Abstract
Background
Degree of rurality and neighbourhood deprivation are important factors in understanding variation in mental illness across populations, but epidemiological data on their relevance to mental illness in home care (HC) clients are limited. We estimated the prevalence of several mental illnesses by these characteristics and their interaction effects among HC clients and matched community comparators in Ontario, Canada.
Method
We conducted a repeated cross-sectional study using linked health administrative data. HC recipients aged ≥18 years were matched to community comparators on demographics, dementia, and comorbidity. Within each study sample, prevalence estimates were calculated and compared across urban/rural residence and binary measures of the 4 dimensions of the Canadian Index of Multiple Deprivation (CIMD). Outcomes were derived with validated case definitions for any mental illness, mood/anxiety disorders, depression and anxiety (with/without drug claims), schizophrenia, bipolar disorder, and suicide attempt. Prevalence ratios (PRs: 95% CIs) were estimated with modified Poisson regression models adjusted for relevant covariates.
Results
We included 479,064 urban- and 72,862 rural-residing HC clients (mean age 73.2 and 72.4 years, respectively). Mental illnesses were less common in rural than urban clients (e.g., any mental illness 31.3% vs. 39.4%), with adjusted PRs between 0.80 and 0.87. Prevalence estimates were significantly higher among clients in areas more marginalized in residential instability and ethno-cultural composition, with more pronounced adjusted PRs for clients in urban than rural settings (e.g., PR [more vs. less marginalized residential instability] for any mental illness: 1.12 (1.12, 1.13) for urban and 1.07 (1.04, 1.10) for rural clients, P < 0.001). In matched community comparators, we observed similar patterns by urban/rural and deprivation measures though mental illness prevalence was significantly lower overall.
Conclusions
Mental illnesses are common among HC clients and vary regionally. Incorporating geographic and socioeconomic indicators into mental health surveillance systems offers insight into mental health disparities that may warrant more targeted interventions.
Keywords: mental illness, home care, prevalence, administrative data, urban/rural residence, marginalization
Résumé
Contexte
Les degrés de ruralité et de défavorisation du quartier sont des facteurs importants pour comprendre la variation en matière de maladie mentale entre les populations, mais les données épidémiologiques sur leur pertinence pour la maladie mentale chez les clients de soins à domicile sont limitées. Nous avons estimé la prévalence de plusieurs maladies mentales en fonction de ces caractéristiques et leurs effets d’interaction chez des clients de soins à domicile et des comparateurs communautaires appariés en Ontario, au Canada.
Méthodes
Nous avons mené une étude transversale répétée à l’aide de données administratives liées à la santé. Les bénéficiaires de soins à domicile âgés de 18 ans et plus ont été appariés à des comparateurs communautaires en fonction des données démographiques, de la démence et des affections concomitantes. Dans chaque échantillon de l’étude, les estimations de prévalence ont été calculées et comparées pour la résidence urbaine/rurale et les mesures binaires des quatre dimensions de l’Indice canadien de défavorisation multiple (ICDM). Les résultats ont été établis à l’aide de définitions de cas validées pour toute maladie mentale, les troubles anxieux/de l’humeur, la dépression et l’anxiété (avec/sans demande de remboursement de médicaments), la schizophrénie, le trouble bipolaire et la tentative de suicide. Les rapports de prévalence (RP : IC à 95%) ont été estimés à l’aide de modèles de régression de Poisson modifiés ajustés en fonction des covariables pertinentes.
Résultats
Nous avons inclus 479 064 clients de soins à domicile en milieu urbain et 72 862 clients en milieu rural (âge moyen de 73,2 ans et de 72,4 ans, respectivement). Les maladies mentales étaient moins fréquentes en milieu rural qu’en milieu urbain (p. ex., toute maladie mentale 31,3% p/r à 39,4%), les RP ajustés se situant entre 0,80 et 0,87. Les estimations de prévalence étaient significativement plus élevées chez les clients des régions plus marginalisées sur le plan de l’instabilité résidentielle et de la composition ethnoculturelle, avec des RP ajustés plus prononcés pour les clients en milieu urbain que rural (p. ex., RP [instabilité résidentielle plus vs moins marginalisée] pour toute maladie mentale : 1,12 (de 1,12 à 1,13) pour les clients urbains et 1,07 (de 1,04 à 1,10) pour les clients ruraux, p < 0,001). Dans les comparateurs communautaires appariés, nous avons observé des tendances semblables selon les régions urbaines/rurales et les mesures de la défavorisation, bien que la prévalence de la maladie mentale ait été significativement plus faible dans l’ensemble.
Conclusions
Les maladies mentales sont courantes chez les clients de soins à domicile et varient selon les régions. L’intégration d’indicateurs géographiques et socioéconomiques dans les systèmes de surveillance de la santé mentale donne un aperçu des disparités en santé mentale qui peuvent justifier des interventions plus ciblées.
