Risk of fractures in patients with multiple sclerosis: record-linkage study.

BACKGROUND: Patients with multiple sclerosis (MS) have been reported to be at higher risk of fracture than other people. We sought to test this hypothesis in a large database of hospital admissions in England. METHODS: We analysed a database of linked statistical records of hospital admissions and death certificates for the whole of England (1999-2010). Rate ratios for fractures were determined, comparing fracture rates in a cohort of all people in England admitted with MS and rates in a comparison cohort. RESULTS: Significantly elevated risk for all fractures was found in patients with MS (rate ratio (RR) = 1.99, 95% confidence interval (CI) = 1.93-2.05)). Risks were particularly high for femoral fractures (femoral neck fracture RR = 2.79 (2.65-2.93); femoral shaft fracture RR 6.69 (6.12-7.29)), and fractures of the tibia or ankle RR = 2.81 (2.66-2.96). CONCLUSIONS: Patients with MS have an increased risk of fractures. Caregivers should aim to optimize bone health in MS patients

Breast cancer in neurofibromatosis type 1 : overrepresentation of unfavourable prognostic factors

Background: An increased breast cancer incidence and poor survival have been reported for women with neurofibromatosis 1 (NF1). To explain the poor survival, we aimed to link the histopathology and clinical characteristics of NF1-associated breast cancers. Methods: The Finnish Cancer Registry and the Finnish NF Registry were cross-referenced to identify the NF1 patients with breast cancer. Archival NF1 breast cancer specimens were retrieved for histopathological typing and compared with matched controls. Results: A total of 32 breast cancers were diagnosed in 1404 NF1 patients during the follow-up. Women with NF1 had an estimated lifetime risk of 18.0% for breast cancer, and this is nearly two-fold compared with that of the general Finnish female population (9.74%). The 26 successfully retrieved archival NF1 breast tumours were more often associated with unfavourable prognostic factors, such as oestrogen and progesterone receptor negativity and HER2 amplification. However, survival was worse in the NF1 group (P = 0.053) even when compared with the control group matched for age, diagnosis year, gender and oestrogen receptor status. Scrutiny of The Cancer Genome Atlas data set showed that NF1 mutations and deletions were associated with similar characteristics in the breast cancers of the general population. Conclusions: These results emphasise the role of the NF1 gene in the pathogenesis of breast cancer and a need for active follow-up for breast cancer in women with NF1.Peer reviewe

An assessment of factors associated with quality of randomized controlled trials for smoking cessation

To reduce smoking-related diseases, a research priority is to develop effective interventions for smoking cessation, and evidence from randomized controlled trials (RCTs) is usually considered to be the most valid. However, findings from RCTs may still be misleading due to methodological flaws. This study aims to assess the quality of 1083 RCTs of smoking cessation interventions in 41 relevant Cochrane Systematic Reviews (CSRs). Logistic regression analysis was performed to identify significant variables associated with the quality of RCTs. It was found that evidence for smoking cessation from RCTs was predominantly from high income countries, and the overall quality was high in only 8.6% of the RCTs. High quality RCTs tended to have a larger sample size, to be more recently published, and conducted in multiple countries belonging to different income categories. In conclusion, the overall quality of RCTs of smoking cessation interventions is far from perfect, and more RCTs in less developed countries are required to generate high grade evidence for global tobacco control. Collaboration between researchers in developed and less developed countries should be encouraged

Risk of self-harm and suicide in people with specific psychiatric and physical disorders: comparisons between disorders using English national record linkage

Background Psychiatric illnesses are known risk factors for self-harm but associations between self-harm and physical illnesses are less well established. We aimed to stratify selected chronic physical and psychiatric illnesses according to their relative risk of self-harm. Design Retrospective cohort studies using a linked dataset of Hospital Episode Statistics (HES) for 1999–2011. Participants Individuals with selected psychiatric or physical conditions were compared with a reference cohort constructed from patients admitted for a variety of other conditions and procedures. Setting All admissions and day cases in National Health Service (NHS) hospitals in England. Main outcome measures Hospital episodes of self-harm. Rate ratios (RRs) were derived by comparing admission for self-harm between cohorts. Results The psychiatric illnesses studied (depression, bipolar disorder, alcohol abuse, anxiety disorders, eating disorders, schizophrenia and substance abuse) all had very high RRs (&gt; 5) for self-harm. Of the physical illnesses studied, an increased risk of self-harm was associated with epilepsy (RR = 2.9, 95% confidence interval [CI] 2.8–2.9), asthma (1.8, 1.8–1.9), migraine (1.8, 1.7–1.8), psoriasis (1.6, 1.5–1.7), diabetes mellitus (1.6, 1.5–1.6), eczema (1.4, 1.3–1.5) and inflammatory polyarthropathies (1.4, 1.3–1.4). RRs were significantly low for cancers (0.95, 0.93–0.97), congenital heart disease (0.9, 0.8–0.9), ulcerative colitis (0.8, 0.7–0.8), sickle cell anaemia (0.7, 0.6–0.8) and Down's syndrome (0.1, 0.1–0.2). Conclusions Psychiatric illnesses carry a greatly increased risk of self-harm as well as of suicide. Many chronic physical illnesses are also associated with an increased risk of both self-harm and suicide. Identifying those at risk will allow provision of appropriate monitoring and support. </jats:sec

Comorbidities in patients with gout prior to and following diagnosis: case-control study

OBJECTIVES: To determine the burden of comorbidities in patients with gout at diagnosis and the risk of developing new comorbidities post diagnosis. METHODS: There were 39 111 patients with incident gout and 39 111 matched controls identified from the UK Clinical Practice Research Data-link. The risk of comorbidity before (ORs) and after the diagnosis of gout (HRs) were estimated, adjusted for age, sex, diagnosis year, body mass index, smoking and alcohol consumption. RESULTS: Gout was associated with adjusted ORs (95% CIs) of 1.39 (1.34 to 1.45), 1.89 (1.76 to 2.03) and 2.51 (2.19 to 2.86) for the Charlson index of 1-2, 3-4 and >/=5, respectively. Cardiovascular and genitourinary diseases, in addition to hyperlipidaemia, hypothyroidism, anaemia, psoriasis, chronic pulmonary diseases, osteoarthritis and depression, were associated with a higher risk for gout. Gout was also associated with an adjusted HR (95% CI) of 1.41 (1.34 to 1.48) for having a Charlson index >/=1. Median time to first comorbidity was 43 months in cases and 111 months in controls. Risks for incident comorbidity were higher in cardiovascular, genitourinary, metabolic/endocrine and musculoskeletal diseases, in addition to liver diseases, hemiplegia, depression, anaemia and psoriasis in patients with gout. After additionally adjusting for all comorbidities at diagnosis, gout was associated with a HR (95% CI) for all-cause mortality of 1.13 (1.08 to 1.18; p<0.001). CONCLUSIONS: The majority of patients with gout have worse pre-existing health status at diagnosis and the risk of incident comorbidity continues to rise following diagnosis. The range of associated comorbidities is broader than previously recognised and merits further evaluation

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK