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Introduction Medico‐administrative databases are increasingly used to study the epidemiology of status epilepticus (SE), targeting hospitalizations with the SE G41 ICD‐10 code. However, the positive predictive value (PPV) of the G41 code, which measures the percentage of true cases among those identified by the code, is unknown. Methods We identified all hospitalizations with a primary or secondary diagnosis coded as G41 in five different hospitals. Medical reports for each hospitalization were reviewed to classify the stays as really related to SE or not, using two distinct approaches (sensitive and specific). The clinical characteristics of SE cases were also extracted. Results Among the 797 hospitalizations identified, the PPV ranged from 85.7% using the sensitive approach to 70.6% with the specific approach. Hospitalizations coded with G41 as the main diagnosis had the highest PPV, whereas codes G411 and G418 showed the lowest PPV. Of the 400 hospitalizations with a G410 (generalized convulsive SE) code, 72.7% were classified as generalized convulsive SE, while 76.5% of the 149 hospitalizations with a G412 (focal SE) code were classified as focal SE. Conclusion Our findings highlight that PPV varies by G41 subtype and diagnostic position. Studies requiring a higher PPV should exclude certain codes or hospitalizations with G41 code only as an associated diagnosis. Further studies are needed to estimate the sensitivity and specificity of G41 code. Sorbonne University Hospitals (5 hospitals, 1.1% of hospitalizations in France Extraction of all hospitalizations with a G41 discharge code (n = 797). Review of medical reports and labeling of hospitalizations using a sensitive and a specific approach. True positive (TP): hospitalization with a G41 code that corresponds to a hospitalization for Status Epilepticus. False positive (FP): hospitalization with a G41 code without Status Epilepticus Positive Predictive Value (PPV) : TP / (TP + FP). The Positive Predictive Value (PPV) of the G41 discharge code for Status Epilepticus ranges from 70.6% to 85.7%. The PPV is higher when the G41 code is the main diagnosis or when the patient is hospitalized in a neurology unit.

The aim was to describe the current use of biological therapies among patients affected by psoriasis and to analyze a drug utilization profile in naïve patients in terms of switching and treatment costs in a Local Health Unit (LHU) of Southern Italy. We conducted an observational retrospective cohort analysis using the health-related administrative databases of a LHU in Southern Italy covering a population of about one million inhabitants. All subjects with a main or secondary diagnosis of psoriasis who received at least one prescription of biological therapies between January 1, 2010 and December 31, 2014 were analyzed. Switching rate was evaluated in naïve patients within the first year of treatment. Drug cost was calculated for all drugs prescribed and comprised both costs for psoriasis drugs and costs for other treatments. About 20% of patients identified with a diagnosis of psoriasis were under treatment with biological drugs. Among 385 subjects treated with biological therapy, 51.2% were in treatment with etanercept and 33% with adalimumab. Among naïve patients, switching rate to a different biological drug, within the first year of treatment, was 7.3%. The per patient yearly drug cost was €10,536: 96.8% for psoriasis-related drugs and 3.2% for other pharmaceutical treatments. The annual average cost per patient switching from the initial treatment was €13,021, while for those who did not switch from the initial treatment, the annual average cost was €10,342, with a significant difference of about €2,680 per patient per year ( =0.002). Our data may be useful in exploring the dynamics that characterize the use of biological therapy within a specific context and to optimize the use of resources for a better management of the disease.

Suicide is a major public health issue in Canada. The quality of health care services, in addition to other individual and population factors, has been shown to affect suicide rates. In publicly managed care systems, such as systems in Canada and the United Kingdom, the quality of health care is manifested at the individual, program and system levels. Suicide audits are used to assess health care services in relation to the deaths by suicide at individual level and when aggregated at the program and system levels. Large health administrative databases comprise another data source used to inform population-based decisions at the system, program and individual levels regarding mental health services that may affect the risk of suicide. This status report paper describes a project we are conducting at the Institut national de santé publique du Québec (INSPQ) with the Quebec Integrated Chronic Disease Surveillance System (QICDSS) in collaboration with colleagues from Wales (United Kingdom) and the Norwegian Institute of Public Health. This study describes the development of quality of care indicators at three levels and the corresponding statistical analysis strategies designed. We propose 13 quality of care indicators, including system-level and several population-level determinants, primary care treatment, specialist care, the balance between care sectors, emergency room utilization, and mental health and addiction budgets, that may be drawn from a chronic disease surveillance system.

