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

Protocol: Are primary care consultations for insomnia associated with dementia in later life?

Insomnia has been defined as a difficulty initiating or maintaining sleep, leading to sleep that is either insufficient or unrefreshing. It is well-established that insomnia is more common in people with dementia, but it is not clear if insomnia predates dementia in these individuals. This latter question is an important one: if it can be shown that people with insomnia are more likely to develop dementia in later life, this may improve our ability to predict an individual’s dementia risk, and possibly to help manage that risk. Several recent studies have found a link between insomnia and later dementia, but typically give little information about the time between the onset of insomnia and the onset of dementia, raising the possibility that the insomnia is an early symptom of dementia, rather than a risk factor or potential cause of the disease. Furthermore, in some studies the link between insomnia and dementia becomes weaker when factors such as depression and sleeping tablet use are taken into account. The proposed study uses primary care records to learn whether people with dementia are more likely to have consulted with their general practitioner (GP) about insomnia 5-10 years earlier, compared to those who do not have dementia

U.S. v. Briggs: Brief of Members of Congress As Amici Curiae in Support of Petitioner

This amicus brief filed in the U.S. Supreme Court case of United States v. Briggs on behalf of a bipartisan group of thirteen members of Congress discusses the absence of any statute of limitation for rape prosecutions within the military. It argues that the Constitution entrusts Congress with authority over military discipline, including the authority to determine what (if any) statutes of limitations apply to crimes that occur within the military. By classifying rape as an “offense punishable by death” and stipulating that “offenses punishable by death” are not subject to statutes of limitations, Congress entrenched the policy that rape within the military is not subject to any statute of limitations at all. Whether the death penalty can be constitutionally imposed for the rape of an adult—an open question in the specialized military context—is wholly irrelevant to the key question in the case: Whether Congress determined that the death penalty is warranted for rape in the military. Congress’ policy judgment—that rape within the military is so heinous and so damaging to military effectiveness that no temporal restriction should be placed on its prosecution—is entitled to respect. Rape in the military has devastating effects on survivors individually and military readiness generally. And the military’s hierarchical command structure can exacerbate the understandable reluctance of rape survivors to come forward and report the crimes committed against them. In light of those considerations, the provisions in the Uniform Code of Military Justice that address rape in the military have been understood for decades to reflect Congress’ intent that those who commit the crime of rape should not be permitted to escape justice by hiding behind the passage of time

Kansas v. Boettger: On Petition for a Writ of Certiorari to the Supreme Court of the State of Kansas

This amicus brief in support of Kansas’ petition for certiorari in Kansas v. Boettger discusses the important issue of whether the First Amendment require proof of specific intent to criminally punish violent threats. The brief argues that the First Amendment does not contain any such requirement and that creating any such requirement would interfere with effective prosecution of domestic violence. The Kansas Supreme Court’s decision over which review is being sought required the state to prove that an abuser had a specific intent to cause fear. If allowed to stand, the decision will make prosecuting and preventing domestic violence even more challenging, without any corresponding benefit. In domestic violence cases, there is rarely direct evidence of specific intent, and domestic-violence victims often struggle to confront their abusers in court. Indeed, the impact of abusers’ psychological, emotional, and physical abuse is often so severe that victims frequently struggle even to seek help. The Kansas Supreme Court’s decision to impose a specific intent requirement in a case involving violent threats is inconsistent with decisions from other courts, the law in over a dozen states, the Model Penal Code, and the history and tradition of the First Amendment. This amicus brief concludes that the Supreme Court should grant certiorari to review the decision below and reverse it

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

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

Could dementia be detected from UK primary care patients’ records by simple automated methods earlier than by the treating physician? A retrospective case-control study

Background: Timely diagnosis of dementia is a policy priority in the United Kingdom (UK). Primary care physicians receive incentives to diagnose dementia;, however, 33% of patients are still not receiving a diagnosis. We explored automating early detection of dementia using data from patients’ electronic health records (EHRs). We investigated: a) how early a machine-learning model could accurately identify dementia before the physician;, b) if models could be tuned for dementia subtype;, and c) what the best clinical features were for achieving detection. Methods: Using EHRs from Clinical Practice Research Datalink in a case-control design, we selected patients aged >65y with a diagnosis of dementia recorded 2000-2012 (cases) and matched them 1:1 to controls; we also identified subsets of Alzheimer’s and vVascular dementia patients. Using 77 coded concepts recorded in the 5 years before diagnosis, we trained random forest classifiers, and evaluated models using Area Under the Receiver Operating Characteristic Curve (AUC). We examined models by: year prior to diagnosis, subtype, and the most important features contributing to classification. Results: 95,202 patients (median age 83y; 64.8% female) were included (50% dementia cases). Classification of dementia cases and controls was poor 2-5 years prior to physician-recorded diagnosis (AUC range 0.55-0.65) but good in the year before (AUC: 0.84). Features indicating increasing cognitive and physical frailty dominated models 2-5 years before diagnosis; in the final year, initiation of the dementia diagnostic pathway (symptoms, screening and referral) explained the sudden increase in accuracy. No substantial differences were seen between all-cause dementia and subtypes. Conclusions: Automated detection of dementia earlier than the treating physician may be problematic, if using only primary care data. Future work should investigate more complex modelling, benefits of linking multiple sources of healthcare data and monitoring devices, or contextualising the algorithm to those cases that the GP would need to investigate

Rationale, experience and ethical considerations underpinning integrated actions to further global goals for health and land biodiversity in Papua New Guinea

The SURFACES project is integrating action on good health and wellbeing (Sustainable Development Goal [SDG] 3) and conservation of life on land (SDG 15) in the threatened rainforests of Papua New Guinea (PNG), and mapping evidence of similar projects worldwide. Our approach is framed by Planetary Health, aiming to safeguard both human health and the natural systems that underpin it. Our rationale is demonstrated through a summary of health needs and forest conservation issues across PNG, and how these play out locally. We outline differing types of integrated conservation and health interventions worldwide, providing examples from Borneo, Uganda, India and elsewhere. We then describe what we are doing on-the-ground in PNG, which includes expansion of a rainforest conservation area alongside the establishment of a nurse-staffed aid post, and an educational intervention conceptually linking forest conservation and health. Importantly, we explore some ethical considerations on the conditionality of medical provision, and identify key challenges to successful implementation of such projects. The latter include: avoiding cross-sectoral blindness and achieving genuine interdisciplinary working; the weak evidence base justifying projects; and temporal-spatial issues. We conclude by suggesting how projects integrating actions on health and conservation SDGs can benefit from (and contribute to) the energy of the emerging Planetary Health movement