Skip to main content

Neighbourhood characteristics related to mental health service use among adults with diabetes: a population-based cohort study in New Brunswick, Canada

Abstract

Objective

It has been postulated that social and economic inequalities may shape the distributions of comorbid diabetes and mental illness. This observational cohort study using linked population-based administrative and geospatial datasets aimed to describe associations between neighbourhood socioenvironments and disorder-specific mental health service use among adults with diabetes in the province of New Brunswick, Canada.

Results

A baseline cohort of 66,275 persons aged 19 and over living with diabetes was identified. One-quarter (26.3%) had used healthcare services for mood and anxiety disorders at least once during the six-year follow-up period 2012/2013–2017/2018. Based on Cox proportional hazards models, the risk of mental health service contacts was significantly higher among those residing in the most materially deprived neighbourhoods [HR: 1.07 (95% CI: 1.01–1.14)] compared to those in the least so, and those in areas characterized with the highest residential instability [HR: 1.13 (95% CI: 1.05–1.22)] compared to those in areas with the lowest instability. Among adults with incident diabetes (N = 4410), age and sex but not neighbourhood factors were related to differential help-seeking behaviours for mental health problems. These findings underscored the gap between theoretical postulations and population-based observations in delineating the syndemics of neighbourhood socioenvironments and mental health outcomes in populations with high diabetes prevalence.

Introduction

Diabetes mellitus is often accompanied by other health problems, which may result in poorer health-related quality of life and greater use of healthcare services versus those living with diabetes alone [1,2,3,4]. Rising incidence and prevalence of diabetes (type 1 and type 2) worldwide is fuelling the need for evidence-informed interventions to prevent or delay common comorbidities at the population level [5,6,7,8]. A growing body of literature is enumerating the mental health implications of the experience of living with diabetes, including higher co-occurrence of depression and other mood and anxiety disorders [9, 10]; however, much of the research has focused on the clinical and epidemiological aspects, with limited attention to the social forces that may contribute to the onset of adverse mental health outcomes among persons with chronic disease [11].

It has been postulated that social and economic inequalities can shape the distributions and exacerbate the syndemic clusterings of comorbid diabetes and mental illness within and across populations [12,13,14,15]. Observational studies from different contexts have found neighbourhood socioeconomic disadvantage and less favourable walking environments to be independently associated with diabetes incidence and prevalence [16,17,18]; the associations of neighbourhood characteristics with mental health disorders and related service use are seemingly less robust [19, 20]. The interaction effects of neighbourhood environments and different health and health system metrics remain complex and the causal pathways are less well understood [21], particularly in populations characterized by smaller urban and rural communities [22]. A recent systematic review highlighted the knowledge gap in having identified a single study quantifying the associations between neighbourhood disadvantage, severe depression, and type 2 diabetes risk [23], with little evidence of synergistic interaction emerging from the reviewed Swedish sample [24].

In research elsewhere, we found only selected socioenvironmental characteristics of local communities were associated with increased mental health service use among older adults surviving myocardial infarction in New Brunswick, one of Canada’s most rapidly aging and most rural provinces [25]. A need for further research to better understand the role of community situations to improve mental health outcomes in populations with high chronic disease burden was identified. For this study, we extend the analysis to uniquely test for associations between neighbourhood factors and mental health service use among all adult New Brunswickers with pre-existing or newly diagnosed diabetes (types 1 or 2). Our aim is to provide insights into the modifying role of neighbourhood socioenvironments on incident disorder-specific mental health service contacts among adults with diabetes in this context of universal medical coverage. Specifically, a population-based observational cohort analysis was conducted, using linked administrative and geospatial datasets to assess the risk of service use for mood and anxiety disorders among patients with diabetes over the period 2012/2013–2017/2018.

