Dataset Information

Unmasking health determinants and health outcomes for urban First Nations using respondent-driven sampling.

ABSTRACT:

Objective

Population-based health information on urban Aboriginal populations in Canada is limited due to challenges with the identification of Aboriginal persons in existing health data sets. The main objective of the Our Health Counts (OHC) project was to work in partnership with Aboriginal stakeholders to generate a culturally relevant, representative baseline health data set for three urban Aboriginal communities in Ontario, Canada.

Design

Respondent-driven sampling (RDS).

Setting

Hamilton, Ontario, Canada.

Participants

The OHC study, in partnership with the De dwa da dehs ney >s Aboriginal Health Access Centre (DAHC), recruited 554 First Nations adults living in Hamilton using RDS.

Results

Among First Nations adults living in Hamilton, 78% earned less than $20 000 per year and 70% lived in the lowest income quartile neighbourhoods. Mobility and crowded living conditions were also highly prevalent. Common chronic diseases included arthritis, hypertension, diabetes and chronic obstructive pulmonary disease and rates of emergency room access were elevated.

Conclusions

RDS is an effective sampling method in urban Aboriginal contexts as it builds on existing social networks and successfully identified a population-based cohort. The findings illustrate striking disparities in health determinants and health outcomes between urban First Nations individuals and the general population which have important implications for health services delivery, programming and policy development.

SUBMITTER: Firestone M

PROVIDER: S-EPMC4091271 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Unmasking health determinants and health outcomes for urban First Nations using respondent-driven sampling.

Firestone M M Smylie J J Maracle S S Spiller M M O'Campo P P

BMJ open 20140709 7

<h4>Objective</h4>Population-based health information on urban Aboriginal populations in Canada is limited due to challenges with the identification of Aboriginal persons in existing health data sets. The main objective of the Our Health Counts (OHC) project was to work in partnership with Aboriginal stakeholders to generate a culturally relevant, representative baseline health data set for three urban Aboriginal communities in Ontario, Canada.<h4>Design</h4>Respondent-driven sampling (RDS).<h4> ...[more]

PMID: 25011988

Similar Datasets

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.

Project description:IntroductionRespondent-driven sampling (RDS) is a variant of a link-tracing design intended for generating unbiased estimates of the composition of hidden populations that typically involves giving participants several coupons to recruit their peers into the study. RDS may generate biased estimates if coupons are distributed non-randomly or if potential recruits present for interview non-randomly. We explore if biases detected in an RDS study were due to either of these mechanisms, and propose and apply weights to reduce bias due to non-random presentation for interview.MethodsUsing data from the total population, and the population to whom recruiters offered their coupons, we explored how age and socioeconomic status were associated with being offered a coupon, and, if offered a coupon, with presenting for interview. Population proportions were estimated by weighting by the assumed inverse probabilities of being offered a coupon (as in existing RDS methods), and also of presentation for interview if offered a coupon by age and socioeconomic status group.ResultsYounger men were under-recruited primarily because they were less likely to be offered coupons. The under-recruitment of higher socioeconomic status men was due in part to them being less likely to present for interview. Consistent with these findings, weighting for non-random presentation for interview by age and socioeconomic status group greatly improved the estimate of the proportion of men in the lowest socioeconomic group, reducing the root-mean-squared error of RDS estimates of socioeconomic status by 38%, but had little effect on estimates for age. The weighting also improved estimates for tribe and religion (reducing root-mean-squared-errors by 19-29%), but had little effect for sexual activity or HIV status.ConclusionsData collected from recruiters on the characteristics of men to whom they offered coupons may be used to reduce bias in RDS studies. Further evaluation of this new method is required.

Dataset Information

Unmasking health determinants and health outcomes for urban First Nations using respondent-driven sampling.

Objective

Design

Setting

Participants

Results

Conclusions

Publications

Unmasking health determinants and health outcomes for urban First Nations using respondent-driven sampling.

Similar Datasets

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