Phenotypic distinctions in depression and anxiety: a comparative analysis of comorbid and isolated cases.

BACKGROUND: Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety. METHODS: Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses. RESULTS: Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10-27), sleep apnea (OR 1.71; p = 1 × 10-22), and type II diabetes (OR 1.74; p = 9 × 10-18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10-25), benign skin neoplasms (OR 1.61; p = 2 × 10-17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10-12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression. CONCLUSIONS: While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.

The importance of social activity to risk of major depression in older adults.

BACKGROUND: Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults. METHODS: We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis. RESULTS: Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level]. CONCLUSION: Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people.

BACKGROUND: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation. METHODS: Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry. RESULTS: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity. CONCLUSIONS: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Atopic asthma as a potentially significant but unrecognized risk factor for Kawasaki disease in children.

OBJECTIVES: Childhood asthma is known to be associated with risks of both respiratory and non-respiratory infections. Little is known about the relationship between asthma and the risk of Kawasaki disease (KD). We assessed associations of asthma status and asthma phenotype (e.g. atopic asthma) with KD. METHODS: We performed a population-based retrospective case-control study, using KD cases between January 1, 1979, and December 31, 2016, and two matched controls per case. KD cases were defined by the American Heart Association diagnostic criteria. Asthma status prior to KD (or control) index dates was ascertained by the two asthma criteria, Predetermined Asthma Criteria (PAC) and Asthma Predictive Index (API, a surrogate phenotype of atopic asthma). We assessed whether 4 phenotypes (both PAC + and API+; PAC + only; API + only, and non-asthmatics) were associated with KD. RESULTS: There were 124 KD cases during the study period. The group having both PAC + and API + was significantly associated with the increased odds of KD, compared to non-asthmatics (odds ratio [OR] 4.3; 95% CI: 1.3 - 14.3). While asthma defined by PAC was not associated with KD, asthma defined by PAC positive with eosinophilia (≥4%) was significantly associated with the increased odds of KD (OR: 6.7; 95% CI: 1.6 - 28.6) compared to non-asthmatics. Asthma status defined by API was associated with KD (OR = 4.7; 95% CI: 1.4-15.1). CONCLUSIONS: Atopic asthma may be associated with increased odds of KD. Further prospective studies are needed to determine biological mechanisms underlying the association between atopic asthma and increased odds of KD.

A diagnostic codes-based algorithm improves accuracy for identification of childhood asthma in archival data sets.

OBJECTIVE: While a single but truncated ICD code (493) had been widely used for identifying asthma in asthma care and research, it significantly under-identifies asthma. We aimed to develop and validate a diagnostic codes-based algorithm for identifying asthmatics using Predetermined Asthma Criteria (PAC) as the reference. METHODS: This is a retrospective cross-sectional study which utilized two different coding systems, the Hospital Adaptation of the International Classification of Diseases, Eighth Revision (H-ICDA) and the International Classification of Diseases, Ninth Revision (ICD-9). The algorithm was developed using two population-based asthma study cohorts, and validated in a validation cohort, a random sample of the 1976-2007 Olmsted County Birth Cohort. Performance of the diagnostic codes-based algorithm for ascertaining asthma status against manual chart review for PAC (gold standard) was assessed by determining both criterion and construct validity. RESULTS: Among eligible 267 subjects of the validation cohort, 50% were male, 70% white, and the median age at last follow-up was 17 (interquartile range, 8.7-24.4) years. Asthma prevalence by PAC through manual chart review was 34%. Sensitivity and specificity of the codes-based algorithm for identifying asthma were 82% and 98% respectively. Associations of asthma-related risk factors with asthma status ascertained by the code-based algorithm were similar to those by the manual review. CONCLUSIONS: The diagnostic codes-based algorithm for identifying asthmatics improves accuracy of identification of asthma and can be a useful tool for large scale studies in a setting without automated chart review capabilities.

Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique.

