Can the use of Bayesian analysis methods correct for incompleteness in electronic health records diagnosis data? Development of a novel method using simulated and real-life clinical data

Background Patient health information is collected routinely in electronic health records (EHRs) and used for research purposes, however, many health conditions are known to be under-diagnosed or under-recorded in EHRs. In research, missing diagnoses result in under-ascertainment of true cases, which attenuates estimated associations between variables and results in a bias towards the null. Bayesian approaches allow the specification of prior information to the model, such as the likely rates of missingness in the data. This paper describes a Bayesian analysis approach which aimed to reduce attenuation of associations in EHR studies focussed on conditions characterised by under-diagnosis. Methods Study 1: We created synthetic data, produced to mimic structured EHR data where diagnoses were under-recorded. We fitted logistic regression (LR) models with and without Bayesian priors representing rates of misclassification in the data. We examined the LR parameters estimated by models with and without priors. Study 2: We used EHR data from UK primary care in a case-control design with dementia as the outcome. We fitted LR models examining risk factors for dementia, with and without generic prior information on misclassification rates. We examined LR parameters estimated by models with and without the priors, and estimated classification accuracy using Area Under the Receiver Operating Characteristic. Results Study 1: In synthetic data, estimates of LR parameters were much closer to the true parameter values when Bayesian priors were added to the model; with no priors, parameters were substantially attenuated by under-diagnosis. Study 2: The Bayesian approach ran well on real life clinic data from UK primary care, with the addition of prior information increasing LR parameter values in all cases. In multivariate regression models, Bayesian methods showed no improvement in classification accuracy over traditional LR. Conclusions The Bayesian approach showed promise but had implementation challenges in real clinical data: prior information on rates of misclassification was difficult to find. Our simple model made a number of assumptions, such as diagnoses being missing at random. Further development is needed to integrated the method into studies using real-life EHR data. Our findings nevertheless highlight the importance of developing methods to address missing diagnoses in EHR data

Healthcare use by children and young adults with cerebral palsy

Aim To link routinely collected health data to a cerebral palsy (CP) register in order to enable analysis of healthcare use by severity of CP. Method The Northern Ireland Cerebral Palsy Register was linked to hospital data. Data for those on the CP register born between 1st January 1981 and 31st December 2009 and alive in 2004 were extracted, forming a CP cohort (n=1684; 57% males, 43% females; aged 0–24y). Frequencies of healthcare events, and the reasons for them, were reported according to CP severity and compared with those without CP who had had at least one hospital attendance in Northern Ireland within the study period. Results Cases of CP represented 0.3% of the Northern Ireland population aged 0 to 24 years but accounted for 1.6% of hospital admissions and 1.6% of outpatient appointments. They had higher rates of elective admissions and multi‐day hospital stays than the general population. Respiratory conditions were the most common reason for emergency admissions. Those with most severe CP were 10 times more likely to be admitted, and four times more likely to attend outpatients, than those with mild CP. Interpretation Linkage between a register and routinely collected healthcare data provided a confirmed cohort of cases of CP that was sufficiently detailed to analyse healthcare use by disease severity

Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches

Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. Methods We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. Results The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Conclusions Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time

Sanitation in urban areas may limit the spread of antimicrobial resistance via flies

Synanthropic filth flies are common where sanitation is poor and fecal wastes are accessible to them. These flies have been proposed as mechanical vectors for the localized transport of fecal microbes including antimicrobial resistant (AMR) organisms and associated antimicrobial resistance genes (ARGs), increasing exposure risks. We evaluated whether an onsite sanitation intervention in Maputo, Mozambique reduced the concentration of enteric bacteria and the frequency of detection of ARGs carried by flies collected in household compounds of low-income neighborhoods. Additionally, we assessed the phenotypic resistance profile of Enterobacteriaceae isolates recovered from flies during the pre-intervention phase. After fly enumeration at study compounds, quantitative polymerase chain reaction was used to quantify an enteric 16S rRNA gene (i.e., specific to a cluster of phylotypes corresponding to 5% of the human fecal microflora), 28 ARGs, and Kirby Bauer Disk Diffusion of Enterobacteriaceae isolates was utilized to assess resistance to eleven clinically relevant antibiotics. The intervention was associated with a 1.5 log10 reduction (95% confidence interval: -0.73, -2.3) in the concentration of the enteric 16S gene and a 31% reduction (adjusted prevalence ratio = 0.69, [0.52, 0.92]) in the mean number of ARGs per fly compared to a control group with poor sanitation. This protective effect was consistent across the six ARG classes that we detected. Enterobacteriaceae isolates–only from the pre-intervention phase–were resistant to a mean of 3.4 antibiotics out of the eleven assessed. Improving onsite sanitation infrastructure in low-income informal settlements may help reduce fly-mediated transmission of enteric bacteria and the ARGs carried by them

A multi-component stair climbing promotional campaign targeting calorific expenditure for worksites; a quasi-experimental study testing effects on behaviour, attitude and intention