Introduction
Mental health disorders contribute significantly to the global burden of disease (GBD) 1 and are associated with considerable disability, morbidity, and poorer quality of life and survival. 2 The personal, economic, and societal costs associated with mental illness3,4 highlight the importance of monitoring mental health trends and needs more closely, particularly in at-risk populations. Between 17% and 20% of older adults have depressive and/or anxiety symptoms 5 and between 1% and 2% have schizophrenia. 6 While GBD data suggest that middle-aged and older populations carry the highest burden of mental disorders, 7 less is known about mental illness in home care (HC) populations. Adults receiving HC services exhibit characteristics that increase their likelihood of experiencing mental illness, including presenting with multimorbidity, cognitive and physical impairment, frailty, and chronic pain.8–10 Combined with ageism and scarce or fragmented mental health services, these characteristics may result in less effective detection and treatment of mental illnesses in HC clients.9,10 Beyond research on depression and depressive symptoms,10–13 few HC studies have estimated the prevalence of anxiety disorders or other mental illnesses and related outcomes, including bipolar disorders, schizophrenia, and suicidal ideation.9,14–18
The likelihood of mental illness among HC clients may be further influenced by the communities in which they live.9,11 While it is recognized that social and structural inequalities contribute to higher rates of mental illness in the general population,19,20 it is not well-understood if rurality or area-level deprivation are independently associated with the prevalence of mental illnesses in HC clients. General population estimates in Canada suggest that some mental illnesses are more prevalent in urban settings, 21 whereas others are more equally distributed across urban and rural residences. 22 Older rural Canadians have reported poorer mental health compared to their urban counterparts. 23 Drawing conclusions on the epidemiology of mental illness in urban versus rural settings has been challenging as findings vary by jurisdiction, reflecting the relevance of population differences in risk and protective factors, 24 including social determinants of health; these factors may partly explain urban/rural health disparities beyond those arising from limited availability of healthcare resources. 25
In Canada, chronic disease surveillance has relied on the use of administrative (health claims) data to monitor mental illness in the general population.26,27 Administrative data can be linked with measures of urban/rural residence and area-based deprivation to better understand patterns of mental health conditions among at-risk population subgroups. Using these linked data sources and validated mental illness case definitions, our objectives were to (i) estimate the prevalence of various mental illnesses in Ontario HC clients and matched community comparators by urban/rural residence and area-level deprivation measures, and (ii) explore whether associations between area-level deprivation and mental illness vary by clients’ urban versus rural residence. The inclusion of matched community comparators allowed for investigation of whether any observed associations were unique to the HC setting.
Methods
Study Design and Setting
This repeated cross-sectional study included adults aged ≥18 years receiving publicly funded HC and a matched community comparator group from Ontario. The study period covered April 1, 2012 to March 31, 2023. The publicly funded Ontario Health Insurance Plan (OHIP) provides universal coverage for medically necessary hospital and physician services, outpatient prescription medications (for those ≥65, receiving HC, long-term care or social assistance), and select health/supportive care services in HC. 28 Individuals with a valid OHIP card and requiring scheduled assistance with activities of daily living, nursing/therapy services for complex comorbidities or other services (e.g., social work) are eligible for HC.
Data Sources
In Ontario, demographic characteristics and health service use are recorded in administrative databases held at ICES. The Registered Persons Database provided subjects’ age, sex, postal code (linked to Census data to derive urban/rural residence and area-based socioeconomic measures), dates of birth, death, and insurance coverage. HC service dates were obtained from mandatory clinical assessments (the Resident Assessment Instrument-Home Care, RAI-HC/interRAI-HC) administered to all clients requiring care for >60 days in one episode. The Discharge Abstract Database provided acute care hospital admission/discharge dates, diagnoses (up to 25, using International Classification of Diseases and Related Health Problems, 10th Revision, Canadian enhancement [ICD-10-CA] codes) and interventions (up to 20, using the Canadian Classification of Health Interventions [CCI] coding system). The National Ambulatory Care Reporting System provided admission/discharge dates, diagnoses (up to 10, coded using ICD-10-CA) and interventions (up to 10, coded using CCI) for visits to emergency departments and community-based ambulatory care centres. The Ontario Mental Health Reporting System provided admission/discharge dates and diagnoses (using International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] or ICD-10-CM codes) for those in designated adult inpatient mental health beds in general/specialty facilities. Physician services claims provided service dates, setting and diagnosis codes using a single 3-digit, modified ICD-9 code, with shadow-billing records for physicians on alternative payment plans. The Ontario Drug Benefit program database provided outpatient prescription medication claims for those covered under the provincial drug program. All datasets (Supplemental Table S1) were linked at the individual level using unique encoded identifiers and analyzed at ICES.
Study Populations
We identified all HC clients aged ≥18 years administered a RAI-HC (or interRAI-HC after April 1, 2018) assessment between April 1, 2012 and March 31, 2023. This assessment is completed at admission and every 6–12 months thereafter. The first assessment in this period served as the index assessment (assessment reference date = index date). We excluded clients if they had invalid sex or registration number, were aged >110 years, were non-residents of the province at index date, had a death date preceding index date, were ineligible for provincial health insurance at index date or for ≥180 continuous days within 5 years before index date, and those with an index date in fiscal 2012 with a RAI assessment in the year prior to focus on new admissions.
Each HC client was matched 1:1 to a comparator (individual residing in the community and not receiving home or long-term care in the 3.5 years before matched index date) by sex, age at index date (± 3 years), region (first 2 digits of postal code), index date (± 3 years), presence/absence of dementia, and comorbidity level (low = 0 or 1 chronic condition(s); moderate = 2 conditions, high = ≥ 3 conditions). Comorbidity was derived as a sum of 7 conditions (hypertension, ischaemic heart disease, diabetes, multiple sclerosis, epilepsy, Parkinsonism, stroke) captured by the Canadian Chronic Disease Surveillance System (CCDSS).26,27,29 All conditions (including dementia) were identified using validated CCDSS case algorithms and assessed during the 5 years before the index date (Supplemental Table S2).
Mental Illness Measures
We applied validated case definitions (Supplemental Table S3) to identify mental illness occurring within an index period defined as the 6 months pre- and post-index date. Included were 4 mental health measures assessed by the CCDSS26,27,29: health service use for any mental illness, mood/anxiety disorders, and schizophrenia, and diagnosis of schizophrenia; and 6 other mental illnesses derived using case definitions validated among populations with comorbidity30–32: depression (specific definition), depression with relevant prescription claims, anxiety, anxiety with relevant prescription claims, bipolar disorders, and suicide attempt. We also examined the annual number of physician visits for mental illness.