BACKGROUND: A subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a broad range of electronic health record (EHR) data sources to automatically identify AIMs accurately and efficiently. METHODS: We established an expert consensus for an operational definition for each AIM from EHR through a modified Delphi technique. A series of questions about the operational definition of 19 AIMS (11 infectious diseases and 8 inflammatory diseases) was generated by a core team of experts who considered feasibility, balance between sensitivity and specificity, and generalizability. Eight internal and 5 external expert panelists were invited to individually complete a series of online questionnaires and provide judgement and feedback throughout three sequential internal rounds and two external rounds. Panelists' responses were collected, descriptive statistics tabulated, and results reported back to the entire group. Following each round the core team of experts made iterative edits to the operational definitions until a moderate (≥ 60%) or strong (≥ 80%) level of consensus among the panel was achieved. RESULTS: Response rates for each Delphi round were 100% in all 5 rounds with the achievement of the following consensus levels: (1) Internal panel consensus: 100% for 8 definitions, 88% for 10 definitions, and 75% for 1 definition, (2) External panel consensus: 100% for 12 definitions and 80% for 7 definitions. CONCLUSIONS: The final operational definitions of AIMs established through a modified Delphi technique can serve as a foundation for developing computational algorithms to automatically identify AIMs from EHRs to enable large scale research studies on patient's multimorbidities associated with asthma.

Long-term Incidence of Kawasaki Disease in a North American Community: A Population-Based Study.

To assess the longitudinal incidence of Kawasaki disease (KD) within the well-defined predominantly White population of Olmsted County, MN. This retrospective cohort study used a population-based medical record linkage system and manual chart reviews to identify children with KD in Olmsted County, MN between January 1, 1979-December 31, 2016. Age- and gender-adjusted incidence rates were calculated using the 2010 U.S. White population. 124 children with KD were confirmed during the study period (median age 3.5, 61% male, 85% White, 9% Asian). The overall age- and gender-adjusted incidence rates for all ages and < 5 years old were 9.8 and 21.4 per 100,000 person-years, respectively. There was an overall increase in incidence up to 1994 followed by plateau, except among children between the ages of 1-5 years. There was also an overall increase in incidence among females compared to males. 24% of children had cardiac complications. While the overall incidence of KD in Olmsted County appears to be stable since 1994, the incidence of KD in subgroups of children 1-5 years old and females seems to have increased. Given the rising trends and one-quarter of children developing cardiac complications, further studies identifying factors driving these trends are warranted.

Relationship between asthma status and antibody response pattern to 23-valent pneumococcal vaccination.

Objective: Asthma poses an increased risk for serious pneumococcal disease, but little is known about the influence of asthma status on the 23-valent serotype-specific pneumococcal antibody response. We examined differences in antibody titers between pre- and post-vaccination with 23-valent pneumococcal polysaccharide vaccine (PPSV-23) in relation to asthma status. Methods: Asthma status was retrospectively ascertained by the Predetermined Asthma Criteria in an existing vaccine cohort through comprehensive medical record review. Twenty-three serotype-specific pneumococcal antibody titers measured at baseline and 4-6 weeks post-vaccination were analyzed. Vaccine responses to PPSV-23 were calculated from pre- to post-vaccine titers for each of the serotypes. Results: Of the 64 eligible and enrolled subjects, 18 (28%) had asthma. Controls (i.e., subjects without asthma) demonstrated a statistically significant fold change response compared to their baseline for all serotypes, while those with asthma did not mount a significant response to serotypes 7F, 22F, and 23F. The overall vaccine response as measured by fold change over baseline was lower in subjects with asthma than controls. Conclusions: Poorer humoral immune responses to PPSV-23 as measured by fold change were more likely to be observed in subjects with asthma compared to controls. We recommend the consideration of asthma status when interpreting vaccine response for immune competence workup through larger studies. Further studies are warranted to replicate these findings.

Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review.

RATIONALE: Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research. OBJECTIVES: We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs). METHODS: The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis). MEASUREMENTS AND MAIN RESULTS: After excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same. CONCLUSIONS: Asthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice.

Automated chart review utilizing natural language processing algorithm for asthma predictive index.

