Part 2 of an overview of epidemiology, and what ArcGIS Insights offers for the analytical needs of the epidemiologist.Key topics with examples covering major areas of epidemiological study and the scope of GIS to provide an analytical framework. _Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Part 1 of an overview of epidemiology, and what ArcGIS Insights offers for the analytical needs of the epidemiologist.Key topics with examples covering major areas of epidemiological study and the scope of GIS to provide an analytical framework. _Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

The triad of host, agent, and environment has become a widely accepted framework for understanding infectious diseases and human health. While modern medicine has traditionally focused on the individual, there is a renewed interest in the role of the environment. Recent studies have shifted from an early-twentieth-century emphasis on individual factors to a broader consideration of contextual factors, including environmental, climatic, and social settings as spatial determinants of health. This shifted focus has been particularly relevant in the context of the COVID-19 pandemic, where the built environment in urban settings is increasingly recognized as a crucial factor influencing disease transmission. However, operationalizing the complexity of associations between the built environment and health for empirical analyses presents significant challenges. This study aims to identify key caveats in the operationalization of spatial determinants of health for empirical analysis and proposes guiding principles for future research. We focus on how the built environment in urban settings was studied in recent literature on COVID-19. Based on a set of criteria, we analyze 23 studies and identify explicit and implicit assumptions regarding the health-related dimensions of the built environment. Our findings highlight the complexities and potential pitfalls, referred to as the ‘spatial trap,' in the current approaches to spatial epidemiology concerning COVID-19. We conclude with recommendations and guiding questions for future studies to avoid falsely attributing a built environment impact on health outcomes and to clarify explicit and implicit assumptions regarding the health-related dimensions.

This dataset contains model-based place (incorporated and census designated places) estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia —at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. These data can be joined with the 2019 Census TIGER/Line place boundary file in a GIS system to produce maps for 36 measures at the place level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=2c3deb0c05a748b391ea8c9cf9903588

Epidemiology is a field of study that looks at patterns of health and disease within a population. This includes the study of factors that contribute to illness. To determine the frequency and causes of illness, epidemiologists focus on studying communities rather than individuals. Use of epidemiology to study characteristics of illnesses and their associated factors can be used to prevent and control public health problems.Epidemiologists study not only infectious diseases, but also environmental exposures to toxins and pollutants, workplace and crime-related injuries, birth defects, mental health, and substance abuse. To characterize these illnesses and conditions, epidemiology depends on statistics to measure rates of incidence, prevalence, and mortality.

Three datasets are provide, 1 (malaria infections data), 2 (densities of Anopheles gambiae) and 3 (geophysical data). The geophysical variables were linked with either malaria infections or densities of An. gambiae outcomes using a TCU_cluster variable, which appears in each of the three datasets. This variable provides the spatial unit of analysis for the study. All data were aggregated at a TCU level, which is a cluster of at least ten houses to a maximum of one hundred.

This dataset contains model-based county-level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the census 2022 county boundary file in a GIS system to produce maps for 40 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This dataset contains model-based county-level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2018 or 2017 county population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 27 measures at the county level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