Urban/Rural Residence and Deprivation Measures
Postal codes in census metropolitan areas with populations ≥10,000 were designated as urban and with <10,000 rural. The 4 area-based measures of deprivation from the Canadian Index of Multiple Deprivation (CIMD) 33 are assigned at the dissemination area level and include: residential instability (reflects proportion of dwellings that are apartments vs owned, recent movers, persons living alone, and median household income), ethno-cultural composition (reflects proportion of foreign born, visible minorities, recent immigrants, and non-English or French speaking), economic dependency (reflects proportion not employed, aged ≥65 years, receiving government transfer payments, and dependency ratios), and situational vulnerability (reflects proportion in homes requiring major repairs, without a high-school diploma, with single parent families, who identify as Indigenous, and median dollar value of dwelling). Levels were collapsed to identify those most marginalized (quintiles 4 and 5) versus less marginalized (quintiles 1–3 and missing).
Other Characteristics
At index date, we captured clients’ age, sex, and area-level household income quintile. The number of non-mental health physician visits in the year before index date was included to reflect healthcare access.
Analysis
We summarized the characteristics of HC clients and community comparators at index date using means (SD), medians (IQR), and frequency (percent) distributions. Standardized differences >0.10 defined meaningful 34 differences (e.g., comparing clients in rural vs. urban settings or most vs. less marginalized areas) within each study sample.
We estimated the crude prevalence of mental illness for each fiscal year for HC clients and their matched comparators by urban/rural residence and most/less marginalized for each CIMD component. Denominators were the study sample in each fiscal year (based on the index date). For the most recent fiscal year (2022), we compared the prevalence of each mental illness for HC clients (and community comparators) by urban/rural and CIMD strata and derived prevalence ratios (PRs), with 95% confidence intervals (CIs), from modified Poisson regression models with a robust variance estimator, 35 accounting for clustering by matched pair.
For HC clients only, these models (all years combined) were further adjusted for the fiscal year of the index date, age, sex, dementia, comorbidity level, urban/rural residence, CIMD dimensions and interaction terms between urban/rural and each CIMD measure, and the number of non-mental health physician visits in the prior year.
All analyses were conducted with SAS Enterprise Guide 8.3, with P < 0.05 defined as the level of statistical significance.
Ethics
Ethics approval was obtained from the University of Waterloo Human Research Ethics Committee (#46451). Data access through ICES was authorized under section 45 of Ontario's Personal Health Information Protection Act.
Results
Supplemental Figure S1 shows the creation of matched HC and community comparator groups.
Baseline Characteristics
Average age was similar for clients in rural and urban areas (mean (SD) age of 72.4 (13.2) and 73.2 (14.2) years, respectively) as was the percentage female (52.3% and 56.7%, respectively) (Table 1, Supplemental Table S4). Urban (vs. rural) clients were more likely to be aged ≥85 years, to have a lower neighbourhood income and to reside in an area that was more marginalized in residential instability and ethno-cultural composition but less marginalized in economic dependency and situational vulnerability. Urban clients also had a higher median number of non-mental health physician visits in the previous year.
Table 1.
Baseline Characteristics for Ontario Home Care Clients by Urban/Rural Residence and CIMD Dimensions. Unless otherwise noted, presented as column n (%).
| Residence | Residential Instability | Ethno-cultural Composition | Economic Dependency | Situational Vulnerability | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Variable | Rural | Urban | Most marginalized | Less marginalized | Most marginalized | Less marginalized | Most marginalized | Less marginalized | Most marginalized | Less marginalized |
| N = 72,862 | N = 479,064 | N = 254,632 | N = 297,294 | N = 263,298 | N = 288,628 | N = 286,000 | N = 265,926 | N = 199,512 | N = 352,414 | |
| Age at index | ||||||||||
| Mean ± SD | 72.40 ± 13.15 | 73.17 ± 14.15 | 73.00 ± 13.97 | 73.13 ± 14.07 | 73.31 ± 14.46 | 72.84 ± 13.61 | 74.17 ± 13.28 a | 71.88 ± 14.69 | 71.21 ± 14.05 a | 74.12 ± 13.90 |
| Age group | ||||||||||
| 18-64 | 17,256 (23.7) | 110,915 (23.2) | 61,481 (24.1) | 66,690 (22.4) | 60,746 (23.1) | 67,425 (23.4) | 58,026 (20.3) a | 70,145 (26.4) | 55,895 (28.0) a | 72,276 (20.5) |
| 65-74 | 19,035 (26.1) | 108,439 (22.6) | 58,387 (22.9) | 69,087 (23.2) | 57,495 (21.8) | 69,979 (24.2) | 65,576 (22.9) | 61,898 (23.3) | 49,873 (25.0) | 77,601 (22.0) |
| 75-84 | 24,844 (34.1) | 159,537 (33.3) | 82,226 (32.3) | 102,155 (34.4) | 86,958 (33.0) | 97,423 (33.8) | 100,313 (35.1) | 84,068 (31.6) | 61,989 (31.1) | 122,392 (34.7) |
