Context:

CDC's Division of Population Health provides cross-cutting set of 124 indicators that were developed by consensus and that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data that are important to public health practice and available for states, territories and large metropolitan areas. In addition to providing access to state-specific indicator data, the CDI web site serves as a gateway to additional information and data resources.

Content:

A variety of health-related questions were assessed at various times and places across the US over the past 15 years. Data is provided with confidence intervals and demographic stratification.

Acknowledgements:

Data was compiled by the CDC.

Inspiration:

• Any interesting trends in certain groups?
• Any correlation between disease indicators and locality hospital spending?

Total-Other-Finance-Cost Time Series for Amedisys Inc. Amedisys, Inc., together with its subsidiaries, provides healthcare services in the United States. It operates through three segments: Home Health, Hospice, and High Acuity Care. The Home Health segment offers a range of services in the homes of individuals for the recovery of patients from surgery, chronic disability, or illness, as well as prevents avoidable hospital readmissions through its skilled nurses; nursing services, rehabilitation therapists specialized in physical, speech, and occupational therapy; and social workers and aides for assisting its patients. The Hospice segment offers services that is designed to provide comfort and support for those who are dealing with a terminal illness, including cancer, heart disease, pulmonary disease, or Alzheimer's. The High Acuity Care offers essential elements of inpatient hospital, skilled nursing facility care, and palliative care to patients in their homes. Amedisys, Inc. was incorporated in 1982 and is headquartered in Baton Rouge, Louisiana.

This dataset contains model-based census tract estimates. 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. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related social needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 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. More information about the methodology can be found at www.cdc.gov/places.

Analysis of ‘COVID-19 high risk individuals per ICU bed’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/covid-19-high-risk-individuals-per-icu-bede on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This dataset contains the data behind the story How One High-Risk Community In Rural South Carolina Is Bracing For COVID-19.

mmsa-icu-beds.csv combines data from the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS), a collection of health-related surveys conducted each year of more than 400,000 Americans, and the Kaiser Family Foundation to show the number of people who are at high risk of becoming seriously ill from COVID-19 per ICU bed in each metropolitan area, micropolitan area or metropolitan division for which we have data.

Being high risk is defined by a number of health conditions and behaviors. Based on the CDC’s list of the relevant underlying conditions that put people at higher risk of serious illness from COVID-19, plus the advice of experts from the Cleveland Clinic, the American Lung Association and the American Heart Association, we counted people as at risk if they’re 65 or older; if they have ever been told they have hypertension, coronary heart disease, a myocardial infarction, angina, a stroke, chronic kidney disease, chronic obstructive pulmonary disease, emphysema, chronic bronchitis or diabetes; if they currently have asthma or a BMI over 40; if they smoke cigarettes every day or some days or use e-cigarettes or vaping products every day or some days; or if they’re currently pregnant. We included every individual who meets at least one of these conditions but counted them only once each, so anyone with multiple conditions doesn’t get counted multiple times. We were not able to include a number of conditions for which we did not have location-based data from the BRFSS, such as liver disease, having smoked, vaped or dabbed marijuana in the last 30 days, and getting cancer treatment or being on immunosuppression medications.

See the data dictionary for column descriptions.

If you find this information useful, please let us know.

License: Creative Commons Attribution 4.0 International License
Source: https://github.com/fivethirtyeight/data/tree/master/covid-geography

This dataset was created by data.world's Admin and contains around 100 samples along with High Risk Per Icu Bed, Icu Beds, technical information and other features such as: - Hospitals - High Risk Per Hospital - and more.

How to use this dataset

• Analyze Total Percent At Risk in relation to High Risk Per Icu Bed
• Study the influence of Icu Beds on Hospitals
• More datasets

Acknowledgements

If you use this dataset in your research, please credit data.world's Admin

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Context

Many people from around the world suffer from chronic kidney diseases. However, if we are able to understand them better then we'll be able to find treatments.

Content

Here you can find data from 500 US Cities and their statistics on Chronic Kidney Diseases in adults ages 18 years and above.

Acknowledgements

This data comes from https://chronicdata.cdc.gov/500-Cities/500-Cities-Chronic-kidney-disease-among-adults-age/dnkc-3whb.

Heart Disease is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. In the United States alone, heart disease claims roughly 647,000 lives each year — making it the leading cause of death. The buildup of plaques inside larger coronary arteries, molecular changes associated with aging, chronic inflammation, high blood pressure, and diabetes are all causes of and risk factors for heart disease. While there are different types of coronary heart disease, the majority of individuals only learn they have the disease following symptoms such as chest pain, a heart attack, or sudden cardiac arrest. This fact highlights the importance of preventative measures and tests that can accurately predict heart disease in the population prior to negative outcomes like myocardial infarctions (heart attacks) taking place