Background Chronic stress and depression are potential risk factors for mild cognitive impairment and dementia, including Alzheimer disease. The aim was to investigate whether any such risk is additive. Methods Cohort study including 1 362 548 people (665 997 women, 696 551 men) with records in the Region Stockholm administrative healthcare database (VAL). Exposure was a recorded ICD-10 diagnosis of chronic stress, depression, or both, recorded in 2012 or 2013. Outcome was a diagnosis of Alzheimer disease, other dementia, or mild cognitive impairment recorded from 2014 through 2022. Odds ratios with 99% confidence intervals (CI) adjusted for age, sex, neighborhood socioeconomic status, diabetes, and cardiovascular disorders were calculated. Results During the exposure period, 4 346 patients were diagnosed with chronic stress, 40 101 with depression, and 1 898 with both. The average age at baseline was around 40 years in all groups. In the fully adjusted model, the odds ratio of Alzheimer disease was 2.45 (99% CI 1.22–4.91) in patients with chronic stress, 2.32 (99% CI 1.85–2.90) in patients with depression, and 4.00 (99% CI 1.67–9.58) in patients with chronic stress and depression. The odds ratio of mild cognitive impairment was 1.87 (99% CI 1.20–2.91) in patients with chronic stress, 2.85 (99% CI 2.53–3.22) in patients with depression, and 3.87 (99% CI 2.39–6.27) in patients with both. When other dementia was analyzed, the odds ratio was significant only in patients with depression, 2.39 (99% CI 1.92–2.96). Conclusions Documented chronic stress increased the risk of mild cognitive impairment and Alzheimer disease. The same was seen with depression. The novel finding is the potential additive effect of chronic stress to depression, on risk of MCI and AD.

This study aimed to determine whether higher intakes of Na, added sugars and saturated fat are prospectively associated with all-cause mortality and CVD incidence and mortality in a diverse population. The nationally representative Canadian Community Health Survey-Nutrition 2004 was linked with the Canadian Vital Statistics – Death Database and the Discharge Abstract Database (2004–2011). Outcomes were all-cause mortality and CVD incidence and mortality. There were 1722 mortality cases within 115 566 person-years of follow-up (median (interquartile range) of 7·48 (7·22–7·70) years). There was no statistically significant association between Na density or energy from saturated fat and all-cause mortality or CVD events for all models investigated. The association of usual percentage of energy from added sugars and all-cause mortality was significant in the base model with participants consuming 11·47 % of energy from added sugars having 1·34 (95 % CI 1·01, 1·77) times higher risk of all-cause mortality compared with those consuming 4·17 % of energy from added sugars. Overall, our results did not find statistically significant associations between the three nutrients and risk of all-cause mortality or CVD events at the population level in Canada. Large-scale linked national nutrition datasets may not have the discrimination to identify prospective impacts of nutrients on health measures.

Real-world data have become increasingly important in medical science and healthcare. A new, effective, and practically feasible statistical design is needed to unlock the potential of real-world data that decision-makers and practitioners can use to meet people’s healthcare needs. In the first half of the study, we validated our proposed new method by simulation, and in the second half, we conducted a clinical study on actual real-world data. We proposed the “Exact Matching Algorithm Using Administrative Health Claims Database Equivalence Factors (AHCDEFs)” using a target trial emulation framework. The simulation trials were conducted 500 times independently, considering the misclassification and chance errors of all variables and competing events of outcome. Two conventional methods, multivariate and propensity score analyses, were compared. Next, we estimated the effect of specific health guidance provided in Japan on the prevention of diabetes onset and medical expenditures. Our proposed novel method for real-world data returns improved estimates and fewer type I errors (the probability of erroneously determining that there is a difference when, in fact, there is no difference) than conventional methods. We quantitatively demonstrated the effectiveness of specific health guidance in Japan in preventing the onset of diabetes and reducing medical expenditures during five years. We proposed a new method for analyzing real-world data and an exact-matching algorithm using AHCDEFs. The larger the number of patients available for analysis, the more the AHCDEFs that can be matched, thereby removing the influence of confounding factors. This method will generate significant evidence when applied to real-world data.

Introduction: The use of Real-World Evidence (RWE) is gaining increasing relevance in oncology, offering a complementary perspective to randomised clinical trials (RCTs). In Italy, administrative databases represent a promising source to explore treatment patterns and distribution of molecular subtypes in metastatic breast cancer(mBC). This study was aimed at evaluating the feasibility and accuracy of using administrative data to identify andcharacterise molecular subtypes of mBC in a setting of real clinical practice in Italy.Methods: Retrospective observational study conducted on a sample of about 4 million assisted patients. Datafrom different administrative flows were used to identify patients with mBC and to assign them to one of thethree main molecular subtypes (HR+/HER2-, HER2+, TNBC) through the use of proxies based on prescriptions ofdrugs and diagnostic codes.Results: Between January 2019 and June 2023, the observed distribution of subtypes was HR+/HER2- (74%),HER2+ (15%), and TNBC (11%), in line with the ranges reported in the literature. The model demonstrated a highconcordance with pre-existing epidemiological data.Conclusions: Administrative data are confirmed as a valid resource to describe the molecular landscape of mBCin Italy. The RWE emerges as a crucial tool to support clinical and regulatory decisions, promoting the approachof personalised medicine and the optimisation of healthcare resources.