| 85+ | 11,727 (16.1) a | 100,173 (20.9) | 52,538 (20.6) | 59,362 (20.0) | 58,099 (22.1) | 53,801 (18.6) | 62,085 (21.7) | 49,815 (18.7) | 31,755 (15.9) a | 80,145 (22.7) |
| Sex | ||||||||||
| Female | 38,077 (52.3) | 271,834 (56.7) | 148,116 (58.2) | 161,795 (54.4) | 151,895 (57.7) | 158,016 (54.7) | 162,710 (56.9) | 147,201 (55.4) | 111,197 (55.7) | 198,714 (56.4) |
| Residence | ||||||||||
| Urban | — | — | 236,748 (93.0) a | 242,316 (81.5) | 261,550 (99.3) a | 217,514 (75.4) | 240,393 (84.1) a | 238,671 (89.8) | 166,518 (83.5) a | 312,546 (88.7) |
| Rural | — | — | 17,845 (7.0) a | 53,744 (18.1) | 1,701 (0.6) a | 69,888 (24.2) | 45,573 (15.9) a | 26,016 (9.8) | 32,973 (16.5) a | 38,616 (11.0) |
| Neighbourhood Income Quintile | ||||||||||
| Missing/Unknown | 1,463 (2.0) a | 400 (0.1) | 317 (0.1) | 1,546 (0.5) | 203 (0.1) | 1,660 (0.6) | 271 (0.1) | 1,592 (0.6) | 238 (0.1) | 1,625 (0.5) |
| Quintile 1 (lowest) | 16,613 (22.8) a | 138,017 (28.8) | 133,986 (52.6) a | 20,644 (6.9) | 85,947 (32.6) a | 68,683 (23.8) | 106,679 (37.3) a | 47,951 (18.0) | 106,442 (53.4) a | 48,188 (13.7) |
| Quintile 2 | 15,944 (21.9) | 107,300 (22.4) | 69,400 (27.3) a | 53,844 (18.1) | 61,783 (23.5) | 61,461 (21.3) | 68,038 (23.8) | 55,206 (20.8) | 53,313 (26.7) a | 69,931 (19.8) |
| Quintile 3 | 14,510 (19.9) | 87,994 (18.4) | 28,608 (11.2) a | 73,896 (24.9) | 45,617 (17.3) | 56,887 (19.7) | 46,841 (16.4) a | 55,663 (20.9) | 24,248 (12.2) a | 78,256 (22.2) |
| Quintile 4 | 12,804 (17.6) | 76,198 (15.9) | 13,389 (5.3) a | 75,613 (25.4) | 37,320 (14.2) | 51,682 (17.9) | 34,955 (12.2) a | 54,047 (20.3) | 10,736 (5.4) a | 78,266 (22.2) |
| Quintile 5 (highest) | 11,528 (15.8) | 69,155 (14.4) | 8,932 (3.5) a | 71,751 (24.1) | 32,428 (12.3) a | 48,255 (16.7) | 29,216 (10.2) a | 51,467 (19.4) | 4,535 (2.3) a | 76,148 (21.6) |
| Residential instability | ||||||||||
| Most marginalized | 17,884 (24.5) a | 236,748 (49.4) | — | — | 144,587 (54.9) a | 110,045 (38.1) | 160,977 (56.3) a | 93,655 (35.2) | 136,607 (68.5) a | 118,025 (33.5) |
| Ethno-cultural composition | ||||||||||
| Most marginalized | 1,748 (2.4) a | 261,550 (54.6) | 144,587 (56.8) a | 118,711 (39.9) | — | — | 126,188 (44.1) a | 137,110 (51.6) | 94,641 (47.4) | 168,657 (47.9) |
| Economic dependency | ||||||||||
| Most marginalized | 45,607 (62.6) a | 240,393 (50.2) | 160,977 (63.2) a | 125,023 (42.1) | 126,188 (47.9) a | 159,812 (55.4) | — | — | 125,312 (62.8) a | 160,688 (45.6) |
| Situational vulnerability | ||||||||||
| Most marginalized | 32,994 (45.3) a | 166,518 (34.8) | 136,607 (53.6) a | 62,905 (21.2) | 94,641 (35.9) | 104,871 (36.3) | 125,312 (43.8) a | 74,200 (27.9) | — | — |
| Dementia b | 1,580 (2.2) | 14,522 (3.0) | 7,183 (2.8) | 8,919 (3.0) | 8,776 (3.3) | 7,326 (2.5) | 7,881 (2.8) | 8,221 (3.1) | 4,842 (2.4) | 11,260 (3.2) |
| Comorbidity count | ||||||||||
| Mean ± SD | 1.16 ± 0.99 | 1.24 ± 0.98 | 1.22 ± 0.98 | 1.24 ± 0.99 | 1.27 ± 0.98 | 1.19 ± 0.99 | 1.22 ± 0.97 | 1.24 ± 1.00 | 1.25 ± 1.00 | 1.22 ± 0.98 |
| Comorbidity level | ||||||||||
| Low (0,1) | 48,892 (67.1) | 308,008 (64.3) | 165,427 (65.0) | 191,473 (64.4) | 165,907 (63.0) | 190,993 (66.2) | 186,203 (65.1) | 170,697 (64.2) | 126,884 (63.6) | 230,016 (65.3) |
| Moderate (2) | 16,551 (22.7) | 118,995 (24.8) | 62,365 (24.5) | 73,181 (24.6) | 67,669 (25.7) | 67,877 (23.5) | 70,175 (24.5) | 65,371 (24.6) | 49,675 (24.9) | 85,871 (24.4) |
| High (3+) | 7,419 (10.2) | 52,061 (10.9) | 26,840 (10.5) | 32,640 (11.0) | 29,722 (11.3) | 29,758 (10.3) | 29,622 (10.4) | 29,858 (11.2) | 22,953 (11.5) | 36,527 (10.4) |
| Hypertension b | 35,302 (48.5) a | 269,135 (56.2) | 140,650 (55.2) | 163,787 (55.1) | 155,294 (59.0) a | 149,143 (51.7) | 158,522 (55.4) | 145,915 (54.9) | 107,595 (53.9) | 196,842 (55.9) |
| Ischemic heart disease b | 14,903 (20.5) | 93,077 (19.4) | 48,921 (19.2) | 59,059 (19.9) | 48,796 (18.5) | 59,184 (20.5) | 57,137 (20.0) | 50,843 (19.1) | 39,956 (20.0) | 68,024 (19.3) |
| Diabetes b | 22,118 (30.4) | 148,753 (31.1) | 79,797 (31.3) | 91,074 (30.6) | 84,888 (32.2) | 85,983 (29.8) | 87,516 (30.6) | 83,355 (31.3) | 67,404 (33.8) | 103,467 (29.4) |
| Multiple sclerosis b | 658 (0.9) | 4,662 (1.0) | 2,353 (0.9) | 2,967 (1.0) | 2,388 (0.9) | 2,932 (1.0) | 2,451 (0.9) | 2,869 (1.1) | 1,806 (0.9) | 3,514 (1.0) |
| Epilepsy b | 1,370 (1.9) | 10,663 (2.2) | 5,711 (2.2) | 6,322 (2.1) | 5,912 (2.2) | 6,121 (2.1) | 5,635 (2.0) | 6,398 (2.4) | 4,814 (2.4) | 7,219 (2.0) |
| Parkinsonism b | 1,711 (2.3) | 13,839 (2.9) | 6,192 (2.4) | 9,358 (3.1) | 7,783 (3.0) | 7,767 (2.7) | 7,825 (2.7) | 7,725 (2.9) | 4,200 (2.1) | 11,350 (3.2) |
| Stroke b | 8,585 (11.8) | 54,178 (11.3) | 27,929 (11.0) | 34,834 (11.7) | 29,657 (11.3) | 33,106 (11.5) | 31,202 (10.9) | 31,561 (11.9) | 22,743 (11.4) | 40,020 (11.4) |
| Number non-MH physician visitsc | ||||||||||
| Mean ± SD | 21.58 ± 29.07 | 24.09 ± 32.43 | 23.55 ± 31.46 | 23.93 ± 32.48 | 24.69 ± 33.40 | 22.90 ± 30.67 | 23.12 ± 30.13 | 24.44 ± 33.91 | 23.57 ± 32.35 | 23.86 ± 31.82 |
| Median (IQR) | 13 (6-26) a | 15 (7-29) | 15 (7-28) | 15 (7-29) | 15 (7-30) | 14 (6-28) | 15 (7-28) | 15 (7-29) | 14 (6-28) | 15 (7-29) |
Abbreviations: CIMD = Canadian Index of Multiple Deprivation; IQR = interquartile range; MH = mental health; SD = standard deviation.
