scholarly journals Racial/ethnic differences in cardiovascular outcomes in a universal healthcare system: insights from the CARTaGENE cohort

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
M D'Entremont ◽  
E L Couture ◽  
M Nguyen ◽  
J Ni ◽  
A Yan ◽  
...  

Abstract Background While prior studies have shown racial/ethnic differences in cardiovascular (CV) outcomes within private or mixed health care systems, it remains uncertain whether inequalities in cardiovascular outcomes exist between different races and ethnicities in universal health care contexts. We aimed to determine whether there are racial/ethnicity disparities in long-term CV outcomes within a single-payer universal health care system. Methods The CARTaGENE study is a population-based prospective cohort study with enrollment of 19,996 individuals between 40–69 years in 2009, in the province of Quebec, Canada. Participants residing in four large metropolitan areas were randomly chosen from the provincial health insurance registry by strata of age, sex, and postal codes. Follow-up was available up to 2016. For this analysis, we retained only participants without prior known CV disease. The primary composite endpoint was time to the first CV event or intervention (CV death, acute coronary syndrome, heart failure, coronary revascularization, ischemic stroke, or peripheral vascular event or revascularization). We used unadjusted and adjusted Cox proportional hazard models to evaluate the association of self-defined race/ethnicity with the primary endpoint. Results There were 17,802 eligible participants with a mean age of 51 years (52.5% females) with 111,312 person-years of follow-up (median follow-up of 6.6 years). South Asian (SA) participants had the highest prevalence of diabetes mellitus (29%) and hypertension (32%). After adjustment for age and sex, SA ethnicity was associated with a 95% relative increase in risk for CV events, while East/Southeast Asian (ESA) ethnicity was associated with a 42% relative decrease in risk for CV events compared to White participants. After further adjustment for socioeconomic status and CV risk factors, ESA ethnicity remained associated with a similar decreased CV risk. In contrast, the association of SA ethnicity with increased CV risk was attenuated after full adjustment for baseline characteristics (Table 1). Conclusions Racial/ethnic disparities in long-term CV outcomes are present in a single-payer universal healthcare setting. ESA ethnicity was associated with a lower risk of long-term CV outcomes. Future studies are needed to corroborate the reduced risk of long-term major CV events associated with ESA ethnicity. Understanding the reasons related to potential CV protection with ESA ethnicity could facilitate endeavors to reduce long-term CV outcomes in other races/ethnicities. FUNDunding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): McGill Health University Center

2018 ◽  
Author(s):  
Anthony Waruru ◽  
Agnes Natukunda ◽  
Lilly M Nyagah ◽  
Timothy A Kellogg ◽  
Emily Zielinski-Gutierrez ◽  
...  

BACKGROUND A universal health care identifier (UHID) facilitates the development of longitudinal medical records in health care settings where follow up and tracking of persons across health care sectors are needed. HIV case-based surveillance (CBS) entails longitudinal follow up of HIV cases from diagnosis, linkage to care and treatment, and is recommended for second generation HIV surveillance. In the absence of a UHID, records matching, linking, and deduplication may be done using score-based persons matching algorithms. We present a stepwise process of score-based persons matching algorithms based on demographic data to improve HIV CBS and other longitudinal data systems. OBJECTIVE The aim of this study is to compare deterministic and score-based persons matching algorithms in records linkage and matching using demographic data in settings without a UHID. METHODS We used HIV CBS pilot data from 124 facilities in 2 high HIV-burden counties (Siaya and Kisumu) in western Kenya. For efficient processing, data were grouped into 3 scenarios within (1) HIV testing services (HTS), (2) HTS-care, and (3) within care. In deterministic matching, we directly compared identifiers and pseudo-identifiers from medical records to determine matches. We used R stringdist package for Jaro, Jaro-Winkler score-based matching and Levenshtein, and Damerau-Levenshtein string edit distance calculation methods. For the Jaro-Winkler method, we used a penalty (р)=0.1 and applied 4 weights (ω) to Levenshtein and Damerau-Levenshtein: deletion ω=0.8, insertion ω=0.8, substitutions ω=1, and transposition ω=0.5. RESULTS We abstracted 12,157 cases of which 4073/12,157 (33.5%) were from HTS, 1091/12,157 (9.0%) from HTS-care, and 6993/12,157 (57.5%) within care. Using the deterministic process 435/12,157 (3.6%) duplicate records were identified, yielding 96.4% (11,722/12,157) unique cases. Overall, of the score-based methods, Jaro-Winkler yielded the most duplicate records (686/12,157, 5.6%) while Jaro yielded the least duplicates (546/12,157, 4.5%), and Levenshtein and Damerau-Levenshtein yielded 4.6% (563/12,157) duplicates. Specifically, duplicate records yielded by method were: (1) Jaro 5.7% (234/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.4% (308/6993) within care, (2) Jaro-Winkler 7.4% (302/4073) within HTS, 0.5% (6/1091) in HTS-care, and 5.4% (378/6993) within care, (3) Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care, and (4) Damerau-Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care. CONCLUSIONS Without deduplication, over reporting occurs across the care and treatment cascade. Jaro-Winkler score-based matching performed the best in identifying matches. A pragmatic estimate of duplicates in health care settings can provide a corrective factor for modeled estimates, for targeting and program planning. We propose that even without a UHID, standard national deduplication and persons-matching algorithm that utilizes demographic data would improve accuracy in monitoring HIV care clinical cascades.


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