The choropleth map is a device used for the display of socioeconomic data associated with an areal partition of geographic space. Cartographers emphasize the need to standardize any raw count data by an area-based total before displaying the data in a choropleth map. The standardization process converts the raw data from an absolute measure into a relative measure. However, there is recognition that the standardizing process does not enable the map reader to distinguish between low–low and high–high numerator/denominator differences. This research uses concentration-based classification schemes using Lorenz curves to address some of these issues. A test data set of nonwhite birth rate by county in North Carolina is used to demonstrate how this approach differs from traditional mean–variance-based systems such as the Jenks’ optimal classification scheme.

Context

The dataset defines the geographic polygon shapes of the prefectures of Japan. You can use it for plotting Mapbox Choropleth maps by the plotly package conveniently. It is a small modification from the dataset at https://github.com/dataofjapan/land/blob/master/japan.geojson.

Content

For each prefecture, an id is assigned. The id naming is something like 'Kyoto' which means for the Kyoto prefecture, and 'Okinawa' which means for the Okinawa prefecture.

Acknowledgements

It is a small modification from the original dataset at https://github.com/dataofjapan/land/blob/master/japan.geojson. I have added id for each element so that it can be conveniently used for plotting Mapbox Choropleth maps.

When creating choropleth maps, mapmakers often bin (i.e. group, classify) quantitative data values into groups to help show that certain areas fall within a similar range of values. For instance, a mapmaker may divide counties into groups of high, middle, and low life expectancy (measured in years). It is well known that different binning methods (e.g. natural breaks, quantiles) yield different groupings, meaning the same data can be presented differently depending on how it is divided into bins. To help guide a wide variety of users, we present a new, open-source, web-based, geospatial visualization tool, Exploropleth, that lets users interact with a catalog of established data binning methods, and subsequently compare, customize, and export custom maps. This tool advances the state of the art by providing multiple binning methods in one view and supporting administrative unit reclassification on-the-fly. We interviewed 16 cartographers and geographic information systems (GIS) experts from 13 government organizations, non-government organizations (NGOs), and federal agencies who identified opportunities to integrate Exploropleth into their existing mapmaking workflow, and found that the tool has the potential to educate students as well as mapmakers with varying levels of experience. Exploropleth is open-source and publicly available at https://exploropleth.github.io.

Percent of Households without a Computer in San Bernardino County, CABy Fernando HernandezThis map visualizes the percent of households without a computer in San Bernardino County, CA. The values are organized into four classes defined by the mean and standard deviation of the values, identified by the single hue progression of the choropleth map where the darker hue indicates a higher percentage of households without a computer. A Light Gray Base map is used to emphasize the hue progressions and clearly delineate census tract and county boundaries. SourcesACS Internet Access by Age and Race Variables - BoundariesLight Gray Canvas BaseHuman Geography Label (English)

This dataset contains a comprehensive collection of geographic shapefiles representing the boundaries of countries and territories worldwide. The shapefiles define the outlines of each nation and are based on the most recent and accurate geographical data available. The dataset includes polygon geometries that accurately represent the territorial extent of each country, making it suitable for various geographical analyses, visualizations, and spatial applications.

Content: The dataset comprises shapefiles in the ESRI shapefile format (.shp) along with associated files (.shx, .dbf, etc.) that contain the attributes of each country, such as country names, ISO codes, and other relevant information. The polygons in the shapefiles correspond to the land boundaries of each nation, enabling precise mapping and spatial analysis.

Use Cases: This dataset can be utilized in a wide range of applications, including but not limited to:

• Creating choropleth maps to visualize and analyze various socio-economic indicators by country.
• Conducting spatial analysis to study population distribution, territorial areas, and geographic trends.
• Performing geopolitical research and country-level comparisons.
• Integrating with other datasets to enrich geographic analyses and insights.

Source: The shapefile data is sourced from reputable and authoritative geographic databases, ensuring its accuracy and reliability for diverse applications.

