Rainfall, the density of mice and autumn crop production could be used as predictors of HFRS transmission in low-lying epidemic foci.

ObjectivesPopulation ageing has been associated with an increase in comorbid chronic disease, functional dependence, disability and associated higher health care costs. Frailty Syndromes have been proposed as a way to define this group within older persons. We explore whether frailty syndromes are a reliable methodology to quantify clinically significant frailty within hospital settings, and measure trends and geospatial variation using English secondary care data set Hospital Episode Statistics (HES).SettingNational English Secondary Care Administrative Data HES.ParticipantsAll 50 540 141 patient spells for patients over 65 years admitted to acute provider hospitals in England (January 2005—March 2013) within HES.Primary and secondary outcome measuresWe explore the prevalence of Frailty Syndromes as coded by International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10) over time, and their geographic distribution across England. We examine national trends for admission spells, inpatient mortality and 30-day readmission.ResultsA rising trend of admission spells was noted from January 2005 to March 2013(daily average admissions for month rising from over 2000 to over 4000). The overall prevalence of coded frailty is increasing (64 559 spells in January 2005 to 150 085 spells by Jan 2013). The majority of patients had a single frailty syndrome coded (10.2% vs total burden of 13.9%). Cognitive impairment and falls (including significant fracture) are the most common frailty syndromes coded within HES. Geographic variation in frailty burden was in keeping with known distribution of prevalence of the English elderly population and location of National Health Service (NHS) acute provider sites. Overtime, in-hospital mortality has decreased (>65 years) whereas readmission rates have increased (esp.>85 years).ConclusionsThis study provides a novel methodology to reliably quantify clinically significant frailty. Applications include evaluation of health service improvement over time, risk stratification and optimisation of services.

Parkinson’s disease (PD) is a neurological disorder that is associated with both motor and non-motor symptoms (NMS). The management of PD is primarily via pharmaceutical treatment; however, non-pharmaceutical interventions have become increasingly recognized in the management of motor and NMS. In this review, the efficacy of physical activity, including physiotherapy and occupational therapy, as an intervention in NMS will be assessed. The papers were extracted between the 20th and 22nd of June 2016 from PubMed, Web of Science, Medline, Ovid, SportsDiscuss, and Scopus using the MeSH search terms “Parkinson’s,” “Parkinson,” and “Parkinsonism” in conjunction with “exercise,” “physical activity,” “physiotherapy,” “occupational therapy,” “physical therapy,” “rehabilitation,” “dance,” and “martial arts.” Twenty studies matched inclusion criteria of having 10 or more participants with diagnosed idiopathic PD participating in the intervention as well as having to evaluate the effects of physical activity on NMS in PD as controlled, randomized intervention studies. The outcomes of interest were NMS, including depression, cognition, fatigue, apathy, anxiety, and sleep. Risk of bias in the studies was evaluated using the Cochrane Collaboration’s tool for assessing risk of bias. Comparability of the various intervention methods, however, was challenging due to demographic variability and methodological differences. Nevertheless, physical activity can positively impact the global NMS burden including depression, apathy, fatigue, day time sleepiness, sleep, and cognition, thus supporting its therapeutic potential in neurodegenerative conditions such as PD. It is recommended that further adequately powered studies are conducted to assess the therapeutic role of physical activity on both motor and non-motor aspects of PD. These studies should be optimally designed to assess non-motor elements of disease using instruments validated in PD.

ObjectivesPopulation ageing may result in increased comorbidity, functional dependence and poor quality of life. Mechanisms and pathophysiology underlying frailty have not been fully elucidated, thus absolute consensus on an operational definition for frailty is lacking. Frailty scores in the acute medical care setting have poor predictive power for clinically relevant outcomes. We explore the utility of frailty syndromes (as recommended by national guidelines) as a risk prediction model for the elderly in the acute care setting.SettingEnglish Secondary Care emergency admissions to National Health Service (NHS) acute providers.ParticipantsThere were N=2 099 252 patients over 65 years with emergency admission to NHS acute providers from 01/01/2012 to 31/12/2012 included in the analysis.Primary and secondary outcome measuresOutcomes investigated include inpatient mortality, 30-day emergency readmission and institutionalisation. We used pseudorandom numbers to split patients into train (60%) and test (40%). Receiver operator characteristic (ROC) curves and ordering the patients by deciles of predicted risk was used to assess model performance. Using English Hospital Episode Statistics (HES) data, we built multivariable logistic regression models with independent variables based on frailty syndromes (10th revision International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10) coding), demographics and previous hospital utilisation. Patients included were those >65 years with emergency admission to acute provider in England (2012).ResultsFrailty syndrome models exhibited ROC scores of 0.624–0.659 for inpatient mortality, 0.63–0.654 for institutionalisation and 0.57–0.63 for 30-day emergency readmission.ConclusionsFrailty syndromes are a valid predictor of outcomes relevant to acute care. The models predictive power is in keeping with other scores in the literature, but is a simple, clinically relevant and potentially more acceptable measurement for use in the acute care setting. Predictive powers of the score are not sufficient for clinical use.

Objectives: (1) To examine the feasibility to link climate data with monthly incidence of Ross River virus (RRv). (2) To assess the impact of climate variability on the RRv transmission. Design: An ecological time series analysis was performed on the data collected between 1985 to 1996 in Queensland, Australia. Methods: Information on the notified RRv cases was obtained from the Queensland Department of Health. Climate and population data were supplied by the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. Spearman's rank correlation analyses were performed to examine the relation between climate variability and the monthly incidence of notified RRv infections. The autoregressive integrated moving average (ARIMA) model was used to perform a time series analysis. As maximum and minimum temperatures were highly correlated with each other (r s =0.75), two separate models were developed. Results: For the eight major cities in Queensland, the climate-RRv correlation coefficients were in the range of 0.12 to 0.52 for maximum and minimum temperatures, −0.10 to 0.46 for rainfall, and 0.11 to 0.52 for relative humidity and high tide. For the whole State, rainfall (partial regression coefficient: 0.017 (95% confidence intervals 0.009 to 0.025) in Model I and 0.018 (0.010 to 0.026) in Model II), and high tidal level (0.030 (0.006 to 0.054) in Model I and 0.029 (0.005 to 0.053) in Model II) seemed to have played significant parts in the transmission of RRv in Queensland. Maximum temperature was also marginally significantly associated with the incidence of RRv infection. Conclusion: Rainfall, temperature, and tidal levels may be important environmental determinants in the transmission cycles of RRv disease.