{"data":{"id":"10.4121/4598c213-94cb-4227-8c0c-131732629c79","type":"dois","attributes":{"doi":"10.4121/4598c213-94cb-4227-8c0c-131732629c79","prefix":"10.4121","suffix":"4598c213-94cb-4227-8c0c-131732629c79","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Van Thieu","nameType":"Personal","givenName":"Van","familyName":"Thieu","nameIdentifiers":[{"nameIdentifier":"0009-0003-9637-0195","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Diabetes Risk Indicators Event Logs (NHIS 2019-2024)"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Health and Support Services","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"9202"},{"subject":"Specific Population Health","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"9205"},{"subject":"Information Systems","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0806"},{"subject":"Public Health (excl. Specific Population Health)","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"9204"},{"subject":"Health","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"92"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"process mining"},{"subject":"event log"},{"subject":"XES"},{"subject":"NHIS"},{"subject":"diabetes"},{"subject":"public health"},{"subject":"health survey"},{"subject":"chronic disease"},{"subject":"inductive miner"},{"subject":"pm4py"},{"subject":"Time: 2019/2024"}],"contributors":[{"name":"University of Information Technology VNU-HCM, Ho Chi Minh City, Vietnam","nameType":"Organizational","contributorType":"Other","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-07-27","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"References","relatedIdentifier":"https://github.com/thieuanhvan/nhis-diabetes","relatedIdentifierType":"URL"},{"relationType":"References","relatedIdentifier":"https://github.com/thieuanhvan/pm-nhis-discovery","relatedIdentifierType":"URL"},{"relationType":"References","relatedIdentifier":"https://www.cdc.gov/nchs/nhis/2019nhis.htm","relatedIdentifierType":"URL"},{"relationType":"References","relatedIdentifier":"https://www.cdc.gov/nchs/nhis/2020nhis.htm","relatedIdentifierType":"URL"},{"relationType":"References","relatedIdentifier":"https://www.cdc.gov/nchs/nhis/2021nhis.htm","relatedIdentifierType":"URL"},{"relationType":"References","relatedIdentifier":"https://www.cdc.gov/nchs/nhis/2022nhis.htm","relatedIdentifierType":"URL"},{"relationType":"References","relatedIdentifier":"https://www.cdc.gov/nchs/nhis/2023nhis.htm","relatedIdentifierType":"URL"},{"relationType":"References","relatedIdentifier":"https://www.cdc.gov/nchs/nhis/2024nhis.htm","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/4598c213-94cb-4227-8c0c-131732629c79.v1","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/4598c213-94cb-4227-8c0c-131732629c79.v2","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["application/gzip, text/markdown, application/x-yaml"],"version":"2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Diabetes Risk Indicators Event Logs (NHIS 2019-2024)\u003cbr\u003eXES event logs derived from the U.S. CDC National Health Interview Survey (NHIS) Sample Adult public-use files, packaged for process mining research.\u003cbr\u003eSix event logs (one per survey year, 2019-2024) totalling 154,465 traces and approximately 2.32 million events. Each trace represents one respondent; each event represents a discretized health indicator measurement placed in a clinically-grounded layered order.\u003cbr\u003eFILES\u003cbr\u003eOne XES event log per survey year (year: traces, events, diabetic prevalence, uncompressed size):- cdc_nhis_2019_eventlog.xes - 2019: 27,115 traces, 406,725 events, 8.88% diabetic, 103 MB- cdc_nhis_2020_eventlog.xes - 2020: 26,677 traces, 400,155 events, 8.78% diabetic, 101 MB- cdc_nhis_2021_eventlog.xes - 2021: 25,024 traces, 375,360 events, 8.80% diabetic, 95 MB- cdc_nhis_2022_eventlog.xes - 2022: 23,246 traces, 348,690 events, 8.83% diabetic, 88 MB- cdc_nhis_2023_eventlog.xes - 2023: 24,882 traces, 373,230 events, 9.19% diabetic, 94 MB- cdc_nhis_2024_eventlog.xes - 2024: 27,521 traces, 412,815 events, 9.29% diabetic, 104 MB\u003cbr\u003eTotal: 154,465 traces, 2,316,975 events. Files are uploaded gzip-compressed (.xes.gz); pm4py and ProM auto-detect gzip on read.