BackgroundAlthough the burden of the coronavirus disease 2019 (COVID-19) has been different across communities in the US, little is known about the disparities in COVID-19 burden in North Dakota (ND) and yet this information is important for guiding planning and provision of health services. Therefore, the objective of this study was to identify geographic disparities of COVID-19 hospitalization risks in ND.MethodsData on COVID-19 hospitalizations from March 2020 to September 2021 were obtained from the ND Department of Health. Monthly hospitalization risks were computed and temporal changes in hospitalization risks were assessed graphically. County-level age-adjusted and spatial empirical Bayes (SEB) smoothed hospitalization risks were computed. Geographic distributions of both unsmoothed and smoothed hospitalization risks were visualized using choropleth maps. Clusters of counties with high hospitalization risks were identified using Kulldorff's circular and Tango's flexible spatial scan statistics and displayed on maps.ResultsThere was a total of 4,938 COVID-19 hospitalizations during the study period. Overall, hospitalization risks were relatively stable from January to July and spiked in the fall. The highest COVID-19 hospitalization risk was observed in November 2020 (153 hospitalizations per 100,000 persons) while the lowest was in March 2020 (4 hospitalizations per 100,000 persons). Counties in the western and central parts of the state tended to have consistently high age-adjusted hospitalization risks, while low age-adjusted hospitalization risks were observed in the east. Significant high hospitalization risk clusters were identified in the north-west and south-central parts of the state.ConclusionsThe findings confirm that geographic disparities in COVID-19 hospitalization risks exist in ND. Specific attention is required to address counties with high hospitalization risks, especially those located in the north-west and south-central parts of ND. Future studies will investigate determinants of the identified disparities in hospitalization risks.

OverviewThe multiple hazard index for the United States Counties was designed to map natural hazard relating to exposure to multiple natural disasters. The index was created to provide communities and public health officials with an overview of the risks that are prominent in their county, and to facilitate the comparison of hazard level between counties. Most existing hazard maps focus on a single disaster type. By creating a measure that aggregates the hazard from individual disasters, the increased hazard that results from exposure to multiple natural disasters can be better understood. The multiple hazard index represents the aggregate of hazard from eleven individual disasters. Layers displaying the hazard from each individual disaster are also included.

The hazard index is displayed visually as a choropleth map, with the color blue representing areas with less hazard and red representing areas with higher hazard. Users can click on each county to view its hazard index value, and the level of hazard for each individual disaster. Layers describing the relative level of hazard from each individual disaster are also available as choropleth maps with red areas representing high, orange representing medium, and yellow representing low levels of hazard.Methodology and Data CitationsMultiple Hazard Index

The multiple hazard index was created by coding the individual hazard classifications and summing the coded values for each United States County. Each individual hazard is weighted equally in the multiple hazard index. Alaska and Hawaii were excluded from analysis because one third of individual hazard datasets only describe the coterminous United States.

Avalanche Hazard

University of South Carolina Hazards and Vulnerability Research Institute. “Spatial Hazard Events and Losses Database”. United States Counties. “Avalanches United States 2001-2009”. < http://hvri.geog.sc.edu/SHELDUS/

Downloaded 06/2016.

Classification

Avalanche hazard was classified by dividing counties based upon the number of avalanches they experienced over the nine year period in the dataset. Avalanche hazard was not normalized by total county area because it caused an over-emphasis on small counties, and because avalanches are a highly local hazard.

None = 0 AvalanchesLow = 1 AvalancheMedium = 2-5 AvalanchesHigh = 6-10 Avalanches

Earthquake Hazard

United States Geological Survey. “Earthquake Hazard Maps”. 1:2,000,000. “Peak Ground Acceleration 2% in 50 Years”. < http://earthquake.usgs.gov/hazards/products/conterminous/

. Downloaded 07/2016.

Classification

Peak ground acceleration (% gravity) with a 2% likelihood in 50 years was averaged by United States County, and the earthquake hazard of counties was classified based upon this average.

Low = 0 - 14.25 % gravity peak ground accelerationMedium = 14.26 - 47.5 % gravity peak ground accelerationHigh = 47.5+ % gravity peak ground acceleration

Flood Hazard

United States Federal Emergency Management Administration. “National Flood Hazard Layer”. 1:10,000. “0.2 Percent Annual Flood Area”. < https://data.femadata.com/FIMA/Risk_MAP/NFHL/

. Downloaded 07/2016.

