Clinical Epidemiology最新文献

Background: The Danish national hospital registers are a rich source of data for psychiatric epidemiology. The potential to identify diagnosed mental disorders in the Danish population has resulted in a plethora of important research studies. However, no recent review of these research sources exists.

Objective: To review the content, data quality and epidemiological considerations regarding mental disorders in the Danish hospital registers.

Methods: We provide information on the setting, history, and data access regarding mental disorders in the Danish hospital registers (defined according to the International Classification of Diseases, Tenth Revision, F group Mental and behavioural disorder codes). We describe the registration of mental disorders over time in the Danish hospital registers, by providing counts of individuals diagnosed with mental disorders each year, by types of mental disorders, diagnosis, hospital contact, and contact unit. We also provide information on considerations when using these data sources, including data quality. For this, we searched PubMed to identify validation studies of coding of mental disorder diagnoses.

Results: Between 1969 and 2022, 1,136,280 individuals (10.7% of all individuals who resided in Denmark in this period) have been registered as having a mental disorder as a primary or secondary diagnosis in a psychiatric unit in Denmark at least once. The type of contact and unit diagnoses were made in varied by mental disorder type. The literature indicates that a large proportion of coded diagnoses in the Danish hospital registers of mental disorders were considered valid.

Conclusion: We provide an overview of the available data for investigating mental disorders in the Danish hospital registers. We raise awareness of the limitations researchers should be aware of when using this data and discuss their implications. Careful consideration should be given to these during study design and when reporting findings from register-based studies on diagnosed mental disorders.

Background: Carbon monoxide (CO) poisoning represents a significant contributor to injury burden across Asia. This study seeks to assess the burden of CO poisoning in Asia from 1990 to 2021 utilizing data from the Global Burden of Disease Study (GBD) 2021.

Methods: Data on the burden of CO poisoning across 49 Asian countries were extracted from GBD 2021. The variations in burden were analyzed according to year, gender, location, age, and Socio-Demographic Index (SDI). Analyses included Joinpoint analysis to evaluate temporal trends, the age-period-cohort model to assess disease burden trends, the slope index of inequality and concentration index for assessing health disparities, frontier analysis for estimating potential outcomes based on developmental stages, and the auto-regressive integrated moving average model to predict the disability-adjusted life year (DALY) rates.

Results: During 1990-2021, the age-standardized incidence rate (average annual percent change (AAPC) = -0.83, 95% CI: -0.94 to -0.73), age-standardized mortality rate (AAPC = -2.01, 95% CI: -2.20 to -1.81), and DALY rates (AAPC = -2.39, 95% CI: -2.54 to -2.23) for CO poisoning across Asia declined. In 2021, females experienced a lower burden than males, and countries in higher latitudes bore a greater burden. The burden was more pronounced in extreme age groups, with an elevated cohort risk in the 1967-1971 birth cohort (relative risk (RR) = 1.045, 95% CI: 0.96-1.14). Health inequality analyses showed a reduction in disparities between countries with varying SDI levels. Frontier analysis identified potential improvements in reducing the burden across different countries. However, there was no notable correlation between this burden and SDI levels. The age-standardized disability-adjusted life year rate is predicted to continue declining from 2022 to 2030.

Conclusion: This study analyzes the burden of CO poisoning in Asia, revealing a decline from 1990 to 2021, with variations across countries and higher burdens in males and extreme age groups. It suggests a reduction in health inequalities and forecasts a continued decline in the burden by 2030.

Purpose: We aimed to develop a Natural Language Processing (NLP) algorithm to extract cognitive scores from electronic health records (EHR) data and compare them with cognitive function recorded by Centers for Medicare & Medicaid Services (CMS)-mandated clinical assessments in nursing homes and home health visits.

Patients and methods: We identified a cohort of Medicare beneficiaries who had either the Minimum Data Set (MDS) or Outcome and Assessment Information Set (OASIS) linked to EHR data from the Research Patient Data Registry (Mass General Brigham, Boston, MA) from 2010 to 2019. We applied an NLP approach to identify the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) scores from unstructured clinician notes in EHR. Using the NLP-extracted MoCA or MMSE scores from EHR, we compared mean differences of extracted MoCA or MMSE by cognition status determined by MDS (impaired vs intact cognition) and OASIS (severe impairment vs intact cognition) data, respectively.

