Prevalence and determinants of healthcare avoidance during the COVID-19 pandemic: A population-based cross-sectional study.

BACKGROUND: During the Coronavirus Disease 2019 (COVID-19) pandemic, the number of consultations and diagnoses in primary care and referrals to specialist care declined substantially compared to prepandemic levels. Beyond deferral of elective non-COVID-19 care by healthcare providers, it is unclear to what extent healthcare avoidance by community-dwelling individuals contributed to this decline in routine healthcare utilisation. Moreover, it is uncertain which specific symptoms were left unheeded by patients and which determinants predispose to healthcare avoidance in the general population. In this cross-sectional study, we assessed prevalence of healthcare avoidance during the pandemic from a patient perspective, including symptoms that were left unheeded, as well as determinants of healthcare avoidance. METHODS AND
FINDINGS: On April 20, 2020, a paper COVID-19 survey addressing healthcare utilisation, socioeconomic factors, mental and physical health, medication use, and COVID-19-specific symptoms was sent out to 8,732 participants from the population-based Rotterdam Study (response rate 73%). All questionnaires were returned before July 10, 2020. By hand, prevalence of healthcare avoidance was subsequently verified through free text analysis of medical records of general practitioners. Odds ratios (ORs) for avoidance were determined using logistic regression models, adjusted for age, sex, and history of chronic diseases. We found that 1,142 of 5,656 included participants (20.2%) reported having avoided healthcare. Of those, 414 participants (36.3%) reported symptoms that potentially warranted urgent evaluation, including limb weakness (13.6%), palpitations (10.8%), and chest pain (10.2%). Determinants related to avoidance were older age (adjusted OR 1.14 [95% confidence interval (CI) 1.08 to 1.21]), female sex (1.58 [1.38 to 1.82]), low educational level (primary education versus higher vocational/university 1.21 [1.01 to 1.46), poor self-appreciated health (per level decrease 2.00 [1.80 to 2.22]), unemployment (versus employed 2.29 [1.54 to 3.39]), smoking (1.34 [1.08 to 1.65]), concern about contracting COVID-19 (per level increase 1.28 [1.19 to 1.38]) and symptoms of depression (per point increase 1.13 [1.11 to 1.14]) and anxiety (per point increase 1.16 [1.14 to 1.18]). Study limitations included uncertainty about (perceived) severity of the reported symptoms and potentially limited generalisability given the ethnically homogeneous study population.
CONCLUSIONS: In this population-based cross-sectional study, 1 in 5 individuals avoided healthcare during lockdown in the COVID-19 pandemic, often for potentially urgent symptoms. Healthcare avoidance was strongly associated with female sex, fragile self-appreciated health, and high levels of depression and anxiety. These results emphasise the need for targeted public education urging these vulnerable patients to timely seek medical care for their symptoms to mitigate major health consequences.

Introduction

In the first months of 2020, the number of confirmed COVID-19 cases in Europe began to rise, to which many European countries responded with restrictive measures aimed at limiting individual mobility in order to prevent overwhelming their healthcare systems [1-3]. On March 11, 2021, a year after COVID-19 was declared a pandemic by WHO, 40.5 million European citizens had been infected with the virus, and 904,100 had died [4]. During this year, the main focus was on facilitating acute medical care, while most scheduled and preventive care was cancelled or postponed [5,6]. Consequently, the number of consultations and diagnoses in primary care related to chronic diseases, such as cancer, cardiovascular diseases, and mental illnesses, as well as referrals to hospitals for these indications, declined in the first 6 months of 2020 compared to 2019 [7-11].

Thus far, the observed changes in healthcare utilisation are exclusively based on registry data of diagnoses instead of the actual symptoms experienced by individuals in the general population [7-9]. Detailed data on healthcare-seeking behaviour in primary care from a patient perspective provide complementary insights in the symptoms that were left unheeded and by whom. During the pandemic, especially during national lockdowns, individuals might refrain from seeing their general practitioner (GP) because they perceive their symptoms as too insignificant for burdening their physician, or not worth the risk of a COVID-19 infection [8,9]. Although many symptoms in primary care are self-limiting, urgent medical evaluation is essential for some in order to mitigate health damage. For example, symptoms that signal (transient) cardiovascular or cerebrovascular events could, if left untreated, lead to major health consequences. The collateral damage resulting from the pandemic is, therefore, not limited to patients who have been infected with COVID-19, but also affects vulnerable groups of individuals who experience difficulties or are afraid to access their primary care physician [12]. For this reason, the aim of this study is to expand our knowledge of healthcare avoidance in order to mitigate damage to population health in the aftermath of this or future pandemics, and of other disasters that could affect healthcare-seeking behaviour of citizens. Studies that so far focused on the patients’ perspective in relation to healthcare avoidance identified several groups at risk of avoiding healthcare. However, these studies have mainly been conducted in the United States, which do not have a primary care gatekeeper system as most European countries do [13-15]. This gatekeeper system provides a unique opportunity to meticulously assess changes in healthcare-seeking behaviour during the pandemic, since patients always have to contact their GP before they can be referred to a medical specialist [16].

In this cross-sectional study, embedded within an ongoing prospective cohort study, we determined prevalence of healthcare avoidance in the general population during the COVID-19 pandemic by combining self-reported healthcare-seeking behaviour with medical records of GPs. We also assessed the specific symptoms that were left unheeded, while specifically paying attention to the perception of the patient instead of the healthcare provider, and we sought to establish which potential determinants were associated with healthcare avoidance.

