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Understanding the COVID-19 pandemic through bayesian spatio-temporal modeling of several outcomes 通过几种结果的贝叶斯时空建模来理解COVID-19大流行

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-07-18 DOI: 10.1016/j.sste.2025.100737

Lander Rodriguez-Idiazabal , Miguel Angel Martinez-Beneito , Jose M. Quintana , Julia Garcia-Asensio , Maria Jose Legarreta , Nere Larrea , Irantzu Barrio

Understanding the spatio-temporal dynamics of past pandemics and the factors driving these patterns can enhance preparedness against future pandemics. This study aimed to investigate the COVID-19 pandemic by analyzing the spatio-temporal variations in infections, hospitalizations, deaths and reinfections.

We conducted a retrospective analysis of data from the adult population of the Basque Country at Primary Care Unit level from March 1, 2020 to January 9, 2022. Using a Bayesian hierarchical spatio-temporal model, we estimated relative risks for each outcome, accounting for the effects of a deprivation index, urbanicity, and COVID-19 testing rates.

SARS-CoV-2 infections and mortality followed similar risk patterns, with a strong clustering in highly populated areas. Hospitalization risks were influenced by proximity to hospitals, revealing potential access barriers in remote areas. High reinfection risks were predominantly localized in the northwest coast of our region. Increased testing rates were associated with higher risks across all outcomes. Urbanicity showed positive associations with hospitalizations (relative risk, [95 % credible interval]: 1.22, [1.12–1.33]) and infections (1.34, [1.15–1.57]). Similarly, deprivation was positively associated with hospitalization risks (1.09, [1.04–1.15]) and mortality risks (1.07, [1.02–1.12]), reflecting the increased vulnerability of socioeconomically disadvantaged populations.

This comprehensive analysis of various COVID-19 outcomes provides valuable insights into the pandemic’s spatio-temporal dynamics and highlights key improvement areas. Addressing healthcare access disparities in rural areas and focusing on the deprived populations could help mitigate the impact of future pandemics. This approach could be extended to other regions to inform specific public health strategies.

了解过去大流行的时空动态以及推动这些格局的因素，可以加强对未来大流行的防范。本研究旨在通过分析感染、住院、死亡和再感染的时空变化，了解新冠肺炎大流行的情况。我们对2020年3月1日至2022年1月9日巴斯克地区初级保健单位的成年人口数据进行了回顾性分析。使用贝叶斯分层时空模型，我们估计了每个结果的相对风险，考虑了剥夺指数、城市化和COVID-19检测率的影响。SARS-CoV-2感染和死亡率也遵循类似的风险模式，在人口密集地区有很强的聚集性。住院风险受到距离医院近的影响，这揭示了偏远地区可能存在的准入障碍。再感染高危人群主要集中在西北沿海地区。检测率的增加与所有结果的高风险相关。城市化与住院（相对危险度，[95%可信区间]:1.22,[1.12-1.33]）和感染（1.34,[1.15-1.57]）呈正相关。同样，剥夺与住院风险（1.09,[1.04-1.15]）和死亡风险（1.07,[1.02-1.12]）呈正相关，反映了社会经济弱势群体的脆弱性增加。这份对2019冠状病毒病各种结果的综合分析提供了对大流行时空动态的宝贵见解，并突出了关键的改进领域。解决农村地区获得医疗保健的差距问题，并把重点放在贫困人口身上，可能有助于减轻未来流行病的影响。这种做法可以推广到其他区域，为具体的公共卫生战略提供信息。

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引用次数: 0

Detecting the occurrence of suicide clusters at city block scale: evidence from a 26-year data series 城市街区尺度自杀集群发生的检测：来自26年数据序列的证据

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-07-01 DOI: 10.1016/j.sste.2025.100734

SA Estay , G Rivera , JC Olivares , R Fuentes-Ferrada , M Gotelli , C Heskia , C Rojas-Carvajal , J Santander , HC Lungenstrass , T Baader

