Method Of Case Identification Research Articles

The Prevalence of Sickle Cell Disease in Colorado and Methodologies of the Colorado Sickle Cell Data Collection Program: Public Health Surveillance Study.

Sickle cell disease (SCD) is a genetic blood disorder that affects approximately 100,000 individuals in the United States, with the highest prevalence among Black or African American populations. While advances in care have improved survival, comprehensive state-level data on the prevalence of SCD remain limited, which hampers efforts to optimize health care services. To address this gap, the Colorado Sickle Cell Data Collection (CO-SCDC) program was established in 2021 as part of the Centers for Disease Control and Prevention's initiative to enhance surveillance and public health efforts for SCD. The objectives of this study were to describe the establishment of the CO-SCDC program and to provide updated estimates of the prevalence and birth prevalence of SCD in Colorado, including geographic dispersion. Additional objectives include evaluating the accuracy of case identification methods and leveraging surveillance activities to inform public health initiatives. Data were collected from Health Data Compass (a multi-institutional data warehouse) containing electronic health records from the University of Colorado Health and Children's Hospital Colorado for the years 2012-2020. Colorado newborn screening program data were included for confirmed SCD diagnoses from 2001 to 2020. Records were linked using the Colorado University Record Linkage tool and deidentified for analysis. Case definitions, adapted from the Centers for Disease Control and Prevention's Registry and Surveillance System for Hemoglobinopathies project, classified cases as possible, probable, or definite SCD. Clinical validation by hematologists was performed to ensure accuracy, and prevalence rates were calculated using 2020 US Census population estimates. In 2019, 435 individuals were identified as living with SCD in Colorado, an increase of 16%-40% over previous estimates, with the majority (n=349, 80.2%) identifying as Black or African American. The median age of individuals was 19 years. The prevalence of SCD was highest in urban counties, with concentrations in Arapahoe, Denver, and El Paso counties. Birth prevalence of SCD increased from 11.9 per 100,000 live births between 2010 and 2014 to 20.1 per 100,000 live births between 2015 and 2019 with 58.5% (n=38) of cases being hemoglobin (Hb) SS or HbSβ0 thalassemia subtypes. The study highlighted a 67% (n=26) increase in SCD births over the decade, correlating with the growth of the Black or African American population in the state. The CO-SCDC program successfully established the capacity to perform SCD surveillance and, in doing so, identified baseline prevalence estimates for SCD in Colorado. The findings highlight geographic dispersion across Colorado counties, highlighting the need for equitable access to specialty care, particularly for rural populations. The combination of automated data linkage and clinical validation improved case identification accuracy. Future efforts will expand surveillance to include claims data to better capture health care use and address potential underreporting. These results will guide public health interventions aimed at improving care for individuals with SCD in Colorado.

Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records.

ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases. We examined the Mass General Brigham (MGB) repository of clinical documentation from 12/1/1979 to 5/11/2021, using expert-curated keywords and ICD codes to identify a large cohort of potential AAV cases. Three labeled datasets (I, II, III) were created, each containing note sections. We trained and evaluated a range of machine learning and deep learning algorithms for note-level classification, using metrics like positive predictive value (PPV), sensitivity, F-score, area under the receiver operating characteristic curve (AUROC), and area under the precision and recall curve (AUPRC). The deep learning model was further evaluated for its ability to classify AAV cases at the patient-level, compared with rule-based algorithms in 2,000 randomly chosen samples. Datasets I, II, and III comprised 6,000, 3,008, and 7,500 note sections, respectively. Deep learning achieved the highest AUROC in all three datasets, with scores of 0.983, 0.991, and 0.991. The deep learning approach also had among the highest PPVs across the three datasets (0.941, 0.954, and 0.800, respectively). In a test cohort of 2,000 cases, the deep learning model achieved a PPV of 0.262 and an estimated sensitivity of 0.975. Compared to the best rule-based algorithm, the deep learning model identified six additional AAV cases, representing 13% of the total. The deep learning model effectively classifies clinical note sections for AAV diagnosis. Its application to EHR notes can potentially uncover additional cases missed by traditional rule-based methods.