\u003cbr\u003eSCHEMA\u003cbr\u003eEach trace contains the same 15 events in a fixed clinical order:- Stage 1 (Demographics): AgeGroup, Sex, Income, Education- Stage 2 (Anthropometric): BMI- Stage 3 (Physiological): HighBP, HighChol, CholCheck- Stage 4 (Lifestyle): Smoker- Stage 5 (Self-rated): GenHlth, MentHlth- Stage 6 (Functional/HC): DiffWalk, AnyHealthcare- Stage 7 (Outcomes): Stroke, HeartDiseaseorAttack\u003cbr\u003eThe schema is identical across all six years, so cross-year analyses are directly supported.\u003cbr\u003eTrace attributes:- concept:name (string): case ID (respondent index within the year)- diabetes_label (\"0\" / \"1\"): whether the respondent reported a diabetes diagnosis- nhis_year (string): survey year\u003cbr\u003eEvent attributes:- concept:name (string): activity label (e.g. Normal_BMI, High_BP)- org:resource (string): variable name (e.g. BMI, HighBP)- time:timestamp (datetime): synthetic hourly timestamp (see note below)- lifecycle:transition (string): always \"complete\"\u003cbr\u003eActivity vocabulary (37 unique activities), by variable:- AgeGroup: Age=YoungAge(18-39), Age=MiddleAge(40-64), Age=SeniorAge(65+)- Sex: Female, Male- Income: Low_Income, Mid_Income, High_Income- Education: Low_Edu, Mid_Edu, High_Edu- BMI: Underweight, Normal_BMI, Overweight, Obese- HighBP: Normal_BP, High_BP- HighChol: Normal_Chol, High_Chol- CholCheck: No_CholCheck, Has_CholCheck- Smoker: Non_Smoker, Smoker- GenHlth: Good_Health, Fair_Health, Poor_Health- MentHlth: Good_MentHlth, Moderate_MentHlth, Poor_MentHlth- DiffWalk: No_DiffWalk, Has_DiffWalk- AnyHealthcare: No_Healthcare, Has_Healthcare- Stroke: No_Stroke, Has_Stroke- HeartDiseaseorAttack: No_HeartDisease, Has_HeartDisease\u003cbr\u003eSYNTHETIC TIMESTAMPS\u003cbr\u003eNHIS is a cross-sectional household survey: all 15 events for a single respondent share the same survey date in reality. The hourly timestamps in the XES files exist only to satisfy the XES standard's temporal axis requirement and to encode the layered ordering. They have no real-world interpretation. Do not use these timestamps for sojourn time, throughput time, or other time-based analyses.\u003cbr\u003eDISCRETIZATION NOTES\u003cbr\u003e- BMI uses the NHIS pre-computed BMICAT_A 4-level category (Underweight / Normal / Overweight / Obese) corresponding to WHO cutoffs at 18.5, 25.0, and 30.0.- AgeGroup uses NHIS AGEP_A (raw age in years) recoded to the BRFSS _AGEG5YR 13-level convention, then binned to three life stages (18-39, 40-64, 65+).- MentHlth is an approximation: NHIS provides a 5-level frequency (DEPFREQ_A / ANXFREQ_A) which is mapped to estimated days/30 ({0, 1, 4, 30}) and then binned to Good (0) / Moderate (1, 4) / Poor (30). This differs from BRFSS, which provides continuous days 0-30 directly.- Income uses NHIS 5-level coding (INCGRP_A / RATCAT_A) with bins tightened from BRFSS defaults to give a roughly 25/35/40 Low/Mid/High split.- AnyHealthcare is derived from NOTCOV_A with inverted polarity (NHIS records lack of coverage; this dataset records presence of coverage).\u003cbr\u003eYEAR COVERAGE AND DESIGN NOTES\u003cbr\u003e- Why six years (2019-2024)? This range covers pre-COVID (2019), COVID years (2020-2021), and post-COVID (2022-2024), enabling cross-period cohort comparisons.- Why is PhysActivity not included? NHIS only fields physical activity questions in 2020/2022/2024. To keep the activity vocabulary identical across all six years (a requirement for clean cross-year process mining), PhysActivity is excluded from the common schema.- Why isn't there a PhysHlth variable? The NHIS Sample Adult file does not include a variable comparable to BRFSS PHYSHLTH (\"days physical health not good in past 30\") across 2019-2024. Rather than fabricate one from injury-specific limitation variables, the column was dropped.- Why isn't there a NoDocbcCost variable? No analog is fielded consistently across NHIS Sample Adult 2019-2024.\u003cbr\u003eSOURCE DATA\u003cbr\u003eGenerated from publicly-available NHIS Sample Adult public-use files:- 2019: https://www.cdc.gov/nchs/nhis/2019nhis.htm- 2020: https://www.cdc.gov/nchs/nhis/2020nhis.htm- 2021: https://www.cdc.gov/nchs/nhis/2021nhis.htm- 2022: https://www.cdc.gov/nchs/nhis/2022nhis.htm- 2023: https://www.cdc.gov/nchs/nhis/2023nhis.htm- 2024: https://www.cdc.gov/nchs/nhis/2024nhis.htm\u003cbr\u003eThe raw XPT/CSV files are redistributed by the source under U.S. Government public-domain status.