Cardiovascular disease subtypes, physical disability and workforce participation: A cross-sectional study of 163,562 middle-aged Australians

Muhammad Shahdaat Bin Sayeed; Grace Joshy; Ellie Paige; Emily Banks; Rosemary Korda

doi:10.1371/journal.pone.0249738

. 2021 Apr 8;16(4):e0249738. doi: 10.1371/journal.pone.0249738

Cardiovascular disease subtypes, physical disability and workforce participation: A cross-sectional study of 163,562 middle-aged Australians

Muhammad Shahdaat Bin Sayeed ^1,^*, Grace Joshy ¹, Ellie Paige ¹, Emily Banks ¹, Rosemary Korda ¹

Editor: Thomas Behrens²

PMCID: PMC8031377 PMID: 33831054

Abstract

Background

Workforce participation is reduced among people with cardiovascular disease (CVD). However, detailed quantitative evidence on this is limited. We examined the relationship of CVD to workforce participation in older working-age people, by CVD subtype, within population subgroups and considering the role of physical disability.

Methods

Questionnaire data (2006–2009) for participants aged 45–64 years (n = 163,562) from the population-based 45 and Up Study (n = 267,153) were linked to hospitalisation data through the Centre for Health Record Linkage. Prior CVD was from self-report or hospitalisation. Modified Poisson regression estimated adjusted prevalence ratios (PRs) for non-participation in the workforce in people with versus without CVD, adjusting for sociodemographic factors.

Results

There were 19,161 participants with CVD and 144,401 without. Compared to people without CVD, workforce non-participation was greater for those with CVD (40.0% vs 23.5%, PR = 1.36, 95%CI = 1.33–1.39). The outcome varied by CVD subtype: myocardial infarction (PR = 1.46, 95%CI = 1.36–1.55); cerebrovascular disease (PR = 1.92, 95%CI = 1.80–2.06); heart failure (PR = 1.83, 95%CI = 1.68–1.98) and peripheral vascular disease (PR = 1.76, 95%CI = 1.65–1.88). Workforce non-participation in those with CVD versus those without was at least 21% higher in all population subgroups examined, with PRs ranging from 1.75 (95%CI = 1.65–1.85) in people aged 50–55 years to 1.21 (95%CI = 1.19–1.24) among those aged 60–64. Compared to people with neither CVD nor physical functioning limitations, those with physical functional limitations were around three times as likely to be out of the workforce regardless of CVD diagnosis; participants with CVD but without physical functional limitations were 13% more likely to be out of the workforce (PR = 1.13, 95%CI = 1.07–1.20).

Conclusions

While many people with CVD participate in the workforce, participation is substantially lower, especially for people with cerebrovascular disease, than for people without CVD, highlighting priority areas for research and support, particularly for people experiencing physical functioning limitations.

Introduction

Cardiovascular disease (CVD) remains a leading contributor to global burden of disease internationally [1] and is the second largest contributor to burden of disease in Australia [2], despite declining CVD mortality globally. With improving CVD survival, there is an increasing need to consider the consequences of living with CVD, for individuals and society. Many people with CVD are disabled to the extent that their core activities, including self-care, mobility, and communication are affected [3, 4], as is their ability to engage socially and participate in the workforce [5]. The effect of CVD on workforce participation is of particular importance. It not only affects the overall health and financial well-being of the person living with CVD [6], but also has consequences for society given the substantial economic benefits of retaining people in the workforce [7], an increasingly important issue as the population ages.

Studies outside Australia, primarily set in Europe, have shown that CVD is associated with poorer workforce outcomes, including higher rates of unemployment, exit from paid employment, receipt of disability pension and early retirement [8–13]. In Australia, studies have reported lower productivity in work [14], lower income [15–17] and higher retirement due to ill-health [18] among people with CVD compared to those general population, but there is no large-scale comparative evidence on workforce participation in people with and without CVD. Furthermore, CVD is heterogeneous and there is a lack of comparative evidence on workforce participation across with different types of CVD and in different population subgroups. There is also limited information on why people with CVD may be less likely to participate in the workforce. In particular, although physical functional limitations are common among people with CVD [19] and are a key determinant of employment [20], no studies have examined the extent to which this might account for the lower workforce participation among people with CVD.

In this study we aimed to quantify workforce participation in a large Australian population-based study of 45-64-year-old men and women, comparing levels of non-participation in the workforce in people with versus without CVD, overall and according to CVD subtypes including ischaemic heart disease (IHD) and its subgroup myocardial infarction (MI), cerebrovascular disease, heart failure (HF) and peripheral arterial disease (PAD). We also aimed to examine whether the relationship between CVD and workforce participation varied by socio-demographic and health factors, and in particular, the extent to which co-existing physical functioning limitations–as a measure of physical disability–might explain differences in workforce participation between people with and without CVD. Such evidence is likely to be useful to inform CVD survivors, their care, policy and practice and in modelling CVD outcomes and costs.

Materials and methods

Study population and data sources

We used data from the Sax Institute’s 45 and Up Study, a population-based study of 267 153 people aged 45 and over in New South Wales (NSW) (~10% of the population in that age group), randomly sampled from the Medicare Australia database. Individuals joined the study by completing a postal questionnaire (available at https://www.saxinstitute.org.au/our-work/45-up-study/questionnaires/) and providing consent for follow-up through linkage to a range of routinely-collected data. The majority of participants were sampled in 2008 and the median baseline questionnaire date is February 2008. Persons aged 80 years and over and those living in rural and remote areas were oversampled by a factor of two. The response rate to mailed invitations was estimated to be 18%, representing around 10% of the NSW population aged 45 years and older [21]. This study used cross-sectional data from the baseline survey which included self-reported data from participants on socio-demographic, family, work and health. We also used linked data from the NSW Admitted Patient Data Collection to identify CVD diagnoses prior to study enrolment. The Admitted Patient Data Collection is a complete census of patients admitted to hospitals in NSW (available from 2001), covering public and private hospitals and private day procedure centres, and includes information on diagnosis, and procedures. The 45 and Up Study data and administrative health datasets were probabilistically linked through the Centre for Health Record Linkage (CHeReL) with false positive and negative rates of <0.5% and <0.1%, respectively. The participants with invalid data on age or date of recruitment (n = 454), those with linkage errors (n = 195) and those aged 65 years or over (n = 102,942) were excluded. There were 163,562 participants who were of working age (45-<65 years old) with known CVD status at baseline (70 458 men, 93 104 female) (details in S1-S4 Figs, S1-S3 Tables in S1 File).

