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Determinants of worse care for non‐COVID‐19 health or disability needs in Australia in the first month of COVID‐19 restrictions: A national survey – Cicuttini – – Health & Social Care in the Community

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1 INTRODUCTION

The COVID-19 pandemic has had major impacts on healthcare directly through overwhelming healthcare systems, and indirectly, though change in healthcare delivery to accommodate social distancing introduced to reduce the spread of the virus (Duckett, 2020; Stevens, 2020). This has significantly affected access to non-COVID-related healthcare (Marshall & Abbs, 2020; Thorlby et al., 2020). Emergency department visits in the USA declined 42% during the early COVID-19 pandemic, with the proportion of infectious disease-related visits being four times higher (Garcia et al., 2020; Hartnett et al., 2020). The World Health Organisation (WHO) has reported that prevention and treatment services for non-communicable diseases in 155 countries were severely disrupted during a 3-week period in May (Brunier & Harris, 2020). There have been fewer admissions for common emergencies such as heart attacks (Garcia et al., 2020; Hartnett et al., 2020), limited hospital capacity for screening (Palmer et al., 2020), care disruption for chronic diseases including delayed chemotherapies (Kutikov et al., 2020) and patients with other chronic diseases changing their medications without professional consultation (Michaud et al., 2020). There is concern that reduction in the management of non-COVID-19 conditions might result in a major increase of other medical conditions as was seen previously, for example in the 2002–2004 SARS outbreak, where chronic-care hospitalisations for diabetes plummeted during the crisis but skyrocketed afterwards (Huang et al., 2009).

Although on the 29 March 2020 (Woods, 2020), the Australian Government introduced strict social distancing rules, travel for purposes of healthcare was not restricted, however, services such as non-urgent elective surgery were cancelled (Duckett, 2020). To date, the Australian healthcare system has not been overwhelmed by COVID-19 cases and there has been a rapid adoption of Government funded telehealth services for medical care (Duckett, 2020). Although attempts have been made to transition to alternative methods of healthcare delivery, the concern remains that those with chronic illnesses or disabilities have either been forced to postpone care, or have elected to do so, in the face of concerns regarding COVID-19.

To date, there has been no population-based study that has examined access to the newly modified healthcare delivery system, although Medicare data which list services subsidised by the Australian government, suggest that visits to primary care physicians in Australia have dropped by 10% since March 2020 (nearly 100,000 fewer visits compared with the same time last year) (Tsirtsakis, 2020). Furthermore, it remains unknown how accessibility to health/disability care services is affected by sociodemographic, psychological and COVID-19-related factors. Understanding person-related barriers to access health/disability care services has the potential to inform strategies aimed at identifying and targeting those most at risk of missing important health and disability care. In Australia, we are uniquely positioned to address this as our health systems were not overwhelmed by COVID-19 cases and free telehealth (including telephone-only) healthcare options were provided within the national healthcare system, Medicare, removing cost as a barrier (Duckett, 2020). Thus, the aim of this study was to examine self-reported ability to obtain the necessary health/disability care for non-COVID-19 health conditions under the COVID-19 restrictions and risk factors for this.

2 METHODS

2.1 Design and procedures

A short, anonymously completed, self-report survey of people living in Australia aged at least 18 years. The survey was built in Qualtrics Insight Platform (Qualtrics, Provo, UT). It was available online from 3 April 2020, 4 days after COVID-19 restrictions were implemented in Australia, to midnight on 2 May 2020. A link to the survey was hosted on the Monash University website and information about it was distributed widely on news and social media and through organisational and personal networks. Approval to conduct the study was provided by the Monash University Human Research Ethics Committee (No. 2020-24080-42716).

2.2 Data collection

Each person completed a questionnaire assessing demographic, socioeconomic and study-specific, fixed-response-option questions and widely used standardised psychometric instruments.

2.2.1 Sociodemographic characteristics

Study-specific questions with fixed response options were used to ascertain age, postcode, gender, whether born overseas or in Australia, living circumstances and occupation.

Data on State, urban/rural residence and Socioeconomic Indices for Areas (SEIFA) were derived from respondent’s postcode using the most recent Australian Bureau of Statistics data.

2.2.2 Ability to obtain the necessary care for non-COVID-19 health conditions or disability

This was assessed using a single fixed-choice study-specific question ‘Have you been able to get the care you need for non-COVID-19 health conditions or a disability? Four options of the answers were ‘Yes; there’s been no change in my access to health or disability care’; ‘Yes; my access to health or disability care has been better’; ‘No; my access to health or disability care has been worse’ and ‘I haven’t needed health or disability care’.

