Data API

Introduction

Let's breakdown an example data call:

http://api.datausa.io/api/?show=cip&sumlevel=all

In its most basic form, all API calls required two parameters: a show variable and a sumlevel variable. In this example we are showing "cip" (aka CIP, Classification of Instructional Programs) across all sumlevels. CIP codes are available at four basic sumlevels: 2-digit (high level), 4 digit, 6-digit (most detailed) and all (2, 4 and 6).

If we only want the high level CIP codes, we could ask for:

http://api.datausa.io/api/?show=cip&sumlevel=2

If we look at the response, we are receiveing many columns. If we are only interested an a particular column we can ask for just that:

http://api.datausa.io/api/?show=cip&sumlevel=2&required=grads_total

If we want multiple columns, we can provide a comma-separated list to required:

http://api.datausa.io/api/?show=cip&sumlevel=2&required=grads_men,grads_women

Query Parameters

parameter	accepted values	description
force	schema_name.table_name (Example)	Forces the use of a particular data table.
limit	integer	Limits the number of rows returned by the query.
order	any available column name	Column name to use for ordering the resulting data array.
show (required)	any available attribute	A comma-separated list of attributes to show in the query.
sort	`desc` or `asc`	Changes the sort order of the returned data array.
sumlevel (required)	any available sumlevel for the given attribute	This restricts the data fetched to only display the specified sumlevel(s). If more than one "show" attribute is specified, sumlevel must be a comma-separated list with a value for each attribute.
required	any available column name	A comma-separated list of column names to be returned in the query.
where	see documentation	Advanced filtering of columns, similar to the WHERE clause on SQL.
year	`latest`, `oldest`, `all`, 4-digit year	Filters the returned data to the given year.

Basic Filtering

There are two main ways to filter rows in the DataUSA API. One way is through strict equality. For example, if you were looking at all top level NAICS:

http://api.datausa.io/api?show=naics&sumlevel=0

You could filter the results to only show data from NAICS code 23, by adding &naics=23 to the URL:

http://api.datausa.io/api?show=naics&sumlevel=0&naics=23

This is true for any column: you may restrict API results to that column through strict equality by using column_name=value.

Advanced Filtering

Sometimes, however, it's necessary to filter not merely by equality but by other mechanisms. Using the where query parameter provides access to more complicated expressions. The basic syntax of using where is: &where**=column_name:condition

Let's say we wanted to filter the results of an API call to only show top-level industries containing more than an estimated 10M people in the workforce. Here is how we would write that:

http://api.datausa.io/api/?show=naics&sumlevel=0&where=num_ppl:>10000000

Here is a list of the available expressions:

expression	operator	example syntax
greater than	>	&where=num_ppl:>10000000
less than	<	&where=num_ppl:<10000000
string starts with	^	&where=naics:^23
string ends with	$ (placed after text)	&where=naics:3$
not equal (integer)	!	&where=avg_wage_rank:!1
not equal (string)	str!	&where=geo:str!04000US25

Substitutions

Sometimes, when requesting data for a specific attribute, the underlying dataset may only contain data for a higher level. For example, the County Health Records (CHR) dataset only contains geographies down to the county, so when requesting health data for a deeper geography the API will automatically find the closest parent. Take this example:

http://api.datausa.io/api/?show=geo&required=diabetes&sumlevel=all&geo=31000US14460

Here we are requesting the diabetes rate for the Boston-Cambridge-Newtown, MA-NH Metro Area, but because that depth is not available for this dataset, the API returns data for the state of Massachusetts. When looking at the JSON data that is returned, this substitution is passed as the "subs" key:

{
  "data": ...,
  "headers": ...,
  "source": ...,
  "subs": {
    "geo": "04000US25"
  },
  "logic": ...
}

Limitations

The API mirrors the underlying aggregated table structure. Consequently, currently not all possible variables are available for simultaneous retrieval from the API. Check each dataset's table lists to understand supported variable combinations. To address this issue we have created the Join API.