Case identification of non-traumatic brain injury in youth using linked population data

BackgroundPopulation-level administrative data provides a cost-effective means of monitoring health outcomes and service needs of clinical populations. This study aimed to present a method for case identification of non-traumatic brain injury in population-level data and to examine the association with sociodemographic factors.MethodsAn estimated resident population of youth aged 0–24 years was constructed using population-level datasets within the New Zealand Integrated Data Infrastructure. A clinical consensus committee reviewed the International Classification of Diseases Ninth and Tenth Editions codes and Read codes for inclusion in a case definition. Cases were those with at least one non-traumatic brain injury code present in the five years up until 30 June 2018 in one of four databases in the Integrated Data Infrastructure. Rates of non-traumatic brain injury were examined, both including and excluding birth injury codes and across age, sex, ethnicity, and socioeconomic deprivation groups.ResultsOf the 1 579 089 youth aged 0–24 years on 30 June 2018, 8154 (0.52%) were identified as having one of the brain injury codes in the five-years to 30 June 2018. Rates of non-traumatic brain injury were higher in males, children aged 0–4 years, Māori and Pacific young people, and youth living with high levels of social deprivation.ConclusionThis study presents a comprehensive method for case identification of non-traumatic brain injury using national population-level administrative data.

Evaluating active leprosy case identification methods in six districts of Nepal

BackgroundNepal has achieved and sustained the elimination of leprosy as a public health problem since 2009, but 17 districts and 3 provinces with 41% (10,907,128) of Nepal’s population have yet to eliminate the disease. Pediatric cases and grade-2 disabilities (G2D) indicate recent transmission and late diagnosis, respectively, which necessitate active and early case detection. This operational research was performed to identify approaches best suited for early case detection, determine community-based leprosy epidemiology, and identify hidden leprosy cases early and respond with prompt treatment.MethodsActive case detection was undertaken in two Nepali provinces with the greatest burden of leprosy, Madhesh Province (40% national cases) and Lumbini Province (18%) and at-risk prison populations in Madhesh, Lumbini and Bagmati provinces. Case detection was performed by (1) house-to-house visits among vulnerable populations (n = 26,469); (2) contact examination and tracing (n = 7608); in Madhesh and Lumbini Provinces and, (3) screening prison populations (n = 4428) in Madhesh, Lumbini and Bagmati Provinces of Nepal. Per case direct medical and non-medical costs for each approach were calculated.ResultsNew case detection rates were highest for contact tracing (250), followed by house-to-house visits (102) and prison screening (45) per 100,000 population screened. However, the cost per case identified was cheapest for house-to-house visits [Nepalese rupee (NPR) 76,500/case], followed by contact tracing (NPR 90,286/case) and prison screening (NPR 298,300/case). House-to-house and contact tracing case paucibacillary/multibacillary (PB:MB) ratios were 59:41 and 68:32; female/male ratios 63:37 and 57:43; pediatric cases 11% in both approaches; and grade-2 disabilities (G2D) 11% and 5%, respectively. Developing leprosy was not significantly different among household and neighbor contacts [odds ratios (OR) = 1.4, 95% confidence interval (CI): 0.24–5.85] and for contacts of MB versus PB cases (OR = 0.7, 95% CI 0.26–2.0). Attack rates were not significantly different among household contacts of MB cases (0.32%, 95% CI 0.07–0.94%) and PB cases (0.13%, 95% CI 0.03–0.73) (χ2 = 0.07, df = 1, P = 0.9) and neighbor contacts of MB cases (0.23%, 0.1–0.46) and PB cases (0.48%, 0.19–0.98) (χ2 = 0.8, df = 1, P = 0.7). BCG vaccination with scar presence had a significant protective effect against leprosy (OR = 0.42, 0.22–0.81).ConclusionsThe most effective case identification approach here is contact tracing, followed by house-to-house visits in vulnerable populations and screening in prisons, although house-to-house visits are cheaper. The findings suggest that hidden cases, recent transmission, and late diagnosis in the community exist and highlight the importance of early case detection.

