Impact of the COVID-19 Pandemic on Administration of Selected Routine Childhood and Adolescent Vaccinations — 10 U.S. Jurisdictions, March–September 2020

Temporary disruptions in routine and nonemergency medical care access and delivery have been observed during periods of considerable community transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) ( 1 ). However, medical care delay or avoidance might increase morbidity and mortality risk associated with treatable and preventable health conditions and might contribute to reported excess deaths directly or indirectly related to COVID-19 ( 2 ). To assess delay or avoidance of urgent or emergency and routine medical care because of concerns about COVID-19, a web-based survey was administered by Qualtrics, LLC, during June 24–30, 2020, to a nationwide representative sample of U.S. adults aged ≥18 years. Overall, an estimated 40.9% of U.S. adults have avoided medical care during the pandemic because of concerns about COVID-19, including 12.0% who avoided urgent or emergency care and 31.5% who avoided routine care. The estimated prevalence of urgent or emergency care avoidance was significantly higher among the following groups: unpaid caregivers for adults* versus noncaregivers (adjusted prevalence ratio [aPR] = 2.9); persons with two or more selected underlying medical conditions † versus those without those conditions (aPR = 1.9); persons with health insurance versus those without health insurance (aPR = 1.8); non-Hispanic Black (Black) adults (aPR = 1.6) and Hispanic or Latino (Hispanic) adults (aPR = 1.5) versus non-Hispanic White (White) adults; young adults aged 18–24 years versus adults aged 25–44 years (aPR = 1.5); and persons with disabilities § versus those without disabilities (aPR = 1.3). Given this widespread reporting of medical care avoidance because of COVID-19 concerns, especially among persons at increased risk for severe COVID-19, urgent efforts are warranted to ensure delivery of services that, if deferred, could result in patient harm. Even during the COVID-19 pandemic, persons experiencing a medical emergency should seek and be provided care without delay ( 3 ). During June 24–30, 2020, a total of 5,412 (54.7%) of 9,896 eligible adults ¶ completed web-based COVID-19 Outbreak Public Evaluation Initiative surveys administered by Qualtrics, LLC.** The Human Research Ethics Committee of Monash University (Melbourne, Australia) reviewed and approved the study protocol on human subjects research. This activity was also reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. †† Respondents were informed of the study purposes and provided electronic consent before commencement, and investigators received anonymized responses. The 5,412 participants included 3,683 (68.1%) first-time respondents and 1,729 (31.9%) persons who had completed a related survey §§ during April 2–8, 2020. Among the 5,412 participants, 4,975 (91.9%) provided complete data for all variables in this analysis. Quota sampling and survey weighting ¶¶ were employed to improve cohort representativeness of the U.S. population by gender, age, and race/ethnicity. Respondents were asked “Have you delayed or avoided medical care due to concerns related to COVID-19?” Delay or avoidance was evaluated for emergency (e.g., care for immediate life-threatening conditions), urgent (e.g., care for immediate non–life-threatening conditions), and routine (e.g., annual check-ups) medical care. Given the potential for variation in interpretation of whether conditions were life-threatening, responses for urgent and emergency care delay or avoidance were combined for analysis. Covariates included gender; age; race/ethnicity; disability status; presence of one or more selected underlying medical conditions known to increase risk for severe COVID-19; education; essential worker status***; unpaid adult caregiver status; U.S. census region; urban/rural classification ††† ; health insurance status; whether respondents knew someone who had received a positive SARS-CoV-2 test result or had died from COVID-19; and whether the respondents believed they were at high risk for severe COVID-19. Comparisons within all these subgroups were evaluated using multivariable Poisson regression models §§§ with robust standard errors to estimate prevalence ratios adjusted for all covariates, 95% confidence intervals, and p-values to evaluate statistical significance (α = 0.05) using the R survey package (version 3.29) and R software (version 4.0.2; The R Foundation). As of June 30, 2020, among 4,975 U.S. adult respondents, 40.9% reported having delayed or avoided any medical care, including urgent or emergency care (12.0%) and routine care (31.5%), because of concerns about COVID-19 (Table 1). Groups of persons among whom urgent or emergency care avoidance exceeded 20% and among whom any care avoidance exceeded 50% included adults aged 18–24 years (30.9% for urgent or emergency care; 57.2% for any care), unpaid caregivers for adults (29.8%; 64.3%), Hispanic adults (24.6%; 55.5%), persons with disabilities (22.8%; 60.3%), persons with two or more selected underlying medical conditions (22.7%; 54.7%), and students (22.7%; 50.3%). One in four unpaid caregivers reported caring for adults who were at increased risk for severe COVID-19. TABLE 1 Estimated prevalence of