Suicide clusters have profound negative impacts in the affected communities. Despite the fact that the occurrence of this phenomenon is a rare event, it has been observed across diverse cultural contexts. Suicide clusters are commonly described as a higher number of suicides than expected by chance, occurring at relatively small time/space scales. In particular, point clusters have been reported as occurring in areas ranging from hundreds of meters to several kilometers and lasting from a few days to several months or years. Nevertheless, effective prevention of suicide clusters requires robust estimation of their spatial and temporal scales, especially at short distances such as the city-block scale. This study analyzes data from the city of Valdivia, Chile (1996–2021) using a DBSCAN-based approach. We detected seven suicide clusters at very small scale in different years. In the clusters, the distances between suicides were <300 m (two to three city blocks). For the entire period, 6 % of the suicides occurred in a cluster. These clusters contain between three and four suicides, each with higher prevalence of men and people over 30 years old. Our results provide important insights for implementing preventive actions at the neighborhood scale.

自杀集群对受影响社区产生了深远的负面影响。尽管这种现象的发生是罕见的，但在不同的文化背景下都有观察到。自杀集群通常被描述为自杀人数高于偶然预期，发生在相对较小的时间/空间尺度上。特别是，据报道，点簇发生在数百米到几公里的范围内，持续时间从几天到几个月或几年。然而，有效预防自杀集群需要对其空间和时间尺度进行可靠的估计，特别是在距离较短的地方，如城市街区尺度。本研究使用基于dbscan的方法分析了智利瓦尔迪维亚市（1996-2021）的数据。我们在不同年份发现了7个非常小规模的自杀集群。在这些集群中，自杀之间的距离为300米（两到三个城市街区）。在整个研究期间，6%的自杀事件发生在一个群体中。这些群集包含3至4起自杀事件，每起事件中男性和30岁以上人群的患病率较高。我们的研究结果为在社区范围内实施预防措施提供了重要的见解。

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引用次数: 0

Spatiotemporal analysis of dengue fever in tourist destinations using a Time-Lagged DCCAC approach 基于时滞DCCAC方法的旅游目的地登革热时空分析

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-06-19 DOI: 10.1016/j.sste.2025.100730

Jéssica B. Oliveira , Thiago B. Murari , Hernane B. de B. Pereira , Marcelo A. Moret , Claudia Andrea L. Cardoso

Background:

Dengue is one of the most important neglected tropical diseases in the world and is spread rapidly through human movement, especially throughout intermunicipal, national and international routes. The Pantanal is the largest wetland in the world and is a UNESCO World Heritage Site spanning the states of Mato Grosso do Sul and Mato Grosso. In addition, the Pantanal of Mato Grosso do Sul is an important tourist hub.

Methods:

This study addresses the spread of dengue in different regions, focusing on the Pantanal of Mato Grosso do Sul. The objective of our study is to evaluate the spread of dengue, using the time-lagged Detrended Cross-Correlation Analysis Coefficient (DCCAC) method to provide data that will help in discussions of protocols to combat dengue in the region.

Results:

Through the time-lagged DCCAC, it was possible to identify similar behaviors in the lagged DCCAC comovements in different regions, including Bolivia and Paraguay and states in Brazil, such as Mato Grosso do Sul, Mato Grosso and others.

Conclusion:

This study suggests the importance of cooperation between these regions to fight the disease in an integrated way and share information about the behavior of dengue cases in each area. Implementing a shared system across a network formed by these regions can effectively combat dengue and allow the regions to work together to identify and address the factors that affect the behavior of dengue cases.

背景：登革热是世界上最重要的被忽视的热带病之一，通过人类活动，特别是通过城市间、国家和国际途径迅速传播。潘塔纳尔湿地是世界上最大的湿地，是联合国教科文组织世界遗产，横跨南马托格罗索州和马托格罗索州。此外，南马托格罗索州的潘塔纳尔也是一个重要的旅游中心。方法：本研究以南马托格罗索州潘塔纳尔为研究对象，探讨登革热在不同地区的传播情况。本研究的目的是评估登革热的传播情况，利用时滞去趋势相互关联分析系数（DCCAC）方法提供数据，帮助讨论该地区防治登革热的方案。结果：通过时间滞后的DCCAC，可以识别不同地区（包括玻利维亚和巴拉圭）和巴西州（如南马托格罗索州、马托格罗索州等）滞后的DCCAC运动中的相似行为。结论：本研究提示各地区应加强合作，以综合方式防治登革热，并共享各地区登革热病例行为信息。在由这些地区组成的网络中实施共享系统可以有效地防治登革热，并使这些地区能够共同努力，确定和处理影响登革热病例行为的因素。

