Autism, autistic traits and multiple risk behaviours in adolescence: a longitudinal birth cohort study.

BACKGROUND: Multiple risk behaviours (MRBs), typically beginning in adolescence, are associated with increased risk of adverse health and social outcomes. The association between autism and MRBs is little understood. METHODS: Data were from the Avon Longitudinal Study of Parents and Children, an UK-based longitudinal, birth cohort study. Exposures were diagnosed autism and four autistic traits: social communication difficulties, pragmatic language, repetitive behaviours and reduced sociability. Outcomes were participation in up to 14 risk behaviours, including alcohol consumption, smoking, risky sexual behaviours and physical inactivity. Outcome data were collected at ages approximately 12, 14, 16 and 18. RESULTS: Up to 4300 participants were included in latent basis growth curve analyses with adjustment for confounders. Social communication difficulties were associated with an above average level of MRBs engagement at ~12 years (mean difference ß 0.26; 95% CI 0.13-0.40), and above average rate of engagement from ages ~12-18 (ß 0.08; 95% CI 0.02-0.13). Repetitive behaviours were associated with above average levels of engagement in MRBs at ~12 years (ß 0.24; 95% CI 0.09-0.38). Contrastingly, reduced sociability was associated with a reduced rate of engagement in MRBs from ages ~12-18 (ß -0.06; 95% CI -0.11 to -0.02). In sex-specific analyses, persisting differences in MRB engagement patterns from ages ~12-18 were observed in males with social communication difficulties and females with reduced sociability temperament. CONCLUSIONS: Having elevated levels of some autistic traits appear to have differentiated effects on MRB engagement patterns. These findings could reflect difficulties fitting in and/or coping mechanisms relating to difficulties with fitting in.

Bias amplification of unobserved confounding in pharmacoepidemiological studies using indication-based sampling.

PURPOSE: Estimating causal effects in observational pharmacoepidemiology is a challenging task, as it is often plagued by confounding by indication. Restricting the sample to those with an indication for drug use is a commonly performed procedure; indication-based sampling ensures that the exposed and unexposed are exchangeable on the indication-limiting the potential for confounding by indication. However, indication-based sampling has received little scrutiny, despite the hazards of exposure-related covariate control. METHODS: Using simulations of varying levels of confounding and applied examples we describe bias amplification under indication-based sampling. RESULTS: We demonstrate that indication-based sampling in the presence of unobserved confounding can give rise to bias amplification, a self-inflicted phenomenon where one inflates pre-existing bias through inappropriate covariate control. Additionally, we show that indication-based sampling generally leads to a greater net bias than alternative approaches, such as regression adjustment. Finally, we expand on how bias amplification should be reasoned about when distinct clinically relevant effects on the outcome among those with an indication exist (effect-heterogeneity). CONCLUSION: We conclude that studies using indication-based sampling should have robust justification - and that it should by no means be considered unbiased to adopt such approaches. As such, we suggest that future observational studies stay wary of bias amplification when considering drug indications.

Mendelian randomisation with coarsened exposures.

A key assumption in Mendelian randomisation is that the relationship between the genetic instruments and the outcome is fully mediated by the exposure, known as the exclusion restriction assumption. However, in epidemiological studies, the exposure is often a coarsened approximation to some latent continuous trait. For example, latent liability to schizophrenia can be thought of as underlying the binary diagnosis measure. Genetically driven variation in the outcome can exist within categories of the exposure measurement, thus violating this assumption. We propose a framework to clarify this violation, deriving a simple expression for the resulting bias and showing that it may inflate or deflate effect estimates but will not reverse their sign. We then characterise a set of assumptions and a straight-forward method for estimating the effect of SD increases in the latent exposure. Our method relies on a sensitivity parameter which can be interpreted as the genetic variance of the latent exposure. We show that this method can be applied in both the one-sample and two-sample settings. We conclude by demonstrating our method in an applied example and reanalysing two papers which are likely to suffer from this type of bias, allowing meaningful interpretation of their effect sizes.

