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Original Investigation | Health Policy

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Reports of Forgone Medical Care Among US Adults
During the Initial Phase of the COVID-19 Pandemic
Kelly E. Anderson, MPP; Emma E. McGinty, PhD, MS; Rachel Presskreischer, MS; Colleen L. Barry, PhD, MPP

Abstract

IMPORTANCE The coronavirus disease 2019 (COVID-19) pandemic has caused major disruptions in
the US health care system.

OBJECTIVE To estimate frequency of and reasons for reported forgone medical care from March to
mid-July 2020 and examine characteristics of US adults who reported forgoing care.

DESIGN, SETTING, AND PARTICIPANTS This survey study used data from the second wave of the
Johns Hopkins COVID-19 Civic Life and Public Health Survey, fielded from July 7 to July 22, 2020.
Respondents included a national sample of 1337 individuals aged 18 years or older in the US who were
part of National Opinion Research Center’s AmeriSpeak Panel.

EXPOSURES The initial period of the COVID-19 pandemic in the US, defined as from March to mid-
July 2020.

MAIN OUTCOMES AND MEASURES The primary outcomes were missed doses of prescription
medications; forgone preventive and other general medical care, mental health care, and elective
surgeries; forgone care for new severe health issues; and reasons for forgoing care.

RESULTS Of 1468 individuals who completed wave 1 of the Johns Hopkins COVID-19 Civic Life and
Public Health Survey (70.4% completion rate), 1337 completed wave 2 (91.1% completion rate). The
sample of respondents included 691 (52%) women, 840 non-Hispanic White individuals (63%), 16

non-Hispanic Black individuals (12%), and 223 Hispanic individuals (17%). The mean (SE) age of
respondents was 48 (0.78) years. A total of 544 respondents (41%) forwent medical care from March
through mid-July 2020. Among 1055 individuals (79%) who reported needing care, 544 (52%)
reported forgoing care for any reason, 307 (29%) forwent care owing to fear of severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, and 75 (7%) forwent care owing to
financial concerns associated with the COVID-19 pandemic. Respondents who were unemployed,
compared with those who were employed, forwent care more often (121 of 186 respondents [65%]
vs 251 of 503 respondents [50%]; P = .01) and were more likely to attribute forgone care to fear of
SARS-CoV-2 transmission (78 of 186 respondents [42%] vs 120 of 503 respondents [24%]; P = .002)
and financial concerns (36 of 186 respondents [20%] vs 28 of 503 respondents [6%]; P = .001).
Respondents lacking health insurance were more likely to attribute forgone care to financial concerns
than respondents with Medicare or commercial coverage (19 of 88 respondents [22%] vs 32 of 768
respondents [4%]; P < .001). Frequency of and reasons for forgone care differed in some instances by race/ethnicity, socioeconomic status, age, and health status.

(continued)

Key Points
Question What are the frequency of
and reasons for reported forgone

medical care from March to mid-July

2020, the initial phase of the

coronavirus disease 2019 (COVID-19)

pandemic in the US?

Findings In this national survey of 1337
participants, 41% of respondents

reported forgoing medical care from

March through mid-July 2020. Among

adults who reported needing care

during this period, more than half

reported forgoing care for any reason,

more than one-quarter reported

forgoing care owing to fear of severe

acute respiratory syndrome coronavirus

2 transmission, and 7% reported

forgoing care owing to financial

concerns.

Meaning This survey study found that
there was a high frequency of forgone

care from March to mid-July 2020, with

respondents commonly attributing the

causes of forgone care to repercussions

of the COVID-19 pandemic.

+ Supplemental content
Author affiliations and article information are
listed at the end of this article.

Open Access. This is an open access article distributed under the terms of the CC-BY License.

JAMA Network Open. 2021;4(1):e2034882. doi:10.1001/jamanetworkopen.2020.34882 (Reprinted) January 21, 2021 1/11

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Abstract (continued)

CONCLUSIONS AND RELEVANCE This survey study found a high frequency of forgone care among
US adults from March to mid-July 2020. Policies to improve health care affordability and to reassure
individuals that they can safely seek care may be necessary with surging COVID-19 case rates.

JAMA Network Open. 2021;4(1):e2034882. doi:10.1001/jamanetworkopen.2020.34882

Introduction

During the initial months of the coronavirus disease 2019 (COVID-19) pandemic, the US health care
system experienced major disruptions, with temporary closures of medical practices, cancellation of
elective procedures, and the shift of many services to telehealth delivery.1 These disruptions may
have led individuals to forgo medical care. Forgoing care for chronic and emergent conditions can
lead to increased complications and costs. Additionally, missing preventive care, such as cancer
screenings, can result in a delayed diagnosis. Since the pandemic onset, hospitals have reported
substantial declines in emergency department (ED) visits for severe health issues, including heart
attacks and strokes.2

Several factors may have influenced individuals’ decisions to forgo medical care during the
COVID-19 pandemic. In March 2020, many state and local governments issued emergency public
health orders, such as stay-at-home orders and bans on elective procedures, which either
discouraged or prohibited certain types of medical care.1 These suspensions were not lifted until late
spring or early summer 2020. Furthermore, many medical practices voluntarily closed in the early
weeks of the pandemic, either to redirect their personnel to COVID-19 response or to reduce risk of
transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that
causes COVID-19. Many individuals feared that seeking in-person medical care could expose them to
SARS-CoV-2.

In addition, the financial downturn caused by the COVID-19 pandemic increased unemployment
rates and reduced employee working hours. In the first 4 months of the pandemic, more than 48
million individuals filed for unemployment benefits.3 Because health insurance is tied to employment
for many US adults, layoffs have also resulted in more than 12 million individuals losing coverage since
March 2020.4 Resulting financial concerns may have influenced individuals’ decisions to obtain or
forgo care.

Several studies have sought to quantify changes in medical care during the pandemic using
electronic health record (EHR) or insurance claims data. A study by Westgard et al5 found a 49%
decline in ED visits comparing visits in the 28 days before and 28 days after the state emergency
declaration using EHR data from an urban trauma center.5 Using data from 9 cardiac catheterization
laboratories, a study by Garcia et al6 estimated a 38% decline in cardiac catheterizations, comparing
data from March 2020 with data from 2019 and earlier in 2020. Similarly, a study by Bhatt et al7

estimated a 43% reduction in hospitalizations for cardiovascular conditions in March 2020
compared with March 2019, using data from a large health system. While these studies provide a
useful snapshot of changes in health care utilization, they do not provide a nationally representative
picture of forgone care or assess the mechanisms behind reductions in care. Understanding reasons
individuals forgo care is particularly important for designing clinical and policy interventions targeted
to barriers to obtaining care. Furthermore, these prior studies focused on care for severe health
issues and did not examine preventive care, mental health care, or prescription medication
continuity.

To our knowledge, no published research has quantified the frequency of and factors associated
with forgone medical care during the initial phase of the COVID-19 pandemic in a representative
sample of US adults. We fielded a nationally representative survey to determine the frequency and
types of forgone medical care among adults and the reasons identified for cancelling or not seeking
care from March through mid-July 2020. We examined the sociodemographic characteristics of

JAMA Network Open | Health Policy Reports of Forgone Medical Care Among US Adults During the Initial Phase of the COVID-19 Pandemic

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respondents forgoing medical care and assessed whether prevalence differed for certain at-risk
groups, including individuals who were unemployed, lacked health insurance, or had chronic health
conditions. Finally, we examined 2 specific reasons respondents may have forgone medical care: fear
of exposure to SARS-CoV-2 and the financial repercussions of the COVID-19 pandemic.

Methods

All data reported in this survey study come from wave 2 of the Johns Hopkins COVID-19 Civic Life and
Public Health Survey, fielded July 7 to 22, 2020, using the National Opinion Research Center’s
(NORC) AmeriSpeak Panel. Prior to enrolling individuals in the AmeriSpeak Panel, NORC obtained
written informed consent. This study was approved by the Johns Hopkins Bloomberg School of
Public Health institutional review board. This study is reported following the American Association
for Public Opinion Research (AAPOR) reporting guideline.

The AmeriSpeak Panel is a probability-based panel designed to be representative of the US
adult population. The panel is drawn from NORC’s area probability sample and US Postal Service
addresses and covers 97% of US households.8 The AmeriSpeak panel’s recruitment rate is 34% and
includes approximately 35 000 individuals. Our sample was drawn from this panel, and respondents
completed the survey online.

We developed a 16-item module to assess health status and forgone medical care from March
to the time of survey data collection in July 2020 (eAppendix in the Supplement). Possible types of
forgone medical care included missed prescription medications, missed scheduled preventive care
visits, missed scheduled general medical outpatient visits (ie, physical health care, other than
preventive care, delivered in an office setting), missed scheduled mental health outpatient visits,
missed elective surgical procedures, or emergent health issues warranting general medical or mental
health care for which the respondent did not receive care. In the survey, we asked respondents to
distinguish between care received through telehealth (not classified as forgone care) and missed or
forgone care. We defined a new health issue as severe if a respondent reported a severity score of 4
or 5 on a 5-point Likert scale. In addition to the aggregate measure that included all of the categories
of forgone care, we also developed a measure of forgone planned medical care that included
prescription medications, scheduled preventive care visits, scheduled general medical outpatient
visits, scheduled mental health outpatient visits, and elective surgical procedures but did not include
new health issues.

We calculated prevalence of forgone medical care overall and by type of care among all
respondents and among the subset who reported needing care. Then, among individuals who
reported needing care, we calculated prevalence of forgone medical care by sociodemographic and
clinical characteristics and tested whether group differences were statistically significant. We also
analyzed group differences based on race/ethnicity, as the COVID-19 pandemic has
disproportionately affected Black, Hispanic, and Indigenous communities.9-11 We classified individual
race/ethnicity based on self-reported race/ethnicity using response options defined by NORC. Finally,
we tested whether frequency of forgone medical care differed by employment and health
insurance status.

Statistical Analysis
All counts and percentages reported in this study are survey weighted To test whether frequency of
forgone medical care differed between subgroups, we used Pearson χ2 tests. We considered a
difference to be statistically significant if the 2-sided P value was less than .05. We conducted
analyses in Stata statistical software version 16 (StataCorp), applying survey weights to calculate
nationally representative estimates. Data were analyzed from July 30 to September 3, 2020.

JAMA Network Open | Health Policy Reports of Forgone Medical Care Among US Adults During the Initial Phase of the COVID-19 Pandemic

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Results

Of 1468 individuals who completed wave 1 of the survey (70.4% completion rate), 1337 completed
wave 2 (91.1% completion rate). Among 1337 wave 2 respondents, 691 (52%) were women, and the
mean (SE) age was 48 (0.78) years. A total of 840 respondents (63%) reported their race/ethnicity
as non-Hispanic White, 160 respondents (12%) reported their race/ethnicity as non-Hispanic Black,
223 respondents (17%) reported their race/ethnicity as Hispanic, and the remaining 115 respondents
(9%) reported another race and non-Hispanic ethnicity (eTable in the Supplement).

