A COVID-19 Lesson: Better Health Emergency Preparedness Standards Are Needed

Abstract

This article reports on an assessment of the value of 4 widely recognized standards of health sector emergency preparedness as predictors of effective preparedness for, and response to, the COVID-19 pandemic in the United States. The standards are sponsored by the National Health Security Preparedness Index (NHSPI), the Trust for America's Health (TFAH), the Emergency Management Accreditation Program (EMAP), and the Public Health Accreditation Board (PHAB). The measure of effectiveness was states' cumulative COVID-19 deaths per 100,000 population, from January 21, 2020, through January 20, 2022. Linear regression analysis found no statistically significant associations when controlling for 3 intervening variables. Cross-tabulation of states' preparedness status with their COVID-19 death rates found that high NHSPI and TFAH preparedness scores were generally, but not uniformly, associated with lower death rates. EMAP and PHAB accreditation had negligible association with low or high death rates. Lack of accreditation was associated with lower death rates. Higher prior state public health spending related to COVID-19 preparedness and higher state household income, an indicator of state economic strength, were associated with lower death rates. States with Democratic control of the legislative and executive branches of government generally had substantially lower death rates than states with Republican control. A science-based, practice-oriented research initiative is recommended to improve the predictive power of health sector preparedness standards and to enhance protection for US residents from large-scale future health threats.

Introduction

Extensive efforts have been made in recent decades to improve the emergency preparedness of US healthcare systems, hospitals, and public health departments, triggered by the 9/11 and anthrax terrorist attacks of 2001, the 2009 H1N1 influenza outbreak, extreme hurricanes, vast wildfires, and other disasters. Federal, state, and local governments have invested billions of dollars in this effort, supplemented by the work of philanthropic foundations, professional associations, and other health-oriented organizations.

An integral part of this effort has been the development of standards intended to guide health sector preparedness for large-scale emergencies. This study assessed the value of such standards as predictors of effective preparedness and response. The research hypothesis was that US states (including the District of Columbia) whose hospitals and public health departments had made greater progress toward meeting such standards would achieve better COVID-19-related health outcomes during the pandemic than states whose hospitals and public health departments had made less progress.

As of January 20, 2022, 2 years after the US Centers for Disease Control and Prevention (CDC) began publishing COVID-19 mortality data, the pandemic had caused 856,288 US deaths.¹ Communities of color have borne a disproportionate burden.² These tragic outcomes have resulted in large part from demands on hospitals and public health departments perhaps not equaled since the 1918-1920 influenza pandemic. An American Journal of Public Health editorial critiques public health departments' response to the COVID-19 crisis³ and the National Academy of Medicine is leading an initiative to reassess hospital crisis standards of care, an issue of great concern to clinicians and administrators during the pandemic.⁴ This assessment contributes to these and other efforts to strengthen the US health sector against such threats.

The preparedness standards examined here are delivered by 4 national nonprofit organizations: the National Health Security Preparedness Index (NHSPI), the Trust for America's Health (TFAH), the Emergency Management Accreditation Program (EMAP), and the Public Health Accreditation Board (PHAB). While the scope of this study included only the jurisdictional level of US states and the District of Columbia, a notable program that focuses on local public health department emergency preparedness is Project Public Health Ready, managed by the National Association of County and City Health Officials.

Two of the standards are closely related to 2 emergency preparedness frameworks developed by the US Department of Health and Human Services. One framework is provided by the CDC Public Health Emergency Preparedness Program (PHEP), which funds all states and selected localities and tribes to strengthen their public health systems for all-hazards threats. The CDC's grantee guidance, Public Health Emergency Preparedness and Response Capabilities: National Standards for State, Local, Tribal, and Territorial Public Health,⁵ identifies 15 capabilities grantees should work toward “to advance the emergency preparedness and response of state and local public health systems.” The CDC requires all grantees to use their PHEP funds to strengthen those capabilities and to evaluate their emergency readiness using the companion CDC Operational Readiness Review Guidance.⁶ The second framework is the Office of the Assistant Secretary for Preparedness and Response (ASPR) Hospital Preparedness Program (HPP) grantee guidance, 2017-2022 Health Care Preparedness and Response Capabilities,⁷ which identifies 4 capabilities its grantees must strengthen using their HPP funding. Grantees include hospitals, healthcare coalitions, nursing homes, clinical services provided by public health departments, and other providers. The PHEP and HPP programs coordinate closely. Their joint funding announcement for the period 2017-2022 explicitly charged all grantees to work toward meeting the goals articulated in the 2 capabilities documents.⁸