Methods

Study population

This cross-sectional study was embedded within the ongoing population-based Rotterdam Study, a prospective cohort study conceived to investigate the aetiology and natural history of chronic diseases in mid- and late-life [17]. The Rotterdam Study was initiated in 1990, when 7,983 residents of the district Ommoord in Rotterdam who were 55 years and older started their participation in the study [17]. Since then, the size of actively contributing, living participants remained largely stable, with 3 new study waves that have been initiated over time—while other participants passed away. In 2000, the cohort has been expanded with residents 55 years and older (RS-II, N = 3,011). In 2006, 3,932 participants aged 45 years and older enrolled (R-III). In 2016, the most recent wave was initiated with 3,368 participants aged 40 and over contributing to the study (RS-IV). Since 1990, a total of 17,931 participants have taken part in the Rotterdam Study. All participants were extensively examined at study entry and subsequent follow-up every 3 to 6 years [17]. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist). The prospective analysis plan of the current study has been included as a supporting information (S1 File).

Data collection

For this cross-sectional study, we identified all participants that were still alive and actively taking part in the Rotterdam Study on April 8, 2020 (N = 9,008). At that time, 8,732 participants (96.9%) were not hospitalised or living in nursing homes, thus included in the current study. In the Dutch healthcare system, residents of nursing homes are under direct and daily medical supervision of a geriatrician or nursing home physician, limiting potential healthcare avoidance. On April 20, 2020, we have invited the noninstitutionalised participants to fill out a paper COVID-19 questionnaire about the preceding period starting from the first confirmed COVID-19 infection in the Netherlands on February 27, 2020, which also indicated the start of the first wave of COVID-19. The questionnaire addressed healthcare utilisation, socioeconomic factors, mental and physical health, medication use, and COVID-19–specific symptoms. A detailed description of the methods including validation of the questionnaire has been reported elsewhere [18].

Assessment of healthcare avoidance

Participants were asked to report whether they had experienced symptoms in the preceding weeks for which they otherwise would have contacted their GP or medical specialist but now did not do so because of COVID-19. They were also provided with a prespecified list of both potentially urgent symptoms (such as palpitations, chest pain, and limb weakness) and generic symptoms (such as lower back pain) to enable them to indicate for which symptoms they had avoided healthcare. Since lower back pain is generally self-limiting, we have specifically used this symptom to contrast with other symptoms that might have required urgent medical evaluation. We subsequently checked the GP records by hand from December 2019 until December 2020 of all participants who reported having experienced symptoms for which they did not seek medical attention. The GP is generally the first to contact when an individual has symptoms. Visits to emergency departments or medical specialists are documented by the GP as well. In presence of self-reported symptoms in the questionnaire, healthcare avoidance would have, therefore, been reflected by the absence of physical, telephone, and administrative consultations in the medical records kept by the GP. These records contained narrative data, which means that the notes of the GP had been entered in a free text instead of structured format using prespecified diagnostic codes [19]. While the latter would have mainly included basic information such as laboratory results and patient demographics, narrative medical records are more accurate and detailed in scope, also including information on comorbidities, medication use, physical exams, the GP’s impression, and treatment plan [19]. Analysis of these records resulted in a detailed overview of the healthcare-seeking behaviour of healthcare avoiding participants. We defined 3 levels of certainty of healthcare avoidance: “definite,” “probable,” and “possible.” Participants who did not contact their GP for the symptoms they had mentioned on the questionnaire were definite healthcare avoiders. In case they had reached out to their GP more than 2 weeks after they had filled out the questionnaire, indicating a delay in healthcare-seeking behaviour, they were considered a probable healthcare avoider. The remaining participants, who had had contact with their GP despite reporting themselves as healthcare avoiders, were labelled as possible healthcare avoiders. The GP records also gave us the opportunity to compare consultation rates between 3 control months prior to the first wave of COVID-19, with consultation rates during the months of the lockdown itself.

Determinants related to healthcare avoidance

Based on literature, we have prespecified the following determinants of healthcare avoidance for inclusion in the questionnaire: age, sex, self-appreciated health (excellent; very good; good; fair; poor), occupational status (working; on sick leave; unemployed; retired; other), alcohol consumption and smoking status (self-reported use during the 14 days prior to filling out the questionnaire), concern about contracting COVID-19 (never; rarely; sometimes; often; almost continuously), depression (weighted score on 10 out of 20 questions from the Center for Epidemiological Studies Depression (CESD) scale, with a maximum score of 29), and anxiety (weighted score on 7 out of 14 questions from the Hospital Anxiety and Depression Scale (HADS), with a maximum score of 20) [9,10,13,20-24]. We have also asked participants about their medical history, including a history of chronic diseases (such as cancer; heart disease; stroke; chronic lung disease; neurodegenerative disease; diabetes; mental illness). The educational level of participants (primary education; low/intermediate general or lower vocational; intermediate vocational or higher general; higher vocational or university) was retrieved from earlier measurements in 2015 (cohorts I, II, and III) and 2020 (cohort IV), according to the International Standard Classification of Education (ISCED) by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) [25].

Statistical analyses

Characteristics of the study population that were measured on a continuous scale were represented by the mean and standard deviation, whereas categorical variables were presented as the total number of observations with corresponding percentages. Age was subdivided into 3 categories to calculate age-specific prevalence of healthcare avoidance. Missing values in determinants (all less than 1.3% missing) were imputed using the fully conditional specification method with a maximum number of 10 iterations. We did not find evidence for issues of multicollinearity.