BACKGROUND: Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria. METHODS: This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n = 87) and validated on a test cohort (n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma. RESULTS: Among the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6-6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8-10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively. CONCLUSION: NLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.

Heterogeneity of asthma and the risk of celiac disease in children.

BACKGROUND: Although human leukocyte antigen (HLA)-DR and HLA-DQ genes and gluten play crucial roles in developing celiac disease (CD), most patients with these risk factors still do not develop CD, which indicates additional unrecognized risk factors. OBJECTIVE: To determine the association between asthma and the risk of CD in children. METHODS: We conducted a population-based retrospective case-control study in children who resided in Olmsted County, Minnesota. We identified children with CD (cases) between January 1, 1997, and December 31, 2014, and compared these with children without CD (controls) (1:2 matching). Asthma status was ascertained by using the predetermined asthma criteria (PAC) and the asthma predictive index (API). Data analysis included conditional logistic regression models and an unsupervised network analysis by using an independent phenome-wide association scan (PheWAS) data set. RESULTS: Although asthma status as determined by using PAC was not associated with the risk of CD (odds ratio [OR] 1.4 [95% confidence interval {CI}, 0.8-2.5]; p = 0.2), asthma status by using the API was significantly associated (OR 2.8 [95% CI, 1.3-6.0]; p = 0.008). A subgroup analysis indicated that children with both asthma as determined by using PAC and a family history of asthma had an increased risk of CD compared with those without asthma (OR 2.28 [95% CI, 1.11-4.67]; p = 0.024). PheWAS data showed a cluster of asthma single nucleotide polymorphisms and patients with CD. CONCLUSION: A subgroup of children with asthma who also had a family history of asthma seemed to be at an increased risk of CD, and, thus, the third factor that underlies the risk of CD might be related to genetic factors for asthma. Heterogeneity of asthma plays a role in determining the risk of asthma-related comorbidity.

What accounts for the association between late preterm births and risk of asthma?

BACKGROUND: Although results of many studies have indicated an increased risk of asthma in former late preterm (LPT) infants, most of these studies did not fully address covariate imbalance. OBJECTIVE: To compare the cumulative frequency of asthma in a population-based cohort of former LPT infants to that of matched term infants in their early childhood, when accounting for covariate imbalance. METHODS: From a population-based birth cohort of children born 2002-2006 in Olmsted County, Minnesota, we assessed a random sample of LPT (34 to 36 6/7 weeks) and frequency-matched term (37 to 40 6/7 weeks) infants. The subjects were followed-up through 2010 or censored based on the last date of contact, with the asthma status based on predetermined criteria. The Kaplan-Meier method was used to estimate the cumulative incidence of asthma during the study period. Cox models were used to estimate the hazard ratio and 95% confidence interval for the risk of asthma, when adjusting for potential confounders. RESULTS: LPT infants (n = 282) had a higher cumulative frequency of asthma than did term infants (n = 297), 29.9 versus 19.5%, respectively; p = 0.01. After adjusting for covariates associated with the risk of asthma, an LPT birth was not associated with a risk of asthma, whereas maternal smoking during pregnancy was associated with a risk of asthma. CONCLUSION: LPT birth was not independently associated with a risk of asthma and other atopic conditions. Clinicians should make an effort to reduce exposure to smoking during pregnancy as a modifiable risk factor for asthma.

Asthma as a risk factor for zoster in adults: A population-based case-control study.