BACKGROUND: Accumulation of lifestyle physical activity is a current aim of health promotion, with increased stair climbing one public health target. While the workplace provides an opportunity for regular stair climbing, evidence for effectiveness of point-of-choice interventions is equivocal. This paper reports a new approach to worksite interventions, aimed at changing attitudes and, hence, behaviour. METHODS: Pre-testing of calorific expenditure messages used structured interviews with members of the public (n = 300). Effects of multi-component campaigns on stair climbing were tested with quasi-experimental, interrupted time-series designs. In one worksite, a main campaign poster outlining the amount of calorific expenditure obtainable from stair climbing and a conventional point-of-choice prompt were used (Poster alone site). In a second worksite, additional messages in the stairwell about calorific expenditure reinforced the main campaign (Poster + Stairwell messages site). The outcome variables were automated observations of stair and lift ascent (28,854) and descent (29,352) at baseline and for three weeks after the intervention was installed. Post-intervention questionnaires for employees at the worksites assessed responses to the campaign (n = 253). Analyses employed Analysis of Variance with follow-up Bonferroni t-tests (message pre-testing), logistic regression of stair ascent and descent (campaign testing), and Bonferroni t-tests and multiple regression (follow-up questionnaire). RESULTS: Pre-testing of messages based on calorific expenditure suggested they could motivate stair climbing if believed. The new campaign increased stair climbing, with greater effects at the Poster + Stairwell messages site (OR = 1.52, 95% CI = 1.40-1.66) than Posters alone (OR = 1.24, 95% CI = 1.15-1.34). Follow-up revealed higher agreement with two statements about calorific outcomes of stair climbing in the site where they were installed in the stairwell, suggesting more positive attitudes resulted from the intervention. Future intentions for stair use were predicted by motivation by the campaign and beliefs that stair climbing would help weight control. CONCLUSIONS: Multi-component campaigns that target attitudes and intentions may substantially increase stair climbing at work

Quantitative Microbial Risk Assessment of Pediatric Infections Attributable to Ingestion of Fecally Contaminated Domestic Soils in Low-Income Urban Maputo, Mozambique.

Rigorous studies of water, sanitation, and hygiene interventions in low- and middle-income countries (LMICs) suggest that children are exposed to enteric pathogens via multiple interacting pathways, including soil ingestion. In 30 compounds (household clusters) in low-income urban Maputo, Mozambique, we cultured Escherichia coli and quantified gene targets from soils (E. coli: ybbW, Shigella/enteroinvasive E. coli (EIEC): ipaH, Giardia duodenalis: β-giardin) using droplet digital PCR at three compound locations (latrine entrance, solid waste area, dishwashing area). We found that 88% of samples were positive for culturable E. coli (mean = 3.2 log10 CFUs per gram of dry soil), 100% for molecular E. coli (mean = 5.9 log10 gene copies per gram of dry soil), 44% for ipaH (mean = 2.5 log10), and 41% for β-giardin (mean = 2.1 log10). Performing stochastic quantitative microbial risk assessment using soil ingestion parameters from an LMIC setting for children 12-23 months old, we estimated that the median annual infection risk by G. duodenalis was 7100-fold (71% annual infection risk) and by Shigella/EIEC was 4000-fold (40% annual infection risk) greater than the EPA's standard for drinking water. Compounds in Maputo, and similar settings, require contact and source control strategies to reduce the ingestion of contaminated soil and achieve acceptable levels of risk

Statistical evidence of transitioning open-vent activity towards a paroxysmal period at Volcán Santiaguito (Guatemala) during 2014–2018

Long-term eruptive activity at the Santiaguito lava dome complex, Guatemala, is characterised by the regular occurrence of small-to-moderate size explosions from the active Caliente dome. Between November 2014 and December 2018, we deployed a seismo-acoustic network at the volcano, which recorded several changes in the style of eruption, including a period of elevated explosive activity in 2016. Here, we use a new catalogue of explosions to characterise changes in the eruptive regime during the study period. We identify four different phases of activity based on changes in the frequency and magnitude of explosions. At the two ends of the spectrum of repose times we find pairs of explosions with near-identical seismic and acoustic waveforms, recorded within 1–10 min of one another, and larger explosions with recurrence times on the order of days to weeks. The magnitude-frequency relationship for explosions at Santiaguito is well described by a power-law; we show that changes in b-value between eruptive regimes reflect temporal and spatial changes in rupture mechanisms, likely controlled by variable magma properties. We also demonstrate that the distribution of inter-explosion repose times between and within phases is well represented by a Poissonian process. The Poissonian distribution describing repose times changes between and within phases as the source dynamics evolve. We find that changes in source properties restrict the extrapolation of explosive behaviour to within a given eruptive phase, limiting the potential for long-term assessments of anticipated eruptive behaviour at Santiaguito

Automated detection of patients with dementia whose symptoms have been identified in primary care but have no formal diagnosis: a retrospective case-control study using electronic primary care records

Objectives UK statistics suggest only two-thirds of patients with dementia get a diagnosis recorded in primary care. General practitioners (GPS) report barriers to formally diagnosing dementia, so some patients may be known by GPS to have dementia but may be missing a diagnosis in their patient record. We aimed to produce a method to identify these â known but unlabelled' patients with dementia using data from primary care patient records. Design Retrospective case-control study using routinely collected primary care patient records from Clinical Practice Research Datalink. Setting UK general practice. Participants English patients aged >65 years, with a coded diagnosis of dementia recorded in 2000-2012 (cases), matched 1:1 with patients with no diagnosis code for dementia (controls). Interventions Eight coded and nine keyword concepts indicating symptoms, screening tests, referrals and care for dementia recorded in the 5 years before diagnosis. We trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, random forest). Primary and secondary outcomes The outcome variable was dementia diagnosis code; the accuracy of classifiers was assessed using area under the receiver operating characteristic curve (AUC); the order of features contributing to discrimination was examined. Results 93 426 patients were included; the median age was 83 years (64.8% women). Three classifiers achieved high discrimination and performed very similarly. AUCs were 0.87-0.90 with coded variables, rising to 0.90-0.94 with keywords added. Feature prioritisation was different for each classifier; commonly prioritised features were Alzheimer's prescription, dementia annual review, memory loss and dementia keywords. Conclusions It is possible to detect patients with dementia who are known to GPS but unlabelled with a diagnostic code, with a high degree of accuracy in electronic primary care record data. Using keywords from clinic notes and letters improves accuracy compared with coded data alone. This approach could improve identification of dementia cases for record-keeping, service planning and delivery of good quality care