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引用次数: 0

Identifying hotspots of malaria incidence and mortality for tailored interventions in Cameroon using routine data from 2011 to 2021: A Bayesian space-time variability modeling 利用2011 - 2021年的常规数据确定喀麦隆疟疾发病率和死亡率的热点地区：贝叶斯时空变异性模型

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-06-18 DOI: 10.1016/j.sste.2025.100733

Fottsoh Fokam Arnold , Fati Kirakoya-Samadoulougou , Ateba Marcelin , Fosso Jean , Yazoume Ye , Sekou Samadoulougou

Malaria remains a health challenge globally, particularly in Cameroon. Despite the emphasis of recent studies to provide estimates of spatio-temporal variations in malaria in the country, these studies have overlooked an important aspect of disease surveillance, which is the monitoring and assessing of spatio-temporal risk in hotspots. This study addresses this gap by examining the ex-ante dynamics of malaria risk clustering. It identifies health districts in Cameroon with a high potential to be hotspots based on uncomplicated incidence, severe incidence, and deaths. The authors used the malaria routine data from 189 contiguous health districts in Cameroon from January 2011 - December 2021 to achieve this. By fitting a Bayesian spatiotemporal model, the classification of the spatial trend into hotspots, coldspots, and neutral-spots, and the classification of the differential time trends into increasing, decreasing, and stable trends were defined to assess the ex-ante risk of hotspot. As findings, 42.9 % (81), 44.9 % (85) and 28.6 % (54) districts show a decreasing trend for the uncomplicated, severe, and malaria deaths, respectively. However, 44.9 % (85), 37.6 % (71), and 32.8 % (62) districts show an increasing trend for the uncomplicated, severe, and malaria deaths, respectively, including 24.3 % (46), 23.3 % (44), and 21.7 % (41) identified with a high likelihood of becoming hotspots for uncomplicated, severe, and malaria deaths, respectively. This trend suggests that these neutral-spot and coldspots health districts, especially those with increasing trends, are at risk of becoming hotspots. Although aggregated health facility data may not accurately reflect individual-level risk and could be influenced by varying surveillance and case management practices, integrating a Bayesian space-time variability modeling into Cameroon's malaria surveillance system can help pinpoint ex-ante hotspots and facilitate a proactive and targeted interventions.

疟疾仍然是全球，特别是喀麦隆的一项健康挑战。尽管最近的研究强调提供该国疟疾时空变化的估计，但这些研究忽视了疾病监测的一个重要方面，即监测和评估热点地区的时空风险。本研究通过检查疟疾风险聚类的事前动态来解决这一差距。它根据简单发病率、严重发病率和死亡率确定了喀麦隆具有高潜力成为热点的卫生区。为了实现这一目标，作者使用了2011年1月至2021年12月期间喀麦隆189个相邻卫生区的疟疾常规数据。通过拟合贝叶斯时空模型，将空间趋势划分为热点、冷点和中性，并将时间差异趋势划分为增加、减少和稳定趋势，以评估热点的事前风险。结果显示，42.9%（81个）、44.9%（85个）和28.6%（54个）县的无并发症死亡、严重死亡和疟疾死亡分别呈下降趋势。然而，分别有44.9%（85）、37.6%（71）和32.8%（62）的地区显示出无并发症、严重和疟疾死亡人数增加的趋势，其中24.3%（46）、23.3%（44）和21.7%（41）的地区被确定为极有可能成为无并发症、严重和疟疾死亡人数多的地区。这一趋势表明，这些中性点和冷点卫生区，特别是那些有上升趋势的卫生区，有成为热点的危险。尽管汇总的卫生设施数据可能不能准确反映个人层面的风险，并可能受到不同监测和病例管理实践的影响，但将贝叶斯时空变变性模型整合到喀麦隆的疟疾监测系统中，可以帮助查明事前热点，并促进积极和有针对性的干预措施。