1274. Long-term Sustainability of Improvements in Antibiotic Prescribing for Inpatient Skin and Soft Tissue Infections after Implementation of Local Treatment Guidelines

Abstract Background Institutional guidelines for the management of inpatient skin and soft tissue infections (SSTIs) were implemented at an academic public safety-net hospital in 2009. This led to an immediate and marked decrease in unnecessary use of broad-spectrum antibiotics and durations of therapy. The objective of this study was to determine if these changes in prescribing have been sustained over time. Methods This was a retrospective review of patients hospitalized with cellulitis or abscess at Denver Health Medical Center between July 2016 and August 2022 (maintenance period). Case identification and data collection methods were identical to those used in a previous publication describing pre-intervention (January to December 2007) and intervention (July 2009 to July 2010) period data. The main outcomes were the proportion of patients exposed to an antibiotic with broad-spectrum gram-negative or antipseudomonal activity and the median duration of therapy. Results A total of 186 patients from the maintenance period were included for comparison with those from the pre-intervention (n = 169) and intervention (n = 175) periods. In the maintenance period, the median patient age was 41 years and 74% were male. The most common comorbid condition was injection drug use (27%). Overall clinical characteristics were similar between the three periods. The proportion of patients exposed to an antibiotic with broad gram-negative activity was 66% during the pre-intervention period, 33% during the intervention period, and 27% during the maintenance period. The proportion of patients exposed to an antibiotic with antipseudomonal activity was 28%, 18%, and 16% during the pre-intervention, intervention, and maintenance periods, respectively. The median total duration of therapy was 13 (IQR 10-15), 10 (IQR 9-12), and 8 (IQR 7-10) days in the pre-intervention, intervention, and maintenance periods, respectively. Conclusion Reductions in use of broad-spectrum antibiotics and durations of therapy for inpatients with SSTIs were sustained 12 years after implementation of a local treatment guideline. These findings add to the evidence that local guidelines are an effective antibiotic stewardship tool and demonstrate that resultant improvements in prescribing can be sustained over prolonged periods. Disclosures Timothy C. Jenkins, MD, Basilea: Clinical events adjudication committee

Estimating Sepsis Incidence Using Administrative Data and Clinical Medical Record Review

Despite the large health burden, reliable data on sepsis epidemiology are lacking; studies using International Statistical Classification of Diseases and Related Health Problems (ICD)-coded hospital discharge diagnosis for sepsis identification suffer from limited sensitivity. Also, ICD data do not allow investigation of underlying pathogens and antimicrobial resistance. To generate reliable epidemiological estimates by linking data from a population-based database to a reference standard of clinical medical record review. This was a retrospective, observational cohort study using a population-based administrative database including all acute care hospitals of the Scania region in Sweden in 2019 and 2020 to identify hospital-treated sepsis cases by ICD codes. From this database, clinical medical records were also selected for review within 6 strata defined by ICD discharge diagnosis (both with and without sepsis diagnosis). Data were analyzed from April to October 2022. Hospital and population incidences of sepsis, case fatality, antimicrobial resistance, and temporal dynamics due to COVID-19 were assessed, as well as validity of ICD-10 case identification methods compared with the reference standard of clinical medical record review. Out of 295 531 hospitalizations in 2019 in the Scania region of Sweden, 997 patient medical records were reviewed, among which 457 had sepsis according to clinical criteria. Of the patients with clinical sepsis, 232 (51%) were female, and 357 (78%) had at least 1 comorbidity. The median (IQR) age of the cohort was 76 (67-85) years. The incidence of sepsis in hospitalized patients according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria in 2019 was 4.1% (95% CI, 3.6-4.5) by medical record review. This corresponds to an annual incidence rate of 747 (95% CI, 663-832) patients with sepsis per 100 000 population. No significant increase in sepsis during the COVID-19 pandemic nor a decrease in sepsis incidence when excluding COVID-19 sepsis was observed. Few sepsis cases caused by pathogens with antimicrobial resistance were found. The validity of ICD-10-based case identification in administrative data was low. In this cohort study of sepsis epidemiology, sepsis was a considerable burden to public health in Sweden. Supplying administrative data with information from clinical medical records can help to generate reliable data on sepsis epidemiology.

