Socioeconomic status in HCV infected patients – risk and prognosis

Abstract

Background and aims

It is unknown whether socioeconomic status (SES) is a risk factor for hepatitis C virus (HCV) infection or a prognostic factor following infection.

Methods

From Danish nationwide registries, we obtained information on three markers of SES: employment, income, and education. In a case control design, we examined HCV infected patients and controls; conditional logistic regression was employed to obtain odds ratios (ORs) for HCV infection for each of the three SES markers, adjusting for the other two SES markers, comorbidity, and substance abuse. In a cohort design, we used Cox regression analysis to compute mortality rate ratios (MRRs) for each of the three SES markers, adjusting for the other two SES markers, comorbidity level, age, substance abuse, and gender.

Results

When compared to employed persons, ORs for HCV infection were 2.71 (95% confidence interval [CI]: 2.24–3.26) for disability pensioners and 2.24 (95% CI: 1.83–2.72) for the unemployed. When compared to persons with a high income, ORs were 1.64 (95% CI: 1.34–2.01) for low income persons and 1.19 (95% CI: 1.02–1.40) for medium income persons. The OR was 1.35 (95% CI: 1.20–1.52) for low education (no more than basic schooling). When compared to employed patients, MRRs were 1.71 (95% CI: 1.22–2.40) for unemployed patients and 2.24 (95% CI: 1.63–3.08) for disability pensioners. When compared to high income patients, MRRs were 1.47 (95% CI: 1.05–2.05) for medium income patients and 1.64 (95% CI: 1.13–2.34) for low income patients. Educational status was not associated with mortality.

Conclusion

Low SES was associated with an increased risk of HCV infection and with poor prognosis in HCV infected patients.

Keywords:

• survival
• socioeconomic status
• risk factor
• prognosis

Supplementary Materials

Supplementary material 1

Definitions of alcohol abuse and injection drug use (IDU) Alcohol abuse

A record in Danish National Registry of Patients of International Classification of Diseases (ICD)-8 codes: 291.00–291.99, 571.09, 571.10, 303.00–89, and 303.91–99 and ICD-10 codes: K70.0–K70.9, F10.2–F10.9, and G31.2 before study inclusion.

IDU

A record in Danish National Registry of Patients of ICD-8 codes: 304.09–304.99 and ICD-10 codes: F11.0–F19.9, T40.0–T40.9, and/or registration in The Registry of Drug Abusers Undergoing Treatment before study inclusion.

Supplementary material 2

Table S1 Coding algorithm used for comorbid conditions in the Charlson Comorbidity Index

Acknowledgment

This study was funded by Augustinusfonden, which had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosure

N Obel received grants from Roche, Bristol-Meyers Squibb, Merck Sharp and Dohme, GlaxoSmithKline, Abbott, Boehringer Ingelheim, Janssen-Cilag, and Swedish-Orphan Drugs. PB Christensen received grants from Roche and Schering-Plough. The remaining authors declare no conflicts of interest in this work.