The role of familial predisposition in imaging-confirmed atraumatic rotator cuff tears.

BACKGROUND: The etiology of atraumatic rotator cuff tears is not completely understood. Limited data suggest the role of genetic and familial predisposition in the etiology of rotator cuff tears. The purpose of this study was to assess whether there is an increased likelihood of rotator cuff tears in family members of patients with rotator cuff tears vs. those without tears. This would provide evidence for whether there is an association between familial predisposition and rotator cuff tearing. METHODS: Patients presenting to a shoulder clinic were recruited in this study. They provided information on personal medical history, shoulder symptoms, and family history of rotator cuff tears. The diagnosis of rotator cuff tears was based on imaging (magnetic resonance imaging or computed tomography arthrogram) confirmation of a structural defect in the rotator cuff. The association between family history of rotator cuff problems and the likelihood of an imaging-confirmed rotator cuff tear diagnosis was evaluated using multivariate logistic regression, adjusting for age, sex, race/ethnicity, cigarette smoking, hypertension, diabetes, and depression. RESULTS: In our cohort of 2335 patients, 52.6% (n = 1229) of patients had a rotator cuff tear. Among patients with tears, 17.9% (n = 220) of patients reported a family history of rotator cuff issues vs. 11.1% (n = 123) in patients without tears. A family history of rotator cuff problems was significantly associated with the diagnosis of an imaging-confirmed rotator cuff tear (odds ratio [OR] 2.24, 95% confidence interval [CI] 1.71, 2.95). Other confounding variables such as increasing age (OR 1.06, 95% CI 1.05, 1.07) and Hispanic race/ethnicity as compared to non-Hispanic white race/ethnicity (OR 1.48, 95% CI 1.07, 2.05) were significantly associated with rotator cuff tears. Sex, smoking, hypertension, diabetes, and depression were not significantly associated with rotator cuff tearing. CONCLUSION: Our study shows that individuals with rotator cuff tears were more than 2 times as likely to have a family member with a tear as compared to patients without tears. Increasing age and patients who identified as being of Hispanic ancestry were also significantly associated with higher odds of rotator cuff tears.

Conducting public involvement in dementia research: The contribution of the European Working Group of People with Dementia to the ROADMAP project.

BACKGROUND: Dementia outcomes include memory loss, language impairment, reduced quality of life and personality changes. Research suggests that outcomes selected for dementia clinical trials might not be the most important to people affected. OBJECTIVE: One of the goals of the 'Real world Outcomes across the Alzheimer's Disease spectrum for better care: Multi-modal data Access Platform' (ROADMAP) project was to identify important outcomes from the perspective of people with dementia and their caregivers. We review how ROADMAP's Public Involvement shaped the programme, impacted the research process and gave voice to people affected by dementia. DESIGN: The European Working Group of People with Dementia (EWGPWD) were invited to participate. In-person consultations were held with people with dementia and caregivers, with advance information provided on ROADMAP activities. Constructive criticism of survey content, layout and accessibility was sought, as were views and perspectives on terminology and key concepts around disease progression. RESULTS: The working group provided significant improvements to survey accessibility and acceptability. They promoted better understanding of concepts around disease progression and how researchers might approach measuring and interpreting findings. They effectively expressed difficult concepts through real-world examples. CONCLUSIONS: The role of the EWGPWD in ROADMAP was crucial, and its impact was highly influential. Involvement from the design stage helped shape the ethos of the programme and ultimately its meaningfulness. PUBLIC CONTRIBUTION: People with dementia and their carers were involved through structured consultations and invited to provide feedback on project materials, methods and insight into terminology and relevant concepts.

Valuing Alzheimer's disease drugs: a health technology assessment perspective on outcomes.

