Association between Serum 25-Hydroxy Vitamin D Levels and the Prevalence of Adult-Onset Asthma

The major circulating metabolite of vitamin D (25(OH)D) has been implicated in the pathogenesis for atopic dermatitis, asthma and other allergic diseases due to downstream immunomodulatory effects. However, a consistent association between 25(OH)D and asthma during adulthood has yet to be found in observational studies. We aimed to test the association between 25(OH)D and asthma during adulthood and hypothesised that this association would be stronger in non-atopic participants. Using information collected on the participants of the 1958 birth cohort, we developed a novel measure of atopic status using total and specific IgE values and reported history of eczema and allergic rhinitis. We designed a nested case-control analysis, stratified by atopic status, and using logistic regression models investigated the association between 25(OH)D measured at age 46 years with the prevalence of asthma and wheezy bronchitis at age 50 years, excluding participants who reported ever having asthma or wheezy bronchitis before the age of 42. In the fully adjusted models, a 10 nmol/L increase in serum 25(OH)D prevalence had a significant association with asthma (aOR 0.94; 95% CI 0.88–1.00). There was some evidence of an atopic dependent trend in the association between 25(OH)D levels and asthma. Further analytical work on the operationalisation of atopy status would prove useful to uncover whether there is a role for 25(OH)D and other risk factors for asthma

The effect of ambient temperature on type-2-diabetes: case-crossover analysis of 4+ million GP consultations across England.

BACKGROUND: Given the double jeopardy of global increases in rates of obesity and climate change, it is increasingly important to recognise the dangers posed to diabetic patients during periods of extreme weather. We aimed to characterise the associations between ambient temperature and general medical practitioner consultations made by a cohort of type-2 diabetic patients. Evidence on the effects of temperature variation in the primary care setting is currently limited. METHODS: Case-crossover analysis of 4,474,943 consultations in England during 2012-2014, linked to localised temperature at place of residence for each patient. Conditional logistic regression was used to assess associations between each temperature-related consultation and control days matched on day-of-week. RESULTS: There was an increased odds of seeking medical consultation associated with high temperatures: Odds ratio (OR) = 1.097 (95% confidence interval = 1.041, 1.156) per 1 °C increase above 22 °C. Odds during low temperatures below 0 °C were also significantly raised: OR = 1.024 (1.019, 1.030). Heat-related consultations were particularly high among diabetics with cardiovascular comorbidities: OR = 1.171 (1.031, 1.331), but there was no heightened risk with renal failure or neuropathy comorbidities. Surprisingly, lower odds of heat-related consultation were associated with the use of diuretics, anticholinergics, antipsychotics or antidepressants compared to non-use, especially among those with cardiovascular comorbidities, although differences were not statistically significant. CONCLUSIONS: Type-2 diabetic patients are at increased odds of medical consultation during days of temperature extremes, especially during hot weather. The common assumption that certain medication use heightens the risk of heat illness was not borne-out by our study on diabetics in a primary care setting and such advice may need to be reconsidered in heat protection plans

Pathogen seasonality and links with weather in England and Wales: A big data time series analysis

This is the final version. Available on open access from BMC via the DOI in this record.Background: Many infectious diseases of public health importance display annual seasonal patterns in their incidence. We aimed to systematically document the seasonality of several human infectious disease pathogens in England and Wales, highlighting those organisms that appear weather-sensitive and therefore may be influenced by climate change in the future. Methods: Data on infections in England and Wales from 1989 to 2014 were extracted from the Public Health England (PHE) SGSS surveillance database. We conducted a weekly, monthly and quarterly time series analysis of 277 pathogen serotypes. Each organism's time series was forecasted using the TBATS package in R, with seasonality detected using model fit statistics. Meteorological data hosted on the MEDMI Platform were extracted at a monthly resolution for 2001-2011. The organisms were then clustered by K-means into two groups based on cross correlation coefficients with the weather variables. Results: Examination of 12.9 million infection episodes found seasonal components in 91/277 (33%) organism serotypes. Salmonella showed seasonal and non-seasonal serotypes. These results were visualised in an online Rshiny application. Seasonal organisms were then clustered into two groups based on their correlations with weather. Group 1 had positive correlations with temperature (max, mean and min), sunshine and vapour pressure and inverse correlations with mean wind speed, relative humidity, ground frost and air frost. Group 2 had the opposite but also slight positive correlations with rainfall (mm, > 1 mm, > 10 mm). Conclusions: The detection of seasonality in pathogen time series data and the identification of relevant weather predictors can improve forecasting and public health planning. Big data analytics and online visualisation allow the relationship between pathogen incidence and weather patterns to be clarified.Medical Research Council (MRC)National Institute for Health Research (NIHR)National Institute of Health Research (NIHR

