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Assessing the potential utility of commercial 'big data' for health research: Enhancing small-area deprivation measures with Experian™ Mosaic groups.

ABSTRACT: In contrast to area-based deprivation measures, commercial datasets remain infrequently used in health research and policy. Experian collates numerous commercial and administrative data sources to produce Mosaic groups which stratify households into 15 groups for marketing purposes. We assessed the potential utility of Mosaic groups for health research purposes by investigating their relationships with Indices of Multiple Deprivation (IMD) for the British population. Mosaic groups showed significant associations with IMD quintiles. Correspondence Analysis revealed variations in patterns of association, with Mosaic groups either showing increasing, decreasing, or some mixed trends with deprivation quintiles. These results suggest that Experian's Mosaics additionally measure other aspects of socioeconomic circumstances to those captured by deprivation measures. These commercial data may provide new insights into the social determinants of health at a small area level.

SUBMITTER: Wami WM

PROVIDER: S-EPMC6686722 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Assessing the potential utility of commercial 'big data' for health research: Enhancing small-area deprivation measures with Experian™ Mosaic groups.

Wami Welcome M WM Dundas Ruth R Molaodi Oarabile R OR Tranter Mette M Leyland Alastair H AH Katikireddi Srinivasa Vittal SV

Health & place 20190521

In contrast to area-based deprivation measures, commercial datasets remain infrequently used in health research and policy. Experian collates numerous commercial and administrative data sources to produce Mosaic groups which stratify households into 15 groups for marketing purposes. We assessed the potential utility of Mosaic groups for health research purposes by investigating their relationships with Indices of Multiple Deprivation (IMD) for the British population. Mosaic groups showed signifi ...[more]

PMID: 31125848

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Project description:ObjectivesMost research on health inequalities uses aggregated deprivation scores assigned to the small area where the patient lives; however, the concordance between aggregate area-level deprivation measures and personal deprivation experienced by individuals living in the area is poorly understood. Our objective was to examine the agreement between individual and ecological deprivation. We tested the concordance between metrics of income, occupation and education at individual and area levels, and assessed the reliability of area-based deprivation measures to predict individual deprivation circumstances.SettingEngland and Wales.ParticipantsA cancer patient cohort of 9547 individuals extracted from the Office for National Statistics Longitudinal Study.OutcomesWe quantified the concordance between measures of income, occupation and education at individual and area level. In addition, we used ROC (receiver operating characteristic) curves and the area under the curve (AUC) to assess the reliability of area-based deprivation measures to predict individual deprivation circumstances.ResultsWe found low concordance between individual-level and area-level indicators of deprivation (Cramer's V statistics range between 0.07 and 0.20). The most commonly used indicator in health inequalities research, area-based income deprivation, was a poor predictor of individual income status (AUC between 0.56 and 0.59), whereas education and occupation were slightly better predictors (AUC between 0.62 and 0.65). The results were consistent across sexes and across six major cancer types.ConclusionsOur results indicate that ecological deprivation measures capture only part of the relationship between deprivation and health outcomes, especially with respect to income measurement. This has important implications for our understanding of the relationship between deprivation and health, and, as a consequence, healthcare policy. The results have a wide-reaching impact for the way in which we measure and monitor inequalities, and in turn, fund and organise current UK healthcare policy aimed at reducing them.