Assessing the accuracy of the International Classification of Disease (ICD) framework in the identification of patients with chronic subdural haematoma from hospital records

BackgroundChronic subdural hematoma (CSDH) is one of the commonest neurosurgical pathologies with an increasing incidence. Observational studies of routine care have demonstrated high perioperative morbidity and approximately 10% mortality at one year. The development, implementation, and evaluation of a potential care framework relies on an accurate and reproducible method of case identification and case ascertainment. With this manuscript, we report on the accuracy of diagnostic ICD codes for identifying patients with CSDH from retrospective electronic data and explore whether basic demographic data could improve the identification of CSDH. MethodsData were collected retrospectively from the hospital administrative system between 2014 and 2018 of all patients coded with either S065 or I620. Analysis of the ICD codes in identifying patients with CSDH diagnosis was calculated using the caretR package in RStudioR,.and stepwise logistic regression analysis was performed to evaluate the best predictive model for CSDH. ResultsA total of 1861 patients were identified. Of these, 189 (10.2%) had a diagnosis of non-traumatic SDH (I620) and 1672 (89.8%) traumatic subdural haematomas (S065). Variables that identified CSDH as a diagnosis on univariate logistic regression included male sex (Odds Ratios (OR) - 1.606 (1.197–2.161), elderly age (OR) - 1.023 (1.015–1.032) per year for age (p < 0.001) and shorter length of hospital stay. Using stepwise regression against AIC the best model to predict CSDH included male sex, older age, and shorter LOS. The calculated sensitivity for identifying CSDH with the model is 88.4% with a specificity of 84.5% and PPV of 87.9%. ConclusionCSDH is a common neurosurgical pathology with increasing incidence and ongoing unmet clinical need. We demonstrate that case ascertainment for research purposes can be improved with the incorporation of additional demographic data but at the expense of significant case exclusion.

Global epidemiology of factor XI deficiency: A targeted review of the literature and foundation reports.

Hereditary factor XI (FXI) deficiency is a rare coagulation disorder that may result in excessive bleeding requiring intervention to restore haemostasis. The aim of this review was to report the current knowledge of the worldwide incidence and prevalence of FXI deficiency. A targeted PubMed search using terms related to FXI deficiency was conducted to identify studies published from April 2002 through April 2022. A manual search supplemented the electronic search. Studies were eligible for data abstraction if they reported population-based incidence proportions/rates or prevalence proportions for FXI deficiency. The electronic and manual searches returned 253 publications. After applying exclusion criteria, seven publications were included in the analysis, including a global report from the World Federation of Haemophilia (WFH). Six publications provided information on the prevalence of FXI deficiency that included 74 countries and regions. The estimated prevalence of FXI in the WFH report ranged from 0/100,000 in several countries to 55.85/100,000 individuals in the United Kingdom. Prevalence estimates in the PubMed findings ranged from .1 to 246.2/1,000,000 inhabitants with varying methods of case identification and time periods of analysis. One study estimated the incidence of FXI deficiency in Yecla, Spain at 2% of blood donors and .09% of hospital inpatients/outpatients with activated partial thromboplastin time (aPTT) tests. FXI deficiency is rare across the world, but additional steps could be taken to improve incidence and prevalence estimation, for example, development of a consistent FXI deficiency definition and incorporating genetic testing into a clinical routine to better identify and characterise cases.

Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis.

BackgroundFollowing COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition.ObjectiveThis study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not.MethodsWe used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics–defined LC symptoms between groups. We used descriptive statistics and logistic regression.ResultsThe long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P<.001) with uncomplicated COVID-19.ConclusionsOur LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.

Systematic Review of Prevalence Studies of Progressive Supranuclear Palsy and Corticobasal Syndrome.

ABSTRACTBackgroundHigh‐quality prevalence studies are important in estimating the burden of disease in a population, thus informing priority setting, resource allocation, delivery, and use of health services.ObjectivesThis study was undertaken to systematically review the methods and results of previous prevalence studies of progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) and make recommendations for future studies.MethodsA total of 2 authors independently identified original articles that described the prevalence of PSP or CBS using several comprehensive and overlapping search strategies, assessed study quality, and extracted relevant data. Descriptive and pooled analyses were performed as appropriate.ResultsA total of 16 studies were identified in PSP and 9 studies in CBS, with highly heterogeneous methods of case definition, identification, and verification in identified studies. Few studies were deemed of necessary quality or methodological homogeneity to justify a full meta‐analysis. In addition, few studies reported age‐ and sex‐stratified results. The best 3 prevalence studies in PSP gave a pooled rate of 7.1 per 100,000 per year, whereas the pooled rate in 2 CBS studies was roughly 3 times lower at 2.3 per 100,000 per year. Based on crude rates, there was little evidence to suggest clear sex differences in the prevalence of PSP or CBS or that the prevalence of PSP had increased over time, but some evidence to suggest that prevalence may increase with increasing age.ConclusionGiven the paucity of prevalence studies in PSP and CBS, further high‐quality prevalence studies are necessary.