2.2.3 Mental health

Psychological symptoms experienced over the previous fortnight were assessed using PHQ-9 and GAD-7, and optimism about the future in a study-specific question.

Patient health questionnaire 9 (PHQ-9)

The PHQ-9 (Kroenke et al., 2001) is an easily understood self-report 9-item scale asking respondents to endorse each depressive symptom as ‘0’ (not experienced) to ‘3’ (experienced nearly every day). Aggregated responses yield a scale indicative of symptom severity. Formally validated against diagnostic psychiatric interviews, a PHQ-9 score ≥10 has sensitivity of 88% and specificity of 88% for Major Depression. PHQ-9 scores of 5–9 represent mild, 10–14 moderate, 15–19 moderately severe and ≥20 severe depressive symptoms. We defined moderate, moderately severe or severe (PHQ-9 score ≥10) as clinically significant depressive symptoms.

Generalised anxiety disorder scale (GAD-7)

The GAD-7 (Spitzer et al., 2006) is a 7-item scale assessing common symptoms of anxiety that uses same response options as PHQ-9, is easily understood and acceptable. In formal validation against psychiatric interviews a GAD-7 score ≥10 has sensitivity of 89% and specificity of 82% to detect Generalised Anxiety Disorder. Scores of 5–9 represent mild, 10–14 moderate and 15–21 severe anxiety. Higher scores are strongly associated with functional impairment. We defined clinically significant symptoms of anxiety as moderate, or severe anxiety (GAD-7 score ≥10).

2.2.4 Experience of COVID 19 and the COVID-19 restrictions

Study-specific questions assessed:

  1. Direct experience of COVID-19: whether the respondent had been diagnosed with or tested for COVID-19, or lived with or knew someone with COVID-19: yes/no.
  2. Whether a job had been lost because of COVID-19 restrictions: yes/no.
  3. Worry about contracting COVID-19: a visual analogue scale with scores from 0 (not at all worried) to 10 (extremely worried). We defined highly worried about contracting COVID-19 as score ≥8.
  4. How badly COVID-19 restrictions had affected daily life: a visual analogue scale with scores from 0 (not at all badly) to 10 (very badly). We defined high adverse impact of restrictions (scale score ≥8)

2.3 Statistical analysis

A sample size of 2,635 people is required to estimate the prevalence of people not able to obtain the care needed for non-COVID-19 health conditions or a disability under the COVID-19 restrictions (expected at 30%) at the precision of 2.5% taking into account design effect = 2.

Data were analysed in four stages.

  1. Population prevalence rates and 95% CIs of people not able to obtain the care needed for non-COVID-19 health conditions/disability under the COVID-19 restrictions were estimated, adjusting for differences in sociodemographic characteristics between the sample and the Australian population. The adjustment was made using weights for proportions of age groups, genders, SEIFA deciles and states in the sample and the corresponding information in the population (Australian Bureau of Statistics’, 2019).
  2. The raw prevalence rates of people not able to obtain the care needed among people who had any healthcare need were calculated across psycho-social and demographic characteristics using crosstabs.
  3. Multiple logistic regression analysis was performed to examine the associations between psycho-social and demographic characteristics and being not able to obtain the care needed among people who had any healthcare need.
  4. Population attributable fraction (PAF) was calculated to determine the proportion (%) of worse access to non-COVID-19 health/disability care that could be attributed to the independent risk factors identified in this study. These were estimated using the ‘punafcc’ command in Stata, which implements the method recommended by Greenland and Drescher (Greenland & Drescher, 1993).

All analyses were conducted in the whole sample or subgroups of age (below 45 years vs. 45 years and older). This age was chosen as 45 years was the age where the Council of Australian Governments’ Better Health for all Australians Action Plan recommended the ‘well person’s health check’ in general practice’ to address the high prevalence of behavioural risk factors for non-communicable disease in the population. Only complete data were included in analyses, which were conducted using STATA Version 16 (StataCorp., College Station, TX).

2.4 Patient and public involvement

Patients were not directly involved in the development, implementation or interpretation of this research study because of the urgency of conducting the study and reporting the results.

3 FINDINGS

Of the 15,121 respondents who accessed the online survey, 13,829 (91.5%) contributed complete data and were included in the analysis. Those included in the analyses tended to be of higher socioeconomic status, female and aged over 40 years compared to the general population (Table 1). More than 44.1% of participants were from Victoria, the Australian state where the study was based.

