背景:芥末硫(SM)是一种化学战剂,对健康具有严重的短期和长期影响。在八年的伊伊拉克冲突中,成千上万的伊朗人受到SM的影响,并受到永久性伤害,而他们的医疗保健利用(HCU)和医疗支出仍然存在社会经济失衡。这项研究旨在描述2018年至2021年伊朗SM暴露幸存者的HCU;确定高风险地区;并对社会经济群体的利用进行不平等分析,以通过控制关键决定因素来缩小差距。
方法:从2018年10月到2021年6月,退伍军人和烈士事务基金会(VMAF)记录了58,888名活着的战争幸存者,肺,和皮肤疾病。清理数据集并删除垃圾代码后,我们定义了11个与HCU相关的变量,并使用贝叶斯时空模型预测了未来几年的HCU.我们使用零膨胀泊松(ZIP)模型探索了个体水平的HCU和决定因素之间的关联,并使用LocalMoran的I.
结果:置信度≥90%,我们在伊朗发现了11个HCU集群。我们发现HCU1)的预期数量随着年龄的增加而增加,幸存者眼睛和肺部并发症的严重程度,财富指数(WI),预期寿命(LE),和医院床位比率;2)随着皮肤并发症的增加而减少,受教育年限(YOS),城市化,医院病床数,在床上的停留时间(LOS),和床位占用率(BOR)。HCU的集中度指数(CInd)以及年龄和财富群体的相关成本均为正值,然而,HCU的CI值和YOS总成本的符号,城市化,LOS,和医院床位比率组不相同。
结论:我们观察到老年人口的亲富不平等趋势以及更高的HCU和支出。最后,卫生政策应解决潜在的社会经济不平等问题,以减少SM暴露人群中的HCU差距。此外,政策制定者应根据HCU的热点分配资源。
BACKGROUND: Sulfur Mustard (SM) is a chemical warfare agent that has serious short-term and long-term effects on health. Thousands of Iranians were exposed to SM during the eight-year Iran-Iraq conflict and permanently injured while the socioeconomic imbalance in their healthcare utilization (HCU) and health expenditures remains. This
study aims to describe the HCU of SM-exposed survivors in Iran from 2018 to 2021; identify high-risk areas; and apply an inequality analysis of utilization regarding the socioeconomic groups to reduce the gap by controlling crucial determinants.
METHODS: From Oct 2018 to June 2021, the Veterans and Martyrs Affairs Foundation (VMAF) recorded 58,888 living war survivors with eye, lung, and skin ailments. After cleaning the dataset and removing junk codes, we defined 11 HCU-related variables and predicted the HCU for the upcoming years using Bayesian spatio-temporal models. We explored the association of individual-level HCU and determinants using a Zero-inflated Poisson (ZIP) model and also investigated the provincial hotspots using Local Moran\'s I.
RESULTS: With ≥ 90% confidence, we discovered eleven HCU clusters in Iran. We discovered that the expected number of HCU 1) rises with increasing age, severity of complications in survivors\' eyes and lungs, wealth index (WI), life expectancy (LE), and hospital beds ratio; and 2) decreases with growing skin complications, years of schooling (YOS), urbanization, number of hospital beds, length of stay (LOS) in bed, and bed occupancy rate (BOR). The concentration index (CInd) of HCU and associated costs in age and wealth groups were all positive, however, the signs of CInd values for HCU and total cost in YOS, urbanization, LOS, and Hospital beds ratio groups were not identical.
CONCLUSIONS: We observed a tendency of pro-rich inequity and also higher HCU and expenditures for the elderly population. Finally, health policies should tackle potential socioeconomic inequities to reduce HCU gaps in the SM-exposed population. Also, policymakers should allocate the resources according to the hotspots of HCU.