Association between social deprivation and incidence of first seizures and epilepsy: A prospective population-based cohort.

ObjectiveEpidemiologic studies have investigated whether social deprivation is associated with a higher incidence of epilepsy, and results are conflicting, especially in children. The mechanisms underlying a potential association are unclear. This study examines whether there is an association between social deprivation and the incidence of first seizures (unprovoked and provoked) and new diagnosis of epilepsy by comparing incidence across an area‐level measure of deprivation in a population‐based cohort.MethodsMultiple methods of case identification followed by individual case validation and classification were carried out in a defined geographical area (population 542 868) to identify all incident cases of first provoked and first unprovoked seizures and new diagnosis of epilepsy presenting during the calendar year 2017. An area‐level relative deprivation index, based on 10 indicators from census data, was assigned to each patient according to registered address and categorized into quintiles from most to least deprived.ResultsThe annual incidence of first unprovoked seizures (n = 372), first provoked seizures (n = 189), and new diagnosis of epilepsy (n = 336) was highest in the most deprived areas compared to the least deprived areas (incidence ratios of 1.79 [95% confidence interval (CI) = 1.26–2.52], 1.55 [95% CI = 1.04–2.32], and 1.83 [95% CI = 1.28–2.62], respectively). This finding was evident in both adults and children and in those with structural and unknown etiologies of epilepsy.SignificanceThe incidence of first seizures and new diagnosis of epilepsy is associated with more social deprivation. The reason for this higher incidence is likely multifactorial.

Association Between High-Need Education-Based Funding and School Suspension Rates for Autistic Students in New Zealand

Autistic students often experience poor educational outcomes that have implications for later life, including unemployment, interactions with the criminal justice system, increased risk for substance abuse, and low socioeconomic status. Improving educational outcomes is critical for ensuring that autistic young people can reach their potential. To quantify differences in suspension rates between autistic and nonautistic students and to assess whether high-need education-based funding for autistic students is associated with reduced rates of school suspension. This national cohort study used linked health and education data from New Zealand's Integrated Data Infrastructure. Data were obtained for students aged 5 to 16 years from January 1 to December 31, 2018, and analyzed July 7, 2021, to January 1, 2022. A novel case identification method was used to identify autistic students. High-need education-based funding (Ongoing Resourcing Scheme [ORS]) obtained before 2019. Rates of suspension from school. Crude and adjusted analyses of the association between suspension rates and autism among the full population with adjustment made for sociodemographic characteristics (sex, age, ethnicity, deprivation, and urban or rural profile of residence) were conducted using complete-case, 2-level random intercept logistic multivariable regressions. To assess the association between ORS funding and suspension, analysis was restricted to autistic students. Of the 736 911 students in the study population, 9741 (1.3%) were identified as autistic (median [SD] age, 10 [3.2] years; 7710 [79.1%] boys), and 727 170 (98.7%) as nonautistic (median [SD] age, 10 [3.4] years; 369 777 [50.9%] boys). School suspension was experienced by 504 autistic students (5.2%) and 13 845 nonautistic students (1.9%). After adjustment for demographic characteristics, autistic students had significantly higher odds of suspension than their nonautistic peers (adjusted odds ratio, 2.81; 95% CI, 2.55-3.11). Of the 9741 autistic students, 2895 (29.7%) received high-need education-based (ORS) funding. Suspensions were experienced by 57 autistic students (2.0%) with high-need funding and 447 autistic students (6.5%) without high-need funding. After adjustment for demographic characteristics, co-occurring conditions, and level of disability support need, autistic students with high-need funding had significantly lower odds of suspension than autistic students without high-need funding (adjusted odds ratio, 0.29; 95% CI, 0.21-0.40). In this cohort study, the findings of disparities in suspension rates between autistic and nonautistic students underscore the challenges faced in providing inclusive education for all young people, regardless of disability status. This study found that high-need funding was associated with reduced suspension rates among autistic students, suggesting that if appropriate supports are afforded to autistic students, a more inclusive education can be provided.