BACKGROUND: We recently reported an increased risk of herpes zoster (shingles or zoster) in children with asthma, but little is known about whether the same is true for adults with asthma. OBJECTIVE: We determined whether asthma is associated with an increased risk of zoster in adults. METHODS: This study was designed as a population-based case-control study. Zoster cases during the study period were identified among adults (aged ≥50 years) who resided in Olmsted County, Minnesota. We compared the frequency of asthma between zoster cases and birthday- and sex-matched control subjects (1:2 matching) without a history of zoster. Asthma status was ascertained based on predetermined criteria. A conditional logistic regression model was used to assess the association of asthma with risk of zoster. RESULTS: A total of 371 zoster cases and their 742 matched control subjects were enrolled. Of the 371 cases, 246 (66%) were female, 348 (94%) were white, and the mean ± SD age was 66.8 ± 10.7 years. Twenty-three percent (n = 87) of zoster cases had a history of asthma compared with 15% (n = 114) of control subjects. Controlling for pertinent covariates and confounders, there was a significant association between a history of asthma and risk of zoster (adjusted odds ratio, 1.70; 95% CI, 1.20-2.42; P = .003). The population attributable risk percentage for asthma was about 10%. CONCLUSIONS: Asthma is an unrecognized risk factor for zoster in adults. Consideration should be given to immunizing adults with asthma aged more than 50 years as a target group.

Accumulating evidence for a role of TCF7L2 variants in bipolar disorder with elevated body mass index.

OBJECTIVES: Bipolar disorder (BD) is a complex disease associated with various hereditary traits, including a higher body mass index (BMI). In a prior genome-wide association study, we found that BMI modified the association of rs12772424 - a common variant in the gene encoding transcription factor 7-like 2 (TCF7L2) - with risk for BD. TCF7L2 is a transcription factor in the canonical Wnt pathway, involved in multiple disorders, including diabetes, cancer and psychiatric conditions. Here, using an independent sample, we evaluated 26 TCF7L2 single nucleotide polymorphisms (SNPs) to explore further the association of BD with the TCF7L2-BMI interaction. METHODS: Using a sample of 662 BD cases and 616 controls, we conducted SNP-level and gene-level tests to assess the evidence for an association between BD and the interaction of BMI and genetic variation in TCF7L2. We also explored the potential mechanism behind the detected associations using human brain expression quantitative trait loci (eQTL) analysis. RESULTS: The analysis provided independent evidence of an rs12772424-BMI interaction (p = 0.011). Furthermore, while overall there was no evidence for SNP marginal effects on BD, the TCF7L2-BMI interaction was significant at the gene level (p = 0.042), with seven of the 26 SNPs showing SNP-BMI interaction effects with p < 0.05. The strongest evidence of interaction was observed for rs7895307 (p = 0.006). TCF7L2 expression showed a significant enrichment of association with the expression of other genes in the Wnt canonical pathway. CONCLUSIONS: The current study provides further evidence suggesting that TCF7L2 involvement in BD risk may be regulated by BMI. Detailed, prospective assessment of BMI, comorbidity, and other possible contributing factors is necessary to explain fully the mechanisms underlying this association.

Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records.

BACKGROUND: Major depressive disorder (MDD) is often comorbid with other chronic mental and physical health conditions. Although the literature widely acknowledges the association of many chronic conditions with the risk of MDD, the relative importance of these conditions on MDD risk in the presence of other conditions is not well investigated. In this study, we aimed to quantify the relative contribution of selected chronic conditions to identify the conditions most influential to MDD risk in adults and identify differences by age. METHODS: This study used electronic health record (EHR) data on patients empanelled with primary care at Mayo Clinic in June 2013. A validated EHR-based algorithm was applied to identify newly diagnosed MDD patients between 2000 and 2013. Non-MDD controls were matched 1:1 to MDD cases on birth year (±2 years), sex, and outpatient clinic visits in the same year of MDD case diagnosis. Twenty-four chronic conditions defined by Chronic Conditions Data Warehouse were ascertained in both cases and controls using diagnosis codes within 5 years of index dates (diagnosis dates for cases, and the first clinic visit dates for matched controls). For each age group (45 years or younger, between 46 and 60, and over 60 years), conditional logistic regression models were used to test the association between each condition and subsequent MDD risk, adjusting for educational attainment and obesity. The relative influence of these conditions on the risk of MDD was quantified using gradient boosting machine models. RESULTS: A total of 11,375 incident MDD cases were identified between 2000 and 2013. Most chronic conditions (except for eye conditions) were associated with risk of MDD, with different association patterns observed depending on age. Among 24 chronic conditions, the greatest relative contribution was observed for diabetes mellitus for subjects aged ≤ 60 years and rheumatoid arthritis/osteoarthritis for those over 60 years. CONCLUSIONS: Our results suggest that specific chronic conditions such as diabetes mellitus and rheumatoid arthritis/osteoarthritis may have greater influence than others on the risk of MDD.