Classification

The National Flood Hazard Layer 0.2 Percent Annual Flood Area was spatially intersected with the United States Counties layer, splitting flood areas by county and adding county information to flood areas. Flood area was aggregated by county, expressed as a fraction of the total county land area, and flood hazard was classified based upon percentage of land that is susceptible to flooding. National Flood Hazard Layer does not cover the entire United States; coverage is focused on populated areas. Areas not included in National Flood Hazard Layer were assigned flood risk of Low in order to include these areas in further analysis.

Low = 0-.001% area susceptibleMedium = .00101 % - .005 % area susceptibleHigh = .00501+ % area susceptible

Heat Wave Hazard

United States Center for Disease Control and Prevention. “National Climate Assessment”. Contiguous United States Counties. “Extreme Heat Events: Heat Wave Days in May - September for years 1981-2010”. Downloaded 06/2016.

Classification

Heat wave was classified by dividing counties based upon the number of heat wave days they experienced over the 30 year time period described in the dataset.

Low = 126 - 171 Heat wave DaysMedium = 172 – 187 Heat wave DaysHigh = 188 – 255 Heat wave Days

Hurricane Hazard

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Atlantic Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Pacific Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

Classification

Atlantic and Pacific datasets were merged. Tropical storm and disturbance tracks were filtered out leaving hurricane tracks. Each hurricane track was assigned the value of the category number that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as being more hazardous. Values describing each hurricane event were aggregated by United States County, normalized by total county area, and the hurricane hazard of counties was classified based upon the normalized value.

Landslide Hazard

United States Geological Survey. “Landslide Overview Map of the United States”. 1:4,000,000. “Landslide Incidence and Susceptibility in the Conterminous United States”. < https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download

. Downloaded 07/2016.

Classification

The classifications of High, Moderate, and Low landslide susceptibility and incidence from the study were numerically coded, the average value was computed for each county, and the landslide hazard was classified based upon the average value.

Long-Term Drought Hazard

United States Drought Monitor, Drought Mitigation Center, United States Department of Agriculture, National Oceanic and Atmospheric Administration. “Drought Monitor Summary Map”. “Long-Term Drought Impact”. < http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx >. Downloaded 06/2016.

Classification

Short-term drought areas were filtered from the data; leaving only long-term drought areas. United States Counties were assigned the average U.S. Drought Monitor Classification Scheme Drought Severity Classification value that characterizes the county area. County long-term drought hazard was classified based upon average Drought Severity Classification value.

Low = 1 – 1.75 average Drought Severity Classification valueMedium = 1.76 -3.0 average Drought Severity Classification valueHigh = 3.0+ average Drought Severity Classification value

Snowfall Hazard

United States National Oceanic and Atmospheric Administration. “1981-2010 U.S. Climate Normals”. 1: 2,000,000. “Annual Snow Normal”. < http://www1.ncdc.noaa.gov/pub/data/normals/1981-2010/products/precipitation/

. Downloaded 08/2016.

Classification

Average yearly snowfall was joined with point location of weather measurement stations, and stations without valid snowfall measurements were filtered out (leaving 6233 stations). Snowfall was interpolated using least squared distance interpolation to create a .05 degree raster describing an estimate of yearly snowfall for the United States. The average yearly snowfall raster was aggregated by county to yield the average yearly snowfall per United States County. The snowfall risk of counties was classified by average snowfall.

None = 0 inchesLow = .01- 10 inchesMedium = 10.01- 50 inchesHigh = 50.01+ inches

Tornado Hazard

United States National Oceanic and Atmospheric Administration Storm Prediction Center. “Severe Thunderstorm Database and Storm Data Publication”. 1: 2,000,000. “United States Tornado Touchdown Points 1950-2004”. < https://catalog.data.gov/dataset/united-states-tornado-touchdown-points-1950-2004-direct-download

. Downloaded 07/2016.

Classification

Each tornado touchdown point was assigned the value of the Fujita Scale that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as more hazardous. Values describing each tornado event were aggregated by United States County, normalized by total county area, and the tornado hazard of counties was classified based upon the normalized value.

Volcano Hazard

Smithsonian Institution National Volcanism Program. “Volcanoes of the World”. “Holocene Volcanoes”. < http://volcano.si.edu/search_volcano.cfm

. Downloaded 07/2016.