Main text

Materials and methods

Study setting

Located in eastern Canada, the semi-rural province of New Brunswick is characterized with high rates of diabetes and of mental health service use. Diabetes prevalence stood at 10.7% and incidence at 0.8% in 2016, both rates significantly higher than the national averages (8.8% and 0.6% respectively) [26]. Meanwhile, 10.9% had used healthcare services for a mood or anxiety disorder in the same year, slightly above the national average (10.3%) [26]. The provincial diabetes prevention and control strategy identified the need to address comorbid mental health challenges as well as interactions among social and economic factors [27], although associated baseline measures and benchmarks for use in practice and research to inform sustainable investments were lacking, underscoring the need for improved evidence.

Study design and target population

Following research approaches detailed elsewhere [25], we linked longitudinally multiple person-level provincial administrative health datasets with area-based socioenvironmental datasets for the population of New Brunswick, Canada. Four pseudonymized administrative datasets were used: resident registrations for public healthcare insurance, vital statistics, annual case ascertainments for diabetes (types 1 and 2 combined), and annual ascertainments for the use of healthcare services for mood and anxiety disorders. Record linkages were performed deterministically using patients’ (scrambled) insurance numbers. Given the context of universal medical insurance, the data captured virtually all physician and hospital services among all residents, and this according to age, sex, and place of residence. Based on annual residential postal code information, each individual was assigned a series of neighbourhood-level indicators of socioenvironmental characteristics from datasets made available through the Canadian Urban Environmental Health Research Consortium [28]. The data were accessed in the secure computing environment of the New Brunswick Institute for Research, Data and Training (NB-IRDT) [29].

The study cohort comprised all adults 19 years and over and residing in New Brunswick ever diagnosed or newly diagnosed with diabetes in the baseline fiscal year of 2012/2013. These individuals were then followed over six years of observation, that is, to the end of the 2017/2018 fiscal year. Patients who died or moved away from the province during this period were censored. The case ascertainments were tallied using an algorithm for identifying population-based diabetes prevalence and incidence from administrative health data in accordance with validated data standards of the Canadian Chronic Disease Surveillance System (CCDSS) [8, 30]. Since type 1 diabetes is most often diagnosed in childhood and adolescence [7], incident cases among the adult population are assumed to chiefly reflect type 2 diabetes.

The outcome of interest was patients’ use of medical or hospital services at least once in a given year for a mood disorder (e.g., depression, neurosis, affective psychosis), an anxiety disorder (e.g., social phobia, panic disorder, dream anxiety disorder), or both. Ascertainments of service use for mood and anxiety disorders based on administrative information drew on validated CCDSS case definitions [31, 32].

Neighbourhood characteristics

The mental health implications for five different indicators of neighbourhood socioenvironments among persons with diabetes were considered. These included four area-based composite indicators of social and economic inequality collated in the Canadian Marginalization Index: material deprivation (e.g., proportion of low-income families, unemployment, homes needing major repair); residential instability (e.g., level of crowding, residential ownership, residential mobility); ethnic concentration (proportions of recent immigrants and visible minorities); and population dependency (e.g., labour force participation, proportion of seniors) [28, 33]. The fifth socioenvironmental indicator was derived from the Canadian Active Living Environments dataset, a summary index of features of communities that support active living, such as densities of homes, parks, footpaths, and transit stops [34]. The geocoded measures were ranked into nationally standardized groupings for New Brunswick’s 1454 census dissemination areas.

Statistical analysis

Cox proportional hazards regression analysis was used to assess the associations between health service contacts for mood and anxiety disorders and neighbourhood characteristics among the adult population with prevalent or incident diabetes. To control for recent history of common mental disorders, patients having used mental health services in the 3 years preceding baseline (that is, based on retrospective data from 2009/2010 to 2011/2012) were excluded from the analysis. Individuals’ age was included as a time-varying confounding factor over the period of observation and sex as a time-invariant confounder in the models. Adjusted hazard ratios (HRs) and bootstrapped 95% confidence intervals (CIs) were generated for each predictor using Stata v15 statistical software. Population counts were rounded to a base of five to reinforce the confidential nature of the administrative health data. We followed the RECORD (REporting of studies Conducted using Observational Routinely collected health Data) guidelines in our reporting [35].