A total of 544 respondents, representing an estimated 41% of US adults , reported forgoing
medical care during the initial phase of the COVID-19 pandemic in the US from March through
mid-July 2020 (Figure 1), including 108 respondents (8%) who reported missing 1 or more doses of
a prescription medicine typically picked up from a retail pharmacy, 387 respondents (29%) who
reported missing a preventive care visit, 343 respondents (26%) who reported missing an outpatient
general medical appointment, 105 respondents (8%) who reported missing an outpatient mental
health appointment, 77 respondents (6%) who reported missing an elective surgery, and 38
respondents (3%) who reported not receiving health care for a new severe mental or physical
health issue.

Among 1055 respondents (79%) who reported needing care from March to mid-July 2020, 544
(52%) reported forgoing care, including 108 of 725 respondents (15%) who typically picked up
prescription medication and who missed 1 or more doses, 387 of 664 respondents (58%) with
scheduled preventive care, 343 of 688 respondents (50%) with scheduled general medical care, and
105 of 227 respondents (46%) with scheduled mental health care reporting missing visits. Among
127 respondents who had scheduled an elective surgical procedure in the initial phase of the
pandemic, 77 respondents (60%) reported forgoing their surgical procedure. Finally, 38 of 74
respondents (51%) with a severe mental or physical health issue that emerged after the start of the
pandemic reported forgoing care.

Among 535 respondents who reported missing any planned medical care, including missed
prescription medications or missed scheduled appointments or procedures, 337 (63%) attributed
missed care to a medical practice being closed (either temporarily or permanently), 307 (57%)
attributed missed care to fear of SARS-CoV-2 exposure, and 75 (7%) attributed missed care to
financial repercussions of the COVID-19 pandemic (Figure 2). While medical practice closure was the
most common reason for missing care, 174 respondents (56%) who reported missing care owing to

Figure 1. Share of Respondents Forgoing Medical Care From March Through Mid-July 2020

Survey respondents, %
40 60 8020

Any forgone care

Prescription medication

Scheduled preventive care

Scheduled general medical appointment

Scheduled mental health appointment

Scheduled elective surgery

New severe issue

Overall Among individuals who reported needing care

Forgone medical care includes missing 1 or more doses
of a medicine the respondent typically picked-up or
had someone else pick-up from a retail pharmacy;
missing a scheduled health care visit, including a
preventive care visit, general medical outpatient visit,
mental health outpatient visit, or elective surgical
procedure; or not receiving care for a new severe
(defined based on self-report as severity 4-5 on a scale
of 1-5) physical or mental health issue. Individuals
could report multiple types of forgone care during the
period of March through mid-July 2020.

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fear of SARS-CoV-2 exposure and 39 respondents (52%) who reported missing planned care owing
to the financial repercussions of the COVID-19 pandemic did not report medical practice closure as a
reason for forgoing care.

Among 108 respondents reporting a missed dose of medication, 44 respondents (41%)
attributed it to fear of COVID-19 and 23 respondents (21%) cited financial repercussions of the
COVID-19 pandemic. Among 387 respondents who reported missing scheduled preventive care,
scheduled general medical care, or scheduled mental health care, more than half of respondents
attributed the missed care to practice closure and fear of COVID-19 exposure, and less than 10% of
respondents attributed the forgone care to financial concerns owing to COVID-19 (Figure 2). Practice
closure and fear of SARS-CoV-2 transmission were also the most common reasons reported for
missing a scheduled elective surgery; more than one-quarter of respondents reported the financial
repercussions of the COVID-19 pandemic as a reason for forgoing elective surgery (Figure 2).

While the proportion of respondents reporting forgone medical care did not vary by sex,
differences were found by race/ethnicity, age, household income, employment status, and health
insurance status (Table 1). A larger share of Hispanic respondents reported missed prescription
medications compared with non-Hispanic White respondents (33 of 109 respondents [30%] vs 50
of 482 respondents [10%]; P = .004). Compared with adults aged 65 years or older, higher
proportions of respondents reported missed medication in age groups 18 to 34 years (45 of 204
respondents [22%] vs 10 of 160 respondents [6%]; P = .004) and 35 to 49 years (29 of 182
respondents [16%]; P = .01). Respondents in households with lower incomes (ie, <$35 000/year) more often reported missing medication compared with respondents in households with an income of $35 000 to $74 999 per year (66 of 244 respondents [27%] vs 26 of 226 respondents [12%]; P = .01).

Respondents who were unemployed or not working owing to disability, compared with
individuals who were employed, reported higher frequency of any forgone medical care (121 0f 186

Figure 2. Reasons Reported for Forgoing Care Among Respondents Who Missed Planned Care
From March Through Mid-July 2020

0
Survey respondents, %
40 60 8020

Missed any planned care (n = 535)

Missed ≥1 dose of prescription medication (n = 108)

Missed a scheduled preventive care appointment (n = 387)

Missed a scheduled general medical appointment (n = 343)

Missed a scheduled mental health appointment (n = 105)

Missed a scheduled elective surgery (n = 77)

Practitioner’s office being closed

Reported reason for forgoing care

Fear of SARS-CoV-2 exposure

Financial repercussions of COVID-19 pandemic

Respondents were prompted to select the reasons
that best described why they missed taking a dose(s)
of medication or missed a previously scheduled health
care appointment. Respondents were allowed to
select more than 1 reason. Practitioner practice being
closed was not a response option for individuals who
reported missing a dose of prescription medication.
COVID-19 indicates coronavirus disease 2019; SARS-
CoV-2, severe acute respiratory syndrome
coronavirus 2.

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respondents [65%] vs 251 of 503 respondents [50%]; P = .01), missed doses of prescription
medication (46 of 117 respondents [39%] vs 46 of 367 respondents [13%]; P < .001), and missed scheduled medical care (111 of 159 respondents [70%] vs 225 of 405 respondents [56%]; P = .02). Compared with individuals with commercial health insurance or Medicare, those insured through Medicaid reported higher frequency of missed prescription medications (41 of 114 respondents [36%] vs 52 of 517 respondents [10%]; P < .001).

Frequency of forgone medical care varied by self-reported health status, number of prescription
medications taken, and presence of a mental health condition (Table 2). Respondents who rated
their health as fair or poor more often reported missing prescription medication compared with
individuals who rated their health as excellent (35 of 149 respondents [24%] vs 5 of 41 respondents
[11%]; P = .03), and those with 1 or more prescriptions reported forgoing any medical care less often
than those with no prescription medication use (443 of 902 respondents [49%] vs 99 of 149
respondents [66%]; P = .005). Similarly, individuals with a mental health condition more often
reported missing medication than individuals without a mental health condition (49 of 184
respondents [26%] vs 59 of 541 respondents [11%]; P = .004). No differences were detected in

Table 1. Respondents Who Reported Needing Care Reporting Forgone Medical Care From March Through Mid-July 2020, by Sociodemographic Characteristics

Characteristic

Any forgone medical care (N = 1055)a Missed dose of medicine (n = 725) Missed scheduled medical care (n = 873)b

No./total
No. (%) 95% CI, % P value

No./total
No. (%) 95% CI, % P value
No./total
No. (%) 95% CI, % P value

Sex

Men 234/458 (51) 44.9-57.1 [Reference] 41/299 (14) 9.0-20.4 [Reference] 211/384 (55) 48.5-61.5 [Reference]

Women 310/597 (52) 46.3-57.7 .81 66/426 (16) 10.3-22.9 .66 290/490 (59) 52.8-65.2 .38

Race/ethnicity

White, non-Hispanic 356/697 (51) 46.1-56.0 [Reference] 50/482 (10) 7.0-15.2 [Reference] 337/600 (56) 50.8-61.4 [Reference]

Black, non-Hispanic 58/121 (48) 36.1-60.1 .65 18/81 (22) 11.3-37.7 .06 48/85 (56) 43.7-68.3 .98

Other, non-Hispanic 48/94 (51) 35.9-66.3 .99 7/52 (13) 2.8-43.0 .78 39/70 (57) 38.9-72.9 .96

Hispanic 82/143 (57) 45.1-69.0 .34 33/109 (30) 16.7-48.6 .004 77/119 (64) 50.7-76.0 .27

Age group

≥65 125/262 (48) 41.4-54.2 [Reference] 10/160 (6) 3.4-11.4 [Reference] 119/231 (51) 44.5-58.1 [Reference]

50-64 155/276 (56) 48.9-62.6 .09 23/179 (13) 7.7-20.5 .08 146/242 (61) 52.9-67.6 .07

35-49 125/241 (52) 44.6-59.4 .39 29/182 (16) 10.3-24.5 .01 116/199 (58) 50.1-66.2 .19

18-34 139/276 (50) 39.3-61.6 .68 45/204 (22) 12.2-36.6 .004 120/202 (59) 45.8-71.7 .29

Household income, per y

<$35 000 167/327 (51) 42.6-59.6 .94 66/244 (27) 18.0-38.3 .01 149/264 (56) 46.4-65.7 .96

$35 000-$74 999 177/344 (52) 44.1-58.9 [Reference] 26/226 (12) 6.6-19.6 [Reference] 163/288 (57) 49.1-63.8 [Reference]

≥$75 000 200/384 (52) 46.1-57.7 .93 16/255 (6) 3.7-9.9 .09 189/321 (59) 52.3-65.1 .65

Employment status

Currently employed 251/503 (50) 43.9-55.9 [Reference] 46/367 (13) 8.4-18.7 [Reference] 225/405 (56) 49.0-61.9 [Reference]

Unemployed or not working
owing to disability

121/186 (65) 55.2-73.7 .01 46/117 (39) 25.1-55.2 <.001 111/159 (70) 60.0-78.3 .02

Retired or providing unpaid
family caregiving

129/271 (48) 41.3-54.2 .63 8/161 (5) 2.3-11.4 .05 125/239 (52) 45.3-59.1 .50

Insurance coverage

Commercial or Medicare 387/768 (50) 46.1-54.8 [Reference] 52/517 (10) 7.0-14.0 [Reference] 360/645 (56) 51.1-60.3 [Reference]

Medicaid 86/142 (61) 44.4-75.4 .23 41/114 (36) 20.0-55.7 <.001 75/116 (65) 45.1-80.4 .37

Uninsured 45/88 (50) 35.4-65.5 .99 12/56 (21) 9.5-40.9 .09 40/61 (65) 48.3-79.3 .28

b Scheduled medical care includes scheduled preventive care visits, scheduled general
medical outpatient visits, scheduled mental health outpatient visits, and elective
surgical procedures.