A new socioeconomic status measure for vaccine research in children using individual housing data: a population-based case-control study.

BACKGROUND: We recently developed HOUSES, an individual housing-based socioeconomic status (SES) measurement for health disparities research. We assessed whether HOUSES was associated with risk of pertussis and pertussis vaccine up-to-date status in children. METHODS: The study utilized a previous population-based case-control study cohort assembled during the 2004-2005 pertussis outbreak. We collected data on pertussis vaccine status (up-to-date status) at the time of the index date. Using a z-score for housing value, actual square footage, and numbers of bedrooms and bathrooms, HOUSES was formulated in continuous variable and categorized into quartiles. Vaccine up-to-date status was compared among subjects with different SES as measured by HOUSES using a chi-square test and logistic regression models. RESULTS: Of the 391 eligible pediatric subjects (median age of 13.1 years with male sex of 55 %), 363 (93 %) were successfully geocoded to formulate HOUSES index. HOUSES was not associated with the risk of pertussis (p = 0.82). Pertussis vaccine up-to-date statuses were 79, 86, 83, and 94 % for children in the first (the lowest SES), second, third, and fourth quartiles of HOUSES, respectively (p = 0.03). HOUSES as a continuous variable was associated with pertussis vaccine up-to-date status (adjusted OR: 1.15 per increment of one unit of HOUSES, 95 % CI: 1.04-1.27, p = 0.008). CONCLUSION: While HOUSES is not associated with the risk of pertussis, it predicts vaccine up-to-date status among children with different SES. HOUSES may be a useful tool for vaccine delivery research among children.

Association between history of psychosis and cardiovascular disease in bipolar disorder.

OBJECTIVES: To determine whether clinical features of bipolar disorder, such as history of psychosis, and cardiovascular disease (CVD) risk factors contribute to a higher risk of CVD among patients with bipolar disorder. METHODS: This cross-sectional study included a sample of 988 patients with bipolar I or bipolar II disorder or schizoaffective bipolar type confirmed by the Structured Clinical Interview for DSM-IV-TR disorders (SCID). Medical comorbidity burden was quantified utilizing the Cumulative Illness Severity Rating Scale (CIRS). This 13-item organ-based scale includes cardiac disease severity quantification. Confirmed by medical record review, patients who scored 1 (current mild or past significant problem) or higher in the cardiac item were compared by logistic regression to patients who scored 0 (no impairment), adjusting for CVD risk factors that were selected using a backwards stepwise approach or were obtained from the literature. RESULTS: In a multivariate model, age [odds ratio (OR) = 3.03, 95% confidence interval (CI): 1.66-5.54, p < 0.0001], hypertension (OR = 2.43, 95% CI: 1.69-3.55, p < 0.0001), and history of psychosis (OR = 1.48, 95% CI: 1.03-2.13, p = 0.03) were associated with CVD. When CVD risk factors from the literature were added to the analysis, age (OR = 3.19, 95% CI: 1.67-6.10, p = 0.0005) and hypertension (OR = 2.46, 95% CI: 1.61-3.76, p < 0.01) remained significant, with psychosis being at the trend level (OR = 1.43, 95% CI: 0.96-2.13, p = 0.08). CONCLUSIONS: The phenotype of psychotic bipolar disorder may reflect higher illness severity with associated cardiac comorbidity. Further studies are encouraged to clarify the effect of the disease burden (i.e., depression), lifestyle, and treatment interventions (i.e., atypical antipsychotics) on this risk association.

Quantifying the importance of disease burden on perceived general health and depressive symptoms in patients within the Mayo Clinic Biobank.

