Table 1 Characteristics of included studies
Author & year of publication | Study design (Accrual) | City and Province | Research question / study objectives | Data collection | Total sample size (n) | Limitations reported by authors |
|---|---|---|---|---|---|---|
Cloos, Ndao, Aho, et al., (2020) [23] | Cross-sectional (2016 to 2017) | Montréal, Québec | To examine the association between precarious migration status and self-perceived health in Montréal | -Snowball sampling, local media campaign in community, and recruitment through health clinic -Face-to-face questionnaire -Subsequent focus groups | 806 | -Potentially unrepresentative sample -Recruiting uninsured in a clinic could introduce selection bias -Self-reporting could introduce social desirability bias -Potential for misreporting -Lack of power due to sample size -No control for confounding effects -Cross-sectional study design makes it difficult to establish causality |
Ridde, Aho, Ndao, et al., (2020) [22] | Cross-sectional (2016 to 2017) | Montréal, Québec | To examine the unmet health care needs and its associated factors among Medicare uninsured migrants residing in Montréal | -Snowball sampling, local media campaign in community, and recruitment through health clinic -Face-to-face questionnaire -Subsequent focus groups | 806 | -Certain social groups (Chinese and Anglo-Caribbean migrants) were underrepresented -Participants could have been surveyed twice given that no personal information was collected to identify participants -Risk for interviewer bias is possible -Did not collect objective data (such as health outcomes) -Cross-sectional study design makes it difficult to establish causality |
Darling, Bennett, Burton, et al., (2019) [47] | Retrospective cohort (2012 to 2015) | Ontario | To analyze the characteristics, health service utilization, and clinical outcomes of Ontario residents not covered by Ontario Health Insurance Plan (OHIP) that receive services from midwives | - BORN-Ontario records meeting criteria during specified time period (pregnancy to 6 weeks postpartum) for all midwifery clients | 55,634 | -Did not do chi-square analysis to see whether the proportions differ by insurance status -BORN-Ontario registry does not provide a reason why individuals are uninsured and whether they had non-OHIP insurance |
Hynie, Ardern, & Robertson (2016) [48] | Cross-sectional (9 consecutive years, 2002/3 to 2010/11) | Ontario | To compare the diagnoses, severity, and outcomes associated with acute care visits by Ontario residents with and without insurance | -Data of all emergency visits in the National Ambulatory Care Reporting System (NACRS) | 44,489,750 (unique emergency department visits) | -Repeat visits may have caused an increase in the number of uninsured -Those excluded due to homelessness could have been uninsured -NACRS data represent number of unique visits, not individuals -Cross-sectional study design makes it difficult to establish causality |
Bunn, Fleming, Rzeznikiewiz, et al., (2013) [41] | Retrospective cohort (2005 to 2009) | Toronto, Ontario | To determine demographic and diagnostic information about the medically uninsured patient population and compare it with that of the medically insured patient population at a primary care centre | -Medical charts and billing records to determine number of uninsured individuals -Random sampling used to obtain insured individuals | 95 | -Lack of power due to sample size -Low external validity -Members of uninsured group were uninsured for a number of reasons; heterogeneous group -Unknown if all participants were screened for all of the diagnoses investigated -No control for confounding effects -Internal validity of this study was limited by the fact that medical charts of 7 uninsured patients and 2 insured patients could not be located -Unclear whether the two groups were comparable in all fronts except for insurance status; only compared income, age, and sex |
Gagnon, Merry, & Haase (2013) [42] | Prospective cohort (2006 to 2009) | Toronto, Ontario, Montréal, Québec and Vancouver, British Columbia | To determine predictors (social, biomedical, migration, and health service) of emergency cesarean delivery in order to develop a better understanding of disparities in emergency cesarean delivery rates between Canadian-born and migrant women | -Convenience & alternate sampling -Recruited through the Childbearing Health and Related Services Needs of Newcomers study -Medical chart review and interviewer-assisted validated questionnaire | 1,025 | -Heterogeneity of comparison group -Uninsured population was not defined. Unclear whether sample included refugees, asylum-seekers, or immigrants -No analysis of maternity unit characteristics -Full regression results were not presented -Canadian-born women were included in the original study but they did not act as a comparator here |
Rousseau, Laurin-Lamothe, Rummens, et al., (2013) [45] | Retrospective cohort (2008 to 2009) | Montréal, Québec and Toronto, Ontario | To examine the differences in help-seeking and service delivery across migratory statuses, institutions and provinces | -Chart review of patient records from 3 hospitals (2 in Montréal, 1 in Toronto) -Charts were randomly sampled from a curated list of uninsured files -Hospital 1 (Montréal) randomly selected 500 files for review -Hospital 2 (Montréal) reviewed all files (805) without a health insurance number -Hospital 3 (Toronto) reviewed 902 files (576 refugee claimants with IFHP coverage and 406 uninsured immigrant, refugee or undocumented patients without provincial coverage) | 2,035 | -Due to the retrospective chart review design, sociodemographic variables were unavailable or missing and could not be accounted for -No control for confounding effects -Potential differences across hospitals were not studied |
Wilson-Mitchell & Rummens, (2013) [43] | Retrospective cohort (2007 to 2010) | Toronto, Ontario | To examine the relationship between insurance status and perinatal outcomes | -Chart review of hospital records -Insured patients were randomly selected -Uninsured patients were obtained from hospital record lists using self-pay payment codes | 453 | -Retrospective chart reviews may be inaccurate or inconsistent -Low external validity -Lack of power due to sample size -Researchers could not match uninsured to insured because demographic information was either inaccurate or not recorded -Other information, such as place of birth, was not recorded in the chart |
Wiedmeyer, Lofters, & Rashid, (2012) [44] | Retrospective cohort (2004 to 2008) | Toronto, Ontario | To examine if refugee women at a community health centre were appropriately screened for cervical cancer, and what characteristics affect whether they were screened | -Chart review of all patient records from the community health centre from 2004–2008 (sampling not necessary) - Database search of all registered clients of Access Alliance Multicultural Health and Community Services meeting criteria within the specified timeline | 357 | -Lack of power due to sample size -Low external validity -Did not analyze provider effects (such as male or female physician, demeanor) |
Jarvis, Munoz, Graves, et al., (2011) [46] | Retrospective cohort (2004 to 2007) | Montréal, Québec | To assess prenatal and perinatal health outcomes among uninsured pregnant women in Montréal | -Random sampling to obtain insured cohort and convenience sampling to obtain uninsured cohort -Database and chart record audit during specified time period | 143 | -Difficult population to study as uninsured are often undocumented -Study is not representative of uninsured women with no prenatal care (low external validity) -One of the family health centres provided financial assistance to women -Difficult to collect sociodemographic information -Confounders may have been missed |