Classification

Volcano coordinate locations from spreadsheet were mapped and aggregated by United States County. Volcano count was normalized by county area, and the volcano hazard of counties was classified based upon the number of volcanoes present per unit area.

None = 0 volcanoes/100 kilometersLow = 0.000915 - 0.007611 volcanoes / 100 kilometersMedium = 0.007612 - 0.018376 volcanoes / 100 kilometersHigh = 0.018377- 0.150538 volcanoes / 100 kilometers

Wildfire Hazard

United States Department of Agriculture, Forest Service, Fire, Fuel, and Smoke Science Program. “Classified 2014 Wildfire Hazard Potential”. 270 meters. < http://www.firelab.org/document/classified-2014-whp-gis-data-and-maps

. Downloaded 06/2016.

Classification

The classifications of Very High, High, Moderate, Low, Very Low, and Non-Burnable/Water wildfire hazard from the study were numerically coded, the average value was computed for each county, and the wildfire hazard was classified based upon the average value.

Public protest represents an important sanction on rulers and institutions. Protest is a quotidian phenomenon in South Africa; perhaps the defining element of post-apartheid political life. Geographic representations of protest abound – typically dot distribution maps – but these merely confirm that more protests occur where there are more people. Visualisations of protest per capita and protestors per capita (or ‘general propensity’), which are best rendered as choropleth maps, are well-placed to overcome this limitation. The South African Police Services' database of protest is the largest publicly-available single-country protest event database. Having used machine learning to classify 89,000 protest events, I locate each within one of the country's 234 municipalities, and depict these events using counts, count per capita, and the general propensity. This reveals a proportionally high number of rural protests, and that municipalities hosting major industries, along with provincial seats of government, present the highest propensity for protest.

Overview:

The Federal Emergency Management Agency (FEMA) and Argonne National Laboratory (Argonne) created the Resilience Analysis and Planning Tool (RAPT) to support state, local, tribal, territorial analysis in identifying focus areas for building resilience and response capabilities. RAPT is a GIS webmap tool with clickable layers of community resilience indicators, hazard data, and infrastructure data and widgets to help with analysis. Where available from the Census Bureau, RAPT provides data at the census tract level (population sizes between 1,200 and 8,000 people) allowing an enhanced level of analysis.Community Resilience Challenges Indicator Methodology

FEMA and Argonne conducted analysis of peer-reviewed research and identified 20 commonly used community resilience challenges indicators, 11 with a population focus and 9 with a community focus. A complete description of the methodology is provided in Community Resilience Challenges Indicator Analysis: County-Level Analysis of Commonly Used Indicators from Peer-Reviewed Research: 2023 Update. First, an initial literature review identified six meta-analyses of peer-reviewed community resilience assessment methodologies published in the five year period 2013-2018. Next, the research team catalogued each referenced assessment methodology, ultimately identifying 73 distinct methodologies. Argonne retained eight assessment methodologies because they met the following criteria:

Based on a subnational unit of analysis to correspond to U.S. county-level data, Applicable to multiple hazards, Had a pre-disaster focus, Used quantitative measures, Used a publicly available methodology, and Used publicly available data sources.

The research team then identified more than 100 quantitative indicators used within these eight methodologies and selected only those indicators cited in three or more. This process resulted in the 20 commonly used indicators. Using common statistical methods and data structuring, the research team created five bins of data for each indicator and produced choropleth maps of the United States showing county-level data for each indicator. Finally, the research team developed a method to aggregate county-level data from all 20 indicators and sorted each U.S. County into five bins denoting relative resilience.

Hazard Layers:

RAPT includes GIS layers of historic hazard data and risk assessments for tornadoes, tropical storms, seismic, wildfire, and flooding. Real-time watch and warning notifications from the National Weather Service are also provided. Jurisdictions can click on multiple hazard layers at a time to see a more comprehensive view of a hazard risk.

Infrastructure Layers:

The infrastructure layers in RAPT are drawn from the Homeland Infrastructure Foundation-Level Data (HIFLD) Subcommittee Online Community and include components of community lifelines and other datapoints, such as hospitals, nursing homes, fire stations, mobile home parks, and school locations.

