The use of a bayesian hierarchy to develop and validate a co-morbidity score to predict mortality for linked primary and secondary care data from the NHS in England

Background: We have assessed whether the linkage between routine primary and secondary care records provided an opportunity to develop an improved population based co-morbidity score with the combined information on co-morbidities from both health care settings. Methods: We extracted all people older than 20 years at the start of 2005 within the linkage between the Hospital Episodes Statistics, Clinical Practice Research Datalink, and Office for National Statistics death register in England. A random 50% sample was used to identify relevant diagnostic codes using a Bayesian hierarchy to share information between similar Read and ICD 10 code groupings. Internal validation of the score was performed in the remaining 50% and discrimination was assessed using Harrell’s C statistic. Comparisons were made over time, age, and consultation rate with the Charlson and Elixhauser indexes. Results: 657,264 people were followed up from the 1st January 2005. 98 groupings of codes were derived from the Bayesian hierarchy, and 37 had an adjusted weighting of greater than zero in the Cox proportional hazards model. 11 of these groupings had a different weighting dependent on whether they were coded from hospital or primary care. The C statistic reduced from 0.88 (95% confidence interval 0.88–0.88) in the first year of follow up, to 0.85 (0.85–0.85) including all 5 years. When we stratified the linked score by consultation rate the association with mortality remained consistent, but there was a significant interaction with age, with improved discrimination and fit in those under 50 years old (C=0.85, 0.83–0.87) compared to the Charlson (C=0.79, 0.77–0.82) or Elixhauser index (C=0.81, 0.79–0.83). Conclusions: The use of linked population based primary and secondary care data developed a co-morbidity score that had improved discrimination, particularly in younger age groups, and had a greater effect when adjusting for co-morbidity than existing scores

Sensitivity of the UK clinical practice research datalink to detect neurodevelopmental effects of medicine exposure in utero:comparative analysis of an antiepileptic drug-exposed cohort

Introduction: Electronic healthcare data have several advantages over prospective observational studies, but the sensitivity of data on neurodevelopmental outcomes and its comparability with data generated through other methodologies is unknown. Objectives: The objectives of this study were to determine whether data from the UK Clinical Practice Research Datalink (CPRD) produces similar risk estimates to a prospective cohort study in relation to the risk of neurodevelopmental disorders (NDDs) following prenatal antiepileptic drug (AED) exposure. Methods: A cohort of mother–child pairs of women with epilepsy (WWE) was identified in the CPRD and matched to a cohort without epilepsy. The study period ran from 1 January 2000 to 31 March 2007 and children were required to be in the CPRD at age 6 years. AED exposure during pregnancy was determined from prescription data and children with an NDD diagnosis by 6 years were identified from Read clinical codes. The prevalence and risk of NDDs was calculated for mother–child pairs in WWE stratified by AED regimen and for those without epilepsy. Comparisons were made with the results of the prospective Liverpool and Manchester Neurodevelopment Group study which completed assessment on 201 WWE and 214 without epilepsy at age 6 years. Results: In the CPRD, 1018 mother–child pairs to WWE and 6048 to women without epilepsy were identified. The CPRD identified a lower prevalence of NDDs than the prospective study. In both studies, NDDs were more frequently reported in children of WWE than women without epilepsy, although the CPRD risk estimate was lower (2.16 vs. 0.96%, p < 0.001 and 7.46 vs. 1.87%, p = 0.0128). NDD prevalence differed across AED regimens but the CPRD data did not replicate the significantly higher risk of NDDs following in utero monotherapy valproate exposure (adjusted odds ratio [ORadj] 2.02, 95% confidence interval [CI] 0.52–7.86) observed in the prospective study (ORadj 6.05, 95% CI 1.65–24.53). Conclusion: It was possible to identify NDDs in the CPRD; however, the CPRD appears to under-record these outcomes. Larger studies are required to investigate further

Metabolic engineering of the iodine content in Arabidopsis

Plants are a poor source of iodine, an essential micronutrient for human health. Several attempts of iodine biofortification of crops have been carried out, but the scarce knowledge on the physiology of iodine in plants makes results often contradictory and not generalizable. In this work, we used a molecular approach to investigate how the ability of a plant to accumulate iodine can be influenced by different mechanisms. In particular, we demonstrated that the iodine content in Arabidopsis thaliana can be increased either by facilitating its uptake with the overexpression of the human sodium-iodide symporter (NIS) or through the reduction of its volatilization by knocking-out HOL-1, a halide methyltransferase. Our experiments show that the iodine content in plants results from a balance between intake and retention. A correct manipulation of this mechanism could improve iodine biofortification of crops and prevent the release of the ozone layer-threatening methyl iodide into the atmosphere

Case-finding for common mental disorders of anxiety and depression in primary care: an external validation of routinely collected data

