Does Health & Her app use improve menopausal symptoms? A longitudinal cohort study

Objectives: The Health & Her app provides menopausal women with a means of monitoring their symptoms, symptom triggers and menstrual periods, and enables them to engage in a variety of digital activities designed to promote well-being. This study aimed to examine whether sustained weekly engagement with the app is associated with improvements in menopausal symptoms. Design: A pre–post longitudinal cohort study. Setting: Analysed data collected from Health & Her app users. Participants: 1900 women who provided symptom data via the app across a 2-month period. Primary and secondary outcome measures: Symptom changes from baseline to 2 months was the outcome measure. A linear mixed effects model explored whether levels of weekly app engagement influenced symptom changes. Secondary analyses explored whether app-usage factors such as total number of days spent logging symptoms, reporting triggers, reporting menstrual periods and using in-app activities were independently predictive of symptom changes from baseline. Covariates included hormone replacement therapy use, hormonal contraceptive use, present comorbidities, age and dietary supplement use. Results: Findings demonstrated that greater engagement with the Health & Her app for 2 months was associated with greater reductions in symptoms over time. Daily use of in-app activities and logging symptoms and menstrual periods were each independently associated with symptom reductions. Conclusions: This study demonstrated that greater weekly engagement with the app was associated with greater reductions in symptoms. It is recommended that women be made aware of menopause-specific apps, such as that provided by Health & Her, to support them to manage their symptoms

Incidence, Prevalence, and Health Care Outcomes in Idiopathic Intracranial Hypertension

Objective: To characterise trends in incidence, prevalence, and healthcare outcomes in the idiopathic intracranial hypertension (IIH) population in Wales using routinely collected healthcare data.Methods: We used and validated primary and secondary care IIH diagnosis codes within the Secure Anonymised Information Linkage databank, to ascertain IIH cases and controls, in a retrospective cohort study between 2003 and 2017. We recorded body mass index (BMI), deprivation quintile, CSF diversion surgery and unscheduled hospital admissions in case and control cohorts.Results: We analysed 35 million patient years of data. There were 1765 cases of IIH in 2017 (85% female). The prevalence and incidence of IIH in 2017 was 76/100,000 and 7.8/100,000/year, a significant increase from 2003 (corresponding figures=12/100,000 and 2.3/100,000/year) (p<0.001). IIH prevalence is associated with increasing BMI and increasing deprivation. The odds ratio for developing IIH in the least deprived quintile compared to the most deprived quintile, adjusted for gender and BMI, was 0.65 (95% CI 0.55 to 0.76). 9% of IIH cases had CSF shunts with less than 0.2% having bariatric surgery. Unscheduled hospital admissions were higher in the IIH cohort compared to controls (rate ratio=5.28, p<0.001) and in individuals with IIH and CSF shunts compared to those without shunts (rate ratio=2.02, p<0.01).Conclusions: IIH incidence and prevalence is increasing considerably, corresponding to population increases in BMI, and is associated with increased deprivation. This has important implications for healthcare professionals and policy makers given the comorbidities, complications and increased healthcare utilization associated with II

COVID-19 vaccination uptake in people with epilepsy in wales

Purpose: People with epilepsy (PWE) are at increased risk of severe COVID-19. Assessing COVID-19 vaccine uptake is therefore important. We compared COVID-19 vaccination uptake for PWE in Wales with a matched control cohort. Methods: We performed a retrospective, population, cohort study using linked, anonymised, Welsh electronic health records within the Secure Anonymised Information Linkage (SAIL) Databank (Welsh population=3.1 million).We identified PWE in Wales between 1st March 2020 and 31st December 2021 and created a control cohort using exact 5:1 matching (sex, age and socioeconomic status). We recorded 1st, 2nd and booster COVID-19 vaccinations.Results: There were 25,404 adults with epilepsy (127,020 controls). 23,454 (92.3%) had a first vaccination, 22,826 (89.9%) a second, and 17,797 (70.1%) a booster. Comparative figures for controls were: 112,334 (87.8%), 109,057 (85.2%) and 79,980 (62.4%).PWE had higher vaccination rates in all age, sex and socioeconomic subgroups apart from booster uptake in older subgroups. Vaccination rates were higher in older subgroups, women and less deprived areas for both cohorts. People with intellectual disability and epilepsy had higher vaccination rates when compared with controls with intellectual disability. Conclusions: COVID-19 vaccination uptake for PWE in Wales was higher than that for a matched control group