Purpose Improving the understanding of co-existing chronic diseases prior to and after the diagnosis of cancer may help to facilitate therapeutic decision making in clinical practice. This study aims to examine patterns of comorbidities in Canadian women with breast cancer. Methods We conducted a retrospective cohort study using provincial linked administrative health datasets from British Columbia, Canada, between 2000 and 2013. Women diagnosed with breast cancer between 2005 and 2009 were identified. The index date was defined as the date of diagnosis of breast cancer. Subsets of the breast cancer cohort were identified based on the absence of individual type of comorbidity of interest within 5 years prior to breast cancer diagnosis. For each subset, cases were then individually matched by year of birth at 1:2 ratios with controls without a history of cancer and the individual type of comorbidity of interest within 5 years prior to the assigned index year, matching with the year of breast cancer diagnosis of the corresponding case. Baseline comorbidities were measured over a 1-year period prior to the index date using two comorbidity indices, Rx-Risk-V and Aggregated Diagnosis Groups (ADG). Cox regression model was used to assess the development of seven specific comorbidities after the index date between women with breast cancer and non-cancer women. Results The most prevalent baseline comorbidity in the breast cancer cohort measured using the Rx-Risk-V model was cardiovascular conditions (39.0%), followed by pain/pain-inflammation (34.8%). The most prevalent category measured using the ADG model was major signs or symptoms (71.8%), followed by stable chronic medical conditions (52.2%). The risks of developing ischemic heart disease, heart failure, depression, diabetes, osteoporosis, and hypothyroidism were higher in women with breast cancer compared to women without cancer, with the hazard ratios ranging from 1.09 (95 CI% 1.03–1.16) for ischemic heart disease to 2.10 (95% CI 1.99–2.21) for osteoporosis in the model adjusted for baseline comorbidity measured using Rx-Risk-V score. Conclusion Women with breast cancer had a higher risk of developing new comorbidities than women without cancer. Development of coordinated care models to manage multiple chronic diseases among breast cancer patients is warranted.

Background Mental healthcare provision is undergoing substantial reconfiguration in many regions of the world. Such changes require a broad, evidence-based approach incorporating epidemiological data and information on local needs. The objective of this study was to estimate the prevalence of schizophrenia spectrum disorders (SSD) in the Lazio region and its geographical distribution using regional administrative healthcare databases. Methods Cases of SSD (15–64 years old) were identified using an algorithm based on data from the hospital discharge registry (ICD IX CM: 295, 297, 298 [excl. 298.0], 299) and the ticket exemption database [code 044], between 2006 and 2019. We calculated crude, age- and gender-specific prevalence estimates on December 31, 2019. We also calculated age- and gender-adjusted prevalence to compare prevalence in different regional areas. Results We identified 18,371 cases. The overall prevalence was 5.03 per 1000 population (95% CI 4.96–5.10). Age-adjusted prevalence estimates were 4.18 (95% CI 4.09–4.27) per 1000 for women and 5.92 (95% CI 5.81–6.04) per 1000 for men. The prevalence was higher among older age groups, in both genders. There were differences in prevalence within the region, ranging from 4.25/1000 in the province of Viterbo to 5.42/1000 in Rome and 6.02/1000 in the province of Frosinone. When we analysed the subcategories of SSD, the three most frequent conditions were schizophrenia, schizoaffective disorder, and psychosis NOS. In general, the prevalence was higher in men for all the conditions but delusional disorders and brief psychosis. Conclusions Our results show that the overall prevalence of SSD among adults in the Lazio region is similar to those published in previous reviews, but an uneven regional distribution was observed. While possible underestimation must be considered, administrative databases represent a valuable source of information for epidemiological surveillance and healthcare planning.

Google Trends (GT) data have shown promising results as a complementary tool to classical surveillance approaches. However, GT data are not necessarily provided by a representative sample of patients and may be skewed toward demographic and clinical groups that are more likely to use the internet to search for their health. In this study, we aimed to assess whether GT-based models perform differently in distinct population subgroups. To assess that, we analyzed a case study on asthma hospitalizations. We analyzed all hospitalizations with a main diagnosis of asthma occurring in 3 different countries (Portugal, Spain, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold for the same countries and time period were retrieved from GT. We estimated the correlation between GT data and the weekly occurrence of asthma hospitalizations (considering separate asthma admissions data according to patients' age, sex, ethnicity, and presence of comorbidities). In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations (for the different aforementioned subgroups) for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Overall, correlation coefficients between GT on the pseudo-influenza syndrome topic and asthma hospitalizations ranged between 0.33 (in Portugal for admissions with at least one Charlson comorbidity group) and 0.86 (for admissions in women and in White people in Brazil). In the 3 assessed countries, forecasted hospitalizations for 2015-2016 correlated more strongly with observed admissions of older versus younger individuals (Portugal: Spearman ρ=0.70 vs ρ=0.56; Spain: ρ=0.88 vs ρ=0.76; Brazil: ρ=0.83 vs ρ=0.82). In Portugal and Spain, forecasted hospitalizations had a stronger correlation with admissions occurring for women than men (Portugal: ρ=0.75 vs ρ=0.52; Spain: ρ=0.83 vs ρ=0.51). In Brazil, stronger correlations were observed for admissions of White than of Black or Brown individuals (ρ=0.92 vs ρ=0.87). In Portugal, stronger correlations were observed for admissions of individuals without any comorbidity compared with admissions of individuals with comorbidities (ρ=0.68 vs ρ=0.66). We observed that the models based on GT data may perform differently in demographic and clinical subgroups of participants, possibly reflecting differences in the composition of internet users' health-seeking behaviors.