Outcome: Workforce participation

The main outcome of interest was non-participation in paid work (yes/no). This was based on responses to the following two questions: “What is your current work status?” and “About how many hours each week do you usually spend doing the following—paid work, voluntary/unpaid work”?. Those indicating valid paid hours (> 0 and <100) or work status as at least one of “In full time paid work”, “In part time paid work”, “Self-employed”, “Partially retired” were classified as participating in the workforce; of the remaining participants, those indicating work status as “Doing unpaid work”, “Completely retired/pensioner”, “Studying”, “Looking after home/family”, “Disabled/sick”, “Unemployed”, “Other” were classified as not participating in the workforce.

Secondary outcomes included paid work hours/week among those in paid work, retirement (yes/no), and retirement due to ill-health (yes/no) among those not in paid work. Paid work hours/week was defined based on responses to the following questions: “What is your current work status?” and “About how many hours each week do you usually spend doing the following?—paid work, voluntary/unpaid work”. Zero or non-zero positive values less than 100 were considered valid paid hours per week. Further logical checks was applied with workforce participation status variable (S4 Table in S1 File).

Retirement was defined from question that asked ‘If you are partially or completely retired, why did you retire?’. Participants with valid records for any of the options (“Reached usual retirement age”, “Lifestyle reasons”, “To care for family members/friend”, “Ill health”, “Made redundant”, “Made redundant”, “Could not find a job”, “Other”) were defined as ‘retired’ and those without any of these options were defined as ‘not retired’. Following logical checks, participants not in the work force were classified as “yes (retired due to ill health)” if they chose “ill health”, and otherwise as “no (retired for other reasons)”.

Further outcome specific exclusions for missing or invalid data applied for each outcome. Among the 163,562 participants included in the study, 131 had missing data on workforce participation status. Of the 121,816 participants in the work force, 7752 had missing/invalid data on the number of paid hours per week and of the 41,615 participants not in the work force, 11,961 had invalid data on retirement due to ill health (S1 Fig in S1 File).

Main exposure: Cardiovascular disease

Participants were classified as either having CVD or not. CVD was defined as self-reported heart disease, stroke or blood clot on the baseline questionnaire, or at least one hospital admission in the five years prior to entering the study with a major CVD diagnosis code, as identified in any diagnostic or a procedure code fields in the linked hospital admissions data (details in S5 Table in S1 File) [22]. A five-year window was used to ensure uniform probability of identification of previous diagnoses from administrative data for all participants. We also sub-categorised participants based on hospitalisations for the following CVD subtypes (yes/no): IHD (ICD-AM codes: I20-I25), MI (ICD-AM codes: I21, I22 and I23), cerebrovascular disease (ICD-AM codes: I61, I63, I64), PAD (ICD-AM codes: I70-I74) and HF (ICD-AM codes: I50, I11.0, I13.0, I13.2). The ‘other CVD’ group consisted of those participants with CVD who had self-reported CVD or any CVD codes other than the CVD subtype as mentioned earlier. The participants with hospitalisation for a particular CVD subtype may or may not have had another types of CVD hospitalisations. In the sensitivity analyses, we further stratified people with CVD into self-reported CVD only, CVD hospitalisation only or both, and in combination of several CVD subtype where sample size permitted.

Other variables of interest

Sociodemographic and health variables known to be associated with workforce participation [23–32], were used to define population subgroups. The sociodemographic variables included: age, sex, remoteness of residence (categorised as major cities, inner regional and more remote), marital status (categorised as married/defacto and single/widowed/divorced), education attainment (categorised as tertiary, certificates/diploma/trade and high school or less), language spoken at home other than English (LOTE) (yes/no) and born in Australia/New Zealand (yes/no). The health-related variables included: body mass index (BMI, kg/m²) categorised as underweight (15-<18.5), normal weight (18.5-<25), overweight (25-<30), obese (30–50); alcohol consumption (number of alcoholic drinks per week categorised as non-drinkers (zero drinks per week), moderate drinkers (>0-<15 drinks per week), heavy drinkers (≥15 drinks per week)); smoking status (non-smoker, past-smoker, current smoker); self-reported doctor-diagnosed diabetes/cancer/osteoarthritis (yes/no for each) and physical functioning limitations. The cut-points of alcohol consumption broadly reflect Australian guidelines on low-risk consumption [33]. Physical functioning limitations were assessed using the Medical Outcomes Study–Physical Functioning (MOS-PF) subscale which was based on 10 questionnaire items assessing varying levels of physical functioning [34]. Physical functioning limitations scores ranged from 0 to 100, with higher scores representing fewer limitations, and were grouped into four categories: no limitation (score of 100); minor limitation (score 90–99); moderate limitation (60–89); and severe limitation (score 0–59) [35] (details in S6 Table in S1 File). These variables were based on the self-reported data from the baseline survey (2006–2009) of the 45 and Up Study, except for sex (obtained from Medicare Australia database [21]) and remoteness of residence (derived from the mean Accessibility Remoteness Index of Australia Plus score [36]).

Statistical analysis

Descriptive statistics were used to summarise characteristics of the study population and distribution of outcomes by CVD status. Modified Poisson regression with robust error variance [37] was used to estimate prevalence ratios (PRs) for non-participation in the paid workforce in relation to CVD. Models were sequentially adjusted, initially adjusting for age-group (5-year age bands) and sex [model 1], noting no sex by CVD interaction was observed, and then additionally adjusted for region of residence and education [model 2]. Further statistical adjustments were not done as the objective was to compare prevalences and lived experiences; causality cannot be established in this cross-sectional analysis. We also estimated PRs separately within population subgroups; chi-square tests for heterogeneity were used to assess heterogeneity between subgroups. To examine the potential contribution of physical functioning to the CVD-workforce participation relationship, we modelled the joint categorisation of CVD and physical functioning limitations on workforce participation; those with no CVD and no physical functioning limitations were used as the reference group. Participants with missing values for workforce participation were excluded from all analyses. There were no missing data in the main exposure (CVD status), age or sex. Missing values for the factors used in the model adjustments were included in the analysis as separate categories.

Self-reported CVD and hospital recorded CVD was considered separately in the first sensitivity analysis, the hospital recorded CVD subtypes were considered after excluding participants with multiple CVD subtypes in the second sensitivity analysis, and combination of more than one CVD subtypes in the third sensitivity analysis.