TABLE 1.
Sociodemographic characteristics of the study sample (N = 13,829)

All ages (N = 13,829) People aged <45 years (N = 5,026) People aged >= 45 years (N = 8,803)
Age group
18–29 1337 (9.7) 1337 (26.6)
30–39 2294 (16.6) 2294 (45.6)
40–49 2854 (20.6) 1395 (27.8) 1459 (16.6)
50–59 3064 (22.2) 3064 (34.8)
60–69 2833 (20.5) 2833 (32.2)
70+ 1447 (10.5) 1447 (16.4)
Gender
Female 10,434 (75.5) 4053 (80.6) 6381 (72.5)
Male 3328 (24.1) 933 (18.6) 2395 (27.2)
Non-binary 67 (0.5) 40 (0.8) 27 (0.3)
State of residence
New South Wales (NSW) 2753 (19.9) 863 (17.2) 1890 (21.5)
Victoria 6105 (44.1) 2558 (50.9) 3547 (40.3)
Queensland 1939 (14) 584 (11.6) 1355 (15.4)
Western Australia (WA) 1177 (8.5) 420 (8.4) 757 (8.6)
South Australia (SA) 836 (6) 258 (5.1) 578 (6.6)
Tasmania 445 (3.2) 103 (2) 342 (3.9)
Australian Capital Territory (ACT) 465 (3.4) 192 (3.8) 273 (3.1)
Northern Territory (NT) 109 (0.8) 48 (1) 61 (0.7)
SEIFAa quintiles
Quintile 1 (Lowest socioeconomic position) 1093 (7.9) 313 (6.2) 780 (8.9)
Quintile 2 1541 (11.1) 421 (8.4) 1120 (12.7)
Quintile 3 2228 (16.1) 748 (14.9) 1480 (16.8)
Quintile 4 3038 (22) 1203 (23.9) 1835 (20.8)
Quintile 5 (Highest socioeconomic position) 5929 (42.9) 2341 (46.6) 3588 (40.8)
Living situation
On your own 2660 (19.2) 596 (11.9) 2064 (23.4)
With othersb 9630 (69.6) 3636 (72.3) 5994 (68.1)
With children only 578 (4.2) 181 (3.6) 397 (4.5)
Other 961 (6.9) 613 (12.2) 348 (4)
Country of birth
Overseas 3150 (22.8) 1068 (21.2) 2082 (23.7)
Occupation
Currently having a job 8330 (60.2) 3556 (70.8) 4774 (54.2)
Doing unpaid workc 1146 (8.3) 440 (8.8) 706 (8)
Student 1343 (9.7) 1015 (20.2) 328 (3.7)
Retired 3010 (21.8) 15 (0.3) 2995 (34)

6,712 (46.4%) of the total population indicated that they had a need for healthcare/disability services, 42.1% of those aged <45 years and 50.3% in those aged ≥45 years. Among these participants, 31.6% of those aged <45 years and 24.3% of those aged ≥45 years reported worse access to health/disability care services. In the total population, 14.2% [95% CI 12.3%–16.3%] aged <45 years and 11.9% [95% CI 10.9%–12.9%] aged ≥45 years of age responded that their access to health or disability care has been worse (Table 2).

TABLE 2.
Ability to access non-COVID-19 health or disability under the COVID-19 restrictions

Total

(N = 13,829)

People aged <45 years

(N = 5,026)

People aged >=45 years

(N = 8,803)

Yes; there have been no change in my access to health or disability care 31.7 [30.4; 33.1] 26.0 [23.8; 28.3] 36.8 [35.3; 38.3]
Yes; my access to health or disability care has been better 1.8 [1.5; 2.1] 1.9 [1.5; 2.5] 1.7 [1.3; 2.1]
No; my access to health or disability care has been worse 13.0 [11.9; 14.1] 14.2 [12.3; 16.3] 11.9 [10.9; 12.9]
I haven’t needed health or disability care 53.6 [52; 55.1] 57.9 [55.2; 60.6] 49.7 [48.1; 51.2]

Note

  • Data cell: Prevalence (%) [95% CI], post-stratification weighted by: State, Socioeconomic Indices for Areas decile, gender and age.

Details of the characteristics of participants who identified as having a need for health/disability care (n = 6,712) are summarised in Table 3. We determined which factors were independently associated with worse access to necessary health/disability care in the whole population and stratified by age <45 years and >=45 years (Table 4). We excluded the non-binary participants from these analyses because the numbers were too small. Having any experience of COVID-19, high levels of restrictions, clinically significant depressive symptoms and clinically significant anxiety were associated with worse access to health/disability care irrespective of age group. For participants <45 years, doing unpaid work, (OR1.52, 95% CI 1.13–2.06) or being a student (OR 1.43, 95% CI 1.12–1.82), and for participants >=45 years: doing unpaid work (OR 1.51, 95% CI 1.17–1.95) or having their main source income being government benefits (OR 1.44, 95% CI 1.18–1.76), were associated with worse access to health/disability care.

