., the information is displayed with charts and other graphics, not just in tabular form), and we anticipate that more states will publish COVID-19 dashboards in the coming days.
States are reporting a wide number (ranging from 4 to 13) and type of indicators in their dashboards, most of which are updated at least daily.
Many states are also starting to show trends in these data points over time. The most common indicators reported on a state dashboard include:
- Number of total cases
- Number of total deaths
- Number of cases by county
- Map of cases by county
- Number of tests completed
- Number of cases by age group
- Number of cases by gender
- Number of deaths by county
- Number of hospitalizations
Other key indicators that some states are reporting that may be of interest include:
- Total number of recovered cases (i.e., cases that are no longer required to isolate)
- Number of hospitalizations that require ventilation
- Number of deaths by age/gender/race/ethnicity
- Case rate per 100,000 people by county
- Number of cases by race/ethnicity
- Number of cases by congregate living setting (e.g., long-term care, assisted living, dorms, jails, correctional settings, etc.)
- Number of tests completed by laboratory type (e.g., public vs. commercial labs)
- Number of tests completed by race/ethnicity
- Number of calls to a state’s COVID-19 hotline or number of hits on a COVID-19 website
It is important to note that states may be defining indicators that appear initially similar in different ways. For example, some states report “hospitalizations” as the total number of cases who have ever been hospitalized, while other states report “hospitalizations” as the current number of hospitalizations on a certain day. As a result, users should be cautious about making comparisons across states. In most cases, states have not identified the sources of their dashboard data beyond indicating that data is maintained by the respective state’s health department (or equivalent) and that it comes from a variety of sources such as state and local public health surveillance data, lab data (including public health, hospital, and commercial labs), and hospital reporting systems, among others. While the quality of the data being reported is difficult to assess currently (and is therefore reported as provisional), many states have acknowledged that data on confirmed cases represent an undercount due to a lack of widespread testing. Similarly, states have suggested that data on the number of deaths from COVID-19 may also change as post-mortem testing expands and guidance on how to record COVID-19 deaths is established. As mentioned below, we encourage states to include information on data quality, such as levels of missing data, where possible.
COVID-19 Dashboard Best Practices
Audience: Before designing a dashboard, make sure to clearly identify who is the intended audience. Different levels of detail, explanation, or source information may be necessary depending on whether the intended audience is state agency leadership, political leadership, or the general public. It is also important to think about what medium you will be using to reach the audience. Will the dashboard only be published on a website? Will it be available on a mobile device? Or, might you want to print it as a handout or post it on social media?
Organization and layout: Prioritize key measures. Because timeliness of these data is so important, the dashboard needs to have enough data points to convey key information, but limited enough to update quickly. It is helpful to have a landing page that makes all indicators visible to users with limited scrolling, but also provides users with the ability to “drill down” to more detail—comparisons, methodology, etc. If it is not possible to show all indicators, there should be an obvious and intuitive option for the user to “hover” over a list and get an “at a glance” view of the available content. The following example shows Florida’s COVID-19 Dashboard landing page, which implements many of these best practices.
Source: Florida COVID-19 Data and Surveillance Dashboard. Accessed March 30, 2020.
Think about any potential layout in terms of a story. It is helpful to group indicators into high-level categories with headings (e.g., overview, demographics, hospitalizations, etc.). This provides additional context for interpreting the data without the need for lengthy text descriptions. In addition, many dashboards are modular in nature so that visual elements can be replaced as information relevancy changes over time (e.g., information on likely source of exposure may become less relevant over time, while information on health care workforce exposure may become more relevant).
Health equity: In order to understand the potentially disproportionate impact COVID-19 may have on communities of color, low-income, and other populations that face health disparities, it will be important for states to track both COVID-19 related cases and health outcome data disaggregated for key subpopulations, such as gender, race and ethnicity, geography (e.g., urban vs. rural), and insurance status. Several states are reporting data by race/ethnicity, which is critical as early reports suggest that the crisis is disproportionately impacting communities of color. For states that do report data on race, we recommend including detail about the scope of the missing data (and the reason, if possible) to help users interpret the findings. In the example below, North Carolina’s dashboard reports confirmed cases and deaths broken down by race and ethnicity. North Carolina also clearly states the data’s limitations—i.e., the number of cases for which race and ethnicity data are missing.
