Characterizing the Temporal Regularities of Crime

There are a lot of studies that show that criminal activities exhibit certain temporal and spatial regularities. However, they often focus on either specific cities or types of crime and cannot clearly explain the patterns for the crime. What are the temporal patterns at the microlevel spatial scale? How general? Understanding the regularities of urban crime is important because it can help us improve the economy and safety of the cities and maintain harmony. This study analyzes the theft and burglary crime data from five cities in the United States. We successfully find the spatiotemporal patterns of two types of crime in different time series across cities.

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

<b>Purpose. </b>A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. <p><b>Methods. </b>We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (<i> n </i>= 1,136,301 patients) was identified using a rule-based phenotype method. A multi-step procedure was then used to identify type 2 diabetes–related symptoms based on <i>International Classification of Diseases</i>,<i> </i>9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. </p> <p><b>Results.</b> Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies.</p> <p><b>Conclusion.</b> To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified. </p>

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

OBJECTIVE A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. Methods We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes–related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Results Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. Conclusion To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

<b>Purpose. </b>A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. <p><b>Methods. </b>We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (<i> n </i>= 1,136,301 patients) was identified using a rule-based phenotype method. A multi-step procedure was then used to identify type 2 diabetes–related symptoms based on <i>International Classification of Diseases</i>,<i> </i>9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. </p> <p><b>Results.</b> Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies.</p> <p><b>Conclusion.</b> To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified. </p>