ObjectiveTo examine the correlation between SIRI and the probability of cardiovascular mortality as well as all-cause mortality in individuals with chronic kidney disease.MethodsA cohort of 3,262 participants from the US National Health and Nutrition Examination Survey (NHANES) database were included in the study. We categorized participants into five groups based on the stage of chronic kidney disease. A weighted Cox regression model was applied to assess the relationship between SIRI and mortality. Subgroup analyses, Kaplan–Meier survival curves, and ROC curves were conducted. Additionally, restricted cubic spline analysis was employed to elucidate the detailed association between SIRI and hazard ratio (HR).ResultsThis study included a cohort of 3,262 individuals, of whom 1,535 were male (weighted proportion: 42%), and 2,216 were aged 60 or above (weighted proportion: 59%). Following adjustments for covariates like age, sex, race, and education, elevated SIRI remained a significant independent risk factor for cardiovascular mortality (HR=2.50, 95%CI: 1.62-3.84, p

Context

The respiratory disease causes an immense health burden. It is estimated that worldwide 235 million people suffer from asthma, more than 200 million people have chronic obstructive pulmonary disease (COPD), 65 million endure moderate-to-severe COPD, 1–6% of the adult population (more than 100 million people) experience sleep-disordered breathing, 9.6 million people develop tuberculosis (TB) annually, millions live with Pulmonary Hypertension and more than 50 million people struggle with occupational lung diseases,more than 1 billion people suffering from chronic respiratory conditions. At least 2 billion people are exposed to the toxic effects of biomass fuel consumption, 1 billion are exposed to outdoor air pollution and 1 billion are exposed to tobacco smoke. Each year, 4 million people die prematurely from chronic respiratory disease.To analyze pulmonary diseases we collected the data from the local health department of Albuquerque,NM,US. The Data containing different attributes to identify the disease and nature.

Content

This data was collected from public health department to identify different chronic respiratory diseases across the state of NM,US. This data consists of different attributes like Name,age,sex,diseases,treatment and nature.Here Name,Sex,Diseases,Treatment and Nature are string values and some of them like Sex and nature are categorical values.This data contains 37000+ records till now and it has been updated regularly in quarterly basis.

Acknowledgements

We would like to thank public health department of New Mexico for their cooperation and consider our request.

The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project between all of the states in the United States and participating US territories and the Centers for Disease Control and Prevention (CDC).

BRFSS’s objective is to collect uniform state-specific data on health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services related to the leading causes of death and disability in the United States. BRFSS conducts both landline and mobile phone-based surveys with individuals over the age of 18. General factors assessed by the BRFSS in 2020 included health status and healthy days, exercise, insufficient sleep, chronic health conditions, oral health, tobacco use, cancer screenings, and access to healthcare.

Section Names:

1. Record Identification -> Columns 0 to 8
2. Land Line Introduction -> Columns 9 to 20
3. Cell Phone Introduction -> Columns 21 to 30
4. Respondent Sex -> Column 31
5. Health Status -> Column 32 a. Healthy Days -> Columns 33 to 35 b. Health Care Access -> Columns 36 to 39 c. Exercise -> Column 40 d. Inadequate Sleep -> Column 41 e. Chronic Health Conditions -> Columns 42 to 54 f. Oral Health -> Columns 55 and 56
6. Demographics -> Columns 57 to 69
7. Disability -> Columns 70 to 75
8. Tobacco Use -> Columns 71 to 75
9. Alcohol Consumption -> Columns 76 to 79
10. Immunization -> Columns 80 to 83
11. Falls -> Columns 84 and 85
12. Seatbelt Use and Drinking and Driving -> Columns 86 and 87
13. Breast and Cervical Cancer Screening -> Columns 88 to 94
14. Prostate Cancer Screening -> Columns 95 to 100
15. Colorectal Cancer Screening -> Columns 101 to 110
16. HIV/AIDS -> Columns 111 to 113
17. Diabetes -> Columns 114 to 124
18. ME/CFS -> Columns 125 to 127
19. Hepatitis Treatment -> Columns 128 to 133
20. Health Care Access -> Column 134
21. Cognitive Decline -> Columns 135 to 140
22. Caregiver -> Columns 141 to 149
23. E-Cigarettes -> Columns 150 and 151
24. Marijuana Use -> Columns 152 to 154
25. Lung Cancer Screening -> Columns 155 to 158
26. Cancer Survivorship: a. Type of Cancer -> Columns 159 to 162 b. Course of Treatment -> Columns 163 to 170 c. Pain Management -> Columns 171 and 172
27. Prostate Cancer Screening Decision Making -> Columns 173 and 174
28. HPV Vaccination -> Columns 175 and 176
29. Tetanus Diphtheria (Tdap) (Adults) -> Column 177
30. Place of Flu Vaccination -> Column 178
31. Sex at Birth -> Column 179
32. Sexual Orientation and Gender Identity (SOGI) -> Columns 180 to 182
33. Adverse Childhood Experience -> Columns 183 to 193
34. Random Child Selection -> Columns 194 and 195
35. Childhood Asthma Prevalence -> Columns 196 and 197
36. Questionnaire Version -> Column 198
37. Questionnaire Language -> Column 199
38. Urban Rural -> Columns 200 and 201
39. Weighting Variables -> Columns 202 to 207, 212 to 215
40. Child Demographic Variables -> Columns 208 to 210
41. Child Weighting Variables -> Column 211
42. Calculated Variables -> Columns 216 to 228, 236 to 278
43. Calculated Race Variables -> Columns 229 to 235

Acknowledgements

This dataset has been published annually by the CDC since 1984. You can find the original dataset as a ASCII format and past years data from here

Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [2020].