TABLE 3.
Participant characteristics based on access to health/disability care needs during month 1 of COVID-19 restrictions in those who identified as having care needs (N = 6,712)

All ages (N = 6,712) People aged <45 years (N = 2,318) People aged >= 45 years (N = 4,394)
No change/better Worse Total No change/better Worse Total No change/better Worse Total
Gender
Female 3677 (72.1) 1424 (27.9) 5101 (100) 1326 (68.2) 619 (31.8) 1945 (100) 2351 (74.5) 805 (25.5) 3156 (100)
Male 1212 (77.4) 354 (22.6) 1566 (100) 246 (71.3) 99 (28.7) 345 (100) 966 (79.1) 255 (20.9) 1221 (100)
Non-binary 22 (48.9) 23 (51.1) 45 (100) 13 (46.4) 15 (53.6) 28 (100) 9 (52.9) 8 (47.1) 17 (100)
State of residence
NSW/ Victoriaa 3057 (72.6) 1156 (27.4) 4213 (100) 1044 (68.3) 484 (31.7) 1528 (100) 2013 (75) 672 (25) 2685 (100)
Other state/territory 1854 (74.2) 645 (25.8) 2499 (100) 541 (68.5) 249 (31.5) 790 (100) 1313 (76.8) 396 (23.2) 1709 (100)
Remoteness
Major cities 1750 (73.7) 625 (26.3) 2375 (100) 414 (68.4) 191 (31.6) 605 (100) 1336 (75.5) 434 (24.5) 1770 (100)
Regional/remote areas 3161 (72.9) 1176 (27.1) 4337 (100) 1171 (68.4) 542 (31.6) 1713 (100) 1990 (75.8) 634 (24.2) 2624 (100)
SEIFAb quintiles
Quintile 1 (lowest SEP) 376 (67.5) 181 (32.5) 557 (100) 85 (59) 59 (41) 144 (100) 291 (70.5) 122 (29.5) 413 (100)
Quintile 2 564 (72.3) 216 (27.7) 780 (100) 138 (66.4) 70 (33.7) 208 (100) 426 (74.5) 146 (25.5) 572 (100)
Quintile 3 811 (75.1) 269 (24.9) 1080 (100) 255 (72.7) 96 (27.4) 351 (100) 556 (76.3) 173 (23.7) 729 (100)
Quintile 4 1110 (75.2) 366 (24.8) 1476 (100) 394 (71.3) 159 (28.8) 553 (100) 716 (77.6) 207 (22.4) 923 (100)
Quintile 5 (highest SEP) 2050 (72.7) 769 (27.3) 2819 (100) 713 (67.1) 349 (32.9) 1062 (100) 1337 (76.1) 420 (23.9) 1757 (100)
Living situation
On your own 960 (71) 393 (29.1) 1353 (100) 181 (66.1) 93 (33.9) 274 (100) 779 (72.2) 300 (27.8) 1079 (100)
With othersc 3462 (74.8) 1167 (25.2) 4629 (100) 1187 (70.1) 506 (29.9) 1693 (100) 2275 (77.5) 661 (22.5) 2936 (100)
With children only 199 (66.1) 102 (33.9) 301 (100) 59 (59.6) 40 (40.4) 99 (100) 140 (69.3) 62 (30.7) 202 (100)
Other 290 (67.6) 139 (32.4) 429 (100) 158 (62.7) 94 (37.3) 252 (100) 132 (74.6) 45 (25.4) 177 (100)
Country of birth
Australia 3865 (72.4) 1474 (27.6) 5339 (100) 1289 (67.4) 624 (32.6) 1913 (100) 2576 (75.2) 850 (24.8) 3426 (100)
