Table contains estimated percentage of adults ages 18 years and older who reported ever being diagnosed with diabetes by a healthcare provider. Data are presented at zip code level. Data are downloaded from the AskCHIS Neighborhood Edition and are not direct estimates. For more information on the methodology used to calculate estimates, please visit healthpolicy.ucla.edu. Data for zip code 95053 are not available. Source: California Health Interview Survey, AskCHIS Neighborhood Edition, 2018 CHIS data. Exported on June 1, 2022.METADATA:notes (String): Lists table title, notes, sourceszip_code (Numeric): Geography IDestimate (Numeric): Estimate of adults with diabetesunit (String): Unit used for the estimate (Percent)CI (Numeric): 95% confidence interval for the estimate

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...

Table contains estimated percentage of adults ages 18 years and older who report ever being diagnosed with asthma by a healthcare provider. Data are at zip code level. Data are downloaded from the AskCHIS Neighborhood Edition and are not direct estimates. For more information on the methodology used to calculate estimates, please visit healthpolicy.ucla.edu. Data for zip codes 94305 and 95053 are not available. Source: California Health Interview Survey, AskCHIS Neighborhood Edition, 2018 CHIS data. Exported on June 1, 2022.METADATA:notes (String): Lists table title, notes, sourceszip_code (Numeric): Geography IDestimate (Numeric): Estimate of adults with asthmaunit (String): Unit used for the estimate (Percent)CI (Numeric): 95% confidence interval for the estimate

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.

Malaria, a life-threatening disease transmitted by mosquitoes, affects millions of people worldwide. Treatment and prevention efforts such as insecticide-treated mosquito nets and rapid diagnostic tests significantly decreased the number of malaria cases in Africa. Find out where malaria rates increased or decreased from 2000 to 2015. This layer displays the change in malaria rates from 2000 to 2015 among children in Sub-Saharan Africa.Click here to view this layer in an interactive app.Source: The Malaria Atlas Project

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

Colorado County BRFSS Binge Drinking Prevalence represents the Percent of Adults who Binge Drink calculated from the 2018-2022 Colorado Behavioral Risk Factor Surveillance System (County Estimates) data set. These data represent the estimated prevalence of Binge Drinking among adults (Age 18+) for each county in Colorado. Binge Drinking is defined for males as having five or more drinks on one occasion and for females as having four or more drinks on one occasion within the past 30 days. Binge Drinking is calculated from the number of days alcohol was consumed in the past 30 days, and the average number of drinks consumed on those days. Data is suppressed if there was not enough data to calculate a reliable estimate. The estimate for each county was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2018-2022). This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment (CDPHE), including the Alcohol Outlet Density StoryMap. COUNTY (County Name)FULL (Full County Name)LABEL (Proper County Name)County FIPS (County FIPS Code as String)NUM FIPS (County FIPS Code as Number)CENT LAT (County Centroid Latitude)CENT LONG (County Centroid Longitude)US FIPS (Full FIPS Code)Binge Percent (County estimate for prevalence of Binge Drinking among adults Age 18+)Lower Confidence Limit (Lower 95% Confidence Interval for Binge Percent Value)Upper Confidence Limit (Upper 95% Confidence Interval for Binge Percent Value)Years (2018-2022)

The study (see credits for link) presents an analysis of the malaria burden in sub-Saharan Africa between 2000 and 2015, and quantifies the effects of the interventions that have been implemented to combat the disease. The authors find that the prevalence of Plasmodium falciparum infection has been reduced by 50% since 2000 and the incidence of clinical disease by 40%, and that interventions have averted approximately 663 million clinical cases since 2000, with insecticide-treated bed nets being the largest contributor.

This service is a time-aware image service showing the modelled parasite prevalence rate of Plasmodium falciparum malaria for each year. Units are percentage prevalence of the Pf parasite in children aged 2-10.

This layer represents the Percent of Adults ever diagnosed with Asthma calculated from the 2014-2017 Colorado Behavioral Risk Factor Surveillance System (County or Regional Estimates) data set. These data represent the estimated prevalence of Asthma among adults (Age 18+) for each county in Colorado. Asthma is defined as ever being diagnosed with Asthma by a doctor, nurse, or other health professional, and still having the condition. Regional estimates were used if there was not enough sample size to calculate a single county estimate. The estimate for each county was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).

