Multistate analysis of prospective Legionnaires' disease cluster detection using SaTScan, 2011-2015

Uncovering wastewater treatment plants as possible sources of legionellosis clusters through spatial statistics approach and environmental analysis

Wastewater treatment plants (WWTPs) are suspected reservoirs of Legionella pneumophila (Lp). The required aeration and mixing steps lead to the emission and dispersion of bioaerosols potentially harboring Lp. The aim of the project is to evaluate municipal WWTPs as a possible source of legionellosis through the statistical analysis of case clusters. A space-time scanning statistical method was implemented in SaTScan software to identify and analyze WWTPs located within and close to spatiotemporal clusters of legionellosis detected in Quebec between 2016 and 2020. In parallel, WWTPs were ranked according to their pollutant load, flow rate and treatment type. These parameters were used to evaluate the WWTP susceptibility to generate and disperse bioaerosols. Results show that 37 of the 874 WWTPs are located inside a legionellosis cluster study zone, including six of the 40 WWTPs ranked most susceptible. In addition, two susceptible WWTPs located within an extended area of 2.5 km from the study zone (2.5-km buffer) were included, for a total of 39 WWTPs. The selected 39 WWTPs were further studied to document proximity of population, dominant wind direction, and surrounding water quality. Samples collected from the influent and the effluent of six selected WWTPs revealed the presence of Legionella spp. in 92.3% of the samples. Lp and Lp serogroupg 1 (Lp sg1) were detected below the limit of quantification in 69% and 46% of the samples, respectively. The presence of Legionella in wastewater and the novel statistical approach presented here provides information to the public health authorities regarding the investigation of WWTPs as a possible source of Legionella exposure, sporadic cases, and clusters of legionellosis.

Geographic Variation in Late-Stage Cervical Cancer Diagnosis

Importance

There are stark disparities in cervical cancer burden in the United States, notably by race and ethnicity and geography. Late-stage diagnosis is an indicator of inadequate access to and utilization of screening.

Objective

To identify geospatial clusters of late-stage cervical cancer at time of diagnosis in Texas.

Design, Setting, and Participants

This population-based cross-sectional study used incident cervical cancer data from the Texas Cancer Registry from 2014 to 2018 of female patients aged 18 years or older. Late-stage cervical cancer cases were geocoded at the census tract level (n = 5265) using their residential coordinates (latitude and longitude) at the time of diagnosis. Statistical analysis was performed from April to September 2023.

Exposures

Census tract of residence at diagnosis.

Main Outcome and Measures

Late-stage cervical cancer diagnosis (ie, cases classified by the National Cancer Institute Surveillance, Epidemiology and End Results summary stages 2 to 4 [regional spread] or 7 [distant metastasis]). A Poisson probability-based model of the SaTScan purely spatial scan statistics was applied at the census tract-level to identify geographic clusters of higher (hot spots) or lower (cold spots) proportions than expected of late-stage cervical cancer diagnosis and adjusted for age.

Results

Among a total of 6484 female patients with incident cervical cancer cases (mean [SD] age, 48.7 [14.7] years), 2300 (35.5%) were Hispanic, 798 (12.3%) were non-Hispanic Black, 3090 (47.6%) were non-Hispanic White, and 296 (4.6%) were other race or ethnicity. Of the 6484 patients, 2892 with late-stage diagnosis (mean [SD] age, 51.8 [14.4] years were analyzed. Among patients with late-stage diagnosis, 1069 (37.0%) were Hispanic, 417 (14.4%) were non-Hispanic Black, 1307 (45.2%) were non-Hispanic White, and 99 (3.4%) were other race or ethnicity. SaTScan spatial analysis identified 7 statistically significant clusters of late-stage cervical cancer diagnosis in Texas, of which 4 were hot spots and 3 were cold spots. Hot spots included 1128 census tracts, predominantly in the South Texas Plains, Gulf Coast, and Prairies and Lakes (North Texas) regions. Of the 2892 patients with late-stage cervical cancer, 880 (30.4%) were observed within hot spots. Census tract-level comparison of characteristics of clusters suggested that hot spots differed significantly from cold spots and the rest of Texas by proportions of racial and ethnic groups, non-US born persons, and socioeconomic status.

Conclusions and Relevance

In this cross-sectional study examining geospatial clusters of late-stage cervical cancer diagnosis, place-based disparities were found in late-stage cervical cancer diagnosis in Texas. These findings suggest that these communities may benefit from aggressive cervical cancer interventions.