The CDC and ASPR do not publish assessments or rankings of their PHEP and HPP grantees' preparedness progress. The CDC developed the NHSPI in 2012 as a means to measure PHEP grantees' progress⁹ and later transferred management of the annual NHSPI report to independent academic institutions. As of June 2022, 2 of the 10 members of the NHSPI advisory council were former directors of PHEP and HPP, signaling a continuing relationship between the NHSPI organization and those programs.

The NHSPI and TFAH annual index reports are the only public sources of systematic, empirical information on national progress in development of health-oriented, state-level emergency preparedness. The purpose of the NHSPI is to “track the nation's progress in preparing for, responding to, and recovering from the health consequences of disasters, disease outbreaks, and other large-scale emergencies.”¹⁰ The index encompasses 6 health sector domains that substantially reflect the PHEP and HPP capabilities and uses 130 measures to assess state-level progress. The TFAH index is intended to “give state officials benchmarks for progress, point out gaps within their states [sic] all-hazards preparedness, and provide data to compare states' performance against like jurisdictions.”¹¹ The index uses 10 measures, 9 of which are NHSPI measures, similarly relying on the CDC and ASPR standards frameworks. While NHSPI and TFAH do not explicitly characterize their indices as standards, they use numerical preparedness scores to rank the states. We treat the NHSPI and TFAH indices as proxies for the US Department of Health and Human Services standards. For both indices, PHAB and EMAP accreditation is included as a preparedness indicator.

PHAB designed its voluntary accreditation standards within a different health-oriented framework, the widely recognized 10 Essential Public Health Services,¹² first developed in 1994 and revised in 2020. The essential services comprise all the public health functions the designers consider necessary to protect and advance the health of a given population or jurisdiction. The 32 PHAB standards belong to 10 domains, 4 of which PHAB describes as directly relevant to emergency preparedness. PHAB deems an accredited public health department able “to appropriately respond to public health emergencies” of all kinds, including infectious disease outbreaks.¹³ While the standards do not explicitly cite the CDC or ASPR frameworks, a crosswalk document shows PHEP grantees how they can use their PHEP accomplishments to support their application for PHAB accreditation.

EMAP publishes voluntary accreditation standards to “promote consistent quality in emergency management programs” operated by governments, higher education institutions, and independent government authorities. EMAP defines the scope of all-hazards emergency management to “encompass all organizations with emergency/disaster functions in a jurisdiction,” including hospitals and public health departments, and cites pandemic influenza, a close analog to COVID-19, as within the scope of its standards.¹⁴ Its Emergency Management Standard uses an emergency management framework that encompasses all types of natural and human-caused emergencies.¹⁵ Each entity that applies for EMAP accreditation must demonstrate its compliance with 66 detailed standards. For example, it must have an emergency operations plan that assigns responsibility for provision of public health and medical services to a designated entity.

This study analyzed the dependency of states' COVID-19 death rates (deaths per 100,000 population) during the period January 21, 2020, through January 20, 2022, on states' level of emergency preparedness, as reflected in their NHSPI and TFAH preparedness scores and in their EMAP and PHAB accreditation.

Researchers have pointed to several other types of factors that may influence COVID-19 health outcomes in the United States. Among these are public policies, prior health status, demographic and socioeconomic characteristics, and interventions used to prevent COVID-19 infection and treat affected patients. The scope of this assessment was limited to the role of 3 policy-related intervening factors in influencing state COVID-19 death rates: funding levels, especially for state and local public health departments; state economic strength; and political party control of government. Examples of pertinent research include Gostin and Nuzzo,¹⁶ Miller et al,¹⁷ and Shvetsova et al.¹⁸ Indicators selected for these variables were mean per capita state government spending for COVID-19-related purposes (comparable data on hospital spending was not available); state median household income, an indicator of state economic strength; and Democratic and Republican party control of the legislative and executive branches of state government.