Obtaining structured clinical data from unstructured data using natural language processing software

ABSTRACT Background Free text documents in healthcare settings contain a wealth of information not captured in electronic healthcare records (EHRs). Epilepsy clinic letters are an example of an unstructured data source containing a large amount of intricate disease information. Extracting meaningful and contextually correct clinical information from free text sources, to enhance EHRs, remains a significant challenge. SCANR (Swansea University Collaborative in the Analysis of NLP Research) was set up to use natural language processing (NLP) technology to extract structured data from unstructured sources. IBM Watson Content Analytics software (ICA) uses NLP technology. It enables users to define annotations based on dictionaries and language characteristics to create parsing rules that highlight relevant items. These include clinical details such as symptoms and diagnoses, medication and test results, as well as personal identifiers.   Approach To use ICA to build a pipeline to accurately extract detailed epilepsy information from clinic letters. Methods We used ICA to retrieve important epilepsy information from 41 pseudo-anonymized unstructured epilepsy clinic letters. The 41 letters consisted of 13 ‘new’ and 28 ‘follow-up’ letters (for 15 different patients) written by 12 different doctors in different styles. We designed dictionaries and annotators to enable ICA to extract epilepsy type (focal, generalized or unclassified), epilepsy cause, age of onset, investigation results (EEG, CT and MRI), medication, and clinic date. Epilepsy clinicians assessed the accuracy of the pipeline. Results The accuracy (sensitivity, specificity) of each concept was: epilepsy diagnosis 98% (97%, 100%), focal epilepsy 100%, generalized epilepsy 98% (93%, 100%), medication 95% (93%, 100%), age of onset 100% and clinic date 95% (95%, 100%). Precision and recall for each concept were respectively, 98% and 97% for epilepsy diagnosis, 100% each for focal epilepsy, 100% and 93% for generalized epilepsy, 100% each for age of onset, 100% and 93% for medication, 100% and 96% for EEG results, 100% and 83% for MRI scan results, and 100% and 95% for clinic date. Conclusions ICA is capable of extracting detailed, structured epilepsy information from unstructured clinic letters to a high degree of accuracy. This data can be used to populate relational databases and be linked to EHRs. Researchers can build in custom rules to identify concepts of interest from letters and produce structured information. We plan to extend our work to hundreds and then thousands of clinic letters, to provide phenotypically rich epilepsy data to link with other anonymised, routinely collected data

Educational attainment of children born to mothers with epilepsy

102.Lacey AS, Pickrell OW, Thomas RH, Kerr MP, White CP, Rees MI (2018)

Health care utilization and mortality for people with epilepsy during COVID ‐19: A population study

Objective: This study was undertaken to characterize changes in health care utilization and mortality for people with epilepsy (PWE) during the COVID‐19 pandemic. Methods: We performed a retrospective study using linked, individual‐level, population‐scale anonymized health data from the Secure Anonymised Information Linkage databank. We identified PWE living in Wales during the study “pandemic period” (January 1, 2020–June 30, 2021) and during a “prepandemic” period (January 1, 2016–December 31, 2019). We compared prepandemic health care utilization, status epilepticus, and mortality rates with corresponding pandemic rates for PWE and people without epilepsy (PWOE). We performed subgroup analyses on children (65 years old), those with intellectual disability, and those living in the most deprived areas. We used Poisson models to calculate adjusted rate ratios (RRs). Results: We identified 27 279 PWE who had significantly higher rates of hospital (50.3 visits/1000 patient months), emergency department (55.7), and outpatient attendance (172.4) when compared to PWOE (corresponding figures: 25.7, 25.2, and 87.0) in the prepandemic period. Hospital and epilepsy‐related hospital admissions, and emergency department and outpatient attendances all reduced significantly for PWE (and all subgroups) during the pandemic period. RRs [95% confidence intervals (CIs)] for pandemic versus prepandemic periods were .70 [.69–.72], .77 [.73–.81], .78 [.77–.79], and .80 [.79–.81]. The corresponding rates also reduced for PWOE. New epilepsy diagnosis rates decreased during the pandemic compared with the prepandemic period (2.3/100 000/month cf. 3.1/100 000/month, RR = .73, 95% CI = .68–.78). Both all‐cause deaths and deaths with epilepsy recorded on the death certificate increased for PWE during the pandemic (RR = 1.07, 95% CI = .997–1.145 and RR = 2.44, 95% CI = 2.12–2.81). When removing COVID deaths, RRs were .88 (95% CI = .81–.95) and 1.29 (95% CI = 1.08–1.53). Status epilepticus rates did not change significantly during the pandemic (RR = .95, 95% CI = .78–1.15). Significance: All‐cause non‐COVID deaths did not increase but non‐COVID deaths associated with epilepsy did increase for PWE during the COVID‐19 pandemic. The longer term effects of the decrease in new epilepsy diagnoses and health care utilization and increase in deaths associated with epilepsy need further research