Overseas 1046 (76.2) 327 (23.8) 1373 (100) 296 (73.1) 109 (26.9) 405 (100) 750 (77.5) 218 (22.5) 968 (100)
Occupation
Currently having a job 2839 (74.7) 964 (25.4) 3803 (100) 1156 (72.3) 444 (27.8) 1600 (100) 1683 (76.4) 520 (23.6) 2203 (100)
Doing unpaid workd 422 (61.6) 263 (38.4) 685 (100) 155 (58.9) 108 (41.1) 263 (100) 267 (63.3) 155 (36.7) 422 (100)
Student 397 (62.4) 239 (37.6) 636 (100) 269 (60.3) 177 (39.7) 446 (100) 128 (67.4) 62 (32.6) 190 (100)
Retired 1253 (78.9) 335 (21.1) 1588 (100) 5 (55.6) 4 (44.4) 9 (100) 1248 (79) 331 (21) 1579 (100)
Main source of income from government benefits
No 4146 (74.7) 1406 (25.3) 5552 (100) 1433 (70) 615 (30) 2048 (100) 2713 (77.4) 791 (22.6) 3504 (100)
Yes 765 (66) 395 (34.1) 1160 (100) 152 (56.3) 118 (43.7) 270 (100) 613 (68.9) 277 (31.1) 890 (100)
Any experience of COVID-19e
No 4137 (74.1) 1450 (26) 5587 (100) 1275 (68.8) 577 (31.2) 1852 (100) 2862 (76.6) 873 (23.4) 3735 (100)
Yes 774 (68.8) 351 (31.2) 1125 (100) 310 (66.5) 156 (33.5) 466 (100) 464 (70.4) 195 (29.6) 659 (100)
Lost job due to COVID-19
No 4538 (73.6) 1624 (26.4) 6162 (100) 1427 (68.6) 653 (31.4) 2080 (100) 3111 (76.2) 971 (23.8) 4082 (100)
Yes 373 (67.8) 177 (32.2) 550 (100) 158 (66.4) 80 (33.6) 238 (100) 215 (68.9) 97 (31.1) 312 (100)
Worried about contracting COVID-19
Less worried 4055 (74.6) 1383 (25.4) 5438 (100) 1352 (69.8) 586 (30.2) 1938 (100) 2703 (77.2) 797 (22.8) 3500 (100)
Very worried 856 (67.2) 418 (32.8) 1274 (100) 233 (61.3) 147 (38.7) 380 (100) 623 (69.7) 271 (30.3) 894 (100)
Impact of restrictions
Low (scale score <8) 3795 (76.7) 1156 (23.4) 4951 (100) 1190 (71.6) 471 (28.4) 1661 (100) 2605 (79.2) 685 (20.8) 3290 (100)
High (scale score ≥8) 1116 (63.4) 645 (36.6) 1761 (100) 395 (60.1) 262 (39.9) 657 (100) 721 (65.3) 383 (34.7) 1104 (100)
Depressive symptoms
Mild depressive symptoms 3792 (80) 949 (20) 4741 (100) 1054 (76.7) 321 (23.4) 1375 (100) 2738 (81.3) 628 (18.7) 3366 (100)
Moderate/severe, depressive symptoms 1119 (56.8) 852 (43.2) 1971 (100) 531 (56.3) 412 (43.7) 943 (100) 588 (57.2) 440 (42.8) 1028 (100)
Symptoms of anxiety
None or mild 3995 (78.8) 1078 (21.3) 5073 (100) 1132 (75.7) 363 (24.3) 1495 (100) 2863 (80) 715 (20) 3578 (100)
Moderate/severe 916 (55.9) 723 (44.1) 1639 (100) 453 (55) 370 (45) 823 (100) 463 (56.7) 353 (43.3) 816 (100)