delay or avoidance of medical care because of concerns related to COVID-19, by type of care and respondent characteristics — United States, June 30, 2020 Characteristic No. (%)† Type of medical care delayed or avoided* Urgent or emergency Routine Any %† P-value§ %† P-value§ %† P-value§ All respondents 4,975 (100) 12.0 — 31.5 — 40.9 — Gender Female 2,528 (50.8) 11.7 0.598 35.8 <0.001 44.9 <0.001 Male 2,447 (49.2) 12.3 27.0 36.7 Age group, yrs 18–24 650 (13.1) 30.9 <0.001 29.6 0.072 57.2 <0.001 25–44 1,740 (35.0) 14.9 34.2 44.8 45–64 1,727 (34.7) 5.7 30.0 34.5 ≥65 858 (17.3) 4.4 30.3 33.5 Race/Ethnicity White, non-Hispanic 3,168 (63.7) 6.7 <0.001 30.9 0.020 36.2 <0.001 Black, non-Hispanic 607 (12.2) 23.3 29.7 48.1 Asian, non-Hispanic 238 (4.8) 8.6 31.3 37.7 Other race or multiple races, non-Hispanic¶ 150 (3.0) 15.5 23.9 37.3 Hispanic, any race or races 813 (16.3) 24.6 36.4 55.5 Disability** Yes 1,108 (22.3) 22.8 <0.001 42.9 <0.001 60.3 <0.001 No 3,867 (77.7) 8.9 28.2 35.3 Underlying medical condition†† No 2,537 (51.0) 8.2 <0.001 27.9 <0.001 34.7 <0.001 One 1,328 (26.7) 10.4 33.0 41.2 Two or more 1,110 (22.3) 22.7 37.7 54.7 2019 household income, USD <25,000 665 (13.4) 13.9 0.416 31.2 0.554 42.8 0.454 25,000–49,999 1,038 (20.9) 11.1 30.9 38.6 50,000–99,999 1,720 (34.6) 12.5 30.5 41.1 ≥100,000 1,552 (31.2) 11.2 33.0 41.4 Education Less than high school diploma 65 (1.3) 15.6 0.442 24.7 0.019 37.9 0.170 High school diploma 833 (16.7) 12.3 28.1 38.1 Some college 1,302 (26.2) 13.6 29.7 40.3 Bachelor's degree 1,755 (35.3) 11.2 34.8 43.6 Professional degree 1,020 (20.5) 10.9 31.2 39.5 Employment status Employed 3,049 (61.3) 14.6 <0.001 31.5 0.407 43.3 <0.001 Unemployed 630 (12.7) 8.7 34.4 39.5 Retired 1,129 (22.7) 5.3 29.9 33.8 Student 166 (3.3) 22.7 30.5 50.3 Essential worker status§§ Essential worker 1,707 (34.3) 19.5 <0.001 32.4 0.293 48.0 <0.001 Nonessential worker 1,342 (27.0) 8.4 30.3 37.3 Unpaid caregiver status¶¶ Unpaid caregiver for adults 1,344 (27.0) 29.8 <0.001 41.0 <0.001 64.3 <0.001 Not unpaid caregiver for adults 3,631 (73.0) 5.4 27.9 32.2 U.S. Census region*** Northeast 1,122 (22.6) 11.0 0.008 33.9 0.203 42.5 0.460 Midwest 936 (18.8) 8.5 32.0 38.7 South 1,736 (34.9) 13.9 29.6 40.7 West 1,181 (23.7) 13.0 31.5 41.5 Rural/Urban classification††† Urban 4,411 (88.7) 12.3 0.103 31.5 0.763 41.2 0.216 Rural 564 (11.3) 9.4 30.9 38.2 Health insurance status Yes 4,577 (92.0) 12.4 0.036 32.6 <0.001 42.3 <0.001 No 398 (8.0) 7.8 18.4 24.8 Know someone with positive test results for SARS-CoV-2§§§ Yes 989 (19.9) 8.8 0.004 40.7 <0.001 46.6 <0.001 No 3,986 (80.1) 12.8 29.2 39.5 Knew someone who died from COVID-19 Yes 364 (7.3) 10.1 0.348 41.4 <0.001 46.3 0.048 No 4,611 (92.7) 12.2 30.7 40.5 Believed to be in group at high risk for severe COVID-19 Yes 981 (19.7) 10.0 0.050 42.5 <0.001 49.4 <0.001 No 3,994 (80.3) 12.5 28.8 38.8 Abbreviations: CI = confidence interval; COVID-19 = coronavirus disease 2019; USD = U.S. dollars. * The types of medical care avoidance are not mutually exclusive; respondents had the option to indicate that they had delayed or avoided more than one type of medical care (i.e., routine medical care and urgent/emergency medical care). † Statistical raking and weight trimming were employed to improve the cross-sectional June cohort representativeness of the U.S. population by gender, age, and race/ethnicity according to the 2010 U.S. Census. § The Rao-Scott adjusted Pearson chi-squared test was used to test for differences in observed and expected frequencies among groups by characteristic for avoidance of each type of medical care (e.g., whether avoidance of routine medical care differs significantly by gender). Statistical significance was evaluated at a threshold of α = 0.05. ¶ “Other” race includes American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or Other. ** Persons who had a disability were defined as such based on a qualifying response to either one of two questions: “Are you limited in any way in any activities because of physical, mental, or emotional condition?” and “Do you have any health conditions that require you to use special equipment, such as a cane, wheelchair, special bed, or special telephone?” https://www.cdc.gov/brfss/questionnaires/pdf-ques/2015-brfss-questionnaire-12-29-14.pdf. †† Selected underlying medical conditions known to increase the risk for severe COVID-19 included in this analysis were obesity, diabetes, high blood pressure, cardiovascular disease, and any type of cancer. Obesity is defined as body mass index ≥30 kg/m2 and was calculated from self-reported height and weight (https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html). The remaining conditions were assessed using the question “Have you ever been diagnosed with any of the following conditions?” with response options of 1) “Never”; 2) “Yes, I have in the past, but don’t have it now”; 3) “Yes I have, but I do not regularly take medications or receive treatment”; and 4) “Yes I have, and I am regularly taking medications or receiving treatment.” Respondents who answered that they have been diagnosed and chose either response 3 or 4 were considered as having the specified medical condition. §§ Essential worker status was self-reported. ¶¶ Unpaid caregiver status was self-reported. Unpaid caregivers for adults were defined as having provided unpaid care to a relative or friend aged ≥18 years at any time in the last 3 months. Examples provided to survey respondents included helping with personal needs, household chores, health care tasks, managing a person’s finances, taking them to a doctor’s appointment, arranging for outside services, and visiting regularly to see how they are doing. *** Region classification was determined by using the U.S. Census Bureau’s Census Regions and Divisions. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. ††† Rural-urban classification was determined by using self-reported ZIP codes according to the Federal Office of Rural Health Policy definition of rurality. https://www.hrsa.gov/rural-health/about-us/definition/datafiles.html. §§§ For this question, respondents were asked to select the following statement, if applicable: “I know someone who has tested positive for COVID-19.” In the multivariable Poisson regression models, differences within groups were observed for urgent or emergency care avoidance (Figure) and any care avoidance (Table 2). Adjusted prevalence of urgent or emergency care avoidance was significantly higher among unpaid caregivers for adults versus noncaregivers (2.9; 2.3–3.6); persons with two or more selected underlying medical conditions versus those without those conditions (1.9; 1.5–2.4); persons with health insurance versus those without health insurance (1.8; 1.2–2.8); Black adults (1.6; 1.3–2.1) and Hispanic adults (1.5; 1.2–2.0) versus White adults; young adults aged 18–24 years versus adults aged 25–44 years (1.5; 1.2–1.8); and persons with disabilities versus those without disabilities (1.3; 1.1–1.5). Avoidance of urgent or emergency care was significantly lower among adults aged ≥45 years than among younger adults. FIGURE Adjusted prevalence ratios* , † for characteristics § , ¶ , ** , †† associated with delay or avoidance of urgent or emergency medical care because of concerns related to COVID-19 — United States, June 30, 2020 Abbreviation: COVID-19 = coronavirus disease 2019. * Comparisons within subgroups were evaluated using Poisson regressions used to calculate a prevalence ratio adjusted for all characteristics shown in figure. † 95% confidence intervals indicated with error bars. § “Other” race includes American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or Other. ¶ Selected underlying medical conditions known to increase the risk for severe COVID-19 were obesity, diabetes, high blood pressure, cardiovascular disease, and any type of cancer. Obesity is defined as body mass index ≥30 kg/m2 and was calculated from self-reported height and weight (https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html). The remaining conditions were assessed using the question “Have you ever been diagnosed with any of the following conditions?” with response options of 1) “Never”; 2) “Yes, I have in the past, but don’t have it now”; 3) “Yes I have, but I do not regularly take medications or receive treatment”; and 4) “Yes I have, and I am regularly taking medications or receiving treatment.” Respondents who answered that they have been diagnosed and chose either response 3 or 4 were considered as having the specified medical condition. ** Essential worker status was self-reported. For the adjusted prevalence ratios, essential workers were compared with all other respondents (including those who were nonessential workers, retired, unemployed, and students). †† Unpaid caregiver status was self-reported. Unpaid caregivers for adults were defined as having provided unpaid care to a relative or friend aged ≥18 years to help them take care of themselves at any time in the last 3 months. The figure is a forest plot showing the adjusted prevalence ratios for characteristics associated with delay or avoidance of urgent or emergency medical care because of concerns related to COVID-19, in the United States, as of June 30, 2020. TABLE 2 Characteristics associated with delay or avoidance of any medical care because of concerns related to COVID-19 — United States, June 30, 2020 Characteristic Weighted* no. Avoided or delayed any medical care aPR† (95% CI†) P-value† All respondents 4,975 — — — Gender Female 2,528 Referent — — Male 2,447 0.81 (0.75–0.87)§ <0.001 Age group, yrs 18–24 650 1.12 (1.01–1.25)§ 0.035 25–44 1,740 Referent — — 45–64 1,727 0.80 (0.72–0.88)§ <0.001 ≥65 858 0.72 (0.64–0.81)§ <0.001 Race/Ethnicity White, non-Hispanic 3,168 Referent — — Black, non-Hispanic 607 1.07 (0.96–1.19) 0.235 Asian, non-Hispanic 238 1.04 (0.91–1.18) 0.567 Other race or multiple races, non-Hispanic¶ 150 0.87 (0.71–1.07) 0.196 Hispanic, any race or races 813 1.15 (1.03–1.27)§ 0.012 Disability** Yes 1,108 1.33 (1.23–1.43)§ <0.001 No 3,867 Referent — — Underlying medical condition†† No 2,537 Referent — — One 1,328 1.15 (1.05–1.25)§ 0.004 Two or more 1,110 1.31 (1.20–1.42)§ <0.001 Education Less than high school diploma 65 0.72 (0.53–0.98)§ 0.037 High school diploma 833 0.79 (0.71–0.89)§ <0.001 Some college 1,302 0.85 (0.78–0.93)§ 0.001 Bachelor's degree 1,755 Referent — — Professional degree 1,020 0.90 (0.82–0.98)§ 0.019 Essential workers vs others§§ Essential workers 1,707 1.00 (0.92–1.09) 0.960 Other respondents (nonessential workers, retired persons, unemployed persons, and students) 3,268 Referent — — Unpaid caregiver status¶¶ Unpaid caregiver