Download as CSV

To download the data as a CSV, simply add csv into the URL as follows:

http://api.datausa.io/api/csv/?show=cip&sumlevel=2&required=grads_men,grads_women

IPEDS

Tuition

column name	description
year	4-digit year value
cip	course ID
university	university ID
sector	sector ID
oos_tuition	out of state tuition
oos_fee	out of state fee
state_tuition	in-state tuition
state_fee	in-state fee
district_tuition	in-district tuition
district_fee	in-district fee
state_tuition_grads	state tuition (graduate students)
district_tuition_grads	district tuition (graduate students)
oss_tuition_grads	out of state tuition (graduate students)
state_fee_grads	state fee (graduate students)
district_fee_grads	district fee (graduate students)
oss_fee_grads	out of state fee (graduate students)

Enrollment

column name	description
year	4-digit year value
university	university ID
geo_id	location ID
enrolled_nonresident	enrolled non-residents
enrolled_hawaiian	enrolled hawaiian
enrolled_unknown	enrolled unknown Race
enrolled_men	enrolled men
enrolled_multiracial	enrolled multiracial
enrolled_hispanic	enrolled hispanic
enrolled_black	enrolled black
enrolled_asian	enrolled asian
enrolled_white	enrolled white
enrolled_total	enrolled total
enrolled_native	enrolled native
enrolled_women	enrolled women

Graduates

column name	description
year	4-digit year value
university	university ID
cip	course ID
degree	degree ID
geo_id	location ID
grads_total	total graduates
grads_men	men graduates
grads_women	women graduates
grads_black_men	black men graduates
grads_multi	multiracial graduates
grads_white_women	white women graduates
grads_hispanic_men	hispanic men graduates
grads_nonresident	non-resident graduates
grads_native	native American graduates
grads_hispanic	hispanic graduates
grads_hawaiian	hawaiian and Pacific Islander graduates
grads_multi_men	multiracial men graduates
grads_black	black graduates
grads_native_women	native women graduates
grads_multi_women	multiracial women graduates
grads_asian_women	asian women graduates
grads_unknown	unknown race graduates
grads_hawaiian_men	hawaiian men graduates
grads_hawaiian_women	hawaiian women graduates
grads_asian	asian graduates
grads_nonresident_women	non-resident women graduates
grads_unknown_men	unknown race men graduates
grads_native_men	native American men graduates
grads_asian_men	asian men graduates
grads_unknown_women	unknown race women graduates
grads_black_women	black women graduates
grads_white_men	white men graduates
grads_white	white graduates
grads_nonresident_men	non-resident men graduates
grads_hispanic_women	hispanic women graduates

ACS PUMS

column name	description
num_records	number of PUMS records collapsed to generate the estimate
num_records_ft	number of full-time records collapsed to generate the estimate
num_records_pt	number of part-time records collapsed to generate the estimate
avg_wage	average wage estimate
avg_wage_ft	average wage full-time employees estimate
avg_wage_pt	average wage part-time employees estimate
avg_wage_moe	average wage margin of error
avg_wage_ft_moe	average wage full-time margin of error
avg_wage_pt_moe	average wage part-time margin of error
num_ppl	estimated number of people in the workforce
num_ppl_ft	estimated number of full-time employed people
num_ppl_pt	estimated number of part-time employed people
num_ppl_moe	estimated number of people margin of error
num_ppl_ft_moe	estimated number of people full-time margin of error
num_ppl_pt_moe	estimated number of people part-time margin of error
avg_age	average age estimate
avg_age_ft	average age full-time employees estimate
avg_age_pt	average age part-time employees estimate
avg_age_moe	average age margin of error
avg_age_ft_moe	average age margin of error full-time employees
avg_age_pt_moe	average age margin of error part-time employees

Note: num_ppl represents people over the age of 16 in the workforce. In the ygb table, num_ppl is replaced by num_over5 to represent all people over the age of 5 years old.

Aggregation Methodology

Data USA incorporates 4 universes from the ACS PUMS dataset (the default usually being "Workforce"). Here are is the aggregation criteria for these workforces:

Workforce Universe (e.g. num_ppl)

WAGP > 0
AGEP >= 16
ESR is one of: 1, 2, 4, 5

Full-time Universe (e.g. num_ppl_ft)

WAGP > 0
AGEP >= 16
WKHP >= 35
ESR is one of: 1, 2, 4, 5

Part-time Universe (e.g. num_ppl_pt)

WAGP > 0
AGEP >= 16
WKHP < 35
ESR is one of: 1, 2, 4, 5

5+ Universe (only for num_over5)

AGEP >= 5

All wage estimate values are adjusted using the ADJINC variable.