Evidence That Nine Autistic Women Out of Ten Have Been Victims of Sexual Violence.

BackgroundResearch indicates that sexual violence affects about 30% of women in the general population and between two to three times as much for autistic women.Materials and MethodsWe investigated prevalence of sexual abuse, autistic traits and a range of symptoms, using an online survey addressed to the women of the French autistic community (n = 225). We assessed victimization through an open question and through a specific questionnaire, derived from the Sexual Experiences Survey-Short Form Victimization.ResultsBoth case identification methods yielded high figures: 68.9% victimization (open question) compared to 88.4% (standardized questionnaire). Two thirds of the victims were very young when they were first assaulted: among 199 victims, 135 were aged 18 or below and 112 participants were aged 15 or below. 75% of participants included in our study reported several aggressions. Analyses indicate that primo-victimization was highly correlated to revictimization and that being young increased that risk. Young victims were also at higher risk of developing post-traumatic stress disorder. A third of the victims reported the assault. 25% of those were able to file a complaint (n = 12) and/or receive care (n = 13). For the remainder 75%, reporting did not lead to action.DiscussionThose findings indicate a very large proportion of victims of sexual assault among autistic women, consistently with previous research. The World Health Organization states unambiguously that sexual violence is systemic and that vulnerable individuals are preferably targeted by offenders. We therefore postulate that it would be erroneous to consider that victimization of autistic women is mainly due to autism. On the contrary, autism seems to be just a vulnerability factor. Some authors propose that educating potential victims to better protect themselves would help preventing abuse. We reviewed this proposition in the light of our results and found it to be impossible to apply since more than half of the victims were below or at the age of consent. Literature about sexual violence is discussed. Large-scale prevention programs proposed by World Health Organization and the Center for Disease Control aim at cultural changes in order to diminish gender inequality, that they identify as the very root of sexual violence.

A quantitative comparison of two measures of postpartum depression

BackgroundStudies investigating the prevalence and risk factors for postpartum depression (PPD) have used different definitions. Some studies have used a high score on the Edinburgh Postnatal Depression Scale (EPDS) to define PPD, whereas others have used information on antidepressant medication use and/or diagnostic information on treatment for depression at a psychiatric hospital. We wanted to compare results using these two approaches to evaluate to what degree results can be compared. Moreover we wanted to evaluate, whether use of EPDS or PPAT (defined below) leads to identification of different risk factor profiles.MethodsWe identified women who delivered a child between 1 January 2014 and 31 December 2016 in Copenhagen or in one of the municipalities that were part of the Danish Health Visitors’ Child Health Database. The potential risk factors were demographic factors and pregnancy- and obstetrical events. Outcomes of interest were an EPDS score ≥ 13, use of antidepressants (ATC: N06A) and/or a diagnosis of depression (F32) within six months after birth. Use of antidepressants and/or diagnosis of depression will be referred to as postpartum antidepressant treatment (PPAT). Agreement between EPDS ≥ 13 and PPAT was evaluated by the kappa coefficient. Associations between risk factors and the two outcomes (EPDS ≥ 13 and PPAT) were estimated by risk ratios (RR) using log-linear binomial regression. Presence of a systematic difference between RRs based on EPDS ≥ 13 (RREPDS≥13) and PPAT (RRPPAT) was evaluated in a meta-regression approach weighted by inverse-variance and with logarithm of the RRs as outcome.ResultsThe estimated PPD prevalence using EPDS ≥ 13 was 3.2% and of PPAT 0.4%. The agreement between the two measures was small (Kappa = 0.08), but their risk factor profile was very similar with no systematic difference between them.ConclusionsUsing the two different methods of case identification produced different prevalence estimates, but a similar risk factor profile. The differences in estimated prevalence and low agreement suggest that the two measures identify different potential PPD cases and using only one of the methods in defining PPD would underestimate PPD prevalence. The similar risk factor profile suggests that the considered risk factors are involved in the general development of PPD.