Results

Study population

Among the New Brunswick population aged 19 and over at baseline (N = 621,385 in 2012/2013), 71,560 had been diagnosed with diabetes mellitus. After excluding individuals with a recent history of prevalent mental disorders (N = 2305) and those without complete residential history information in the province over the 6-year follow-up period (N = 2980), the cohort for analysis included 66,275 adults with diabetes residing in one of 1374 neighbourhoods. The subcohort of those newly diagnosed with diabetes in the year counted 4410 individuals.

Descriptives

Of the cohort of adults with diabetes, at least two-thirds were residing in neighbourhoods characterized by higher material deprivation (quintiles 4–5), higher population dependency, and low active living friendliness (Fig. 1). In the New Brunswick context, relatively few were residing in areas characterized by high ethnic concentration (8% in quintiles 4–5), as expected based on research findings elsewhere on the province’s adult population with chronic disease [25].

Fig. 1
figure 1

Percentage distribution of the adult population with diabetes by individual and neighbourhood-level characteristics, New Brunswick, Canada

Over the 6-year period of observation, 26.3% of the study population had used healthcare services at least once for a mood or anxiety disorder; the proportion was 19.3% among adults with newly diagnosed with diabetes (Table 1). Usage rates were higher among women than men and among younger adults than seniors, echoing results from our earlier investigation of mental health service contacts among adults with cardiac disorders [25]. While rates were somewhat higher among those residing in neighbourhoods characterized with greater material deprivation, the directions and magnitudes of the relationships between mental health service use and the various neighbourhood factors were less clear compared with those observed for individual demographics.

Table 1 Percent of the adult population with diabetes having used healthcare services for a mood or anxiety disorder, by individual and neighbourhood-level characteristics

Risk of mental health service contacts

Results from the proportional hazards models showed significantly increased risk of using health services for mood and anxiety disorders among adults ever diagnosed with diabetes residing in neighbourhoods characterized by the greatest material deprivation compared with their more affluent counterparts (HR: 1.07 [95% CI: 1.01–1.14], p < 0.05), and among those residing in the most residentially unstable areas compared with the least unstable areas (HR: 1.13 [95% CI: 1.05–1.22]), after adjusting for age and sex (Table 2, model 1). Other observed associations between characteristics of local environments and mental health service contacts among prevalent diabetes cases were mostly not statistically significant. Among the adult population with incident diabetes, none of the neighbourhood-level indicators—only individuals’ demographics—exercised a discernible influence on the outcome variable (Table 2, model 2).

Table 2 Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between individual and neighbourhood-level characteristics and risk of healthcare use for a mood or anxiety disorder among adults with diabetes

Discussion

This novel study assessed the role of neighbourhood environments on mental health comorbidities among adults with diabetes in a publicly funded healthcare system, an underexplored area in terms of bridging the gap between theoretical postulation and population-based observation. Drawing on linked province-wide administrative and geospatial datasets tracking individuals’ healthcare service contacts over a 6-year follow-up period, and controlling for prior record of mental disorders, only partial associations were found between selected socioenvironmental characteristics of local communities and the risk of healthcare use for mood and anxiety disorders among adults with prevalent diabetes in the Province of New Brunswick, Canada. Notably, the risk of mental health service contacts was significantly higher among those residing in the most materially deprived neighbourhoods (HR: 1.07 [95% CI: 1.01–1.14]) compared to those in the least deprived areas, and among those residing in neighbourhoods characterized with the highest degree of residential instability (HR: 1.13 [95% CI: 1.05–1.22]) compared to those in areas with the lowest instability. No significant associations were found when distinguishing the analysis among adults with incident diabetes, that is, beyond the biodemographic variables of age and sex.

Although there is much evidence of benefits to physical health of favourable neighbourhood socioenvironments, the present findings are generally consistent with observational studies elsewhere reporting limited evidence of clearly convincing associations between mental health outcomes with neighbourhood characteristics in aging populations. Previous small-area studies based on large datasets have found no significant associations between depressive symptoms and other mood or anxiety disorders with active living environments in Canada [20], between mental disorders with neighbourhood income rank in selected Chinese cities [19], or between depression and type 2 diabetes risk with differing levels of neighbourhood socioeconomic deprivation in Sweden [24]. An investigation from Brazil indicated some geographic clustering of depression and diabetes, but at the (large-scale) state level, which may have reflected heterogeneity in access to diagnostic services [15]. Our study adds to the nascent research into neighbourhood effects on comorbid diabetes and mental illness [23], and reinforces the need for more empirical examinations on the syndemics of neighbourhood socioenvironments and mental health implications in populations with high diabetes burden.