Methods

Data

Data on cumulative state COVID-19 deaths per 100,000 population, as of January 20, 2022, came from the CDC COVID Data Tracker, except for New York state.¹ Death rate data for New York state came from statista.¹⁹

NHSPI and TFAH represent each state's preparedness score as a single, composite number. The study used the scores that had been published most immediately before the onset of the pandemic in the United States. NHSPI scores were based on 2019 data.²⁰ TFAH scores were based on 2019-2020 data.¹¹ For TFAH scores, states were divided into 3 groups of 17 each: low scores (4.0 to 5.0), midrange scores (5.1 to 5.8), and high scores (5.9 to 6.6). For NHSPI scores, states were divided into 3 groups—16 with low scores (5.8 to 6.6), 18 with midrange scores (6.7 to 6.9), and 17 with high scores (7.0 to 7.4)—to avoid dividing states with identical scores between 2 groups, as noted later in the discussion on limitations.

Each state's accreditation status as of December 2020 was assigned to 1 of 3 categories—accredited by EMAP, accredited by PHAB, and not accredited—as reported by EMAP and PHAB.^21,22

Data on state public health department spending for COVID-19-related purposes came from a study that generated a smoothed, 3-year (2016-2018) value for mean per capita state spending for each state (excluding California and the District of Columbia),²³ based on US Census Bureau Census of Governments reports.²⁴ The authors defined “COVID-related purposes” to include “communicable disease control,” related “functional capabilities,” and “all hazards preparedness and response.” States were ranked by mean per capita spending level and were divided into 3 groups: 16 with low spending ($0 to $19.40), 17 with midrange spending ($20.41 to $30.18), and 16 with high spending ($36.42 to $175.34). State median household data for 2019, the data most recently published before onset of the pandemic, came from the US Census Bureau American Community Survey.²⁵

Data on 2020-2021 party control of state governments came from the National Conference of State Legislatures (for 49 states)^26,27 and from Ballotpedia for the District of Columbia and Nebraska governments, where legislators are nominally nonpartisan.^28,29 Each state's party control was assigned to 1 of 3 categories: states where Democrats held the office of the governor and a majority of seats in all chambers of the state legislature; states where Republicans held the office of the governor and a majority of seats in all chambers; and “divided” states in which 1 party had a majority of seats in at least 1 chamber, while a different party had majority control of the other chamber or held the office of governor between 2020 and 2021.

Analytical Methods

The study conducted 2 lines of analysis. In both, NHSPI and TFAH scores, state spending for COVID-19-related purposes, and state median household income were treated as continuous variables. Accreditation and party control were treated as categorical variables.

First, linear regression analyses tested the association between state preparedness and COVID-19 death rates without controlling for intervening variables, and also tested that association while controlling for the 3 intervening variables simultaneously. Regression analyses used the R package (R Core Team, Vienna, Austria). Table 1 provides descriptive statistics for regression variables.

Table 1.

Linear Regression Descriptive Statistics

Continuous Variables	Minimum/Maximum	Mean (SD)
Deaths per 100,000 population	75/359	193 (68)
NHSPI preparedness scores	58/74	6.76 (0.42)
TFAH preparedness scores	4.0/6.6	9.98 (5.34)
Median per capita spending for COVID-19-related purposes	$0.0/$175.34	$35.99 ($33.59)
Median household income	$45,792/$92,266	$64,538 ($10,460)

Categorical Variables (n = 51)	n (%)
EMAP accreditation	37 (72.5)
PHAB accreditation	39 (76.5)
No accreditation	8 (15.7)
Party control
Democratic control	16 (31.4)
Republican control	22 (43.1)
Divided	13 (25.5)

The data used for this table came from the US Centers for Disease Control and Prevention,¹ statista,¹⁹ the National Health Security Preparedness Index,²⁰ Trust for America's Health,¹¹ Alfonso et al,²³ the US Census Bureau,²⁴ the Emergency Management Accreditation Program,²¹ the Public Health Accreditation Board,²² the National Conference of State Legislatures,^26,27 and Ballotpedia.^28,29

Abbreviations: NHSPI, National Health Security Preparedness Index; TFAH, Trust for America's Health; EMAP, Emergency Management Accreditation Program; PHAB, Public Health Accreditation Board.