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reported frequency of forgone medical care by other chronic health conditions examined, including
heart disease, lung disease, or high blood pressure, diabetes, or high cholesterol.

We identified differences in the reasons stated for forgoing medical care by employment and
health insurance status (Figure 3). Compared with adults who were employed, adults who were
unemployed more often attributed forgone medical care to fear of SARS-CoV-2 exposure (78 of 186
respondents [42%] vs 120 of 503 respondents [24%]; P = .002) and to financial repercussions of the
pandemic (36 of 186 respondents [20%] vs 28 of 503 respondents [6%]; P = .001). Respondents
without insurance reported forgoing medical care owing to financial concerns more often than
respondents with commercial or Medicare health care coverage (19 of 88 respondents [22%] vs 32
of 768 respondents [4%]; P < .001). Respondents with Medicaid coverage, compared with respondents with commercial or Medicare coverage, more often reported forgoing care owing to concerns about SARS-CoV-2 exposure (70 of 142 respondents [50%] vs 203 of 768 respondents [26%]; P = .003) and financial concerns (21 of 142 respondents [15%] vs 32 of 768 respondents [4%]; P = .03). We also examined whether there were differences in reporting forgoing care owing to practice closures, but did not find statistically significant differences based on employment status or insurance coverage.

Table 2. Share of Respondents Who Reported Needing Care Who Reported Forgone Medical Care From March Through Mid-July 2020, by Clinical Characteristics

Characteristic
Any forgone medical care (N = 1055)a Missed dose of medicine (n = 725) Missed scheduled medical care (n = 873)b
No./total
No. (%) 95% CI, % P value
No./total
No. (%) 95% CI, % P value
No./total
No. (%) 95% CI, % P value

Self-reported health

Excellent 56/92 (61) 46.0-74.7 [Reference] 5/41 (11) 2.8-3.4 [Reference] 56/82 (69) 53.4-81.0 [Reference]

Very good 173/363 (48) 41.8-53.8 .11 25/254 (10) 5.7-16.3 .88 167/298 (56) 49.3-62.5 .13

Good 198/391 (51) 43.7-57.7 .21 43/281 (15) 9.6-23.1 .21 174/322 (54) 45.9-61.7 .09

Fair or poor 116/210 (56) 45.1-65.5 .54 35/149 (24) 13.0-39.4 .03 104/172 (61) 50.7-69.9 .36

Uses ≥1 prescription
medications

No 99/149 (66) 55.4-75.7 [Reference] NA NA NA 95/145 (65) 54.1-75.0 [Reference]

Yes 443/902 (49) 44.7-53.6 .005 108/725 (15) 11.0-19.7 NA 404/725 (56) 50.8-60.6 .12

Has high blood pressure,
diabetes, or high cholesterol

No 337/650 (52) 46.1-57.6 [Reference] 71/433 (16) 10.8-23.8 [Reference] 309/519 (60) 53.1-65.7 [Reference]

Yes 207/405 (51) 45.4-56.7 .85 37/292 (13) 8.5-18.4 .37 192/354 (54) 48.0-60.1 .23

Has heart disease, such as a
heart attack, coronary heart
disease, angina, congestive
heart failure, or other heart
problems

No 500/979 (51) 46.7-55.5 [Reference] 94/675 (14) 10.0-19.0 [Reference] 463/803 (58) 52.8-62.2 [Reference]

Yes 44/76 (58) 44.9-70.1 .33 13/50 (27) 13.0-47.4 .10 38/70 (55) 41.2-67.5 .69

Has lung disease, such as
chronic bronchitis or
emphysema

No 515/1003 (51) 47.0-55.7 [Reference] 103/695 (15) 10.9-20.0 [Reference] 475/829 (57) 52.5-61.9 [Reference]

Yes 29/52 (56) 39.7-71.0 .59 4/30 (14) 5.8-29.4 .86 26/44 (59) 43.0-73.1 .85

≥1 Mental health conditions

No 402/809 (50) 45.1-54.3 [Reference] 59/541 (11) 7.6-15.4 [Reference] 373/669 (56) 50.6-60.8 [Reference]

Yes 142/246 (58) 48.2-66.7 .13 49/184 (26) 16.3-39.9 .004 128/204 (63) 53.0-71.1 .22

a Forgone medical care includes missing 1 or more doses of a medicine the respondent
typically picked-up or had someone else pick up from a retail pharmacy; missing a
scheduled health care visit, including a preventive care visit, general medical
outpatient visit, mental health outpatient visit, or elective surgical procedure; or not
receiving care for a new severe (defined based on self report as severity 4-5 on a scale
of 1-5) physical or mental health issue. Individuals could report multiple types of
forgone care.
b Scheduled medical care includes scheduled preventive care visits, scheduled general
medical outpatient visits, scheduled mental health outpatient visits, and elective
surgical procedures.
JAMA Network Open | Health Policy Reports of Forgone Medical Care Among US Adults During the Initial Phase of the COVID-19 Pandemic

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Discussion

This survey study found that in a population representative of the overall US adult population, 41%
of adults reported forgone care from March through mid-July 2020. Previous studies have found that
individuals sometimes chose to forgo care prior to the COVID-19 pandemic; for example, the Kaiser
Family Foundation estimated that in 2018, 13% of White individuals, 17% of Black individuals, and
21% of Hispanic individuals forwent care owing to cost.12 However, our results suggest that the
COVID-19 pandemic exacerbated the problem, with individuals reporting closed practitioner offices,
fear of exposure to SARS-CoV-2, and the financial repercussions of the pandemic as common reasons
for forgoing care during this period.

These national survey results are consistent with research using insurance claims and EHR data
that documented declines in the use of health care services to treat severe health issues during the
first several months of the COVID-19 pandemic within specific health systems.5-7 Our results extend
existing research on forgone medical care by quantifying changes at the national level, considering
a larger set of health care services, and examining the underlying reasons reported for forgoing care
during the initial phase of the pandemic.

The most common reason respondents reported for missing scheduled care was owing to office
closure. The Coronavirus Aid, Relief, and Economic Security (CARES) Act13 included $175 billion to
provide financial relief to medical practices and hospitals during the COVID-19 pandemic, and such
funding may have helped practices that initially closed to reopen after putting additional safety
precautions in place to prevent the spread of COVID-19. Proactive outreach from health care
practitioner offices to reschedule cancelled appointments through in-person care or telehealth may
help limit the long-term consequences of this forgone medical care. Telehealth can also help
individuals continue to receive health care when they are concerned about exposure to
SARS-CoV-2.14 States and the federal government have supported telehealth by temporarily
loosening licensing, electronic prescribing, and written consent laws.15-17 Additionally, many payers
have temporarily increased the types of services that can be delivered via telehealth and
reimbursement for telehealth services.18 Continuing to provide financial and regulatory support for
telehealth is important to ensure that practitioners offer this service for the duration of the
pandemic. However, older adults who are uncomfortable with technology and individuals with
limited internet connectivity may struggle to access or may be hesitant to use telehealth.19 It is
important for practitioners and insurers to support patient use of telehealth and to ensure that

Figure 3. Reasons Reported for Forgoing Planned Care Among Respondents Who Reported Needing Care
by Employment and Health Insurance Status

0
Survey respondents, %
40 60 8020

Currently employed

Unemployed or not working because of disability

Retired or providing unpaid family caregiving

Commercial insurance or Medicare

Uninsured

Medicaid

Currently employed
Unemployed or not working because of disability
Retired or providing unpaid family caregiving
Commercial insurance or Medicare
Uninsured
Medicaid

Fe
ar

o
f S

A
R

S-
Co

V
-2

ex
po

su
re

Fi
na

nc
ia

l r
ep

er
cu

ss
io

ns
o

f
CO

V
ID

-1
9

pa
nd

em
ic

P =.002

P =.39

P =.12

P =.003

P =.001

P =.03

P <.001

P =.03

Responses are based on the time period of March
through mid-July 2020, during the initial phase of the
coronavirus disease 2019 (COVID-19) pandemic in the
United States. SARS-CoV-2 indicates severe acute
respiratory syndrome coronavirus 2.

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telehealth can be accessed using a variety of internet speeds and devices, for example by offering
audio-only (telephone) services.20,21

Among respondents who reported missing planned care, 14% reported the financial
repercussions of the COVID-19 pandemic as a reason for forgoing care, and among the subset who
reported missing prescription medication, nearly 1 in 4 respondents reported financial reasons for
missing medications. Several policies can offer better financial protection to patients experiencing
financial distress owing to the pandemic. Within the 38 states plus Washington, District of Columbia,
that have expanded Medicaid, enrollment in Medicaid can improve health care affordability for
individuals who have lost health insurance or were uninsured when the pandemic began. The $600
boost to weekly unemployment benefits during the first 4.5 months of the pandemic may have also
mitigated some of the potentially harmful financial outcomes of the COVID-19 pandemic on people
with health care needs. More individuals who are unemployed may forgo medical care as their
unemployment benefits expire. Our results suggest that Medicare had a protective association, with
older adults reporting much lower frequency of missed medication compared with other age groups.
Conditioning businesses’ relief payments on keeping furloughed employees enrolled in their health
insurance is another strategy that may prevent forgone care owing to cost concerns. Employers
receiving federal assistance, such as the employee retention tax credit, are currently allowed, but not
required, to pay for health insurance for furloughed employees.22

Limitations
This study has several limitations. First, our sample size may have inhibited our ability to detect
statistically significant differences in the frequency and reasons of forgone medical care, particularly
when analyzing certain subgroups. Second, there may have been heterogeneity in responses to the
COVID-19 pandemic owing to differences in timing and extent of the pandemic and public health
responses in different locales not captured in our survey. Third, our survey items on forgone medical
care were generated for this study, preventing us from directly comparing our findings with
frequency of forgone medical care before the COVID-19 pandemic. Fourth, the AmeriSpeak panel
used probability-based recruitment aligning with best-practice survey research standards, but results
may be susceptible to sampling biases. Fifth, we did not have information on the employment or
health insurance status of a respondent’s entire household. If a family member lost employment or
health insurance owing to the pandemic, it could financially affect decision-making within the entire
household about whether to seek or forgo care. Sixth, our analysis did not capture all types of
forgone medical care; for example, we did not consider missed doses of mail-order drugs.

Conclusions

The findings of this survey study suggest that as the United States is experiencing another wave of
surging SARS-CoV-2 infections, it will be important to track whether interventions to enhance health
system safety provide the public with sufficient confidence to seek medical care. As emergency
financial measures enacted by the US Congress and unemployment benefits expire, ensuring the
affordability of needed health care services for individuals financially impacted by COVID-19 is
critical.

ARTICLE INFORMATION
Accepted for Publication: December 4, 2020.

Published: January 21, 2021. doi:10.1001/jamanetworkopen.2020.34882

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Anderson KE
et al. JAMA Network Open.