Limitations

Some limitations to this investigation are noted, including ones inherited from our data sources and linkages as originally applied to the older adult population in New Brunswick presenting with a different cardiometabolic condition [25]. Firstly, it is likely the prevalence of mental health service use was underestimated, since the administrative datasets excluded information on service use from exclusively community-based settings or private practices. In addition, we lacked linkable individual-level data on lifestyle behaviours and socioeconomic characteristics potentially influencing health outcomes. Specific to this enquiry, the CCDSS methodology used here for estimating the population with diabetes from administrative medical and hospital records does not distinguish between types 1 and 2 of the disease, which differ in etiology and healthcare responses [36]. More broadly, the generalizability of results from our study context—one characterized by uniquely smaller urban and rural settlements—is uncertain.

Availability of data and materials

The datasets used in this study are not readily available because restrictions apply to the accessibility of these confidential data, which were used under license for the current study. Requests to access the datasets for research purposes should be directed to the NB-IRDT (www.unb.ca/nbirdt). Requests for accessing the geocoded datasets of socioenvironmental indices may be directed to the Canadian Urban Environmental Health Research Consortium (www.canue.ca).

Abbreviations

CCDSS:

Canadian Chronic Disease Surveillance System

CI:

Confidence interval

HR:

Hazard ratio

NB-IRDT:

New Brunswick Institute for Research, Data and Training

References

  1. Gruneir A, Markle-Reid M, Fisher K, Reimer H, Ma X, Ploeg J. Comorbidity burden and health services use in community-living older adults with diabetes mellitus: a retrospective cohort study. Can J Diabetes. 2016;40(1):35–42.

    Article  Google Scholar 

  2. Hutter N, Schnurr A, Baumeister H. Healthcare costs in patients with diabetes mellitus and comorbid mental disorders—a systematic review. Diabetologia. 2010;53(12):2470–9.

    CAS  Article  Google Scholar 

  3. Lopez-de-Andres A, Carrasco-Garrido P, Esteban-Hernandez J, Gil-de-Miguel Á, Jiménez-García R. Characteristics and hospitalization costs of patients with diabetes in Spain. Diabetes Res Clin Pract. 2010;89(1):e2-4.

    Article  Google Scholar 

  4. Maddigan SL, Feeny DH, Majumdar SR, Farris KB, Johnson JA. Understanding the determinants of health for people with type 2 diabetes. Am J Public Health. 2006;96(9):1649–55.

    Article  Google Scholar 

  5. NCD Risk Factor Collaboration. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet. 2016;387(10027):1513–30.

    Article  Google Scholar 

  6. Geiss LS, Wang J, Cheng YJ, Thompson TJ, Barker L, Li Y, et al. Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980–2012. JAMA. 2014;312(12):1218–26.

    CAS  Article  Google Scholar 

  7. Maahs DM, West NA, Lawrence JM, Mayer-Davis EJ. Epidemiology of type 1 diabetes. Endocrinol Metab Clin N Am. 2010;39(3):481–97.

    Article  Google Scholar 

  8. Feely A, Lix LM, Reimer K. Estimating multimorbidity prevalence with the Canadian Chronic Disease Surveillance System. Health Promot Chronic Dis Prev Can. 2017;37(7):215–22.

    Article  Google Scholar 

  9. Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001;24(6):1069–78.

    CAS  Article  Google Scholar 

  10. Robinson DJ, Coons M, Haensel H, Vallis M, Yale J-F. Diabetes and mental health. Can J Diabetes. 2018;42:S130–41.

    Article  Google Scholar 

  11. Gallo JJ, Joo JH, Visvanathan K, McGinty EE, Thrul J, Holingue C. An idea whose time has come: promoting health equity by preventing the syndemic of depression and medical comorbidity. Am J Geriatric Psychiatry. 2021;29(1):12–4.

