The more troubling news from the 2021 Index release is that large differences in health security levels persist across states and communities, with the lowest levels of health security found in geographic areas with the highest levels of social and economic vulnerability. By producing the Index annually since 2013, we can clearly see that health security levels have improved at an uneven pace across the United States in recent years. As a result, large segments of the country are left under-protected and vulnerable to health and economic burdens triggered by COVID-19.

The 2021 release of the Index provides an important window into the consequences of failing to achieve a more equitable health security system across the United States. In earlier releases of the Index—prior to the COVID-19 pandemic—it was difficult to see the clear connections between health security levels and health outcomes as they varied across the country. Now, in the midst of the pandemic, the 2021 Index release clearly demonstrates that areas with lower health security levels experienced significantly higher mortality due to COVID-19. These results document how inequities in health security contribute to inequities in health outcomes within the context of a large-scale health emergency.

Most importantly, results from the 2021 release of the Index demonstrate that gaps and inequities in health security are amenable to solutions. Every state achieved improvements in health security levels in at least one domain over the past eight years and most states also bolstered their overall health security levels. For example, Louisiana achieved one of the largest gains in health security of any state in 2020 despite serving many residents with high levels of social and economic vulnerability and despite confronting simultaneous health emergencies. If all states achieve sustained rates of improvement observed in Louisiana, the nation as a whole can eliminate geographic inequities and reach a strong national health security level of at least 9.0 in as few as five years.

This year’s Index results show that inequities in health security are not inevitable. A uniformly high-functioning system is within reach by targeting additional resources and assistance to places that experience the lowest health security levels and by supporting meaningful approaches to regional coordination and cross-sector collaboration.

We hope the 2021 release of the Index can inform these types of improvements as the United States continues to recover from the pandemic. Please spend some time with the 2021 Key Findings report for a deeper dive into health security patterns and trends observed across the country. For a summary of the health security patterns in your specific area, take a look at the state profile for your individual state.  We invite you to conduct your own analyses of health security measures by downloading the Health Security Data Explorer. For ideas on using the Index data to mobilize change in your community, see the Innovator’s Guide to the National Health Security Preparedness Index and other resources on the Index website.

Glen P. Mays PhD, MPH is the chair and a professor in the Department of Health Systems, Management and Policy in the Colorado School of Public Health at CU Anschutz. His research examines delivery and financing systems for health services, with a focus on estimating their effects on population health and economic efficiency.

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What Can Be Learned from Updated Results?

Results from the 2020 release of the Index show that national health security capabilities continue to gain strength, but at a rate that has slowed down over time. Most states experienced improvements in health security levels over the past year, but at widely varying levels. Large geographic differences in preparedness remain, and these differences have grown larger over time in selected domains. Jurisdictions are responding to a growing array of risks and hazards by stretching constrained human, financial, and technological resources and making difficult trade-offs.

How can states and communities use this updated information? Coronavirus transmissions appear to be leveling off in a growing number of U.S. regions, giving these areas some breathing room and a chance to take stock of their capabilities and needs. With a large set of 130 measures, the Index allows users to identify strengths and weaknesses across a broad range of sectors and capabilities.

As new resources become available through federal coronavirus aid packages, the Index can help emergency preparedness stakeholders identify where vulnerabilities exist and where reinforcements are most urgently needed. Users can quickly identify leading states in each domain of preparedness and in regions across the United States, allowing for peer networking and cross-jurisdictional learning to occur.

What Can Findings Tell Us About COVID-19 Response?

The Index includes many measures that are directly relevant to coronavirus response efforts, including measures of laboratory testing capabilities, epidemiologist staffing, hospital surge capacity, nursing home infection control, household broadband access, and employer-provided paid time off. Effective responses to COVID-19 require coordination of resources across a broad range of public and private sectors. The Index integrates data from a broad range of sectors to offer a comprehensive view of preparedness.

Figure: Higher Scores on the 2020 Health Security Index Are Associated with Significantly Lower Risk-adjusted County COVID-19 Mortality Rates

Note: point estimates with 95% confidence intervals are shown as horizontal lines.  Estimates are derived from risk-adjusted mortality regression models using generalized estimating equations. 

To gain additional perspectives on pandemic response, Index results can be linked with emerging state and local data on COVID-19 (see figure). To date, states with higher health security levels have experienced significantly lower risk-adjusted COVID-19 deaths per 100,000 population – an encouraging trend that can be monitored over time. The figure above shows how county-level risk-adjusted mortality rates change in response to a one point increase in Index scores. These results can help stakeholders craft targeted responses for regions that face higher mortality risks combined with lower preparedness levels.

What Index Changes Were Introduced in 2020?