BACKGROUND: Deficits in health-related quality of life (HRQOL) may be associated with worse patient experiences, outcomes and even survival. While there exists evidence to identify risk factors associated with deficits in HRQOL among patients with individual medical conditions such as cancer, it is less well established in more general populations without attention to specific illnesses. This study used patients with a wide range of medical conditions to identify contributors with the greatest influence on HRQOL deficits. METHODS: Self-perceived general health and depressive symptoms were assessed using data from 21,736 Mayo Clinic Biobank (MCB) participants. Each domain was dichotomized into categories related to poor health: deficit (poor/fair for general health and ≥3 for PHQ-2 depressive symptoms) or non-deficit. Logistic regression models were used to test the association of commonly collected demographic characteristics and disease burden with each HRQOL domain, adjusting for age and gender. Gradient boosting machine (GBM) models were applied to quantify the relative influence of contributors on each HRQOL domain. RESULTS: The prevalence of participants with a deficit was 9.5 % for perception of general health and 4.6 % for depressive symptoms. For both groups, disease burden had the strongest influence for deficit in HRQOL (63 % for general health and 42 % for depressive symptoms). For depressive symptoms, age was equally influential. The prevalence of a deficit in general health increased slightly with age for males, but remained stable across age for females. Deficit in depressive symptoms was inversely associated with age. For both HRQOL domains, risk of a deficit was associated with higher disease burden, lower levels of education, no alcohol consumption, smoking, and obesity. Subjects with deficits were less likely to report that they were currently working for pay than those without a deficit; this association was stronger among males than females. CONCLUSIONS: Comorbid health burden has the strongest influence on deficits in self-perceived general health, while demographic factors show relatively minimal impact. For depressive symptoms, both age and comorbid health burden were equally important, with decreasing deficits in depressive symptoms with increasing age. For interpreting patient-reported metrics and comparison, one must account for comorbid health burden.

Comparative analysis of online health queries originating from personal computers and smart devices on a consumer health information portal.

BACKGROUND: The number of people using the Internet and mobile/smart devices for health information seeking is increasing rapidly. Although the user experience for online health information seeking varies with the device used, for example, smart devices (SDs) like smartphones/tablets versus personal computers (PCs) like desktops/laptops, very few studies have investigated how online health information seeking behavior (OHISB) may differ by device. OBJECTIVE: The objective of this study is to examine differences in OHISB between PCs and SDs through a comparative analysis of large-scale health search queries submitted through Web search engines from both types of devices. METHODS: Using the Web analytics tool, IBM NetInsight OnDemand, and based on the type of devices used (PCs or SDs), we obtained the most frequent health search queries between June 2011 and May 2013 that were submitted on Web search engines and directed users to the Mayo Clinic's consumer health information website. We performed analyses on "Queries with considering repetition counts (QwR)" and "Queries without considering repetition counts (QwoR)". The dataset contains (1) 2.74 million and 3.94 million QwoR, respectively for PCs and SDs, and (2) more than 100 million QwR for both PCs and SDs. We analyzed structural properties of the queries (length of the search queries, usage of query operators and special characters in health queries), types of search queries (keyword-based, wh-questions, yes/no questions), categorization of the queries based on health categories and information mentioned in the queries (gender, age-groups, temporal references), misspellings in the health queries, and the linguistic structure of the health queries. RESULTS: Query strings used for health information searching via PCs and SDs differ by almost 50%. The most searched health categories are "Symptoms" (1 in 3 search queries), "Causes", and "Treatments & Drugs". The distribution of search queries for different health categories differs with the device used for the search. Health queries tend to be longer and more specific than general search queries. Health queries from SDs are longer and have slightly fewer spelling mistakes than those from PCs. Users specify words related to women and children more often than that of men and any other age group. Most of the health queries are formulated using keywords; the second-most common are wh- and yes/no questions. Users ask more health questions using SDs than PCs. Almost all health queries have at least one noun and health queries from SDs are more descriptive than those from PCs. CONCLUSIONS: This study is a large-scale comparative analysis of health search queries to understand the effects of device type (PCs vs. SDs) used on OHISB. The study indicates that the device used for online health information search plays an important role in shaping how health information searches by consumers and patients are executed.