    Article  Google Scholar 

  12. McCurley JL, Gutierrez AP, Bravin JI, Schneiderman N, Reina SA, Khambaty T, et al. Association of social adversity with comorbid diabetes and depression symptoms in the Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study: a syndemic framework. Ann Behav Med. 2019;53(11):975–87.

    Article  Google Scholar 

  13. Mendenhall E. Beyond comorbidity: a critical perspective of syndemic depression and diabetes in cross-cultural contexts. Med Anthropol Q. 2016;30(4):462–78.

    Article  Google Scholar 

  14. Mendenhall E, Kohrt BA, Norris SA, Ndetei D, Prabhakaran D. Non-communicable disease syndemics: poverty, depression, and diabetes among low-income populations. Lancet. 2017;389(10072):951–63.

    Article  Google Scholar 

  15. Diderichsen F, Andersen I. The syndemics of diabetes and depression in Brazil—an epidemiological analysis. SSM Popul Health. 2019;7(100318):1–6.

    Google Scholar 

  16. Kivimäki M, Vahtera J, Tabák AG, Halonen JI, Vineis P, Pentti J, et al. Neighbourhood socioeconomic disadvantage, risk factors, and diabetes from childhood to middle age in the Young Finns Study: a cohort study. Lancet Public Health. 2018;3(8):e365–73.

    Article  Google Scholar 

  17. Creatore MI, Glazier RH, Moineddin R, Fazli GS, Johns A, Gozdyra P, et al. Association of neighborhood walkability with change in overweight, obesity, and diabetes. JAMA. 2016;315(20):2211–20.

    CAS  Article  Google Scholar 

  18. Bilal U, Auchincloss AH, Diez-Roux AV. Neighborhood environments and diabetes risk and control. Curr Diab Rep. 2018;18(62):1–10.

    Google Scholar 

  19. Chiavegatto Filho ADP, Sampson L, Martins SS, Yu S, Huang Y, He Y, et al. Neighbourhood characteristics and mental disorders in three Chinese cities: multilevel models from the World Mental Health Surveys. BMJ Open. 2017;7(10):e017679.

    Article  Google Scholar 

  20. Lukmanji A, Williams JVA, Bulloch AGM, Dores AK, Patten SB. The association of active living environments and mental health: a Canadian epidemiological analysis. Int J Environ Res Public Health. 2020;17(6):1910.

    Article  Google Scholar 

  21. Yen IH, Michael YL, Perdue L. Neighborhood environment in studies of health of older adults: a systematic review. Am J Prev Med. 2009;37(5):455–63.

    Article  Google Scholar 

  22. Terashima M, Rainham DGC, Levy AR. A small-area analysis of inequalities in chronic disease prevalence across urban and non-urban communities in the Province of Nova Scotia, Canada, 2007–2011. BMJ Open. 2014;4(5):e004459.

    Article  Google Scholar 

  23. Walsan R, Bonney A, Mayne DJ, Pai N, Feng X, Toms R. Serious mental illness, neighborhood disadvantage, and type 2 diabetes risk: a systematic review of the literature. J Prim Care Community Health. 2018;9:215013271880202.

    Article  Google Scholar 

  24. Mezuk B, Chaikiat Å, Li X, Sundquist J, Kendler KS, Sundquist K. Depression, neighborhood deprivation and risk of type 2 diabetes. Health Place. 2013;23:63–9.

    Article  Google Scholar 

  25. Foroughi I, Gupta N, Crouse DL. Healthcare service use for mood and anxiety disorders following acute myocardial infarction: a cohort study of the role of neighbourhood socioenvironmental characteristics in a largely rural population. Int J Environ Res Public Health. 2020. https://doi.org/10.3390/ijerph17144939.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Public Health Agency of Canada. Public Health Infobase: Canadian chronic disease surveillance system. Ottawa; 2019.