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Corresponding Author: Kelly E. Anderson, MPP, Department of Health Policy and Management, Johns Hopkins
Bloomberg School of Public Health, 624 N Broadway, Room 428, Baltimore, MD 21205 (kelly.anderson@jhu.edu).

Author Affiliations: Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public
Health, Baltimore, Maryland.

Author Contributions: Ms Anderson had full access to all of the data in the study and takes responsibility for the
integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Anderson, McGinty, Barry.

Drafting of the manuscript: Anderson.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Anderson.

Obtained funding: McGinty, Barry.

Administrative, technical, or material support: McGinty, Barry.

Supervision: McGinty, Barry.

Conflict of Interest Disclosures: Ms Anderson previous employment from The Lewin Group outside the
submitted work. No other disclosures were reported.

Funding/Support: The Johns Hopkins Bloomberg School of Public Health and the Johns Hopkins University
Alliance for a Healthier World’s 2020 COVID-19 Launchpad Grant funded data collection. The Agency for
Healthcare Research and Quality provides tuition and stipend support for Ms Anderson (grant No.
T32HS000029). The National Institute of Mental Health provides tuition and stipend support for Ms
Presskreischer (grant No. T32MH109436).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection,
management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and
decision to submit the manuscript for publication.

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retention-credits-amount-of-allocable-qualified-health-plan-expenses-faqs#65

SUPPLEMENT.
eAppendix. Question Wording for Forgone Medical Care, Health Insurance, and Employment Survey Questions
eTable. Characteristics of Study Sample

JAMA Network Open | Health Policy Reports of Forgone Medical Care Among US Adults During the Initial Phase of the COVID-19 Pandemic

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https://www.irs.gov/newsroom/covid-19-related-employee-retention-credits-amount-of-allocable-qualified-health-plan-expenses-faqs#65

Association Between Rotating Night Shift Work and Risk
of Coronary Heart Disease Among Women
Céline Vetter, PhD; Elizabeth E. Devore, ScD; Lani R. Wegrzyn, ScD; Jennifer Massa, ScD; Frank E. Speizer, MD;
Ichiro Kawachi, MD, ScD; Bernard Rosner, PhD; Meir J. Stampfer, MD, DrPH; Eva S. Schernhammer, MD, DrPH

IMPORTANCE Prospective studies linking shift work to coronary heart disease (CHD) have
been inconsistent and limited by short follow-up.

OBJECTIVE To determine whether rotating night shift work is associated with CHD risk.

DESIGN, SETTING, AND PARTICIPANTS Prospective cohort study of 189 158 initially healthy
women followed up over 24 years in the Nurses’ Health Studies (NHS [1988-2012]:
N = 73 623 and NHS2 [1989-2013]: N = 115 535).

EXPOSURES Lifetime history of rotating night shift work (�3 night shifts per month in
addition to day and evening shifts) at baseline (updated every 2 to 4 years in the NHS2).

MAIN OUTCOMES AND MEASURES Incident CHD; ie, nonfatal myocardial infarction, CHD
death, angiogram-confirmed angina pectoris, coronary artery bypass graft surgery, stents,
and angioplasty.

RESULTS During follow-up, 7303 incident CHD cases occurred in the NHS (mean age at
baseline, 54.5 years) and 3519 in the NHS2 (mean age, 34.8 years). In multivariable-adjusted
Cox proportional hazards models, increasing years of baseline rotating night shift work was
associated with significantly higher CHD risk in both cohorts. In the NHS, the association
between duration of shift work and CHD was stronger in the first half of follow-up than in the
second half (P=.02 for interaction), suggesting waning risk after cessation of shift work.
Longer time since quitting shift work was associated with decreased CHD risk among ever
shift workers in the NHS2 (P<.001 for trend).

Baseline History of Rotating Night Shift Work P Value
for
TrendNone <5 y 5-9 y ≥10 y

NHS cohort

CHD incidence ratea 425.5 435.1 525.7 596.9

HR (95% CI)b 1 [Reference] 1.02 (0.97-1.08) 1.12 (1.02-1.22) 1.18 (1.10-1.26)

<.001

First half of follow-up

CHD incidence ratea 367.3 382.4 483.1 494.4

HR (95% CI)b 1 [Reference] 1.10 (1.01-1.21) 1.19 (1.03-1.39) 1.27 (1.13-1.42) <.001

Second half of
follow-up

CHD incidence ratea 436.6 424.8 520.7 556.2

HR (95% CI)b 1 [Reference] 0.98 (0.92-1.05) 1.08 (0.96-1.21) 1.13 (1.04-1.24) .004

NHS2 cohort

CHD incidence ratea 122.6 130.6 151.6 178.0

HR (95% CI)b 1 [Reference] 1.05 (0.97-1.13) 1.12 (0.99-1.26) 1.15 (1.01-1.32) .01
a Age-adjusted rates per 100 000 person-years.
b Multivariable-adjusted hazard ratio (HR).

CONCLUSIONS AND RELEVANCE Among women who worked as registered nurses, longer
duration of rotating night shift work was associated with a statistically significant but small
absolute increase in CHD risk. Further research is needed to explore whether the association
is related to specific work hours and individual characteristics.

JAMA. 2016;315(16):1726-1734. doi:10.1001/jama.2016.4454

Author Video Interview and
JAMA Report Video at
jama.com

Supplemental content at
jama.com

Author Affiliations: Author
affiliations are listed at the end of this
article.

Corresponding Author: Céline
Vetter, PhD, Channing Division of
Network Medicine, 181 Longwood
Ave, Boston, MA 02115 (celine.vetter
@channing.harvard.edu).

Research

Original Investigation

1726 (Reprinted) jama.com

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S ocietal and economic demands push toward an in-crease of employees’ 24-hour availability in health caresettings as well as in service and security industries. The
resulting disruption of social and biological rhythms, occur-
ring especially during shift work, has been hypothesized to in-
crease chronic disease risk,1-5 and suggestive evidence sup-
ports an association between shift work and coronary heart
disease (CHD), metabolic disorders, and cancer.6

In 1995, Kawachi et al7 examined the association be-
tween rotating night shift work and CHD in the Nurses’ Health
Study (NHS) over 4 years of follow-up and reported a 51% sig-
nificant increase in CHD risk (nonfatal myocardial infarction
[MI] and CHD death) among women with more than 6 years
of rotating night shift work after multivariable adjustment
(incidence rate per 100 000 person-years, 156.1 compared with
75.4 among women who never worked night shifts). A recent
systematic meta-analysis reported a 24% elevated CHD risk
associated with most types of shift work but noted signifi-
cant heterogeneity in exposure assessment and study de-
signs across studies.8 The present study reassessed the asso-
ciation of rotating night shift work and coronary health in the
Nurses’ Health Studies (NHS and NHS2) with 24 years of
follow-up and examined manifestations of CHD (angiogram-
confirmed angina pectoris, coronary artery stents, angio-
plasty, and coronary artery bypass graft [CABG] surgery),
in addition to nonfatal MI and CHD death. Additionally, pos-
sible differences in this association over time, including ef-
fects of time since quitting shift work, were explored. The
study also examined the excess risk of CHD associated with
shift work among women without diabetes, hypertension, or
hypercholesterolemia—potential comorbid mediators of CHD.

Methods
Study Population
The NHS and NHS2 are ongoing, prospective cohort studies. The
NHS began in 1976 when 121 701 female registered US nurses
aged 30 to 55 years responded to a baseline questionnaire.9 The
NHS2 started in 1989 and included 116 430 female registered US
nurses aged 25 to 42 years. In both cohorts, biennial follow-up
questionnaires have been mailed to update information on
medical history, lifestyle factors, and newly diagnosed dis-
eases. Follow-up rates were high in both cohorts, with approxi-
mately 90% participation at each 2-year cycle. This study
was reviewed and approved by the Brigham and Women’s Hos-
pital Institutional Review Board; completion of the self-
administered questionnaire was considered informed con-
sent, so the requirement for oral or written consent was waived.

Rotating Night Shift Work Assessment
In the NHS, lifetime years of exposure to rotating night shift
work (defined as ≥3 night shifts per month, in addition to day
and evening shifts) was queried once, in 1988. In the NHS2,
women indicated in 1989 how many years of rotating night shift
work they had worked, with updates in 1991, 1993, 1997, 2001,
2005, and 2007; retrospective assessments for shift work in
1995, 1999, and 2003 were included on the 2001 and 2005

questionnaires, respectively. The analyses used baseline as-
sessments of lifetime shift work history in each cohort (1988
for NHS and 1989 for NHS2), as well as cumulative shift work
exposure through 2007 in the NHS2. In all analyses, night shift
work information was carried forward for 1 questionnaire cycle
in the case of missing data.

Ascertainment of CHD
On baseline and follow-up questionnaires, participants were
asked to report physician-diagnosed CHD events. Those who
reported nonfatal MI were asked for medical record access so
that exposure-blinded physicians could confirm self-reported
nonfatal MI. Nonfatal MI was confirmed using the World Health
Organization criteria, which required diagnostic electrocardio-
graphic findings or elevated enzyme levels in addition to typi-
cal symptoms.10 Participant deaths were identified through the
National Death Index, next of kin, or postal authorities, with pri-
mary cause of death being determined by autopsy reports, hos-
pital records, and death certificates. The primary outcome was
incident CHD, including self-reported cases of CABG surgery,
angina pectoris (confirmed by angiogram), angioplasty, and
coronary artery stents, in addition to nonfatal MI and CHD death
(including fatal MI), whichever came first. Secondary analyses
were restricted to nonfatal MI and CHD death.

Covariate Assessment
In both cohorts, biennial questionnaires were used to collect
information on medical history, anthropometric data, diet, and
lifestyle. Most variables were updated biennially from base-
line onward; physical activity and dietary data were obtained
approximately every 4 years. Dietary habits were assessed using
a semiquantitative, validated food frequency questionnaire11

calculating the Alternative Healthy Eating Index, which has pre-
viously been found to be a reliable predictor of CHD in these
cohorts.12 Parity was updated until 1996 and 2009 for the NHS
and NHS2, respectively, and subsequently carried forward. Par-
ticipants’ husbands’ educational attainment (a proxy for so-
cioeconomic status assessed in 1992 in NHS and in 1999 in
NHS2), family history of MI before age 60 years (1976 and 1984
in NHS and 1989, 1997, and 2001 in NHS2), and race (2004 in
NHS and 1989 and 2005 in NHS2) were not updated through-
out follow-up. Usual sleep duration assessed in 1986, 2000,
and 2008 (NHS) and 2001 (NHS2), and social support (assessed
by asking whether participants had a confidant) in 1992, 2000,
2004, and 2008 (NHS) and in 1993 (NHS2) were not regularly
updated throughout follow-up.

