European Programme for Intervention Epidemiology Training, European Centre for Disease Prevention and Control, Stockholm, Sweden; Public Health Agency of Sweden, Solna, Sweden.
Public Health Agency of Sweden, Solna, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Lancet Infect Dis. 2019 Feb;19(2):165-176. doi: 10.1016/S1473-3099(18)30485-7. Epub 2018 Dec 14.
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.
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.
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).
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.
The Public Health Agency of Sweden.
尽管低社会经济地位与非传染性疾病之间的关联已得到充分证实，但社会经济因素对许多传染病的影响尚不清楚，尤其是在高收入国家。我们研究了 29 种传染病与社会经济特征之间的关系。
我们在瑞典进行了一项个体匹配病例对照研究。我们将年龄在 18-65 岁之间、2005 年至 2014 年期间在瑞典被诊断出患有 29 种传染病之一的人定义为病例。病例与性别、年龄和居住县的 5 名随机选择的对照者进行个体匹配。我们从电子国家传染病报告和传染病登记处（SmiNet）中提取了 29 种传染病的数据。我们从瑞典人口登记处提取了病例和对照者的出生国、教育和就业状况以及收入信息。我们使用条件逻辑回归计算了调整后的匹配比值比（amOR），以检查传染病或传染病组与出生地、教育、就业和收入之间的关系。
我们纳入了 2005 年 1 月 1 日至 2014 年 12 月 31 日期间登记的 173729 例病例和 868645 名对照者。侵袭性细菌疾病、血源传染性疾病、结核病和抗生素耐药性感染患者更有可能失业（amOR 1.59，95%CI 1.49-1.70；amOR 3.62，3.48-3.76；amOR 1.88，1.65-2.14；amOR 1.73，1.67-1.79），受教育程度较低（amOR 1.24，1.15-1.34；amOR 3.63，3.45-3.81；amOR 2.14，1.85-2.47；amOR 1.07，1.03-1.12），收入最低（amOR 1.52，1.39-1.66；amOR 3.64，3.41-3.89；amOR 3.17，2.49-4.04；amOR 1.2，1.14-1.25）。相比之下，食源性和水源性传染病患者比对照组更不可能失业（amOR 0.74，95%CI 0.72-0.76）、受教育程度较低（amOR 0.75，0.73-0.77）和收入最低（amOR 0.59，0.58-0.61）。
这些发现表明，在一个具有普遍医疗保健的平等主义高收入国家，传染病仍存在持续的社会经济不平等现象。我们建议利用这些发现来确定优先干预措施，并作为监测解决健康方面社会经济不平等问题的方案的基线。
瑞典公共卫生局。
