This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

This dataset contains information on the number of deaths and age-adjusted death rates for the five leading causes of death in 1900, 1950, and 2000. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

Content

Health, United States is an annual report on trends in health statistics, find more information at http://www.cdc.gov/nchs/hus.htm.

Context

This is a dataset hosted by the Centers for Disease Control and Prevention. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CDC Data using Kaggle and all of the data sources available through the CDC organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by CATHY PHAM on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under NA

This dataset of U.S. mortality trends since 1900 highlights trends in age-adjusted death rates for five selected major causes of death.

Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).

Revisions to the International Classification of Diseases (ICD) over time may result in discontinuities in cause-of-death trends.

SOURCES

CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.

2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

3. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf.

4. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

The Centers for Disease Control (CDC) dataset provides the number of infant deaths, and the rate of deaths to infants for every 1000 live births by maternal residents of the US. The CDC only reports numbers of births for counties with populations of 100,000 or more and number and rate of infant deaths for counties with populations of 250,000 or more. It suppresses the rate where there are fewer than 20 deaths reported.

Adult mortality data are taken from the National Center for Health Statistics’ Compressed Mortality file as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. The Compressed mortality file provides the number and rate of deaths, by age group and cause of death as reported through the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10).

Data on PolicyMap represent deaths from Alzheimer’s disease, cancer, coronary heart disease, chronic lower respiratory disease, COVID-19, stroke, and chronic lower respiratory disease among those aged 45 or older, from 2000 through 2015. Deaths from homicide, suicide, motor vehicle traffic, and accidental injury for all age groups. These causes have topped the CDC’s list of leading causes of death since 2005. Underlying cause-of-death is indicated on the death certificate by the physician. The National Center for Health Statistics determines one cause of death when more than one cause or condition is entered by the physician. PolicyMap shows mortality data from 2000 through 2021.

Adults ages 35 and older are used as a base category for deaths from disease because these age groups represent most of the deaths from the four leading causes. Rates are calculated per 100,000 population 35 and over in the source data using population estimates based on 2000 and 2010 U.S. Census counts.

The CDC’s National Center for Health Statistics released an estimated model of drug overdose data in its Data Visualization Gallery. Smoothed crude death rate estimates were generated using Hierarchical Bayesian models with spatial and temporal random effects. Bayesian hierarchical modeling “borrows strength” across geographic areas and allows estimates to be generated for counties that have small populations. Updated county-level estimates now include point estimates rather than estimate ranges. The CDC adds a disclaimer to this dataset that in certain states and years, for example New Jersey (2009) and West Virginia (2005, 2009), the rates may be lower than expected due to a large number of unresolved cases or misclassification of ICD-10 codes. More information on the CDC’s methodology is available here.

Opioid and narcotic poisoning data comes from the CDC’s Multiple Cause of Death files. Drug overdose deaths were classified using the Tenth Revision (ICD-10) of the International Classification of Disease underlying-cause-of-death codes for drug poisonings (overdose): X40-44 (unintentional), X60-64 (suicide), X85 (homicide), and Y10–Y14 (undetermined intent).

The types of opioid involved in drug overdose deaths were classified following the ICD-10 codes: and T40.1 (heroin), T40.2 (natural and semisynthetic opioids), T40.3 (methadone), and T40.4 (synthetic opioids, other than methadone). The category for all opioid overdoses includes all these categories (T40.1, T40.2, T40.3, and T40.4). T40.0 (opium) was not included since fewer than 10 people are reported each year as having died from opium overdose in the nation. Deaths involving multiple types of opioids are recorded in each applicable category, therefore the US totals may include overcounting.

Heroin is an illegally-made semi-synthetic opioid derived from morphine. “Natural and semisynthetic opioids” is a category of prescription opioids, which includes natural opioid analgesics (codeine, morphine, etc.) and semi-synthetic opioid analgesics (hydrocodone, hydromorphone, oxycodone, and oxymorphone), but excludes heroin.

Methadone is a prescribed synthetic opioid used to treat moderate to severe pain, and also withdrawal symptoms in those addicted to heroin or other narcotics. “Synthetic opioids, other than methadone” is a category of opioids commonly available by prescription and includes drugs such as fentanyl and tramadol, but excludes methadone. The CDC does not differentiate between deaths from pharmaceutical fentanyl and illegally-made fentanyl, and deaths from both forms are included in the data.