Fresh tissue procurement and preparation for multicompartment and multimodal analysis of the prostate tumor microenvironment.

BackgroundProstatic cancers include a diverse microenvironment of tumor cells, cancer‐associated fibroblasts, and immune components. This tumor microenvironment (TME) is a known driving force of tumor survival after treatment, but the standard‐of‐care tissue freezing or fixation in pathology practice limit the use of available approaches/tools to study the TME's functionality in tumor resistance. Thus, there is a need for approaches that satisfy both clinical and laboratory endpoints for TME study. Here we present methods for clinical case identification, tissue processing, and analytical workflow that are compatible with standard histopathology while enabling molecular and functional interrogation of prostate TME components.MethodsWe first performed a small retrospective review to identify cases where submission of alternate prostate tissue slices and a parallel live tissue processing protocol complement traditional histopathology and enable viable multicompartment analysis of the TME. Then, we tested its compatibility with commonly employed methods to study the microenvironment including quantification of components both in situ and after tissue dissociation. We also evaluated tissue digestion conditions and cell isolation techniques to aid various molecular and functional endpoints.ResultsWe identified Gleason Grade Group 3+ clinical cases where tumor volume was sufficient to allow slicing of unfixed tissue and distribution of alternating tissue slices to standard‐of‐care histopathology and viable multi‐modal TME analyses. No single method was found that preserved cellular sub‐types for all downstream readouts; instead, tissues were further divided so techniques could be catered to each endpoint. For instance, we show that incorporating the protease dispase into tissue dissociation improves viability for culture and functional analyses but hinders immune cell analysis by flow cytometry. We also found that flow activated cell sorting provides highly pure cell populations for quantitative reverse‐transcription polymerase chain reaction and RNA‐seq while isolation using antibody‐labeled paramagnetic particles facilitated functional coculture experiments.ConclusionsThe identification of candidate cases and use of these techniques enable translational research and the development of molecular and functional assays to facilitate prostate TME study without compromising standard‐of‐care histopathological diagnosis. This allows bridging clinical histopathology and further interrogation of the prostate TME and promises to advance our understanding of tumor biology and unveil new predictive and prognostic markers of prostate cancer progression.

The global cost of epilepsy: A systematic review and extrapolation.

Global action for epilepsy requires information on the cost of epilepsy, which is currently unknown for most countries and regions of the world. To address this knowledge gap, the International League Against Epilepsy Commission on Epidemiology formed the Global Cost of Epilepsy Task Force. We completed a systematic search of the epilepsy cost-of-illness literature and identified studies that provided a comprehensive set of direct health care and/or indirect costs, followed standard methods of case identification and cost estimation, and used data on a representative population or subpopulation of people with epilepsy. Country-specific costs per person with epilepsy were extracted and adjusted to generate an average cost per person in 2019 US dollars. For countries with no cost data, estimates were imputed based on average costs per person of similar income countries with data. Per person costs for each country were then applied to data on the prevalence of epilepsy from the Global Burden of Disease collaboration adjusted for the treatment gap. One hundred one cost-of-illness studies were included in the direct health care cost database, 74 from North America or Western Europe. Thirteen studies were used in the indirect cost database, eight from North America or Western Europe. The average annual cost per person with epilepsy in 2019 ranged from $204 in low-income countries to $11432 in high-income countries based on this highly skewed database. The total cost of epilepsy, applying per person costs to the estimated 52.51million people in the world with epilepsy and adjusting for the treatment gap, was $119.27 billion. Based on a summary and extrapolations of this limited database, the global cost of epilepsy is substantial and highly concentrated in countries with well-developed health care systems, higher wages and income, limited treatment gaps, and a relatively small percentage of the epilepsy population.

