Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
Department of Geography and Health Data Research, Western Michigan University, Kalamazoo, Michigan, USA.
Sex Transm Infect. 2018 Aug;94(5):353-358. doi: 10.1136/sextrans-2017-053350. Epub 2018 Jan 22.
We modelled individual vulnerability to STI using personal history of infection and neighbourhood characteristics.
Retrospective chlamydia and gonorrhoea data of reported confirmed cases from Kalamazoo County, Michigan for 2012 through 2014 were analysed. Unique IDs were generated from the surveillance data in collaboration with local health officials to track the individual STI histories. We then examine the concept that individuals with similar STI histories form a 'peer' group. These peer group include: (1) individuals with a single chlamydia; (2) individuals with single gonorrhoea; (3) individuals with repeated cases of one type of STI and (4) individuals that were diagnosed with both infections during the study period. Using Kernel density estimation, we generated densities for each peer group and assigned the intensity of the infection to the location of the individual. Finally, the individual vulnerability was characterised through ordinary least square regression (OLS) using demographics and socioeconomic variables.
In an OLS regression adjusted for frequency of infection, individual vulnerability to STI was only consistently significant for race and neighbourhood-level socioeconomic status (SES) in all the models under consideration. In addition, we identified six areas in three townships in Kalamazoo County that could be considered for unique interventions based on overlap patterns among peer groups.
The results provide evidence that individual vulnerability to STI has some dependency on individual contextual (race) and exogenous factors at the neighbourhood level such as SES, regardless of that individual's personal history of infection. We suggest place-based intervention strategies be adopted for planning STI interventions instead of current universal screening of at-risk populations.
利用感染史和社区特征来建立个体易感染性病(STI)模型。
分析了 2012 年至 2014 年密歇根州卡拉马祖县报告的确诊衣原体和淋病病例的回顾性数据。通过与当地卫生官员合作,从监测数据中生成唯一 ID,以跟踪个体的 STI 病史。然后,我们研究了具有相似 STI 病史的个体形成“同伴”群体的概念。这些同伴群体包括:(1)单个衣原体感染者;(2)单个淋病感染者;(3)重复感染一种 STI 的个体;(4)在研究期间同时感染两种感染的个体。使用核密度估计法,我们为每个同伴群体生成密度,并将感染强度分配给个体的位置。最后,使用普通最小二乘回归(OLS),根据人口统计学和社会经济变量来描述个体易感性。
在调整感染频率的 OLS 回归中,个体易感性在所有考虑的模型中仅与种族和社区层面的社会经济地位(SES)显著相关。此外,我们在卡拉马祖县的三个镇中确定了六个区域,这些区域可能需要根据同伴群体之间的重叠模式进行独特的干预。
结果表明,个体易感性与个体的社会背景(种族)和社区层面的外部因素(如 SES)有关,而与个体的感染史无关。我们建议采用基于地点的干预策略来规划 STI 干预措施,而不是当前对高危人群的普遍筛查。