5-year Estimates

Geographies, industries and occupation classifications can change in the middle of a single 5-year PUMS estimate. Since data are recorded in the classification in which they are collected, these changes add additional uncertainty to the PUMS 5-year estimate. In order to compensate in many cases for the lack of 1:1 mappings between old and new classifications, we will, apply a randomized redistribution from the old classification system to the new one. For occupations and industries the probability is based upon the Census Bureau's Industry and Occupation Conversion Rates tables. For geography (the issue only arises with PUMA-level data) it is based upon IPUMS' crosswalk methodology.

Column Mapping

Data USA	Base PUMS
birthplace	POBP
cip	FOD1P
degree	SCHL
naics	NAICSP
soc	SOCP
race	RAC1P
age	AGEP
sex	SEX

American Community Survey (ACS)

Data USA provides a view onto several American Community survey data tables. These tables include information on: population, income, veteran status, nativity, poverty, race, property, transportation, language and more. Below are a list of the support data tables and variables for ACS:

yg

Sources: B01002, B01003, B19013, B08136, B08006, B05001, B25003, B25077, B16001

column name	description
year	4-digit year value
geo	location ID
age	median Age
pop	population
non_us_citizens	percentage of population that are Non-US Citizens
mean_commute_minutes	mean Commute Time in Minutes
income	median Household Income
owner_occupied_housing_units	percentage of housing units that are Owner occupied
median_property_value	median property value
median_property_value_moe	median property value, margin of error
pop_rank	rank of population (for its sumlevel)
income_rank	rank of income (for its sumlevel)
us_citizens	percentage of population that are US citizen
non_eng_speakers_pct	percentage of population that speak a language other than English

yg_conflict

Source: B21002 Universe: Civilian Veterans 18 Years and Over

column name	description
year	4-digit year value
geo	location ID
conflict_total	total number of Veterans
conflict_wwii	total number of World War II Veterans
conflict_korea	total number of Korean War Veterans
conflict_vietnam	total number of Vietnam Veterans
conflict_gulf90s	total number of Gulf War (90s) Veterans
conflict_gulf01	total number of Gulf War (01-present) Veterans

yg_income

Source: B19013 Universe: Households

column name	description
year	4-digit year value
geo	location ID
income_black	median income for Black people
income_native	median income for Native American people
income_asian	median income for Asian people
income_hawaiian	median income for Hawaiian people
income_white	median income for White people
income_hispanic	median income for Hispanic people
income_2ormore	median income for people of two or more

yg_income_distribution

Source: B19001 Universe: Households

column name	description
year	4-digit year value
geo	location ID
income_under10	number of households whose income is $10K or less
income_10to15	number of households whose income is between $10-15K
income_15to20	number of households whose income is between $15-20K
income_20to25	number of households whose income is between $20-25K
income_25to30	number of households whose income is between $25-30K
income_30to35	number of households whose income is between $30-35K
income_35to40	number of households whose income is between $35-40K
income_40to45	number of households whose income is between $40-45K
income_45to50	number of households whose income is between $45-50K
income_50to60	number of households whose income is between $50-60K
income_50to60	number of households whose income is between $50-60K
income_60to75	number of households whose income is between $60-75K
income_75to100	number of households whose income is between $75-100K
income_100to125	number of households whose income is between $100-125K
income_125to150	number of households whose income is between $125-150K
income_150to200	number of households whose income is between $150-200K
income_200over	number of households whose income is $200K+
totalhouseholds	total number of households

yg_nativity_age

Source: B06001 Universe: Total Population in the United States

column name	description
year	4-digit year value
geo	location ID
nativity_foreign	number of foreign born (non-Native citizens)
nativity_foreign_under5	population of Foreign born people under the age of 5
nativity_foreign_5to17	foreign born people 5-17 years old
nativity_foreign_18to24	foreign born people 18-24 years old
nativity_foreign_25to34	foreign born people 25-34 years old
nativity_foreign_35to44	foreign born people 35-44 years old
nativity_foreign_45to54	foreign born people 45-54 years old
nativity_foreign_55to59	foreign born people 54-59 years old
nativity_foreign_60to61	foreign born people 60-61 years old
nativity_foreign_62to64	foreign born people 62-64 years old
nativity_foreign_65to74	foreign born people 65-74 years old
nativity_foreign_75over	foreign born people 75+ years old
nativity_us	number of US natives (includes those born in the US as well as those born as natives outside the US)
nativity_us_under5	number of US natives under 5 years old
nativity_us_5to17	US natives 5-17 years old
nativity_us_18to24	US natives 18-24 years old
nativity_us_25to34	US natives 25-34 years old
nativity_us_35to44	US natives 35-44 years old
nativity_us_45to54	US natives 45-54 years old
nativity_us_55to59	US natives 54-59 years old
nativity_us_60to61	US natives 60-61 years old
nativity_us_62to64	US natives 62-64 years old
nativity_us_65to74	US natives 65-74 years old
nativity_us_75over	US natives 75+ years old

