A Bayesian Spatial Scan Statistic for Multinomial Data

Spatial scan statistics are commonly used to detect clustering. We present a Bayesian spatial scan statistic for multinomial data. After validating our method with a simulation study, we use it to detect clusters of SARS-CoV-2 infection/immunity in South Carolina.

Detecting high-risk neighborhoods and socioeconomic determinants for common oral diseases in Germany

BACKGROUND

Ideally, health services and interventions to improve dental health should be tailored to local target populations. But this is not the standard. Little is known about risk clusters in dental health care and their evaluation based on small-scale, spatial data, particularly among under-represented groups in health surveys. Our study aims to investigate the incidence rates of major oral diseases among privately insured and self-paying individuals in Germany, explore the spatial clustering of these diseases, and evaluate the influence of social determinants on oral disease risk clusters using advanced data analysis techniques, i.e. machine learning.

METHODS

A retrospective cohort study was performed to calculate the age- and sex-standardized incidence rate of oral diseases in a study population of privately insured and self-pay patients in Germany who received dental treatment between 2016 and 2021. This was based on anonymized claims data from BFS health finance, Bertelsmann, Dortmund, Germany. The disease history of individuals was recorded and aggregated at the ZIP code 5 level (n = 8871).

RESULTS

Statistically significant, spatially compact clusters and relative risks (RR) of incidence rates were identified. By linking disease and socioeconomic databases on the ZIP-5 level, local risk models for each disease were estimated based on spatial-neighborhood variables using different machine learning models. We found that dental diseases were spatially clustered among privately insured and self-payer patients in Germany. Incidence rates within clusters were significantly elevated compared to incidence rates outside clusters. The relative risks (RR) for a new dental disease in primary risk clusters were min = 1.3 (irreversible pulpitis; 95%-CI = 1.3-1.3) and max = 2.7 (periodontitis; 95%-CI = 2.6-2.8), depending on the disease. Despite some similarity in the importance of variables from machine learning models across different clusters, each cluster is unique and must be treated as such when addressing oral public health threats.

CONCLUSIONS

Our study analyzed the incidence of major oral diseases in Germany and employed spatial methods to identify and characterize high-risk clusters for targeted interventions. We found that private claims data, combined with a network-based, data-driven approach, can effectively pinpoint areas and factors relevant to oral healthcare, including socioeconomic determinants like income and occupational status. The methodology presented here enables the identification of disease clusters of greatest demand, which would allow implementing more targeted approaches and improve access to quality care where they can have the most impact.

Detecting the spatial clustering of exposure-response relationships with estimation error: a novel spatial scan statistic

Detecting the spatial clustering of the exposure-response relationship (ERR) between environmental risk factors and health-related outcomes plays important roles in disease control and prevention, such as identifying highly sensitive regions, exploring the causes of heterogeneous ERRs, and designing region-specific health intervention measures. However, few studies have focused on this issue. A possible reason is that the commonly used cluster-detecting tool, spatial scan statistics, cannot be used for multivariate spatial datasets with estimation error, such as the ERR, which is often defined by a vector with its covariance estimated by a regression model. Such spatial datasets have been produced in abundance in the last decade, which suggests the importance of developing a novel cluster-detecting tool applicable for multivariate datasets with estimation error. In this work, by extending the classic scan statistic, we developed a novel spatial scan statistic called the estimation-error-based scan statistic (EESS), which is applicable for both univariate and multivariate datasets with estimation error. Then, a two-stage analytic process was proposed to detect the spatial clustering of ERRs in practical studies. A published motivating example and a simulation study were used to validate the performance of EESS. The results show that the clusters detected by EESS can efficiently reflect the clustering heterogeneity and yield more accurate ERR estimates by adjusting for such heterogeneity.

Sequential Data-Mining for Adverse Events After Recombinant Herpes Zoster Vaccination Using the Tree-Based Scan Statistic

Tree-based scan statistics have been successfully used to study the safety of several vaccines without prespecifying health outcomes of concern. In this study, the binomial tree-based scan statistic was applied sequentially to detect adverse events in days 1-28 compared with days 29-56 after recombinant herpes zoster (RZV) vaccination, with 5 looks at the data and formal adjustment for the repeated analyses over time. IBM MarketScan data on commercially insured persons ≥50 years of age receiving RZV during January 1, 2018, to May 5, 2020, were used. With 999,876 doses of RZV included, statistically significant signals were detected only for unspecified adverse effects/complications following immunization, with attributable risks as low as 2 excess cases per 100,000 vaccinations. Ninety percent of cases in the signals occurred in the week after vaccination and, based on previous studies, likely represent nonserious events like fever, fatigue, and headache. Strengths of our study include its untargeted nature, self-controlled design, and formal adjustment for repeated testing. Although the method requires prespecification of the risk window of interest and may miss some true signals detectable using the tree-temporal variant of the method, it allows for early detection of potential safety problems through early initiation of ongoing monitoring.

Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic

The spatial scan statistics based on the Poisson and binomial models are the most common methods to detect spatial clusters in disease surveillance. These models rely on Monte-Carlo simulation which are time consuming. Moreover, frequently, datasets present over-dispersion which cannot be handled by them. Thus, we have the following goals. First, we propose irregularly shaped spatial scan for the Bell, Poisson, and binomial. The Bell distribution has just one parameter but it is capable of handling over-dispersed datasets. Second, we apply these scan statistics to big maps. A fast version, without Monte-Carlo simulation, for the proposed Poisson and binomial scans is introduced. Intensive simulation studies are carried out to assess the quality of the proposals. In addition, we show the time improvement of the fast scan versions over their traditional ones. Finally, we end the paper with an application on the detection of irregular shape small nodules in a medical image.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12561-022-09353-7.

A Broad Safety Assessment of the Recombinant Herpes Zoster Vaccine

The recombinant herpes zoster vaccine (RZV), approved as a 2-dose series in the United States in October 2017, has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome after vaccination was identified after approval, and questions remain about other possible adverse events. This data-mining study assessed RZV safety in the United States using the self-controlled tree-temporal scan statistic, scanning data on thousands of diagnoses recorded during follow-up to detect any statistically unusual temporal clustering of cases within a large hierarchy of diagnoses. IBM MarketScan data on commercially insured persons at least 50 years of age receiving RZV between January 1, 2018, and May 5, 2020, were used, including 56 days of follow-up; 1,014,329 doses were included. Statistically significant clustering was found within a few days of vaccination for unspecified adverse effects, complications, or reactions to immunization or other medical substances/care; fever; unspecified allergy; syncope/collapse; cellulitis; myalgia; and dizziness/giddiness. These findings are consistent with the known safety profile of this and other injected vaccines. No cluster of Guillain-Barré syndrome was detected, possibly due to insufficient sample size. This signal-detection method has now been applied to 5 vaccines, with consistently plausible results, and seems a promising addition to vaccine-safety evaluation methods.