Second, the study divided the states into 3 groups according to their COVID-19 death rates (low, midrange, and high) and, in lieu of additional regression analyses, cross-tabulated those groups with 3 groupings of NHSPI and TFAH preparedness scores (low, midrange, and high) and with the 3 accreditation groupings. Similar cross-tabulations were conducted for the 3 intervening variables.

Results

Regression analysis produced 1 statistically significant relationship: high TFAH preparedness scores were associated with high COVID-19 death rates (P < .01) when uncontrolled for intervening variables. That result dropped to P = .35 when controlled for the 3 intervening variables (Table 2). The second line of analysis produced 6 principal results, as follows.

Table 2.

Association Between State Cumulative COVID-19 Deaths^a and State Emergency Preparedness Scores and Accreditation Status

Preparedness Indicator	Unadjusted Association		Adjusted Association for 3 Controls^b
Preparedness Indicator	Score (95% Wald CI)	P Value	Score (95% Wald CI)	P Value
NHSPI preparedness score	-36.49 (-84.75 to 11.77)	.14	3.81 (-46.59 to 54.21)	.88
TFAH preparedness score	5.50 (1.96 to 9.04)	<.01	2.14 (-2.28 to 6.55)	.35
EMAP accreditation	52.00 (-35.76 to 139.76)	.25	23.09 (-58.76 to 104.95)	.58
PHAB accreditation	-1.08 (-78.48 to 76.31)	.98	-29.52 (-106.71 to 47.67)	.46

Per 100,000 population, January 21, 2020 to January 20, 2022. ^bControls: state mean per capita spending for COVID-19-related purposes, 2016-2018; state median household income, 2020; party control of legislative and executive branches of state government, 2020-2021.

Abbreviations: EMAP, Emergency Management Accreditation Program; NHSPI, National Health Security Preparedness Index; PHAB, Public Health Accreditation Board; TFAH, Trust for America's Health.

NHSPI Scores

As Table 3 shows, a majority (n = 10, 59%) of the 17 states with high NHSPI preparedness scores experienced low death rates. Of the 18 states with midrange NHSPI scores, 7 (39%) had midrange death rates and 8 (44%) had high-range death rates. Of the 16 states with low NHSPI scores, 7 (44%) had midrange death rates and 5 (31%) had high death rates.

Table 3.

Association Between State NHSPI Preparedness Scores and Cumulative COVID-19 Deaths

NHSPI Preparedness Scores	COVID-19 Death Rates^a
NHSPI Preparedness Scores	16 States With High Rates	18 States With Midrange Rates	17 States With Low Rates
17 states with high scores	3 (18%)MA, RI, GA	4 (24%)MO, CT, ND, IA	10 (59%)MD, WI, NC, CO, VA, NE, DC, NH, ME, VT
18 states with midrange scores	8 (44%)AZ, AL, NJ, NY, TN, AR and MI^b, PA	7 (39%)SC, KY, MT, TX, IL, DE and KS^b	3 (17%)MN, OR, UT
16 states with low scores	5 (31%)MS, LA, WV, IN, OK	7 (44%)FL and NM^b, SD, NV, WY, OH, ID	4 (25%)CA, AK, WA, HI

Notes: Percentages are rounded to the nearest whole number. Within each cell, states are listed in order by death rate, from highest to lowest.

The data used for this table came from the US Centers for Disease Control and Prevention,¹ statista,¹⁹ and the National Health Security Preparedness Index.²⁰

Per 100,000 population, January 21, 2020 to January 20, 2022. ^bPairs of states with identical scores.

Abbreviation: NHSPI, National Health Security Preparedness Index.