yg_poverty

Source: B17001 Universe: Population for Whom Poverty Status Is Determined

column name	description
year	4-digit year value
geo	location ID
income_below_poverty	population living below the poverty line, for whom poverty status is determined
pop_poverty_status	population for whom poverty status is determined
poverty_female	females with income below poverty level
poverty_male	males with income below poverty level
poverty_female_5	females under 5 years old with income below poverty level
poverty_female_6to11	females 6-11 years old with income below poverty level
poverty_female_12to14	females 12-14 years old with income below poverty level
poverty_female_15	females 15 years old with income below poverty level
poverty_female_16to17	females 16-17 years old with income below poverty level
poverty_female_18to24	females 18-24 years old with income below poverty level
poverty_female_25to34	females 25-34 years old with income below poverty level
poverty_female_35to44	females 35-44 years old with income below poverty level
poverty_female_45to54	females 45-54 years old with income below poverty level
poverty_female_55to64	females 55-64 years old with income below poverty level
poverty_female_65to74	females 65-74 years old with income below poverty level
poverty_female_75over	females 75+ years old with income below poverty level
poverty_male_5	males under 5 years old with income below poverty level
poverty_male_6to11	males 6-11 years old with income below poverty level
poverty_male_12to14	males 12-14 years old with income below poverty level
poverty_male_15	males 15 years old with income below poverty level
poverty_male_16to17	males 16-17 years old with income below poverty level
poverty_male_18to24	males 18-24 years old with income below poverty level
poverty_male_25to34	males 25-34 years old with income below poverty level
poverty_male_35to44	males 35-44 years old with income below poverty level
poverty_male_45to54	males 45-54 years old with income below poverty level
poverty_male_55to64	males 55-64 years old with income below poverty level
poverty_male_65to74	males 65-74 years old with income below poverty level
poverty_male_75over	males 75+ years old with income below poverty level

yg_poverty_race

Source: B17001A, B17001B, B17001C, B17001D, B17001E, B17001F Universe: Population for Whom Poverty Status Is Determined

column name	description
year	4-digit year value
geo	location ID
poverty_asian	asian population below poverty level
poverty_white	white population below poverty level
poverty_black	black population below poverty level
poverty_native	american Indian and Alaska Native Alone population below poverty level
poverty_other	other Race population below poverty level
poverty_hawaiian	native Hawaiian and Other Pacific Islander population below poverty level
poverty_2ormore	2 or more races population below poverty level
poverty_white_non_hispanic	white non-Hispanic population below poverty level
poverty_hispanic	hispanic population below poverty level

yg_property_tax

Source: B25102 Universe: Total population Owner-occupied Housing Units (Only looking at units with a mortgage)

column name	description
year	4-digit year value
geo	location ID
propertytax_less800	owner-occupied housing units with a mortgage and property taxes less than $800
propertytax_800to1500	owner-occupied housing units with a mortgage and property taxes $800-$1,499
propertytax_1500to2000	owner-occupied housing units with a mortgage and property taxes $1,500-$1,999
propertytax_2000to3000	owner-occupied housing units with a mortgage and property taxes $2,000-$2,999
propertytax_3000over	owner-occupied housing units with a mortgage and property taxes $3000+
propertytax_none	owner-occupied housing units with a mortgage and no property taxes