TABLE 4.
Sub-group multiple logistic regression predicting health or disability care having been worse/or more difficult to access during the COVID-19 restrictions among people having any health need

All ages Model with people aged <45 years Model with people aged >= 45 years
aOR 95% CI aOR 95% CI aOR 95% CI
Age (in year) 0.99 [0.98; 0.99] 1.00 [0.98; 1.01] 0.97 [0.96; 0.98]
Male vs. Female 0.88 [0.76; 1.01] 0.90 [0.69; 1.18] 0.89 [0.75; 1.05]
NSW/Victoriaa vs. Other state/territory 0.95 [0.84; 1.07] 0.98 [0.81; 1.2] 0.92 [0.79; 1.07]
Major city vs. regional/remote areas 0.96 [0.83; 1.11] 0.94 [0.74; 1.21] 0.99 [0.82; 1.2]
SEIFAb quintiles
Quintile 1 (lowest SEP) Ref. Ref. Ref.
Quintile 2 0.87 [0.68; 1.12] 0.90 [0.56; 1.44] 0.88 [0.65; 1.19]
Quintile 3 0.70 [0.55; 0.89] 0.67 [0.44; 1.03] 0.72 [0.54; 0.96]
Quintile 4 0.69 [0.55; 0.88] 0.71 [0.47; 1.07] 0.68 [0.51; 0.92]
Quintile 5 (highest SEP) 0.85 [0.67; 1.07] 0.92 [0.62; 1.36] 0.82 [0.61; 1.09]
Living situation
With othersc Ref. Ref. Ref.
On your own 1.17 [1.01; 1.35] 1.06 [0.80; 1.41] 1.23 [1.03; 1.46]
With children only 1.22 [0.94; 1.59] 1.34 [0.82; 2.09] 1.09 [0.77; 1.52]
Other 1.01 [0.80; 1.27] 1.15 [0.86; 1.56] 0.93 [0.63; 1.35]
Born overseas vs. born in Australia 0.87 [0.75; 1.01] 0.83 [0.64; 1.07] 0.88 [0.74; 1.06]
Occupation
Currently having a job Ref. Ref. Ref.
Doing unpaid workd 1.51 [1.25; 1.84] 1.52 [1.13; 2.06] 1.51 [1.17; 1.95]
Student 1.28 [1.06; 1.56] 1.43 [1.12; 1.82] 1.20 [0.85; 1.7]
Retired 1.03 [0.85; 1.25] 1.23 [0.27; 5.52] 1.25 [1; 1.57]
Main source of income from government benefits 1.33 [1.12; 1.57] 1.20 [0.88; 1.64] 1.44 [1.18; 1.76]
Any experience of COVID-19e 1.21 [1.04; 1.4] 1.05 [0.84; 1.33] 1.34 [1.1; 1.63]
Lost job because of COVID-19 0.96 [0.77; 1.18] 0.83 [0.6; 1.15] 1.11 [0.84; 1.48]
Very worried about contracting COVID-19f 1.14 [0.99; 1.31] 1.11 [0.87; 1.43] 1.13 [0.95; 1.36]
High adverse impact of restrictionsg 1.38 [1.21; 1.57] 1.24 [1.01; 1.53] 1.51 [1.27; 1.78]
Clinically significant depressive symptomsh 1.86 [1.59; 2.17] 1.62 [1.28; 2.07] 2.03 [1.66; 2.48]
Clinically significant symptoms of anxietyi 1.54 [1.31; 1.81] 1.65 [1.3; 2.1] 1.46 [1.17; 1.82]

Note

  • aOR: Adjusted odds ratio from the multiple logistic regression models including all factors in this table; Bolded aORs are statistically significant; Ref.: reference category.

We examined the contribution of the independent risk factors we identified as associated with worse access to health/disability care during month-1 of the COVID-19 restrictions using the population attributable fraction (PAF) (Table 5). In those <45 years, the PAF was 45.9% (95% CI 37.2%–53.4%) in the presence of one or more when one or more of the following factors: doing unpaid caregiving work for dependent family members; being a student; having depressive symptoms; or having symptoms of anxiety. The population attributable fraction for a high adverse impact of restrictions on worse access to health/disability care was 7.1% (1.0%; 12.8%) with the PAF being 49.6% (40.1–57.6) when this was added to the previous risk factors. In those aged ≥45 years, the PAF was 42.9% (95% CI 36.9–48.4) in the presence of one or more of the following factors: having depressive symptoms; having symptoms of anxiety; doing unpaid work; living alone; being in lowest socioeconomic quintile; or having main source of income from government benefits. When we considered COVID-19-related factors, the population attributable fraction for worse access to health/disability care due to a high adverse impact of restrictions was 11.9% (95% CI 7.8%–15.8%) and any experience of COVID-19 was 4.7% (95% CI 2.0%–7.3%) (the respondent had been diagnosed with or tested for COVID-19, or lived with or knew someone with COVID-19). The PAF was 44.1% (36.0%–51.2%) when we added these to the previous risk factors.

TABLE 5.
Population impact of the independent risk factors associated with worse access to health/disability care during month-1 of the COVID-19 restrictions

Population attributable fraction (95% CI) in those aged <45 years

Population attributable fraction (95% CI) in those aged >=45 years

Having depressive symptoms 21.4 (12.6; 29.3) 20.9 (16.6; 24.8)
Having symptoms of anxiety 19.9 (12.3; 26.9) 10.6 (5.6; 15.4)
Doing unpaid work 7.8 (1.0; 14.1) 8.4 (4.6; 12.1)
Having main source of income from government benefits 7.9 (4.3; 11.5)
Being a student 5.9 (1.4; 10.1)
Living alone 5.2 (1.2; 0.9)
Being in lowest socioeconomic quintile 2.6 (0.5; 4.8)
One or more of the above 45.9 (37.2; 53.4) 42.9 (36.9; 48.4)
Specific Covid-19 related factors
High adverse impact of restrictions 7.1 (1.0; 12.8) 11.9 (7.8; 15.8)
Any experience of COVID-19a 4.7 (2.0; 7.3)
One or more of all the factors, including COVID-19 related 49.6 (40.1; 57.6) 44.1 (36.0; 51.2)

Note

  • Bolded statistics are statistically significant.