The effects of meteorological conditions and daylight on nature-based recreational physical activity in England

This is the final version. Available on open access from Elsevier via the DOI in this recordMeteorological conditions affect people’s outdoor physical activity. However, we know of no previous research into how these conditions affect physical activity in different types of natural environments – key settings for recreational physical activity, but ones which are particularly impacted by meteorological conditions. Using responses from four waves (2009–2013) of a survey of leisure visits to natural environments in England (n = 47,613), visit dates and locations were ascribed estimates of energy expenditure (MET-minutes) and assigned meteorological data. We explored relationships between MET-minutes in natural environments (in particular, parks, woodlands, inland waters, and coasts) and the hourly maxima of air temperature and wind speed, levels of rainfall, and daylight hours using generalised additive models. Overall, we found a positive linear relationship between MET-minutes and air temperature; a negative linear relationship with wind speed; no relation with categories of rainfall; and a positive, but non-linear relationship with daylight hours. These same trends were observed for park-based energy expenditure, but differed for visits to other natural environments: only daylight hours were related to energy expenditure at woodlands; wind speed and daylight hours affected energy expenditure at inland waters; and only air temperature was related to energy expenditure at coasts. Natural environments support recreational physical activity under a range of meteorological conditions. However, distinct conditions do differentially affect the amount of energy expenditure accumulated in a range of natural environments. The findings have implications for reducing commonly-reported meteorological barriers to both recreational physical activity and visiting natural environments for leisure, and begin to indicate how recreational energy expenditure in these environments could be affected by future climate change.National Institute for Health Research (NIHR)European Commissio

Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data

Background: Telemonitoring of symptoms and physiological signs has been suggested as a means of early detection of exacerbations of chronic obstructive pulmonary disease (COPD) with a view to instituting timely treatment. However, current algorithms to identify exacerbations result in frequent false positive results and increased workload. Machine learning, when applied to predictive modelling, can determine patterns of risk factors useful for improving quality of predictions. Objective: To establish if machine learning techniques applied to telemonitoring datasets improve prediction of hospital admissions, decisions to start steroids, and to determine if the addition of weather data further improves such predictions. Methods: We used daily symptoms, physiological measures and medication data, with baseline demography, COPD severity, quality of life, and hospital admissions from a pilot and large randomised controlled trial of telemonitoring in COPD. In addition, we linked weather data from the UK Meteorological Office. We used feature selection and extraction techniques for time-series to construct up to 153 predictive patterns (features) from symptom, medication, and physiological measurements. The resulting variables were used for the construction of predictive models fitted to training sets of patients and compared to common algorithms. Results: We had a mean 363 days of telemonitoring data from 135 patients. The two most practical traditional score-counting algorithms, restricted to cases with complete data resulted in AUC estimates of 0.60 [CI 95% 0.51, 0.69] and 0.58 [0.50, 0.67] for predicting admissions based on a single day’s readings. However, in a real-world scenario allowing for missing data, with greater numbers of patient daily data and hospitalisations (N = 57,150, N+=17), the performance of all the traditional algorithms fell, including those based on two days data. One of the most frequently used algorithms performed no better than chance. Machine learning models demonstrated significant improvements; the best machine learning algorithm based on 57,150 episodes resulted in an aggregated AUC = 0.73 [0.67, 0.79]. Addition of weather data measurements resulted in a negligible improvement in the predictive performance of the best model (AUC = 0.74 [0.69, 0.79]). In order to achieve an 80% true positive rate (sensitivity), the traditional algorithms were associated with an 80% false positive rate: our algorithm halved this rate to approximately 40% (specificity approximately 60%). The machine learning algorithm was moderately superior to the best standard algorithm (AUC = 0.77 [0.74, 0.79] v AUC = 0.66 [0.63, 0.68]) at predicting the need for steroids. Conclusions: The early detection and management of COPD remains an important goal given the huge personal and economic costs of the condition. Machine learning approaches, which can be tailored to an individual’s baseline profile and can learn from experience of the individual patient are superior to existing predictive algorithms show promise in achieving this goal