Rapid surveillance of COVID-19 by timely detection of geographically robust, alive and emerging hotspots using Particle Swarm Optimizer

A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first detected in Wuhan city in December 2019 and has affected 216 countries with 9473214 confirmed cases and 484249 deaths globally as on June 26th, 2020. Also, this outbreak continues to grow in many countries like the United States of America (U.S.), Brazil, India, and Russia. To ensure rapid surveillance and better decision-making by government authorities in different countries, it is vital to identify alive and emerging hotspots within a country promptly. State-of-the-art methods based on space-time scan statistics (like SaTScan) are not geographically robust. Also, due to the enumeration of many Spatio-temporal cylinders, the computation cost of Spatio-temporal SaTScan (ST-SaTScan) is very high. In the applications like COVID-19 where we need to detect the emerging hotspots daily as soon as the new count of cases gets updated, ST-SaTScan seems inefficient. Therefore, this paper proposes a Particle Swarm Optimizer-based scheme to timely detect geographically robust, alive, and emerging COVID-19 hotspots in a country. Timely detection can help government officials design better control strategies like increasing testing in hotspots, imposing stricter containment rules, or setting up temporary hospital beds. Performance of ST-SaTScan and proposed scheme have been analyzed for four worst-hit U.S. states for the incubation period of 14 days between June 11th, 2020, and June 24th, 2020. Results indicate that the proposed scheme detects hotspots of a higher likelihood ratio (a measure to indicate the significance of hotspot) than ST-SaTScan in significantly less time. We also applied the proposed scheme to detect the emerging COVID-19 hotspots in all states of the U.S. During the study period, the proposed scheme has detected 104 emerging COVID-19 hotspots.

A spatiotemporal data mining study to identify high-risk neighborhoods for out-of-hospital cardiac arrest (OHCA) incidents

Out-of-hospital cardiac arrest (OHCA) is a worldwide health problem. The aim of the study is to utilize the territorial-wide OHCA data of Hong Kong in 2012-2015 to examine its spatiotemporal pattern and high-risk neighborhoods. Three techniques for spatiotemporal data mining (SaTScan's spatial scan statistic, Local Moran's I, and Getis Ord Gi*) were used to extract high-risk neighborhoods of OHCA occurrence and identify local clusters/hotspots. By capitalizing on the strengths of these methods, the results were then triangulated to reveal "truly" high-risk OHCA clusters. The final clusters for all ages and the elderly 65+ groups exhibited relatively similar patterns. All ages groups were mainly distributed in the urbanized neighborhoods throughout Kowloon. More diverse distribution primarily in less accessible areas was observed among the elderly group. All outcomes were further converted into an index for easy interpretation by the general public. Noticing the spatial mismatches between hospitals and ambulance depots (representing supplies) and high-risk neighborhoods (representing demands), this setback should be addressed along with public education and strategic ambulance deployment plan to shorten response time and improve OHCA survival rate. This study offers policymakers and EMS providers essential spatial evidence to assist with emergency healthcare planning and informed decision-making.

A review of legionnaires' disease and public water systems - Scientific considerations, uncertainties and recommendations

Legionella is an opportunistic premise plumbing pathogen and causative agent of a severe pneumonia called Legionnaires' Disease (LD). Cases of LD have been on the rise in the U.S. and globally. Although Legionella was first identified 45 years ago, it remains an 'emerging pathogen." Legionella is part of the normal ecology of a public water system and is frequently detected in regulatory-compliant drinking water. Drinking water utilities, regulators and public health alike are increasingly required to have a productive understanding of the evolving issues and complex discussions of the contribution of the public water utility to Legionella exposure and LD risk. This review provides a brief overview of scientific considerations important for understanding this complex topic, a review of findings from investigations of public water and LD, including data gaps, and recommendations for professionals interested in investigating public water utilities. Although the current literature is inconclusive in identifying a public water utility as a sole source of an LD outbreak, the evidence is clear that minimizing growth of Legionella in public water utilities through proper maintenance and sustained disinfectant residuals, throughout all sections of the water utility, will lead to a less conducive environment for growth of the bacteria in the system and the buildings they serve.

Epidemiological Trends and Hotspots of Other Infectious Diarrhea (OID) in Mainland China: A Population-Based Surveillance Study From 2004 to 2017

Background: The incidence of other infectious diarrhea (OID) ranked second in class C notifiable disease in China. It has posed a great threat to public health of all age groups. The aim of this study was to investigate the epidemiological trends and hotspots of OID in mainland China. Materials and Methods: Incidence and mortality data for OID stratified by date, age and region from 2004 to 2017 was extracted from the data-center of China public health science. Joinpoint regression and space-time analyses were performed to explore the epidemiological trends and hotspots of OID. Results: The average annual incidence of OID was 60.64/100,000 and it showed an increased trend in the mainland China especially after 2006 (APC = 4.12, 95 CI%: 2.06-6.21). Children of 0-4 year age group accounts for 60.00% (5,820,897/11,414,247) of all cases and its incidence continuously increased though 2004-2017 (APC = 6.65, 95 CI%: 4.39-8.96). The first-level spatial and temporal aggregation areas were located in Beijing and Tianjin, with the gathering time from 2005/1/1 to 2011/12/31 (RR = 5.52, LLR = 572893.59, P < 0.001). The secondary spatial and temporal aggregation areas covered Guangdong, Guangxi, Hainan and Guizhou from 2011/1/1 to 2017/12/31 (RR = 1.98, LLR = 242292.72, P < 0.001). OID of Tianjin and Beijing presented a decreased trend since 2006. However, the incidence of OID in Guangdong, Guangxi, Hainan and Guizhou showed increased trends through 2004-2017. Conclusion: Our study showed that OID showed a constantly increasing trend and brought considerable burden in China especially in the 0-4 age group. The high-risk periods and clusters of regions for OID were identified, which will help government develop disease-specific and location-specific interventive measures.