Statistical Analyses
Age- and multivariable-adjusted Cox proportional hazards
models were used to estimate hazard ratios (HRs) and 95%
confidence intervals across rotating night shift work catego-
ries (none, <5, 5-9, and ≥10 years). Women with no history of rotating night shift work comprised the reference category in all analyses. Calculations of P values for trend were based on the midpoint of rotating night shift work categories, with the highest category conservatively set to 10; the reported P value was based on the Wald test. The proportional hazards assump- tion was tested by including an interaction of shift work

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(ie, midpoint of categories) by time in all models, and its sig-
nificance was evaluated using the Wald statistic. In sensitiv-
ity analyses, the outcome was restricted to nonfatal MI and CHD
death. Additional sensitivity analyses were restricted to par-
ticipants with no baseline history of major comorbidities po-
tentially mediating CHD (ie, diabetes, hypertension, and hy-
percholesterolemia) and censored women who reported any
of these conditions throughout follow-up.

The following cardiovascular disease risk factors were in-
cluded in multivariable-adjusted models: family history of MI
before age 60 years, diet quality (Alternative Healthy Eating
Index,12 without the alcohol and multivitamin components, in
quintiles), physical activity (metabolic equivalent task–hours per
week, in quintiles), body mass index (BMI, calculated as weight
in kilograms divided by height in meters squared: <25, 25-29, 30-35, or >35), cumulative pack-years smoked (continuous), al-
cohol intake (none, 0.1-5, 5.1-10, 10.1-20, or >20 g/d), parity (nul-
liparous, 1, 2, or ≥3 children), menopausal status (premenopaus-
al or postmenopausal), hormone therapy (premenopausal, ever,
or never), race (white, black, or other), husband’s highest edu-
cational level (high school diploma or less, college degree, or
graduate school level or similar), multivitamin use (yes or no),
acetaminophen use (yes or no), nonsteroidal anti-inflammatory
drug use (yes or no), aspirin use (yes or no), hypertension (yes or
no), diabetes (yes or no), and hypercholesterolemia (yes or no).
In additional analyses, models were adjusted for sleep duration
(<6, 6-7, 8-9, or ≥10 hours per day) and social support (yes or no). Dummy variables were used to indicate missing covariate val- ues. For missing information on pack-years of smoking, the me- dian among smokers was imputed; in the case of missing BMI, information was carried forward once. On average, 9.5% of co- variate information was missing across 24 years of follow-up.

In the NHS2, analyses also examined the association be-
tween cumulative time since quitting rotating night shift work
(never, current, <12, 12-24, or >24 years) and CHD risk. Time since
quitting rotating night shift work was estimated based on life-
time reports of exposure in 1989 and updated shift work infor-
mation throughout follow-up. If women reported rotating night
shifts at baseline only, time since quitting shift work was esti-
mated by subtracting 21 years (assumed age at starting shift
work) and the lower bound of the categorically reported dura-
tion of rotating night shift work from their age in 1989.

In additional secondary analyses, potential effect modi-
fication by BMI (<25, 25-30, or >30) was examined, adjusting
continuously for BMI within each stratum. To evaluate poten-
tial interactions, the log likelihood ratio test was used to com-
pare models with and without cross-product interaction terms;
corresponding P values were based on χ2 statistics.

The a priori hypothesis was that rotating night shift work in-
creased CHD risk, and all secondary analyses were preplanned.
Analyses were conducted with SAS software, version 9.4 (SAS
Institute Inc) with a 2-sided significance threshold of P < .05.

Results
A total of 103 525 NHS participants answered the 1988 ques-
tionnaire. Of these, women with CHD, stroke, or cancer

(n = 14 065) and those who did not answer the shift work
question in 1988 (n = 15 837) were excluded, leaving 73 623
women for analysis. In the NHS2, 116 430 women answered
the baseline questionnaire (1989), of whom 895 reported
stroke or CHD prior to baseline, so that after the same exclu-
sions, 115 535 women were left for analysis. For the NHS2
analysis with updated shift work information, women who
did not answer shift work questions for 2 consecutive cycles
(on average, 8.7% per cycle) were censored. Women were
excluded from further follow-up after any self-reported
stroke, incident CHD, or death.

During 24 years of follow-up, a total of 10 822 incident CHD
cases were observed (7303 in NHS and 3519 in the younger
NHS2). Table 1 describes age and age-adjusted (within-
cohort) characteristics of the study population across catego-
ries of lifetime years of rotating night shift work at baseline.
Compared with women in the NHS, women in the NHS2 were
younger, more likely to be nulliparous, had slightly lower al-
cohol consumption, reported fewer pack-years of smoking, had
fewer comorbid conditions, and took fewer medications and
multivitamin supplements. With increasing duration of rotat-
ing night shift work, women were heavier in both cohorts. Also,
in the NHS, a lower proportion of women had husbands with
graduate-level education across increasing categories of shift
work, while pack-years of smoking and self-reports of hyper-
tension increased; in the NHS2, a greater proportion of nul-
liparous women and acetaminophen users were observed with
increasing duration of rotating night shift work.

Compared with women without a history of rotating night
shift work (incidence rates, 425.5 and 122.6 per 100 000 per-
son-years in the NHS and NHS2, respectively), women who
worked less than 5 years of shift work at baseline did not have
a significantly increased CHD risk in age-adjusted analyses
(Table 2 and Table 3), but there was a significant association
between longer durations of shift work and CHD risk (in the
NHS: incidence rate per 100 000 person-years for 5-9 years,
525.7; HR, 1.21 [95% CI, 1.11-1.33]; incidence rate for ≥10 years,
596.9; HR, 1.36 [95% CI, 1.27-1.46]; P<.001 for trend; in the NHS2: incidence rate for 5-9 years, 151.6; HR, 1.22 [95% CI, 1.08- 1.38]; incidence rate for ≥10 years, 178.0; HR, 1.34 [95% CI, 1.17- 1.53]; P<.001 for trend).

Multivariable adjustment for known CHD risk factors
attenuated these estimates, but the elevated risk observed
for 5 years or more of shift work persisted in the NHS (multi-
variable HR for 5-9 years, 1.12 [95% CI, 1.02-1.22]; multivari-
able HR for ≥10 years, 1.18 [95% CI, 1.10-1.26]; P<.001 for trend), and for 10 years or more of shift work in the NHS2 (multivariable HR for 5-9 years, 1.12 [95% CI, 0.99-1.26]; mul- tivariable HR for ≥10 years, 1.15 [95% CI, 1.01-1.32]; P = .01 for trend).

In the NHS, there was a significant interaction between
rotating night shift work exposure and time ( by 2-year
period, P<.001 for interaction) (Table 2), suggesting that CHD risk associated with shift work changes over time. During the first half of follow-up, higher effect estimates and signifi- cantly elevated risks also were observed with shorter dura- tions of shift work exposure (incidence rate per 100 000 person-years for <5 years, 382.4; multivariable HR, 1.10 [95%

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CI, 1.01-1.21]; incidence rate for 5-9 years, 483.1; multivariable
HR, 1.19 [95% CI, 1.03-1.39]; incidence rate for ≥10 years,
494.4; multivariable HR, 1.27 [95% CI, 1.13-1.42]; P < .001 for

trend and P = .02 for interaction for first vs second half of
follow-up). In the second half of follow-up, compared with
women who never worked rotating night shifts (incidence

Table 1. Age and Age-Adjusted Characteristics of Participating Women at Baseline by Rotating Night Shift Work Historya

Characteristics

Rotating Night Shift Work Exposure (≥3 Night Shifts Per Month)

NHS (1988) NHS2 (1989)
None
(n=30 012)

<5 y (n=30 122)

5-9 y
(n=4955)

≥10 y
(n=8534)

None
(n=43 657)

<5 y (n=56 179)

5-9 y
(n=9866)

≥10 y
(n=5833)

Age, mean (SD), y 54.0 (7.1) 54.3 (7.1) 54.9 (7.1) 56.2 (6.9) 34.8 (4.7) 34.5 (4.7) 35.1 (4.2) 37.1 (3.6)

White race, No. (%) 29 390 (98) 29 424 (98) 4832 (98) 8250 (97) 42 075 (96) 53 501 (95) 9337 (95) 5479 (95)

Parity,

No. (%)

Nulliparous 1434 (5) 1795 (6) 351 (7) 539 (6) 12 111 (28) 17 814 (31) 3440 (36) 1795 (37)

1 or 2 children 10 415 (34) 10 650 (35) 1761 (36) 2853 (35) 23 249 (53) 28 704 (51) 4889 (50) 2926 (48)

≥3 children 17 750 (60) 17 211 (57) 2743 (55) 4956 (57) 8290 (19) 9653 (18) 1536 (15) 1109 (16)

Parental history of MI
at age <60 y, No. (%)

4893 (16) 5081 (17) 879 (18) 1516 (18) 6105 (14) 8294 (15) 1670 (17) 1011 (16)

Body mass index,
mean (SD)b

25.2 (4.8) 25.4 (4.8) 26.0 (5.3) 26.6 (5.4) 23.9 (4.9) 24.0 (5.0) 24.8 (5.5) 25.1 (5.8)

No. (%)

<25 18 206 (61) 17 910 (59) 2683 (54) 4242 (50) 31 400 (72) 39 851 (71) 6365 (65) 3420 (62)

25-29.9 7926 (27) 8107 (27) 1455 (29) 2559 (30) 7693 (18) 10 300 (18) 2068 (21) 1330 (22)

30-34.9 2645 (9) 2877 (10) 545 (11) 1116 (13) 2837 (7) 3723 (7) 853 (9) 606 (9)

≥35 1235 (4) 1228 (4) 272 (6) 617 (7) 1727 (4) 2305 (4) 580 (6) 477 (7)

Pack-years of smoking,
median (IQR)c

18 (7-34) 18 (6-34) 20 (7-35) 24 (10-39) 10 (5-16) 9 (5-16) 10 (5-17) 11 (6-19)

Husband holds graduate
school degree, No. (%)

5841 (19) 6346 (21) 840 (17) 1028 (12) 9351 (21) 13 810 (25) 2079 (21) 1090 (18)

Alcohol intake, median
(IQR), g/dd

1.8 (0-7.6) 1.9 (0-8.3) 1.8 (0-7.3) 1.1 (0-6.2) 0.9 (0-3.1) 0.9 (0-3.7) 0.9 (0-3.6) 0.9 (0-2.9)

Alternative Healthy Eating
Index score (2010),
mean (SD)e

45.7 (10.5) 46.0 (10.4) 46.0 (10.3) 45.3 (10.1) 43.6 (10.5) 44.3 (10.5) 44.2 (10.4) 44.1 (10.3)

Physical activity, median
(IQR), MET-hours/wkf

7.9
(2.9-20.2)

9.1
(3.4-20.9)

9.0
(3.4-21.5)

8.4
(3.2-21.5)

