Socioeconomic disparities associated with 29 common infectious diseases in Sweden, 2005-14: an individually matched case-control study.

Background: Although the association between low socioeconomic status and non-communicable diseases is well established, the effect of socioeconomic factors on many infectious diseases is less clear, particularly in high-income countries. We examined the associations between socioeconomic characteristics and 29 infections in Sweden.

Methods: We did an individually matched case-control study in Sweden. We defined a case as a person aged 18-65 years who was notified with one of 29 infections between 2005 and 2014, in Sweden. Cases were individually matched with respect to sex, age, and county of residence with five randomly selected controls. We extracted the data on the 29 infectious diseases from the electronic national register of notified infections and infectious diseases (SmiNet). We extracted information on country of birth, educational and employment status, and income of cases and controls from Statistics Sweden's population registers. We calculated adjusted matched odds ratios (amOR) using conditional logistic regression to examine the association between infections or groups of infections and place of birth, education, employment, and income.

Findings: We included 173 729 cases notified between Jan 1, 2005, and Dec 31, 2014 and 868 645 controls. Patients with invasive bacterial diseases, blood-borne infectious diseases, tuberculosis, and antibiotic-resistant infections were more likely to be unemployed (amOR 1·59, 95% CI 1·49-1·70; amOR 3·62, 3·48-3·76; amOR 1·88, 1·65-2·14; and amOR 1·73, 1·67-1·79, respectively), to have a lower educational attainment (amOR 1·24, 1·15-1·34; amOR 3·63, 3·45-3·81; amOR 2·14, 1·85-2·47; and amOR 1·07, 1·03-1·12, respectively), and to have a lowest income (amOR 1·52, 1·39-1·66; amOR 3·64, 3·41-3·89; amOR 3·17, 2·49-4·04; and amOR 1·2, 1·14-1·25, respectively). By contrast, patients with food-borne and water-borne infections were less likely than controls to be unemployed (amOR 0·74, 95% CI 0·72-0·76), to have lower education (amOR 0·75, 0·73-0·77), and lowest income (amOR 0·59, 0·58-0·61).

Interpretation: These findings indicate persistent socioeconomic inequalities in infectious diseases in an egalitarian high-income country with universal health care. We recommend using these findings to identify priority interventions and as a baseline to monitor programmes addressing socioeconomic inequalities in health.

Funding: The Public Health Agency of Sweden.