Epilepsy and the risk of COVID ‐19‐related hospitalization and death: A population study

Objective: People with epilepsy (PWE) may be at an increased risk of severe COVID‐19. It is important to characterize this risk to inform PWE and for future health and care planning. We assessed whether PWE were at higher risk of being hospitalized with, or dying from, COVID‐19. Methods: We performed a retrospective cohort study using linked, population‐scale, anonymized electronic health records from the SAIL (Secure Anonymised Information Linkage) databank. This includes hospital admission and demographic data for the complete Welsh population (3.1 million) and primary care records for 86% of the population. We identified 27 279 PWE living in Wales during the study period (March 1, 2020 to June 30, 2021). Controls were identified using exact 5:1 matching (sex, age, and socioeconomic status). We defined COVID‐19 deaths as having International Classification of Diseases, 10th Revision (ICD‐10) codes for COVID‐19 on death certificates or occurring within 28 days of a positive SARS‐CoV‐2 polymerase chain reaction (PCR) test. COVID‐19 hospitalizations were defined as having a COVID‐19 ICD‐10 code for the reason for admission or occurring within 28 days of a positive SARS‐CoV‐2 PCR test. We recorded COVID‐19 vaccinations and comorbidities known to increase the risk of COVID‐19 hospitalization and death. We used Cox proportional hazard models to calculate hazard ratios. Results: There were 158 (.58%) COVID‐19 deaths and 933 (3.4%) COVID‐19 hospitalizations in PWE, and 370 (.27%) deaths and 1871 (1.4%) hospitalizations in controls. Hazard ratios for COVID‐19 death and hospitalization in PWE compared to controls were 2.15 (95% confidence interval [CI] = 1.78–2.59) and 2.15 (95% CI = 1.94–2.37), respectively. Adjusted hazard ratios (adjusted for comorbidities) for death and hospitalization were 1.32 (95% CI = 1.08–1.62) and 1.60 (95% CI = 1.44–1.78). Significance: PWE are at increased risk of being hospitalized with, and dying from, COVID‐19 when compared to age‐, sex‐, and deprivation‐matched controls, even when adjusting for comorbidities. This may have implications for prioritizing future COVID‐19 treatments and vaccinations for PWE

Validating epilepsy diagnoses in routinely collected data

Purpose: Anonymised, routinely-collected healthcare data is increasingly being used for epilepsy research. We validated algorithms using general practitioner (GP) primary healthcare records to identify people with epilepsy from anonymised healthcare data within the Secure Anonymised Information Linkage (SAIL) databank in Wales, UK. Method: A reference population of 150 people with definite epilepsy and 150 people without epilepsy was ascertained from hospital records and linked to records contained within SAIL (containing GP records for 2.4 million people). We used three different algorithms, using combinations of GP epilepsy diagnosis and anti-epileptic drug (AED) prescription codes, to identify the reference population. Results: Combining diagnosis and AED prescription codes had a sensitivity of 84% (95% ci 77–90) and specificity of 98% (95–100) in identifying people with epilepsy; diagnosis codes alone had a sensitivity of 86% (80–91) and a specificity of 97% (92–99); and AED prescription codes alone achieved a sensitivity of 92% (70–83) and a specificity of 73% (65–80). Using AED codes only was more accurate in children achieving a sensitivity of 88% (75–95) and specificity of 98% (88–100). Conclusion: GP epilepsy diagnosis and AED prescription codes can be confidently used to identify people with epilepsy using anonymised healthcare records in Wales, U