We have employed binary logistic regression analyses to assess the association between determinants and healthcare avoidance. These analyses were conducted in 3 different steps. First, we have investigated the association between a particular determinant, 2 confounders (age and sex), and healthcare avoidance (model 1). Then, we have repeated these analyses while adding another confounder to the model, which was a history of self-reported chronic diseases (model 2). Finally, we have conducted multivariable logistic analyses adjusting for all considered determinants in this study (model 3). Results were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). To evaluate the robustness of our findings, we have conducted 4 sensitivity analyses. First, we have stratified main analyses between participants who reported symptoms that might have warranted urgent medical assessment (chest pain, palpitations, limb weakness, difficulty speaking or facial drooping, and self-perceived cancer-related symptoms) and the remaining symptoms of a more generic nature that were listed in the questionnaire (lower back pain, sudden onset dizziness, memory complaints, fluid retention (oedema), elevated blood pressure, attempts to stop or reduce smoking, nausea or vomiting, sudden (temporary) vision loss, and dysregulation of diabetes). Second, we explored healthcare avoidance among participants that would most likely differ in their healthcare utilisation behaviour due to comorbidities by stratifying individuals with or without a history of any chronic disease. Third, we have compared the main analyses of definite and probable to possible healthcare avoiders to verify whether effect estimates would differ between these different levels of healthcare avoidance. Finally, as a result of the peer review process, we have additionally stratified the analyses between the self-reported chronic diseases included in this study to examine whether the strength of the associations would differ depending on the type of disease. Data were handled and analysed with the Statistical Package for the Social Sciences software (SPSS), version 25.0. Level of statistical significance (alpha) was set at 0.05.

Details of ethical approval

The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare, and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www.trialregister.nl) and into the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalogue number NTR6831. All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians.

Results

Characteristics

The response rate of the questionnaire was 73% (N = 6,241). All questionnaires were returned before July 10, 2020. We excluded 9.3% (N = 585) that did not have complete data on the questions concerning healthcare utilisation. These participants were more often women, were of a lower educational level, and less often had a chronic disease (Table A in S1 Tables). Nonresponders to the questionnaire had a comparable mean age and ethnic background to responders, were slightly more often female and of a lower educational level (Table B in S1 Tables). The final population size consisted of 5,656 individuals. In Table 1, it is shown that 1,142 participants (20.2%) have avoided healthcare despite experiencing symptoms. Most of those participants considered to be healthcare avoiders (42.3%) were in the age category 65 to 79 years, compared to 27.8% below age 65 and 29.9% from the age of 80 onwards. Healthcare avoiders were more likely to be women (66.4%) and to have a history of any chronic disease (77.8%).

Table 1

Characteristics of the study population (N = 5,656). Values are numbers (percentages) unless stated otherwise.

			Population total (N = 5,656)	Nonavoiders (N = 4,514)	Avoiders (N = 1,142)
Age, years (mean, SD)			69.4 (11.5)	68.8 (11.3)	71.7 (11.9)
Age categories		<65 years	1,880 (33.2)	1,562 (34.6)	318 (27.8)
		65–79 years	2,589 (45.8)	2,106 (46.7)	483 (42.3)
		≥80 years	1,187 (20.9)	846 (18.7)	341 (29.9)
Women			3,266 (57.7)	2,508 (55.6)	758 (66.4)
History of chronic diseases	Any		3,661 (64.7)	2,772 (61.4)	889 (77.8)
	Cancer		812 (14.4)	601 (13.3)	211 (18.5)
	Heart disease		1,640 (29.0)	1,224 (27.1)	416 (36.4)
	Stroke		418 (7.4)	292 (6.5)	126 (11.0)
	Chronic lung disease		795 (14.1)	569 (12.6)	226 (19.8)
	Neurodegenerative disease		97 (1.7)	68 (1.5)	29 (2.5)
	Diabetes		547 (9.7)	391 (8.7)	156 (13.7)
	Mental illness		257 (4.5)	154 (3.4)	103 (9.0)
Educational level	Primary education		343 (6.1)	241 (5.3)	102 (8.9)
	Low/intermediate general or lower vocational		1,875 (33.2)	1,454 (32.2)	421 (36.9)
	Intermediate vocational or higher general		1,807 (31.9)	1,452 (32.2)	355 (31.1)
	Higher vocational or university		1,579 (27.9)	1,330 (29.5)	249 (21.8)
Self-appreciated health	Poor		62 (1.1)	24 (0.5)	38 (3.4)
	Fair		731 (12.9)	433 (9.6)	298 (26.7)
	Good		3,197 (56.5)	2,577 (57.1)	620 (55.6)
	Very good		1,153 (20.4)	1,029 (22.8)	124 (11.1)
	Excellent		416 (7.4)	380 (8.4)	36 (3.2)
Occupational status	Working (full time, part-time, self-employed)		1,578 (27.9)	1,375 (30.5)	203 (17.8)
	On sick leave		61 (1.1)	45 (1.0)	16 (1.4)
	Unemployed		161 (2.8)	117 (2.6)	44 (3.9)
	Retired		3,407 (60.2)	2,684 (59.5)	723 (63.3)
	Other		289 (5.1)	197 (4.4)	92 (8.1)
Alcohol consumption; yes			3,092 (54.7)	2,545 (56.4)	547 (47.9)
Current smoking; yes			591 (10.4)	456 (10.1)	135 (11.8)
Concern contracting COVID-19	Never		893 (15.8)	742 (16.4)	151 (13.2)
	Rarely		1,766 (31.2)	1,486 (32.9)	280 (24.5)
	Sometimes		2,417 (42.7)	1,910 (42.3)	507 (44.4)
	Often		446 (7.9)	296 (6.6)	150 (13.1)
	Almost continuously		76 (1.3)	44 (1.0)	32 (2.8)
Symptoms of depression (weighted score ≥ 10)			911 (16.1)	554 (12.3)	357 (31.3)
Symptoms of anxiety (weighted score ≥ 7)			889 (15.7)	549 (12.2)	340 (29.8)

CI, confidence interval; COVID-19, Coronavirus Disease 2019; N, number of participants; SD, standard deviation.