Results: Our study cohort had 7419 patients who had MDS (19.7%) or OASIS (80.3%) assessments, with a mean age of 80 (SD=7) years and 60% female. In EHR, the NLP algorithm extracted cognitive test scores with 97% accuracy (95% CI: 92-99%) for MoCA and 100% accuracy (95% CI: 84-100%) for MMSE. In MDS, the mean difference in extracted MoCA was -5.6 (95% CI: -8.7, -2.4, p=0.0008), and the mean difference in extracted MMSE was -7.9 (95% CI: -12.4, -3.5, p=0.0012). In OASIS, the mean difference in extracted MoCA and extracted MMSE was -4.8 (95% CI: -9.1, -0.6, p=0.0006) and -4.5 (95% CI: -9.5, -0.5, p=0.0182), respectively.

Conclusion: We developed an NLP algorithm to accurately extract cognitive scores from unstructured EHR, and these extracted cognitive scores were well correlated with cognition function recorded in CMS-mandated clinical assessments. This could help researchers identify patients with various degrees of cognitive impairment in EHR-based research.

Background: The impact of the coronavirus disease 2019 (COVID-19) pandemic on asthma medication trajectories, including changes in medication type or dosage, remains unclear. This study compared dispensing patterns among adults who initiated asthma inhalers before pandemic (cohorts 2014 and 2017) and during pandemic (cohort 2020).

Methods: We performed retrospective inception cohort study using University of Groningen IADB.nl community pharmacy dispensing database. Individuals aged 16-44 years who initiated inhaled asthma treatment in 2014, 2017, or 2020 were followed for 2 years. Treatment steps (1-5) were based on the Global Initiative for Asthma (GINA) guideline. Primary outcomes included time to treatment step switch (step-up or step-down) and time to first oral prednisolone/prednisone, and were compared between cohorts using Cox regression models.

Results: In 2014, 2017 and 2020, 1193, 960 and 730 patients initiated asthma inhalers, respectively. In all cohorts, more than half of the patients initiated treatment at the lowest step. During 2-year follow-up, fewer patients switched their treatment steps in 2020 when compared with 2014 (adjusted hazard ratio (aHR): 0.86 (95% confidence interval (CI): 0.76-0.99). From 2014 to 2020, the likelihood of treatment stepping-down decreased over time, with a 21% in 2017 (aHR: 0.79 (0.68-0.92)) and 24% in 2020 (aHR: 0.76 (0.64-0.90)) compared to 2014, while the likelihood of stepping-up did not change significantly. The risk of taking oral prednisolone/prednisone was also significantly lower in 2020 (aHR: 0.76 (0.61-0.94)).

Conclusion: During the pandemic, fewer asthma patients switched treatment steps and took oral prednisolone/prednisone. Since 2014, fewer individuals stepping down medication, with a decrease of 21% in 2017 and 24% in 2020. Possible drivers include improved adherence, better asthma control, and increased telemedicine use-trends that predate and have been accelerated by the pandemic. Research incorporating clinical data is necessary to confirm these hypotheses.

Purpose: To date, complete and long-term registrations of diseases and events are not available in every situation. As a useful proxy, medication usage data is very promising. For instance, real-world dispensing data from pharmacies are attractive because of the high validity of drug dispensing data, large sample sizes, and long-term registration. However, before application as a proxy, validity must be assessed. Therefore, in this study, we aim to assess the validity of various medicines used as a proxy for major adverse cardio-cerebrovascular events (MACCE), that is, to identify an incident or previous hospitalization for a MACCE.

Patients and methods: Using the claims database of a large Dutch healthcare insurer, we estimated the concordance between hospitalization claims for MACCE and specific claims for dispensings to treat MACCE in a cohort of patients on primary preventive antihypertensive and/or antihyperlipidemic therapy between 2013 and 2020.

Results: In a cohort of more than 110,000 patients, a dispensing of either vitamin K antagonists, platelet aggregation inhibitors, or nitrates was predictive of an incident hospitalization for a MACCE between 2013 and 2020, with a sensitivity of 71.5% (95% CI: 70.4-72.5%) and specificity of 93.2% (95% CI 91.1-93.4%), and any history of hospitalization for a MACCE (prevalence) with a sensitivity of 86.9% (95% CI: 86.5-87.3%) and specificity of 81.9 (956% CI: 81.6-82.1%), while positive predicted value remains low. Sensitivity analyses across age, sex, and patients with asthma/COPD or diabetes showed a similarly good performance.

