Systematic Multi-Domain Alzheimer's Risk Reduction Trial (SMARRT): Study Protocol.

This article describes the protocol for the Systematic Multi-domain Alzheimer's Risk Reduction Trial (SMARRT), a single-blind randomized pilot trial to test a personalized, pragmatic, multi-domain Alzheimer's disease (AD) risk reduction intervention in a US integrated healthcare delivery system. Study participants will be 200 higher-risk older adults (age 70-89 years with subjective cognitive complaints, low normal performance on cognitive screen, and ≥ two modifiable risk factors targeted by our intervention) who will be recruited from selected primary care clinics of Kaiser Permanente Washington, oversampling people with non-white race or Hispanic ethnicity. Study participants will be randomly assigned to a two-year Alzheimer's risk reduction intervention (SMARRT) or a Health Education (HE) control. Randomization will be stratified by clinic, race/ethnicity (non-Hispanic white versus non-white or Hispanic), and age (70-79, 80-89). Participants randomized to the SMARRT group will work with a behavioral coach and nurse to develop a personalized plan related to their risk factors (poorly controlled hypertension, diabetes with evidence of hyper or hypoglycemia, depressive symptoms, poor sleep quality, contraindicated medications, physical inactivity, low cognitive stimulation, social isolation, poor diet, smoking). Participants in the HE control group will be mailed general health education information about these risk factors for AD. The primary outcome is two-year cognitive change on a cognitive test composite score. Secondary outcomes include: 1) improvement in targeted risk factors, 2) individual cognitive domain composite scores, 3) physical performance, 4) functional ability, 5) quality of life, and 6) incidence of mild cognitive impairment, AD, and dementia. Primary and secondary outcomes will be assessed in both groups at baseline and 6, 12, 18, and 24 months

Interrupted time series analysis to evaluate the performance of drug overdose morbidity indicators shows discontinuities across the ICD-9-CM to ICD-10-CM transition

Introduction: On 1 October 2015, the USA transitioned from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, 10th Revision (ICD-10-CM). Considering the major changes to drug overdose coding, we examined how using different approaches to define all-drug overdose and opioid overdose morbidity indicators in ICD-9-CM impacts longitudinal analyses that span the transition, using emergency department (ED) and hospitalisation data from six states’ hospital discharge data systems. Methods: We calculated monthly all-drug and opioid overdose ED visit rates and hospitalisation rates (per 100 000 population) by state, starting in January 2010. We applied three ICD-9-CM indicator definitions that included identical all-drug or opioid-related codes but restricted the number of fields searched to varying degrees. Under ICD-10-CM, all fields were searched for relevant codes. Adjusting for seasonality and autocorrelation, we used interrupted time series models with level and slope change parameters in October 2015 to compare trend continuity when employing different ICD-9-CM definitions. Results: Most states observed consistent or increased capture of all-drug and opioid overdose cases in ICD10-CM coded hospital discharge data compared with ICD-9-CM. More inclusive ICD-9-CM indicator definitions reduced the magnitude of significant level changes, but the effect of the transition was not eliminated. Discussion: The coding change appears to have introduced systematic differences in measurement of drug overdoses before and after 1 October 2015. When using hospital discharge data for drug overdose surveillance, researchers and decision makers should be aware that trends spanning the transition may not reflect actual changes in drug overdose rates

Understanding emergency hospital admission of older people

This report sets out and discusses the findings of our study to gain an improved understanding of the drivers of emergency hospital admissions of older people in England and to formulate evidence-based scenarios for possible future trends in these emergency admissions. Commissioned by the Department of Health

Pediatric observation status: Are we overlooking a growing population in children's hospitals?

BACKGROUND: Inpatient administrative datasets often exclude observation stays, as observation is considered to be outpatient care. The extent to which this status is applied to pediatric hospitalizations is not known. OBJECTIVE: To characterize trends in observation status code utilization and 1‐day stays among children admitted from the emergency department (ED), and to compare patient characteristics and outcomes associated with observation versus inpatient stays. DESIGN: Retrospective longitudinal analysis of the 2004–2009 Pediatric Health Information System (PHIS). SETTING: Sixteen US freestanding children's hospitals contributing outpatient and inpatient data to PHIS. PATIENTS: Admissions to observation or inpatient status following ED care in study hospitals. MEASUREMENTS: Proportions of observation and 1‐day stays among all admissions from the ED were calculated each year. Top ranking discharge diagnoses and outcomes of observation were determined. Patient characteristics, discharge diagnoses, and return visits were compared for observation and 1‐day stays. RESULTS: The proportion of short‐stays (including both observation and 1‐day stays) increased from 37% to 41% between 2004 and 2009. Since 2007, observation stays have outnumbered 1‐day stays. In 2009, more than half of admissions from the ED for 6 of the top 10 ranking discharge diagnoses were short‐stays. Fewer than 25% of observation stays converted to inpatient status. Return visits and readmissions following observation were no more frequent than following 1‐day stays. CONCLUSIONS: Children admitted under observation status make up a substantial proportion of acute care hospitalizations. Analyses of inpatient administrative databases that exclude observation stays likely result in an underestimation of hospital resource utilization for children. Journal of Hospital Medicine 2012; © 2012 Society of Hospital MedicinePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93720/1/1923_ftp.pd

Chapters in the Epidemiology of child and adolescent mental health: risk factors, prevention, treatment and outcomes

