Clinical Epidemiology最新文献

Introduction: Previous research has shown associations between eczema and psoriasis and anxiety and depression. We investigated whether associations are consistent across different settings of ascertainment for depression and anxiety, including interview and survey responses from UK Biobank (a large longitudinal cohort recruiting individuals aged 40-69 years between 2006-2010), and linked primary care data, with the aim of drawing more reliable conclusions through triangulation.

Methods: In cross-sectional studies, we estimated associations between eczema or psoriasis and anxiety or depression, defining anxiety or depression as 1) self-reported previous diagnosis at UK Biobank recruitment interview; 2) PHQ-9/GAD-7 score indicating depression or anxiety from a UK Biobank mental health follow-up survey in 2016; and 3) diagnosis in linked primary care electronic health record data.

Results: We analysed 230,047 people with linked Biobank and primary care data. We found poor agreement between the data sources for eczema, psoriasis, anxiety, and depression. Eg, 9474 had a previous eczema diagnosis in primary care data, 4069 self-reported previous eczema diagnosis at the UK biobank interview, and 1536 had eczema in both data sources (for depression 40,455; 13,320; and 9588 respectively). Having eczema or psoriasis (recorded in primary care or baseline interview) was associated with higher odds of anxiety and depression. Eg, the adjusted odds ratio for depression comparing those with eczema to those without was greater than 1 when defining the outcome from 1) the recruitment interview (1.36, 95% confidence interval 1.27-1.45); 2) the follow-up survey (1.24, 1.09-1.39), and 3) primary care records (1.56, 1.50-1.62).

Discussion: Our findings support increased prevalence of mental illness in people with psoriasis and eczema across multiple data sources, which should be considered in planning of mental health services. However, we found poor agreement in disease ascertainment between settings, with implications for data interpretation in electronic health records.

Purpose: To determine the relationship between perceived social support and viral suppression among young adults with perinatally-acquired HIV (YAPHIV).

Participants and methods: We included YAPHIV ≥18 years enrolled in AMP Up, a study of PHACS (Pediatric HIV/AIDS Cohort Study), with social support evaluations and ≥1 HIV viral load (VL) measured over the next year. We evaluated emotional, instrumental, and friendship social support via the NIH Toolbox. We defined social support, measured at study entry and year 3 (if available), as low (T-score ≤40), average (41-59) or high (≥60). We defined viral suppression as all VL <50 copies/mL over the one year after social support measures. We fit multivariable Poisson regression models using generalized estimating equations, and evaluated transition from pediatric to adult care as an effect modifier.

Results: Among 444 YAPHIV, low emotional and instrumental support and friendship at entry were reported by 37%, 32% and 36%. Over the next year, 44% were virally suppressed. Of 136 with year 3 data, 45% were suppressed. Average or high levels of all three social support measures were associated with higher likelihood of viral suppression. Instrumental support was associated with viral suppression among those in pediatric (adjusted proportion suppressed among those with average/high vs low support=51.2% vs 28.9%; risk ratio (RR)=1.77, 95% confidence interval (CI)=1.37, 2.29), but not adult care (40.0% vs 40.8%; RR=0.98, 95% CI=0.67, 1.44).

Conclusion: Sufficient social support increases likelihood of viral suppression among YAPHIV. Strategies to enhance social support may promote viral suppression as YAPHIV prepare for adult clinical care transition.

Purpose: Children of mothers with prenatal depression have elevated risk for depression later in life. Pregnant women are hesitant to use antidepressants due to fear of adverse fetal effects. To inform prevention, this study examined associations between maternal prenatal depression and antidepressant use, and adolescent depressive symptoms and suicidality.

Patients and methods: Prospective data from 74,695 mother-adolescent dyads from the Kaiser Permanente Northern California integrated healthcare delivery system were used. Three prenatal exposure groups were examined: maternal depression and antidepressants (Med); depression and no antidepressants (No-Med); neither depression nor antidepressants (NDNM). Adolescent depressive symptoms (Patient Health Questionnaire-2 score ≥3) and suicidality were assessed for 12- to 18-year-olds. Associations were analyzed using mixed effects logistic regression, adjusted for confounders.