TFAH Scores

Of the 17 states with high TFAH scores, a majority (n = 10, 59%) had low death rates. (Table 4) Of the 17 states with midrange TFAH scores, 7 (41%) had midrange death rates and 4 (24%) had high-range death rates. Of the 17 states with low TFAH scores, a majority (n = 9, 53%) had midrange death rates and 7 (41%) had high death rates.

Table 4.

Association Between State TFAH Preparedness Scores and Cumulative COVID-19 Deaths

TFAH Preparedness Scores	COVID-19 Death Rates^a
TFAH Preparedness Scores	16 States With High Rates	18 States With Midrange Rates	17 States With Low Rates
17 states with high scores	5 (29%)AZ, NY, MA, RI, PA	2 (12%)CT, IL	10 (59%)MD, WI, NC, CO, VA, DC, NH, ME, UT, VT
17 states with midrange scores	4 (24%)AL, LA, AR and MI^b	7 (41%)FL and NM^b, MO, ND, IA, DE and KS^b	6 (35%)MN, CA, NE, OR, AK, WA
17 states with low scores	7 (41%)MS, NJ, TN, WV, IN, GA, OK	9 (53%)SC, SD, KY and NV^b, MT, WY, OH, TX, ID	1 (6%)HI

Notes: Percentages are rounded to the nearest whole number. Within each cell, states are listed in order by death rate, from highest to lowest.

The data used for this table came from the US Centers for Disease Control and Prevention,¹ statista,¹⁹ and Trust for America's Health.¹¹

Per 100,000 population, January 21, 2020 to January 20, 2022. ^bPairs of states with identical scores.

Abbreviation: TFAH, Trust for America's Health.

Accreditation

EMAP accreditation had a positive, but small, association with high death rates: as Table 5 shows, 14 (38%) of the 37 EMAP-accredited states had high death rates. There was no positive or negative association between PHAB accreditation and the death rates of 39 states accredited by PHAB. Lack of accreditation was generally associated with lower death rates than was accreditation: of the 8 unaccredited states, 3 (38%) had low death rates, 4 (50%) had midrange death rates, and 1 (13%) had a high death rate.

Table 5.

Association Between State EMAP and PHAB Accreditation Status and Cumulative COVID-19 Deaths

Accreditation Status	COVID-19 Death Rates^a
Accreditation Status	28 States With High Rates	29 States With Midrange Rates	27 States With Low Rates
37 states accredited by EMAP	14 (38%)MS, AZ, AL, NJ, LA, NY, TN, MI, AR, MA, RI, PA, GA, OK	12 (32%)FL, SC, NV, MO, CT, ND, OH, IA, IL, KS, DE, ID	11 (30%)MD, WI, CA, NC, CO, VA, NE, DC, WA, UT, VT
39 states accredited by PHAB	13 (33%)MS, AZ, AL, NJ, LA, NY, AR, MA, IN, RI, PA, GA, OK	13 (33%)NM, FL, SC, MT, MO, CT, ND, OH, IA, IL, KS, DE, ID	13 (33%)MD, WI, MN, CA, CO, VA, NE, DC, OR, WA, ME, UT, VT
8 states not accredited	1 (13%)WV	4 (50%)SD, KY, WY, TX	3 (38%)NH, AK, HI

Notes: Percentages are rounded to the nearest whole number. Within each cell, states are listed in order by death rate, from highest to lowest.

The data used for this table came from the US Centers for Disease Control and Prevention,¹ statista,¹⁹ the Emergency Management Accreditation Program,²¹ and the Public Health Accreditation Board.²²

Per 100,000 population, January 21, 2020 to January 20, 2022.

Abbreviations: EMAP, Emergency Management Accreditation Program; PHAB, Public Health Accreditation Board.

Public Health Spending

States with high 2016-2018 public health spending,²³ deemed by separate research to be related to COVID-19 preparedness, generally experienced substantially lower death rates than states with midrange or low spending (Table 6). Of the 17 states with midrange spending, 8 (47%) had high death rates, while a majority (10 [63%]) of the states with low-range spending had midrange death rates. (These findings may have been influenced by the absence of spending data for California and the District of Columbia.)