Mental illnesses are a substantial burden in Canada and worldwide. Early life conditions and experiences make individuals more susceptible to developing diseases. The primary goal of this thesis is to understand mental health issues in children and adolescents and to provide a basis for prevention planning and policy. The four core studies in this thesis utilize a variety of epidemiological methods and data sources. The first study, a systematic review and meta-analysis of longitudinal studies, found that early childhood maltreatment is a strong risk factor for the latter onset of depression and anxiety disorders. Proportion attributable fractions (PAFs) indicated a very large reduction in depression and anxiety could result from reducing childhood maltreatment. The second study explored epigenetic changes (DNA methylation) linked to depression. This systematic review found inconsistent results for candidate genes (e.g. BDNF, SLC6A4, NR3C1, OXTR, and others) and genome-wide studies. There was high heterogeneity in terms of experimental and statistical methodologies among the studies. Future studies should apply standardized experimental and laboratory methodologies and adopt longitudinal designs to trace changes overtime. The third study using clinical administrative data examined whether current child and adolescent mental health services effectively improved clients’ psychosocial functioning. Treatment was found to be effective though the initial severity of the problem affected outcomes. While shortening the length of treatment might improve resource use efficiency, it would be detrimental to some clients. Personalized treatment is required to meet clients’ specific needs. Finally, the potential iatrogenic effects (Bipolar Disorder (BPAD)) of pharmacological treatment (stimulant) of children and adolescents for ADHD is examined using a cohort study design and provincial administrative data. After adjusting for psychiatric comorbidity, it was found that stimulant use by itself does not lead to the development of BPAD, but rather the severity of the initial disease and comorbidity are predictors of future BPAD. The clear message of this research is that early reduction in risk factor exposure in utero and in childhood and adolescence and the early treatment of mental health problems has a very positive effect in reducing the onset and further development of psychiatric diseases and mental health problems

Mining High Impact Combinations of Conditions from the Medical Expenditure Panel Survey

The condition of multimorbidity — the presence of two or more medical conditions in an individual — is a growing phenomenon worldwide. In the United States, multimorbid patients represent more than a third of the population and the trend is steadily increasing in an already aging population. There is thus a pressing need to understand the patterns in which multimorbidity occurs, and to better understand the nature of the care that is required to be provided to such patients. In this thesis, we use data from the Medical Expenditure Panel Survey (MEPS) from the years 2011 to 2015 to identify combinations of multiple chronic conditions (MCCs). We first quantify the significant heterogeneity observed in these combinations and how often they are observed across the five years. Next, using two criteria associated with each combination -- (a) the annual prevalence and (b) the annual median expenditure -- along with the concept of non-dominated Pareto fronts, we determine the degree of impact each combination has on the healthcare system. Our analysis reveals that combinations of four or more conditions are often mixtures of diseases that belong to different clinically meaningful groupings such as the metabolic disorders (diabetes, hypertension, hyperlipidemia); musculoskeletal conditions (osteoarthritis, spondylosis, back problems etc.); respiratory disorders (asthma, COPD etc.); heart conditions (atherosclerosis, myocardial infarction); and mental health conditions (anxiety disorders, depression etc.). Next, we use unsupervised learning techniques such as association rule mining and hierarchical clustering to visually explore the strength of the relationships/associations between different conditions and condition groupings. This interactive framework allows epidemiologists and clinicians (in particular primary care physicians) to have a systematic approach to understand the relationships between conditions and build a strategy with regards to screening, diagnosis and treatment over a longer term, especially for individuals at risk for more complications. The findings from this study aim to create a foundation for future work where a more holistic view of multimorbidity is possible

Effects of Social Determinants of Health in Progression to Type 2 Diabetes

The prevalence of prediabetes and diabetes in the United States and around the world has increased faster than expected in the last 30 years. The economic burden this costs a nation can be astronomic both in terms of expense and loss in productivity. One-third of U.S. adults, 86 million people, have prediabetes. Effective management is needed that can reach these 86 million, and others at high risk, to reduce their progression to diagnosed Type 2 diabetes. After the literature review, there was not enough literature to support how these led to the progression to diabetes. The abundant literature is centered on how to prevent complications and improve the quality of life of those living with type 2 diabetes. This paper will focus on the longitudinal association between these social determinants and how they may predispose to the progression to Type 2 diabetes

Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.

OBJECTIVE: Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning. MATERIALS AND METHODS: We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, ICD-9, ICD-10, and PheWAS, and evaluated the prediction performances of these terminologies on 2 different tasks: the risk prediction of heart failure in diabetes patients and the risk prediction of pancreatic cancer. Two popular models were evaluated: logistic regression and a recurrent neural network. RESULTS: For logistic regression, using UMLS delivered the optimal area under the receiver operating characteristics (AUROC) results in both dengue hemorrhagic fever (81.15%) and pancreatic cancer (80.53%) tasks. For recurrent neural network, UMLS worked best for pancreatic cancer prediction (AUROC 82.24%), second only (AUROC 85.55%) to PheWAS (AUROC 85.87%) for dengue hemorrhagic fever prediction. DISCUSSION/CONCLUSION: In our experiments, terminologies with larger vocabularies and finer-grained representations were associated with better prediction performances. In particular, UMLS is consistently 1 of the best-performing ones. We believe that our work may help to inform better designs of predictive models, although further investigation is warranted

Estimating the costs of gender-based violence in the European Union

The purpose of the study is to identify and recommend appropriate methodologies to measure the cost of gender-based and intimate partner violence in EU-28 Member States. To define gender-based and intimate partner violence for this study we draw on the definitions advanced by the Declaration on the Elimination of Violence Against Women (UN 1993) and Council of Europe (2011) respectively. These authorities focus on the forms of violence, violence perpetrated by intimate partners and other family members (domestic violence) and sexual violence that are disproportionality perpetrated against and disproportionality impact women