\u003cbr\u003eLISTWISE DELETION\u003cbr\u003eRows containing missing values in any of the 16 required columns (15 event columns plus Diabetes_binary) were dropped before event log construction. The trace counts above reflect post-deletion counts.\u003cbr\u003eFORMAT\u003cbr\u003eXES 1.0 (IEEE 1849-2016), the standard interchange format for event logs in process mining. Files can be loaded with any XES-compliant tool, including pm4py, ProM, Disco, and Apromore.\u003cbr\u003e from pm4py.objects.log.importer.xes import importer as xes_importer log = xes_importer.apply(\"cdc_nhis_2023_eventlog.xes\")\u003cbr\u003eLICENSE\u003cbr\u003eThis dataset is released under the Creative Commons Attribution 4.0 International licence (CC BY 4.0). The underlying NHIS Sample Adult files are U.S. Government public-use data (public domain in the United States); CC BY 4.0 applies to this derived event-log compilation and asks that reuse cite the dataset and its associated publication.\u003cbr\u003eCITATION\u003cbr\u003eA citation file (CITATION.cff) accompanies this release.\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[{"geoLocationPlace":"United States","geoLocationPoint":{"pointLatitude":39.8283,"pointLongitude":-98.5795}}],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.4121/4598C213-94CB-4227-8C0C-131732629C79</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Van Thieu</creatorName>
      <givenName>Van</givenName>
      <familyName>Thieu</familyName>
      <nameIdentifier nameIdentifierScheme="https://orcid.org/">0009-0003-9637-0195</nameIdentifier>
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  </creators>
  <titles>
    <title>Diabetes Risk Indicators Event Logs (NHIS 2019-2024)</title>
  </titles>
  <publisher>4TU.ResearchData</publisher>
  <publicationYear>2026</publicationYear>
  <resourceType resourceTypeGeneral="Dataset">Dataset</resourceType>
  <subjects>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="9202">Health and Support Services</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="9205">Specific Population Health</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="0806">Information Systems</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="9204">Public Health (excl. Specific Population Health)</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="92">Health</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="08">Information and Computing Sciences</subject>
    <subject>process mining</subject>
    <subject>event log</subject>
    <subject>XES</subject>
    <subject>NHIS</subject>
    <subject>diabetes</subject>
    <subject>public health</subject>
    <subject>health survey</subject>
    <subject>chronic disease</subject>
    <subject>inductive miner</subject>
    <subject>pm4py</subject>
    <subject>Time: 2019/2024</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Other">
      <contributorName nameType="Organizational">University of Information Technology VNU-HCM, Ho Chi Minh City, Vietnam</contributorName>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2026-07-27</date>
  </dates>
  <language>en</language>
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    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://github.com/thieuanhvan/nhis-diabetes</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://github.com/thieuanhvan/pm-nhis-discovery</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://www.cdc.gov/nchs/nhis/2019nhis.htm</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://www.cdc.gov/nchs/nhis/2020nhis.htm</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://www.cdc.gov/nchs/nhis/2021nhis.htm</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://www.cdc.gov/nchs/nhis/2022nhis.htm</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://www.cdc.gov/nchs/nhis/2023nhis.htm</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References">https://www.cdc.gov/nchs/nhis/2024nhis.htm</relatedIdentifier>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Diabetes Risk Indicators Event Logs (NHIS 2019-2024)&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;XES event logs derived from the U.S. CDC National Health Interview Survey (NHIS) Sample Adult public-use files, packaged for process mining research.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Six event logs (one per survey year, 2019-2024) totalling 154,465 traces and approximately 2.32 million events. Each trace represents one respondent; each event represents a discretized health indicator measurement placed in a clinically-grounded layered order.