Project description:Data from Northeast Scotland for 11,803 cancer patients (diagnosed 2007-13) were linked to UK Censuses to explore relationships between hospital travel-time, timely-treatment and one-year-mortality, adjusting for both area and individual-level socioeconomic status (SES). Adjusting for area-based SES, those living >60 minutes from hospital received timely-treatment more often than those living <15 minutes. Substituting individual-level SES changed little. Adjusting for area-based SES those living >60 minutes from hospital died within one year more often than those living <15 minutes. Again, substituting individual-level SES changed little. In Northeast Scotland distance to services, rather than individual SES, likely explains poorer rural cancer survival.Background and objectiveThe Northeast and Aberdeen Scottish Cancer and Residence (NASCAR) study found rural-dwellers are treated quicker but more likely to die within a year of a cancer diagnosis. A potential confounder of the relationship between geography and cancer mortality is socioeconomic status (SES). We linked the original NASCAR cohort to the UK Censuses of 2001 and 2011, at an individual level, to explore the relationship between travel time to key healthcare facilities, timely cancer treatment and one-year mortality adjusting for both area and individual-level markers of socioeconomic status.MethodsA data linkage study of 11803 patients examined the association between travel times, timely treatment and one-year mortality with adjustment for area, and for individual-level, markers of socioeconomic status.ResultsFollowing adjustment for area-based SES measures those living more than 60 minutes from the cancer treatment centre were significantly more likely to be treated within 62 days of GP referral than those living within 15 minutes (Odds Ratio [OR]) 1.41; 95% (Confidence Interval [CI]) 1.23, 1.60]. Replacing area-based with individual-level SES measures from UK Censuses made little impact on the results [OR 1.39; 95% CI 1.22, 1.57].Following adjustment for area-based SES measures of socioeconomic status those living more than 60 minutes from the cancer treatment centre were significantly more likely to die within one year than those living closer by [OR 1.22; 95% CI 1.08, 1.38]. Again, replacing area-based with individual-level SES measures from UK Censuses made little impact on the result [OR 1.20; CI 1.06, 1.35].ConclusionsDistribution of individual measures of socioeconomic status did not differ significantly between rural and urban cancer patients. The relationship between distance to service, timely treatment and one-year survival were the same adjusting for both area-based and individual SES. Overall, it seems that distance to services, rather than personal characteristics, influences poorer rural cancer survival.

Project description:BackgroundIn ToxCast™ Phase I, the U.S. EPA commissioned screening of 320 pesticides, herbicides, fungicides, and other chemicals in a series of high-throughput assays. The agency also developed a toxicological prioritization tool, ToxPi, to facilitate using ToxCast™ assays to predict biological function.ObjectivesWe asked whether top-scoring PPAR? activators identified in ToxCast™ Phase I were genuine PPAR? activators and inducers of adipogenesis. Next, we identified ToxCast™ assays that should predict adipogenesis, developed an adipogenesis ToxPi, and asked how well the ToxPi predicted adipogenic activity.MethodsWe used transient transfection to test the ability of ToxCast™ chemicals to modulate PPAR? and RXR?, and differentiation assays employing 3T3-L1 preadipocytes and mouse bone marrow-derived mesenchymal stem cells (mBMSCs) to evaluate the adipogenic capacity of ToxCast™ chemicals.ResultsOnly 5/21 of the top scoring ToxCast™ PPAR? activators were activators in our assays, 3 were PPAR? antagonists, the remainder were inactive. The bona fide PPAR? activators we identified induced adipogenesis in 3T3-L1 cells and mBMSCs. Only 7 of the 17 chemicals predicted to be active by the ToxPi promoted adipogenesis, 1 inhibited adipogenesis, and 2 of the 7 predicted negatives were also adipogenic. Of these 9 adipogenic chemicals, 3 activated PPAR?, and 1 activated RXR?.ConclusionsToxCast™ PPAR? and RXR? assays do not correlate well with laboratory measurements of PPAR? and RXR? activity. The adipogenesis ToxPi performed poorly, perhaps due to the performance of ToxCast™ assays. We observed a modest predictive value of ToxCast™ for PPAR? and RXR? activation and adipogenesis and it is likely that many obesogenic chemicals remain to be identified.CitationJanesick AS, Dimastrogiovanni G, Vanek L, Boulos C, Chamorro-García R, Tang W, Blumberg B. 2016. On the utility of ToxCast™ and ToxPi as methods for identifying new obesogens. Environ Health Perspect 124:1214-1226;?http://dx.doi.org/10.1289/ehp.1510352.

Dataset Information

Assessing the potential utility of commercial 'big data' for health research: Enhancing small-area deprivation measures with Experian™ Mosaic groups.

Publications

Assessing the potential utility of commercial 'big data' for health research: Enhancing small-area deprivation measures with Experian™ Mosaic groups.

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