In supplementary analysis, the PRs for retirement and retirement due to ill health were estimated using similar methods as those for non-participation in paid workforce, and a generalised linear model assuming a Poisson distribution and log link function was used to estimate mean of paid work hours per week.

Analyses was carried out using SAS software version 9.4 and R version 3.5.2 [38].

Ethics approval

Individuals gave written informed consent to take part in the study, including consent for linkage of their data to population health databases. The conduct of the 45 and Up Study was approved by the University of New South Wales Human Research Ethics Committee (HREC). Ethics approval for this project was obtained from the NSW Population and Health Services Research Ethics Committee (Reference: HREC/12/CIPHS/31) and the Australian National University Human Research Ethics Committee (Reference: 2012/504).

Results

Characteristics of the study participants

There were 163,562 study participants: 19,161 (11.7%) with CVD and 144,401 (88.3%) without CVD. 121,816 participants (74.5%) were in the paid workforce, of whom 11,480 (9.4%) had CVD; and 155,723 participants (>95%) had valid paid work hours per week (i.e. ≥ 0 and <100). There were 43,397 participants who had retired, 29,654 of retirees had not been working in any form and 5,970 of whom had CVD (20.1%). The sociodemographic profile of participants with and without CVD was similar except that the CVD group had higher proportions of men and older participants (Table 1). Participants with CVD had a poorer health profile than those without CVD, with higher levels of smoking, obesity, comorbid diseases and moderate/severe functional limitation (Table 1).

Table 1. Sociodemographic and health related characteristics of study participants.

	People with CVD	People without CVD
Total participants (n = 163562)	19161	144401
Percentage (%)	11.7 (19161/ 163562)	88.3 (144401/ 163562)
Age (years)
Mean (sd)	57.5 (5.18)	55.0 (5.38)
45–49	11.0 (2110)	22.7 (32759)
50–54	19.7 (3767)	27.1 (39173)
55–59	29.8 (5712)	27.5 (39733)
60–64	39.5 (7572)	22.7 (32736)
Sex
Men	51.5 (9873)	42.0 (60585)
Women	48.5 (9288)	58.0 (83816)
Region of residence
Major cities	49.5 (9477)	51.9 (75002)
Inner regional	36.1 (6911)	34.7 (50058)
More remote	12.4 (2370)	11.4 (16392)
Marital status
single/widowed/separated	23.1 (4425)	20.1 (29042)
Married/defacto	76.3 (14613)	79.3 (114549)
Education attainment
No school certificate	12.8 (2460)	7.8 (11225)
Certificate/diploma/trade	64.1 (12277)	62.3 (89897)
Tertiary	22.0 (4206)	29.0 (41853)
Language spoken at home other than English (Yes)	8.5 (1623)	10.0 (14443)
Country of birth in Australia/NZ	79.6 (15244)	78.0 (112655)
Alcohol consumption
None (0 drink per week)	34.5 (6604)	28.9 (41680)
Moderate drinkers (1-14drinks per week)	48.2 (9237)	54.6 (78857)
Heavy drinkers (15 or more drinks per week)	15.6 (2988)	15.1 (21793)
Smoking status
Current	10.7 (2046)	9.3 (13367)
Past	40.9 (7839)	33.0 (47590)
Never	48.0 (9194)	57.5 (82979)
BMI Category
Underweight (15-<18.5)	0.9 (169)	0.9 (1369)
Normal weight (18.5-<25)	24.3 (4659)	34.7 (50047)
Overweight (25-<30)	36.3 (6959)	36.1 (52120)
Obese (30 to 50)	31.3 (6005)	21.6 (31159)
Medical History: Cancer: Yes	15.2 (2919)	10.7 (15416)
Medical History: Diabetes: Yes	14.4 (2753)	5.4 (7804)
Medical History: Osteoarthritis: Yes	4.8 (921)	2.8 (4026)
Physical functioning limitations
No limitation	22.7 (4347)	41.7 (60166)
Minor limitation	24.6 (4714)	26.7 (38603)
Moderate limitation	24.2 (4645)	16.1 (23275)
Severe limitation	18.5 (3536)	6.1 (8812)

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Note: % missing responses (CVD, No CVD): region of residence (2.1, 2); marital status (0.6, 0.6); education attainment (1.1, 1), country of birth (0.8, 0.8), alcohol per week (1.7, 1.4); smoking status (0.4, 0.3); BMI (7.1, 6.7); physical functioning limitations (10.0, 9.4). sd refers to standard deviation, NZ refers to New Zealand, BMI refers to Body Mass Index.

CVD and workforce participation

Overall, 60% of people with CVD were participating in the workforce, compared to 76% of people without CVD (Table 2). Mean weekly working hours per week among those in paid work was slightly lower overall for people with CVD than without CVD. Of all participants, 42% of people with CVD had retired, compared to 25% of people without CVD. Of the retirees who were not in paid workforce in any form, 53% of people with CVD had retired due to ill health, compared to 26% of people without CVD. In every 5-year age bracket (45-<50 to 60-<65), non-participation was higher, weekly work hours slightly lower, retirement and retirement due to ill health higher in men and women with CVD than without CVD (Fig 1).

Table 2. Workforce participation, paid work hours per week, retirement patterns and physical functioning limitations among study participants.

	People with CVD	People without CVD
Total N	19161	144401
In workforce^*	59.9 (11480)	76.4 (110336)
In full time paid work	28.0 (5358)	38.8 (55971)
Self-employed	14.0 (2675)	17.3 (24913)
In part time paid work	15.4 (2950)	19.7 (28512)
Partially retired	6.8 (1297)	5.3 (7644)
Not in workforce	40.0 (7657)	23.5 (33958)
Doing unpaid work	5.8 (1102)	5.3 (7681)
Completely retired/pensioner	21.5 (4113)	12.3 (17729)
Studying	1.7 (319)	2.1 (3053)
Looking after home/family	9.9 (1902)	11.2 (16182)
Disabled/sick	14.9 (2859)	4.4 (6410)
Unemployed	3.8 (732)	3.1 (4502)
Other	2.0 (380)	1.7 (2419)
Paid hours of work (for those in workforce)
N	10506	103558
Mean, SD	34.9, 15.6	35.9, 14.8
Median [inter quartile range]	38 [25, 45]	38 [26, 45]
Retirement reasons (among those who retired and not in the workforce)
Total N	5970	23684
Retired due to ill health	53.0 (3166)	26.3 (6238)
Retired due to other reasons	47.0 (2804)	73.7 (17446)
Reached usual retirement age	7.9 (474)	12.2 (2896)
Lifestyle reasons	20.7 (1237)	32.4 (7684)
To care for family member/friend	10.9 (648)	14.9 (3537)
Made redundant	10.6 (630)	11.7 (2770)
Could not find a job	5.6 (332)	5.4 (1277)
Other	9.8 (584)	14.1 (3328)

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One person might be in more than one category of sub-groups of those in workforce, not in workforce and retired due to other reasons.