IntroductionIn December 2019, several cases of pneumonia of an unknown origin appeared in China. Previously, in that same year, the World Health Organization (WHO) had already published a list of the ten major global health issues which included the risk of a pandemic from respiratory diseases [1]. Later, in January 2020, the cause of the pneumonia cases detected in China was identified as being a new coronavirus of the Severe Acute Respiratory Syndrome (SARS-CoV-2) [2].The first records of SARS-CoV-2 infection, identified in Wuhan, China, spread quickly causing the territorial spread of contagions across dozens of countries. This lead the World Health Organization (WHO) to declare a pandemic, at the time more than 100 thousand cases of infection had already been detected in 114 countries and a total number of deaths higher than 4.000 [3].Geographical analysis of diseases are common in scientific literature [4, 5, 6] and Geographic Information Systems (GIS) and Spatial Analysis techniques have proven to be useful for studying how they spread across space and time [7, 8]. The spatial dimension plays a key role in epidemiological studies partly due to the growing development of technologies in terms of algorithms and processing capacity that allows the modeling of epidemiological phenomena [8]. COVID-19 studies GIS-based are just as important to understand unknown attributes of the disease in this time of great uncertainty, although, only a few studies have focused on geographic hotspots analysis and have tried to unveil the community drivers associated with the spatial patterns of local transmission [9].ObjectivesThis applied study is twofold. First seeks to highlight the importance of geographical factors in the current context; and second, uses geographic analysis methods and techniques, especially spatial statistic methods, to create evidence-based knowledge upon COVID-19 spatial spread, as well as its patterns and trends.In this way, ArcGIS Pro, Esri’s GIS software, is used in a space-time approach to synthetize the most relevant spatial dynamics. The specific objectives of the study are:1. Analyze the spatial patterns of the pandemic diffusion;2. Identify important transmission clusters;3. Identify spatial determinants of the disease spread.Study Area and DataThe study area is mainland Portugal at a municipal scale, due to being the finest scale of analysis with epidemiological information available in the official reports of the Direção-Geral da Saúde (DGS). Portugal has been severely affected by the pandemic and various spatial dynamics can be identified through time, since the patterns of incidence have changed in successive waves. In this way, the study is focused on various moments during the first year of incidence of the disease, capturing the most important patterns, tendencies and processes. Data used for this analysis is the epidemiological information of DGS [10], for the epidemiologic dimension, and Instituto Nacional de Estatística (INE) database [11] and Carta Social [12] for the variables that will be used as independent variables grouped in 3 dimensions: economic, sociodemographic and mobility (Figure 1).Figure 1 - Variables and respective dimensions of analysisMethodologyThe methodology is divided in 2 parts (Figure 2): the first is related to data acquisition, editing, management and integration in GIS, and the second is in relation to the modeling itself, in order to respond to the objectives which comprises of 3 phases: (i) space-time analysis of confirmed cases of infections to understand the diffusion processes; (ii) analysis of hot spots, clusters and outliers to identify the different patterns and tendencies over time and (iii) ordinary least squares regression (OLS) to identify the most important determinant spatial factors and drivers of the virus propagation.Figure 2 - Methodology flowResultsResults demonstrate an initial tendency of a hierarchical diffusion process, from centers of larger population densities to those of which are less dense (Figure 3), which is replaced and dominated in following periods by contagion expansion. Geographically, the first confirmed cases appeared in coastal cities and progressively penetrated into the interior of the country with a strong spatial association with the main roads and the population size of the territorial units.Figure 3 - Evolution of confirmed cases and hot spots, clusters and outliers of incidence rate by municipalityThe Norte region, namely the Porto metropolitan region, recorded a very high rate of incidence in all periods and broke records in the numbers of new cases, except in the third wave, after the Christmas and New Year festivities, in which the number of new cases was the highest ever in every region and specially in Centro region inland municipalities.The results of OLS (Figure 4) are in line with other studies [13, 14] and show that there is a significant relationship in regard to family size that is visible during almost every period, demonstrating that it is difficult to avoid contagion between cohabitants. Population density also appears as important in various moments, although with lower coefficients.Figure 4 – Ordinary Least Squares resultsEmployment concentrations also appear with a strong spatial relationship with the incidences, as well as the socioeconomic conditions that appear to be represented by different variables (beneficiaries of unemployment benefits, social reintegration allowance, declared income, proportion of house-ownership).The importance of mobility in the virus’s propagation is confirmed, both by type of usual mode of transport and commuting time. The interrelation between school students and incidence may also indicate that increased mobility associated with school attendance is relevant for propagation.ConclusionsArcGIS Pro proved to be crucial and an added value for geographical visualization and for the use of spatial statistics methods, essential in providing evidence-based knowledge about the spatial dynamics of COVID-19 in mainland Portugal. The COVID-19 waves demonstrated different spatial behaviours, with different patterns and thus different community drivers. Income, mobility, population density, family size and employment concentrations appear as the most important spatial determinants. Results are in line with scientific literature and prove the relevance of spatial approaches in epidemiology.References1 - WHO - World Health Organization. (2019). Ten threats to global health in 2019. https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-20192 - WHO - World Health Organization. (2020a). Coronavirus disease 2019 (COVID-19): situation report, 94. https://apps.who.int/iris/handle/10665/3318653 - WHO - World Health Organization. (2020e). WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-20204 - Gould, P. (1993). The slow plague : a geography of the AIDS pandemic. Blackwell Publishers. https://books.google.pt/books?id=u3Z9QgAACAAJ&dq5 - Cliff, A. D., Hagget, P., Ord, J. K., & Versey, G. R. (1981). Spatial diffusion : an historical geography of epidemics in an island community. Cambridge University Press Cambridge ; New York. https://books.google.pt/books?id=OIaqxwEACAAJ&dq6 - Arroz, M. E. (1979). Difusão espacial da hepatite infecciosa. Finisterra - Revista Portuguesa de Geografia, LV(14) DOI: https://doi.org/10.18055/Finis22377 - Lyseen, A.K.; Nøhr, C.; Sørensen, E.M.; Gudes, O.; Geraghty, E.M.; Shaw, N.T.; Bivona-Tellez, C. (2014). A review and framework for categorizing current research and development in health related geographical information systems (GIS) studies. Yearb Med. Inform. https://doi.org/10.15265%2FIY-2014-00088 - Pfeiffer, D.; Robinson, T; Stevenson, M.; Stevens, K.; Rogers, D.; Clements, A. (2008). Spatial Analysis in Epidemiology. Oxford University Press. https://books.google.pt/books/about/Spatial_Analysis_in_Epidemiology.html?id=niTDr3SIEhUC&redir_esc=y9 - Franch-Pardo, I.; Napoletano, B.M.; Rosete-Verges, F.; Billa, L. Spatial analysis and GIS in the study of COVID-19. A review. (2020). Sci.10 – DGS – Direção-Geral da Saúde (2020). Relatório de Situação. Lisboa: Ministério da Saúde – Direção-Geral da Saúde. https://covid19.min-saude.pt/relatorio-de-situacao/11 – INE – Instituto Nacional de Estatística (s.d.). Portal do INE. Base de dados. https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_base_dados&contexto=bd&selTab=tab212 – GEP – Gabinete de Estratégia e Planeamento (2018). Carta Social. Ministério do Trabalho, Solidariedade e Segurança Social. www.cartasocial.pt13 – Sousa, P., Costa, N. M., Costa, E. M., Rocha, J., Peixoto, V. R., Fernandes, A. C., Gaspar, R., Duarte-Ramos, F., Abrantes, P., & Leite, A. (2021). COMPRIME - Conhecer mais para intervir melhor: Preliminary mapping of municipal level determinants of covid-19 transmission in Portugal at different moments of the 1st epidemic wave. Portuguese Journal of Public Health. https://doi.org/10.1159/00051433414 – Andersen, L. M.; Harden, S. R.; Sugg, M. M.; Runkle, J. D.; Lundquist, T. E. (2021). Analyzing the spatial determinants of local Covid-19 transmission in the United States. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142396