Another breakdown important for monitoring equity is geography. Nearly all states are reporting data at the county level. It may also be helpful for states to present information that compares metrics in urban versus rural areas, as the unique challenges of the virus (e.g., overcrowding in densely populated areas vs. more limited hospital resources in rural areas) differ significantly by these factors. There are several approaches to defining urban versus rural areas and each have advantages and disadvantages, but given that states are already collecting COVID-19 information at the county level, it may be most straightforward to disaggregate information using the Census definition that classifies counties as “completely rural”, “mostly rural”, and “mostly urban”.
In addition to providing data by race and geography, it would be ideal if states could provide additional subpopulation breakdowns such as primary language, socioeconomic status (e.g., education, income, occupation), and disability status, if the data is available. Due to the rapid emergency response required to address the COVID-19 outbreak, we realize states may not initially have the time or bandwidth to produce a broad range of subpopulation analysis or to conduct additional analyses of their demographic data, such as looking at the intersectionality of data (e.g., by race and gender). However, those types of analyses will be increasingly important as states seek to understand disparities in COVID-19 treatment access, morbidity, and mortality.
Date and time-stamps: Because these indicators are subject to change so rapidly, it will be important to date and time-stamp any dashboard updates. In addition to date-stamping the entire dashboard, also consider adding the date (and source information) to any graphic that could potentially be used as a stand-alone item in another report or on social media. For example, the graphic below represents the age distribution of a state’s COVID-19 cases and is labeled “As of 3/31/2020” so that it’s clear what time period this represents, even when the image is viewed separately from the overall dashboard.
Data labels, definitions, and sources: Provide clear data labels and documentation. Although you should avoid “cluttering” a dashboard with extensive text, it is also important to provide the audience with information about data definitions and sources. Below is an example from North Dakota’s data dashboard showing how they included definitions for each of their six key indicators below their visualization. If space is limited, it is fine to provide hyperlinks to more detailed information on these factors. However, the links should be tested regularly to ensure they are still “live” and taking users to the correct information. Source: North Dakota COVID-19 Dashboard. Accessed March 30, 2020. |
Time-series data: Visually displaying time-series data is an effective way to track changes. In order to improve readability, try to ensure that all time-trended data on the dashboard starts with the same date and covers the same time period, if possible. For example, although deaths and hospitalizations began ramping up at different times, these two time-trended graphs on Ohio’s dashboard start on the same date and cover the same time period. States may also choose to have a dual-axis marking both the date and the week (as shown in the first figure at the top of the page). This helps users understand the broader context of the trends being displayed. Source: State of Ohio COVD-19 Dashboard. Accessed March 30, 2020. |
Visualizations: Choose visualizations that are clean and compliant with a range of browsers. Simple visualizations can also help users interpret more complex data “at a glance.” For example, many dashboards use up or down arrows to indicate whether most recent data show improvements or declines. Make sure visualizations require limited manual data manipulation. For example, the visual to the right was created so that it links to a back-end Excel spreadsheet, which is easily refreshed.
Documentation to support data updates: After constructing your customized dashboard, create an “instruction sheet” outlining all of the steps necessary to update the data on an ongoing basis, including:
- Which specific cells or inputs need daily updates
- What data sources are being used and where the data is located
- How and where to document what data was pulled and when
This detailed “instruction sheet” is especially important in the event that the individual who normally updates the data is absent or leaves—that way someone else can easily complete the update.
Support for the development of this expert perspective was provided by the Robert Wood Johnson Foundation. The views expressed here do not necessarily reflect the views of the Foundation.
SHADAC Expertise
Data Analytics and Visualization
SHADAC is a nationally recognized research center in the collection and analysis of survey data and has long maintained specific expertise in the access and use of federal data, sought after by a variety of clients. SHADAC’s State Health Compare, supported by the Robert Wood Johnson Foundation, is a user-friendly and easily accessible online data tool for obtaining state-level estimates on a range of topics related to health and health care. Additionally, SHADAC researchers are experienced in effective visualization in the form of maps, infographics, state profiles, and chartbooks, which can play a critical role in the dissemination of policy-relevant data and communication of data implications.