*% missing responses of workforce participation (Total, CVD, No CVD): (0.08, 0.13, 0.07).

After adjusting for sociodemographic characteristics (age, sex, region of residence and education), workforce non-participation was 36% higher among people with any CVD compared to people without CVD [prevalence ratio (PR) = 1.36 (95% CI: 1.33–1.39)]. Workforce non-participation varied by CVD subtype, with PRs of 1.46 (95% CI: 1.41–1.50) for IHD and 1.92 (95% CI: 1.80–2.06) for cerebrovascular disease (Fig 2). Sensitivity analysis indicate that those with CVD hospitalisation had somewhat higher PRs of non-participation in the paid workforce compared to those with self-reported CVD only (S7 Table in S1 File) and people with only one type of CVD had slightly lower PRs of non-participation in the paid workforce than those with more than one type of CVD (S8 and S9 Tables in S1 File). A similar pattern of results was seen for work hours among those who are in paid workforce, retirement among all participants and retirement due to ill health of retirees who had not been working in any form (S5-S7 Figs, S10-S18 Tables in S1 File).

CVD and workforce participation among population subgroups

Workforce participation was associated with a number of sociodemographic factors among both people with and without CVD, including age, marital status, education, country of birth, alcohol consumption, smoking status and having medical history of osteoarthritis (Fig 3, S8 and S9 Figs in S1 File). When workforce participation was compared in people with and without CVD within sociodemographic and health subgroups, workforce non-participation remained higher among people with CVD compared to those without CVD, regardless of population subgroup. However, PRs were significantly higher among younger people, men, those who were not married/de facto, those without tertiary qualifications and those who were current smokers. Although the absolute crude prevalence of workforce non-participation was higher in older compared to younger age groups (irrespective of CVD status), the relation of CVD and workforce non-participation became weaker with increasing age (Fig 3).

CVD, physical functioning limitations and workforce participation

Workforce participation was lower in those with greater physical functional limitations—among both those with and without CVD—but non-participation was higher among those with CVD in all sub-groups based on physical functioning limitations (Fig 4). Among participants with no physical functioning limitations, about one in 5 were not working—21% of those with CVD and 16% of those without CVD; among participants with severe functioning limitations, 73% of those with CVD, and 60% of those without CVD, were not working. After adjustment for sociodemographic variables, compared to those without CVD and no functional limitations, participants without physical functional limitations but with CVD were 13% more likely to be out of the workforce (PR = 1.13, 95%CI = 1.07–1.20). Those with severe functioning limitations were 3 times as likely to be out of the workforce if they had CVD [PR = 2.91 (95% CI: 2.82–3.00)] and 2.7 times as likely if they did not have CVD [PR = 2.70 (95% CI: 2.63–2.77)] (Fig 4).

Discussion

In this large population-based Australian study of older working-age adults, people with CVD were around 36% more likely than those without CVD to be out of the workforce. Non-participation was highest for people with cerebrovascular disease, peripheral arterial disease and heart failure and lowest for other types of CVD. Lower workforce participation among people with CVD was evident regardless of age, sex and other sociodemographic and health-related characteristics. Importantly, physical functioning was strongly related to workforce participation, and in people without physical functioning limitations, participation rates in those with and without CVD were similar, while people with CVD and severe physical functioning limitations were almost three times as likely as those without CVD and no functional limitations to not to be in paid work.

We know of no other studies in Australia that have compared workforce participation in people with and without CVD using large-scale population data. Our study results are broadly comparable to studies set in Canada, Japan, and Europe, which have consistently shown that people with CVD are less likely to participate in the workforce than people without CVD [12, 23, 39]. It is somewhat difficult to compare the magnitude of our findings with these studies, given the variation in study design, definition of workforce participation, case definition of CVD, and selection of the comparison population. For example, a cross-sectional investigation with a study population from 10 European countries found that people with stroke had a 11% higher odds of being unemployed [23] compared to those without stroke. A study with participants in France with stroke were 50% more likely to be out of workforce [12]. The magnitude of the association between stroke and workforce participation from these European studies was lower than the 92% increase in odds of being out of the workforce for those with vs. without cerebrovascular disease in our study.

We could not find any other study that reported workforce participation in absolute and relative terms among different population sub-groups based on sociodemographic and health-related factors. Similar to a previous report [23], our results indicate that women were more likely to be out the paid workforce compared to men in absolute terms, but the magnitude of the relative association between CVD and workforce participation was greater among men than women. We could not find other study to compare our findings that people with CVD who were single, had education less than tertiary education or were current smokers were more likely in absolute and relative terms to be out of the workforce compared to those without CVD. However, there were some studies to compare the secondary outcomes in our investigations but those varied by countries of the previously reported studies. For example, participants in Italy with MI had 50% higher chance to retire early [24], compared to 27% in our study. Previous studies from 10 European countries have reported on the extent on early retirement of people with CVD subtype such as stroke [23] but comparison could not be made due to variation in the definition of the CVD subtype as indicated in this study.

While our study, like others, highlights the relatively high prevalence of workforce non-participation among people with CVD and a stronger relationship in younger people is also consistent with the idea that CVD is the driving force, it should not be assumed that CVD is the cause without additional evidence. However, cohort studies in Europe and Canada provide evidence consistent with a casual explanation [8–11, 13, 40, 41]. For example, one study from the Netherlands analysed various exit mechanisms from paid employment (such as, via unemployment benefits, via early retirement benefits etc.) and reported that in comparison to those without CVD, people with incident CVD were more likely to leave paid employment regardless of the exit mechanisms [41]. Nevertheless, the relationship between CVD and workforce participation, particularly when measured in terms of government benefits, is highly specific to the social welfare structure of the countries in which study participants live and work. In other studies, risk of leaving employment increased with severe CVD subtypes such as coronary heart disease, stroke and MI [9–11, 13, 40] which are also broadly similar to those reported in our investigation, especially participants living with multiple CVD subtype. Another underlying reason for higher workforce non-participation in people with CVD could be physical disability since physical functioning has been shown to decline following incident CVD [41] and this study has indicated that those with higher physical functioning limitations had the highest probability of being out of paid-work.

