Kikuti Mariana, Cunha Geraldo M, Paploski Igor A D, Kasper Amelia M, Silva Monaise M O, Tavares Aline S, Cruz Jaqueline S, Queiroz Tássia L, Rodrigues Moreno S, Santana Perla M, Lima Helena C A V, Calcagno Juan, Takahashi Daniele, Gonçalves André H O, Araújo Josélio M G, Gauthier Kristine, Diuk-Wasser Maria A, Kitron Uriel, Ko Albert I, Reis Mitermayer G, Ribeiro Guilherme S
Centro de Pesquisas Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil; Instituto de Saúde Coletiva, Universidade Federal da Bahia, Salvador, Bahia, Brazil.
Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil.
PLoS Negl Trop Dis. 2015 Jul 21;9(7):e0003937. doi: 10.1371/journal.pntd.0003937. eCollection 2015.
Few studies of dengue have shown group-level associations between demographic, socioeconomic, or geographic characteristics and the spatial distribution of dengue within small urban areas. This study aimed to examine whether specific characteristics of an urban slum community were associated with the risk of dengue disease.
METHODOLOGY/PRINCIPAL FINDINGS: From 01/2009 to 12/2010, we conducted enhanced, community-based surveillance in the only public emergency unit in a slum in Salvador, Brazil to identify acute febrile illness (AFI) patients with laboratory evidence of dengue infection. Patient households were geocoded within census tracts (CTs). Demographic, socioeconomic, and geographical data were obtained from the 2010 national census. Associations between CTs characteristics and the spatial risk of both dengue and non-dengue AFI were assessed by Poisson log-normal and conditional auto-regressive models (CAR). We identified 651 (22.0%) dengue cases among 2,962 AFI patients. Estimated risk of symptomatic dengue was 21.3 and 70.2 cases per 10,000 inhabitants in 2009 and 2010, respectively. All the four dengue serotypes were identified, but DENV2 predominated (DENV1: 8.1%; DENV2: 90.7%; DENV3: 0.4%; DENV4: 0.8%). Multivariable CAR regression analysis showed increased dengue risk in CTs with poorer inhabitants (RR: 1.02 for each percent increase in the frequency of families earning ≤1 times the minimum wage; 95% CI: 1.01-1.04), and decreased risk in CTs located farther from the health unit (RR: 0.87 for each 100 meter increase; 95% CI: 0.80-0.94). The same CTs characteristics were also associated with non-dengue AFI risk.
CONCLUSIONS/SIGNIFICANCE: This study highlights the large burden of symptomatic dengue on individuals living in urban slums in Brazil. Lower neighborhood socioeconomic status was independently associated with increased risk of dengue, indicating that within slum communities with high levels of absolute poverty, factors associated with the social gradient influence dengue transmission. In addition, poor geographic access to health services may be a barrier to identifying both dengue and non-dengue AFI cases. Therefore, further spatial studies should account for this potential source of bias.
关于登革热的研究很少表明人口统计学、社会经济或地理特征与小城市地区登革热的空间分布之间存在群体层面的关联。本研究旨在探讨城市贫民窟社区的特定特征是否与登革热疾病风险相关。
方法/主要发现：2009年1月至2010年12月，我们在巴西萨尔瓦多一个贫民窟唯一的公共急救单位开展了强化的社区监测，以识别有登革热感染实验室证据的急性发热性疾病（AFI）患者。患者家庭在普查区（CTs）内进行了地理编码。人口统计学、社会经济和地理数据来自2010年全国人口普查。通过泊松对数正态模型和条件自回归模型（CAR）评估CTs特征与登革热和非登革热AFI的空间风险之间的关联。我们在2962例AFI患者中识别出651例（22.0%）登革热病例。2009年和2010年有症状登革热的估计风险分别为每10000名居民21.3例和70.2例。所有四种登革热血清型均被识别出，但DENV2占主导（DENV1：8.1%；DENV2：90.7%；DENV3：0.4%；DENV4：0.8%）。多变量CAR回归分析显示，居民较贫困的CTs登革热风险增加（家庭收入≤最低工资1倍的频率每增加1%，相对风险RR：1.02；95%置信区间CI：1.01 - 1.04），而距离卫生单位较远的CTs风险降低（每增加100米，RR：0.87；95%CI：0.80 - 0.94）。相同的CTs特征也与非登革热AFI风险相关。
结论/意义：本研究突出了有症状登革热给巴西城市贫民窟居民带来的沉重负担。社区社会经济地位较低与登革热风险增加独立相关，表明在绝对贫困程度高的贫民窟社区，与社会梯度相关的因素影响登革热传播。此外，获得卫生服务的地理便利性差可能是识别登革热和非登革热AFI病例的一个障碍。因此，进一步的空间研究应考虑到这种潜在的偏差来源。
Zotero 插件