yg_property_value

Source: B25075 Universe: Total population Owner-occupied Housing Units

column name	description
year	4-digit year value
geo	location ID
propertyval_less10k	number of units with property value less than $10,000
propertyval_10kto15k	$10,000 to $14,999
propertyval_15kto20k	$15,000 to $19,999
propertyval_20kto25k	$20,000 to $24,999
propertyval_25kto30k	$25,000 to $29,999
propertyval_30kto35k	$30,000 to $34,999
propertyval_35kto40k	$35,000 to $39,999
propertyval_40kto50k	$40,000 to $49,999
propertyval_50kto60k	$50,000 to $59,999
propertyval_60kto70k	$60,000 to $69,999
propertyval_70kto80k	$70,000 to $79,999
propertyval_80kto90k	$80,000 to $89,999
propertyval_90kto100k	$90,000 to $99,999
propertyval_100kto125k	$100,000 to $124,999
propertyval_125kto150k	$125,000 to $149,999
propertyval_150kto175k	$150,000 to $174,999
propertyval_175kto200k	$175,000 to $199,999
propertyval_200kto250k	$200,000 to $249,999
propertyval_250kto300k	$250,000 to $299,999
propertyval_300kto400k	$300,000 to $399,999
propertyval_400kto500k	$400,000 to $499,999
propertyval_500kto750k	$500,000 to $749,999
propertyval_750kto1M	$750,000 to $999,999
propertyval_1Mormore	$1,000,000 or more

yg_race

Source: B03002 Universe: Total population

column name	description
year	4-digit year value
geo	location ID
pop_native	population that is American Indian and Alaska Native
pop_black	population that is Black or African American
pop_white	population that is White
pop_asian	population that is Asian
pop_hawaiian	population that is Native Hawaiian and Other Pacific Islander
pop_other	population that is some other race
pop_2ormore	population that is 2 or more races
pop_latino	population that is Hispanic or Latino

yg_tenure

Source: B25003 Universe: Occupied Housing Units

column name	description
year	4-digit year value
geo	location ID
households_renter_occupied	number of renter-occupied households
households_owner_occupied	number of owner-occupied households
households	number of households

yg_transport

Source: B08301 Universe: Workers 16 Years and Over

column name	description
year	4-digit year value
geo	location ID
transport_bicycle	number of workers who commute by bicycle
transport_carpooled	number of workers who commute by carpool
transport_drove	number of workers who commute by driving alone?
transport_motorcycle	number of workers who commute by carpool
transport_other	number of workers who commute by other methods
transport_publictrans	number of workers who commute by public transit (excluding taxi)
transport_taxi	number of workers who commute by taxi
transport_walked	number of workers who commute by walking
transport_home	number of workers who work at home
workers	total number of workers

yg_travel_time

Source: B08303 Universe: Workers 16 Years and Over Who Did Not Work at Home

column name	description
year	4-digit year value
geo	location ID
travel_less5	number of households whose commute time is less than 5 minutes
travel_5to9	number of households whose commute time is 5-9 minutes
travel_10to14	number of households whose commute time is 10-14 minutes
travel_15to19	number of households whose commute time is 15-19 minutes
travel_20to24	number of households whose commute time is 20-24 minutes
travel_25to29	number of households whose commute time is 25-29 minutes
travel_30to34	number of households whose commute time is 30-34 minutes
travel_35to39	number of households whose commute time is 35-39 minutes
travel_40to44	number of households whose commute time is 40-44 minutes
travel_45to59	number of households whose commute time is 45-59 minutes
travel_60to89	number of households whose commute time is 60-89 minutes
travel_90over	number of households whose commute time is 90+ minutes

yg_vehicles

Source: B08014 Universe: Workers 16 Years and Over in Households

column name	description
year	4-digit year value
geo	location ID
vehicles_none	households with no vehicles
vehicles_1	households with 1 vehicle
vehicles_2	households with 2 vehicles
vehicles_3	households with 3 vehicles
vehicles_4	households with 4 vehicles
vehicles_5over	households with 5+ vehicles

ygi_num_emp

Source: C24030 Universe: Civilian Employed Population 16 Years and Over

column name	description
year	4-digit year value
geo	location ID
num_emp	number of employees
num_emp_rca	RCA metric based on the number of employees in a given year and geography for a particular industry

ygl_speakers

Source: B16001 Universe: Population 5 Years and Over

column name	description
year	4-digit year value
geo	location ID
language	language ID
num_speakers	number of speakers of the language

ygo_med_earnings

Source: B24012 Universe: Civilian Employed Population 16 Years and Over With Earnings

column name	description
year	4-digit year value
geo	location ID
acs_occ	occupation ID
med_earnings	median earnings

ygo_num_emp

Source: C24010 Universe: Civilian Employed Population 16 Years and Over

column name	description
year	4-digit year value
geo	location ID
acs_occ	occupation ID
num_emp	number of employees
num_emp_male	number of male employees
num_emp_female	number of female employees
num_emp_rca	number of employees RCA
num_emp_rca	RCA metric based on the number of employees in a given year and geography for a particular industry

O*NET

Data USA uses the Skills.txt file from revision 19.0 of the O*NET Database.