Malaria, a life-threatening disease transmitted by mosquitoes, affects millions of people worldwide. This map highlights malaria rates among children age 2 to 10 in Sub-Saharan Africa in 2000 and 2015. Treatment and prevention efforts such as insecticide-treated mosquito nets and rapid diagnostic tests significantly decreased the number of malaria cases in Africa. Find out where malaria rates increased or decreased from 2000 to 2015. Click here to view this map in an interactive app.Source: The Malaria Atlas Project

This map provides model-based county level estimates for selected chronic conditions' prevalence and number of adults >=18 years with the condition using the Behavioral Risk Factor Surveillance System (BRFSS) 2018 survey, the Census Bureau 2018 county population estimates and the American Community Survey (ACS) 2014-2018 data. BRFSS respondents were classified as having an underlying medical condition- if they answered “yes” to any of the following questions: “Have you ever been told by a doctor, nurse, or other health professional that you have: chronic obstructive pulmonary disease (COPD), emphysema or chronic bronchitis; heart disease (angina or coronary heart disease, heart attack or myocardial infarction); diabetes; or chronic kidney disease.” Respondents were asked to self-report height and weight which was used to calculate BMI. Obesity was defined as BMI≥30 kg per sq meter. A created variable any condition captured persons having any of these 5 conditions.Detailed method is available at https://www.cdc.gov/mmwr/volumes/69/wr/mm6929a1.htm?s_cid=mm6929a1_w.Data is available at https://stacks.cdc.gov/view/cdc/90519POC: hgl6@cdc.gov

Indicator 2.1.1Prevalence of undernourishment.Methodology:The index is obtained as the cumulative probability that the daily habitual dietary energy intake (x) is below the lower end of the range of normal dietary energy requirements for that actor, or average individual (MDER), as in the formula below:PoU=∫_(x <MDER) f(x| DEC; CV; Skew)dxWhere f(x) is the probability density function of an individual's calorie consumption.The parameters necessary to calculate the index are: average level of dietary energy consumption (DEC); A cut-off point defined as the minimum dietary energy requirement (MDER); Coefficient of variation (CV) as a parameter to calculate inequality in food consumption; The skewness parameter (Skew) represents the asymmetry in the distribution. The DEC as well as the MDER are updated annually, with the former calculated from the FAO Food Balance Tables Where DEC, CV and Skew are the means, coefficient of variation and skewness characterizing the distribution of usual dietary energy intake levels across the population. The average distribution of dietary energy intake levels for the average individual in a population (DEC) corresponds, by definition, to the average level of daily food consumption per capita in the population. The various parameters of the model (DEC = µ), standard deviation = σ, and the distribution of the z-score dietary energy consumption function can be estimated as follows:σ = √(ln⁡(〖CV〗^(2 ) )+1 )µ=ln(µ)-σ^2/2z=(ln(λ)-µ)/σWhere ln(λ) is the Napierian logarithm of the average calories required per person as a final value. The coefficient of variation (CV) of the usual food consumption of the representative individual in the population and the coefficient of skewness (Skew) is also determined from the household survey data according to the methodology of the FAO Statistics Division. See:https://www.fao.org/3/i4046e/i4046e.pdfData Source:The Ministry of Public Health.

Goal 3: Ensure healthy lives and promote well-being for all at all agesChild health17,000 fewer children die each day than in 1990, but more than six million children still die before their fifth birthday each year.Since 2000, measles vaccines have averted nearly 15.6 million deaths.Despite global progress, an increasing proportion of child deaths are in sub-Saharan Africa and Southern Asia. Four out of every five deaths of children under age five occur in these regions.India’s Under Five Mortality (U5MR) declined from 125 per 1,000 live births in 1990 to 49 per 1,000 live births in 2013. Maternal healthGlobally, maternal mortality has fallen by almost 50% since 1990.In Eastern Asia, Northern Africa and Southern Asia, maternal mortality has declined by around two-thirds. But, the maternal mortality ratio – the proportion of mothers that do not survive childbirth compared to those who do – in developing regions is still 14 times higher than in the developed regions.Only half of women in developing regions receive the recommended amount of health care.From a Maternal Mortality Rate (MMR) of 437 per 100,000 live births in 1990-91, India came down to 167 in 2009. Delivery in institutional facilities has risen from 26% in 1992-93 to 72% in 2009. HIV/AIDSBy 2014, there were 13.6 million people accessing antiretroviral therapy, an increase from just 800,000 in 2003.New HIV infections in 2013 were estimated at 2.1 million, which was 38% lower than in 2001.At the end of 2013, there were an estimated 35 million people living with HIV.At the end of 2013, 240,000 children were newly infected with HIV.India has made significant strides in reducing the prevalence of HIV and AIDS across different types of high-risk categories. Adult prevalence has come down from 0.45 percent in 2002 to 0.27 in 2011.Data source: https://niti.gov.in/sites/default/files/SDG-India-Index-2.0_27-Dec.pdfPlease find detailed metadata here.This web layer is offered by Esri India, for ArcGIS Online subscribers, If you have any questions or comments, please let us know via content@esri.in.