SHADAC's State Health Compare
Analysts and policymakers can use State Health Compare to view measures of insurance coverage, access, cost, utilization, and outcomes—as well as social and economic measures related to health. State Health Compare allows users to compare these measures across states and look at trends over time through user-generated maps, bar charts, trend lines, and tables. Users can also explore these measures within states by characteristics (e.g., age, race/ethnicity, and education level). State Health Compare hosts more than 45 measures and estimates are available for timespans ranging from 4 to 21 years - drawing from 15 different sources. Click here for a full list of measures, data sources, and data years currently available, and check out our short tutorial video to learn tips and tricks for using State Health Compare!
On-Demand Analysis of Federal Survey Data
On a task-by-task basis, SHADAC conducts quantitative analyses for the Medicaid and CHIP Payment and Access Commission (MACPAC) using a broad array of federal survey data, including the American Community Survey (ACS), National Ambulatory Medical Care Survey (NAMCS), National Electronic Health Records Survey (NEHRS), National Health Interview Survey (NHIS), National Survey of Children's Health (NSCH), National Survey on Drug Use and Health (NSDUH), Medical Expenditure Panel Survey (MEPS), Pregnancy Risk Assessment Monitoring System (PRAMS), and the Survey of Income and Program Participation (SIPP), among others. As part of this work, SHADAC assists MACPAC in scaling work to meet their information needs, producing sound estimates, and developing documentation that supports the reproducibility of results over time. Key SHADAC staff execute diverse statistical methods ranging from simple descriptive statistics and t-tests to multivariate logistic regression and maintain Special Sworn Status to facilitate access and use of restricted data at the Minnesota Census Research Data Center (MnRDC). Our work has contributed to a wide range of products, including: 2022 MACStats: Medicaid and CHIP Data Book; Experiences in Lesbian Gay Bisexual Transgender Medicaid Beneficiaries (Brief); Experiences in Accessing Care by Race and Ethnicity (Brief); June 2021 Report to Congress on Medicaid and CHIP; Health Needs of Adults Involved in the Criminal Justice System (Brief); Rural and Urban Health Care (Brief); Adolescents’ Use of Behavioral Health Services (Brief); Pregnant Women and Medicaid (Brief).
IPUMS Health Surveys (2006-Present)
Since the early 2000s, SHADAC’s Director has led an effort to harmonize the data and documentation produced by the NHIS, which was expanded in 2016 to include the MEPS. The IPUMS Health Survey project allows users to create free, individual-level customized data extracts for analysis to support time trend analysis through consistent variable coding and production of publically available microdata. This work involves the creation of more than 2,000 integrated variables with comprehensive metadata, and through this work SHADAC has developed expertise and a deep understanding of the publically available NHIS and MEPS data, supporting our work using NHIS restricted data in the RDC.
Examples of SHADAC data visualization products
50-State Snapshots and Profiles - 50-state snapshots and profiles provide an overview of individual state-level data for all 50 states and the District of Columbia on specialized topics, such as health insurance coverage or health care access and affordability. Snapshots and profiles often include not only data for the state overall, but also breakdowns by year, by demographic groups, and national or state-to-state comparison.
Room to Grow: Inequities in Children's Health Insurance Coverage
Measuring State-level Disparities in Unhealthy Days
A State-level Look at Flu Vaccination Rates among Key Population Subgroups
Infographics - SHADAC produces a wide variety of infographics as a clear and concise way to convey complex data analysis in an engaging visual format. Infographics often accompany shorter blog posts and deeper analyses, and can also stand alone as part of communication or public engagement with broader audiences.
CPS ASEC: 2020 vs. 2021: National-level Changes in Uninsurance Rates
2021 Employer-Sponsored Insurance Access and Cost in the United States
2020 NHIS: State vs. National Estimates of Health Insurance Coverage
Interactive Visualizations - SHADAC has expanded our portfolio of interactive data visualizations to provide users with an opportunity to easily and visually sort and compare data across states.
State Survey Research Activity
Selected Health Equity Activity in State Medicaid Programs
Explore more of SHADAC’s data visualization products here.