The Index measurement set and methodology remain virtually unchanged from last year. We introduced one new measure into the Index based on recommendations from the field: a measure of nursing home compliance with federal standards for infection control. This addition appears particularly relevant to the COVID-19 pandemic, given the heightened infection rates observed in long-term care facilities across the United States.

One notable change to our dissemination process this year is the rethinking of the “Index preview period” used in previous years to give emergency preparedness stakeholders an early look at the results and a structured process to provide feedback. To avoid placing additional demands on these officials during the COVID-19 response and to provide information as quickly as possible to the field, we are releasing the results directly to the field without holding a full review and comment period. One downside of this minimally invasive approach, of course, is that we have fewer opportunities to screen for possible errors in the many data sources used in the Index. Such errors have been rare in previous releases of the Index, but they occasionally do occur in some data sources, particularly those that rely on self-reported information from agencies and facilities. For this reason, we invite Index users to ask questions and provide feedback on an ongoing basis at any time that is convenient by sending an email to our research team at systemsforaction@ucdenver.edu. If we identify the need to correct any data source based on this feedback, we will release corrections as soon as possible on the Index website.

We hope you find useful information and new insight about the geography and dynamics of health security in this latest release of the Index. We look forward to incorporating the lessons learned from current health security experiences into future releases of the Index.

Glen P. Mays PhD, MPH is the chair and a professor in the Department of Health Systems, Management and Policy in the Colorado School of Public Health at CU Anschutz. His research examines delivery and financing systems for health services, with a focus on estimating their effects on population health and economic efficiency.

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Results from the National Health Security Preparedness Index clearly demonstrate that health security is not simply a governmental responsibility.  Individual businesses and the private sector at large contribute to many of the health security measures that comprise the Index, such as by offering paid time off and telecommuting options for employees, promoting vaccination coverage in the workforce, supporting workers who train and volunteer for their local Medical Reserve Corps, and participating in emergency planning and exercises organized by regional healthcare coalitions and networks.

Panelists shared key insights for both health and business stakeholders as they consider strategies for strengthening health security and preparedness activities, including:

• The importance of leveraging the supply chain to prepare for events by collaborating on contingency plans to avoid large-scale business disruptions;
• Increasing awareness about the need for preparedness plans among the business community, especially for small businesses with little influence over suppliers;
• How business can foster social cohesion—often business owners work closely in the community and will need to rise above competition to recover from an adverse event;
• Businesses as a catalyst for volunteerism in their workforce; and
• Harnessing technology to plan, respond, and recover, for both large and small companies.

We also know health security and preparedness require cross-sector collaboration and a multipronged approach, and we were pleased that our participants joined from a variety of backgrounds. A plurality came from governmental public health, with significant representation from the private sector and academia.

Figure: Webinar attendees

The diversity of our attendees led to questions on a wide-range of topics, including:

• Global pandemics are arguably the only catastrophic threat that can simultaneously hit a business’s employees, customers, and suppliers worldwide. Do you think most corporate CEOs are fully aware of the risk and adequately engaged in ensuring that all parts of the house (business continuity, HR, medical services) are resourced and supported? Are most companies doing drills?
• As a Public Health Emergency Preparedness Coordinator through a Health Department, where should the line be drawn between helping private businesses to prepare vs. just working towards community preparedness?
• How do you handle the moral hazard aspect of private markets, like healthcare, that may see these regional treatment facilities as the primary source for handling high-consequence pathogens and therefore cut down on preparedness and training?

Panelists mentioned the following resources during the discussion:

• National Health Security Preparedness Index measures
• Intel CSR Report highlighting preparedness efforts
• Global Health Security Agenda: Private Sector Roundtable backgrounder

We are excited to continue engaging stakeholders from many different backgrounds and improving health security and preparedness in all communities. Follow the conversation on Twitter @NHSPIndex and stay tuned for more webinars on the role we all can play in health security.

Glen P. Mays PhD, MPH is the chair and a professor in the Department of Health Systems, Management and Policy in the Colorado School of Public Health at CU Anschutz. His research examines delivery and financing systems for health services, with a focus on estimating their effects on population health and economic efficiency.

The post In Review: Business and Health Security: The Bottom Line on Preparedness appeared first on NHSPI.

The National Health Security Preparedness Index measures the nation’s health security and preparedness—that is, the nation’s ability to prepare for, respond to, and recover from large-scale health threats. The Preparedness Index measures health security from a broad, multi-sectoral perspective using nearly 140 measures from more than 50 different sources. Here we examine three of these measures: paid time off (PTO), telecommuting, and high-speed internet access from home, in the context of health security, preparedness, and equity. These factors have at least three things in common: they enhance compliance with social distancing policies used in infectious disease outbreaks; they highlight the private-sector role in the nation’s health preparedness; and they bring to the fore important equity issues. For prime working-age adults between 25 and 54 years old, an estimated 81 percent have broadband access at home, approximately 62 percent have some form of PTO, and about 30 percent can telecommute when they are away from their usual workplace. However, there are significant differences based on income and education levels, with individuals at lower income and education levels reporting lower percentages of PTO, broadband access, and telecommuting. Our analysis of individual-level U.S. Census data reveals statistically significant independent effects of education and income on whether an individual has PTO, broadband at home, or can telecommute. This analysis illustrates how the less advantaged can be affected differently by disease outbreaks, disasters, and large-scale emergencies—and how workplace practices can either exacerbate or ameliorate health security.

