Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and gender. Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.' Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)   RRR: 3 digits represent race/ethnicity     All - Overall     AIA - American Indian and Alaska Native, non-Hispanic     API - Asian and Pacific Islander, non-Hispanic     BLK - Black, non-Hispanic     HIS - Hispanic     WHT - White, non-Hispanic   S: 1 digit represents sex/gender     A - All    F - Female     M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound.  Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and gender).At least one of the following 3 criteria: At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

2014 to 2016, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by gender and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke http://www.cdc.gov/dhdsp/maps/atlas

Analysis of ‘Stroke Mortality Data Among US Adults (35+) by State/Territory and County’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a555fdcf-e15b-4813-b232-e5ef863a272e on 12 February 2022.

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

2012 to 2014, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by gender and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke http://www.cdc.gov/dhdsp/maps/atlas

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

Analysis of ‘Stroke Mortality Data Among US Adults (35+) by State/Territory and County – 2016-2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b24a2fd1-cebe-4182-a772-aaf6bf10a136 on 28 January 2022.

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

2016 to 2018, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by gender and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke http://www.cdc.gov/dhdsp/maps/atlas

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

Background: While geographic disparities in stroke mortality are well documented, there are no data describing geographic variation in recurrent stroke. Accordingly, we evaluated geographic variations in 1-year recurrent ischemic stroke rates in the USA with adjustment for patient characteristics. Methods: One-year recurrent stroke rates for ischemic stroke (International Classification of Diseases, 9th Revision codes 433, 434 and 436) following hospital discharge were calculated by county for all fee-for-service Medicare beneficiaries from 2000 to 2002. The rates were standardized and smoothed using a bayesian conditional autoregressive model that was risk-standardized for patients’ age, gender, race/ethnicity, prior hospitalizations, Deyo comorbidity score, acute myocardial infarction, congestive heart failure, diabetes, hypertension, dementia, cancer, chronic obstructive pulmonary disease and obesity. Results: The overall 1-year recurrent stroke rate was 9.4% among 895,916 ischemic stroke patients (mean age: 78 years; 56.6% women; 86.6% White, 9.7% Black and 1.2% Latino/Hispanic). The rates varied by geographic region and were highest in the South and in parts of the West and Midwest. Regional variation was present for all racial/ethnic subgroups and persisted after adjustment for individual patient characteristics. Conclusions: Almost 1 in 10 hospitalized ischemic stroke patients was readmitted for an ischemic stroke within 1 year. There was heterogeneity in recurrence patterns by geographic region. Further work is needed to understand the reasons for this regional variability.

Analysis of ‘Stroke Mortality Data Among US Adults (35+) by State/Territory and County – 2017-2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/6e009bb0-ee1b-4243-a6dc-6c3986422e09 on 12 February 2022.

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

2017 to 2019, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by gender and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke http://www.cdc.gov/dhdsp/maps/atlas

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

Create maps of U.S. stroke hospitalization rates among Medicare fee-for-service beneficiaries aged 65 and older, by county. Data can be stratified by race/ethnicity and gender. Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceHospitalization data were obtained from the Centers for Medicare and Medicaid Services Medicare Provider Analysis and Review (MEDPAR) file, Part A and the Master Beneficiary Summary File (MBSF). International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes: 430-434, 436-438; principle (i.e., first-listed) diagnosis. Medicare fee-for-service beneficiaries 65 and older were included. Visit the Atlas of Heart Disease and Stroke Statistical Methods pages for more detailed Medicare data inclusion criteria.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.' Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., BLK_M_65UP) RRR: 3 digits represent race/ethnicity All - Overall BLK - Black, non-Hispanic HIS - Hispanic WHT - White, non-Hispanic S: 1 digit represents sex/gender A - All F - Female M - Male������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 1,000 black Medicare beneficiaries aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 1,000 Medicare beneficiaries. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and gender).At least one of the following 3 criteria: At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

2013 to 2015, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by gender and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke http://www.cdc.gov/dhdsp/maps/atlas