OBJECTIVES: Due to the nature of Alzheimer's disease (AD), health technology assessment (HTA) agencies might face considerable challenges in choosing appropriate outcomes and outcome measures for drugs that treat the condition. This study sought to understand which outcomes informed previous HTAs, to explore possible reasons for prioritizations, and derive potential implications for future assessments of AD drugs. METHOD: We conducted a literature review of studies that analyzed decisions made in HTAs (across disease areas) in three European countries: England, Germany, and The Netherlands. We then conducted case studies of technology assessments conducted for AD drugs in these countries. RESULTS: Overall, outcomes measured using clinical scales dominated decisions or recommendations about whether to fund AD drugs, or price negotiations. HTA processes did not always allow the inclusion of outcomes relevant to people with AD, their carers, and families. Processes did not include early discussion and agreement on what would constitute appropriate outcome measures and cut-off points for effects. CONCLUSIONS: We conclude that in order to ensure that future AD drugs are valued appropriately and timely, early agreement with various stakeholders about outcomes, outcome measures, and cut-offs is important.

Real-world evidence in Alzheimer's disease: The ROADMAP Data Cube.

INTRODUCTION: The ROADMAP project aimed to provide an integrated overview of European real-world data on Alzheimer's disease (AD) across the disease spectrum. METHODS: Metadata were identified from data sources in catalogs of European AD projects. Priority outcomes for different stakeholders were identified through systematic literature review, patient and public consultations, and stakeholder surveys. RESULTS: Information about 66 data sources and 13 outcome domains were integrated into a Data Cube. Gap analysis identified cognitive ability, functional ability/independence, behavioral/neuropsychiatric symptoms, treatment, comorbidities, and mortality as the outcomes collected most. Data were most lacking in caregiver-related outcomes. In general, electronic health records covered a broader, less detailed data spectrum than research cohorts. DISCUSSION: This integrated real-world AD data overview provides an intuitive visual model that facilitates initial assessment and identification of gaps in relevant outcomes data to inform future prospective data collection and matching of data sources and outcomes against research protocols.

Identifying dementia cases with routinely collected health data: A systematic review.

INTRODUCTION: Prospective, population-based studies can be rich resources for dementia research. Follow-up in many such studies is through linkage to routinely collected, coded health-care data sets. We evaluated the accuracy of these data sets for dementia case identification. METHODS: We systematically reviewed the literature for studies comparing dementia coding in routinely collected data sets to any expert-led reference standard. We recorded study characteristics and two accuracy measures-positive predictive value (PPV) and sensitivity. RESULTS: We identified 27 eligible studies with 25 estimating PPV and eight estimating sensitivity. Study settings and methods varied widely. For all-cause dementia, PPVs ranged from 33%-100%, but 16/27 were >75%. Sensitivities ranged from 21% to 86%. PPVs for Alzheimer's disease (range 57%-100%) were generally higher than those for vascular dementia (range 19%-91%). DISCUSSION: Linkage to routine health-care data can achieve a high PPV and reasonable sensitivity in certain settings. Given the heterogeneity in accuracy estimates, cohorts should ideally conduct their own setting-specific validation.

Genetic liability to rheumatoid arthritis on autism and autistic traits: polygenic risk score and Mendelian randomization analyses.

Higher prevalence of autism in offspring born to mothers with rheumatoid arthritis has been reported in observational studies. We investigated (a) the associations between maternal and offspring's own genetic liability for rheumatoid arthritis and autism-related outcomes in the offspring using polygenic risk scores (PRS) and (b) whether the effects were causal using Mendelian randomization (MR). Using the latest genome-wide association (GWAS) summary data on rheumatoid arthritis and individual-level data from the Avon Longitudinal Study of Parents and Children, United Kingdom, we constructed PRSs for maternal and offspring genetic liability for rheumatoid arthritis (single-nucleotide polymorphism [SNP] p-value threshold 0.05). We investigated associations with autism, and autistic traits: social and communication difficulties, coherence, repetitive behaviours and sociability. We used modified Poisson regression with robust standard errors. In two-sample MR analyses, we used 40 genome-wide significant SNPs for rheumatoid arthritis and investigated the causal effects on risk for autism, in 18,381 cases and 27,969 controls of the Psychiatric Genetics Consortium and iPSYCH. Sample size ranged from 4992 to 7849 in PRS analyses. We found little evidence of associations between rheumatoid arthritis PRSs and autism-related phenotypes in the offspring (maternal PRS on autism: RR 0.89, 95%CI 0.73-1.07, p = 0.21; offspring's own PRS on autism: RR 1.11, 95%CI 0.88-1.39, p = 0.39). MR results provided little evidence for a causal effect (IVW OR 1.01, 95%CI 0.98-1.04, p = 0.56). There was little evidence for associations between genetic liability for rheumatoid arthritis on autism-related outcomes in offspring. Lifetime risk for rheumatoid arthritis has no causal effects on autism.