Conclusion: Claims for the dispensings of vitamin K antagonists, platelet aggregation inhibitors, and/or nitrates can be validly used as a proxy for new and previous hospitalization for MACCE in patients on primary preventive therapy. Further study is required to assess the validity of such dispensing claims for non-hospitalized cerebro-cardiovascular events and whether the results are generalizable in non-Dutch cohorts.

Background: Mental disorders such as depression and anxiety are common for women of reproductive age and impact pregnancy and parenting. Invasive Group B Streptococcus disease (iGBS) is a leading cause of neonatal morbidity and mortality worldwide. Little is known about the short and long-term risk of common mental disorders in birthing parents whose infants had iGBS in the first 89 days after birth. We aimed to examine the risk of depression and anxiety in birthing parents with iGBS-affected infants in a cohort study with prospectively collected data from Danish registries.

Materials and methods: Using Danish healthcare registries from 1997 to 2018, we obtained data on iGBS-affected children and their birthing parents. A comparison cohort was randomly sampled (1:50) through risk-set sampling, and matched on persons' age, year of child´s birth, and parity. The risk of using antidepressant medicines and depression or anxiety diagnosis was analyzed with cumulative incidence function and in Cox proportional hazards regression models.

Results: During the study period, we identified 1,552 women with iGBS-affected child and 76,879 matched comparators. During a median follow-up of 9∙9 years, the cumulative incidence of antidepressants use among birthing parents with iGBS-affected children was 31% (95% confidence interval, CI: 28-34%), as compared with 29% (95% CI: 28-30%) among members of the comparison cohort (hazard ratio 1∙12 [95% CI: 1∙01-1∙25]). A 16% increase in the rate of diagnosed depression or anxiety was observed in the overall follow-up period.

Conclusion: Our findings provide evidence of a slightly increased risk of antidepressant use and diagnosed depression or anxiety in parents who gave birth to children with a history of iGBS compared to a matched cohort of birthing parents whose infants did not develop iGBS. Our findings highlight the importance of addressing the mental health needs of birthing parents affected by their children' iGBS.

Purpose: We aimed to analyse the characteristics related to non-attendance at general outpatient hospital clinics in patients aged ≥18 years. An increased focus has been directed towards patient non-attendance at hospital appointments as it is related to patient risk and waste of resources in the healthcare system.

Patients and methods: In this cohort study, we retrieved data from the entire Region of Southern Denmark on i) non-attendance from the electronic medical journals from January 1, 2021 until December 31, 2022, and ii) data on all attended appointments from Danish health registries in the same period. We analysed the overall proportion of non-attendance, and characteristics of patients with non-attendance, relative to those who attended an appointment. We examined hospital type and patient demographics (age, sex, comorbidity, socioeconomic factors).

Results: Included were 614,157 patients, of which 12,244 were patients with a first non-attendance and 601,913 patients with a first attended appointment. The overall non-attendance proportion was 2.0%. The most prevalent type of underlying disease among non-attendant patients was musculoskeletal/connective tissue diseases (10%), which was also the most prevalent group of diseases among patients who attended hospital appointments (11%). In the regression model, compared to those who attended, the two strongest associations for non-attendance were patients aged 18-34 years, aOR=2.69 (95% CI 2.52-2.85), and patients diagnosed with mental/behavioral disorders, aOR=2.60 (95% CI 2.39-2.82). Other sociodemographic factors were associated with non-attendance including male sex (aOR=1.90 (95% CI 1.82-1.96)), patients aged 35-54 years (aOR=1.89 (95% CI 1.78-2.01)), living alone (aOR=1.72 (95% CI 1.65-1.79)), and not Danish nationality (aOR=1.65 (95% CI 1.57-1.74)).

Conclusion: Based on data from the Region of Southern Denmark (corresponding to 20% of the Danish population), the non-attendance proportion was low (2.0%). More research is needed, including other data-sets validating our findings, validation of registration practices, and qualitative research aspects of non-attendance.

Purpose: We aimed to identify the association between obesity and nonalcoholic fatty liver disease (NAFLD) and to quantify the mediating effects of insulin resistance (IR) and chronic inflammation through observational studies and Mendelian randomization (MR).

Patients and methods: In the current study, three IR-related indicators and three indicators of inflammation were included. The individual and combined mediated effects of IR and inflammation in the association between obesity and NAFLD were investigated in two cross-sectional studies, the Fuqing Cohort from China and the National Health and Nutrition Examination Survey (NHANES). Total, direct, and indirect effects were estimated through direct counterfactual imputation estimation, and the proportion of mediating effects was calculated. We applied a two-step MR to determine the causal mediating role of IR and chronic inflammation in the pathway between obesity and NAFLD by using single nucleotide polymorphisms as instrumental variables to predict obesity, IR, and inflammation genetically.