Skills by Course

We crosswalk the O*NET codes to CIP codes using data from the National Crosswalk Service Center and compute mean level values for each CIP at 2, 4 and 6-digit levels.

column name	description
cip	course ID
skill	O*NET skill identifier
value	value describing the importance of the skill to the course
value_rca	RCA calculation for the skill value

Skills by Occupation

We use the level values (SCALEID=LV) for each skill. In addition the values provided directly by ONET we also compute additional values based on parent grouping. This allows to provide reference data for an occupation's parent grouping if it is not available in the ONET skills database.

column name	description
soc	occupation ID
skill	O*NET Skill identifier
value	value describing the importance of the skill to the course
value_rca	RCA calculation for the skill value

Bureau of Labor and Statistics (BLS)

Data USA contains growth projections for industries and occupations, as well as CES data on employment from the Bureau of Labor and Statistics.

Industry Growth

column name	description
naics	industry ID
emp_2002_thousands	number of employees in 2002 (in thousands)
emp_2012_thousands	number of employees in 2012 (in thousands)
emp_2022_thousands	number of employees in 2022 (in thousands)

Occupation Growth

column name	description
soc	occupation ID
emp_2012_thousands	number of employees in 2012 (in thousands)
emp_2022_thousands	number of employees in 2022 (in thousands)

CES Data

column name	description
year	4-digit year value
naics	industry ID
employees_thousands	employees (in thousands)
avg_hrly_earnings	average hourly earnings
avg_wkly_hrs	average weekly hours

County Health Rankings (CHR)

Data USA contains data from the University of Wisconsin's County Health Rankings. Visit the CHR website for more detailed documentation.

column name	description
year	4-digit year value
geo	location ID (State or County)
primary_care_physicians	ratio of the population to total primary care physicians
dentists	ratio of the county population to total dentists in the county
mental_health_providers	ratio of the county population to the number of mental health providers including psychiatrists, psychologists, licensed clinical social workers, counselors, marriage and family therapists and advanced practice nurses specializing in mental health care
other_primary_care_providers	number of other primary care provders per the population of a county, which include nurse practitioners, physician assistants, and clinical nurse specialists
adult_smoking	percentage of adults that reported currently smoking
adult_obesity	percentage of adults that report BMI >= 30
excessive_drinking	percentage of adults that report excessive drinking
motor_vehicle_crash_deaths	motor vehicle crash deaths per 100,000 population
homicide_rate	number or deaths due to homicide per 100,000 population
sexually_transmitted_infections	number of newly diagnosed chlamydia cases per 100,000 population
health_care_costs	amount of price-adjusted Medicare reimbursements per enrollee
diabetes	percentage of adults aged 20 and above with diagnosed diabetes
hiv_prevalence_rate	number of persons living with a diagnosis of human immunodeficiency virus (HIV) infection per 100,000 population
violent_crime	number of reported violent crime offenses per 100,000 population
alcoholimpaired_driving_deaths	percentage of driving deaths with alcohol involvement
premature_death	years of potential life lost before age 75 per 100,000 population
poor_or_fair_health	percentage of adults reporting fair or poor health (age-adjusted)
poor_physical_health_days	average number of physically unhealthy days reported in past 30 days (age-adjusted)
poor_mental_health_days	average number of mentally unhealthy days reported in past 30 days (age-adjusted)
low_birthweight	percentage of live births with low birthweight (< 2500 grams)
food_environment_index	takes both proximity to healthy foods and income into account
physical_inactivity	percentage of adults aged 20 and over reporting no leisure-time physical activity
access_to_exercise_opportunities	percentage of individuals who live reasonably close to a location for physical activity
teen_births	number of births per 1,000 female population, ages 15-19
uninsured	percentage of the population under age 65 that has no health insurance coverage
preventable_hospital_stays	the hospital discharge rate for ambulatory care-sensitive conditions per 1,000 fee-for-service Medicare enrollees
diabetic_screening	percentage of diabetic fee-for-service Medicare patients ages 65-75 whose blood sugar control was monitored in the past year using a test of their glycated hemoglobin (HbA1c) levels
mammography_screening	percentage of female fee-for-service Medicare enrollees age 67-69 that had at least one mammogram over a two-year period
high_school_graduation	percentage of the ninth-grade cohort in public schools that graduates from high school in four years
some_college	percentage of the population ages 25-44 with some post-secondary education, such as enrollment in vocational/technical schools, junior colleges, or four-year colleges, including individuals who pursued education following high school but did not receive a degree
children_in_poverty	percentage of children under age 18 living in poverty
children_in_singleparent_households	percentage of children in family households that live in a household headed by a single parent (male or female head of household with no spouse present)
social_associations	number of social associations per 10,000 population, including membership organizations such as civic organizations, bowling centers, golf clubs, fitness centers, sports organizations, religious organizations, political organizations, labor organizations, business organizations, and professional organizations
injury_deaths	number of deaths due to injury per 100,000 population
polution_ppm	average daily density of fine particulate matter in micrograms per cubic meter (PM2.5)
drinking_water_violations	percentage of population being served by community water systems with health-based drinking water violations
severe_housing_problems	percentage of households with at least 1 or more of the following housing problems: housing unit lacks complete kitchen facilities, housing unit lacks complete plumbing facilities, household is severely overcrowded, and/or household is severely cost burdened
population_living_in_a_rural_area	percentage of population living in a rural area
premature_ageadjusted_mortality	years of potential life lost before age 75 per 100,000 population (age-adjusted to the 2000 US population)
infant_mortality	number of deaths among children less than one year of age per 1,000 live births
child_mortality	number of deaths among children under age 18 per 100,000 population
food_insecurity	percentage of the population who did not have access to a reliable source of food during the past year
limited_access_to_healthy_foods	percentage of the population who are low income and do not live close to a grocery store
drug_poisoning_deaths	number of deaths due to drug poisoning per 100,000 population
uninsured_adults	percentage of the population ages 18 to 65 that has no health insurance coverage
uninsured_children	percentage of children under age 19 without health insurance
could_not_see_doctor_due_to_cost	percentage of adults who could not see a doctor in the past 12 months because of cost
children_eligible_for_free_lunch	percentage of children enrolled in public schools eligible for free lunch
unemployment	percentage of the civilian labor force, age 16 and older, that is unemployed but seeking work
income_inequality	ratio of household income at the 80th percentile to that at the 20th percentile
driving_along_to_work	percentage of the workforce that usually drives alone to work
long_commute__driving_alone	percentage of commuters, among those who commute to work by car, truck, or van alone, who drive longer than 30 minutes to work each day
population_that_is_not_proficient_in_english	percentage of population that is not proficient in English
population_estimate	resident population.
median_household_income	income at which half the households earn more and half the households earn less

