A cross-sectional analysis of the relationship between diabetes and health access barriers in an urban First Nations population in Canada

Abstract

ObjectiveThis study explores the relationship between health access barriers and diabetes in an urban First Nations population in Canada.DesignData from a self-identified urban First Nations population were collected using respondent-driven sampling (RDS). As no clear approach for regression modelling of RDS data is available, two logistic regression modelling approaches, including survey-based logistic and generalised linear mixed models, were used to explore the relationship between diabetes and health barriers of interest, including access to healthcare, food, housing and socioeconomic factors.SettingHamilton, Ontario, Canada.ParticipantsThis cross-sectional study used data collected from the Our Health Counts study, in partnership with the De dwa da dehs nye>s Aboriginal Health Centre, which recruited 554 First Nations adults living in Hamilton using RDS.ResultsAfter adjusting for covariates, multivariable regression techniques showed a statistically significant relationship between a self-reported diagnosis of diabetes and a lack of culturally appropriate care among urban First Nations peoples (OR: 12.70, 95% CI 2.52 to 57.91). There was also a trend towards a relationship between diabetes and not having a doctor available in the area, feeling that healthcare provided was inadequate and a lack of available healthcare services in the area.ConclusionsUrban First Nations peoples who felt the health service they received was not culturally appropriate were more likely to have diabetes, compared with those who did not feel the service they received was culturally inappropriate. Establishing more healthcare services that integrate First Nations cultures and traditions could improve access to care and the course of treatment for urban First Nations peoples living with diabetes.

Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study

“…Models using regression weights had poor coverage. The GLMM model fit with the declining correlation structure suggested by Beckett et al [22] exhibited low parameter coverage rate, despite an acceptable error rate, due to underestimation of the parameter variance. This was also the only model for which there were any problems with convergence; 1–13% of the simulated RDS samples did not result in sensible standard errors (reported as either infinite or zero).…”

“…Fewer authors have attempted to control for clustering; Lima et al attempted to control for homophily (related to clustering) by incorporating the outcome value of the recruiter as an independent variable [21] and Schwartz et al used robust Poisson regression ‘accounting for clustering’ of participants within the same seed [13]. We found only one study which used both weighted regression and controlled for clustering; those authors used weighted regression and modelled dependence among observations with two methods and found similar results with both [22]. Treatment of clustering is the thornier of the two statistical issues with RDS regression, because clusters, if they exist, may be difficult to identify.…”

“…For the surveylogistic models fit in SAS (models 9, 10) the strata and class commands were used to define observations within recruiters within seeds. Several methods were used for the GLMM models: the glmer function was used to model unstructured covariance within seeds (models 11–12, 28–29), glimmix was used to model first-order auto regressive correlation along recruitment chains (models 13) and immediate recruiters as the clustering unit, with exchangeable correlation structure (model 14), glmmPQL in the glmm package [33] was used to model a declining correlation structure as described in Beckett et al [22], in which the correlation decreases with increased distance along the recruitment trees (model 15). Finally, in the GEE models, geeglm from the geepack package [30] was used to fit an independent working covariance structure within recruiters (models 16–17, 30–31), and glimmix was used to fit auto-regression correlation along recruitment lines (model 18) and exchangeable working correlation structures within recruiter (models 19–23).…”

Exploring Sex-Based Differences in Concussion Knowledge, Attitudes and Resources in Young First Nations Hockey Players: A Cross-Sectional Survey from Ontario, Canada

“…Data collected on knowledge, attitudes, reporting behaviour and sources of information about concussion by sex can help to build customised health education programmes for Indigenous young boys and girls to promote health. Targeted health education programmes have demonstrated effectiveness in Indigenous populations (Beckett, Firestone, McKnight, Smylie, & Rotondi, 2018;Smylie et al, 2018).…”

Barriers and Mitigating Strategies to Healthcare Access in Indigenous Communities of Canada: A Narrative Review

“…Distal barriers necessitate that organizations review and revise their hiring policies and processes, performance evaluation, recognition and compensation to support the commitment to equitable health [25,67]. Practices include developing and delivering culturally sensitive healthcare services by: hiring Indigenous HCPs, or non-Indigenous staff who are motivated to work in Indigenous communities [25,58], developing anti-racism policies, and providing cross-cultural training [86]. Since 2016, Ontario requires mandatory cultural competency training for all public service employees including HCPs [87]; similarly, all existing and new Alberta Health Services staff are required to undergo Indigenous cultural competency training [88].…”