Background The robustness of epidemiological research using routinely collected primary care electronic data to support policy and practice for common mental disorders (CMD) anxiety and depression would be greatly enhanced by appropriate validation of diagnostic codes and algorithms for data extraction. We aimed to create a robust research platform for CMD using population-based, routinely collected primary care electronic data. Methods We developed a set of Read code lists (diagnosis, symptoms, treatments) for the identification of anxiety and depression in the General Practice Database (GPD) within the Secure Anonymised Information Linkage Databank at Swansea University, and assessed 12 algorithms for Read codes to define cases according to various criteria. Annual incidence rates were calculated per 1000 person years at risk (PYAR) to assess recording practice for these CMD between January 1st 2000 and December 31st 2009. We anonymously linked the 2799 MHI-5 Caerphilly Health and Social Needs Survey (CHSNS) respondents aged 18 to 74 years to their routinely collected GP data in SAIL. We estimated the sensitivity, specificity and positive predictive value of the various algorithms using the MHI-5 as the gold standard. Results The incidence of combined depression/anxiety diagnoses remained stable over the ten-year period in a population of over 500,000 but symptoms increased from 6.5 to 20.7 per 1000 PYAR. A ‘historical’ GP diagnosis for depression/anxiety currently treated plus a current diagnosis (treated or untreated) resulted in a specificity of 0.96, sensitivity 0.29 and PPV 0.76. Adding current symptom codes improved sensitivity (0.32) with a marginal effect on specificity (0.95) and PPV (0.74). Conclusions We have developed an algorithm with a high specificity and PPV of detecting cases of anxiety and depression from routine GP data that incorporates symptom codes to reflect GP coding behaviour. We have demonstrated that using diagnosis and current treatment alone to identify cases for depression and anxiety using routinely collected primary care data will miss a number of true cases given changes in GP recording behaviour. The Read code lists plus the developed algorithms will be applicable to other routinely collected primary care datasets, creating a platform for future e-cohort research into these conditions. Keyword

Health and Employment after Fifty (HEAF):A new prospective cohort study

BackgroundDemographic trends in developed countries have prompted governmental policies aimed at extending working lives. However, working beyond the traditional retirement age may not be feasible for those with major health problems of ageing, and depending on occupational and personal circumstances, might be either good or bad for health. To address these uncertainties, we have initiated a new longitudinal study.Methods/designWe recruited some 8000 adults aged 50–64 years from 24 British general practices contributing to the Clinical Practice Research Datalink (CPRD). Participants have completed questionnaires about their work and home circumstances at baseline, and will do so regularly over follow-up, initially for a 5-year period. With their permission, we will access their primary care health records via the CPRD. The inter-relation of changes in employment (with reasons) and changes in health (e.g., major new illnesses, new treatments, mortality) will be examined.DiscussionCPRD linkage allows cost-effective frequent capture of detailed objective health data with which to examine the impact of health on work at older ages and of work on health. Findings will inform government policy and also the design of work for older people and the measures needed to support employment in later life, especially for those with health limitations

Selective recruitment designs for improving observational studies using electronic health records

Large‐scale electronic health records (EHRs) present an opportunity to quickly identify suitable individuals in order to directly invite them to participate in an observational study. EHRs can contain data from millions of individuals, raising the question of how to optimally select a cohort of size n from a larger pool of size N . In this article, we propose a simple selective recruitment protocol that selects a cohort in which covariates of interest tend to have a uniform distribution. We show that selectively recruited cohorts potentially offer greater statistical power and more accurate parameter estimates than randomly selected cohorts. Our protocol can be applied to studies with multiple categorical and continuous covariates. We apply our protocol to a numerically simulated prospective observational study using an EHR database of stable acute coronary disease patients from 82 089 individuals in the U.K. Selective recruitment designs require a smaller sample size, leading to more efficient and cost‐effective studies

The incidence of first venous thromboembolism in and around pregnancy using linked primary and secondary care data: a population based cohort study from England and comparative meta-analysis

Background: Recent linkage between primary and secondary care data has provided valuable information for studying heath outcomes that may initially present in different health care settings. The aim of this study was therefore, twofold: to use linked primary and secondary care data to determine an optimum definition for estimating the incidence of first VTE in and around pregnancy; and secondly to conduct a systematic literature review of studies on perinatal VTE incidence with the purpose of comparing our estimates. Methods: We used primary care data from the Clinical Practice Research Datalink (CPRD), which incorporates linkages to secondary care contained within Hospital Episode Statistics (HES) between 1997 and 2010 to estimate the incidence rate of VTE in the antepartum and postpartum period. We systematically searched the literature on the incidence of VTE during antepartum and postpartum periods and performed a meta-analysis to provide comparison. Findings: Using combined CPRD and HES data and a restrictive VTE definition, the absolute rate during the antepartum period and first six weeks postpartum (early postpartum) were 99 (95%CI 85–116) and 468 (95%CI 391–561) per 100,000 person-years respectively. These were comparable to the pooled estimates from our meta-analysis (using studies after 2005) during the antepartum period (118/100,000 person-years) and early postpartum (424/100,000 person-years). When we used only secondary care data to identify VTE events, incidence was lower during the early postpartum period (308/100,000 person-years), whereas relying only on primary care data lead to lower incidence during the time around delivery, but higher rates during the postpartum period (558/100,000 person-years). Conclusion: Using combined CPRD and HES data gives estimates of the risk of VTE in and around pregnancy that are comparable to the existing literature. It also provides more accurate estimation of the date of VTE diagnosis which will allow risk stratification during specific pregnancy and postpartum periods