for adults 1,344 1.64 (1.52–1.78)§ <0.001 Not unpaid caregiver for adults 3,631 Referent — — U.S. Census region*** Northeast 1,122 Referent — — Midwest 936 0.93 (0.83–1.04) 0.214 South 1,736 0.90 (0.82–0.99)§ 0.028 West 1,181 0.99 (0.89–1.09) 0.808 Rural/Urban classification††† Urban 4,411 1.00 (0.89–1.12) 0.993 Rural 564 Referent — — Health insurance status Yes 4,577 1.61 (1.31–1.98)§ <0.001 No 398 Referent — — Know someone with positive test results for SARS-CoV-2§§§ Yes 989 1.22 (1.12–1.33)§ <0.001 No 3,986 Referent — — Knew someone who died from COVID-19 Yes 364 0.99 (0.88–1.12) 0.860 No 4,611 Referent — — Believed to be in a group at high risk for severe COVID-19 Yes 981 1.33 (1.23–1.44)§ <0.001 No 3,994 Referent — — Abbreviations: aPR = adjusted prevalence ratio; CI = confidence interval; COVID-19 = coronavirus disease 2019. * Statistical raking and weight trimming were employed to improve the cross-sectional June cohort representativeness of the U.S. population by gender, age, and race/ethnicity according to the 2010 U.S. Census. † Comparisons within subgroups were evaluated using Poisson regressions used to calculate a prevalence ratio adjusted for all characteristics listed, as well as a 95% CI and p-value. Statistical significance was evaluated at a threshold of α = 0.05. § P-value calculated using Poisson regression among respondents within a characteristic is statistically significant at levels of p<0.05. ¶ “Other” race includes American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or Other. ** Persons who had a disability were defined based on a qualifying response to either one of two questions: “Are you limited in any way in any activities because of physical, mental, or emotional condition?” and “Do you have any health conditions that require you to use special equipment, such as a cane, wheelchair, special bed, or special telephone?” https://www.cdc.gov/brfss/questionnaires/pdf-ques/2015-brfss-questionnaire-12-29-14.pdf. †† Selected underlying medical conditions known to increase the risk for severe COVID-19 were obesity, diabetes, high blood pressure, cardiovascular disease, and any type of cancer. Obesity is defined as body mass index ≥30 kg/m2 and was calculated from self-reported height and weight (https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html). The remaining conditions were assessed using the question “Have you ever been diagnosed with any of the following conditions?” with response options of 1) “Never”; 2) “Yes, I have in the past, but don’t have it now”; 3) “Yes I have, but I do not regularly take medications or receive treatment”; and 4) “Yes I have, and I am regularly taking medications or receiving treatment.” Respondents who answered that they have been diagnosed and chose either response 3 or 4 were considered as having the specified medical condition. §§ Essential worker status was self-reported. For the adjusted prevalence ratios, essential workers were compared with all other respondents (including those who were nonessential workers, retired, unemployed, and students). ¶¶ Unpaid caregiver status was self-reported. Unpaid caregivers for adults were defined as having provided unpaid care to a relative or friend aged ≥18 years at any time in the last 3 months. Examples provided to survey respondents included helping with personal needs, household chores, health care tasks, managing a person’s finances, taking them to a doctor’s appointment, arranging for outside services, and visiting regularly to see how they are doing. *** Region classification was determined by using the U.S. Census Bureau’s Census Regions and Divisions. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. ††† Rural/urban classification was determined by using self-reported ZIP codes according to the Federal Office of Rural Health Policy definition of rurality. https://www.hrsa.gov/rural-health/about-us/definition/datafiles.html. §§§ For this question, respondents were asked to select the following statement, if applicable: “I know someone who has tested positive for COVID-19.” Discussion As of June 30, 2020, an estimated 41% of U.S. adults reported having delayed or avoided medical care during the pandemic because of concerns about COVID-19, including 12% who reported having avoided urgent or emergency care. These findings align with recent reports that hospital admissions, overall emergency department (ED) visits, and the number of ED visits for heart attack, stroke, and hyperglycemic crisis have declined since the start of the pandemic ( 3 – 5 ), and that excess deaths directly or indirectly related to COVID-19 have increased in 2020 versus prior years ( 2 ). Nearly one third of adult respondents reported having delayed or avoided routine medical care, which might reflect adherence to community mitigation efforts such as stay-at-home orders, temporary closures of health facilities, or additional factors. However, if routine care avoidance were to be sustained, adults could miss opportunities for management of chronic conditions, receipt of routine vaccinations, or early detection of new conditions, which might worsen outcomes. Avoidance of both urgent or emergency and routine medical care because of COVID-19 concerns was highly prevalent among unpaid caregivers for adults, respondents with two or more underlying medical