Note: Only a small % were missing information on urban/rural residence (<0.5% for less marginalized; <0.02% for most marginalized); missing and rural categories were combined. Missing information on CIMD dimensions ranged from 3.4–7.1%; missing and less marginalized categories were combined.
Denotes values with meaningful differences (comparing rural vs urban or most marginalized vs less marginalized for each CIMD dimension), standardized difference > 0.1.
5 years prior to index date; c1 year prior to start of index year.
Clients’ mean age was higher for those in an area more marginalized in economic dependency and lower for those in areas more marginalized in situational vulnerability. Clients in more marginalized areas were more likely to have lower neighbourhood income levels. Each CIMD measure was generally positively associated with the others except for a negative association between ethno-cultural composition and economic dependency. Descriptive findings were similar for the community comparators (Supplemental Table S5).
Prevalence of Mental Illness by Urban/Rural Residence
The most prevalent mental illnesses among urban HC clients (shown here for the last fiscal year) were any mental illness (39.4%), mood/anxiety disorders (28.3%), depression-specific (22.0%), and anxiety (21.9%) (Figure 1A). Case algorithms that included drug claims resulted in higher prevalence estimates of depression (27.4%) and anxiety (30.9%). Prevalence rose slightly for the most common illnesses around 2020/2021, though generally returned to pre-2020 levels by 2022. For less common conditions, prevalence estimates for urban HC clients were schizophrenia (2.63%), health service use for schizophrenia (2.44%), bipolar disorders (3.16%), and suicide attempt (0.71%).
Figure 1.
Annual prevalence of mental illness in Ontario home care clients by (A) urban versus (B) rural residence and community comparators by (C) urban versus (D) rural residence, fiscal years 2012–2022.
Prevalence estimates among rural HC clients followed similar patterns but were generally lower: any mental illness (31.3%), mood/anxiety disorders (21.1%), depression-specific (16.1%), depression with drug claims (23.2%), anxiety (16.5%), anxiety with drug claims (23.8%), schizophrenia (1.89%), health service use for schizophrenia (1.69%), bipolar disorders (2.09%), and suicide attempt (0.77%) (Figure 1B). Similar findings were observed by urban/rural residence for matched community comparators, though estimates were lower relative to HC clients (Figures 1(C) and (D)).
Examining the most recent fiscal year, prevalence estimates for all mental illnesses, except suicide attempt, were significantly lower among rural than urban HC clients (PRs = 0.66–0.85) (Figure 2A). PRs for community comparators were generally similar (Figure 2B).
Figure 2.
Prevalence of mental illness in Ontario (A) home care clients and (B) community comparators by urban/rural residence, fiscal year 2022. Note: S = suppressed due to low cell count. Caution is warranted in the interpretation of the prevalence ratio for suicide attempt given small cells.
Similar patterns comparing urban versus rural HC clients and community comparators were evident for the number of physician visits for each mental illness (Supplemental Figures S2 and S3).
Prevalence of Mental Illness by CIMD Dimensions
Across study years, the annual prevalence of mental illness was higher for HC clients in areas more (vs. less) marginalized in residential instability and ethno-cultural composition, comparable or higher for clients in areas more (vs. less) marginalized in situational vulnerability, and lower for clients in areas more (vs. less) marginalized in economic dependency (Supplemental Figures S4–S7).
In the most recent fiscal year, prevalence estimates were significantly higher for HC clients residing in areas more (vs. less) marginalized in residential instability (PRs = 1.1–1.88, particularly for less common illnesses), more (vs. less) marginalized in ethno-cultural composition (PRs = 1.08–1.43) except for suicide attempt, and more (vs. less) marginalized in situational vulnerability for select illnesses (PRs = 1.03–1.81, particularly for less common illnesses) (Figure 3). Conversely, prevalence estimates were significantly lower for HC clients residing in areas more (vs. less) marginalized in economic dependency (PRs = 0.86–0.96). For community comparators, similar findings were observed for each CIMD strata with some minor differences and lower prevalence estimates overall (Supplemental Figure S8).
Figure 3.
Prevalence of mental illness in Ontario home care clients by 4 CIMD dimensions: (A) residential instability, (B) ethno-cultural composition, (C) economic dependency, and (D) situational vulnerability, fiscal year 2022.