Results: Maternal prenatal depression was associated with higher odds of adolescent depressive symptoms (Med odds ratio [OR]: 1.50, 95% confidence interval [CI]: 1.23-1.84; No-Med OR: 1.59, CI: 1.34-1.88) and suicidality (Med OR: 2.36, CI: 1.67-3.34; No-Med OR: 1.54, CI: 1.10-2.14) compared to no prenatal depression (NDNM). Adolescents exposed to prenatal depression and antidepressants were not at greater odds of depressive symptoms (Med OR: 0.95, CI: 0.74-1.21) compared to those not exposed to antidepressants (No-Med). However, they showed non-significant but greater odds of suicidality (Med OR: 1.54, CI: 0.99-2.39).

Conclusion: Our findings suggest that maternal prenatal depression is associated with adolescent depressive symptoms and suicidality, and that exposure to antidepressants in utero does not increase risk of depressive symptoms, specifically. While not statistically significant, the increased odds of suicidality among adolescents exposed to antidepressants suggest a possible association; however, further investigation is needed. After replication, the findings of this study may inform shared clinical decision-making when considering options regarding antidepressant use for the treatment of maternal prenatal depression.

Purpose: To validate two register-based algorithms classifying type 1 (T1D) and type 2 diabetes (T2D) in a general population using Danish register data.

Patients and methods: After linking data on prescription drug usage, hospital diagnoses, laboratory results and diabetes-specific healthcare services from nationwide healthcare registers, diabetes type was defined for all individuals in Central Denmark Region age 18-74 years on 31 December 2018 according to two distinct register-based classifiers: 1) a novel register-based diabetes classifier incorporating diagnostic hemoglobin-A1C measurements, the Open-Source Diabetes Classifier (OSDC), and 2) an existing Danish diabetes classifier, the Register for Selected Chronic Diseases (RSCD). These classifications were validated against self-reported data from the Health in Central Denmark survey - overall and stratified by age at onset of diabetes. The source-code of both classifiers was made available in the open-source R package osdc.

Results: A total of 2633 (9.0%) of 29,391 respondents reported having any type of diabetes, divided across 410 (1.4%) self-reported cases of T1D and 2223 (7.6%) cases of T2D. Among all self-reported diabetes cases, 2421 (91.9%) were classified as diabetes cases by both classifiers. In T1D, sensitivity of OSDC-classification was 0.773 [95% CI 0.730-0.813] (RSCD: 0.700 [0.653-0.744]) and positive predictive value (PPV) 0.943 [0.913-0.966] (RSCD: 0.944 [0.912-0.967]). In T2D, sensitivity of OSDC-classification was 0.944 [0.933-0.953] (RSCD: 0.905 [0.892-0.917]) and PPV 0.875 [0.861-0.888] (RSCD: 0.898 [0.884-0.910]). In age at onset-stratified analyses of both classifiers, sensitivity and PPV were low in individuals with T1D onset after age 40 and T2D onset before age 40.

Conclusion: Both register-based classifiers identified valid populations of T1D and T2D in a general population, but sensitivity was substantially higher in OSDC compared to RSCD. Register-classified diabetes type in cases with atypical age at onset of diabetes should be interpreted with caution. The validated, open-source classifiers provide robust and transparent tools for researchers.

Purpose: High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data.

Methods: Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009-2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not.

Results: A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8-87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8-87.8%), and accuracy of 96.7% (95% CI 95.4-97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4-91.3%), PPV of 84.1% (95% CI 74.4-91.3%), and an accuracy of 96.8% (95% CI 95.4-97.9%).

Conclusion: Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise.

Background: Existing research exploring associations between atopic eczema (AE) or psoriasis, and severe mental illness (SMI - ie, schizophrenia, bipolar disorder, other psychoses) is limited, with longitudinal evidence particularly scarce. Therefore, temporal directions of associations are unclear. We aimed to investigate associations between AE or psoriasis and incident SMI among adults.

Methods: We conducted matched cohort studies using primary care electronic health records (January 1997 to January 2020) from the UK Clinical Practice Research Datalink GOLD. We identified two cohorts: 1) adults (≥18 years) with and without AE and 2) adults with and without psoriasis. We matched (on age, sex, general practice) adults with AE or psoriasis with up to five adults without. We used Cox regression, stratified by matched set, to estimate hazard ratios (HRs) comparing incident SMI among adults with and without AE or psoriasis.

