Epileptic insanity

In writing on the subject of Epilepsy and its Insanity, I am'Constantly reminded of the difficulty of the subject. In studying this form of insanity one frequently meets with something which seems incompatible with one's previous ideas or observations. This has a tendency to dter one at the outset. However, knowing as I do, that the smallest observations of one man, insignificant as they may seem to him, may yet be of great value to another whose powers of I deduction may be greater, I do not hesitate to commit to paper some observations of my own concerning this fell disease. Here in this large asylum where I come into daily contact with some 200 insane epileptics, I have opportunities not afforded to many. Opportunities which I hope I have made good use of.In the first place, I may state that I will try to make my paper as simple as possible without the aid of any unnecessary padding.I shall start by giving a rough classification of some forms of this epileptic insanity I have met j with here. I do not intend this to be an arbitrary ! classification, but to serve as a guide. One is too apt, now-a-days, to slump all the insane epileptics under the the term "dement". How much this may mean or how little! What a large field of imaginative is left to the hearer! "Oh he's only an epileptic dement", we hear one say, as if that was the beginning and the end of his case. Here then is my classification

SNOT-22 in a Control Population

AIM: To assess SNOT-22 and its subscales in a non-rhinosinusitis UK-wide population.  METHODOLOGY/PRINCIPLE: This analysis uses data from the 'Chronic Rhinosinusitis Epidemiology Study' (CRES) which recruited from 30 centres across the UK, and the Socioeconomic Cost of ChrOnic Rhinosinusitis study' (SocCoR); 250 volunteers without CRS were recruited as part of these studies. Study-specific questionnaires including demographics, socioeconomic factors and past medical history as well as SNOT-22 and SF-36 were distributed. The control (non-CRS) population had no self-reported nasal problems in the past, no chronic conditions undergoing active treatment and no hospital admissions in the preceding 12 months.  RESULTS: The mean SNOT-22 total score overall was 12.0. The mean was 10.2 for males with a median of 6.5, and a mean of 13.2 for females with a median of 9. Females scored significantly more highly than males on the sleep/fatigue and facial domains.  CONCLUSIONS: Our data demonstrate differences in SNOT-22 amongst males and females. These data can be used in future studies for comparison with different disease populations with rhinosinusitis. This article is protected by copyright. All rights reserved

The measurement of commitment to work

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43493/1/11111_2004_Article_BF00972537.pd

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: A systematic analysis for the Global Burden of Disease Study 2015

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods: We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings: Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation: Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding: Bill & Melinda Gates Foundation

Global and national burden of diseases and injuries among children and adolescents between 1990 and 2013 findings from the global burden of disease 2013 study

IMPORTANCE: The literature focuses on mortality among children younger than 5 years. Comparable information on nonfatal health outcomes among these children and the fatal and nonfatal burden of diseases and injuries among older children and adolescents is scarce. OBJECTIVE: To determine levels and trends in the fatal and nonfatal burden of diseases and injuries among younger children (aged < 5 years), older children (aged 5-9 years), and adolescents (aged 10-19 years) between 1990 and 2013 in 188 countries from the Global Burden of Disease (GBD) 2013 study. EVIDENCE REVIEW: Data from vital registration, verbal autopsy studies, maternal and child death surveillance, and other sources covering 14 244 site-years (ie, years of cause of death data by geography) from 1980 through 2013 were used to estimate cause-specific mortality. Data from 35 620 epidemiological sources were used to estimate the prevalence of the diseases and sequelae in the GBD 2013 study. Cause-specific mortality for most causes was estimated using the Cause of Death Ensemble Model strategy. For some infectious diseases (eg, HIVinfection/AIDS, measles, hepatitis B) where the disease process is complex or the cause of death data were insufficient or unavailable, we used natural history models. For most nonfatal health outcomes, DisMod-MR2.0, a Bayesian metaregression tool, was used to meta-analyze the epidemiological data to generate prevalence estimates. FINDINGS: Of the 7.7 (95 uncertainty interval UI, 7.4-8.1) million deaths among children and adolescents globally in 2013,6.28 million occurred amongyounger children, 0.48 million among older children, and 0.97 million among adolescents. In 2013, the leading causes of death were lower respiratory tract infections amongyounger children (905 059 deaths; 95% UI, 810 304-998125), diarrheal diseases among older children (38 325 deaths; 95% UI, 30 365-47 678), and road injuries among adolescents (115186 deaths; 95% UI, 105185-124 870). Iron deficiency anemia was the leading cause of years lived with disability among children and adolescents, affecting 619 (95% UI, 618-621) million in 2013. Large between-country variations exist in mortality from leading causes among children and adolescents. Countries with rapid declines in all-cause mortality between 1990 and 2013 also experienced large declines in most leading causes of death, whereas countries with the slowest declines had stagnant or increasing trends in the leading causes of death. In 2013, Nigeria had a 12% global share of deaths from lower respiratory tract infections and a 38% global share of deaths from malaria. India had 33% of the world's deaths from neonatal encephalopathy. Half of the world's diarrheal deaths among children and adolescents occurred injust 5 countries: India, Democratic Republic of the Congo, Pakistan, Nigeria, and Ethiopia. CONCLUSIONS AND RELEVANCE: Understanding the levels and trends of the leading causes of death and disability among children and adolescents is critical to guide investment and inform policies. Monitoring these trends over time is also key to understanding where interventions are having an impact. Proven interventions exist to prevent or treat the leading causes of unnecessary death and disability among children and adolescents. The findings presented here show that these are underused and give guidance to policy makers in countries where more attention is needed. Copyright 2016 American Medical Association. All rights reserved