  27. New Brunswick Department of Health. A comprehensive diabetes strategy for New Brunswickers 2011–15. Fredericton: Government of New Brunswick; 2011.

  28. Brook JR, Setton EM, Seed E, Shooshtari M, Doiron D, et al. The Canadian urban environmental health research consortium—a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health. BMC Public Health. 2018;18(1):144.

    Article  Google Scholar 

  29. Maillet DC, McDonald JT. New Brunswick Institute for Research, Data and Training: a ten-year partnership between government and academia. In: Handbook on using Administrative Data for Research and Evidence-based policy. Cambridge: Abdul Latif Jameel Poverty Action Lab; 2020. p. 311–46.

  30. Hamm NC, Pelletier L, Ellison J, Tennenhouse L, Reimer K, Paterson JM, et al. Trends in chronic disease incidence rates from the Canadian chronic disease surveillance system. Health Promot Chronic Dis Prev Can. 2019;39(6–7):216–24.

    Article  Google Scholar 

  31. Public Health Agency of Canada. Report from the Canadian Chronic Disease Surveillance System: mood and anxiety disorders in Canada, 2016. Ottawa: Public Health Agency of Canada; 2016.

    Google Scholar 

  32. O’Donnell S, Vanderloo S, McRae L, Onysko J, Patten SB, Pelletier L. Comparison of the estimated prevalence of mood and/or anxiety disorders in Canada between self-report and administrative data. Epidemiol Psychiatry Sci. 2016;25(4):360–9.

    Article  Google Scholar 

  33. Matheson FI, Dunn JR, Smith KLW, Moineddin R, Glazier RH. Development of the Canadian marginalization index: a new tool for the study of inequality. Can J Public Health. 2012;103(8 Suppl 2):S12–6.

    Article  Google Scholar 

  34. Herrmann T, Gleckner W, Wasfi RA, Thierry B, Kestens Y, Ross NA. A pan-Canadian measure of active living environments using open data. Health Rep. 2019;30(5):16–25. https://doi.org/10.25318/82-003-x201900500002-eng.

    Article  Google Scholar 

  35. Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The reporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLoS Med. 2015;12(10):e1001885.

    Article  Google Scholar 

  36. LeBlanc AG, Gao YJ, McRae L, Pelletier C. Twenty years of diabetes surveillance using the Canadian chronic disease surveillance system. Health Promot Chronic Dis Prev Can. 2019;39(11):306–9.

    Article  Google Scholar 

Download references

Acknowledgements

The datasets used for the analysis were accessed in the New Brunswick Institute for Research, Data and Training, located at the University of New Brunswick in Fredericton, Canada. The services and activities of the NB-IRDT are supported by the Government of New Brunswick. The geographic datasets of socioenvironmental indices were made available through the Canadian Urban Environmental Health Research Consortium.

Funding

Financial support was received from the Canadian Institutes of Health Research (Operating Grant DA4-170257) and the New Brunswick Health Research Foundation (Bridge Grant 2018–19). The funding agencies had no role in the design, execution, interpretation, or writing of the study or decision to submit for publication.

Author information

Authors and Affiliations

Authors

Contributions

NG and DLC conceived the study. IF conducted formal data analysis. NG wrote the first draft of the manuscript. All authors contributed to the interpretation of the results. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Neeru Gupta.

Ethics declarations

Ethics approval and consent to participate

Ethics approval for this study using pseudonymized provincial administrative datasets was obtained from the University of New Brunswick’s Research Ethics Board (REB #2017–076). Informed consent was not required to participate in accordance with provincial legislation governing the use and protection of personal information.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gupta, N., Crouse, D.L. & Foroughi, I. Neighbourhood characteristics related to mental health service use among adults with diabetes: a population-based cohort study in New Brunswick, Canada. BMC Res Notes 15, 79 (2022). https://doi.org/10.1186/s13104-022-05966-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13104-022-05966-9

Keywords

  • Diabetes mellitus
  • Mental disorders
  • Social determinants of health
  • Population health
  • Environment design
  • Residence characteristics
  • Public health surveillance
  • Syndemic