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Epidemiological Characteristics and Spatiotemporal Analysis of Occupational Noise–Induced Deafness From 2006 to 2022 in Guangdong, China: Surveillance Study

Epidemiological Characteristics and Spatiotemporal Analysis of Occupational Noise–Induced Deafness From 2006 to 2022 in Guangdong, China: Surveillance Study

When the Moran’s I value is less than 0, it indicates negative spatial autocorrelation. When the Moran’s I value is equal to 0, it indicates a random distribution and no spatial autocorrelation [24]. Local spatial autocorrelation reflects the degree of correlation between each local unit and its neighboring units and is applied to identify high- and low-value clustering of local spatial locations [23].

Shanyu Zhou, Yongshun Huang, Lin Chen, Xianzhong Wen, Shu Wang, Lang Huang, Xudong Li

JMIR Public Health Surveill 2024;10:e57851

Comparison of Different Reverse Transcriptase–Polymerase Chain Reaction–Based Methods for Wastewater Surveillance of SARS-CoV-2: Exploratory Study

Comparison of Different Reverse Transcriptase–Polymerase Chain Reaction–Based Methods for Wastewater Surveillance of SARS-CoV-2: Exploratory Study

SARS-Co V-2 RNA is excreted in the feces of infected individuals [2], and the spatial and temporal changes of the COVID-19 pandemic can therefore be studied from wastewater [3-6]. The need for surveillance of SARS-Co V-2 in wastewater has been demonstrated by the fact that the estimated spread of COVID-19 based on wastewater surveillance has been much higher than would be expected based on clinical cases showing the actual spread of the virus [7].

Annika Länsivaara, Kirsi-Maarit Lehto, Rafiqul Hyder, Erja Sinikka Janhonen, Anssi Lipponen, Annamari Heikinheimo, Tarja Pitkänen, Sami Oikarinen, WastPan Study Group

JMIR Public Health Surveill 2024;10:e53175

Peer Review of “Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018”

Peer Review of “Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018”

Include spatial distribution maps of the incidence rates for 1-2 years during the study period. 5. Add numerical labels to the bars in Figure 1 for a more intuitive understanding. 6. Figure 4 lacks coordinate axes, and there is an incomplete gray box on the horizontal axis, affecting aesthetics. 7. Please provide the formula for calculating the case frequency. 1.

Anonymous

JMIRx Med 2024;5:e60433

Authors’ Response to Peer Reviews of “Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018”

Authors’ Response to Peer Reviews of “Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018”

Include spatial distribution maps of the incidence rates for 1-2 years during the study period. Response: Maps of 2013 and 2018 were added. 5. Add numerical labels to the bars in Figure 1 for a more intuitive understanding. Response: The numerical labels were added. 6. Figure 4 lacks coordinate axes, and there is an incomplete gray box on the horizontal axis, affecting aesthetics. Response: Figure 4 was revised. 7. Please provide the formula for calculating the case frequency.

Ali Hazim Mustafa, Hanan Abdulghafoor Khaleel, Faris Lami

JMIRx Med 2024;5:e60194

Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018

Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018

This study uses official data from the Ministry of Health (Mo H) to identify potential changes in the spatial and temporal occurrence of human brucellosis cases in Iraq from 2007 to 2018. This was a descriptive, retrospective study of the spatial and temporal distribution of human brucellosis from 2007 to 2018. Human brucellosis data were extracted from the surveillance database at the Surveillance Section at the Communicable Diseases Control Center (CDC), Public Health Directorate, Mo H in Iraq.

Ali Hazim Mustafa, Hanan Abdulghafoor Khaleel, Faris Lami

JMIRx Med 2024;5:e54611

Visual “Scrollytelling”: Mapping Aquatic Selfie-Related Incidents in Australia

Visual “Scrollytelling”: Mapping Aquatic Selfie-Related Incidents in Australia

This type of visual storytelling technique using a world map helps illustrate the spatial context of this public health issue. Incident data were acquired via publicly accessible news reports and a Wikipedia repository [2] and cleaned and prepared in Excel (Microsoft Corp). Incidents in aquatic areas (eg, coastal locations and inland waterfalls) were included; those in other settings (eg, falls from artificial structures and incidents involving trains) were excluded.

Samuel Cornell, Amy E Peden

Interact J Med Res 2024;13:e53067

Geospatial Imprecision With Constraints for Precision Public Health: Algorithm Development and Validation

Geospatial Imprecision With Constraints for Precision Public Health: Algorithm Development and Validation

Our second data set contains 58,102 case records from the Medical Examiner Case Archive from Cook County, Illinois, which contains the city of Chicago; we previously used this open data for research on geographic clustering of fatal overdoses [32] and to create an open data pipeline for spatial analyses on substance use disorders [33].

Daniel Harris, Chris Delcher

Online J Public Health Inform 2024;16:e54958

Predictive and Prognostic Biomarkers in Patients With Mycosis Fungoides and Sézary Syndrome (BIO-MUSE): Protocol for a Translational Study

Predictive and Prognostic Biomarkers in Patients With Mycosis Fungoides and Sézary Syndrome (BIO-MUSE): Protocol for a Translational Study

In brief, in skin biopsies, Digital Spatial Profiling will be performed to study immune infiltration in CTCL and how it evolves during the progression of the disease [31]. Single-cell RNA and T-cell receptor sequencing will be applied to sorted CD3+ cells to follow the evolution of the malignant clone over time. Moreover, additional assessments of genetic and epigenetic changes in the tumor and tumor microenvironment will be performed in skin biopsies (Multimedia Appendix 1).

Emma Belfrage, Sara Ek, Åsa Johansson, Hanna Brauner, Andreas Sonesson, Kristina Drott

JMIR Res Protoc 2024;13:e55723