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Our Health Counts Toronto: using respondent-driven sampling to unmask census undercounts of an urban indigenous population in Toronto, Canada.

ABSTRACT: OBJECTIVES:To provide evidence of the magnitude of census undercounts of 'hard-to-reach' subpopulations and to improve estimation of the size of the urban indigenous population in Toronto, Canada, using respondent-driven sampling (RDS). DESIGN:Respondent-driven sampling. SETTING:The study took place in the urban indigenous community in Toronto, Canada. Three locations within the city were used to recruit study participants. PARTICIPANTS:908 adult participants (15+) who self-identified as indigenous (First Nation, Inuit or Métis) and lived in the city of Toronto. Study participants were generally young with over 60% of indigenous adults under the age of 45 years. Household income was low with approximately two-thirds of the sample living in households which earned less than $C20 000 last year. PRIMARY AND SECONDARY OUTCOME MEASURES:We collected baseline data on demographic characteristics, including indigenous identity, age, gender, income, household type and household size. Our primary outcome asked: 'Did you complete the 2011 Census Canada questionnaire?' RESULTS:Using RDS and our large-scale survey of the urban indigenous population in Toronto, Canada, we have shown that the most recent Canadian census underestimated the size of the indigenous population in Toronto by a factor of 2 to 4. Specifically, under conservative assumptions, there are approximately 55 000 (95% CI 45 000 to 73 000) indigenous people living in Toronto, at least double the current estimate of 19 270. CONCLUSIONS:Our indigenous enumeration methods, including RDS and census completion information will have broad impacts across governmental and health policy, potentially improving healthcare access for this community. These novel applications of RDS may be relevant for the enumeration of other 'hard-to-reach' populations, such as illegal immigrants or homeless individuals in Canada and beyond.

SUBMITTER: Rotondi MA

PROVIDER: S-EPMC5770955 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe COVID-19 pandemic has highlighted the role of personal support workers (PSWs) in health care, as well as their work conditions. Our study aimed to understand the characteristics of the PSW workforce, their work conditions and their job security, as well as to explore the health of PSWs and the impact of precarious employment on their health.MethodsOur community-based participatory action research focused on PSWs in the Greater Toronto Area. We administered an online, cross-sectional survey between June and December 2020 using respondent-driven sampling. Data on sociodemographics, employment precarity, worker empowerment and health status were collected. We assessed the association between precarious employment and health using multivariable logistic regression models.ResultsWe contacted 739 PSWs, and 664 consented to participate. Overall, 658 (99.1%) completed at least part of the survey. Using data adjusted for our sampling approach, the participants were predominantly Black (76.5%, 95% confidence interval [CI] 68.2%-84.9%), women (90.1%, 95% CI 85.1%-95.1%) and born outside of Canada (97.4%, 95% CI 94.9%-99.9%). Most worked in home care (43.9%, 95% CI 35.2%-52.5%) or long-term care (34.5%, 95% CI 27.4%-42.0%). Although most participants had at least some postsecondary education (unadjusted proportion = 83.4%, n = 529), more than half were considered low income (55.1%, 95% CI 46.3%-63.9%). Most participants were precariously employed (86.5%, 95% CI 80.7%-92.4%) and lacked paid sick days (89.5%, 95% CI 85.8%-93.3%) or extended health benefits (74.1%, 95% CI 66.8%-81.4%). Nearly half of the participants described their health as less than very good (46.7%, 95% CI 37.9%-55.5%). Employment precarity was significantly associated with higher risk of depression (odds ratio 1.02, 95% CI 1.01-1.03).InterpretationDespite being key members of health care teams, most PSWs were precariously employed with low wages that keep them in poverty; the poor work conditions they faced could be detrimental to their physical and mental health. Equitable strategies are needed to provide decent work conditions for PSWs and to improve their health.

Project description:Respondent-driven sampling is a novel variant of link-tracing sampling for estimating the characteristics of hard-to-reach groups, such as HIV prevalence in sex workers. Despite its use by leading health organizations, the performance of this method in realistic situations is still largely unknown. We evaluated respondent-driven sampling by comparing estimates from a respondent-driven sampling survey with total population data.Total population data on age, tribe, religion, socioeconomic status, sexual activity, and HIV status were available on a population of 2402 male household heads from an open cohort in rural Uganda. A respondent-driven sampling (RDS) survey was carried out in this population, using current methods of sampling (RDS sample) and statistical inference (RDS estimates). Analyses were carried out for the full RDS sample and then repeated for the first 250 recruits (small sample).We recruited 927 household heads. Full and small RDS samples were largely representative of the total population, but both samples underrepresented men who were younger, of higher socioeconomic status, and with unknown sexual activity and HIV status. Respondent-driven sampling statistical inference methods failed to reduce these biases. Only 31%-37% (depending on method and sample size) of RDS estimates were closer to the true population proportions than the RDS sample proportions. Only 50%-74% of respondent-driven sampling bootstrap 95% confidence intervals included the population proportion.Respondent-driven sampling produced a generally representative sample of this well-connected nonhidden population. However, current respondent-driven sampling inference methods failed to reduce bias when it occurred. Whether the data required to remove bias and measure precision can be collected in a respondent-driven sampling survey is unresolved. Respondent-driven sampling should be regarded as a (potentially superior) form of convenience sampling method, and caution is required when interpreting findings based on the sampling method.

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Our Health Counts Toronto: using respondent-driven sampling to unmask census undercounts of an urban indigenous population in Toronto, Canada.

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