Rationale

Social distancing policies, such as school closures and self-quarantine measures, were used during the 2014 Ebola outbreak and the 2009 H1N1 influenza (flu) pandemic to thwart the spread of disease. The efficacy of this approach, however, is largely determined by the extent to which individuals adhere to it. The Centers for Disease Control and Prevention (CDC) estimates that almost 18 of the 26 million H1N1 infected workers in the fall of 2009 took days off from work, but the remaining 8 million workers did not and likely infected another 7 million co-workers.[1]

Broadband access facilitates the continuity of operations during emergencies that can limit the adverse economic impact of disasters. There was, for instance, a citywide closure of Boston after the 2013 Marathon bombing, but some businesses stayed open with teleworkers and experienced limited financial losses. Similarly, health department staff were able to work remotely to maintain critical communications and surveillance activities.

These three factors—PTO, telecommuting, and high-speed internet access from home—can enhance the likelihood individuals will adhere to social distancing and quarantine measures.[2] PTO and telecommuting are two of 17 item measures within the Preparedness Index domain of Countermeasure Management.[3] PTO is an indicator of preparedness and resilience, because it enables one to shelter in place or evacuate during an emergency without experiencing the economic hardship of lost income. Likewise, the ability to telecommute and household access to high-speed internet are vital because, like PTO, if one can work at home, remain economically productive, and shelter in place, it enhances individual security and community resilience.[4] Also, as one of 13 measures in the Incident & Information Management domain,[5] household broadband access reflects the degree to which one can receive timely and up-to-date information during a public health emergency. The Pew Research Center reports that four in ten Americans often get their news online, highlighting the reliance on the internet for staying informed.[6]

Factors Affecting PTO, Telecommuting, & Household Broadband Access

Based on our analysis of prime working age adults from 25 to 54 years old, an estimated 62 percent of workers have some form of PTO,[7] about 30 percent can telecommute when away from their usual workplace,[8] and an estimated 81 percent of households have broadband in the home.[9] However, there are significant differences based on income and education levels, with individuals at lower income and education levels showing comparatively lower percentages.

The numbers in Table 1 illustrate some of the significant differences in these factors across income and education levels. The “gross” numbers, explained in detail below, represent the overall percentages for everyone in that category. We can see, for example, that those without a high school diploma are much less likely to have PTO (33 percent) than those with a bachelor’s degree or higher (70 percent), and workers in the lowest income group (25 percent) are much less likely to have PTO than those in the highest income group (73 percent).

Similarly, individuals with higher income and education levels are more likely to have household broadband and are more likely to telecommute. Individuals, for instance, in the highest income quartile are 1.6 times more likely to have household broadband (95 percent) than those in the lowest income quartile (58 percent)—and are four times more likely to telecommute (46.8 percent compared to 11.7 percent). Likewise, there are big differences across education levels. Those with a bachelor’s degree or higher are 1.7 times more likely to have household broadband (93 percent) than those without a high school diploma (55 percent)—and are 4.9 times more likely to telecommute (44.2 percent compared to 9.1 percent).

Within each of the education and income groups described above, the individuals and households might be quite different from each other and only share membership in the group on the basis of that one factor. That is, among those with a bachelor’s degree or higher there will be members from every income group, both genders, many ages, and all races and ethnicities. We call these the “gross” percentages. However, because so many factors are correlated—like income, education, race, gender, and location of residence—the gross differences do not reveal how much of a digital divide, for example, is due to income (because higher education is associated with higher income), education (since lower income is correlated with lower education), or location of residence (since individuals in metro areas tend to have higher income and educational attainment).

To better address the equity issues represented by the gross differences described above, it is necessary to isolate and identify the “net” differences. Multiple regression analysis allows us to assess the independent or net effects of these factors.[10]  The net effect is an estimate of how individuals and households differ along a single dimension while holding all other factors constant. For example, comparing two individuals with the same education, race, age, gender, and residence—but from different income groups—allows us to estimate the effect of income on whether one has PTO, telecommutes, or has household broadband access. Knowing whether the root cause of the inequity is primarily due to a lack of income, education, or where someone lives can suggest whether the best public policy approach might be subsidizing internet access, launching an information campaign explaining the benefits of broadband, or providing the last mile of wired infrastructure.