conditions, and persons with disabilities. For caregivers who reported caring for adults at increased risk for severe COVID-19, concern about exposure of care recipients might contribute to care avoidance. Persons with underlying medical conditions that increase their risk for severe COVID-19 ( 6 ) are more likely to require care to monitor and treat these conditions, potentially contributing to their more frequent report of avoidance. Moreover, persons at increased risk for severe COVID-19 might have avoided health care facilities because of perceived or actual increased risk of exposure to SARS-CoV-2, particularly at the onset of the pandemic. However, health care facilities are implementing important safety precautions to reduce the risk of SARS-CoV-2 infection among patients and personnel. In contrast, delay or avoidance of care might increase risk for life-threatening medical emergencies. In a recent study, states with large numbers of COVID-19–associated deaths also experienced large proportional increases in deaths from other underlying causes, including diabetes and cardiovascular disease ( 7 ). For persons with disabilities, accessing medical services might be challenging because of disruptions in essential support services, which can result in adverse health outcomes. Medical services for persons with disabilities might also be disrupted because of reduced availability of accessible transportation, reduced communication in accessible formats, perceptions of SARS-CoV-2 exposure risk, and specialized needs that are difficult to address with routine telehealth delivery during the pandemic response. Increasing accessibility of medical and telehealth services ¶¶¶ might help prevent delay of needed care. Increased prevalences of reported urgent or emergency care avoidance among Black adults and Hispanic adults compared with White adults are especially concerning given increased COVID-19-associated mortality among Black adults and Hispanic adults ( 8 ). In the United States, the age-adjusted COVID-19 hospitalization rates are approximately five times higher among Black persons and four times higher among Hispanic persons than are those among White persons ( 9 ). Factors contributing to racial and ethnic disparities in SARS-CoV-2 exposure, illness, and mortality might include long-standing structural inequities that influence life expectancy, including prevalence and underlying medical conditions, health insurance status, and health care access and utilization, as well as work and living circumstances, including use of public transportation and essential worker status. Communities, health care systems, and public health agencies can foster equity by working together to ensure access to information, testing, and care to assure maintenance and management of physical and mental health. The higher prevalence of medical care delay or avoidance among respondents with health insurance versus those without insurance might reflect differences in medical care-seeking behaviors. Before the pandemic, persons without insurance sought medical care much less frequently than did those with insurance ( 10 ), resulting in fewer opportunities for medical care delay or avoidance. The findings in this report are subject to at least five limitations. First, self-reported data are subject to recall, response, and social desirability biases. Second, the survey did not assess reasons for COVID-19–associated care avoidance, such as adherence to public health recommendations; closure of health care provider facilities; reduced availability of public transportation; fear of exposure to infection with SARS-CoV-2; or availability, accessibility, and acceptance or recognition of telemedicine as a means of providing care in lieu of in-person services. Third, the survey did not assess baseline patterns of care-seeking or timing or duration of care avoidance. Fourth, perceptions of whether a condition was life-threatening might vary among respondents. Finally, although quota sampling methods and survey weighting were employed to improve cohort representativeness, this web-based survey might not be fully representative of the U.S. population for income, educational attainment, and access to technology. However, the findings are consistent with reported declines in hospital admissions and ED visits during the pandemic ( 3 – 5 ). CDC has issued guidance to assist persons at increased risk for severe COVID-19 in staying healthy and safely following treatment plans**** and to prepare health care facilities to safely deliver care during the pandemic. †††† Additional public outreach in accessible formats tailored for diverse audiences might encourage these persons to seek necessary care. Messages could highlight the risks of delaying needed care, especially among persons with underlying medical conditions, and the importance of timely emergency care. Patient concerns related to potential exposure to SARS-CoV-2 in health care settings could be addressed by describing facilities’ precautions to reduce exposure risk. Further exploration of underlying reasons for medical care avoidance is needed, including among persons with disabilities, persons with underlying health conditions, unpaid caregivers for adults, and those who face structural inequities. If care were avoided because of concern about