Health Outcome Prioritization in Alzheimer's Disease: Understanding the Ethical Landscape.

BACKGROUND: Dementia has been described as the greatest global health challenge in the 21st Century on account of longevity gains increasing its incidence, escalating health and social care pressures. These pressures highlight ethical, social, and political challenges about healthcare resource allocation, what health improvements matter to patients, and how they are measured. This study highlights the complexity of the ethical landscape, relating particularly to the balances that need to be struck when allocating resources; when measuring and prioritizing outcomes; and when individual preferences are sought. OBJECTIVE: Health outcome prioritization is the ranking in order of desirability or importance of a set of disease-related objectives and their associated cost or risk. We analyze the complex ethical landscape in which this takes place in the most common dementia, Alzheimer's disease. METHODS: Narrative review of literature published since 2007, incorporating snowball sampling where necessary. We identified, thematized, and discussed key issues of ethical salience. RESULTS: Eight areas of ethical salience for outcome prioritization emerged: 1) Public health and distributive justice, 2) Scarcity of resources, 3) Heterogeneity and changing circumstances, 4) Knowledge of treatment, 5) Values and circumstances, 6) Conflicting priorities, 7) Communication, autonomy and caregiver issues, and 8) Disclosure of risk. CONCLUSION: These areas highlight the difficult balance to be struck when allocating resources, when measuring and prioritizing outcomes, and when individual preferences are sought. We conclude by reflecting on how tools in social sciences and ethics can help address challenges posed by resource allocation, measuring and prioritizing outcomes, and eliciting stakeholder preferences.

Use of CT Vs. MRI for Diagnosis of Hip or Pelvic Fractures in Elderly Patients After Low Energy Trauma.

Background: Elderly patients who fall account for more than two million emergency department visits each year. In 4-10 % of these patients, initial plain radiographs do not identify a hip or pelvis fracture later diagnosed with advanced imaging. No consensus exists about what type of imaging, CT or MRI, should be obtained in elderly patients with hip or pelvic pain after a low energy trauma. The purpose of this study was to determine whether CT or MRI is more likely to result in a definitive fracture diagnosis in elderly patients with hip or pelvic pain after low energy trauma. Methods: A retrospective chart review was conducted of all patients who presented to the ED at a single level one trauma center over a 4.5 year period. Inclusion criteria were age greater than fifty years old, presentation with hip and/ or pelvis pain due to a low energy trauma, and radiographic imaging including both plain radiographs and at least one pelvis MRI or CT. Results: Of the 218 patients who met inclusion criteria and had negative initial plain radiographs, CT or MRI later diagnosed a fracture in 69 patients (32%). Seventy eight patients underwent MRI (24 fractures, 32%), 132 underwent CT imaging (41 fractures, 31%), and eight had both CT and MRI (5 fractures, 63%). Patients who underwent CT spent less time in the ED on average (430 minutes) than those who underwent MRI, or MRI and CT (502 minutes and 620 minutes respectively). Patients who underwent CT were just as likely to be diagnosed with a fracture as those who underwent MRI (p= 0.002). We encountered no cases where CT imaging did not identify a fracture that was later identified on MRI. Fifty six patients (26%) had at least one contraindication to MRI. Conclusions: Our study suggests CT may be adequate to rule out hip and pelvic fractures in this patient population. CT may be preferable to MRI based on decreased time spent in the ED and the large percentage of elderly patients with contraindications to MRI.Level of Evidence: III.