This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. F1000Research 2021, 10:1312, DOI: 10.12688/f1000research.75891.1). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..

We also provide a guide which provides a general description of the contents of the four sections in this archive (Documentation_for_Sections_of_Zenodo_Archive.docx). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.

Data Files:

Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"

This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.

Section 2. "Section_2.Localized_Hotspot_Lists.zip"

All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.

Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"

Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).

Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"

Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan).

Software:

This Zenodo archive also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries.

This distribution of the Geostatistical Epidemiology Toolbox does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from DMTI (https://www.dmtispatial.com/canmap/) through the Scholar's Geoportal at the University of Western Ontario (http://geo2.scholarsportal.info/). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).

Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"

The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes.

The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images.

This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).

Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"

In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:

“Ontario_City_Closest_Postal_Code_Identification.pl”

Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.

The output of this program (for the same municipal region being evaluated) is required for the following two Perl

Matched geographic information between dengue visits data and GIS file.

This dataset contains model-based census tract level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the Census tract 2022 boundary file in a GIS system to produce maps for 40 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This database encompasses several files related to cancer data. The first file is an Excel spreadsheet, containing information on newly diagnosed cancer cases from 2014 to 2017. It provides demographic details and specific characteristics of 482,229 cancer patients. We categorized this data according to the International Agency for Research on Cancer (IARC) reporting rules, and cancers with greater incidence rates were identified. To create a geodatabase, individual data was integrated at the county level and combined with population data. Files 2 and 3 contain gender-specific spatial data for the top cancer types and non-melanoma skin cancer. Each file includes county identifications, the number of cancer cases for each cancer type per year, and gender-specific population information. Lastly, there is a user's guide file to help navigate through the data files.