This key finding, that impaired physical functioning is likely to be an important factor underpinning the difference in workforce participation rates between those with and without CVD, has not been reported previously. However, it is consistent with the evidence from a European study reporting a relatively high proportion of people with CVD leave the paid workforce via disability pension [42] compared those without CVD. This may also explain the lower participation rates among people with cerebrovascular disease, peripheral artery disease and heart failure compared to those with ischemic heart disease. This is an important finding as cardiac rehabilitation programs help improve physical functional limitations [43] and hence may improve return to work.

Our large-scale population-based study with linkage to hospital records allowed comparison of workforce non-participation in people with and without CVD, within population subgroups and across CVD subtypes enabling a comprehensive comparative description of workforce participation in individuals living with CVD in the community. However, there was a lack of information on the types of work people were engaged in. This would have been valuable in better understanding the observed workforce participation patterns. Our study population was randomly sampled from a whole-of-population database, and included ~ 10% of the entire population in the target age-group and the response rate was ~18%, consistent with cohort studies of this nature. Generally, participants in cohort studies are healthier than the general population [44]. Though we could not find a comparable age-group similar ours for prevalence of CVD in Australia, the workforce participation rate in our study was 9% higher (74.6% vs 65.2%), than that reported for Australia for the same age group during the same period (2007–08) by the Australian Bureau of Statistics [45, 46]. Hence, while our absolute estimates of CVD prevalence and workforce participation may not be directly representative, PRs, which are based on internal comparisons, are still likely to be generalisable [47].

More precise point estimates of the workforce participation among people with CVD in Australia may be derived from linked administrative data, which do not rely on survey participation. In future, longitudinal investigation of change in workforce participation in Australia after incident CVD would be valuable for enhancing our understanding about the transitions in workforce participation in relation to major CVD events. Our results show the majority of people with CVD were still in the paid workforce but people who have experienced a CVD event, especially those with physical disability, were more vulnerable to not being in the workforce compared to their counterparts. Given the important of paid work for mental health and overall economy [15], policies aiming to help people with CVD or other major illness remain or re-enter the workforce could help lessen this gap. Thus, our findings will be valuable for healthy and successful aging for those with CVD.

Conclusion

While the majority of people with CVD continue to undertake paid work, participation is reduced compared to people without CVD. Lower participation was observed for different CVD subtypes and among different population sub-groups, and workforce non-participation was relatively higher for people who had had a stroke, heart failure or peripheral vascular disease, who were in their 50s. Physical functioning limitations was a key factor in non-participation in the paid workforce both for those with and without CVD. Similar associations were observed for weekly paid work hours among those who had been working, retirement among all study participants and retirement due ill health among retirees who had not been working. The results of this investigation have significant policy implications for healthy and successful ageing for those living with CVD. It underpins the importance of rehabilitation and suggests that social policies to encourage employment among older persons should integrate consideration of the role of chronic disease, including CVD.

Supporting information

S1 File

(DOCX)

Click here for additional data file.^{(162.3KB, docx)}

Acknowledgments

This research was completed using data collected through the 45 and Up Study (www.saxinstitute.org.au). The 45 and Up Study is managed by the Sax Institute in collaboration with major partner Cancer Council NSW; and partners: the National Heart Foundation of Australia (NSW Division); NSW Ministry of Health; NSW Government Family & Community Services–Ageing, Carers and the Disability Council NSW; and the Australian Red Cross Blood Service. We thank the many thousands of people participating in the 45 and Up Study.

Data Availability

This study uses data from the 45 and Up Study, which is managed by the Sax Institute in collaboration with major partner Cancer Council NSW, and partners: the National Heart Foundation of Australia (NSW Division); NSW Ministry of Health; NSW Government Family & Community Services–Ageing, Carers and the Disability Council NSW; and the Australian Red Cross Blood Service. Data supporting the findings from this study are available from the Sax Institute, the NSW Department of Health and the Australian Bureau of Statistics, with data linkage conducted by the NSW Centre for Health Record Linkage. Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Researchers may apply for access to these data with the appropriate data custodian and ethics approvals. Information about data access and governance policies is available at: https://www.saxinstitute.org.au/our-work/45-up-study/for-researchers/.

Funding Statement

Muhammad Shahdaat Bin Sayeed is supported by an Australian Government Research and Training scholarship to undertake a PhD at the Australian National University, Canberra, ACT, Australia. Emily Banks is supported by a Principal Research Fellowship from the National Health and Medical Research Council of Australia (reference 1136128). Ellie Paige is supported by a Postdoctoral Fellowship (reference: 102131) from the National Heart Foundation of Australia. This project was supported by a Project Grant from the National Health and Medical Research Council of Australia (reference 1139539).

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PLoS One. doi: 10.1371/journal.pone.0249738.r001

Decision Letter 0

Thomas Behrens

25 Nov 2020

PONE-D-20-33041

Cardiovascular disease subtypes, physical disability and workforce participation: a cross-sectional study of 163,562 middle-aged Australians

PLOS ONE

Dear Dr. Bin Sayeed,

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Reviewer #2: Yes

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Reviewer #1: The authors examined the relationship of cardiovascular disease to workforce participation in older working-age people. For this purpose they used questionnaire data from a very large population based study assessed between 2006 and 2009 in New South Wales (Australia) and linked it to hospitalisation data. The study size of more than 160,000 subjects is enormous. The study is comprehensibly described and written in good English. Nevertheless, I do have some comments/questions:

Major

1. How do the authors explain the observation that older people (60-64 years) have lower PRs than people aged 50-55 years (see Fig 3)?

2. The data analysed are relatively old. How do the authors assess the transferability of the results to the present day? Could the results be influenced by the financial crisis of 2007-2008?

3. I stumble upon the number of subjects included. Perhaps the relevant section could be made clearer:

• In the abstract the subjects with CVD (19,137) and without CVD (144,294) do not add up to 163,562.

• Please give numbers of excluded subjects, e.g. for the retirees who had been classified as participating in the workforce (line 117).

• How large was the data set finally evaluated?