SARS-CoV-2 exposure or if there were closures or limited options for in-person services, providing accessible telehealth or in-home health care could address some care needs. Even during the COVID-19 pandemic, persons experiencing a medical emergency should seek and be provided care without delay ( 3 ). Summary What is already known about this topic? Delayed or avoided medical care might increase morbidity and mortality associated with both chronic and acute health conditions. What is added by this report? By June 30, 2020, because of concerns about COVID-19, an estimated 41% of U.S. adults had delayed or avoided medical care including urgent or emergency care (12%) and routine care (32%). Avoidance of urgent or emergency care was more prevalent among unpaid caregivers for adults, persons with underlying medical conditions, Black adults, Hispanic adults, young adults, and persons with disabilities. What are the implications for public health practice? Understanding factors associated with medical care avoidance can inform targeted care delivery approaches and communication efforts encouraging persons to safely seek timely routine, urgent, and emergency care.

On March 13, 2020, the president of the United States declared a national emergency in response to the coronavirus disease 2019 (COVID-19) pandemic (1). With reports of laboratory-confirmed cases in all 50 states by that time (2), disruptions were anticipated in the U.S. health care system's ability to continue providing routine preventive and other nonemergency care. In addition, many states and localities issued shelter-in-place or stay-at-home orders to reduce the spread of COVID-19, limiting movement outside the home to essential activities (3). On March 24, CDC posted guidance emphasizing the importance of routine well child care and immunization, particularly for children aged ≤24 months, when many childhood vaccines are recommended.

SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is thought to spread from person to person primarily by the respiratory route and mainly through close contact ( 1 ). Community mitigation strategies can lower the risk for disease transmission by limiting or preventing person-to-person interactions ( 2 ). U.S. states and territories began implementing various community mitigation policies in March 2020. One widely implemented strategy was the issuance of orders requiring persons to stay home, resulting in decreased population movement in some jurisdictions ( 3 ). Each state or territory has authority to enact its own laws and policies to protect the public’s health, and jurisdictions varied widely in the type and timing of orders issued related to stay-at-home requirements. To identify the broader impact of these stay-at-home orders, using publicly accessible, anonymized location data from mobile devices, CDC and the Georgia Tech Research Institute analyzed changes in population movement relative to stay-at-home orders issued during March 1–May 31, 2020, by all 50 states, the District of Columbia, and five U.S. territories.* During this period, 42 states and territories issued mandatory stay-at-home orders. When counties subject to mandatory state- and territory-issued stay-at-home orders were stratified along rural-urban categories, movement decreased significantly relative to the preorder baseline in all strata. Mandatory stay-at-home orders can help reduce activities associated with the spread of COVID-19, including population movement and close person-to-person contact outside the household. Data on state and territorial stay-at-home orders were obtained from government websites containing executive or administrative orders or press releases for each jurisdiction. Each order was analyzed and coded into one of five mutually exclusive categories: 1) mandatory for all persons; 2) mandatory only for persons in certain areas of the jurisdiction; 3) mandatory only for persons at increased risk in the jurisdiction; 4) mandatory only for persons at increased risk in certain areas of the jurisdiction; or 5) advisory or recommendation (i.e., nonmandatory). Jurisdictions that did not issue an order were coded as having no state- or territory-issued order. † These data underwent secondary review and quality assurance checks and were published in a freely available data set ( 4 ). Publicly accessible, anonymized location data from mobile devices were obtained to estimate county-level raw data regarding movement ( 5 ). Population movement was estimated by computing the percentage of individual mobile devices (e.g., mobile phones, tablets, or watches) reporting each day that were completely at home (i.e., had not moved beyond a 150-meter radius of its common nighttime location) within a given county, using a 7-day rolling average to smooth each county’s pre- and postorder time series values. This analysis used four types of order index dates, based only on mandatory orders: 1) the start date of each state or territorial stay-at-home order for each county in that jurisdiction; 2) the relaxation or expiration date of each state or territorial stay-at-home order for each county in that jurisdiction; 3) the effective date of the first state-issued stay-at-home order (i.e., California); and 4) the first date a state-issued stay-at-home order ended (i.e., Alaska). § To assess