The differences in the “net” percentages across income and education groups are significant and important (see Figures 1 and 2). Someone with a bachelor’s degree is nearly 1.6 times more likely to have PTO (69 percent v. 44 percent), more than 1.4 times more likely to have high-speed internet at home (88 percent v. 61 percent), and about 2.6 times more likely to telecommute (40 percent v. 15 percent), assuming all other factors ceteris paribus, compared to the lowest education category, and we see similar patterns across the income quartiles.

Discussion

Inequality—in both opportunity and outcome—is becoming the defining zeitgeist of our era. We typically think about inequality in the context of income, but equity and health also go hand-in-hand. Alonzo Plough, PhD, MPH, chief science officer and vice president of Research, Evaluation, and Learning at the Robert Wood Johnson Foundation, for example, recently described how extreme weather events can have a disproportionate impact on “children, the elderly, people with chronic health conditions, the economically marginalized and communities of color.”[11] And others have raised concerns about the disproportionate vulnerability of lower-income Americans to the Zika virus.[12] While there are many ways in which this relationship can manifest itself, research and analysis confirm what common sense suggests: the less-advantaged are affected differently by disease outbreaks, disasters, and large-scale emergencies.

We illustrate here how lower levels of education and income are associated with a decreased likelihood (both in terms of gross and net percentages) that one will enjoy the benefits of PTO, have household broadband access, or telecommute. Understanding the root causes of these differences and addressing the inequities will enhance health security, preparedness, and community resilience. However, understanding the root causes is not sufficient—community leaders from the private, public, and nonprofit sectors must work together to tackle the root causes of these inequities. By doing so, the health security of the entire community will be enhanced.

Michael T. Childress is a research associate at the Center for Business and Economic Research, Gatton College of Business and Economics, at the University of Kentucky. He is part of the program management office for the National Health Security Preparedness Index.

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[1] Robert Drago and Kevin Miller, “Sick at Work: Infected Employees in the Workplace During the H1N1 Pandemic,” Institute For Women’s Policy Research Briefing Paper, IWPR No. B264, February 2010.

[2] See Anne M. Kavanagh and James E. Fielding, “Leave entitlements, time off work and the household financial impacts of quarantine compliance during an H1N1 outbreak,” BMC Infectious Diseases (2012).

[3] Countermeasure management includes several measures that account for programs, products, and systems necessary to be prepared for, protected from, and resilient against chemical, biological, radiological, nuclear, and explosives (CBRNE) agents and emerging infectious disease threats.

[4] Susan Cutter, et al., “The geographies of community disaster resilience,” Global Environmental Change 29 (2014), pp. 65-77.

[5] Incident & Information Management reflects the ability to: mobilize all critical resources from any source; establish and maintain command, control, and coordination structures within the affected community; provide necessary legal, administrative, and logistical support; and exchange multijurisdictional, multidisciplinary public health and medical-related information, intelligence, plans, and situational awareness.

[6] Pew Research Center, Pathways to news, http://www.journalism.org/2016/07/07/pathways-to-news/.

[7] We pool five years of U.S. Census, Current Population Survey, Annual Social and Economic (ASEC) Supplement data (2012-2016) and limit our sample to prime working age adults, 25 to 54 years old. Our estimate of 62 percent is similar to another that uses a different method and data set—the March 2016 Bureau of Labor Statistics National Compensation Survey (NCS).  The 2016 NCS data estimates 68 percent of workers have paid sick leave, 73 percent have paid vacation, and 75 percent have paid holidays. For more information on the NCS see Employee Benefits Survey http://www.bls.gov/ncs/ebs/.

[8] These estimates are derived from the U.S. Census, Current Population Survey, July 2015, Computer and Internet Use File, and is estimated from the variable PETELEWK: What about telecommuting, or working while away from (you/his/her) usual workplace? (Do you/Does NAME) use the Internet to telecommute or work while away from (your/his/her) usual workplace? We limit the analysis to prime working age adults, 25 to 54 years old.

[9] We base our estimate and analysis on the U.S. Census, Current Population Survey, July 2015, Computer and Internet Use File.  We limit our analysis to heads of household between 25 and 54 years old, the prime working age population.

[10] We use two different, but similar, models for PTO, household broadband, and telecommuting. Both models include dichotomous variables for income quartiles, educational attainment, race, residence, age, and gender. The PTO model also include dichotomous variables for 14 industrial sectors, ranging from agriculture to public administration.

[11] Alonzo L. Plough, “The Impact of Climate Change on Health and Equity,” June 22, 2016, Robert Wood Johnson Foundation http://www.rwjf.org/en/culture-of-health/2016/06/what_does_climatech.html.

[12] Jamila Taylor, “What the Media and Congress Are Missing on Zika and Poverty,” May 24, 2016, https://talkpoverty.org/2016/05/24/media-congress-missing-zika-poverty/.

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