12.3
(4.7-27.4)

14.6
(5.5-31.6)

15.1
(5.8-33.3)

14.2
(5.2-32.1)

Multivitamin use, No. (%) 18 518 (62) 19 011 (63) 3148 (64) 5325 (62) 23 704 (54) 30 053 (54) 5254 (53) 3242 (55)

Aspirin use, No. (%) 18 482 (62) 19 105 (63) 3122 (63) 5374 (63) 4747 (11) 6119 (11) 1195 (12) 827 (13)

NSAID use, No. (%) 9537 (31) 9680 (32) 1575 (32) 2728 (33) 7775 (18) 10 986 (20) 2206 (22) 1409 (22)

Acetaminophen use,
No. (%)g

11 110 (37) 11 315 (37) 1849 (38) 3204 (39) 9229 (21) 12 370 (22) 2292 (23) 1529 (26)

Postmenopausal,
No. (%)

20 735 (71) 21 254 (71) 3674 (72) 6866 (74) 965 (2) 1271 (2) 247 (2) 238 (3)

Current hormone therapy,
No. (%)

6833 (23) 7059 (24) 1122 (22) 1868 (21) 997 (2) 1263 (2) 246 (2) 236 (3)

Self-reported hypertension,
No. (%)

7464 (25) 7641 (26) 1448 (29) 2781 (30) 2270 (5) 2938 (5) 627 (6) 460 (7)

Self-reported diabetes,
No. (%)

1048 (4) 995 (3) 221 (4) 507 (6) 396 (1) 402 (1) 74 (1) 68 (1)

Self-reported
hypercholesterolemia,
No. (%)

6683 (23) 6837 (23) 1171 (23) 2781 (24) 4493 (10) 5809 (10) 1100 (11) 722 (11)

Usual sleep duration,
No. (%), hh

≤6 6978 (23) 7506 (25) 1427 (29) 2901 (34) 8939 (20) 12 230 (22) 2542 (26) 1670 (28)

7 11 299 (38) 11 353 (38) 1770 (36) 2609 (31) 13 835 (32) 17 397 (31) 2779 (28) 1552 (26)

8-9 7661 (26) 7358 (24) 1044 (21) 1709 (19) 9593 (22) 11 178 (20) 1680 (17) 892 (16)

≥10 157 (1) 132 (0) 24 (0) 56 (1) 245 (1) 322 (1) 52 (1) 37 (1)

Social support, No. (%)i 22 288 (94) 22 667 (94) 3617 (93) 6019 (94) 31 370 (94) 39 389 (95) 6822 (95) 3930 (94)

Abbreviations: IQR, interquartile range; MET, metabolic equivalent task; MI,
myocardial infarction; NHS, Nurses’ Health Study; NSAID, nonsteroidal
anti-inflammatory drug.
a Numbers that do not add up to 100% are attributable to missing data.
b Calculated as weight in kilograms divided by height in meters squared.
c Cumulative among smokers.
d Assessed in 1986 for the NHS and in 1991 for the NHS2.

e Assessed in 1986 for the NHS and in 1991 for the NHS2. Higher scores reflect a
healthier diet.12

f Weekly energy expenditure in MET-hours from recreational and leisure time
activities.

g Assessed in 1990 for the NHS and in 1989 for the NHS2.
h Assessed in 1986 for the NHS and in 2001 for the NHS2.
i Assessed in 1992 for the NHS and in 1993 for the NHS2.

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rate per 100 000 person-years, 436.6), only those who
worked 10 years or more of shift work had a significantly
elevated CHD risk (incidence rate, 556.2; multivariable HR,
1.13 [95% CI, 1.04-1.24]; P = .004 for trend). The association
between shift work and CHD risk was not significant in the
last 4 years of follow-up (2008-2012; incidence rate for <5 years, 219.9; multivariable HR, 0.85 [95% CI, 0.70-1.03]; inci- dence rate for 5-9 years, 247.2; multivariable HR, 0.88 [95%

CI, 0.62-1.26]; incidence rate for ≥10 years, 306.3; multivari-
able HR, 1.04 [95% CI, 0.80-1.35]; P = .94 for trend) (eTable 1
in the Supplement).

All categories of rotating night shift work showed a sig-
nificantly elevated CHD risk when shift work history was
cumulatively updated in the NHS2 (inc idence rate per
100 000 person-years for <5 years, 137.4; multivariable HR, 1.12 [95% CI, 1.01-1.24]; incidence rate for 5-9 years, 161.9;

Table 2. Shift Work and Risk of Coronary Heart Disease in the NHSa

Cohort

Baseline History of Rotating Night Shift Workb
P Value for
Trendc

P Value for
Interaction, Shift
Work × TimedNone <5 y 5-9 y ≥10 y

Overall NHS, 1988 to 2012

Cases/person-years 2739/643 774 2857/644 857 568/103 574 1139/173 571

Incidence rate per 100 000 person-years (95% CI)e 425.5
(383.9-467.1)

435.1
(392.8-477.5)

525.7
(410.4-641.1)

596.9
(502.1-691.7)

Age-adjusted model, HR (95% CI) 1 [Reference] 1.02
(0.96-1.07)

1.21
(1.11-1.33)

1.36
(1.27-1.46)

<.001

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 1.02
(0.97-1.08)

1.12
(1.02-1.22)

1.18
(1.10-1.26)

<.001 <.001

First vs second half of follow-up .02

June 1988 to May 2000

Cases/person-years 915/351 568 1021/352 490 213/57 612 455/97 899

Incidence rate per 100 000 person-years (95% CI)e 367.3
(302.4-432.3)

382.4
(316.8-448.1)

483.1
(306.6-659.7)

494.4
(370.1-618.8)

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 1.10
(1.01-1.21)

1.19
(1.03-1.39)

1.27
(1.13-1.42)

<.001 .03

June 2000 to May 2012

Cases/person-years 1824/305 036 1836/305 297 355/48 238 684/79 819

Incidence rate per 100 000 person-years (95% CI)e 436.6
(367.8-505.4)

424.8
(361.8-487.7)

520.7
(377.1-664.3)

556.2
(414.2-754.3)

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 0.98
(0.92-1.05)

1.08
(0.96-1.21)

1.13
(1.04-1.24)

.004 .08

Restricted to myocardial infarction and coronary heart
disease death

June 1988 to May 2000

Cases/person-years 443/353 659 491/354 846 117/58 026 226/99 022

Incidence rate per 100 000 person-years (95% CI)e 173.0
(128.3-217.8)

182.3
(137.2-227.4)

276.2
(142.6-409.9)

236.5
(151.8-321.3)

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 1.12
(0.99-1.28)

1.35
(1.10-1.66)

1.29
(1.09-1.51)

.001 .19

June 2000 to May 2012

Cases/person-years 444/316 989 428/318 083 65/50 714 176/84 689

Incidence rate per 100 000 person-years (95% CI)e 106.6
(73.1-140.0)

92.3
(69.1-115.5)

101.5
(36.4-166.5)

133.3
(76.4-190.1)

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 0.95
(0.83-1.09)

0.77
(0.60-1.00)

1.09
(0.91-1.30)

.84 .56

Abbreviations: HR, hazard ratio; NHS, Nurses’ Health Study.
a A total of 7303 coronary heart disease cases (ie, nonfatal myocardial infarction,

coronary heart disease–attributed death, angiogram-confirmed angina pectoris,
angioplasty, coronary artery bypass graft surgery, and coronary artery stents)
occurred during 24 years of follow-up in the NHS (N = 73 623).

b Assessed in 1988.
c Based on category midpoints, except for �10 years, for which the midpoint

was set to 10 years.
d Based on the interaction between shift work category midpoints (except for

�10 years, for which the midpoint was set to 10 years) and time
(in 2-year cycles).

e Incidence rates and 95% CIs are adjusted to the age distribution of
women who reported no history of rotating night shift work,
separately for each cohort.

f Multivariable-adjusted model included age, physical activity (metabolic
equivalent task–hours per week, in quintiles), diet (Alternative Healthy Eating
Index score,12 in quintiles), alcohol consumption (none, 0.1-5, 5.1-10,
10.1-20, or >20 g/d), pack-years of smoking (continuous), parental history
of myocardial infarction prior to age 60 years (yes or no), menopausal status
(premenopausal vs postmenopausal), parity (nulliparous, 1 child, 2 children,
or �3 children), hormone therapy (ever, never, or premenopausal),
multivitamin use (yes or no), acetaminophen use (yes or no), nonsteroidal
anti-inflammatory drug use (yes or no), aspirin use (yes or no), hypertension
(yes or no), hypercholesterolemia (yes or no), diabetes (yes or no),
body mass index (<25, 25-29.9, 30-34.9, or �35), race (white, black, or other), and husband’s highest educational level (up to high school diploma, college degree, or graduate school or similar).

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multivariable HR, 1.19 [95% CI, 1.04-1.37]; incidence rate for
≥10 years, 190.5; multivariable HR, 1.27 [95% CI, 1.09-1.48];
P<.001 for trend) (Table 3), compared with women without a history of rotating night shift work (incidence rate, 115.8). In the NHS2, CHD risk also decreased with increasing time since quitting shift work (P<.001 for trend) (eTable 2 in the Supplement).

When analyses were restricted to MI and CHD deaths, over-
all, results were similar in the NHS (Table 2) but were attenu-
ated in the NHS2 (Table 3). Results remained largely un-
changed with further adjustment for sleep duration and social
support (eTable 3 in the Supplement).

In women without a history of diabetes, hypertension, or
elevated cholesterol levels, there was a significant trend of
increased CHD risk with longer duration of shift work in the
NHS (P = .004 for trend) (Table 4) but not in the NHS2 (P = .11
for trend).

In analyses stratified by BMI, a significant dose-response
relationship between shift work and CHD risk across all BMI
categories in the NHS was observed (eTable 4 in the Supple-
ment), with highest estimates among obese women (test for

interaction, 10.9; P = .05). In the NHS2, there was a signifi-
cant dose-response relationship between shift work and CHD
risk only in obese women (P = .002 for trend) but not in normal-
weight or overweight women (P=.53 and P=.47 for trend for
normal-weight and overweight women, respectively); the in-
teraction between shift work and BMI was not significant (test
for interaction, 10.4; P = .06).

Discussion
This prospective cohort study examined the association of ro-
tating night shift work with CHD incidence over 24 years of
follow-up and found that 5 years or more of rotating night shift
work was associated with a significantly increased risk of CHD.
The results suggest that recent shift work might be most rel-
evant, as significantly stronger associations were observed in
the first vs second part of follow-up in the NHS (27% vs 13%
increased risk for ≥10 years of rotating night shift work expo-
sure), in addition to an association between decreasing CHD
risk with increasing time since quitting shift work in the NHS2.