Symptoms and healthcare avoidance

Out of 1,142 healthcare avoiders, 16 did not specify their symptoms in the questionnaire, which means that the largest part of the study population (99.7%) included symptomatic patients who did not seek medical care. More than a third (36.3%) of all healthcare avoiding participants reported symptoms, which might have required urgent medical attention, with limb weakness (13.6%), self-perceived cancer-related symptoms (11.5%), palpitations (10.8%), and chest pain (10.2%) being most prevalent (Table 2). Respectively, 60.9% and 49.0% of participants who reported palpitations and chest pain had a history of cardiovascular disease. The high prevalence of lower back pain was mainly driven by a combination of additional symptoms, given that only 119 participants reported lower back pain as the only symptom for which they avoided healthcare.

Table 2

Symptoms for which healthcare was avoided (N = 1,142)*.

		N (%)
Palpitations	All	123 (10.8)
	Among those with a history of CVD	75 (60.9)
Chest pain	All	116 (10.2)
	Among those with a history of CVD	57 (49.0)
Limb weakness		155 (13.6)
Self-perceived cancer-related symptoms		131 (11.5)
Difficulty speaking or facial drooping		23 (2.0)
Sudden (temporary) vision loss		51 (4.5)
Elevated blood pressure		101 (8.8)
Sudden onset dizziness		197 (17.3)
Dysregulation of diabetes		49 (4.3)
Nausea or vomiting		54 (4.7)
Fluid retention (oedema)		139 (12.2)
Memory complaints		177 (15.5)
Attempts to stop or reduce smoking		65 (5.7)
Lower back pain**		369 (32.3)

CVD, cardiovascular disease; N, number of participants.

*1,142 = total number of healthcare avoiders.

**10.4% of the participants (N = 119) reported lower back pain as their only symptom.

36.3% of the participants (N = 414) reported at least one symptom, which should have received direct medical attention (palpitations, chest pain, limb weakness, self-perceived cancer-related symptoms, difficulty speaking).

54.1% of the participants (N = 618) reported one symptom; 44.4% (N = 508) reported 2 or more symptoms; 1.5% (N = 16) did not specify symptoms.

Analysis of GP records

We were able to review the records from 889 out of 1,142 (77.8%) participants who reported that they had avoided healthcare. The remaining 253 (22.2%) medical records could not be retrieved because participants had either not given consent to review their records, or they moved out of the Ommoord district after enrolment in the Rotterdam study, and, therefore, their records were not digitally accessible. Fig 1 shows that most participants met criteria to be a definite healthcare avoider (N = 497). A minority was considered a probable healthcare avoider (N = 171), while possible healthcare avoiders (N = 221) were more prevalent. From the GP records, we also observed that the number of physical consultations plummeted during the first wave of COVID-19 compared to the 3 months prior to the pandemic (44.5% versus 66.2% of all GP consultations).

Fig 1

Flow chart of the study population.

N, number of participants.

ORs for healthcare avoidance were higher among older participants (OR per 10 years increase 1.22 [1.15 to 1.29]; Table 3) and women (1.59 [1.38 to –1.82]). In age- and sex-adjusted models, low compared to high educational attainment was associated with healthcare avoidance (primary education versus higher vocational/university level 1.85 [1.39 to 2.46]). Moreover, the odds for avoidance were higher for participants with poor self-appreciated health (per point decrease 2.13 [1.93 to 2.35]), and for those who were unemployed compared to those who were employed (2.37 [1.60 to 3.51]). Retirement was not related to healthcare avoidance, after accounting for age and sex. Alcohol consumption was associated with a lower OR for avoidance (0.78 [0.68 to 0.89]), and an inverse relationship was found for current smokers (1.35 [1.09 to 1.66]). Increasing concern about contracting COVID-19 was related to higher ORs of healthcare avoidance (per level increase 1.33 [1.24 to 1.43]). Higher ORs were also seen for those who reported symptoms of depression (per point increase 1.13 [1.12 to 1.15]) or anxiety (per point increase 1.17 [1.14 to 1.19]). Additional adjustment for a history of any chronic disease only slightly weakened results. Effect estimates further attenuated in models that were adjusted for all other considered potential determinants, yet largely remained direction consistent.

Table 3

Determinants of healthcare avoidance (N = 5,656).

			Model 1	Model 2	Model 3
			OR (95% CI)	OR (95% CI)	OR (95% CI)
Age, per 10 years increasea			1.22 (1.15–1.29)**	1.14 (1.08–1.21)**	1.01 (0.93–1.10)
Womenb			1.59 (1.38–1.82)**	1.58 (1.38–1.82)**	1.24 (1.06–1.46**
Educational level vs. higher vocational or university	Primary education		1.85 (1.39–2.46)**	1.21 (1.01–1.46)*	1.12 (0.79–1.58)
	Low/intermediate general or lower vocational		1.26 (1.04–1.51)*	1.22 (1.01–1.46)*	0.99 (0.80–1.22)
	Intermediate vocational or higher general		1.24 (1.03–1.48)*	1.21 (1.01–1.46)*	1.08 (0.89–1.31)
Self-appreciated health, per level decrease			2.13 (1.93–2.35)**	2.00 (1.80–2.22)**	1.58 (1.41–1.76)**
Occupational status vs. employed		Retired	1.26 (0.99–1.61)	1.18 (0.92–1.51)	1.34 (1.02–1.76)*
		Unemployed	2.37 (1.60–3.51)**	2.29 (1.54–3.39)**	1.38 (0.88–2.17)
Alcohol consumption			0.78 (0.68–0.89)**	0.81 (0.71–0.92)**	0.90 (0.78–1.05)
Smoking			1.35 (1.09–1.66)**	1.34 (1.08–1.65)**	1.20 (0.95–1.52)
Concern contracting COVID-19, per level increase			1.33 (1.24–1.43)**	1.28 (1.19–1.38)**	1.00 (0.91–1.10)
Level of depression, per score increase			1.13 (1.12–1.15)**	1.13 (1.11–1.14)**	1.08 (1.05–1.11)**
Level of anxiety, per score increase			1.17 (1.14–1.19)**	1.16 (1.14–1.18)**	1.04 (1.01–1.08)*

CI, confidence interval; COVID-19, Coronavirus Disease 2019; N, number of participants; OR, odds ratio.

aadjusted for sex.

badjusted for age.