While medically not considered a narcotic, cocaine is legally classified as such and is included in the CDC’s definition of narcotics along with opioids. The types of narcotics involved in drug overdose deaths were classified following the ICD-10 codes: T40.6 (other and unspecified narcotics), and T40.5 (cocaine). The category for all narcotics overdoses includes T40.1, T40.2, T40.3, T40.4, T40.5 and T40.6.

The methods used to classify deaths on death certificates may lead to a significant undercount of opioid-related deaths, which could inaccurately portray the severity of this public health problem. Because of reporting discrepancies and nonspecific language, it is likely that national statistics underestimate by a substantial fraction the amount of opioid analgesic- and heroin-related deaths. Additionally, the degree of underestimation varies based on states’ death certification systems. For more information undercounting opioid-related deaths visit https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547584/.

To provide context for a given area, it is helpful to also look at how many overdose deaths are recorded with no additional drug information. These were classified according to the ICD-10 code of T50.9 (other or unspecified drugs). For more information on the data visit https://wonder.cdc.gov/mcd-icd10.html.

Health, United States is an annual report on trends in health statistics, find more information at http://www.cdc.gov/nchs/hus.htm.

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

CDC NCHS leading causes of death data for 51 states, 10 causes, spanning 19 years (1999-2017).

In 2023, the leading causes of death among children and adolescents in the United States aged 10 to 14 were unintentional injuries, intentional self-harm (suicide), and cancer. That year, unintentional injuries accounted for around 26 percent of all deaths among this age group. Leading causes of death among older teens Like those aged 10 to 14 years, the leading cause of death among older teenagers in the U.S. aged 15 to 19 years is unintentional injuries. In 2023, unintentional injuries accounted for around 39 percent of all deaths among older teens. However, unlike those aged 10 to 14, the second leading cause of death among teens aged 15 to 19 is assault or homicide. Sadly, the third leading cause of death among this age group is suicide, making suicide among the leading three causes of death for both age groups. Teen suicide Suicide remains a major problem among teenagers in the United States, as reflected in the leading causes of death among this age group. It was estimated that in 2021, around 22 percent of high school students in the U.S. considered attempting suicide in the past year, with this rate twice as high for girls as for boys. The states with the highest death rates due to suicide among adolescents aged 15 to 19 years are New Mexico, Idaho, and Oklahoma. In 2023, the death rate from suicide among this age group in New Mexico was 27.7 per 100,000 population. In comparison, New Jersey, the state with the lowest rate, had just 5.5 suicide deaths among those aged 15 to 19 years per 100,000 population.

A tool that allows to rank all deaths in the USA by the underlying cause of death, sex, race, and age. It is based on data from National Highway Traffic Safety Administration (NHTSA) and from Centers of Disease Control and Prevention (CDC). The NHTSA data are from Fatality Analysis Reporting System (FARS), CDC data are from National Center for Health Statistics. Here both data sets are included together with some sample reports.

The leading causes of death among Black residents in the United States in 2023 included diseases of the heart, cancer, unintentional injuries, and stroke. The leading causes of death for African Americans generally reflect the leading causes of death for the entire United States population. However, a major exception is that death from assault or homicide is the seventh leading cause of death among African Americans but is not among the ten leading causes for the general population. Homicide among African Americans The homicide rate among African Americans has been higher than that of other races and ethnicities for many years. In 2023, around 9,284 Black people were murdered in the United States, compared to 7,289 white people. A majority of these homicides are committed with firearms, which are easily accessible in the United States. In 2023, around 13,350 Black people died by firearms. Cancer disparities There are also major disparities in access to health care and the impact of various diseases. For example, the incidence rate of cancer among African American males is the greatest among all ethnicities and races. Furthermore, although the incidence rate of cancer is lower among African American women than it is among white women, cancer death rates are still higher among African American women.

