Sociodemographic data collection in healthcare settings: An examination of public opinions
Centre for Research on Inner City Health, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada. Medical care
(Impact Factor: 3.23).
02/2011; 49(2):193-9. DOI: 10.1097/MLR.0b013e3181f81edb
Federal, provincial, and municipal organizations in Canada have recently begun to promote an equity agenda for their health systems, but much of the necessary data by which to identify those with social disadvantage are not currently collected.
We conducted a national survey of 1005 Canadian adults to assess the perceived importance of, and concern about, the collection of personal sociodemographic information by hospitals. We also examined public preference for practical approaches to the future collection of such information.
In this sample of Canadian adults, nearly half did not believe it was important for hospitals to collect individual-level sociodemographic data. The majority had concerns that the collection of these data could negatively affect their or others' care; this was especially true among visible minorities and those who have experienced discrimination. There was substantial variation across participant subgroups in their comfort with the collection of various types of information, but greater discomfort in general for current household income, sexual orientation, and education background. There was consistent discomfort reported from older participants. Participants in general were most comfortable providing this type of information to their family physician.
The importance of collecting patient-level equity-relevant data is not widely appreciated in Canada, and our survey has shown that concern about how these data could be misused are high, especially among certain subgroups. Qualitative research to further explore and understand these concerns, patient education about data usage and privacy issues, and using the family doctor's office as a linked electronic data collection point, will likely be important as we move toward high-quality equity measurement.
Available from: Aisha K Lofters
- "Building on a previous study exploring public opinion on this issue with a representative sample of Canadians
, we undertook a mixed methods study to generate a more in-depth understanding of public opinion on the collection of patient sociodemographic information in the province of Ontario. We first surveyed a representative sample of 1,306 Ontario adults regarding their opinions on the collection of personal information (such as family income, education background, ethnic background and sexual orientation) by hospitals for equity measurement purposes. "
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ABSTRACT: Monitoring inequalities in healthcare is increasingly being recognized as a key first step in providing equitable access to quality care. However, the detailed sociodemographic data that are necessary for monitoring are currently not routinely collected from patients in many jurisdictions. We undertook a mixed methods study to generate a more in-depth understanding of public opinion on the collection of patient sociodemographic information in healthcare settings for equity monitoring purposes in Ontario, Canada. The study included a provincial survey of 1,306 Ontarians, and in-depth interviews with a sample of 34 individuals. Forty percent of survey participants disagreed that it was important for information to be collected in healthcare settings for equity monitoring. While there was a high level of support for the collection of language, a relatively large proportion of survey participants felt uncomfortable disclosing household income (67%), sexual orientation (40%) and educational background (38%). Variation in perceived importance and comfort with the collection of various types of information was observed among different survey participant subgroups. Many in-depth interview participants were also unsure of the importance of the collection of sociodemographic information in healthcare settings and expressed concerns related to potential discrimination and misuse of this information. Study findings highlight that there is considerable concern regarding disclosure of such information in healthcare settings among Ontarians and a lack of awareness of its purpose that may impede future collection of such information. These issues point to the need for increased education for the public on the purpose of sociodemographic data collection as a strategy to address this problem, and the use of data collection strategies that reduce discomfort with disclosure in healthcare settings.
Available from: Torunn Pettersen
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ABSTRACT: In a situation where national censuses do not record information on ethnicity, studies of the indigenous Sámi people's health and living conditions tend to use varying Sámi inclusion criteria and categorizations. Consequently, the basis on which Sámi study participants are included and categorized when Sámi health and living conditions are explored and compared differs. This may influence the results and conclusions drawn.
To explore some numerical consequences of applying principles derived from Norway's Sámi Act as a foundation for formalized inclusion criteria in population-based Sámi studies in Norway.
We established 1 geographically based (G1) and 3 individual-based Sámi example populations (I1-I3) by applying diverse Sámi inclusion criteria to data from 17 rural municipalities in Norway north of the Arctic Circle. The data were collected for a population-based study of health and living conditions in 2003-2004 (the SAMINOR study). Our sample consisted of 14,797 participants aged 36-79 years.
The size of the individual-based populations varied significantly. I1 (linguistic connection Sámi) made up 35.5% of the sample, I2 (self-identified Sámi) made up 21.0% and I3 (active language Sámi) 17.7%. They were also noticeably unevenly distributed between the 5 Sámi regions defined for this study. The differences for the other characteristics studied were more ambiguous. For the population G1 (residents in the Sámi language area) the only significant difference found between the Sámi and the corresponding non-Sámi population was for household income (OR=0.69, 95% CI: 0.63-0.74). For the populations I1-I3 there were significant differences on all measures except for I2 and education (OR=1.09, 95% CI: 0.99-1.21).
The choice of Sámi inclusion criterion had a clear impact on the size and geographical distribution of the defined populations but lesser influence on the selected characteristics for the Sámi populations relative to the respective non-Sámi ones.
Available from: Aisha K Lofters
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