Bureau of Economic Analysis (BEA)

Data USA contains data from the Input/Output tables published by the Bureau of Economic Analysis County Health Rankings.

column name	description
year	4-digit year value
industry_iocode	BEA industry ID
commodity_iocode	BEA commodity ID
value_millions	production value in millions USD
industry_level	industry depth level

Dartmouth Atlas of Health

yg_post_discharge

column name	description
year	year
geo	location ID (Nation, State, County)
patients_in_cohort	The number of patients who qualified
patients_readmitted_within_30_days_of_discharge	Patients of patients who return back to hospital within 30 days after discharge
patients_seeing_a_primary_care_physician_within_14_days	Patients of patients who see a primary care physician 2 weeks after discharge
patients_having_an_ambulatory_visit_within_14_days	Patients of patients who require medical procedures within 2 weeks that can be performed on an outpatient basis
patients_having_an_emergency_room_visit_within_30_days	Patients of patients who require an emergency room visit within 30 days of discharge

yg_reimbursements

column name	description
year	year
geo	location ID (Nation, State, County)
total_medicare_enrollees	Total number of Medicare Enrollees
total_reimbursements_a	Total reimbursements per enrollee (age, sex & race-adjusted)
total_reimbursements_b	Total reimbursements per enrollee (price, age, sex & race-adjusted)
hospital_reimbursements_a	Hospital & skilled nursing facility reimbursements per enrollee (age, sex & race-adjusted)
hospital_reimbursements_b	Total number of Medicare Part A hospital reimbursements (price, age, sex & race-adjusted)
physician_reimbursements_a	Physician reimbursements per enrollee (age, sex & race-adjusted)
physician_reimbursements_b	Physician reimbursements per enrollee (price, age, sex & race-adjusted)
outpatient_reimbursements_a	Outpatient facility reimbursements per enrollee (age, sex & race-adjusted)
outpatient_reimbursements_b	Outpatient facility reimbursements per enrollee (price, age, sex & race-adjusted)
home_health_reimbursements_a	Home health agency reimbursements per enrollee (age, sex & race-adjusted)
home_health_reimbursements_b	Home health agency reimbursements per enrollee (price, age, sex & race-adjusted)
hospice_reimbursements_a	Hospice reimbursements per enrollee (age, sex & race-adjusted)
hospice_reimbursements_b	Hospice reimbursements per enrollee (price, age, sex & race-adjusted)
medical_equip_reimbursements_a	Durable medical equipment reimbursements per enrollee (age, sex & race-adjusted)
medical_equip_reimbursements_b	Durable medical equipment reimbursements per enrollee (price, age, sex & race-adjusted)