4. The references should be revised. For example, in some references the PID is given in others it is missing.

Minor

• Should „Language other than English (LOTE)“ describe the mother tongue? (Line 144) Please specify.

• Why were Australia and New Zealand combined when considering the place of birth (Line 144)?

• Please explain the basis for the categorisation of alcohol consumption. How were the cut-offs chosen? How was an alcoholic drink defined? (Lines 146-148)

• How were the cut-offs chosen for physical functioning limitations? (Lines 153-156)

• The information on the exclusion of persons without data regarding workforce participation should be given in the method section. Please specify how many subjects were excluded. (Lines 173/174)

• Table 1: What does „/“ stand for in the 3rd line (Percentage (%))?

• Inaccuracies in Table 2: It should be „[inter quartile range]“ and „To care for family“ should be shown left centred.

• Fig 1: Why do you mention SEM but do not show SEM?

Reviewer #2: Overall, the manuscript provides valuable information on CVD and workforce participation, based on a large sample size.

Please consider following comments:

1. The response rate should already be mentioned in the methods, briefly, and - if applicable - further aspects with regard to sample composition (weighting?, exclusions due to implausible questionnaires?).

2. Outcome measurement:

Full retirement definition was based on giving reasons for retirement, why not just use the information on current work status (“Completely retired/pensioner”)?

Current work status was indicated by multiple choice, did multiple choices occur and how was this handled then?

Participation in work was also defined by paid work hours including 0 hours?

3. I could not find the number of excluded participants with missing data on workforce participation (either text or flow chart).

4. There seems to be some truncated text in the two tables.

5. The overlap of workforce participation/retirement should not be considered in analysis retirement (line 116). Do the precentages in line 210/11 refer to this, though? Then it could also be included in table 2.

6. Line 281: The "and" in the middle should be "or"?

7. As the authors correctly state in the methods, causal relationships cannot be assessed in this study, though implicitly assumed at least in conclusions. Lines 289 ff seem to discuss possible causal relationships, but the paragraph needs more comprehensible and clear formulation. This should also include physical disablity.

8. I am not familiar with "PRs, which are based on internal comparisons" (line 324), what does this mean/what would be the opposite? Despite the reference44, I doubt that it is correct to negate any selection bias in the study. As education was used in the analysis (and is usually associated with CVD), how comparable was it to the general population?

9. As results for physical disablity are a "key finding", they should also be included in the conclusions.

10. "The results of this investigation have significant policy implications for healthy and successful ageing for those living with CVD." (line 339ff) I cannot follow this: What are the implications for health for people with CVD here?

**********

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Reviewer #1: No

Reviewer #2: No

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2021 Apr 8;16(4):e0249738. doi: 10.1371/journal.pone.0249738.r002

Author response to Decision Letter 0

9 Mar 2021

RESPONSE TO REVIEWERS’ COMMENTS

EDITOR'S COMMENTS

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

We have revised the manuscript to confirm with the requirement of the journal.

In your revised cover letter, please address the following prompts:

We will update your Data Availability statement on your behalf to reflect the information you provide.

We have added one paragraph in the cover letter to address the issue of data availability, including the restrictions on sharing a de-identified data, and the ways to access the data.

3. Thank you for stating the following in your Competing Interests section:

'None'

b. This information should be included in your cover letter; we will change the online submission form on your behalf.

In the cover letter above, we have stated about the grants received by authors, and declared that the authors have no other competing interests.

Reviewer #1:

The authors examined the relationship of cardiovascular disease to workforce participation in older working-age people. For this purpose they used questionnaire data from a very large population based study assessed between 2006 and 2009 in New South Wales (Australia) and linked it to hospitalisation data. The study size of more than 160,000 subjects is enormous. The study is comprehensibly described and written in good English. Nevertheless, I do have some comments/questions:

We thank the reviewer for recognising the importance of this work.

Major

1. How do the authors explain the observation that older people (60-64 years) have lower PRs than people aged 50-55 years (see Fig 3)?

The higher PR in the younger age group is not surprising. The PR is the prevalence (proportion) in the exposed divided by the prevalence in the unexposed (baseline) group. Thus, all else being equal, as the prevalence in the baseline group increases, the PR will decrease. Because the prevalence of (adverse) outcomes commonly increase with age, it is very common to see PRs decrease with age, as is the case here.

Crude prevalence (proportion) of workforce non-participation was higher in 60-64 year olds compared to 50-54 year olds, irrespective of CVD status; see the following extract from Fig 3.

Age-group Proportions (%) not in work PR (95% CI)

CVD group No CVD group

50-54 year 25.7 13.7 1.75 (1.65-1.84)

60-64 year 56.0 47.0 1.21 (1.19-1.24)

The following statement has been included in line 253 to 254 in the manuscript:

Although the crude prevalence of workforce non-participation was higher in older compared to younger age groups (irrespective of CVD status), the relation of CVD and workforce non-participation became weaker with increasing age.

2. The data analysed are relatively old. How do the authors assess the transferability of the results to the present day? Could the results be influenced by the financial crisis of 2007-2008?

Population distribution and cardiovascular disease profile in Australia have not drastically changed since these data were collected. Notably, the effect of the financial crisis of 2007-2008 on Australia has been considerably less than in many other countries (ABS, 2010). Regardless, the relative estimates (PRs) comparing those CVD vs without may well be transferable (Rothman et al., 2013). This is particularly so if the financial crisis or other factors have not affected workforce participation disproportionately based on CVD status, but this is unlikely.

Reference:

ABS (Australian Bureau of Statistics), The Global Financial Crisis and its impact on Australia, Year Book Australia 2009–10, Number 91, ABS Canberra, ABS Catalogue No. 1301.0, Chapter 27 — Financial system, 2010, p. 687-88.

Rothman KJ, Gallacher JE, Hatch EE. Why representativeness should be avoided. Int J Epidemiol. 2013 Aug;42(4):1012-4. doi: 10.1093/ije/dys223. PMID: 24062287

3. I stumble upon the number of subjects included. Perhaps the relevant section could be made clearer:

• In the abstract the subjects with CVD (19,137) and without CVD (144,294) do not add up to 163,562.

Thank you for identifying this. The final number of eligible participants was 163,562; this is the number of participants included in the descriptive tables. As outcome-specific exclusions for missing or invalid data were applied for each outcome, the number of participants contributing to regression models were slightly different: 163,431 for workforce participation, 114,064 for paid hours of work and 11,961 for retirement due to ill health. However, there was no missing value for retirement. We have modified the abstract, methods and study flow chart (Fig S1) to make this clearer.