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引用次数: 0

Evaluation of spatial and non-spatial factors on tuberculosis using geospatial information system and fuzzy logic 基于地理空间信息系统和模糊逻辑的结核病空间与非空间因素评价

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-06-05 DOI: 10.1016/j.sste.2025.100729

Mahsa Rakhsh Khorshid , Saeed Behzadi , Alireza Sharifi , Alireza Vafaeinejad , Ziba Abbasian , Hossein Naderi

Tuberculosis is a deadly infectious disease that has not been eradicated yet. The prevalence of this disease is still very high in some parts of the world, so it is considered a deadly disease. Iran is one of the countries which has not yet achieved the ability to eliminate this disease. The prevalence of tuberculosis is relatively higher in some provinces than in the other ones. Sistan and Baluchestan is the province with high rates of tuberculosis. In this paper, the factors affecting tuberculosis are modeled in Sistan and Baluchestan province using Geospatial Information Systems (GIS) and FL. This research contains two general analyzes. In the first analysis, three different scenarios of FL rules are presented. The first two scenarios examine spatial and non-spatial factors respectively. The third scenario also examines the combination of spatial and non-spatial factors simultaneously. As a result, the effect of spatial and non-spatial factors on tuberculosis is obtained. In the second analysis, a spatial scatter density map of tuberculosis is produced according to spatial data. This research reveals that the effects of spatial and non-spatial factors on tuberculosis are 57 % and 43 %, respectively. By comparing the results with samplings, the scatter rate map of tuberculosis is obtained with an accuracy of 71 %.

结核病是一种尚未被根除的致命传染病。这种疾病在世界某些地区的患病率仍然很高，因此被认为是一种致命的疾病。伊朗是尚未具备消灭这种疾病能力的国家之一。一些省份的肺结核患病率相对高于其他省份。锡斯坦和俾路支斯坦是结核病高发的省份。本文利用地理空间信息系统（GIS）和地理空间信息系统（FL）对锡斯坦省和俾路支斯坦省的结核病影响因素进行了建模。在第一个分析中，给出了三种不同的FL规则场景。前两种情景分别考察了空间和非空间因素。第三种情景还同时考察了空间和非空间因素的组合。从而得出空间因素和非空间因素对结核病的影响。在第二种分析中，根据空间数据生成结核的空间散点密度图。研究表明，空间因素和非空间因素对结核病的影响分别为57%和43%。通过与抽样结果的比较，得到了肺结核散点率图，准确率为71%。

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引用次数: 0

Disease mapping with individual level information; a case study of acute myocardial infarction mortality 基于个体水平信息的疾病制图；急性心肌梗死死亡率个案研究

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-04-28 DOI: 10.1016/j.sste.2025.100721

Xavier Puig, Josep Ginebra

When mapping relative mortality risk under specific causes of death in time, one can use small areas and single year mortality data to explore the space time variation in detail. To reduce the variability of the initial mortality risk estimates and help explain their differences, hierarchical Poisson models are typically used. Here we deal with the situation where besides aggregated small-area level data necessary for that, one also has complete individual level data about the presence of certain risk factors in the population, which is now rare but it should become routine in places with universal health coverage using a medical record sharing system. In particular, we consider the convenience of including individual level covariates in the models, and mapping relative mortality risk adjusted for them. That is illustrated by exploring how mortality due to acute myocardial infarction varies in space and in time in Catalonia between 2014 and 2019 using individual data on obesity, diabetes, dyslipidemia and smoking habits.

在绘制特定死亡原因下的相对死亡风险时，可以使用小区域和单年死亡率数据来详细探索时空变化。为了减少初始死亡风险估计的可变性并帮助解释它们之间的差异，通常使用分层泊松模型。在这里，我们处理的情况是，除了必要的汇总小区域数据外，还有关于人口中某些风险因素存在的完整的个人数据，这现在很少见，但在使用医疗记录共享系统的全民健康覆盖地区，它应该成为常规。特别是，我们考虑了在模型中包含个体水平协变量的便利性，以及为它们调整的相对死亡风险映射。通过使用关于肥胖、糖尿病、血脂异常和吸烟习惯的个人数据，探索2014年至2019年加泰罗尼亚急性心肌梗死死亡率在空间和时间上的变化，可以说明这一点。