Multivariable Findings and CIMD—Urban/Rural Interactions
The lower prevalence of mental illnesses among rural HC clients remained statistically significant in models adjusted for various covariates (Table 2). There were statistically significant interactions between residential instability and urban/rural residence for all mental illnesses except mood/anxiety disorders and suicide attempt, illustrating more pronounced PRs (comparing more vs. less marginalized areas) for clients in urban than rural settings. Interaction terms between ethno-cultural composition and urban/rural residence were statistically significant for all mental illnesses except suicide attempt, with more pronounced PRs (more vs. less marginalized) for clients in urban than rural settings for most illnesses. One exception was schizophrenia where the PR (more vs. less marginalized ethno-cultural composition) was more pronounced for rural (1.56) than urban (1.13) clients. A significant interaction term between situational vulnerability and urban/rural residence was observed for schizophrenia only, where the PR was more pronounced for clients in urban settings. The only statistically significant interaction term involving economic dependency was observed for anxiety.
Table 2.
Crude and Adjusted Prevalence Ratios of Mental Illness Associated with Urban/Rural Residence and Each CIMD Dimension (Including Relevant Interaction Effects) Among Ontario Home Care Clients, All Fiscal Years.
| Mental Illness | Crude
a
PR (95% CI) |
Adjusted
b
PR (95% CI) |
Interaction Adjusted
c
PR (95% CI) |
|
|---|---|---|---|---|
| Rural | Urban | |||
| Rural vs Urban (ref) | ||||
| Any mental illness |
0.79 (0.78, 0.80) |
0.87 (0.86, 0.88) |
— | — |
| Mood & Anxiety |
0.74 (0.73, 0.76) |
0.84
(0.83, 0.85) |
— | — |
| Depression |
0.74 (0.73, 0.76) |
0.86
(0.85, 0.88) |
— | — |
| Anxiety |
0.75 (0.74, 0.77) |
0.87
(0.86, 0.89) |
— | — |
| Schizophrenia (CCDSS_schiz) |
0.63 (0.60, 0.67) |
0.80
(0.75, 0.85) |
— | — |
| Bipolar disorder |
0.68 (0.65, 0.72) |
0.81
(0.77, 0.85) |
— | — |
| Suicide attempt | 1.06 (0.97, 1.16) |
1.05 (0.95, 1.15) |
— | — |
| Residential Instability – Most vs Less Marginalized (ref) | ||||
| Any mental illness |
1.13
(1.12, 1.14) |
— |
1.07
(1.04, 1.10) |
1.12
(1.12, 1.13) |
| Mood & Anxiety |
1.13
(1.12, 1.14) |
1.12
(1.11, 1.13) |
— | — |
| Depression |
1.25
(1.24, 1.26) |
— |
1.15
(1.11, 1.20) |
1.24
(1.22, 1.25) |
| Anxiety |
1.21
(1.20, 1.22) |
— |
1.12
(1.08, 1.16) |
1.20
(1.19, 1.22) |
| Schizophrenia (CCDSS_schiz) |
1.94
(1.88, 2.00) |
— |
1.47
(1.30, 1.67) |
1.84
(1.77, 1.91) |
| Bipolar disorder |
1.56
(1.51, 1.60) |
— |
1.21
(1.09, 1.36) |
1.54
(1.49, 1.59) |
| Suicide attempt |
1.44
(1.36, 1.54) |
1.45
(1.35, 1.55) |
— | — |
| Ethno-cultural Composition – Most vs Less Marginalized (ref) | ||||
| Any mental illness |
1.18 (1.17, 1.19) |
— | 1.00 (0.93, 1.08) |
1.11
(1.11, 1.12) |
| Mood & Anxiety |
1.23 (1.22, 1.24) |
— | 1.03 (0.94, 1.12) |
1.15
(1.14, 1.16) |
| Depression |
1.24 (1.23, 1.25) |
— | 0.98 (0.88, 1.08) |
1.14
(1.12, 1.15) |
| Anxiety |
1.24 (1.23, 1.25) |
— | 0.96 (0.86, 1.07) |
1.15
(1.14, 1.16) |
| Schizophrenia (CCDSS_schiz) |
1.34 (1.30, 1.39) |
— |
1.56
(1.19, 2.06) |
1.13
(1.09, 1.17) |
| Bipolar disorder |
1.23 (1.20, 1.27) |
— |
0.64
(0.44, 0.93) |
1.07
(1.03, 1.10) |
| Suicide attempt |
0.86 (0.81, 0.91) |
0.80 (0.75, 0.85) |
— | — |
| Economic Dependency – Most vs Less Marginalized (ref) | ||||
| Any mental illness |
0.93 (0.92, 0.94) |
0.97 (0.96, 0.97) |
— | — |
| Mood & Anxiety |
0.92 (0.92, 0.93) |
0.97 (0.96, 0.98) |
— | — |
| Depression |
0.92 (0.91, 0.93) |
0.97 (0.96, 0.98) |
— | — |
| Anxiety |
0.92 (0.91, 0.93) |
0.97 (0.96, 0.98) |
1.01 (0.98, 1.05) |
0.97
(0.96, 0.98) |
| Schizophrenia (CCDSS_schiz) |
0.82 (0.80, 0.85) |
0.83 (0.80, 0.85) |
— | — |
| Bipolar disorder |
0.89 (0.86, 0.91) |
0.93 (0.91, 0.96) |
— | — |
| Suicide attempt | 0.96 (0.91, 1.03) |
0.94 (0.88, 1.01) |
— | — |
| Situational Vulnerability – Most vs Less Marginalized (ref) | ||||
| Any mental illness |
1.01 (1.00, 1.02) |
0.94 (0.94, 0.95) |
— | — |
| Mood & Anxiety |
0.98 (0.97, 0.98) |
0.91 (0.90, 0.92) |
— | — |
| Depression |
1.07 (1.06, 1.08) |
0.94 (0.93, 0.95) |
— | — |
| Anxiety |
1.02 (1.01, 1.04) |
0.92 (0.91, 0.93) |
— | — |
| Schizophrenia (CCDSS_schiz) |
1.65
(1.59, 1.70) |
— | 0.96 (0.86, 1.08) |
1.26
(1.22, 1.31) |
| Bipolar disorder |
1.31 (1.27, 1.34) |
1.02 (0.99, 1.06) |
— | — |
| Suicide attempt |
1.47
(1.38, 1.56) |
1.17 (1.09, 1.25) |
— | — |
Note. Where there were >1 mental illness measure for the same condition, only one measure was examined in the above models.