• How large was the data set finally evaluated?

We have modified the abstract, methods and study flow chart (Fig S1) to make this clearer.

4. The references should be revised. For example, in some references the PID is given in others it is missing.

We have checked all references again, aiming to provide PIDs. However, not all references had a PID, hence it was not possible to provide a PID for all references. In the first draft, we used Endnote as reference manager but in this revised version the references were edited manually after unlinking those from the main body of the article.

Minor

• Should ‘Language other than English (LOTE)’ describe the mother tongue? (Line 144) Please specify.

The questionnaire asked ‘Do you speak a language other than English at home’ followed by two options to choose from (yes and no). To further clarify, the wording has been modified both in the text and in the table to ‘language spoken at home other than English (yes/no)’.

• Why were Australia and New Zealand combined when considering the place of birth (Line 144)?

New Zealand citizens do not need a visa to live or work in Australia and have access to most of the same entitlements, including health and welfare, and opportunities as Australian citizens. Hence, we grouped Australian and New Zealand citizens as one category and classified participants from other countries as others.

• Please explain the basis for the categorisation of alcohol consumption. How were the cut-offs chosen? How was an alcoholic drink defined? (Lines 146-148)

This variable was categorised with cut points (0, 1-14 and ≥15) aimed to align with the Australian alcohol consumption guidelines to minimise the risk of long-term harm. At the time of data collection, the guideline for low-risk alcohol consumption in Australia was daily consumption of 20 gram of alcohol and the standard drink was defined as 10 gram of alcohol. This meant 14 standard drinks per week was considered low-risk alcohol drink for both men and women in Australia at the time of data collection. The following text with reference is added in the ‘Other variables of interest section’ of main text of the manuscript.

The cut-points of alcohol consumption broadly reflect Australian guidelines on low-risk consumption [33].

References

33. NHMRC (National Health and Medical Research Council). Australian Guidelines To Reduce Health Risks From Drinking Alcohol (NHMRC, Canberra, 2009).

• How were the cut-offs chosen for physical functioning limitations? (Lines 153-156)

We have added a reference for the choice of cut points for physical functioning limitations. The manuscript now reads:

Physical functioning limitations scores ranged from 0 to 100, with higher scores representing fewer limitations, and were grouped into four categories: no limitation (score of 100); minor limitation (score 90–99); moderate limitation (60–89); and severe limitation (score 0–59) [35]

35. Stewart A, Kamberg C. Measuring functioning and well-being: the Medical Outcomes Study Approach.Durham, North Carolina: Duke University Press; 1992

Further details are provided in Supplementary Table S6.

• The information on the exclusion of persons without data regarding workforce participation should be given in the method section. Please specify how many subjects were excluded. (Lines 173/174)

We have updated the methods section of the manuscript to clarify the definition. The following paragraph has been added to the end of ‘Study population and data sources’.

The participants with invalid data on age or date of recruitment (n=454), those with linkage errors (n=195) and those aged 65 years or over (n=102,942) were excluded. There were 163,562 participants who were of working age (45-<65 years old) with known CVD status at baseline (70 458 men, 93 104 female) (details in supplementary file: S1-S4 Figs, S1-S3 Tables).

At the end of ‘Outcome: Workforce participation’ section, the following statement regarding the number of participants excluded is added.

Outcome-specific exclusions for missing or invalid data were applied for each outcome. Of the 163,562 participants included in the study, 131 had missing data on workforce participation status (Supplementary Fig1). Of the 121,816 participants in the work force, 7752 had missing/invalid data on the number of paid hours per week. Of the 41,615 participants not in the work force, 11,961 had invalid data on retirement due to ill health.

• Table 1: What does „/“ stand for in the 3rd line (Percentage (%))?

This section in the table is revised to ‘/163562’ in both columns.

• Inaccuracies in Table 2: It should be „[inter quartile range]“ and „To care for family“ should be shown left centred.

We have updated Table 2.

• Fig 1: Why do you mention SEM but do not show SEM?

The value is very small and thus might not be always visible. A new note ‘‘, and SEM might not be always visible because of small values’’ is embedded in Figure 2. The mean and standard error of mean (SEM) of paid work hours per week according to CVD, age-group are presented for ‘men and women combined’, men and women. These values were used to generate the bar diagram presented in the paper. The mean values were rounded while printing those on top of the figure but while making the bar height, the values upto 5 decimal points were used.

Reviewer #2:

Overall, the manuscript provides valuable information on CVD and workforce participation, based on a large sample size.

We thank the reviewer for recognising the importance of this work.

Please consider following comments:

A detailed description of the 45 and Up study is available as part of a published protocol (Banks et al,. International journal of epidemiology. 2008;37(5):941-7. doi: 10.1093/ije/dym184) so we did not extensively describe it in this paper. However, we have added in further details on the response rate, weighting and exclusion criteria to the revised manuscript. The newly added texts in the ‘Study population and data sources’ section are as follows:

Individuals joined the study by completing a postal questionnaire (available at https://www.saxinstitute.org.au/our-work/45-up-study/questionnaires/) and providing consent for follow-up through linkage to a range of routinely-collected data. The majority of participants joined in 2008, with the majority completing the baseline questionnaire in February 2008. Persons aged 80 years and over and those living in rural and remote areas were oversampled by a factor of two. The response rate to mailed invitations was estimated to be 18%, with the final sample representing around 10% of the NSW population aged 45 years and older.

2. Outcome measurement:

Full retirement definition was based on giving reasons for retirement, why not just use the information on current work status (“Completely retired/pensioner”)?

Most participants chose more than one option from the multiple-choice questions that asked about current work status and many were found to be in apparent conflict regarding retirement status. On the other hand, defining retirement status from the questionnaire that asked about the cause of retirement was straight forward. Note we have used the term ‘retired’, and have not differentiated between ‘full-retirement’ or ‘partial retirement’.

Current work status was indicated by multiple choice, did multiple choices occur and how was this handled then? Participation in work was also defined by paid work hours including 0 hours?

Yes, there were participants choosing more than one option from the multiple-choice questions. Logical checks were done using responses to questions on current work status and ‘paid work hours per week’. We have made this clearer in the revised manuscript (first paragraph of the section ‘Outcomes: Workforce participation’ in page 6). The steps of defining workforce participation might explain further how paid work hours per week contributed to the definition of workforce participation, and how inconsistencies between paid work status and paid work hour per week were resolved. The following text is added in the supplementary section in S4 Table.