yg_prim_care_access

column name	description
year	year
geo	location ID (Nation, State, County)
medicare_beneficiaries_total	Total # of Medicare enrollees
medicare_beneficiaries_black	Total # of Black Medicare enrollees
medicare_beneficiaries_white	Total # of Non-Black Medicare enrollees
patients_with_one_ambulatory_visit_to_pc_total
patients_with_one_ambulatory_visit_to_pc_black
patients_with_one_ambulatory_visit_to_pc_white
diabetic_medicare_enrollees_65_75_total	Total # of diabetic Medicare enrollees between age 65 and 75 having eye exam
diabetic_medicare_enrollees_65_75_black	Total # of diabetic Black Medicare enrollees between age 65 and 75 having eye exam
diabetic_medicare_enrollees_65_75_white	Total # of diabetic Non-Black Medicare enrollees between age 65 and 75 having eye exam
patients_diabetic_medicare_enrollees_65_75_hemoglobin_total	Total # of Medicare enrollees having hemoglobin test
patients_diabetic_medicare_enrollees_65_75_hemoglobin_black	Total # of Black Medicare enrollees having hemoglobin test
patients_diabetic_medicare_enrollees_65_75_hemoglobin_white	Total # of Non-Black Medicare enrollees having hemoglobin test
patients_diabetic_medicare_enrollees_65_75_eye_exam_total	Total # of diabetic Medicare enrollees between age 65 and 75 having eye exam
patients_diabetic_medicare_enrollees_65_75_eye_exam_black	Total # of diabetic Black Medicare enrollees between age 65 and 75 having eye exam
patients_diabetic_medicare_enrollees_65_75_eye_exam_white	Total # of diabetic Non-Black Medicare enrollees between age 65 and 75 having eye exam
patients_diabetic_medicare_enrollees_65_75_lipid_test_total	Total # of diabetic Medicare enrollees between age 65 and 75 having lipids test
patients_diabetic_medicare_enrollees_65_75_lipid_test_black	Total # of diabetic Black Medicare enrollees between age 65 and 75 having lipids test
patients_diabetic_medicare_enrollees_65_75_lipid_test_white	Total # of diabetic Non-Black Medicare enrollees between age 65 and 75 having lipids test
number_of_females_enrolled_67_69_total
number_of_females_enrolled_67_69_black
number_of_females_enrolled_67_69_white
patients_females_67_69_having_mammogram_total
patients_females_67_69_having_mammogram_black
patients_females_67_69_having_mammogram_white
beneficiaries_part_a_eligible_total
beneficiaries_part_a_eligible_black
beneficiaries_part_a_eligible_white
leg_amputations_per_1000_enrollees_total
leg_amputations_per_1000_enrollees_black
leg_amputations_per_1000_enrollees_white
discharges_for_ambulatory_conditions_per_1000_total	Number of hospital discharges for ambulatory sensitive conditions per 1000 medicare enrollees, total
discharges_for_ambulatory_conditions_per_1000_black	Number of hospital discharges for ambulatory sensitive conditions per 1000 medicare enrollees, black
discharges_for_ambulatory_conditions_per_1000_white	Number of hospital discharges for ambulatory sensitive conditions per 1000 medicare enrollees, non-black

| beneficiaries_part_a_eligible_total || | medicare_beneficiaries_total ||

County Business Patterns (CBP)

column name	description
year	year
geo	location ID (Nation, State, County)
naics	industry ID
est	number of establishments
ap	annual payroll
ap_nf	annual payroll noise flag (see CBP methodology)
emp	number of employees
emp_nf	number of employees noise flag (see CBP methodology)

All variables by table

For a complete listing of all tables and their available variables, visit https://api.datausa.io/api/variables/