changes in movement when mandatory state or territorial stay-at-home orders went into effect and ended, counties were first stratified along rural-urban categories to ensure that counties with similar population sizes were grouped together. ¶ A box plot was constructed for each rural-urban category to examine the distribution of county mean percentages of devices at home during the pre- and postorder periods associated with each index date. Because it was not assumed that movement values follow a normal distribution for all counties and periods, a clustered Wilcoxon signed rank test was then performed for each stratum, with counties as clusters, on the constituent counties’ median pre- and postorder values associated with each index date. A lower-tailed test was used for index dates related to the start of state and territorial orders, and an upper-tailed test was used for index dates related to the end of state and territorial orders** ( 6 ). Strata-level statistical significance was assessed at the 99% confidence level (α = 0.01). Analyses were performed using Python (version 3.6; Python Software Foundation) and R (version 3.5; The R Foundation). This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. †† During March 1–May 31, 42 states and territories issued mandatory stay-at-home orders, affecting 2,355 (73%) of 3,233 U.S. counties (Figure 1). The first territorial order was issued by Puerto Rico (March 15), and the first state order by California (March 19). Eight jurisdictions issued only an advisory order or recommendation to stay home, and six did not issue any stay-at-home orders. Most jurisdictions issued multiple orders during the observation period, and coding varied among individual orders. The duration and termination of each order varied by jurisdiction. During the observation period, 22 jurisdictions transitioned from a mandatory order to an advisory order, 11 rescinded or allowed orders to expire without extending, and the order in one jurisdiction was ruled invalid by the state’s supreme court. §§ The first state to rescind or allow a stay-at-home order to expire was Alaska (April 24). Eight jurisdictions had mandatory orders applicable to at least some part of the population that extended beyond May 31. FIGURE 1 Type and duration of COVID-19 state and territorial stay-at-home orders,* by jurisdiction — United States,† March 1–May 31, 2020 Abbreviations: COVID-19 = coronavirus disease 2019; CNMI = Northern Mariana Islands. * Including the type of stay-at-home order implemented, to whom it applied, and the period for which it was in place. † Jurisdictions that did not issue any orders requiring or recommending persons to stay home during the observation period were not included in this figure. Jurisdictions without any orders were American Samoa, Arkansas, Connecticut, Nebraska, North Dakota, and Wyoming. The figure is a line chart showing COVID-19 state and territorial stay-at-home orders in the United States during March 1–May 31, 2020. Differences in county-level mean population movement during the pre- and postorder periods varied by index date and rural-urban strata (Figure 2). Decreased median population movement was observed in 2,295 (97.6%) of the 2,351 counties for which population movement data were available. Mandatory stay-at-home orders were associated with decreased population movement (i.e., higher median percentage of devices at home) during the 28-day period after the order start date, relative to the baseline 28-day period before the order start date. This relationship was significant in all rural-urban strata (Supplementary Table, https://stacks.cdc.gov/view/cdc/92406). Among the 2,355 counties subject to mandatory stay-at-home orders, 436 (19%) had an order that expired on or before May 3, which is the latest possible expiration date that allows for a 28-day postorder observation period. ¶¶ Movement significantly increased (i.e., lower median percentage of devices at home) in the period immediately after the expiration or lifting of orders in all rural-urban strata. FIGURE 2 Distribution of county-level mean percentage of mobile devices at home pre- and postindex date periods (relative to the start and end of stay-at-home orders), by rural-urban classification — United States, March 1–May 31, 2020 The figure is a series of four panels showing the distribution of the county-level mean percentage of mobile devices at home pre- and postindex date periods (start and end of stay-at-home orders), by rural-urban classification in the United States during March 1–May 31, 2020. The 14-day period immediately after the first state stay-at-home order was issued in the United States was associated with a significant decrease in movement in all rural-urban strata relative to the 14-day period immediately preceding its implementation.