*p < 0.05

**p < 0.01.

Model 1: binary logistic regression analyses adjusted for age and sex.

Model 2: the same as model 1, additionally adjusted for a history of self-reported chronic diseases.

Model 3: multivariable logistic analyses adjusted for all determinants presented in Table 3.

Sensitivity analyses

Except for smoking, determinants were most strongly related to healthcare avoidance among participants who reported potentially alarming symptoms compared to those who only reported generic symptoms (Table C in S1 Tables). All determinants were more strongly associated with healthcare avoidance among participants with a history of any chronic disease compared to those without chronic diseases (Table D in S1 Tables). Determinants were stronger related to healthcare avoidance among definite and probable avoiders than possible avoiders, except for concern about contracting COVID-19, which showed comparable ORs (Table E in S1 Tables). Finally, self-appreciated health, concern about contracting COVID-19, and the level of depression and anxiety appeared to be strongly associated with healthcare avoidance among all chronic diseases except neurodegenerative diseases (Table F in S1 Tables).

Discussion

In this cross-sectional study, we found that 1 out of every 5 individuals reported having avoided healthcare during lockdown of the COVID-19 pandemic. Among those, more than a third experienced symptoms that might have warranted urgent medical evaluation, with limb weakness, self-perceived cancer-related symptoms, palpitations, and chest pain being the most prevalent. In multivariable analyses, we have shown that female sex, low self-appreciated health, and high levels of anxiety and depression were associated with healthcare avoidance during the COVID-19 pandemic.

Comparison with previous studies

Previous studies revealed a global trend of declining diagnoses recorded by GPs and a substantial reduction of hospital admissions for acute coronary syndromes, strokes, and transient ischemic attacks (TIAs) during the first wave of COVID-19 [6,8-11,13,26-29]. Our analyses showed that a substantial part of the general population avoided healthcare for symptoms potentially indicating such cardiovascular or cerebrovascular diseases, which can have serious health damaging consequences on both short- and long-term. For instance, the 30-day risk of stroke or other vascular events after a TIA ranges from 3.2% to 17.7%, and the 5-year risk is approximately 6.4% [30,31]. Therefore, healthcare avoidance among participants who reported chest pain, palpitations, or limb weakness is particularly concerning. This does not implicate that healthcare avoidance among participants who experienced atypical symptoms, such as sudden dizziness, vision loss, nausea, or vomiting, is less severe, since these symptoms could signal underlying chronic conditions as well [9].

Several studies have theorised about explanatory mechanisms behind healthcare avoidance during the COVID-19 pandemic. For example, the so-called COVID Stress Syndrome proposes confidence in one’s physical health to be able to overcome a COVID-19 infection as a determinant of healthcare avoidance [21,22]. Individuals with poor perceived health would prefer to avoid physical contact because of their concerns for a severe course of a COVID-19 infection [21]. Our finding that poor self-appreciated health was strongly associated with healthcare avoidance might support this hypothesis. Contrary to what would be expected based on literature [6,8-10], our study showed that self-appreciated health was more strongly associated with healthcare avoidance than concern about contracting COVID-19. This might be explained by the fact that we sent out the questionnaire during the first months of the pandemic, when the potential severity of COVID-19 was not as widely known as it is now.

Strengths and limitations

One of the major strengths of this study is the direct, patient-centred approach. We managed to retrieve self-reported data on healthcare avoidance instead of concentrating on medical records of patients who have already been hospitalised or who visited an outpatient clinic. Moreover, we were able to complement our findings with GP records of a substantial part of the healthcare avoiding participants. Several limitations of this study must also be acknowledged. First, we were unable to assess the actual severity of the symptoms that participants reported in the questionnaire, because they were not medically evaluated at the time. Second, it is unknown how severe participants themselves perceived their symptoms to be, which could have affected their decision whether or not to seek medical attention [14]. Third, to minimise potential selection bias, we have first shown that responders and nonresponders had comparable characteristics, yet the ethnic homogeneity and higher educational attainment of the study population will limit generalisability of our study results to populations with more ethnic diversity or lower educational level. Fourth, participants could have interpreted the question on healthcare avoidance differently, as we asked them to base their responses on the weeks prior to filling out the questionnaire. Nevertheless, more than 90% of our study population (N = 5,151) returned the questionnaire before May 11. From this day onwards, several countermeasures that had been implemented by the Dutch government in March 2020 were lifted, which means that most participants filled out the questionnaire while all of these countermeasures were still present.

Implications

Collectively, findings of our study suggest that healthcare avoidance during COVID-19 may be prevalent among those who are in greater need of it in the population, such as older individuals, those with low perceived health, and those who report symptoms of poor mental health. These findings call for population-wide campaigns urging individuals who are most prone to avoid healthcare to timely reach out to their GP or medical specialist to report both alarming and seemingly insignificant symptoms. Furthermore, physicians should be made aware of which of their patients are most at risk of avoiding healthcare so that they can take a proactive role in approaching these patients, especially now that vaccination strategies are successfully being implemented and regular healthcare is gradually restarting [32].