Cause of Death (COD) Malignant Cancer in Alabama from 1999-2017 I retrieved all Reference and Data information from NCHS website https://catalog.data.gov/dataset/nchs-leading-causes-of-death-united-states Then I cleaned it a little and pulled all information for Just Alabama for the years 1999-2017. Then I just pulled the information of Malignant Cancer in Alabama for the years 1999-2017. The reason I chose Alabama is because it is my home state where I was born and raised. In fact, in this data my Maternal grandparents are sadly apart of this data. I also wanted to add my mother when I get a chance and they update the information for 2021. My mother will also be part of the data My mother and both her parents cause of death was Malignant Cancer. They all lived and passed in Alabama. Also lost multiple Great Aunts and Uncles from Alabama to cancer. Also a few months after my mother past her little brothers wife passed with the same cancer. I do not believe this to be a coincidence. I Hope to do deeper studies later own into this. I also had genetic test done on my self that came back with no reason for all my family to pass with Cancer. This is my first case study on my own. Reference and Data information can be found on NCHS website NCHS - Leading Causes of Death: United States Metadata Updated: April 21, 2022

This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES 1. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. 2. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

Data for deaths by leading cause of death categories are now available in the death profiles dataset for each geographic granularity.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Cause of death categories for years 1999 and later are based on tenth revision of International Classification of Diseases (ICD-10) codes. Comparable categories are provided for years 1979 through 1998 based on ninth revision (ICD-9) codes. For more information on the comparability of cause of death classification between ICD revisions see Comparability of Cause-of-death Between ICD Revisions.

Key Indicators of Heart Disease

2022 annual CDC survey data of 400k+ adults related to their health status

What subject does the dataset cover?

According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect "patterns" in the data that can predict a patient's condition.

Where did the data set come from and what treatments has it undergone?

The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the CDC: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.

What can you do with this data set?

As described above, the original dataset of nearly 300 variables was reduced to 40variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable "HadHeartAttack" as binary ("Yes" - respondent had heart disease; "No" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?

What steps did you use to convert the dataset?

Check out this notebook in my GitHub repository: https://github.com/kamilpytlak/data-science-projects/blob/main/heart-disease-prediction/2022/notebooks/data_processing.ipynb

DescriptionThis dataset includes five tables summarizing mortality and birth outcomes for Cleveland, Ohio. Mortality data provide annual age‑adjusted rates by age, race, and sex (2018–present). Birth data include annual counts and key birth characteristics for Cleveland residents (2012–present). Age‑adjustment is used to support comparisons across populations with different age structures.Five‑Year Leading Causes of Death: Lists the top ten causes of death that account for the largest share of all deaths over a five‑year period. Ohio and U.S. rankings are included for context.Annual Leading Causes of Death: Lists the leading causes of death each year based on the proportion of total deaths. Ohio and U.S. rankings are included for context.Mortality Rates by Cause: Provides annual age‑adjusted mortality rates for each leading cause of death (i.e., cause-specific mortality rates). Ohio and U.S. rates are included for comparison.Mortality Rates: Includes overall mortality rates (not cause-specific) for Cleveland, Ohio, and U.S.Annual Birth Data: Includes annual counts of births, the percentage of births that are preterm (overall and by race and ethnicity), and maternal age at childbirth.Age-Specific Mortality Rates: Provides mortality rates for specific age groups, allowing comparison of death rates across different stages of life. Ohio and U.S. rates are included for comparison.Age-Adjusted Mortality Rate by Race and Ethnicity: Shows annual age‑adjusted mortality rates for Cleveland residents by race and ethnicity, allowing comparison across groups and over time. Ohio and U.S. rates are included for comparison.Age-Adjusted Mortality Rate by Sex: Presents annual age‑adjusted mortality rates for males and females, highlighting differences in mortality patterns by sex. Ohio and U.S. rates are included for comparison. Related ItemsThis dataset is featured in the following app(s): Birth and Mortality DashboardThis dataset is related to the following dataset(s): Infant Mortality DataData GlossarySee Attributes for field definitions.Update FrequencyData will be updated annually in January.ContactCleveland Department of Public Health, Office of Epidemiology and Population HealthAre you looking for data not available in this publication? Access data not included in this publication by submitting a data request to Office of Epidemiology and Population Health: FormSourceCleveland birth and mortality data are provided by the Ohio Department of Health Bureau of Vital Statistics. State and national data come from publicly available CDC WONDER datasets. MethodsBirth and death records were aggregated to summarize patterns in mortality and natality among Cleveland residents. Data were grouped by year, underlying cause of death, demographic characteristics (such as age, sex, and race/ethnicity), and other selected factors relevant to public‑health reporting.Leading causes of death were identified using the standard methodology developed by the National Center for Health Statistics (NCHS), which ranks causes based on the underlying cause of death. Age‑adjusted mortality rates were calculated to allow comparisons across populations with different age distributions.All analyses reflect counts and rates for Cleveland residents. Data suppression rules were applied when necessary to protect confidentiality.