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;FILES&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;One XES event log per survey year (year: traces, events, diabetic prevalence, uncompressed size):&lt;/p&gt;&lt;p&gt;- cdc_nhis_2019_eventlog.xes - 2019: 27,115 traces, 406,725 events, 8.88% diabetic, 103 MB&lt;/p&gt;&lt;p&gt;- cdc_nhis_2020_eventlog.xes - 2020: 26,677 traces, 400,155 events, 8.78% diabetic, 101 MB&lt;/p&gt;&lt;p&gt;- cdc_nhis_2021_eventlog.xes - 2021: 25,024 traces, 375,360 events, 8.80% diabetic, 95 MB&lt;/p&gt;&lt;p&gt;- cdc_nhis_2022_eventlog.xes - 2022: 23,246 traces, 348,690 events, 8.83% diabetic, 88 MB&lt;/p&gt;&lt;p&gt;- cdc_nhis_2023_eventlog.xes - 2023: 24,882 traces, 373,230 events, 9.19% diabetic, 94 MB&lt;/p&gt;&lt;p&gt;- cdc_nhis_2024_eventlog.xes - 2024: 27,521 traces, 412,815 events, 9.29% diabetic, 104 MB&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Total: 154,465 traces, 2,316,975 events. Files are uploaded gzip-compressed (.xes.gz); pm4py and ProM auto-detect gzip on read.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;SCHEMA&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Each trace contains the same 15 events in a fixed clinical order:&lt;/p&gt;&lt;p&gt;- Stage 1 (Demographics):  AgeGroup, Sex, Income, Education&lt;/p&gt;&lt;p&gt;- Stage 2 (Anthropometric): BMI&lt;/p&gt;&lt;p&gt;- Stage 3 (Physiological): HighBP, HighChol, CholCheck&lt;/p&gt;&lt;p&gt;- Stage 4 (Lifestyle):   Smoker&lt;/p&gt;&lt;p&gt;- Stage 5 (Self-rated):   GenHlth, MentHlth&lt;/p&gt;&lt;p&gt;- Stage 6 (Functional/HC): DiffWalk, AnyHealthcare&lt;/p&gt;&lt;p&gt;- Stage 7 (Outcomes):    Stroke, HeartDiseaseorAttack&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The schema is identical across all six years, so cross-year analyses are directly supported.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Trace attributes:&lt;/p&gt;&lt;p&gt;- concept:name (string): case ID (respondent index within the year)&lt;/p&gt;&lt;p&gt;- diabetes_label ("0" / "1"): whether the respondent reported a diabetes diagnosis&lt;/p&gt;&lt;p&gt;- nhis_year (string): survey year&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Event attributes:&lt;/p&gt;&lt;p&gt;- concept:name (string): activity label (e.g. Normal_BMI, High_BP)&lt;/p&gt;&lt;p&gt;- org:resource (string): variable name (e.g. BMI, HighBP)&lt;/p&gt;&lt;p&gt;- time:timestamp (datetime): synthetic hourly timestamp (see note below)&lt;/p&gt;&lt;p&gt;- lifecycle:transition (string): always "complete"&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Activity vocabulary (37 unique activities), by variable:&lt;/p&gt;&lt;p&gt;- AgeGroup: Age=YoungAge(18-39), Age=MiddleAge(40-64), Age=SeniorAge(65+)&lt;/p&gt;&lt;p&gt;- Sex: Female, Male&lt;/p&gt;&lt;p&gt;- Income: Low_Income, Mid_Income, High_Income&lt;/p&gt;&lt;p&gt;- Education: Low_Edu, Mid_Edu, High_Edu&lt;/p&gt;&lt;p&gt;- BMI: Underweight, Normal_BMI, Overweight, Obese&lt;/p&gt;&lt;p&gt;- HighBP: Normal_BP, High_BP&lt;/p&gt;&lt;p&gt;- HighChol: Normal_Chol, High_Chol&lt;/p&gt;&lt;p&gt;- CholCheck: No_CholCheck, Has_CholCheck&lt;/p&gt;&lt;p&gt;- Smoker: Non_Smoker, Smoker&lt;/p&gt;&lt;p&gt;- GenHlth: Good_Health, Fair_Health, Poor_Health&lt;/p&gt;&lt;p&gt;- MentHlth: Good_MentHlth, Moderate_MentHlth, Poor_MentHlth&lt;/p&gt;&lt;p&gt;- DiffWalk: No_DiffWalk, Has_DiffWalk&lt;/p&gt;&lt;p&gt;- AnyHealthcare: No_Healthcare, Has_Healthcare&lt;/p&gt;&lt;p&gt;- Stroke: No_Stroke, Has_Stroke&lt;/p&gt;&lt;p&gt;- HeartDiseaseorAttack: No_HeartDisease, Has_HeartDisease&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;SYNTHETIC TIMESTAMPS&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;NHIS is a cross-sectional household survey: all 15 events for a single respondent share the same survey date in reality. The hourly timestamps in the XES files exist only to satisfy the XES standard's temporal axis requirement and to encode the layered ordering. They have no real-world interpretation. Do not use these timestamps for sojourn time, throughput time, or other time-based analyses.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;DISCRETIZATION NOTES&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;- BMI uses the NHIS pre-computed BMICAT_A 4-level category (Underweight / Normal / Overweight / Obese) corresponding to WHO cutoffs at 18.5, 25.0, and 30.0.