Conclusions

During lockdown in the COVID-19 pandemic, 1 out of 5 individuals in the general population avoided healthcare despite having symptoms. Female sex, fragile self-appreciated health, and high levels of depression and anxiety are particularly associated with healthcare avoidance, often for symptoms that might have required urgent medical assessment. Ongoing longitudinal tracking of the incidence of diseases in this study population will allow quantification of the exact magnitude of collateral health damage due to healthcare avoidance during the COVID-19 pandemic. Future studies should examine healthcare-seeking behaviour among ethnically diverse populations, which remain understudied.

STROBE checklist for cross-sectional studies.

(DOCX)

Click here for additional data file.

Prospective Analysis Plan.

(DOCX)

Click here for additional data file.

Supporting tables.

Table A. Characteristics of excluded participants. Table B. Characteristics of responders versus nonresponders. Table C. Determinants of healthcare avoidance stratified by potentially alarming and generic symptoms. Table D. Determinants of healthcare avoidance among participants with or without a history of any chronic disease. Table E. Determinants of healthcare avoidance stratified by different levels of healthcare avoidance. Table F. Determinants of healthcare avoidance stratified by chronic disease.

(DOCX)

Click here for additional data file.

12 Jul 2021

Dear Dr Splinter,

Thank you for submitting your manuscript entitled "Prevalence and determinants of healthcare avoidance during the COVID-19 pandemic: a population-based study" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

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Kind regards,

Callam Davidson

Associate Editor

PLOS Medicine

14 Sep 2021

Dear Dr. Splinter,

Thank you very much for submitting your manuscript "Prevalence and determinants of healthcare avoidance during the COVID-19 pandemic: a population-based study" (PMEDICINE-D-21-02940R1) for consideration at PLOS Medicine.

Your paper was evaluated by an associate editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. You will understand that we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Your study is observational and therefore causality cannot be inferred. Please remove language that implies causality, such as ‘Vulnerable patients avoided healthcare most’. Refer to associations instead (e.g. fragile self-appreciated health and poor socioeconomic status were associated with healthcare avoidance’). Please check throughout the manuscript.

Please revise your title to include the study design. ‘Prevalence and determinants of healthcare avoidance during the COVID-19 pandemic: a population-based cross-sectional study’, or similar, would be appropriate.

The URL provided in your Data Availability Statement (in your response to the submission form) does not appear to be functioning, please check and correct as necessary.

Please include line numbering in the margin of your manuscript to facilitate the review process.

Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions).

Please combine the Methods and Findings sections into one section, “Methods and findings”.

Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

Please include the study design (cross-sectional), dates that the survey was sent out/returned, and expand on the content of the ‘COVID-19 survey’ in the abstract.

In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Citations should be numerical, in square brackets, and preceding punctuation (e.g. [1]). Please update throughout.

In the final paragraph of the introduction, you refer to the study as a prospective cohort study. While the study was embedded in a prospective cohort study, the design of this study is cross-sectional. Please update as appropriate.

Please review your methods section and ensure that methods are described in the past tense throughout.

Some of the content presented in the methods section (e.g. response rates) may be better located in the results section.

In the results section titled ‘Determinants related to healthcare avoidance’, please remove the term ‘statistically insignificant’ and update to ‘the effect estimates of alcohol consumption and smoking were no longer statistically significant’.

It appears that the analysis you have performed should be described as multivariable rather than multivariate (see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3518362/ for definitions); please edit accordingly.

Please rearrange the Discussion such that the ‘Strengths and limitations’ section follows the ‘Comparison with previous studies’ section (and update numbering of citations as appropriate).

Please remove the ‘Competing interest statement’, ‘Transparency declaration’, ‘Details of funding’, ‘Role of the funding source’, ‘Statement of independence of researchers from funders’, ‘Contributors’, and ‘Data sharing statement’ from the end of the main text. In the event of publication, all of this information will be published as metadata based on your responses to the questions in the submission form.

Please relocate the ‘Details of ethical approval’ section from the end of the main text to the methods section.

Please remove italicised formatting from your references and only use et al after listing the first six authors of a paper. See our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

Thank you for providing your STROBE checklist. Please replace the page numbers with paragraph numbers per section (e.g. "Methods, paragraph 1"), since the page numbers of the final published paper may be different from the page numbers in the current manuscript.

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Comments from the reviewers:

Reviewer #1:

Authors should be attent to some things to be explaine more clearly related to :

* Study design; is not prospective cohort study but it is cross sectional survey, that was apart of the ongoing population-based Rotterdam Study, a prospective cohort study.

* using primary and secondary data, it is ok, but which one is going to be used? or is combine data going to be used as an eligibility? criteria of the study.?

* It needs clearly eligibility of the study for sampling such as age, not hospitalised or living in nursing homes, etc, it's has been written,more accurate in methods as a sample criteria of the study.

* Was questionare valideted? should be explained.

* Respons rate is better to write in the result of the study not in the methods

* Result, discussion, and conclusion was excellent…. added suggestions in conclusion is better. so that immediate action is taken regarding the benefits

Reviewer #2: I confine my remarks to statistical aspects of this paper. The general approach is fine, but I do have a couple issues to resolve before I can recommend publication.

On page 6 the authors say they categorized age. This is a mistake. Categorizing continuous variables increases type 1 and type 2 error and introduces a kind of "magical thinking" that something special happens at the cut points. Leave age continuous and use splines to investigate nonlinearity. I wrote a paper on this: https://medium.com/@peterflom/what-happens-when-we-categorize-an-independent-variable-in-regression-77d4c5862b6c

Also on page 6, please provide some details on how the multiple imputation was done.

