US state chronic-disease disparity signals (CDC CDI burden & equity tracker)

Name: US state chronic-disease disparity signals (CDC CDI burden & equity tracker)
Creator: US State Chronic-Disease Disparity Signals (derived)
License: https://www.cdc.gov/
Keywords: chronic-disease, diabetes, cardiovascular, copd, mental-health, health-equity, disparities, us-states, cdc, momentum, anomaly-detection, forecasting, signals

State-level chronic-disease signals derived from the CDC Chronic Disease Indicators: prevalence, mortality and hospitalization trends for diabetes, cardiovascular disease, COPD and mental health across 55 US states/territories (2019-2023), with year-on-year changes, momentum, 3-sigma anomaly flags vs a trailing 3-year baseline, naive-drift 1-year forecasts, cross-state momentum ranks, and Black-White and female-male disparity gaps. The monetizable health-equity signals layer on top of raw CDC chronic-disease statistics. Raw series: CDC Chronic Disease Indicators (Socrata).

Source: US State Chronic-Disease Disparity Signals (derived)4,680 lignesMis à jour: 22/09/2026

chronic-diseasediabetescardiovascularcopdmental-healthhealth-equitydisparitiesus-statescdcmomentumanomaly-detectionforecastingsignals

Qualité

94.5

Attribution

Centers for Disease Control and Prevention, Chronic Disease Indicators; derived signals by Frontier Data Hub

Schéma

Colonne	Type	Description
date	string	Reference year of the CDC Chronic Disease Indicator observation (yearstart; annual).
country	string
country_code	string
series_id	string	Connector series key: CDC CDI topic code \| state/territory abbreviation \| question code (e.g. DIA\|CA\|Q01).
series_label	string	State/territory name plus the CDC CDI question text, e.g. 'California — Diabetes among adults'.
value	float	Indicator value as published by CDC CDI (datavalue): prevalence in %, or rate per 100,000, depending on the question; see value_type for which form.
value_type	string	CDC CDI datavaluetype for this row (age-adjusted forms preferred for cross-state comparability).
yoy_change_pp	float
yoy_change_pct	float
volatility_30d	float
momentum_3m	float
anomaly_flag	integer
forecast_1m	float
rank	integer
race_gap_black_white_pp	float
sex_gap_female_male_pp	float

Exemple de lignes

date	country	country_code	series_id	series_label	value	value_type	yoy_change_pp	yoy_change_pct	volatility_30d	momentum_3m	forecast_1m	rank	race_gap_black_white_pp	sex_gap_female_male_pp
2019-01-01	United States	USA	COPD\|AK\|Q03	Alaska — Chronic obstructive pulmonary disease among adults	4.6	Age-adjusted Prevalence	—	—	—	—	—	—	—	-0.39999999999999947
2019-01-01	United States	USA	COPD\|AK\|Q04	Alaska — Chronic obstructive pulmonary disease mortality among adults aged 45 years and older, underlying cause	81.7	Age-adjusted Rate	—	—	—	—	—	—	—	24.799999999999997
2019-01-01	United States	USA	COPD\|AK\|Q05	Alaska — Chronic obstructive pulmonary disease mortality among adults aged 45 years and older, underlying or contributing cause	173.7	Age-adjusted Rate	—	—	—	—	—	—	—	-11.099999999999994
2019-01-01	United States	USA	COPD\|AK\|Q08	Alaska — Current smoking among adults with chronic obstructive pulmonary disease	54.4	Age-adjusted Prevalence	—	—	—	—	—	—	—	7.299999999999997
2019-01-01	United States	USA	COPD\|AK\|Q18	Alaska — Hospitalization for chronic obstructive pulmonary disease as any diagnosis, Medicare-beneficiaries aged 65 years and older	42.77	Age-adjusted Rate	—	—	—	—	—	—	4.279999999999994	2.4399999999999977

Télécharger un échantillon

Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

Télécharger l'exemple CSV Télécharger l'exemple JSON

Utiliser avec un LLM

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/cdc_chronic_signals/us_state_chronic_disease_disparity_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/cdc_chronic_signals/us_state_chronic_disease_disparity_signals").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API: https://datazimuts.com/v1/datasets/cdc_chronic_signals/us_state_chronic_disease_disparity_signals

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.