&lt;/p&gt;&lt;p&gt;- AgeGroup uses NHIS AGEP_A (raw age in years) recoded to the BRFSS _AGEG5YR 13-level convention, then binned to three life stages (18-39, 40-64, 65+).&lt;/p&gt;&lt;p&gt;- MentHlth is an approximation: NHIS provides a 5-level frequency (DEPFREQ_A / ANXFREQ_A) which is mapped to estimated days/30 ({0, 1, 4, 30}) and then binned to Good (0) / Moderate (1, 4) / Poor (30). This differs from BRFSS, which provides continuous days 0-30 directly.&lt;/p&gt;&lt;p&gt;- Income uses NHIS 5-level coding (INCGRP_A / RATCAT_A) with bins tightened from BRFSS defaults to give a roughly 25/35/40 Low/Mid/High split.&lt;/p&gt;&lt;p&gt;- AnyHealthcare is derived from NOTCOV_A with inverted polarity (NHIS records lack of coverage; this dataset records presence of coverage).&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;YEAR COVERAGE AND DESIGN NOTES&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;- Why six years (2019-2024)? This range covers pre-COVID (2019), COVID years (2020-2021), and post-COVID (2022-2024), enabling cross-period cohort comparisons.&lt;/p&gt;&lt;p&gt;- Why is PhysActivity not included? NHIS only fields physical activity questions in 2020/2022/2024. To keep the activity vocabulary identical across all six years (a requirement for clean cross-year process mining), PhysActivity is excluded from the common schema.&lt;/p&gt;&lt;p&gt;- Why isn't there a PhysHlth variable? The NHIS Sample Adult file does not include a variable comparable to BRFSS PHYSHLTH ("days physical health not good in past 30") across 2019-2024. Rather than fabricate one from injury-specific limitation variables, the column was dropped.&lt;/p&gt;&lt;p&gt;- Why isn't there a NoDocbcCost variable? No analog is fielded consistently across NHIS Sample Adult 2019-2024.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;SOURCE DATA&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Generated from publicly-available NHIS Sample Adult public-use files:&lt;/p&gt;&lt;p&gt;- 2019: https://www.cdc.gov/nchs/nhis/2019nhis.htm&lt;/p&gt;&lt;p&gt;- 2020: https://www.cdc.gov/nchs/nhis/2020nhis.htm&lt;/p&gt;&lt;p&gt;- 2021: https://www.cdc.gov/nchs/nhis/2021nhis.htm&lt;/p&gt;&lt;p&gt;- 2022: https://www.cdc.gov/nchs/nhis/2022nhis.htm&lt;/p&gt;&lt;p&gt;- 2023: https://www.cdc.gov/nchs/nhis/2023nhis.htm&lt;/p&gt;&lt;p&gt;- 2024: https://www.cdc.gov/nchs/nhis/2024nhis.htm&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The raw XPT/CSV files are redistributed by the source under U.S. Government public-domain status.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;LISTWISE DELETION&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Rows containing missing values in any of the 16 required columns (15 event columns plus Diabetes_binary) were dropped before event log construction. The trace counts above reflect post-deletion counts.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;FORMAT&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;XES 1.0 (IEEE 1849-2016), the standard interchange format for event logs in process mining. Files can be loaded with any XES-compliant tool, including pm4py, ProM, Disco, and Apromore.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt; from pm4py.objects.log.importer.xes import importer as xes_importer&lt;/p&gt;&lt;p&gt; log = xes_importer.apply("cdc_nhis_2023_eventlog.xes")&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;LICENSE&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;This dataset is released under the Creative Commons Attribution 4.0 International licence (CC BY 4.0). The underlying NHIS Sample Adult files are U.S. Government public-use data (public domain in the United States); CC BY 4.0 applies to this derived event-log compilation and asks that reuse cite the dataset and its associated publication.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;CITATION&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;A citation file (CITATION.cff) accompanies this release.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;</description>
  </descriptions>
  <geoLocations>
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      <geoLocationPlace>United States</geoLocationPlace>
      <geoLocationPoint>
        <pointLongitude>-98.57950</pointLongitude>
        <pointLatitude>39.82830</pointLatitude>
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