*Notes: n = 1124. P values reflect difference from reference category *p

PLACES (Population Level Analysis and Community Estimates) is an extension of the original 500 Cities Project and is a collaboration between CDC, the Robert Wood Johnson Foundation (RWJF), and the CDC Foundation (CDCF). The original 500 Cities Project provided city- and census tract-level estimates for chronic disease risk factors, health outcomes, and clinical preventive services use for the 500 largest US cities. PLACES extends these estimates to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTA) across the United States. Data were provided by CDC, Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2018 or 2017), Census Bureau 2010 census population data or annual population estimates for county vintage 2018 or 2017, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates.For more information about the methodology, visit https://www.cdc.gov/places or contact places@cdc.gov.

description:

2013, 2014. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format. This dataset can be joined with city-level spatial data in a geographic information system (GIS) to produce maps of 27 measures at the city-level.
Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

2013, 2014. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format. This dataset can be joined with city-level spatial data in a geographic information system (GIS) to produce maps of 27 measures at the city-level.
Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

Date created: November 2023Update frequency: Daily from Mon to Friday, excluding statutory holidays.Accuracy: Points of consideration for interpretation of the data:The data was extracted by Ottawa Public Health from the Ministry of Health and Long-Term Care’s integrated Public Health Information System (iPHIS) and COVID-19 Case and Contact Management solution (CCM). IPHIS and CCM are dynamic disease reporting systems that allow for ongoing updates to data previously entered. The data extracted from iPHIS and CCM represent a snapshot at the time of extraction and may differ in previous or subsequent reports.Data are presented for confirmed outbreaks and all outbreaks met the outbreak definitions at the time of reporting.Data fields:Type of Outbreak - textOutbreak Name – textFacility Type – textOutbreak Location Details – textStart Date – Date outbreak declaredEnd Date – Date outbreak declared overAetiologic agent - textAuthor: OPH Epidemiology TeamAuthor email: OPH-Epidemiology@ottawa.caMaintainer Organization: Epidemiology & Evidence, Ottawa Public Health

Dengue is one of the major hurdles to the public health in Sri Lanka, causing high morbidity and mortality. The present study focuses on the use of geographical information systems (GIS) to map and evaluate the spatial and temporal distribution of dengue in Sri Lanka from 2009 to 2014 and to elucidate the association of climatic factors with dengue incidence. Epidemiological, population and meteorological data were collected from the Epidemiology Unit, Department of Census and Statistics and the Department of Meteorology of Sri Lanka. Data were analyzed using SPSS (Version 20, 2011) and R studio (2012) and the maps were generated using Arc GIS 10.2. The dengue incidence showed a significant positive correlation with rainfall (p

Variable (year of acquisition).Residual standard deviation is the unit of change for all forest disturbance risk factors.Risk Ratio.Models are adjusted for several sociodemographic and environmental risk factors at the municipality level including: percent of population who migrated in the previous 2 years, male to female ratio, average number of people per household, percent rural population, percent of households living under minimum wage, average transportation costs to the nearest capitol, percent GDP growth from 2000 to 2005, and land cover in 2003 including percent of municipality that was water, remaining forest, and savanna.Interaction between unpaved road density (meters/km2) and % deforestation in a municipality in 2003.

The PLACES (Population Level Analysis and Community Estimates) is an expansion of the original 500 Cities project and is a collaboration between the CDC, the Robert Wood Johnson Foundation (RWJF), and the CDC Foundation (CDCF). The original 500 Cities Project provided city- and census tract-level estimates for chronic disease risk factors (5), health outcomes (13), and clinical preventive services use (9) for the 500 largest US cities. The PLACES Project extends these estimates to all counties, places (incorporated and census designated places), census tracts and ZIP Code Tabulation Areas (ZCTA) across the United States. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2018 or 2017), Census Bureau 2010 census population data or annual population estimates for county vintage 2018 or 2017, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates.The health outcomes include arthritis, current asthma, high blood pressure, cancer (excluding skin cancer), high cholesterol, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, mental health not good for >=14 days, physical health not good for >=14 days, all teeth lost and stroke.The preventive services uses include lack of health insurance, visits to doctor for routine checkup, visits to dentist, taking medicine for high blood pressure control, cholesterol screening, mammography use for women, cervical cancer screening for women, colon cancer screening, and core preventive services use for older adults (men and women).The unhealthy behaviors include binge drinking, current smoking, obesity, physical inactivity, and sleeping less than 7 hours.For more information about the methodology, visit https://www.cdc.gov/places or contact places@cdc.gov.CDC's source webpage.CDC's feature service.