Causes and classification of first unprovoked seizures and newly-diagnosed epilepsy in a defined geographical area- an all-comers analysis

PurposeThe ILAE recently updated the operational definition of epilepsy and the classifications of seizures and epilepsy incorporating aetiology into the classification framework. To date, these classifications have not been applied in any whole population incidence study. MethodsMultiple overlapping methods of case identification were applied to a defined geographical area (population 542,868 adults and children) to identify all first unprovoked seizures and new diagnosis of epilepsy presenting during the calendar year 2017. The 2017 ILAE classification frameworks were applied. Incidence was age-standardised to the 2013 Standard European Population. ResultsThe annual incidence per 100,000 population was 44 for focal epilepsy, 6.8 for generalized epilepsy and 10.9 for unclassified epilepsy (age standardized 56, 6.9 and 11.4, respectively). Focal epilepsy was diagnosed in all age groups, though incidence increased in those ≥55 years of age. Primary generalised epilepsy accounted for 10% (n = 32) of newly diagnosed epilepsy. The most frequently diagnosed aetiology was structural (54%, n = 182). In 30% (n = 102) of newly diagnosed epilepsy, aetiology was not established. ConclusionWe report on the causes of incident first unprovoked seizures and epilepsy in accordance with recently updated ILAE definitions and classification systems employing standard diagnostic investigations. We report a higher proportion of structural aetiology than previous studies, which may reflect incorporation of imaging in aetiology classification. Despite improved access to diagnostic testing, aetiology of a large fraction of first seizures and newly diagnosed epilepsy remains unknown.

Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model

BackgroundHypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk, even with low CHA2DS2-VASc (congestive heart failure, hypertension, age diabetes, previous stroke/transient ischemic attack) scores. Hence, there is a need to understand the pathophysiology of AF/stroke in HCM. In this retrospective study, we develop and apply a data-driven, machine learning–based method to identify AF cases, and clinical/imaging features associated with AF, using electronic health record data. MethodsHCM patients with documented paroxysmal/persistent/permanent AF (n = 191) were considered AF cases, and the remaining patients in sinus rhythm (n = 640) were tagged as No-AF. We evaluated 93 clinical variables; the most informative variables useful for distinguishing AF from No-AF cases were selected based on the 2-sample t test and the information gain criterion. ResultsWe identified 18 highly informative variables that are positively (n = 11) and negatively (n = 7) correlated with AF in HCM. Next, patient records were represented via these 18 variables. Data imbalance resulting from the relatively low number of AF cases was addressed via a combination of oversampling and undersampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. Specifically, an ensemble of logistic regression and naïve Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity = 0.74, specificity = 0.70, C-index = 0.80). ConclusionsOur model (HCM-AF-Risk Model) is the first machine learning–based method for identification of AF cases in HCM. This model demonstrates good performance, addresses data imbalance, and suggests that AF is associated with a more severe cardiac HCM phenotype.

Validated methods for identifying individuals with obesity in health care administrative databases: A systematic review.

SummaryBackgroundHealth care administrative databases are increasingly used for health studies and public health surveillance. Cases of individuals with obesity are selected using case‐identification methods. However, the validity of these methods is fragmentary and particularly challenging for obesity case identification.ObjectiveThe objectives of this systematic review are to (1) determine the case‐identification methods used to identify individuals with obesity in health care administrative databases and (2) to summarize the validity of these case‐identification methods when compared with a reference standard.MethodsA systematic literature search was conducted in six bibliographic databases for the period January 1980 to June 2019 for all studies evaluating obesity case‐identification methods compared with a reference standard.ResultsSeventeen articles met the inclusion criteria. International Classification of Diseases (ICD) codes were the only case‐identification method utilized in selected articles. The performance of obesity‐identification methods varied widely across studies, with positive predictive value ranging from 19% to 100% while sensitivity ranged from 3% to 92%. The sensitivity of these methods was usually low while the specificity was higher.ConclusionWhen obesity is reported in health care administrative databases, it is usually correctly reported; however, obesity tends to be highly underreported in databases. Therefore, case‐identification methods to monitor the prevalence and incidence of obesity within health care administrative databases are not reliable. In contrast, the use of these methods remains relevant for the selection of individuals with obesity for cohort studies, particularly when identifying cohorts of individuals with severe obesity or cohorts where obesity is associated with comorbidities.

Validating the optimal classification approach using International Classification of Diseases, 10th Revision codes to identify dermatology patients with acne

Validating the optimal classification approach using International Classification of Diseases, 10th Revision codes to identify dermatology patients with acne