Table 3. Shift Work and Risk of Coronary Heart Disease in the NHS2a

Cohort

Rotating Night Shift Work Exposure
P Value for
Trendb

P Value for
Interaction, Shift
Work × TimecNone <5 y 5-9 y ≥10 y

Baseline history of shift workd

Cases/person-years 1236/1 007 860 1673/1 296 585 347/226 580 263/132 971

Incidence rate per 100 000 person-years
(95% CI)e

122.6
(105.0-140.3)

130.6
(114.5-146.7)

151.6
(109.2-194.0)

178.0
(123.0-234.0)

Age-adjusted model, HR (95% CI) 1 [Reference] 1.06
(0.99-1.14)

1.22
(1.08-1.38)

1.34
(1.17-1.53)

<.001

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 1.05
(0.97-1.13)

1.12
(0.99-1.26)

1.15
(1.01-1.32)

.01 .54

Restricted to myocardial infarction and coronary
heart disease death

Cases/person-years 151/1 018 680 161/1 311 173 38/229 694 35/135 197

Incidence rate per 100 000 person-years
(95% CI)e

14.8
(9.5-20.2)

12.4
(7.9-16.9)

16.2
(4.3-28.0)

24.4
(7.2-41.6)

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 0.83
(0.66-1.04)

0.98
(0.69-1.41)

1.09
(0.75-1.59)

.71 .55

Updated shift workg

Cases/person-years 589/554 846 1077/872 476 328/222 286 233/118 813

Incidence rate per 100 000 person-years
(95% CI)e

115.8
(91.2-140.4)

137.4
(116.2-158.6)

161.9
(116.3-207.6)

190.5
(125.1-255.8)

Age-adjusted model, HR (95% CI) 1 [Reference] 1.18
(1.06-1.30)

1.40
(1.22-1.61)

1.59
(1.36-1.85)

<.001

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 1.12
(1.01-1.24)

1.19
(1.04-1.37)

1.27
(1.09-1.48)

.001 .84

Abbreviations: HR, hazard ratio; NHS, Nurses’ Health Study.
a A total of 3519 coronary heart disease cases (ie, nonfatal myocardial infarction,

coronary heart disease–attributed death, angiogram-confirmed angina
pectoris, angioplasty, coronary artery bypass graft surgery, and coronary
artery stents) occurred during 24 years of follow-up in the NHS2 (N = 115 535).

b Based on category midpoints, except for �10 years, for which the midpoint
was set to 10 years.

c Based on the interaction between shift work category midpoints (except for
�10 years, for which the midpoint was set to 10 years) and time
(in 2-year cycles).

d Assessed in 1989.
e Incidence rates and 95% CIs are standardized relative to the age distribution

of women who reported no history of rotating night shift work, separately for
each cohort.

f Multivariable-adjusted model including age, physical activity (metabolic
equivalent task–hours per week, in quintiles), diet (Alternative Healthy Eating
Index score,12 in quintiles), alcohol consumption (none, 0.1-5, 5.1-10,
10.1-20, or >20 g/d), pack-years of smoking (continuous), parental history of
myocardial infarction prior to age 60 years (yes or no), menopausal status
(premenopausal vs postmenopausal), parity (nulliparous, 1 child, 2 children,
or �3 children), hormone therapy (ever, never, or premenopausal),
multivitamin use (yes or no), acetaminophen use (yes or no), nonsteroidal
anti-inflammatory drug use (yes or no), aspirin use (yes or no), hypertension
(yes or no), hypercholesterolemia (yes or no), diabetes (yes or no),
body mass index (<25, 25-29.9, 30-34.9, or �35), race (white, black, or other), and husband’s highest educational level (up to high school diploma, college degree, or graduate school level or similar).

g Updated shift work refers to cumulative duration of rotating night shift work
reported up to 2007.

Rotating Night Shift Work and Risk of CHD Among Women Original Investigation Research

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In this younger cohort, when using cumulatively updated shift
work history, a higher CHD risk was observed, with 12%, 19%,
and 27% increased risk for less than 5 years, 5 to 9 years, and
10 years or more of shift work, respectively. Results were simi-
lar overall when restricting to women without hypertension,
diabetes, or hypercholesterolemia, suggesting that these con-
ditions may not be the prime mediators of observed associa-
tions between shift work and CHD. In summary, the present
analysis indicated that rotating night shift work was associ-
ated with increased CHD risk in a duration-dependent man-
ner and that this risk waned over time.

Results were consistent with a recent meta-analysis that
found a 24% increased risk of “any coronary event” in shift
workers despite significant heterogeneity detected across 28
studies, presumably due to heterogeneous outcome and ex-
posure definitions.8 The present study was based on a defini-
tion of rotating night shift work (≥3 night shifts per month) that
has been used extensively in existing literature, although it did
not incorporate more precise intensity measures related to fre-
quency and actual working times.13,14

Lifetime history of rotating night shift work was queried
on average at age 55 years in the NHS, when women are less
likely to begin new shift work schedules; in the NHS2, women
were asked about shift work history when they were in their
mid-30s, with updated shift work assessments throughout
follow-up. In the NHS, CHD risk associated with rotating night
shift work seemed to wane over time, so that after 20 years of

follow-up, the CHD risk associated with 10 years or more of ex-
posure was not significantly elevated. In 1995, Kawachi et al7

reported that 6 years or more of rotating shift work was asso-
ciated with 51% increased CHD risk after multivariable adjust-
ment, based on 4 years of follow-up and 292 CHD cases in the
NHS. The absolute incidence rate difference corresponded to
86.2 per 100 000 person-years (comparing never shift work-
ers with women with a history of ≥10 years of rotating night
shift work) and was of modest magnitude. The rate differ-
ence was also comparable with the one reported in the pres-
ent analysis, when restricting to the primary end points of
Kawachi and colleagues (ie, MI and CHD death) and the first
12 years of follow-up in the NHS (crude absolute incidence rate
difference, 91.6).

Concomitantly, higher risk estimates for updated shift work
were observed in the NHS2, and this CHD risk significantly de-
creased with increasing time since quitting shift work, lend-
ing further support to the suggestion that recent shift work was
particularly relevant for CHD risk—a new finding that war-
rants replication. Overall, the relative CHD risk associated with
rotating night shift work was statistically significant. How-
ever, the increased CHD risk was found in a small group of
women, those who worked 5 or more years on rotating night
shifts (only 15% of all women in the study population). Hence,
the absolute risk and public health impact of night work—
given confirmation of those results—would therefore be small.
Nonetheless, because changes in shift work schedules poten-

Table 4. Shift Work and Risk of Coronary Heart Disease in Women Without Diabetes, Hypertension, or Hypercholesterolemiaa

Cohort
Baseline History of Rotating Night Shift Workb
P Value for
Trendc
P Value for
Interaction, Shift
Work × TimedNone <5 y 5-9 y ≥10 y

NHS, 1988-2012

Cases/person-years 723/319 135 791/316 198 157/47 860 260/75 528

Incidence rate per 100 000 person-years
(95% CI)e

301.4
(243.5-359.2)

323.7
(263.0-384.4)

409.4
(238.3-580.5)

380.0
(255.1-504.9)

Age-adjusted model, HR (95% CI) 1 [Reference] 1.06
(0.96-1.18)

1.37
(1.15-1.63)

1.36
(1.17-1.57)

<.001

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 1.08
(0.97-1.19)

1.29
(1.08-1.54)

1.17
(1.01-1.36)

.004 .24

NHS2, 1989-2013

Cases/person-years 720/748 075 1001/966 924 193/165 593 134/92 148

Incidence rate per 100 000 person-years
(95% CI)e

100.6
(81.1-120.2)

112.1
(93.6-130.7)

122.9
(74.3-171.5)

136.8
(78.8-194.9)

Age-adjusted model, HR (95% CI) 1 [Reference] 1.09
(0.99-1.20)

1.17
(1.00 -1.38)

1.28
(1.06-1.54)

.003

Multivariable-adjusted model, HR (95% CI)f 1 [Reference] 1.09
(0.99-1.20)

1.10
(0.94 -1.30)

1.13
(0.94 -1.36)

.11 .78

Abbreviations: HR, hazard ratio; NHS, Nurses’ Health Study.
a All women who reported any of those comorbidities at baseline or throughout

follow-up were excluded from those analyses, both in the NHS (N = 43 557)
and the NHS2 (N = 98 126).

b Assessed in 1988 for the NHS and in 1989 for the NHS2.
c Based on category midpoints, except for �10 years, for which the midpoint

was set to 10 years.
d Based on the interaction between shift work category midpoints (except for
�10 years, for which the midpoint was set to 10 years) and time
(in 2-year cycles).

e Incidence rates and 95% CIs are standardized to the age distribution of
women who reported no history of rotating night shift work, separately for
each cohort.

f Multivariable-adjusted model including age, physical activity (metabolic
equivalent task–hours per week, in quintiles), diet (Alternative Healthy Eating
Index score,12 in quintiles), alcohol consumption (none, 0.1-5, 5.1-10,
10.1-20, or >20 g/d), pack-years of smoking (continuous), parental history of
myocardial infarction prior to age 60 years (yes or no), menopausal status
(premenopausal vs postmenopausal), parity (nulliparous, 1 child, 2 children,
or �3 children), hormone therapy (ever, never, or premenopausal),
multivitamin use (yes or no), acetaminophen use (yes or no), nonsteroidal
anti-inflammatory drug use (yes or no), aspirin use (yes or no),
body mass index (<25, 25-29.9, 30-34.9, or �35), race (white, black, or other), and husband’s highest educational level (up to high school diploma, college degree, or graduate school level or similar).

Research Original Investigation Rotating Night Shift Work and Risk of CHD Among Women

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tially could reduce such risk, it is important to further explore
the relationship between shift schedules and CHD risk.

In this study, the CHD outcomes examined reflect trends
in CHD care15,16 and included CABG surgery, angiogram-
confirmed angina pectoris, angioplasty, and stents in addition
to MI and CHD death. As stated by Hoffmann,17 an MI repre-
sents a relatively late stage of a long, ongoing disease process18;
to capture earlier manifestations of CHD, the outcome defini-
tion also encompassed angiogram-confirmed angina pectoris
and angioplasty. The analyses demonstrated a significant dose-
response relationship between rotating night shift work expo-
sure and this more comprehensive CHD outcome. In the NHS,
results were similar when restricting analyses to MI and CHD
death—the end points most other studies have examined. In the
NHS2, associations were no longer statistically significant when
analyses were restricted to MI and CHD death. There were many
fewer cases—only 1 in 10 cases was an MI or CHD death—thus,
there was less power to detect a significant association. Age dif-
ferences between the 2 cohorts (mid-50s in the NHS vs mid-
30s in the NHS2 at baseline in 1988 and 1989, respectively) and
technological advances resulting in different standards of care16

may explain these findings. The findings also suggest the im-
portance of evaluating a broader CHD end point in relation to
shift work, as part of the association could otherwise be con-
cealed by secondary and tertiary prevention.