Seasonality and the effects of weather on Campylobacter infections

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: The datasets on MEDMI are described at https://www.data-mashup.org.uk/data/data-library/ and include the SGSS infectious disease dataset. Permissions are required to access these datasets and users require an account to be set up as described at https://www.data-mashup.org.uk/data/accessing-data/.BACKGROUND: Campylobacteriosis is a major public health concern. The weather factors that influence spatial and seasonal distributions are not fully understood. METHODS: To investigate the impacts of temperature and rainfall on Campylobacter infections in England and Wales, cases of Campylobacter were linked to local temperature and rainfall at laboratory postcodes in the 30 days before the specimen date. Methods for investigation included a comparative conditional incidence, wavelet, clustering, and time series analyses. RESULTS: The increase of Campylobacter infections in the late spring was significantly linked to temperature two weeks before, with an increase in conditional incidence of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship to temperature was not linear. Generalized structural time series model revealed that changes in temperature accounted for 33.3% of the expected cases of Campylobacteriosis, with an indication of the direction and relevant temperature range. Wavelet analysis showed a strong annual cycle with additional harmonics at four and six months. Cluster analysis showed three clusters of seasonality with geographic similarities representing metropolitan, rural, and other areas. CONCLUSIONS: The association of Campylobacteriosis with temperature is likely to be indirect. High-resolution spatial temporal linkage of weather parameters and cases is important in improving weather associations with infectious diseases. The primary driver of Campylobacter incidence remains to be determined; other avenues, such as insect contamination of chicken flocks through poor biosecurity should be explored

Seasonality and the effects of weather on Campylobacter infections

Background Campylobacteriosis is a major public health concern. The weather factors that influence spatial and seasonal distributions are not fully understood. Methods To investigate the impacts of temperature and rainfall on Campylobacter infections in England and Wales, cases of Campylobacter were linked to local temperature and rainfall at laboratory postcodes in the 30 days before the specimen date. Methods for investigation included a comparative conditional incidence, wavelet, clustering, and time series analyses. Results The increase of Campylobacter infections in the late spring was significantly linked to temperature two weeks before, with an increase in conditional incidence of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship to temperature was not linear. Generalized structural time series model revealed that changes in temperature accounted for 33.3% of the expected cases of Campylobacteriosis, with an indication of the direction and relevant temperature range. Wavelet analysis showed a strong annual cycle with additional harmonics at four and six months. Cluster analysis showed three clusters of seasonality with geographic similarities representing metropolitan, rural, and other areas. Conclusions The association of Campylobacteriosis with temperature is likely to be indirect. High-resolution spatial temporal linkage of weather parameters and cases is important in improving weather associations with infectious diseases. The primary driver of Campylobacter incidence remains to be determined; other avenues, such as insect contamination of chicken flocks through poor biosecurity should be explored

精索平滑筋肉腫の1例

61歳男.1年半前より左陰嚢内に腫瘤を自覚, 次第に増大してきた.触診上, 腫瘤は精巣と離れており, 精索に付着していた.弾性硬であること, 超音波検査上内部エコーが不均一なことより悪性が疑われ, 高位精巣切除を施行した.病理組織学的検査の結果, 精索原発の平滑筋肉腫であった.画像的に遠隔移転は認めなかった.術後に放射線療法を行った.術後32ヵ月を経過した現在, 再発は認めていないA 61-year-old man presented to our hospital with a 1.5-year history of a gradually enlarging mass in the left scrotum. The mass was apart from the testis and fixed to the spermatic cord. The firm consistency and heterogeneous expression on ultrasonography suggested a malignant tumor. Orchiectomy with high ligation of the spermatic cord was performed and a histological examination revealed leiomyosarcoma of the spermatic cord. Distant metastases were not observed. Because the incidence of local recurrence has been reported to be high, we performed irradiation to control the disease. At 32 months post-surgery he was alive with no evidence of disease