*** The period after the first state relaxed a stay-at-home order was associated with increased population movement at the strata level among states or territories that had not relaxed a stay-at-home order in the same period. ††† Discussion Based on location data from mobile devices, in 97.6% of counties with mandatory stay-at-home orders issued by states or territories, these orders were associated with decreased median population movement after the order start date, relative to the period before the order was implemented. Reduced population movement helps prevent close contact among persons outside the household, potentially limiting exposure to persons infected with SARS-CoV-2. This suggests that stay-at-home orders can help protect the public’s health by limiting potential exposure to SARS-CoV-2 and reducing community transmission of COVID-19. The implementation of stay-at-home orders might affect population movement differently depending on when and where orders are issued and to whom they apply. The observed decrease in population movement after the implementation of the first state-issued mandatory stay-at-home order in California suggests that the implementation of certain public health policies might influence behaviors in other areas, in addition to persons directly subject to the action. However, this observation occurred in the context of other variables, which might have influenced behaviors, including the declaration of COVID-19 as a pandemic, declaration of national or state emergencies, media attention to fatalities and increased demands on hospitals, gathering bans, closures of schools and businesses, and cancellation of sporting events. Increases in population movement were evident among counties in jurisdictions where stay-at-home orders were lifted, as well as in other communities as orders began to lift nationwide. Such increases might be driven in part by persons resuming preorder movement behaviors in response to the lifting of orders where they lived, or in response to perceived reduced risk associated with the lifting of orders elsewhere. Many other factors might have also played a role, and additional studies are needed to determine which factors caused population movement to increase across jurisdictions after the first state stay-at-home order ended. §§§ Further research is needed to assess the impact of reduced population movement and other community mitigation strategies on the spread of COVID-19. For example, understanding the relationship between stay-at-home orders in contiguous counties and movement might explain how same-state and neighboring-state policy changes can affect public health by mitigating or exacerbating external environmental and social factors affecting population movement. ¶¶¶ As the pandemic continues and jurisdictions consider reimplementing mitigation policies, additional studies are needed to assess the impact of reissuing stay-at-home orders. The findings in this report are subject to at least five limitations. First, although relative device coverage largely correlates with U.S. population density, some regions or demographic groups might be over- or underrepresented.**** Second, persons might have multiple mobile devices and might not take certain devices with them when they leave the home (e.g., tablets) or might take multiple devices with them simultaneously (e.g., phones and smart watches). Third, although the clustered Wilcoxon signed rank test is used with counties as clusters because each county’s median pre- and postorder values are paired comparisons rather than independent observations, potential spatial dependence among counties is not addressed. Fourth, this report does not assess whether population movement was affected by nationwide protests during the observation period. †††† Finally, this report analyzes the relationship between stay-at-home orders and population movement and does not assess the complex relationship between stay-at-home orders and illness incidence rates or deaths. Mandatory stay-at-home orders can help reduce activities associated with community spread of COVID-19, including population movement and close person-to-person contact outside the household. Mandatory stay-at-home orders were associated with reduced population movement in most counties during the early months of the COVID-19 pandemic, and the relaxation of those orders was associated with increased movement. Although stay-at-home orders might assist in limiting potential exposure to SARS-CoV-2 and have had public support ( 7 ), such orders substantially disrupt daily life and have resulted in adverse economic impact ( 8 ). Further studies are needed to assess the timing and conditions under which stay-at-home orders might be best used to protect health, minimize negative impacts, and ensure equitable enforcement of community mitigation policies. These findings can inform public policies to potentially slow the spread of COVID-19 and control other communicable diseases in the future. Summary What is already known about this topic? Stay-at-home orders are a community mitigation strategy used to reduce the spread of COVID-19 in the United States. What is added by this report? States and territories that issued mandatory stay-at-home orders experienced decreased population movement in most counties. The period after the first state relaxed a stay-at-home order was associated with increased population movement in states or territories that had not relaxed a stay-at-home order in the same period. What are the implications for public health practice? Stay-at-home orders can reduce activities associated with community spread of COVID-19, including population movement and close person-to-person contact outside the household. These findings can inform future public policies to reduce community spread of COVID-19.