• Participants were considered to be in paid work if:

o Number of hours of paid work hours is valid (0 to <100) OR

o Reported being in full time paid work, in part time paid work, self-employed or partially retired.

• Participants were considered to be not in paid work if:

o Number of hours of paid work hours per week is zero (0) or missing (but not invalid) AND

o Not reported being in fulltime paid work, part time paid work, self-employed or partially retired

• People who are not paid for work automatically received zero (0) for paid work hour per week

• For those who are in “paid” category and entered 0 as paid work hour per week, their paid work status was accepted, and weekly paid work hours were invalidated

• For those who are in “not paid” category and entered valid paid work hours per week, their paid work status was changed, and weekly paid work hours were accepted when the weekly paid work hours were larger than 0.

3. I could not find the number of excluded participants with missing data on workforce participation (either text or flow chart).

The proportion is mentioned at the footnote of Table 2. The exact numbers are now mentioned in response to first reviewer’s query number 3 as above and also, in the supplementary file (S1-S4 Figs and S1-S3 Tables).

4. There seems to be some truncated text in the two tables.

We have updated the both tables.

This has been updated and the exact numbers are now mentioned in the text with relevant wordings. It was explained in query 3 of first reviewer, and further details are provided in the supplementary file (S1-S4 Figs and S1-S3 Tables).

6. Line 281: The "and" in the middle should be "or"?

We have updated this sentence and now in line 295 in the clean version.

This paragraph has been updated and includes the issue of physical disability as a possible underlying mechanism which is further explained in the paragraph that follows it. The wording is as follows:

Another underlying reason for higher workforce non-participation in people with CVD could be physical disability since physical functioning has been shown to decline following incident CVD [41] and this study has indicated that those with higher physical functioning limitations had the highest probability of being out of paid-work.

Reference:

41. Kucharska-Newton A, Griswold M, Yao ZH, Foraker R, Rose K, Rosamond W, et al. Cardiovascular Disease and Patterns of Change in Functional Status Over 15 Years: Findings From the Atherosclerosis Risk in Communities (ARIC) Study. J Am Heart Assoc. 2017;6(3):e004144. pmid: 28249844.

The ratio of prevalences of two groups is called prevalence ratio (PR), and it is analogous to the risk ratio (RR) in cohort studies. Although the absolute proportions estimated in this study will likely not reflect the absolute numbers in the target population, it is reasonable to assume that the relative comparison between groups (in this case the PRs of CVD compared to non-CVD) will be similar in the target population if confounding is minimised (see also response to Reviewer 1, point 2). We have acknowledged the possibility of selection bias but also emphasised the strength of findings from internal comparison in this investigation. The wording of the sentence is now updated as follows:

Hence, while our absolute estimates of CVD prevalence and workforce participation may not be generalisable, PRs, which are based on internal comparisons, are still likely to be generalisable [47].

47. Mealing NM, Banks E, Jorm LR, Steel DG, Clements MS, Rogers KD. Investigation of relative risk estimates from studies of the same population with contrasting response rates and designs. BMC Med Res Methodol. 2010;10:26. pmid: 20356408

We sought to look for other comparable studies but could not find any that investigated the association of CVD, workforce participation and education among study participants similar to ours.

9. As results for physical disablity are a "key finding", they should also be included in the conclusions.

The issue of physical disability was previously mentioned at the end of the sentence (in line 337 of the old version and line 409 of the new version). Now in this version we have deleted the end section of that sentence and added a new sentence stating it as a key finding in the conclusion (in line 362-363 of new clean version), which is as follows.

Physical functioning limitations was a key factor in non-participation in the paid workforce both for those with and without CVD.

The following text is added at the end of the discussion section to clarify the implications (last section of the final paragraph of the discussion section).

Our results show the majority of people with CVD were still in the paid workforce but people who experienced a CVD event, especially those with physical disability, were more vulnerable to not being in the workforce compared to their counterparts. Given the importance of paid work for individual wellbeing and the overall economy [15], policies aimed at helping people with CVD, particularly those with accompanying physical disability, to remain or re-enter the workforce would be of benefit to both individuals and society.

Reference:

15. Schofield D, Shrestha R, Percival R, Passey M, Callander E, Kelly S. The personal and national costs of CVD: impacts on income, taxes, government support payments and GDP due to lost labour force participation. Int J Cardiol. 2013;166(1):68-71. pmid: 22018513.

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PLoS One. doi: 10.1371/journal.pone.0249738.r003

Decision Letter 1

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24 Mar 2021

Cardiovascular disease subtypes, physical disability and workforce participation: a cross-sectional study of 163,562 middle-aged Australians

PONE-D-20-33041R1

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30 Mar 2021

PONE-D-20-33041R1

Cardiovascular disease subtypes, physical disability and workforce participation: a cross-sectional study of 163,562 middle-aged Australians

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Data Availability Statement

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PERMALINK

Cardiovascular disease subtypes, physical disability and workforce participation: A cross-sectional study of 163,562 middle-aged Australians

Muhammad Shahdaat Bin Sayeed

Grace Joshy

Ellie Paige

Emily Banks

Rosemary Korda

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Study population and data sources

Outcome: Workforce participation

Main exposure: Cardiovascular disease

Other variables of interest

Statistical analysis

Ethics approval

Results

Characteristics of the study participants

Table 1. Sociodemographic and health related characteristics of study participants.

CVD and workforce participation

Table 2. Workforce participation, paid work hours per week, retirement patterns and physical functioning limitations among study participants.

Fig 1. Workforce participation characteristics among study participants according to CVD status, sex and age-group.

Fig 2. Non-participation in the workforce: Prevalence and adjusted prevalence ratios in people with and without CVD and according to hospitalisation for CVD subtypes.

CVD and workforce participation among population subgroups

Fig 3. Prevalence ratio of not in workforce in population subgroups based on socio-demographic and health related factors.

CVD, physical functioning limitations and workforce participation

Fig 4. Non-participation in the workforce: Prevalence and adjusted prevalence ratios according to joint categories of physical functioning limitations and CVD.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Thomas Behrens

Roles

Author response to Decision Letter 0

Decision Letter 1

Thomas Behrens

Roles

Acceptance letter

Thomas Behrens

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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