Applying Association Mining and Group Based Trajectory Analyses to Characterize Spatial Temporal Trends of Community-Onset Antibiotic Resistant Staphylococcus aureus Infections in Children

摘要 金黄色葡萄球菌(S.金黄色葡萄球菌）仍然是感染的主要原因，特别是在 是皮肤和软组织感染的主要原因的门诊环境 （统计和科技咨询服务）。S.金黄色葡萄球菌也是一种共生细菌，已知在25-40%的 因此是人类微生物群的一部分。在20世纪90年代末，一种新的克隆 谱系的出现使S.金黄色葡萄球菌的流行源占社区的比例 在整个生命周期中伴随感染，非洲人的风险增加 美国和土著儿童及老年人。随着时间的推移，景观的 抗生素耐药性发生变化，可能是由于多因素作用，包括 非β-内酰胺类抗生素处方过量（对β-内酰胺类抗生素耐药是一个重要标志 耐甲氧西林S. 20年后，非β-内酰胺类S. 金黄色葡萄球菌（对甲氧西林敏感或MSSA）和MRSA对 其他抗生素类，使S.金黄色葡萄球菌是一种多药耐药（MDR）细菌， 导致严重疾病的最主要的抗生素耐药性细菌之一 国际吧虽然众所周知MRSA存在健康差异，但 社区来源的MDR S的差异。金黄色葡萄球菌（和危险因素 与该菌株相关的）与MDR S不同。来源于 从医院环境。社会生态条件在风险中发挥作用 以社区为基础的感染。本研究提出利用抗生素的关联挖掘 耐药表型与S.空间趋势分析检测金黄色葡萄球菌 时间和区域上的传播模式。应用多层次空间相关群- 基于轨迹建模将允许我们检测MDR模式中的“细微”变化， 随时间推移而发生，并描绘出与MDR模式相关的地理区域。 本研究的结果将有助于了解当前 以社区为基础S.金黄色葡萄球菌菌株（基因型及其相关表型） 这导致了SSTI，此外，还解决了识别因素的差距 从而导致这些SSTI的复发。了解菌株特异性将有助于 为预防耐药链球菌的传播奠定了基础。社区金黄色葡萄球菌感染 设置并帮助识别S。金黄色葡萄球菌抗原决定簇 作为葡萄球菌疫苗候选物。这项研究有可能遏制 这种多药耐药细菌对公众健康的威胁越来越大。

Abstract Staphylococcus aureus (S. aureus) remains a leading cause of infections, especially in the outpatient setting where it is the primary cause of skin and soft tissue infections (SSTI). S. aureus also is a commensal bacterium, known to colonize 25-40% of humans and is therefore, part of the human microbiome. In the late 1990s, a new clonal lineage emerged making S. aureus the source of epidemic proportions of community associated infections across the lifespan, with an increased risk among African American and indigenous children and older adults. Over time, the landscape of the antibiotic resistance changed, likely due to multifactorial processes, including the overprescribing of non β-lactam antibiotics (resistance to β-lactam class is the hall mark of methicillin resistant S. aureus (MRSA)).Two decades later, both non β-lactam S. aureus (methicillin sensitive or MSSA) and MRSA have gained additional resistance to other antibiotic classes, making S. aureus a multi drug resistant (MDR) bacteria and one of the top antibiotic resistant germs causing severe illnesses nationally and worldwide. While it has been well known that health disparities exist for MRSA, the disparities seen with community originated forms of MDR S. aureus (and risk factors associated with this strain) are different than those MDR S. aureus which originated from hospital settings. Socioecological conditions play a role in the risks seen with community-based infections. Our study proposes to use association mining of antibiotic resistant phenotypes seen with S. aureus with spatial trend analyses to detect transmission patterns over time and areas. Applying multi-level spatially relevant group- based trajectory modeling will allow us to detect ‘subtle’ changes in MDR patterns that occur over time, and delineate geographic areas associated with MDR patterns. Results generated by this research will contribute to current the knowledge on community-based S. aureus strains (the genotype and its associated phenotypes) which cause SSTI and additionally, address the gap of identifying the factors contributing to recurrence of these SSTI. Understanding strain specificity will serve as the basis to prevent the spread of antibiotic resistant S. aureus infections in community settings and help identify S. aureus antigenic determinants which potentially can serve as staphylococcal vaccine candidates. This study has the potential to curb the upward trajectory and increasing public health threat posed by this multi-drug resistant bacteria.