Adjusted for fiscal year of index date only.
Adjusted for fiscal year of index date, age, sex, dementia status, comorbidity level, urban/rural residence, CIMD dimensions and number of non-mental health physician visits in prior year.
Adjusted for fiscal year of index date, age, sex, dementia status, comorbidity level, urban/rural residence, CIMD dimensions (including all 4 CIMD*urban/rural residence interactions) and number of non-mental health physician visits in prior year.
Bolded indicates p < .05.
Discussion
In this large, repeated cross-sectional study of Ontario HC clients, the prevalence of various mental illnesses was significantly higher among clients residing in urban than rural settings and in areas more marginalized in residential instability, ethno-cultural composition and to a lesser degree, situational vulnerability. These findings remained robust after adjusting for other client characteristics. Among HC clients in urban settings or residing in areas more marginalized in residential instability or ethno-cultural composition, about 4 in 10 had any mental illness. The interaction of urban/rural residence and these CIMD measures illustrated that the significantly higher prevalence of selected mental illnesses evident for clients from more versus less marginalized areas was even greater for those in urban settings. Mental illness prevalence showed similar patterns for matched community comparators though estimates were considerably lower than observed in HC clients.
Previous Canadian and international estimates of depression in HC clients vary between 12.0% and 31.5%10–13,15,16,18 and of anxiety, between 10.6% and 23.1%.9,15,16,18 Our most recent estimates of depression and anxiety were in the middle to upper part of this range. The higher mental illness prevalence observed in urban and some more marginalized settings illustrates key contextual factors that may contribute to variation in estimates. A higher prevalence of depression and anxiety has been associated with race/ethnicity, low income and educational attainment, younger age, and living in a major city,9,11,16 although higher depression in rural areas has also been found. 11 A diagnosis of schizophrenia has been reported in 0.7–2.2% of HC clients,17,18 with adults aged ≥65 having a lower prevalence, 17 while bipolar disorder was evident in 0.7% of a small sample of HC clients from Switzerland. 18 Our estimates for a diagnosis of schizophrenia were at the top range of previous reports, and also higher for bipolar disorders, possibly due to our inclusion of younger HC clients. Suicide attempts in our HC sample were relatively rare and generally comparable to previous reports of self-harm in Ontario HC clients (0.93%). 14 Prevalence estimates for our community comparators were similar to recent Canadian statistics. 36
As the CIMD has not been previously investigated in relation to mental illness in HC clients, there is limited evidence with which to compare our findings. Residential instability, which reflects the tendency of neighbourhood inhabitants to fluctuate over time and, possibly, the stability of supports that these living arrangements promote, 37 was associated with a higher prevalence of mental illness that was most evident in urban clients. This is consistent with previous literature documenting the importance of social support for mental well-being, especially for older adults with multimorbidity 38 or functional limitations. 39
Similarly, higher marginalization in ethno-cultural composition was generally associated with an elevated prevalence of mental illness in urban HC clients. Racialized individuals are exposed to discrimination and barriers to mental health care 40 that increase their risk for mental illness, 19 and immigrants experience stressors associated with adjusting to a new environment that can diminish mental health and eliminate healthy immigrant effects. 41 We may have observed positive associations for clients in urban and not rural settings as immigrants tend to settle in urban centres 42 and may experience other socioeconomic inequities in these settings 43 that place them at higher risk for mental illness. Interestingly, higher marginalization in ethno-cultural composition showed a more pronounced positive association with schizophrenia prevalence and a negative association with bipolar disorder among rural clients. Risk of schizophrenia and bipolar disorder in migrants has been found to be higher and lower, respectively, than in the non-immigrant population. 44 It is possible that immigrants who settle in rural areas are unique in their exposure to past and ongoing personal and environmental risks for these conditions.
Economic dependency diverged from the other CIMD dimensions as it was generally associated with lower mental illness prevalence, perhaps reflecting its association with the proportion of residents in an area who are relatively older. Older adults are less likely than middle-aged or younger adults to meet the criteria for a mental disorder, 36 which may reflect true population differences in prevalence, or lower mental health service use 45 and/or challenges in detecting mental illness in older adults.9,10 The modestly lower prevalence of some mental illnesses (excluding schizophrenia, bipolar disorder and suicide attempt) among those more marginalized in situational vulnerability (adjusted estimates) contrasts with the known association between adverse socioeconomic conditions and risk of mental illness. 19 However, substantial barriers to mental health care exist for the most socioeconomically marginalized in Canada.46,47 Low educational attainment is also related to lower odds of seeking help for mental illness. 45 The most situationally vulnerable may therefore be less likely to present to the healthcare system and be captured in administrative data. Further research is needed to clarify whether and to what extent the CIMD dimensions are related to the likelihood of developing mental illness versus the likelihood of using mental health services.
Strengths and Limitations
Strengths of this study include the investigation of a diverse range of mental illnesses over a 10-year period among an at-risk and understudied population in Ontario. The large HC study population, use of matched comparators and focus on relevant social determinants of health are also key strengths. Some limitations should be noted. As this is a cross-sectional study, causality between associations should not be inferred. Administrative data capture health care use and may underestimate the true prevalence of mental illness. This may be particularly relevant for rural clients with mental illnesses who may face barriers in accessing appropriate health and social care services. Though validated in the general population26,27,29 and sub-populations with chronic illness,30–32 the mental illness case algorithms require further validation in the HC setting. The data lack important characteristics of mental illness, such as severity, trajectory, treatment, and health behaviours. The higher estimates of depression and anxiety when derived from case algorithms including drug claims may reflect more accurate estimates of these illnesses or false positive cases related to the use of antidepressants or anxiolytics to manage neuropsychiatric and behavioural symptoms for clients with dementia. These findings require further investigation. Care should also be taken when interpreting data collected during COVID-19, 48 although our estimates of annual prevalence observed a gradual return to pre-COVID-19 levels by 2022. Additionally, while the urban/rural binary measure improves statistical power and ease of interpretation, it may miss potentially non-linear associations with mental health service use that multiple strata are better positioned to detect. 49 Finally, area-based measures such as the CIMD are aggregate data, meaning inferences based on these data may not hold true at the individual level.
