Neighbourhood immigration, health care utilization and outcomes in patients with diabetes living in the Montreal metropolitan area (Canada): a population health perspective

Abstract

Background: Understanding health care utilization by neighbourhood is essential for optimal allocation of resources, but links between neighbourhood immigration and health have rarely been explored. Our objective was to understand how immigrant composition of neighbourhoods relates to health outcomes and health care utilization of individuals living with diabetes.

Methods: This is a secondary analysis of administrative data using a retrospective cohort of 111,556 patients living with diabetes without previous cardiovascular diseases (CVD) and living in the metropolitan region of Montreal (Canada). A score for immigration was calculated at the neighbourhood level using a principal component analysis with six neighbourhood-level variables (% of people with maternal language other than French or English, % of people who do not speak French or English, % of immigrants with different times since immigration (<5 years, 5-10 years, 10-15 years, 15-25 years)). Dependent variables were all-cause death, all-cause hospitalization, CVD event (death or hospitalization), frequent use of emergency departments, frequent use of general practitioner care, frequent use of specialist care, and purchase of at least one antidiabetic drug. For each of these variables, adjusted odds ratios were estimated using a multilevel logistic regression.

Results: Compared to patients with diabetes living in neighbourhoods with low immigration scores, those living in neighbourhoods with high immigration scores were less likely to die, to suffer a CVD event, to frequently visit general practitioners, but more likely to visit emergency departments or a specialist and to use an antidiabetic drug. These differences remained after controlling for patient-level variables such as age, sex, and comorbidities, as well as for neighbourhood attributes like material and social deprivation or living in the urban core.

Conclusions: In this study, patients with diabetes living in neighbourhoods with high immigration scores had different health outcomes and health care utilizations compared to those living in neighbourhoods with low immigration scores. Although we cannot disentangle the individual versus the area-based effect of immigration, these results may have an important impact for health care planning.

Figure 1

Study cohort. *Diagnosis of diabetes during hospitalization or 3 physician claims within one year with a diagnosis of diabetes.

Figure 2

Map of dissemination areas (DAs) of the census metropolitan area (CMA) of Montreal and the urban core delimitation.

Figure 3

Map of the immigration quintiles in the CMA of Montreal.

Figure 4

Map of the immigration and social deprivation subpopulations in the CMA of Montreal.

Figure 5

Map of the immigration and material deprivation subpopulations in the CMA of Montreal.

Figure 6

Outcomes adjusted odds ratios (OR) associated with immigration by deprivation defined neighbourhoods as compared to low immigration wealthy neighbourhoods (reference group): multilevel logistic regression models. *This is the representation of six multilevel logistic regression models. The dependent variable is indicated at the bottom of each square. The independent categorical variable is the combination of immigration quintiles with social deprivation quintiles (3 top squares) or with material deprivation (3 bottom squares). The reference category refers to neighbourhoods in the first immigration quintile and the first deprivation quintile (low immigration and less deprive neighbourhoods). All models were adjusted for age, sex, being an incident or prevalent diabetes case, having diabetes with complications, presence of comorbidities (hypertension, dyslipidemia, dementia, chronic pulmonary disease, renal disease, connective tissue disease, ulcer disease, mild liver disease, moderate to severe liver disease, any tumor, leukemia, lymphoma, metastatic tumor), and living in the urban core. ICC: intraclass correlation. MOR: median odds ratio.

Figure 7

Health care utilization adjusted odds ratios (OR) associated with immigration by deprivation-defined neighbourhoods as compared to low immigration wealthy neighbourhoods (reference group): multilevel logistic regression models. *This is the representation of eight multilevel logistic regression models. The dependent variable is indicated at the bottom of each square. The independent categorical variable is the combination of immigration quintiles with social deprivation quintiles (4 top squares) or with material deprivation (4 bottom squares). The reference category refers to neighbourhoods in the first immigration quintile and the first deprivation quintile (low immigration and less deprive neighbourhoods). All models were adjusted for age, sex, being an incident or prevalent diabetes case, having diabetes with complications, presence of comorbidities (hypertension, dyslipidemia, dementia, chronic pulmonary disease, renal disease, connective tissue disease, ulcer disease, mild liver disease, moderate to severe liver disease, any tumor, leukemia, lymphoma, metastatic tumor), and living in the urban core. ICC: intraclass correlation. MOR: median odds ratio.