Table 6.

Association Between State Spending for COVID-19-Related Purposes and Cumulative COVID-19 Deaths

State Spending^b,c	COVID-19 Death Rates^a
State Spending^b,c	16 States With High Rates	18 States With Midrange Rates	15 States With Low Rates
16 states with high spending	4 (25%)NJ, NY, RI, GA	2 (13%)SC, DE	10 (63%)MD, WI, NC, NH, OR, AK, WA, UT, HI, VT
17 states with midrange spending	8 (47%)MS, AL, LA, TN, WV, MA, IN, OK	6 (35%)SD, MT, CT, ND, IA, KS	3 (18%)CO, VA, ME
16 states with low spending	4 (25%)AZ, MI, AR, PA	10 (63%)NM, FL, NV, KY, WY, MO, OH, TX, IL, ID	2 (13%)MN, NE

Notes: Percentages are rounded to the nearest whole number. Within each cell, states are listed in order by death rate, from highest to lowest.

The data used for this table came from the US Centers for Disease Control and Prevention,¹ statista,¹⁹ and Alfonso et al.²³

Per 100,000 population, January 21, 2020 to January 20, 2022. ^bOmits California and the District of Columbia. ^cMedian value of states' mean per capita government spending for COVID-19-related purposes, 2016-2018.

Median Household Income

Most states with stronger economies experienced lower death rates than did states with weaker economies (Table 7). This relationship was relatively strong: roughly two-thirds of states with high median household income had low death rates, one-half of states with midrange income had midrange death rates, and almost one-half of states with low income had high death rates.

Table 7.

Association Between State Median Household Income and Cumulative COVID-19 Deaths

State Median Household Income^b	COVID-19 Death Rates^a
State Median Household Income^b	16 States With High Rates	18 States With Midrange Rates	17 States With Low Rates
17 states with high median household income	4 (24%)NJ, NY, MA, RI	2 (12%)CT, DE	11 (65%)MD, MN, CA, CO, VA, DC, NH, AK, WA, UT, HI
17 states with midrange median household income	4 (24%)AZ, MI, PA, GA	9 (53%)SD, NV, WY, ND, IA and TX^c, IL, KS, ID	4 (24%)WI, NE, OR, VT
17 states with low median household income	8 (47%)MS, AL, LA, TN, AR, WV, IN, OK	7 (41%)FL and NM^c, SC, KY, MT, MO, OH	2 (12%)NC, ME

Notes: Percentages are rounded to the nearest whole number. Within each cell, states are listed in order by death rate, from highest to lowest.

The data used for this table came from the US Centers for Disease Control and Prevention,¹ statista,¹⁹ and the US Census Bureau.²⁴

Per 100,000 population, January 21, 2020 to January 20, 2022. ^bMedian value of states' median household income, 2019. ^cPairs of states with identical scores.

Political Party Control

States with Democratic control of both branches of government generally had substantially lower death rates than those with Republican control (Table 8). Of the 17 states with low death rates, 8 (47%) were controlled by the Democratic party and 3 (18%) had Republican control. Of the 16 states with high death rates, 9 (41%) were controlled by the Republican party and 3 (19%) had Democratic control. The 13 states with divided control had death rates generally between those of Democrat-controlled and Republican-controlled states: 6 (46%) had low death rates, 3 (23%) had midrange death rates, and 4 (31%) had high death rates.

Table 8.

Association Between Party Control of State Government Legislative and Executive Branches, 2020-2021, and Cumulative COVID-19 Deaths

Party Control	COVID-19 Death Rates^a
Party Control	16 States With High Rates	18 States With Midrange Rates	17 States With Low Rates
16 states with Democratic control^b	3 (19%)NJ, NY, RI	5 (31%)NM, NV, CT, IL, DE	8 (50%)CA, CO, VA, DC, OR, WA, ME, HI
22 states with Republican control^c	9 (41%)MS, AZ, AL, TN, ARWV, IN, GA, OK	10 (45%)FL, SC, SD, WY, MO, ND, OH, TX, IA, ID	3 (14%)NE, AK, UT
13 states with divided control^d	4 (31%)LA, MI, MA, PA	3 (23%)KY, MT, KS	6 (46%)MD, WI, MN, NC, NH, VT

Notes: Percentages are rounded to the nearest whole number. Within each cell, states are listed in order by death rate, from highest to lowest.