Table 1 Please give the mean and sd for age. (Or median and IQR)

Peter Flom

Reviewer #3: The study is interesting. The concern of health care avoidance was striking at the beginning of the pandemic as evident by the occupancy of the hospital beds that reduced to less than 1/3rd. The authors did a commendable job with the study. The response rate was 73% which is very good for this kind of study, and helped with the robustness of the data.

I have few suggestions/queries:

MAJOR:

1. Page 5, Statistical analysis: I find the Logistic regression models bit confusing. It is stated that model-1 was adjusted for age and sex, but in the table I see there are more predictor variables (controls) than age and sex and same for model-2. Also for model-3, all other considered determinants need to be explained. How were those "considered determinants" considered appropriate for the model-3. It is better for the reliability of the data of authors explained how they chose the predictor variables for LR models. Also please add the Area under ROC for the models with confidence intervals.

MINOR:

2. Aim is missing.

3. In abstract: worry about contracting COVID-19� Better to change 'worry' with concern.

4. Page 4: On April 8th 2020, 9008 out of a total of 18924 participants (47.6%) were still alive. � In 2016 it was 18924, and within 4 years only 47% from the database were alive? Any explanation for this or this is an error in reporting?

5. Page 4 and 5: "Assessment of healthcare avoidance" and "Determinants related to healthcare avoidance". The Definition of the variables is extensive. The methods of assessments and determinants should be left in the main text, the extensive definitions can go to supplementary material preferably as a table.

For example Page 4: "Individuals who withheld from seeking medical care were considered healthcare avoiders.14 Participants were asked whether they had experienced symptoms for which they otherwise would have contacted their GP or medical specialist, but now did not do so because of COVID-19. They were provided with a prespecified list of both symptoms that might have warranted urgent medical assessment and generic symptoms, which made it possible for participants to indicate for which symptoms they had avoided healthcare: palpitations, chest pain, limb weakness, self-perceived cancer-related symptoms (e.g. weight loss, suspicious skin spots), difficulty speaking or facial drooping, vision loss, elevated blood pressure, sudden onset dizziness, dysregulation of diabetes, nausea, fluid retention (oedema), memory complaints, attempts to stop or reduce smoking, and lower back pain. Since lower back pain is generally self-limiting, we specifically used this symptom to contrast with other symptoms that potentially require urgent medical evaluation."� This can be included in supplementary material.

Page 5: "This system enables country-specific divisions of educational levels to be transformed into seven internationally comparable categories.21 Respondents were asked to report their highest level of education attained in the Dutch educational system, after which these results were translated into one of the UNSECO categories, which were eventually merged into four groups. The question 'How do you, in general, appreciate your own health?' was used to assess self-appreciated health, to which respondents could answer 'excellent', 'very good', 'good', 'fair', or 'poor'. We have measured occupational status with the question 'What do you do in everyday life?' with corresponding response categories 'I work (fulltime, part-time, self-employed)', 'I'm on sick leave', 'unemployed', 'retired', or 'other'. Alcohol consumption and smoking status were based on self-reported use during the last 14 days before filling out the questionnaire. Worry about contracting COVID-19 was assessed through the statement 'I worry about contracting COVID-19' with response categories measured on a five-point Likert scale, ranging from 'never' to 'almost continuously'. Respondents were screened for depressive symptoms using ten out of twenty questions from the Center for Epidemiological Studies Depression (CESD) scale, with a weighted maximum score of 29. 22 The higher the score on this scale, the more depressive symptoms participants experienced during the week before completing the questionnaire. Anxiety was measured by seven of out fourteen questions from the Hospital Anxiety and Depression Scale (HADS), which has a weighted maximum score of 20. � This can be included in supplementary material.

6. Page 7: The prevalence of avoidance increased with age, with 16.9% below age 65 and 29.2% the age of 80 onwards. But per table age 65-79 seems to be the largest avoiders � this needs to corrected.

7. Page 8, Sensitivity analysis: Please add area under ROC for all the LR models.

8. Interestingly, poor self-appreciated health had more impact on health care avoidance than worry about contracting COVID-19, OR vs 2.13 vs 1.33 in model-1. This is particular in contrast to what one would assume during the beginning of pandemic. � After the model-1 derivation is explained, it is better to highlight this in discussion portion with re-iteration of Odds ratio.

9. Do authors want to declare the "bias" related to study in Weakness?

10. May be better to replace 'worry of COVID-19' with 'concern of COVID-19', where appropriate.

11. Can add line numbers in the text for furthers revisions?

Reviewer #4: What was the time period the participants were answering about their healthcare utilisation? It's not overly clear in methods. Is it 27.02.20-08.04.20 for all patients? Were all participants specifically asked to recall their healthcare utilisation for the same specific time period e.g. 27.02.20-08.04.20? Or was it the month preceding when they received/completed the questionnaire? This time period could be different for some participants depending on when they completed for questionnaire (you say you received the last questionnaire in July for example). It's not overly clear from the text and more detail is probably warranted. Having a standardised time period for all participants seems like it would be best to reduce any bias. If this isn't possible, accounting for what stage the wave was at may be important as toward the start of the wave 1 people may be more likely to avoid, whereas toward the end of wave 1 people may be less likely to avoid due to lower circulating infection or more comfortable with living with the disease and the measures in place.

Ideally, reporting patients' healthcare utilisation in the same corresponding calendar time period in the previous year/s as when the questionnaire was completed would be good (either in the year before the pandemic e.g. 2019 or many years and average them out e.g. 2014-2019). This may control for seasonal changes in healthcare utilisation as you are only looking at a small time period within 12 months.