Aims To investigate and establish the optimal threshold of rCBF correlates with final infarct volume for Asian populations. Methods/Overaching design This is an observational non-interventional cohort study investigating the optimal ischemic threshold of rCBF comparing Australian and Indonesian ischemic stroke patients. It will be conducted at two main centres, 1) Geelong University Hospital, Australia and (2) National Brain Centre, Jakarta, Indonesia. We believe that the study design will adequately address the overarching aims of my PhD in the elucidation of an optimal relative rCBF threshold for Asian patients. The study in both centres will share the following protocol and will align to the following study design principles and data acquisition as follows: Patient data will be sourced from neuroimaging sequences, including computed tomography perfusion (CTP) rCBF initial ischemic core at presentation, Magnetic Resonance Imaging (MRI) Diffusion Weighted Imaging (DWI) within 72 hours to demonstrate the final infarct volume and CT angiography (CTA). Digital subtraction angiography (DSA) will be reviewed only for patients who proceed to endovascular clot retrieval. Ischemic core volumes will be modelled using different rCBF thresholds. These models provide the basis for comparing and matching the appropriate thresholds in both populations. Consent procedures and clinical waiver processes Jakarta: We will obtain patient consent from Jakarta for neuroimaging (CT perfusion, CT angiography (CTA) and MRI with diffusion weighted imaging (DWI). Either the Emergency Department doctors or neurologists on site will be responsible for obtaining consent from either the patient or from patient’s family. Geelong Neuroimaging (CT perfusion, CT angiography and MRI (with DWI) constitutes the current standard of care for all stroke patients. On this basis, consent is not required. Patient Participation - Inclusion and Exclusion Criteria Inclusion Criteria: 1. Ischemic Stroke up to 24 hours from ictal onset 2. Age >18 years old Exclusion Criteria: 1. intracerebral haemorrhage 2. Pregnant women 3. Asian population in Australia General data collection protocol The preliminary assessment protocol involves collection of patient specific data Standardised per site sourced from a sequence of clinical, observational and imaging Procedures. On arrival at the emergency department, general wards, and stroke unit patients are assessed by the Emergency Department doctors, neurological registrars or designated stroke neurologists: 1). Initial clinical assessment (for all patients including those who proceed to intravenous thrombolysis or endovascular thrombectomy) The following data will be collected in the Clinical Report Form (CRF) as baseline measurement: age, gender, time metrics, vascular risk factors, National Institute of Health Stroke Scale (NIHSS), Modified Rankin Scale (MRS), relevant medical history, current medications. 2). Stroke imaging sequence protocol The following imaging sequences will be performed for all study patients: non-contrast computed tomography (NCCT), CT angiogram, CT perfusion, MRI and digital subtraction angiography (only for those patients who proceed to ECR). NCCT NCCT is critical in its use in exclusion of intracerebral haemorrhage. It is less sensitive but remains useful in the detection of early ischemic changes which include obscuration of grey-white matter and basal ganglia, cortical sulcal effacement and focal parenchymal hypo attenuation[67]. CT angiogram CT angiogram provides information regarding the presence and location of large vessel occlusion. This allows for classification of patients into different stroke territories. In addition, intracranial atherosclerotic disease (ICAD) will be detected[81]. CT perfusion CT perfusion provides information regarding tissue at risk (represented by T Max +6) and predicted ischemic core (represented by rCBF)[75]. Data collected from CT perfusion therefore contributes to the basis of this PhD the characteristics and processing of which will be elaborated in the next section on methodology. MRI Diffusion Weighted Imaging (DWI) MRI (utilizing diffusion weighted imaging sequences, DWI) provides an internationally confirmed standard for quantification of the ischemic core at 24-72 hours[64, 72]. Digital subtraction angiography (DSA) DSA provides data on the degree of reperfusion after endovascular clot retrieval (ECR)/thrombectomy. Follow up clinical assessment 1. mRS and NIHSS at discharge will be collected to assess the improvement. 2. mRS at day 90 will be collected by phone call or during clinical visit. Our preliminary participant inclusion and assessment protocol involves collection of patient specific data; standardised per site sourced from a sequence of clinical, observational and imaging procedures The imaging protocol and associated imaging and data analytical sequence is as follows: Initially a non-contrast-enhanced head CT and/or a CT angiogram will be combined with a Perfusion CT scan. CT perfusion scanning parameters and data acquisition General principles Whole-brain perfusion CT is routinely accomplished using CT systems with a purpose designed data acquisition and wide detector array 8-16 cm system (DAS). Time-resolved scans are used to track the flow of iodinated contrast media through the brain with multiple images (20-40) acquired over the same region of interest (ROI) of target anatomy. Patients are required to remain still during the examination in order to avoid motion misregistration. The examination couch may remain stationary during the entire examination or move back and forth to enable acquisition of date in the required imaging planes dictated by underlying engineering principles. Acquisitions are repeated at specified time intervals (e.g. every second to every 2-3 seconds) for a predetermined duration (e.g. 40-90 seconds). Thick image sections are acquired to minimize image noise and optimize the useful signal to noise (SRN) ratio (section widths are generally set at 5-10 mm). Data are used to generate colour maps of hemodynamic significance, for example cerebral blood volume (CBV) and cerebral blood flow (CBF), mean transit time (MTT) and time to peak (TPP). Dose Management 80 kVp is an international standard that is recognised to increase iodine signal brightness and maintain low radiation dosage per single scan (i.e. one tube rotation). The time interval between scans, and hence the total number of scans over the examination duration, is set carefully, taking into an account the requirements of the analysis algorithm. Dose (tube current) modulation is not generally used, as it interferes with the calculation of the CBV and CBF parameters. Our imaging protocols adopted in Geelong and Jakarta have been designed to align with international standards and guidelines consequently there is technical and clinical standardisation of the imaging techniques performed in both centres. While it is acknowledged that there are differences in the technical design and specifications of commercial imaging systems. We have ensured that our data standardisation and management processes align with (DICOM) standards consequently our pre and post processing of DICOM data in the (RAPID) software is considered compatible between both sites providing the required levels of accuracy and compatibility for comparative analysis. Data acquisition technical specifications per site: Jakarta specific CT perfusion protocol CTP (Model: Phillips ICT 256) 1. Total volume contrast of injection 40 mL Iodine based contrast medium is given with injection rate of bolus at 6mls/sec. 2. The injector and the scanner are started at the same time. 3. Images are acquired every 1.5 seconds for 70 seconds, thus obtaining 35 sets of data. 4. Perfusion coverage is 8cm. 5. Images will be constructed which are 5 mm thick. 6. Total of 640 images will be produced. 7. We use 80kVp and 100mAs for the perfusion. 8. CTP series will be auto sent (by series) to RAPID for post-processing Geelong specific CT perfusion protocol CT Perfusion (Model: Philips ICT Scanner 256) 1. Total volume of 50 mL of Iodine based contrast medium is given as an injection bolus at a rate of 6mL/sec. 2. The injector and the scanner are started at the same time. 3. Images are acquired every 2 seconds for 70 seconds, thus obtaining 35 sets of data. 4. Perfusion coverage is 8cm. 5. Images are 10mm thick and produce 8 images per acquisition. 6. Total of 280 images will be produced. 7. We use 80kVp and 80mAs for the perfusion. 8. CTP series will be auto sent (by series) to RAPID for processing CT Carotid Angiography CTA: (Model: Phillips ICT 256) Jakarta’s Protocol 1. A total volume of 65-75ml of Iodine based contrast is given as an injection bolus at a rate of 5mls/sec. 2. Bolus tracking is used to start the scan. 3. A single image will be taken through the aortic arch and place a ROI in the descending aorta. 4. Wait for 15 seconds after the injection and then take monitoring scans through the arch. When the contrast reaches a predetermined threshold level of 150HU it indicates the beginning of the scan. 5. Generate a thin data set with slice thickness of 0.9 mm. 6. Multiplanar (MPR) images will be created in several planes that are 4mm thickness. 7. 120kVp will be used for angiography. 8. Radiation dose: 31.9 mGy(DLP:801.2 mGycm) Geelong’s Protocol (Model: Philips ICT Scanner 256) 1. A total volume of 65-75ml of Iodine based contrast is given as an injection bolus at a rate of 5mls/sec. 2. Bolus tracking is used to start the scan. 3. A single image will be taken through the aortic arch and place a ROI in the descending aorta. 4. Wait 15 seconds after the injection and then take monitoring scans through the arch. When the contrast reaches a predetermined threshold level of 150HU it indicates the beginning of the scan. 5. Generate a thin data set with slice thickness of 0.8mm. 6.