4 DISCUSSION

Almost half of those responding to this community-based national survey reported needing healthcare/disability services for non-COVID-related health conditions in the first month of COVID-19 restrictions. Of those with a need for healthcare/disability services, 31.6% aged <45 years and 24.3% aged ≥45 years described poor access to health/disability care. Clinically significant depressive or anxiety symptoms, doing unpaid caregiving work and high adverse experiences of COVID-19 restrictions were independently associated with poor access to necessary health/disability care irrespective of age group. In those aged <45 years, being a student, and in those aged ≥45 years, having the main source of income from government benefits, occupying a lower socioeconomic position and living alone were additional, independent risk factors. Approximately 50% of the risk of worse access to necessary health/disability care in those aged <45 years and 44% in those aged ≥45 years could be attributed to these risk factors.

The high prevalence of self-reported worse access to necessary health/disability care in month one of COVID-19 restrictions in Australia is consistent with international experience. Since the COVID-19 pandemic began, across the world increasing severe disruption to prevention and treatment services for non-communicable diseases has been reported (Brunier & Harris, 2020), with reduced presentations for non-COVID-related conditions to hospitals and to primary healthcare services (Garcia et al., 2020; Hartnett et al., 2020). Identifying the barriers faced by the population to access healthcare services related to COVID-19 pandemic will be important to improving better healthcare facilitated by addressing these barriers.

Experiencing clinically significant depressive symptoms was associated with the largest proportion of worse access to non-COVID-19 health/disability care, independent of other factors. Our survey did not distinguish people with a prior history of mental health problems from those experiencing depressive symptoms in response to the restrictions, but population prevalence rates were two to three times higher than estimates generated in non-COVID times (Fisher et al., 2020). Depressed mood is associated with reduced volition, perhaps including for persisting with the additional complexities of arranging and participating in telehealth for general and mental healthcare. If unassisted, psychological factors are associated with worse psychological and non-psychological health outcomes (Henderson et al., 2013; Macavei et al., 2016). Our data suggest that access to health and disability care could be improved by up to 20% in both those aged <45 years and those aged 45 years and older by targeting this risk factor. Among those aged <45 years, having symptoms of anxiety also had a major impact and may need to be targeted to improve access to health and disability care.

It is well recognised that people who are less well-resourced and socially isolated have higher health needs and worse health outcomes (Mirza et al., 2019; Paavola, 2017). We found a significant socioeconomic gradient in access to health and disability care. In those aged <45 years being a student, while in those aged ≥45years being in the lowest socioeconomic position, living alone and having government benefits as the main income source, were associated with worse access to health and disability care. The Australian system provides universal healthcare. Access to primary healthcare providers was available, including via telehealth, at no cost to the patient at the time of this survey, suggesting that the barriers are not merely financial. Nevertheless, this might be related to concerns about affordability. Despite fee-free healthcare being available to minors, concession card holders and patients vulnerable to COVID-19, individual providers are able to levy charges in addition to the government rebates. Thus, it is possible that people with the fewest resources were less able to access affordable care. Younger people relinquished discretionary private health insurance at high rates during this time because of concerns about affordability (Dalzell, 2020). It is also possible that access was limited. Non-urgent surgery was cancelled and postponed. People were fearful that maintaining physical distancing during consultations would be difficult, increasing the risk of contracting COVID-19.

We did not find a difference in access to health and disability care between males and females. However, the descriptive analysis indicated that people who identified as non-binary were more likely to describe having poor access to health/disability care than males and females. Non-binary people already experience high levels of stigma, shame and social inequalities before COVID-19 (Wesp et al., 2019). These experiences can deepen due to the COVID-19 restrictions and result in difficulty in accessing healthcare services (Jacques-Avino et al., 2021).

It was notable that people who had a direct experience either through contracting or being tested for the virus or through knowing someone who had these experiences expressed significantly more difficulty than others in accessing non-COVID-related healthcare. It is possible that the focus on the infection displaced clinical attention from all other aspects of health. Providing strong community-based messaging with an emphasis on the need for continuing care for all medical and disability needs even during the COVID-19 pandemic will be important in order to reduce the risk of a post-COVID-19 rebound in non-COVID-19-related diseases. This will need to be accompanied by appropriately tailored services that provide the needed care in the changing environment.

Our study suggests that approximately 50% of the risk of worse access to necessary health/disability care in those aged <45 years and 44% in those aged over 45 years could be reduced by addressing the risk factors identified in this study. Specifically, in those aged <45 years, doing unpaid caregiving work; being a student; having moderate to severe depressive or anxiety symptoms and experiencing a highly adverse impact of COVID restrictions. In those aged 45 years or older it was having any one of the following: moderate to severe depressive or anxiety symptoms; doing unpaid caregiving work; living alone; being in the lowest socioeconomic position; having government benefits as the main source of income; high impact of COVID-19 restrictions; any experience of COVID-19 and high concerns about contracting COVID-19.