Provisional estimates of death rates. Estimates are presented for each of the 15 leading causes of death plus estimates for deaths attributed to drug overdose, falls (for persons aged 65 and over), human immunodeficiency virus (HIV) disease, homicide, and firearms-related deaths.

Age-adjustment mortality rates are rates of deaths that are computed using a statistical method to create a metric based on the true death rate so that it can be compared over time for a single population (i.e. comparing 2006-2008 to 2010-2012), as well as enable comparisons across different populations with possibly different age distributions in their populations (i.e. comparing Hispanic residents to Asian residents). Age adjustment methods applied to Montgomery County rates are consistent with US Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) as well as Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). PHS Planning and Epidemiology receives an annual data file of Montgomery County resident deaths registered with Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). Using SAS analytic software, MCDHHS standardizes, aggregates, and calculates age-adjusted rates for each of the leading causes of death category consistent with state and national methods and by subgroups based on age, gender, race, and ethnicity combinations. Data are released in compliance with Data Use Agreements between DHMH VSA and MCDHHS. This dataset will be updated Annually.

DescriptionThis dataset includes five tables summarizing mortality and birth outcomes for Cleveland, Ohio. Mortality data provide annual age‑adjusted rates by age, race, and sex (2018–present). Birth data include annual counts and key birth characteristics for Cleveland residents (2012–present). Age‑adjustment is used to support comparisons across populations with different age structures.Five‑Year Leading Causes of Death: Lists the top ten causes of death that account for the largest share of all deaths over a five‑year period. Ohio and U.S. rankings are included for context.Annual Leading Causes of Death: Lists the leading causes of death each year based on the proportion of total deaths. Ohio and U.S. rankings are included for context.Mortality Rates by Cause: Provides annual age‑adjusted mortality rates for each leading cause of death (i.e., cause-specific mortality rates). Ohio and U.S. rates are included for comparison.Mortality Rates: Includes overall mortality rates (not cause-specific) for Cleveland, Ohio, and U.S.Annual Birth Data: Includes annual counts of births, the percentage of births that are preterm (overall and by race and ethnicity), and maternal age at childbirth.Age-Specific Mortality Rates: Provides mortality rates for specific age groups, allowing comparison of death rates across different stages of life. Ohio and U.S. rates are included for comparison.Age-Adjusted Mortality Rate by Race and Ethnicity: Shows annual age‑adjusted mortality rates for Cleveland residents by race and ethnicity, allowing comparison across groups and over time. Ohio and U.S. rates are included for comparison.Age-Adjusted Mortality Rate by Sex: Presents annual age‑adjusted mortality rates for males and females, highlighting differences in mortality patterns by sex. Ohio and U.S. rates are included for comparison. Related ItemsThis dataset is featured in the following app(s): Birth and Mortality DashboardThis dataset is related to the following dataset(s): Infant Mortality DataData GlossarySee Attributes for field definitions.Update FrequencyData will be updated annually in January.ContactCleveland Department of Public Health, Office of Epidemiology and Population HealthAre you looking for data not available in this publication? Access data not included in this publication by submitting a data request to Office of Epidemiology and Population Health: FormSourceCleveland birth and mortality data are provided by the Ohio Department of Health Bureau of Vital Statistics. State and national data come from publicly available CDC WONDER datasets. MethodsBirth and death records were aggregated to summarize patterns in mortality and natality among Cleveland residents. Data were grouped by year, underlying cause of death, demographic characteristics (such as age, sex, and race/ethnicity), and other selected factors relevant to public‑health reporting.Leading causes of death were identified using the standard methodology developed by the National Center for Health Statistics (NCHS), which ranks causes based on the underlying cause of death. Age‑adjusted mortality rates were calculated to allow comparisons across populations with different age distributions.All analyses reflect counts and rates for Cleveland residents. Data suppression rules were applied when necessary to protect confidentiality.

Injuries and violence affect everyone, regardless of age, race, or economic status. In the first half of life, more Americans die from injuries and violence — such as motor vehicle crashes, suicide, or homicides — than from any other cause, including cancer, HIV, or the flu. This makes injury the leading cause of death among persons aged 1-44.