Identifying Parkinson's disease and parkinsonism cases using routinely collected healthcare data: A systematic review.

BACKGROUND: Population-based, prospective studies can provide important insights into Parkinson's disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose. METHODS: We searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity. RESULTS: We identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPV ranged from 56-90% in hospital datasets, 53-87% in prescription datasets, 81-90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPV ranged from 36-88% in hospital datasets, 40-74% in prescription datasets, and was 94% in mortality datasets. Sensitivity ranged from 15-73% in single datasets for PD and 43-63% in single datasets for parkinsonism. CONCLUSIONS: In many settings, routinely collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. However, given the wide range of identified accuracy estimates, we recommend cohorts conduct their own context-specific validation studies if existing evidence is lacking. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.

What outcomes are important to patients with mild cognitive impairment or Alzheimer's disease, their caregivers, and health-care professionals? A systematic review.

INTRODUCTION: Clinical trials involving patients with Alzheimer's disease (AD) continue to try to identify disease-modifying treatments. Although trials are designed to meet regulatory and registration requirements, many do not measure outcomes of the disease most relevant to key stakeholders. METHODS: A systematic review sought research that elicited information from people with AD, their caregivers, and health-care professionals on which outcomes of the disease were important. Studies published in any language between 2008 and 2017 were included. RESULTS: Participants in 34 studies described 32 outcomes of AD. These included clinical (memory, mental health), practical (ability to undertake activities of daily living, access to health information), and personal (desire for patient autonomy, maintenance of identity) outcomes of the disease. DISCUSSION: Evidence elicited directly from the people most affected by AD reveals a range of disease outcomes that are relevant to them but are not commonly captured in clinical trials of new treatments.

Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: A systematic review.

BACKGROUND: Motor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets. We systematically evaluated the accuracy of such datasets in identifying MND cases. METHODS: We performed an electronic search of MEDLINE, EMBASE, Cochrane Library and Web of Science for studies published between 01/01/1990-16/11/2015 that compared MND cases identified in routinely-collected, coded datasets to a reference standard. We recorded study characteristics and two key measures of diagnostic accuracy-positive predictive value (PPV) and sensitivity. We conducted descriptive analyses and quality assessments of included studies. RESULTS: Thirteen eligible studies provided 13 estimates of PPV and five estimates of sensitivity. Twelve studies assessed hospital and/or death certificate-derived datasets; one evaluated a primary care dataset. All studies were from high income countries (UK, Europe, USA, Hong Kong). Study methods varied widely, but quality was generally good. PPV estimates ranged from 55-92% and sensitivities from 75-93%. The single (UK-based) study of primary care data reported a PPV of 85%. CONCLUSIONS: Diagnostic accuracy of routinely-collected health datasets is likely to be sufficient for identifying cases of MND in large-scale prospective epidemiological studies in high income country settings. Primary care datasets, particularly from countries with a widely-accessible national healthcare system, are potentially valuable data sources warranting further investigation.

Efficient high-throughput biological process characterization: Definitive screening design with the ambr250 bioreactor system.

The burgeoning pipeline for new biologic drugs has increased the need for high-throughput process characterization to efficiently use process development resources. Breakthroughs in highly automated and parallelized upstream process development have led to technologies such as the 250-mL automated mini bioreactor (ambr250™) system. Furthermore, developments in modern design of experiments (DoE) have promoted the use of definitive screening design (DSD) as an efficient method to combine factor screening and characterization. Here we utilize the 24-bioreactor ambr250™ system with 10-factor DSD to demonstrate a systematic experimental workflow to efficiently characterize an Escherichia coli (E. coli) fermentation process for recombinant protein production. The generated process model is further validated by laboratory-scale experiments and shows how the strategy is useful for quality by design (QbD) approaches to control strategies for late-stage characterization.