Using RAPT:

RAPT is not a scorecard of resilience but is a tool to help jurisdictions better understand the interplay of factors that may be important for resilience, response, and recovery. Users can choose multiple indicator layers to better understand local challenges to resilience such as population with a disability combined with location of mobile home parks, and historic tornado activity. By combining layers, jurisdictions can conduct analysis of their community to develop targeted outreach and resilience strategies.

You can download the CRCI community and population indicator datasets in Excel format at the link here.

This project provides a comprehensive dataset on intentional homicides in Mexico from 1990 to 2023, disaggregated by sex and state. It includes both raw data and tools for visualization, making it a valuable resource for researchers, policymakers, and analysts studying violence trends, gender disparities, and regional patterns.ContentsHomicide Data: Total number of male and female victims per state and year.Population Data: Corresponding male and female population estimates for each state and year.Homicide Rates: Per 100,000 inhabitants, calculated for both sexes.Choropleth Map Script: A Python script that generates homicide rate maps using a GeoJSON file.GeoJSON File: A spatial dataset defining Mexico's state boundaries, used for mapping.Sample Figure: A pre-generated homicide rate map for 2023 as an example.Requirements File: A requirements.txt file listing necessary dependencies for running the script.SourcesHomicide Data: INEGI - Vital Statistics MicrodataPopulation Data: Mexican Population Projections 2020-2070This dataset enables spatial analysis and data visualization, helping users explore homicide trends across Mexico in a structured and reproducible way.

IntroductionPopulation longevity is a global phenomenon influenced by various factors including social, economic transitions, and medical advancements. The study focused on the population over 95 years old, adopting an approach that integrates data from the 2018 Census and geospatial analysis techniques.MethodsAn ecological study was conducted using anonymized microdata from the 2018 National Population and Housing Census (CNPV). Geographic analysis, choropleth maps, and Kernel density estimation were employed to identify clusters of individuals aged over 95 years.ResultsThe study identified 43,427 individuals aged 95 years or older in Colombia, with concentrations observed in departments such as Antioquia and Bogotá. Analysis by department and municipality revealed variations in rates and sex distribution. Kernel density analysis highlighted clusters in the Valle de Tenza area and other regions.ConclusionThis study sheds light on the geographical distribution of centenarians in Colombia, emphasizing clusters in certain regions. More research is needed to understand the individual and contextual factors underlying successful aging in Colombia and to inform policies to improve the quality of life of older populations.

Chronic wasting disease (CWD) is a fatal neurodegenerative disease among cervids that has steadily spread across the United States and Canada. The year-to-year increase in the geographic spread of this disease among white-tailed deer and mule deer have raised concerns about conserving these species and sustainable big-game hunting. Knowledge of the spatial variation in CWD risk in Kansas, a state that attracts big game hunters nationwide is not fully understood. We explored the spatial variation in CWD risk and the potential effects of habitat-level covariates using surveillance data collected in Kansas from 2005 to 2023, with a Poisson log-Gaussian model in a Bayesian framework. Two models were considered; Model 1 included only spatial random effects and Model 2 included spatial random effects plus non-linear effects of habitat-level covariate. Following satisfactory convergence of model parameters, choropleth maps of posterior mean estimates for the risk of CWD presence, and measures of spatial heterogeneities were plotted. The impacts of the habitat-level covariates were deemed important predictors of CWD as Model 2 outperformed Model 1. The risk of CWD in the northwestern and southcentral portions of the state is likely driven by similar underlying spatial processes; however, no global smoothing effect was observed. The northwestern region is at higher risk for CWD presence but a gradual increase in risk toward the south and eastern sides of the state is apparent. We conclude that the data-driven Poisson log-Gaussian model is useful in assessing CWD and potentially other wildlife diseases from surveillance sources, and the different spatial patterns and habitat-level covariate association have relevance to CWD management in Kansas.