Results: We identified 1,023,232 adults with AE and 4,908,059 without, and 363,210 with psoriasis and 1,801,875 without. After adjusting for matching variables (age, sex, general practice) and potential confounders (deprivation, calendar period) both AE and psoriasis were associated with at least a 17% increased hazard of SMI (AE: HR=1.17,95% CI=1.12-1.22; psoriasis: HR=1.26,95% CI=1.18-1.35). After additionally adjusting for potential mediators (comorbidity burden, harmful alcohol use, smoking status, body mass index, and, in AE only, sleep problems and high-dose glucocorticoids), associations with SMI did not persist for AE (HR=0.98,95% CI=0.93-1.04), and were attenuated for psoriasis (HR=1.14,95% CI=1.05-1.23).

Conclusion: Our findings suggest adults with AE or psoriasis are at increased risk of SMI compared to matched comparators. After adjusting for potential mediators, associations with SMI did not persist for AE, and were attenuated for psoriasis, suggesting that the increased risk may be explained by mediating factors (eg, sleep problems). Our research highlights the importance of monitoring mental health in adults with AE or psoriasis.

Background: The Model for End-Stage Liver Disease (MELD) score predicts disease severity and mortality in cirrhosis. To improve cirrhosis phenotyping in administrative databases lacking laboratory data, we aimed to develop and externally validate claims-based MELD prediction models, using claims data linked to electronic health records (EHR).

Methods: We included adults with established cirrhosis in two Medicare-linked EHR networks (training and internal validation; 2007-2017), and a Medicaid-linked EHR network (external validation; 2000-2014). Using least absolute shrinkage and selection operator (LASSO) with 5-fold cross-validation, we selected among 146 investigator-specified variables to develop models for predicting continuous MELD and relevant MELD categories (MELD<10, MELD≥15 and MELD≥20), with observed MELD calculated from laboratory data. Regression coefficients for each model were applied to the validation sets to predict patient-level MELD and assess model performance.

Results: We identified 4501 patients in the Medicare training set (mean age 75.1 years, 18.5% female, mean MELD=13.0), and 2435 patients in the Medicare validation set (mean age: 74.3 years, 31.7% female, mean MELD=12.3). Our final model for predicting continuous MELD included 112 variables, explaining 58% of observed MELD variability; in the Medicare validation set, the area-under-the-receiver operating characteristic curves (AUC) for MELD<10 and MELD≥15 were 0.84 and 0.90, respectively; the AUC for the model predicting MELD≥20 (using 27 variables) was 0.93. Overall, these models correctly classified 77% of patients with MELD<10 (95% CI=0.75-0.78), 85% of patients with MELD≥15 (95% CI=0.84-0.87), and 87% of patients with MELD≥20 (95% CI=0.86-0.88). Results were consistent in the external validation set (n=2240).

Conclusion: Our MELD prediction tools can be used to improve cirrhosis phenotyping in administrative datasets lacking laboratory data.

Purpose: Because chronic kidney disease (CKD) is often under-coded as a diagnosis in claims data, we aimed to develop claims-based prediction models for CKD phenotypes determined by laboratory results in electronic health records (EHRs).

Patients and methods: We linked EHR from two networks (used as training and validation cohorts, respectively) with Medicare claims data. The study cohort included individuals ≥65 years with a valid serum creatinine result in the EHR from 2007 to 2017, excluding those with end-stage kidney disease or on dialysis. We used LASSO regression to select among 134 predictors for predicting continuous estimated glomerular filtration rate (eGFR). We assessed the model performance when predicting eGFR categories of <60, <45, <30 mL/min/1.73m² in terms of area under the receiver operating curves (AUC).

Results: The model training cohort included 117,476 patients (mean age 74.8 years, female 58.2%) and the validation cohort included 56,744 patients (mean age 73.8 years, female 59.6%). In the validation cohort, the AUC of the primary model (with 113 predictors and an adjusted R² of 0.35) for predicting eGFR <60, eGFR<45, and eGFR <30 mL/min/1.73m² categories was 0.81, 0.88, and 0.92, respectively, and the corresponding positive predictive values for these 3 phenotypes were 0.80 (95% confidence interval: 0.79, 0.81), 0.79 (0.75, 0.84), and 0.38 (0.30, 0.45), respectively.