Results: In the Fuqing Cohort, all obese phenotypes were associated with an elevated NAFLD risk. Moreover, indicators of IR such as homeostatic model assessment of insulin resistance (HOMA-IR) and indicators of inflammation such as C-reactive protein (CRP) were significantly and positively associated with NAFLD risk. Individuals with obesity had significantly higher levels of IR and inflammation indicators compared to non-obese individuals. The indirect proportions of insulin and HOMA-IR accounted for 50.97-66.72% in the associations between obese phenotypes and NAFLD risk, while the proportions of inflammation indicators were < 14%. Similar results were observed in the NHANES analysis. In the MR analysis, the indirect effects of HOMA-IR and CRP were statistically significant with a greater mediated proportion explained by HOMA-IR than CRP.

Conclusion: Through two population-based studies and MR, we found the causal mediation roles of IR and inflammation in the association between obesity and NAFLD, in which HOMA-IR and CRP showed stable, significant mediation effects. Furthermore, HOMA-IR showed a higher mediation effect than CRP. We emphasize the vital role of HOMA-IR in NAFLD monitoring.

Background: The Danish National Patient Registry (DNPR) is a central source of information on hospital contacts for the Danish population and is a key data source for health-related Danish registry studies. The data structure of DNPR was updated from DNPR2 to DNPR3 in 2019, where a key patient-type variable for classification of inpatient, outpatient, or emergency wards was removed. This affects how hospital contacts can be defined and compared across different calendar years.

Aim: To present and compare different algorithms to determine the type of hospital visit (inpatient, outpatient, or emergency) for all hospital visits in Denmark from 2006 to 2021 across DNPR2 and DNPR3.

Methods: The monthly number of hospital visits per 1000 citizens was presented for four different algorithms: 1) a validated approach suggested by Skjøth et al, 2) an approach suggested by the Danish Ministry of Health and Elderly, 3) the latter combined with patient type variables available in DNPR2 only, and 4) a consensus-driven algorithm introduced by Gregersen et al.

Results: Using the same algorithm for DNPR2 and DNPR3 yielded the most similar results across calendar years. The least variation across calendar years was observed for the approach suggested by the Danish Ministry of Health and Elderly, whereas the validated approach suggested by Skjøth et al was more in line with the patient-type variable previously used in DNPR2. When comparing the algorithms, the main difference in the number of hospital visits was observed for inpatient and emergency visits.

Conclusion: We recommend using the same algorithm across DNPR2 and DNPR3. The choice of algorithm should be based on the disease or patient group being studied and by considering how the approaches reflect reality and need in the actual study. We recommend the algorithm suggested by Skjøth et al for the specific clinical situations presented in this study.

Purpose: The g-formula offers a promising approach to analyze long-term dynamic asthma treatment trajectories. This study investigates whether the g-formula can simulate real-world asthma treatment trajectories and predicts subgroup differences in switching behavior.

Patients and methods: This retrospective cohort study identified individuals aged 16- to 45 years who initiated inhaled asthma medication in the Netherlands between 1994 and 2021, from the IADB.nl pharmacy dispensing database. We used the g-formula combined with logistic regression to predict treatment trajectories and their associations with various patient characteristics, such as age, sex, chronic drug treatment for atopic diseases (ATD), cardiovascular diseases (CVD), thyroid diseases, arthritis, diabetes, gastroesophageal reflux disease (GERD), mental health problems (MHP), and immunosuppressants.

Results: The simulations predicted 76% of individuals to switch treatment, on average 2.3 times, with the first switch occurring on average after 8.3 months, which agrees with the real-world observations (77%, 2.3 times and 7.9 months, respectively). Fewer 45-year-olds switched treatment compared to 16-year-olds (74% vs 78%, p < 0.001), but they switched earlier (8.1 vs 8.6 months, p < 0.001) and more frequently (2.4 vs 2.3 times, p < 0.001). Women were more likely to switch compared to men. Patients with ATD, CVD, MHP, or GERD switched significantly less often (p < 0.05).

Conclusion: The g-formula effectively simulates asthma treatment trajectories and found higher age, male sex, ATD, CVD, MHP, and GERD to decrease overall switching behavior. These patients might benefit from earlier intervention or closer monitoring to reduce delays in treatment progression.