Conclusions
Our findings demonstrate the higher prevalence of mental illnesses in HC clients (relative to community comparators) and the importance of urban/rural residence and marginalization to estimates of mental illness in this at-risk population. Greater residential instability and ethno-cultural composition appear to be key drivers of mental health service use, and the magnitude (and in some cases, direction) of healthcare utilization associated with marginalization may further depend on an individual's urban or rural residence. Including HC clients in mental health surveillance is vital for understanding healthcare use and improving services and supports. This is a care setting where there are real opportunities to intervene early, 50 mitigate admissions to long-term care, 51 and support older adults’ ability to age in place. By acknowledging the inequities affecting mental illness in HC clients, we identify key groups for whom targeted public health interventions may be warranted.
Supplemental Material
Supplemental material, sj-docx-1-cpa-10.1177_07067437261468887 for Mental Illness Prevalence Among Ontario Home Care Clients: Variation by Urban/Rural Residence and Deprivation Measures: Prévalence de la maladie mentale chez les clients de soins à domicile en Ontario : variation selon la résidence urbaine/rurale et les mesures de défavorisation by Cindy Wang, Ruth Ann Marrie, Jin Luo, James M. Bolton, Andrea Gruneir, Erind Dvorani, Karl Everett, Zahra Goodarzi, Ping Li, Liisa Jaakkimainen, Colleen J. Maxwell and in The Canadian Journal of Psychiatry
Acknowledgments
In Ontario, this study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). We thank IQVIA Solutions Canada Inc. for the use of their Drug Information File. This document used data adapted from the Statistics Canada Postal CodeOM Conversion File, which is based on data licensed from Canada Post Corporation, and/or data adapted from the Ontario Ministry of Health Postal Code Conversion File, which contains data copied under licence from ©Canada Post Corporation and Statistics Canada. Parts of this material are based on data adapted from Statistics Canada, Census, 2016 and 2021. Parts of this material are based on data and/or information compiled and provided by CIHI and the Ontario MOH. The analyses, conclusions, opinions and statements expressed herein are solely those of the authors and do not reflect those of the Ontario MOH, MLTC or Statistics Canada; no endorsement is intended or should be inferred.
The authors also acknowledge other members of the EMHLTH-CC Research Team: David B. Hogan MD, Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada; Dallas Seitz MD PhD, Division of Geriatric Psychiatry, Centre for Addiction and Mental Health, Toronto, Canada; Matthias Hoben RN Dr rer medic, School of Health Policy and Management, York University, Toronto, Canada; Nathan Herrmann MD, Department of Psychiatry, Sunnybrook Health Science Centre and the University of Toronto, Toronto, Canada; Luke Mondor MSc, ICES, Toronto, Canada; Andrea Iaboni MD DPhil, KITE, Toronto Rehabilitation Institute, University Health Network and Department of Psychiatry, University of Toronto, Toronto, Canada; Kimberley Wilson PhD, Department of Family Relations and Applied Nutrition, University of Guelph, Guelph, Canada; and Walter Wodchis PhD, Institute for Health Policy, Management and Evaluation, University of Toronto and ICES, Toronto, Canada.
Footnotes
ORCID iDs: Cindy Wang https://orcid.org/0009-0000-0931-4370
Ruth Ann Marrie https://orcid.org/0000-0002-1855-5595
James M. Bolton https://orcid.org/0000-0001-6319-5181
Ethical Considerations: This study received ethics clearance from the University of Waterloo Human Research Ethics Committee (#46451) and data access approval from ICES. The use of Ontario data for this study was authorized under section 45 of Ontario's Personal Health Information Protection Act.
Author Contributions: CJM, CW, RAM, JMB, ZG, AG, and JL made a significant contribution to the concept, design, analysis and interpretation of the data; JL, ED, KE, and PL made a significant contribution to the acquisition, analysis and interpretation of data; CW and CJM drafted the initial manuscript, and all authors revised it critically for important intellectual content. All authors approved the final version of the article for publication and agreed to be accountable for all aspects of the work and resolved any issues related to its accuracy or integrity.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Public Health Agency of Canada (Grant No. 2425-HQ-000095). The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada.
Data Availability: The study datasets are held securely in coded form at ICES. Legal data sharing agreements prohibit ICES from making the dataset publicly available. However, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS (email: das@ices.on.ca). The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely on coding templates or macros that are unique to ICES and are, therefore, either inaccessible or may require modification.
Zahra Goodarzi reports receiving honorariums from the Canadian Coalition of Seniors Mental Health. Ruth Ann Marrie was a co-investigator on a study funded in part by Biogen Idec and Roche (no funds to her or her institution). All other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
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Supplementary Materials
Supplemental material, sj-docx-1-cpa-10.1177_07067437261468887 for Mental Illness Prevalence Among Ontario Home Care Clients: Variation by Urban/Rural Residence and Deprivation Measures: Prévalence de la maladie mentale chez les clients de soins à domicile en Ontario : variation selon la résidence urbaine/rurale et les mesures de défavorisation by Cindy Wang, Ruth Ann Marrie, Jin Luo, James M. Bolton, Andrea Gruneir, Erind Dvorani, Karl Everett, Zahra Goodarzi, Ping Li, Liisa Jaakkimainen, Colleen J. Maxwell and in The Canadian Journal of Psychiatry