Conclusion: We developed a claims-based model to determine clinical phenotypes of CKD stages defined by eGFR values. Researchers without access to laboratory results can use the model-predicted phenotypes as a proxy clinical endpoint or confounder and to enhance subgroup effect assessment.

Objective: Little is known about the effect of age at first childbirth on lung function. We aimed to investigate the association between age at first childbirth and lung function in Chinese women and further test whether this association is mediated by body mass index (BMI).

Methods: This cross-sectional study is a partial survey of the China Kadoorie Biobank (CKB) which was conducted in Xinxiang City, Henan Province between 2004 and 2008. A total of 16,584 postmenopausal women aged 30-79 years were enrolled. Multiple linear and logistic regression were used to investigate the association between age at first childbirth and lung function and overweight/obesity. The mediation analysis was performed using the PROCESS procedure for SPSS.

Results: The mean (SD) age at first childbirth was 23.1 (2.7) years. Women with first childbirth aged ≤19 years and 20-22 years had lower lung function than women who gave first childbirth aged 23-25 years. Per 1-year increase in the age at first childbirth was associated with a 3.31 mL increase in FEV1 (95% CI = 1.27-5.35), 3.91 mL increase in FVC (95% CI = 1.63-6.18), 0.15% increase in FEV1, % predicted (95% CI = 0.05-0.24) and 0.14% increase in FVC, % predicted (95% CI = 0.05-0.22). There was no clear association between age at first childbirth and FEV1/FVC ratio. BMI played a contribution to the association between age at first childbirth and FEV1 and the proportion was 16.4% (indirect effect: β = 0.65, 95% CI = 0.46-0.89; total effect: β = 3.96, 95% CI = 1.92-5.99). Similarly, the proportion to FVC, FEV1, % predicted, and FVC, % predicted was 25.0%, 16.6%, and 25.0%, respectively.

Conclusion: Early age at first childbirth was associated with lower lung function and BMI mediated the association. It is important to test lung function and popularize the knowledge of weight control in women who gave first childbirth at an early age.

Purpose: The study aimed to examine the separate population-level contributions of the ethnic and socioeconomic disparities among people with type 2 diabetes mellitus (T2DM) and residence in New Zealand (NZ).

Patients and methods: A prospective cohort enrolled T2DM patients from 01/01/1994 into the Diabetes Care Support Service, a primary care audit program in Auckland, NZ. The cohort was linked to national registry databases (socioeconomic status, pharmaceutical claim, hospitalization, and death registration). Each cohort member was followed up till death or the study end time (31/12/2019), whichever came first. Incident clinical events (stroke, myocardial infarction (MI), heart failure (HF), end-stage renal disease (ESRD), and premature mortality (PM)) were used as outcomes. The attributable fractions (AFs) were estimated for the whole population and for specific population with NZ Europeans (NZE) and/or least deprived population as reference, both unadjusted and with adjustment for covariables by Cox Regression models.

Results: Among 36,267 patients, adjusted population AFs indicated 6.6(-30.8-33.3)% of PM, 17.1(5.8-27.0)% of MI, 35.3(22.6-46.0)% of stroke, 14.3(3.2-24.2)% of HF, and 15.9(6.7-24.2)% of ESRD could be attributed to deprivation; while 14.3(3.3-25.4)% of PM, -3.3(-8.3-1.5)% of MI, -0.5(-6.7-5.3)% of stroke, 4.7(0.3-8.8)% of HF, 13.3(9.9-16.6)% of ESRD could be attributed to ethnicity. Deprivation contributed a significant AF to stroke, while ethnicity was important for ESRD. Gradient of AF for deprivation indicated NZE and Asians were most affected by deprivation across outcomes. Conversely, Māori, with the highest AFs for ethnicity of PM and ESRD, were unaffected by deprivation. At same deprivations, the AFs of MI and stroke were greatest among NZE compared with other ethnic groups; the AF of ESRD was greatest among Māori and Pasifika.

Conclusion: Both socioeconomic deprivation and ethnicity are strongly associated with outcomes in patients with T2DM in NZ, although the extent of the deprivation gradient is greatest among NZE and Asians, and least among Māori.