Historical information on healthcare utilisation from medical records would be good to see whether patients' healthcare utilisation is consistent over time before the pandemic, rather than basing it on one three month period. Even if this is just descriptively. Showing this dip in healthcare utilisation in a form of graph may be more visually striking than only presenting it in tables/text.

More detail on the methods used for the linkage of GP records would be welcomed for clarity.

Including ethnicity and deprivation status (area [postcode derived] or household deprivation) would be useful here as previous evidence shows that these groups (more deprived and ethnic minorities) are less likely to utilise healthcare/have less access to healthcare. Understanding whether patterns and correlates are the same when including these as covariates would be useful. Further understanding whether there are interactions between healthcare utilisation/avoidance and ethnicity/deprivation would be important as it could be informative. Understanding whether the correlates are similar across these groups would be an important public health message. These groups have generally been found to be at higher risk of COVID-19 mortality too. Not sure if the authors agree, but I think a priori there is enough evidence to examine an interaction between these outcomes. Education and occupation status are proxies for deprivation, but not always the best markers.

Again, not sure if authors will agree, but an analysis examining the determinants of healthcare avoidance among participants with specific chronic diseases (rather than all grouped together as in the supplementary data) may be interesting here. Seeing whether there were specific disease states where people avoided healthcare more/if the correlates of these were similar between disease states would be important as not all disease states are the same.

Follow-up associations with outcomes? I think this would addition would strengthen this paper and also contextualise what the healthcare avoidance actually has meant in real terms. The work undertaken so far is still important though.

Multivariable and not multivariate in the 'Determinants related to healthcare avoidance' section. I'm guessing the outcome was only measured once and not repeated measures.

Also, if looking at the determinants/correlates you will be taking the coefficient of each of the covariates in your model, rather than the coefficient of exposure of interest. In that case, checking for collinearity is important here so will need to clarify that in text and accordingly deal with any collinear variables in model.

In limitations need to add in text around the generalisability of the results as these are only in older individuals and any other biases in the cohort from the original study (e.g. could be more affluent than average, more educated, healthier etc).

Any attachments provided with reviews can be seen via the following link:

[LINK]

Submitted filename: Review Comment to manuscript PMEDICINE-D-21-02940R1 rev.HS.-converted.pdf

Click here for additional data file.

18 Oct 2021

Dear Dr. Splinter,

Thank you very much for re-submitting your manuscript "Prevalence and determinants of healthcare avoidance during the COVID-19 pandemic: a population-based cross-sectional study" (PMEDICINE-D-21-02940R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are hoping to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Oct 25 2021 11:59PM.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Line 70: Please update to ‘which determinants are associated with this behaviour’

Line 80: Please replace ‘related to’ with ‘associated with’

Line 85-86: Please update to ‘Importantly, our findings suggest this behaviour may be associated with certain vulnerable groups within the population’

Lines 87-90: Please consider replacing this bullet (which focuses on future studies) with one that briefly discusses study limitations.

Lines 91-93: The findings of this study can be used to develop policy interventions targeted to vulnerable individuals who may be more likely to exhibit health avoidance behaviours.

Line 108: Please replace ‘timely presenting themselves to’ with ‘seeing’

Line 122: Please delete ‘because it allows to characterise the unseen population’ providing you do not feel it alters meaning (to me, it appears redundant).

Line 128: Causality should not be overstated given the cross-sectional design – please update to ‘sought to determine which potential determinants were associated with healthcare avoidance.

Line 142: Please update to ‘This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).’

Line 143-144 and line 168-169: These lines stating ethical approval/consent can be deleted here as you repeat this information again below.

Line 195: Update ‘inquired’ to ‘asked’.

Lines 245 and 247: Update to ‘were of a lower educational level’

Line 249-250: Update to ‘Most of those participants considered to be healthcare avoiders’.

Lines 290-1 and line 321: Update ‘stronger related’ to ‘more strongly associated with’.

Line 298: Update to ‘In this cross-sectional study’

Lines 301-303: Please update to ‘In multivariable analyses, we have shown that female sex, low self-appreciated health, and high levels of anxiety and depression were associated with healthcare avoidance during the COVID-19 pandemic.’

Line 344-346: Please update to ‘findings of our study suggest that healthcare avoidance during COVID-19 may be prevalent amongst those who are in greater need of it in the population, such as older individuals, those with low perceived health and those who report symptoms of poor mental health.’

Line 358: Please update to ‘Future studies should examine healthcare seeking behaviour among ethnically diverse populations which remain understudied.’

Prospective Analysis Plan: Thank you for providing your prospective analysis plan. Please ensure any changes in the analysis-- including those made in response to peer review comments—have been identified as such in the Methods section of the paper, with rationale.

Comments from Reviewers:

Reviewer #2: The authors have addressed my concerns and I now recommend publication.

Peter Flom

Reviewer #3: Thank you for addressing the comments and editing the manuscript.

Any attachments provided with reviews can be seen via the following link:

[LINK]

26 Oct 2021

Dear Dr Licher,

On behalf of my colleagues and the Academic Editor, Dr Sanjay Basu, I am pleased to inform you that we have agreed to publish your manuscript "Prevalence and determinants of healthcare avoidance during the COVID-19 pandemic: a population-based cross-sectional study" (PMEDICINE-D-21-02940R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

When making these updates, please also make the following changes:

* Lines 89-90: Please delete the sentence 'Moreover, we did not systematically inquire self-perceived severity of reported symptoms'

* Lines 223-225: Please update this sentence to 'Finally, as a result of the peer review process, we have additionally stratified the analyses between the self-reported chronic diseases included in this study to examine whether the strength of the associations would differ depending on the type of disease'

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Callam Davidson

Associate Editor

PLOS Medicine