The data used for this table came from the US Centers for Disease Control and Prevention,¹ statista,¹⁹ the National Conference of State Legislatures,^26,27 and Ballotpedia.^28,29

Per 100,000 population, January 21, 2020 to January 20, 2022. ^bDemocrats had a majority of members in all legislative chambers and held the office of governor. ^cRepublicans had a majority of members in all legislative chambers and held the office of governor. ^dOne party had a majority of members in at least 1 legislative chamber while a different party had a majority in the other chamber or held the office of governor.

Limitations

These findings are subject to several limitations. The frequency count in the cross-tabulation tables was too small to support meaningful interpretation of relationships among the variables, for example, whether political control was more predictive of death rates in states with high or low NHSPI and TFAH scores. Additionally, half of the cells in all the cross-tabulation tables contained only 1 to 5 data points. The assessment used a single health outcome indicator, whereas alternative indicators might show patterns different from those reported here. The study assumes that hospitals and public health departments are the most important components of the health sector in determination of COVID-19 death rates; however, other facilities such as community health centers and nursing homes, likely also affected death rates. The state spending dataset omitted 2 jurisdictions. States were divided into 3 groups of 17 states each for analysis of TFAH standards but were divided into groups of 16, 17, and 18 states for NHSPI analysis, to avoid dividing states with identical scores between 2 groups.

Discussion

Expectations and Findings

We initially expected that states whose hospitals and public health departments had attained higher levels of reported emergency health preparedness would experience lower COVID-19 death rates. We also expected that higher prior spending for COVID-19-related purposes and greater state economic prosperity would align with lower death rates, but that party control of state government would not be associated with death rates. State NHSPI and TFAH preparedness scores largely met our expectations. On the other hand, EMAP and PHAB accreditation had no meaningful association with COVID-19 death rates. Lack of accreditation, however, was associated with lower death rates.

Research findings indicate that the 3 types of intervening policy factors studied can shape emergency preparedness in important ways: (1) state spending for purposes related to the health threat, which is determined mainly by state elected officials' appropriations and budgeting policies; (2) the strength of a state's economy, which is shaped in part by elected officials' support for economic development policies; and (3) partisan control of the policymaking system itself.

At least 4 alternative reasons might explain the lack of uniformly positive and strong association found between the 4 standards of health sector emergency preparedness and state COVID-19 death rates. First, the relationship may be weak or variable if some of the capacities and capabilities that are the principle substantive basis for the standards are less important to effective preparedness than standards designers have assumed. Second, it is possible that the standards may vary in relevance to infectious disease outbreaks compared with other types of health emergencies, although all 4 sponsoring organizations assert that their standards address such threats. Third, the standards may align well with threats on the scale of the COVID-19 pandemic, but the indicators and measures used may lack validity and not paint an accurate picture of actual compliance with the standards. Fourth, still other factors may override the ability of hospitals, public health departments, and other health service providers that meet the standards to execute effective response.

While these potential explanations may have some credibility, the gaps observed between the predictive value of the NHSPI and TFAH standards and those of the EMAP and PHAB standards suggest that the design of standards is at the center of the problem. Substantial revision appears necessary if the EMAP and PHAB standards are to become useful predictors of health emergency preparedness levels. There is clearly room for improvement in the NHSPI and TFAH standards as well, given that 42% of states with high NHSPI scores and 41% of states with high TFAH scores experienced either high or midrange death rates; in other words, those states had a roughly 40% chance of experiencing high or midrange death rates, despite their high preparedness scores.

