Do socioeconomic measures improve prediction of cardiovascular disease hospitalization?
| dc.contributor.author | Vasylkiv, Viktoriya | |
| dc.contributor.examiningcommittee | Mignone, Javier (Community Health Sciences) | en_US |
| dc.contributor.examiningcommittee | Delaney, Joseph (Pharmacy) | en_US |
| dc.contributor.supervisor | Lix, Lisa | |
| dc.date.accessioned | 2022-08-25T20:38:42Z | |
| dc.date.available | 2022-08-25T20:38:42Z | |
| dc.date.copyright | 2022-08-25 | |
| dc.date.issued | 2022-08-24 | |
| dc.date.submitted | 2022-08-24T16:28:15Z | en_US |
| dc.date.submitted | 2022-08-25T19:32:56Z | en_US |
| dc.degree.discipline | Community Health Sciences | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Background: Risk prediction models for cardiovascular disease (CVD) hospitalization have improved prediction accuracy when individual-level measures of socioeconomic status (SES) are considered. However, it is unclear how validated, area-level SES measures, available at the population-level in Canada, might improve prediction of CVD hospitalization. Objectives: The research objectives were to (1) test the incremental predictive value of area-level SES measures and (2) compare the incremental predictive performance of different area-level SES measures on time to hospitalization for CVD. Methods: A retrospective cohort design used Manitoba administrative health records from 2014 to 2020 and area-level SES measures from 2016 Statistics Canada Census data. Individuals 40+ years as of April 1st, 2016 were followed until an acute myocardial infarction (AMI) or stroke hospitalization or loss to follow-up. Covariates included age, sex, comorbid conditions, prior healthcare use, AMI/stroke-related prescription medications, and one or more area-level SES measures, including the Socioeconomic Factor Index - Version 2 (SEFI-2), Material Deprivation Index, Social Deprivation Index, and the Canadian Index of Multiple Deprivation (CIMD). Cox proportional hazards models were assessed for model accuracy (area under the curve; AUC), discrimination (net reclassification improvement; NRI and integrated discrimination improvement; IDI) and calibration (Brier score). Results: Overall predictive performance of models containing one or more SES measures (fully-adjusted model; AUC = 0.753 – 0.757) was similar to model containing all other covariates (partially-adjusted model; AUC = 0.753). Discrimination performance was poor or statistically non-significant (NRI = -0.158 – 0.019; IDI = < 0.000). Prediction error was low for all models (Brier score = 0.022). Conclusion: Area-level SES measures did not add predictive value to CVD hospitalization risk models. Risk factors available in administrative health data, like demographics and comorbid conditions, already provide a similar amount of information in terms of predictive ability. Area-level SES measures are useful for characterizing and describing populations but may not have strong predictive value to individual-level outcomes. Further studies are recommended to explore their use in prediction of other health conditions and jurisdictions, and comparisons with individual-level SES measures. | en_US |
| dc.description.note | October 2022 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/36772 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | acute myocardial infarction | en_US |
| dc.subject | stroke | en_US |
| dc.subject | cardiovascular disease | en_US |
| dc.subject | risk prediction modelling | en_US |
| dc.subject | area-level socioeconomic measures | en_US |
| dc.subject | census-based socioeconomic measures | en_US |
| dc.subject | Manitoba | en_US |
| dc.subject | administrative health data | en_US |
| dc.title | Do socioeconomic measures improve prediction of cardiovascular disease hospitalization? | en_US |
| dc.type | master thesis | en_US |
| local.subject.manitoba | yes | en_US |