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Age-Adjusted Incidence Rate (AAIR)Age-adjustment is a statistical method that allows comparisons of incidence rates to be made between populations with different age distributions. This is important since the incidence of most cancers increases with age. An age-adjusted cancer incidence (or death) rate is defined as the number of new cancers (or deaths) per 100,000 population that would occur in a certain period of time if that population had a 'standard' age distribution. In the California Health Maps, incidence rates are age-adjusted using the U.S. 2000 Standard Population.Cancer incidence ratesIncidence rates were calculated using case counts from the California Cancer Registry. Population data from 2010 Census and SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators. Yearly SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators for 5-year incidence rates (2013-2017)According to California Department of Public Health guidelines, cancer incidence rates cannot be reported if based on <15 cancer cases and/or a population <10,000 to ensure confidentiality and stable statistical rates.Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

A Choropleth map created from population density figures released by Statistics New Zealand after the 2013 census. All data has been generalized so no one person can be identified.

Introduction: Though cancer research has traditionally centered on individual-level exposures, there is growing interest in the geography of both cancer and its risk factors. This geographic and epidemiological research has consistently shown that cancer outcomes and their known causal exposures exhibit geographic variation that coincide with area-level socioeconomic status and the composition of neighborhoods. A retrospective study was conducted to evaluate geospatial variation for female breast, cervical, and colorectal cancer incidence in Baltimore City.Materials and Methods: Using a Maryland Cancer Registry dataset of incident breast, cervical, and colorectal cancers (N = 4,966) among Baltimore City female residents diagnosed from 2000 to 2010, spatial and epidemiological analyses were conducted through choropleth maps, spatial cluster identification, and local Moran's I. Ordinary least squares regression models identified characteristics associated with the geospatial clusters.Results: Each cancer type exhibited geographic variation across Baltimore City with the neighborhoods showing high incidence differing by cancer type. Specifically, breast cancer had significant low incidence in downtown Baltimore while cervical cancer had high incidence. The neighborhood covariates associated with the geographic variation also differed by cancer type while local Moran's I identified discordant clusters.Discussion: Cancer incidence varied geographically by cancer type within a single city (county). Small area estimates are needed to detect local patterns of disease when developing health and preventative programs. Given the observed variability of community-level characteristics associated with each cancer type incidence, local information is essential for developing place-, social-, and outcome-specific interventions.

BackgroundWhile the United Kingdom National Health Service aimed to reduce social inequalities in the provision of joint replacement, it is unclear whether these gaps have reduced. We describe secular trends in the provision of primary hip and knee replacement surgery between social deprivation groups.Methods and findingsWe used the National Joint Registry to identify all hip and knee replacements performed for osteoarthritis from 2007 to 2017 in England. The Index of Multiple Deprivation (IMD) 2015 was used to identify the relative level of deprivation of the patient living area. Multilevel negative binomial regression models were used to model the differences in rates of joint replacement. Choropleth maps of hip and knee replacement provision were produced to identify the geographical variation in provision by Clinical Commissioning Groups (CCGs).A total of 675,342 primary hip and 834,146 primary knee replacements were studied. The mean age was 70 years old (standard deviation: 9) with 60% and 56% of women undergoing hip and knee replacements, respectively. The overall rate of hip replacement increased from 27 to 36 per 10,000 person-years and knee replacement from 33 to 46. Inequalities of provision between the most (reference) and least affluent areas have remained constant for both joints (hip: rate ratio (RR) = 0.58, 95% confidence interval [0.56, 0.60] in 2007, RR = 0.59 [0.58, 0.61] in 2017; knee: RR = 0.82 [0.80, 0.85] in 2007, RR = 0.81 [0.80, 0.83] in 2017). For hip replacement, CCGs with the highest concentration of deprived areas had lower overall provision rates, and CCGs with very few deprived areas had higher provision rates. There was no clear pattern of provision inequalities between CCGs and deprivation concentration for knee replacement.Study limitations include the lack of publicly available information to explore these inequalities beyond age, sex, and geographical area. Information on clinical need for surgery or patient willingness to access care were unavailable.ConclusionsIn this study, we found that there were inequalities, which remained constant over time, especially in the provision of hip replacement, by degree of social deprivation. Providers of healthcare need to take action to reduce this unwarranted variation in provision of surgery.

Distribution by municipality in the top 10 municipalities by general rate, of the population greater than or equal to 95 in Colombia, according to the CNPV 2018.

Distribution by municipality of the population greater than or equal to 95 in the Tenza Valley, according to the CNPV 2018.

Distribution by department of the population aged 95 years or older in Colombia, according to the CNPV, 2018.