High Priorities for Research and Development

A science-based, practice-oriented research and development initiative is needed to improve understanding of the association between adoption of health sector preparedness standards and positive health outcomes and, second, to translate that understanding into improved standards. Toward the ultimate goal of comprehensive, sectorwide preparedness for large-scale threats, the scope of the initiative should encompass all jurisdictional levels and all components of the health sector. Health sector actors that rely on standards as important guideposts for development of effective preparedness should participate in setting research priorities. These organizations include, among others, CDC, ASPR, the Federal Emergency Management Agency, their counterpart agencies at state and local levels, and the hospitals, healthcare systems, public health departments, and emergency management agencies they serve. It would be helpful to engage an independent scientific body, such as the National Academy of Medicine, to conduct empirical research for the initiative.

The study findings suggest 3 high priorities for research and development. First, researchers should reexamine the relevance of the capacities and capabilities that have been assumed to undergird existing preparedness standards, including those of the locally focused Project Public Health Ready; assess the value of the indicators and measures linked to those standards; revise or formulate new standards where needed; gauge the predictive power of the standards through modeling, simulations, and field exercises that mirror the complexity and dynamics of large-scale health threats; and evaluate responses to actual health emergencies. Special attention should be given to designing standards that address health threats that inequitably impact people of color, people with disabilities, and marginalized or vulnerable communities.

Second, research can illuminate the implications that different types of intervening and contextual factors have for the value of preparedness standards. For example, detailed analysis of state and local spending on infectious disease-related capacity could help identify the specific uses of funding that most strengthened hospital and public health department response to the COVID-19 pandemic. That information could be used in designing future preparedness standards that can guide organizational budgeting priorities.

Using the “health in all policies” paradigm, researchers could explicate the mechanisms through which states' economic strength and other indicators of societal wellbeing influence the preparedness of their health sectors. For example, stronger economic conditions might translate to higher public investment in preparedness and/or reflect a population that is better informed and more supportive of protective health measures. Better understanding of those relationships could inform the design of standards to reflect policymakers' decisions on pertinent economic development, education, and public information policies.

Additional research is needed on the implications of the governance universe that shapes the ability of health sector entities to prepare for and respond to health emergencies. Legislators, governors, county and city council members, and other elected officials can choose from among a wide range of health emergency policies that individually may have substantially different impacts on health sector preparedness. Research could develop indicators of the views elected officials hold on the proper role of government, trustworthy sources of health information, the suitable balance between health and competing policy priorities, and specific preparedness measures and response interventions that may be suitable for inclusion in preparedness standards.

Third, research could help improve the alignment of multiple preparedness standards with each other and facilitate coordinated response to sector components to shared health threats. The NHSPI and TFAH standards are closely aligned with each other and with the PHEP and HPP capacities frameworks. The PHAB standards are less aligned with those frameworks and the EMAP standards appear to reflect a quite different framework. The National Association of County and City Health Officials reportedly seeks to align its Project Public Health Ready standards with the PHEP and PHAB capabilities while the new Hospital Medical Surge Preparedness Index uses a separate set of indicators specific to hospitals and healthcare systems.^30,31 Research could identify the specific elements of each standard that best predict health sector preparedness and could lead to broader incorporation of those elements into other standards, bringing them into better alignment collectively and contributing to a larger, integrated system of standards.

Conclusion

The COVID-19 pandemic has been a test of fire for the US health sector. The central challenge now is how to best prepare for effective response to inevitable future health threats. Standards that accurately predict the preparedness of the health sector to respond effectively to large-scale health threats are essential to that task.

Much creative and diligent work has gone into the design of existing health sector emergency preparedness standards. Some standards have performed substantially better than others in predicting the success that hospitals and public health departments have had in addressing the COVID-19 pandemic, but none of the 4 standards reviewed here has achieved a uniformly high level of predictive power. A new, science-based research and development initiative is needed to design standards that will better predict the ability of all health sector actors to protect our most vulnerable populations, and all US residents, from infectious disease outbreaks and other future health emergency threats.

Footnotes

Acknowledgments

The author thanks the Institute for Research in Statistics and Its Applications, University of Minnesota, Minneapolis, MN; LeRoy Barnhart, MA, Minneapolis, MN; and 3 anonymous reviewers for their important contributions to this article.

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