引用次数: 0

Integrating data at multiple spatial scales to estimate the local burden of the opioid syndemic 整合多个空间尺度的数据，以估计阿片类药物综合征的当地负担

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-04-22 DOI: 10.1016/j.sste.2025.100720

Eva Murphy , David Kline , Erin McKnight , Andrea Bonny , William C. Miller , Lance Waller , Staci A. Hepler

The opioid epidemic has been particularly severe in Ohio, prompting significant efforts to understand its spatial patterns, mainly using available data at the county level. However, relying solely on county-level analysis can overlook crucial information relevant to localized effects. To address this, we integrate spatially misaligned data observed at the county and ZIP code levels to explore the complex interaction of five opioid-related outcomes, providing a more detailed local understanding of the opioid epidemic. We demonstrate how to map ZIP-code level data to ZIP-code Tabulation Areas (ZCTAs) and relate the county-level and ZCTA-level outcomes to a spatially correlated latent factor. The latent factor is defined on the intersection of the misaligned areal units, which provides a more granular understanding of the opioid epidemic. Furthermore, this approach allows us to identify areas with varying levels of opioid burden and reveals local regions with relatively high burden that county-level analyses might miss. Finally, we highlight the need for careful consideration when relying solely on ZIP code level data for naloxone, as it may lead to misinterpretations, particularly in rural regions.

俄亥俄州的阿片类药物疫情尤为严重，促使人们主要利用县一级的现有数据，努力了解其空间模式。然而，仅仅依靠县级分析可能会忽略与本地化效应相关的关键信息。为了解决这个问题，我们整合了在县和邮政编码层面观察到的空间错位数据，以探索五种阿片类药物相关结果之间复杂的相互作用，从而更详细地了解阿片类药物在当地的流行情况。我们展示了如何将邮政编码级数据映射到邮政编码表区（ZCTA），并将县级和邮政编码表区级结果与空间相关的潜在因素联系起来。该潜在因子定义在错位区域单位的交叉点上，从而提供了对阿片类药物流行的更精细的理解。此外，这种方法还能让我们确定阿片类药物负担程度不同的地区，并揭示县级分析可能遗漏的负担相对较重的局部地区。最后，我们强调在仅依赖邮政编码级别的纳洛酮数据时需要谨慎考虑，因为这可能会导致误读，尤其是在农村地区。

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引用次数: 0

Physician and healthcare partner engagement in the creation of healthfulness indices for West Michigan 医生和医疗保健合作伙伴参与创建健康指数为西密歇根州

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-04-19 DOI: 10.1016/j.sste.2025.100722

Richard Casey Sadler , Samantha Gailey , Erin R. McNeely

Community participatory mapping can direct health research, offering opportunity to build spatial awareness and generate future research. Here we establish healthfulness indices by consulting healthcare system partners for their expert opinions on characteristics they felt influenced health. Partners started from 36 variables and narrowed to 16 in 4 simplified categories. The analytic hierarchy process was used to identify variable and category weights. Opinions were consolidated for each partner sub-group and overall. Map layers were assigned calculated weights and indices were created from weighted layers. Areas with more amenities scored higher, including in and around downtown areas and smaller towns. Lower scores were found in suburban and lower-income urban areas. Variation in maps among subgroups reflect differing priorities in tackling health equity issues. This work increases healthcare partner engagement in built environment work and generates future research pathways. Partners now have a tool for interrogating and communicating the environment’s cumulative impact.

