End of the spectacular decrease in fall-related mortality rate: Men are catching up

Objectives: We determined time trends in numbers and rates of fall-related mortality in an aging population, for men and women. Methods. We performed secular trend analysis of fall-related deaths in the older Dutch population (persons aged 65 years or older) from 1969 to 2008, using the national Official-Cause-of-Death-Statistics. Results. Between 1969 and 2008, the age-adjusted fall-related mortality rate decreased from 202.1 to 66.7 per 100 000 older persons (decrease of 67%). However, the annual percentage change (change per year) in mortality rates was not constant, and could be divided into 3 phases: (1) a rapid decrease until the mid-1980s (men -4.1%; 95% confidence interval [CI] = -4.9, -3.2; women -6.5%; 95% CI, -7.1, -5.9), (2) flattening of the decrease until the mid-1990s (men -1.4%; 95% CI = -2.4, -0.4; women -2.0%; 95% CI = -3.4, -0.6), and (3) stable mortality rates for women (0.0%; 95% CI = -1.2, 1.3) and rising rates for men (1.9%; 95% CI = 0.6, 3.2) over the last decade. Conclusions. The spectacular decrease in fall-related mortality ended in the mid-1990s and is currently increasing in older men at

The Epidemic of Hip Fractures: Are We on the Right Track?

Background: Hip fractures are a public health problem, leading to hospitalization, long-term rehabilitation, reduced quality of life, large healthcare expenses, and a high 1-year mortality. Especially older adults are at greater risk of fractures than the general population, due to the combination of an increased fall risk and osteoporosis. The aim of this study was to determine time trends in numbers and incidence rates of hip fracture-related hospitalizations and admission duration in the older Dutch population. Methods and Findings: Secular trend analysis of all hospitalizations in the older Dutch population (≥65 years) from 1981 throughout 2008, using the National Hospital Discharge Registry. Numbers, age-specific and age-adjusted incidence rates (per 10,000 persons) of hospital admissions and hospital days due to a hip fracture were used as outcome measures in each year of the study. Between 1981 and 2008, the absolute number of hip fractures doubled in the older Dutch population. Incidence rates of hip fracture-related hospital admissions increased with age, and were higher in women than in men. The age-adjusted incidence rate increased from 52.0 to 67.6 per 10,000 older persons. However, since 1994 the incidence rate decreased (percentage annual change -0.5%, 95% CI: -0.7; -0.3), compared with the period 1981-1993 (percentage annual change 2.3%, 95% CI: 2.0; 2.7). The total number of hospital days was reduced by a fifth, due to a reduced admission duration in all age groups. A possible limitation was that data were obtained from a linked administrative database, which did not include information on medication use or co-morbidities. Conclusions: A trend break in the incidence rates of hip fracture-related hospitalizations was observed in the Netherlands around 1994, possibly as a first result of efforts to prevent falls and fractures. However, the true cause of the observation is unknown

Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gaussian Process for Machine Learning (GPML) Algorithms for the Prediction of Norovirus Concentration in Drinking Water Supply

Monitoring of Norovirus in drinking water supply is a complicated, rather expensive, process. Norovirus represent a leading cause of acute gastroenteritis in most developed countries. Modeling of general microbial occurrence in drinking water is a very active field of study and provides reliable information for predicting microbial risks in drinking water. In this work, adaptive neuro-fuzzy inference system (ANFIS) and Gaussian Process for Machine Learning (GPML) are proposed as predicting models for the total number of Norovirus in raw surface water in terms of water quality parameters such as water pH, turbidity, conductivity, temperature and rain. The predictive models were based on data from Nødre Romrike Vannverk water treatment plant in Oslo, Norway. Based on the model performance indices used in this study, the GPML model showed comparable accuracy to the ANFIS model. However, the ANFIS model generally demonstrated more superior prediction ability of the number of Norovirus in drinking water, with lower MSE and MAE values relative to the GPML model. In addition, the ability of the ANFIS model to explain potential effects of interactions among the water quality variables on the number of Norovirus in the raw water makes the technique more efficient for use in water quality modeling