Together, these findings are able to inform explicit strategies to optimise both access to and quality of health and disability care for general populations. They indicate that integrated approaches are needed to understand and address mental health problems as they influence health decision-making and access to care for physical symptoms. In the context of COVID-19, optimising mental and physical health may require a multicomponent public health approach, incorporating psychoeducation and health promotion strategies, online programs targeting self-management, telehealth consultations to address psychological and physical health needs and specialised in-person healthcare. In order to avoid exacerbation of poor health outcomes in less-resourced populations, these will need to be designed to be accessible including to people who do have limited skills in using or access to necessary secure (or private) technology.

These data are, to our knowledge, the first to examine the population prevalence and factors associated with access to healthcare and disability needs during the COVID-19 restrictions. Strengths are the very large and broad representative sample, weighting to reflect the national population use of standardised measures that permit comparisons with equivalent non-COVID-19 populations, and capacity to distinguish worry about contracting COVID-19 from the impacts of restrictions. Response bias was reduced by describing the study in neutral terms and making it short and easy to complete. We acknowledge the limitations that online surveys are less accessible to people who lack computer proficiency, Internet access or English fluency or are in lower socioeconomic positions, or have lower levels of education and their experiences might not be represented. However, this is likely to have underestimated our results regarding access to healthcare/disability. Our population also tended to have more women who tend to access more healthcare (Bertakis et al., 2000; Hansen & Høye, 2015). However, when we examined males and females separately the findings were not significantly different. Recruitment fractions cannot be calculated for online surveys. A short, structured survey cannot gather nuanced information about mental health problems. Cross-sectional surveys identify associations, not causal relationships. For example the association between mental health problems (depression and anxiety) and worse access to necessary healthcare can be a causal relationship in either or both of the directions. Our survey was performed very early in the COVID-19 restrictions (3 April 2020, 4 days after COVID-19 restrictions were implemented in Australia, to midnight on 2 May 2020).

This study has demonstrated the high prevalence of worse access to necessary healthcare and disability needs for non-COVID-19 health conditions in the first month of restrictions in Australia. Whether this will translate into a post-COVID-19 rebound in non-COVID-19-related diseases as experienced post-SARS (Huang et al., 2009) cannot be determined from this study. However, there is concerning and consistent international data that access to non-COVID-19 healthcare has been significantly impacted. We have been able to identify characteristics of those with worse access to healthcare and disability needs. These included low socioeconomic position, psychological symptoms, living alone, being a student and having government income as the main source of income as well as COVID-19 restrictions and concern about getting COVID-19. Although there were some differences in risk factors in those aged <45 years and ≥45 years, experiencing clinically significant depressive symptoms had the greatest impact, with a population attributable fraction of approximately 20% in both populations. Our study suggests that approximately 50% of the worse access to healthcare and disability needs in those <45 years and 44% in those ≥45 years, could be reduced by targeting the identified risk factors. This is likely to have a significant impact on health outcomes, given that the factors we identified also identify those who have more disease and worse health outcomes generally. Although ongoing research will be needed to elucidate the risks identified in this study and monitor whether changes occur over the course of the COVID-19 pandemic, our findings provide strong evidence that ongoing vigilance is needed to monitor and develop strategies to encourage appropriate, ongoing health and disability care in the more vulnerable in our community to prevent a post-COVID-19 rebound in non-COVID-19-related diseases.

ACKNOWLEDGEMENT

The authors are very grateful to the 13,829 Australian residents who contributed their experiences to this research.

    CONFLICT OF INTEREST

    All authors declare that they have no conflicts of interest.

    AUTHORS’ CONTRIBUTIONS

    FC, MH and AW involved in literature research. FC, TT, AW and JF carried out study design. TT and JF carried out data collection. TT also carried out data analysis. FC, TT, MH, AW and JF involved in data interpretation and writing of the manuscript.

    ETHICS APPROVAL

    Approval to conduct the study was provided by the Monash University Human Research Ethics Committee (2020-24080-42716).

    MONASH COVID RESTRICTIONS RESEARCH GROUP

    Professor Jane RW Fisher, Dr Maggie Kirkman, Dr Thach Tran, Dr Karin Hammarberg, Dr Jayagowri Sastry, Ms Hau Nguyen, Dr Heather Rowe, Ms Sally Popplestone, Ms Ruby Stocker and Dr Claire Stubber.

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