2017 to 2019, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by gender and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke http://www.cdc.gov/dhdsp/maps/atlas

Model 1; adjusted for demographics (i.e. age, gender, race-ethnicity, education).Model 2; adjusted for demographics & medical risk factors (i.e. age, sex, race-ethnicity, education, waist circumference, physical activity, moderate alcohol consumption, smoker, diabetes mellitus, systolic blood pressure, coronary artery disease, LDL, HDL).

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:

Acute Myocardial Infarction (AMI)
Asthma
Bladder Cancer
Brain Cancer
Coronary Heart Disease (CHD)
Colorectal Cancer
Chronic Obstructive Pulmonary Disease (COPD)/Chronic Lower Respiratory Diseases
Diabetes
Female Breast Cancer
Female Reproductive Cancer
Heart Failure
Hyperlipidemia (High Blood Cholesterol)
Kidney Cancer
Leukemia
Liver Cancer Lung Cancer Lupus and Connective Tissue Disorders
Melanoma of the Skin
Non-Hodgkin's Lymphoma
Non-melanoma Skin Cancer
Overall Cancer
Overall Heart Disease
Overall Hypertensive Diseases
Pancreatic Cancer
Prostate Cancer Stroke
Thyroid Cancer

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality including those of Non-Communicable conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.