Whether shift work was associated with increased CHD risk
in the absence of hypertension, hypercholesterolemia, and dia-
betes was another question of this study. A previous study
found no association between CHD-related disability and mor-
tality over 22 years in shift workers vs day workers after ex-
cluding individuals with cancer, angina pectoris, nonfatal MI,
obstructive pulmonary disease, hypertension, or diabetes
mellitus prior to baseline.19 In this study, when participants
with hypertension, elevated cholesterol levels, or diabetes were
excluded at baseline and throughout follow-up, a significant
dose-response relationship between rotating night shift work
and CHD risk was observed in the NHS but not in the NHS2.
Overall, this analysis supported the hypothesis that shift work
per se—and the associated disruption of biological and social
rhythms—could have increased CHD risk, even in the ab-
sence of or with only subclinical manifestations of poten-
tially mediating comorbidities such as hypertension, hyper-
cholesterolemia, or diabetes.

Obesity has been associated with a higher risk of CHD,20,21

such as MI and CHD death.22 All analyses were therefore ad-
justed for BMI (updated throughout follow-up), and additional
analyses examined whether the effects of shift work varied by
BMI. There was suggestive evidence for effect modification by
BMI. Although these results warrant replication, women who
were overweight might have been at an even higher risk of CHD
if they simultaneously worked rotating night shifts. Residual con-
founding by BMI could be an alternate explanation; however, as
analyses were adjusted for BMI continuously in each stratum,
this appeared a less likely explanation.

In the past 2 decades, sleep disturbances, psychosocial
stress, and social isolation have been identified as important
contributors to CHD risk.23-27 Therefore, additional analyses
adjusted for sleep and social support, and results remained

largely unchanged. However, given that shift work may affect
both sleep and social support,4 further research in popula-
tions with more extensive information on sleep duration, qual-
ity, and timing as well as work hours seems warranted. In ad-
dition, c irc adian misalignment—where the biologic al,
endogenous rhythm is asynchronous with behavioral cycles
of activity, sleep, and food intake—may be a key mechanism
linking shift work to chronic disease,28,29 including cardiovas-
cular disease.2,3,30 Future studies might also explore whether
an individual’s endogenous biological rhythm (also referred
to as chronotype)31 alters the association between lifetime his-
tory of rotating night shift and CHD risk, as early chronotypes
experience higher levels of circadian misalignment and sleep
curtailment during night shifts32 and might therefore show
higher CHD risk related to rotating night shift work.

This study has several strengths of note. It is large, with
more than 10 000 incident CHD cases over 24 years of follow-
up, and MI and CHD death were confirmed by medical and
death records. Detailed information on a wide range of poten-
tial confounding factors was available, and most of them were
updated regularly throughout follow-up. This study was also
based on one of the few cohorts with detailed lifetime shift
work exposure information.

Several limitations are also noteworthy. Conclusions can
be generalized to women only, and health effects of shift work
and pathways may be different in men and women.33 As in all
observational studies, even though known potential confound-
ing factors were controlled for, confounding due to unmea-
sured differences in behaviors or other factors may still exist.
This study relied on self-reports for angiogram confirmed an-
gina pectoris, CABG surgery, angioplasty and stents, but vali-
dation studies have demonstrated a high accuracy of self-
reports from these participants, all of whom are registered
nurses.34,35 The exposure assessments lacked information on
intensity of night shift work and physiological measures that
may be affected by shift work. Additionally, as information on
permanent night shift work over time was not collected,
women with such schedules might have been included in the
reference group. If permanent night shift workers had a higher
CHD risk compared with never rotating shift workers, this
would have biased results toward the null. Future studies
should include a more detailed assessment of work hours and
job demands, ideally in conjunction with chronotype and sleep
timing measures, to enable more detailed studies of circa-
dian strain on coronary health.14 Furthermore, studying CHD-
related biomarkers (eg, triglycerides, cholesterol levels, ca-
rotid plaque, or hemoglobin A1c)17,36 might be useful in
understanding underlying mechanisms.

Conclusions
Among women who worked as registered nurses, longer
duration of rotating night shift work was associated with a
statistically significant but small absolute increase in CHD
risk. Further research is needed to explore whether the
association is related to specific work hours and individual
characteristics.

Rotating Night Shift Work and Risk of CHD Among Women Original Investigation Research

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ARTICLE INFORMATION

Author Affiliations: Channing Division of Network
Medicine, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School,
Boston, Massachusetts (Vetter, Devore, Speizer,
Rosner, Stampfer, Schernhammer); Department of
Epidemiology, Harvard T. H. Chan School of Public
Health, Boston, Massachusetts (Wegrzyn,
Stampfer, Schernhammer); Department of
Nutrition, Harvard T. H. Chan School of Public
Health, Boston, Massachusetts (Massa, Stampfer);
Department of Environmental Health, Harvard T. H.
Chan School of Public Health, Boston,
Massachusetts (Speizer); Department of Social and
Behavioral Sciences, Harvard T. H. Chan School of
Public Health, Boston, Massachusetts (Kawachi);
Department of Biostatistics, Harvard T. H. Chan
School of Public Health, Boston, Massachusetts
(Rosner); Department of Epidemiology, Center for
Public Health, Medical University of Vienna, Vienna,
Austria (Schernhammer).

Author Contributions: Dr Vetter had full access to
all of the data in the study and takes responsibility
for the integrity of the data and the accuracy of the
data analysis.
Study concept and design: Vetter, Speizer, Stampfer,
Schernhammer.
Acquisition, analysis, or interpretation of data: All
authors.
Drafting of the manuscript: Vetter, Schernhammer.
Critical revision of the manuscript for important
intellectual content: All authors.
Statistical analysis: Vetter, Devore, Wegrzyn, Massa,
Rosner, Schernhammer.
Obtained funding: Speizer, Schernhammer.
Administrative, technical, or material support:
Speizer, Schernhammer.
Study supervision: Rosner, Stampfer,
Schernhammer.

Conflict of Interest Disclosures: All authors have
completed and submitted the ICMJE Form for
Disclosure of Potential Conflicts of Interest and
none were reported.

Funding/Support: This research was supported by
Centers for Disease Control and Prevention/
National Institute for Occupational Safety and
Health grants 5R01OH009803 (to Dr
Schernhammer), UM1CA186107, UM1CA176726,
and R01HL034594. Dr Vetter was additionally
supported by a fellowship from the German
Research Foundation (VE 835/1-1).

Role of the Funder/Sponsor: The sponsor had no
role in the design and conduct of the study;
collection, management, analysis, and
interpretation of the data; preparation, review, or
approval of the manuscript; or decision to submit
the manuscript for publication.

Additional Contributions: We thank the
participants and staff of the Nurses’ Health Study
cohorts for their valuable contributions. In addition,
we also thank Stephanie E. Chiuve, ScD
(Harvard T. H. Chan School of Public
Health and Harvard Medical School, Boston),
for helpful discussions, as well as Jeffrey
Pierre-Paul, PharmD, RPh (Massachusetts College
of Pharmacy and Health Sciences), for his support
in the early stages of the project. Participants did
not receive compensation, and staff were not
compensated outside of their salaries.

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Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Trusted Partner of 9650+ Students for Writing

From brainstorming your paper's outline to perfecting its grammar, we perform every step carefully to make your paper worthy of A grade.

Preferred Writer

Hire your preferred writer anytime. Simply specify if you want your preferred expert to write your paper and we’ll make that happen.

Grammar Check Report

Get an elaborate and authentic grammar check report with your work to have the grammar goodness sealed in your document.

One Page Summary

You can purchase this feature if you want our writers to sum up your paper in the form of a concise and well-articulated summary.

Plagiarism Report

You don’t have to worry about plagiarism anymore. Get a plagiarism report to certify the uniqueness of your work.

Free Features $66FREE

Most Qualified Writer $10FREE
Plagiarism Scan Report $10FREE
Unlimited Revisions $08FREE
Paper Formatting $05FREE
Cover Page $05FREE
Referencing & Bibliography $10FREE
Dedicated User Area $08FREE
24/7 Order Tracking $05FREE
Periodic Email Alerts $05FREE

Our Services

Join us for the best experience while seeking writing assistance in your college life. A good grade is all you need to boost up your academic excellence and we are all about it.

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Academic Writing

We create perfect papers according to the guidelines.

Professional Editing

We seamlessly edit out errors from your papers.

Thorough Proofreading

We thoroughly read your final draft to identify errors.

Thorough Proofreading

We thoroughly read your final draft to identify errors.

Delegate Your Challenging Writing Tasks to Experienced Professionals

Work with ultimate peace of mind because we ensure that your academic work is our responsibility and your grades are a top concern for us!

Check Out Our Sample Work

Dedication. Quality. Commitment. Punctuality

It May Not Be Much, but It’s Honest Work!

Here is what we have achieved so far. These numbers are evidence that we go the extra mile to make your college journey successful.

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Words Written This Week

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Ongoing Orders

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We Analyze Your Problem and Offer Customized Writing

We understand your guidelines first before delivering any writing service. You can discuss your writing needs and we will have them evaluated by our dedicated team.

Clear elicitation of your requirements.
Customized writing as per your needs.

We Mirror Your Guidelines to Deliver Quality Services

We write your papers in a standardized way. We complete your work in such a way that it turns out to be a perfect description of your guidelines.

Proactive analysis of your writing.
Active communication to understand requirements.

We Handle Your Writing Tasks to Ensure Excellent Grades

We promise you excellent grades and academic excellence that you always longed for. Our writers stay in touch with you via email.

Thorough research and analysis for every order.
Deliverance of reliable writing service to improve your grades.

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The two articles needed for 4a and 4b

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Premium Quality

Experienced Writers

On-Time Delivery

24/7 Customer Support

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Order Tracking

Areas of Expertise

Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Areas of Expertise

Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Trusted Partner of 9650+ Students for Writing

Preferred Writer

Grammar Check Report

One Page Summary

Plagiarism Report

Free Features $66FREE

Our Services

Academic Writing

Professional Editing

Thorough Proofreading

Thorough Proofreading

Delegate Your Challenging Writing Tasks to Experienced Professionals

Check Out Our Sample Work

It May Not Be Much, but It’s Honest Work!

0+

Happy Clients

0+

Words Written This Week

0+

Ongoing Orders

0%

Customer Satisfaction Rate

Process as Fine as Brewed Coffee

Share Your Requirements

Place Order & Deposit Funds

Release Payment to Your Writer

See How We Helped 9000+ Students Achieve Success

We Analyze Your Problem and Offer Customized Writing

We Mirror Your Guidelines to Deliver Quality Services

We Handle Your Writing Tasks to Ensure Excellent Grades