社区参与式绘图可以指导卫生研究，为建立空间意识和开展未来研究提供机会。在此，我们通过咨询医疗保健系统合作伙伴对他们认为影响健康的特征的专家意见来建立健康指数。合作伙伴从36个变量开始，缩小到16个，简化为4个类别。采用层次分析法确定变量和类别的权重。对每个合作伙伴分组和整体的意见进行了合并。地图层被分配计算权重，并从加权层创建索引。拥有更多便利设施的地区得分更高，包括市中心及周边地区和小城镇。郊区和低收入城市地区的得分较低。分组间地图的差异反映了处理卫生公平问题的不同重点。这项工作增加了医疗保健合作伙伴对建筑环境工作的参与，并产生了未来的研究途径。合作伙伴现在有了一个工具来询问和交流环境的累积影响。

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引用次数: 0

A multivariate generalized logistic approach with spatially varying nonlinear components for modeling epidemic data 一种具有空间变化非线性分量的流行病数据建模的多元广义逻辑方法

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-04-04 DOI: 10.1016/j.sste.2025.100718

Marcos O. Prates , Dani Gamerman , Samuel F. Candido , Luis M. Castro

This work considers the joint analysis of time series for epidemiological count data of neighboring regions. The joint analysis involves parameter estimation and prediction of future outcomes. The literature concentrated on imposing similarities on components of the linear predictor for the mean. However, some hierarchical model specifications for the mean contain non-linear components with similar behavior over neighboring regions. This paper proposes the use of spatial specification for these components. Parametric forms based on a data-driven approach are assumed for the waves of epidemic counts, and multiple waves are considered. The resulting model is tested in simulation studies and applied to real data. Model evaluation is based on the fitting and prediction capabilities. An illustration is provided by the analysis of counts of COVID19 cases, and it compares favorably against alternative models. Finally, the paper concludes with a discussion of the proposed methodology.

本文考虑了相邻地区流行病学统计数据的时间序列联合分析。联合分析包括参数估计和对未来结果的预测。文献集中于对均值的线性预测器的组成部分施加相似性。然而，对于均值的一些层次模型规范包含在相邻区域上具有相似行为的非线性分量。本文提出了对这些组件使用空间规范的方法。假设了基于数据驱动方法的流行病计数波的参数形式，并考虑了多个波。该模型已在仿真研究中得到验证，并应用于实际数据。模型评价是基于拟合和预测能力。对covid - 19病例数的分析提供了一个例证，与其他模型相比，它具有优势。最后，对本文提出的研究方法进行了讨论。

引用次数: 0

A Bayesian spatial measurement error approach to incorporate heterogeneous population-at-risk uncertainty in estimating small-area opioid mortality rates 估算小区域阿片类药物死亡率时纳入异质性高危人群不确定性的贝叶斯空间测量误差方法

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology

Pub Date : 2025-03-31 DOI: 10.1016/j.sste.2025.100719

Emily N. Peterson , Rachel C. Nethery , Jarvis T. Chen , Loni P. Tabb , Brent A. Coull , Frederic B. Piel , Lance A. Waller

Monitoring small-area geographical population trends in opioid mortality has significant implications for informing preventative resource allocation. A common approach to estimating small-area opioid mortality uses a standard disease mapping method where population-at-risk estimates (denominators) are treated as fixed. This assumption ignores the uncertainty in small-area population estimates, potentially biasing risk estimates and underestimating their uncertainties. We compare a Bayesian Spatial Berkson Error model and a Bayesian Spatial Classical Error model to a naive approach that treats denominators as fixed. Using simulations, we illustrate potential bias from ignored population-at-risk uncertainty. We apply these methods to obtain 2020 opioid mortality risk estimates for 159 counties in Georgia. Assessing differences in bias and uncertainty across approaches can improve the accuracy of small-area opioid risk estimates, guiding public health interventions, policies, and resource allocation.

监测小区域阿片类药物死亡率的地理人口趋势对告知预防性资源分配具有重要意义。估计小区域阿片类药物死亡率的一种常用方法是使用标准疾病制图方法，其中将高危人口估计值（分母）视为固定值。这种假设忽略了小区域人口估计的不确定性，可能会使风险估计产生偏差，并低估其不确定性。我们将贝叶斯空间伯克逊误差模型和贝叶斯空间经典误差模型与将分母视为固定的朴素方法进行比较。通过模拟，我们说明了被忽视的风险人群不确定性的潜在偏差。我们应用这些方法获得格鲁吉亚159个县的2020年阿片类药物死亡风险估计。评估不同方法在偏倚和不确定性方面的差异，可以提高小区域阿片类药物风险估计的准确性，指导公共卫生干预措施、政策和资源分配。

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引用次数: 0