Step 1 Identify the dynamic

indexes and transform to sta

Step 1. Identify the dynamic

indexes and transform to static ones. Firstly, analyse the attribute of safety assessment indexes on dangerous goods transport enterprise and identify the dynamic indexes. Then treat them statically according to the way described in [10], as showed in the following. (1) According SAR302503 936091-26-8 to the principle combining with qualitative and quantitative, the dynamic index’s attribute value recorded for k times in different periods is defined as follows: M(k)=m1k,m2k,m3k,…,mnkT k=1,2,…. (1) And the weight vector and weight vector set of corresponding index in different period are given as follows: u(k)=u1(k),u2(k),…,un(k)∈Uk,U(k)=u1(k),u2(k),…,un(k) ∣ ∑j=1nuj(k)=1,  k=1,2,…. (2) (2) Calculate static value of all dynamic indexes using the following formula: M=mj1+∑k=2ujkΔmjk ∣ Δmjk=mjk−mjk−1, k=1,2,…;j=1,2,…,n. (3) Step 2. Calculate multi-index assessment matrix as follows: B′=b11′b12′⋯b1n′b21′b22′⋯b2n′⋮⋮⋯⋮bm1′bm2′⋯bmn′,

(4) where b ij′ is the weight of index i given by expert j; standardize B′, and then we get B = (b ij)m×n, and b ij ∈ [0,1]; the value of b ij depends on the following situations. If the situation becomes better when the value of b ij is median, then: bij=2max⁡j⁡bij′−min⁡j⁡bij′/2−bij′max⁡j⁡bij′−min⁡j⁡bij′. (5) If the situation is better when the value of b ij becomes bigger, then: bij=bij′−min⁡j⁡bij′max⁡j⁡bij′−min⁡j⁡bij′. (6) If the situation is better when the value of b ij becomes smaller, then: bij=max⁡j⁡bij′−bij′max⁡j⁡bij′−min⁡j⁡bij′. (7) Step

3. Define the entropy weight of every assessment index according to the following method. (1) Among assessment of indexes with experts, the entropy of index is defined as follows: Hi=−1ln⁡n∑j=1nfijln⁡fij i=1,2,…,m, (8) wheref ij = b ij/∑j=1 n b ij. Note that ln f ij has no sense when f ij = 0, thus defining f ij as f ij = (1 + b ij)/(1 + ∑j=1 n b ij). (2) Calculate entropy weight of every assessment index in expression of W j = (λ i)1×m, wherein Carfilzomib λ i = (1 − H i)/(m − ∑i=1 m H i), and ∑i=1 m λ i = 1. Step 4. Identify positive ideal point and negative ideal point. After getting entropy weight, we can introduce λ i into standardized matrix B′ and then get normalized matrix: B * = (b ij *)m×n, wherein b ij * = λ i b ij. Thus positive ideal point and nP + = (p 1 +, p 2 +,…, p m +)T negative ideal point, P + and P −, respectively, can be expressed as follows: P−=p1−,p2−,…,pm−T.

These data can be used to compare socioeconomic inequalities for

These data can be used to compare socioeconomic inequalities for several conditions, providing insight into a healthcare system with no direct financial barriers to treatment (the National Health

Service in England). We aimed to selleck product assess socioeconomic inequalities in the burden of illness (estimated by validated scales, biomarker and reported symptoms) of angina, cataract, depression, diabetes and osteoarthritis, and compare them with inequalities in self-reported medical diagnosis and treatment, in order to determine whether key components of healthcare were received equitably. Methods We obtained data from the ELSA cohort, an interview survey of a sample of the population aged 50 years or older in England. The sample was selected from households that had previously responded to the Health Survey for England, and drawn from selected postcode sectors stratified by health authority and deprivation to be representative of adults aged 50 or more living in private households in England.15 Participants are interviewed in their homes or care homes every 2 years about a wide range of health, economic and social topics. We used data collected from core participants

who had been interviewed in any of four waves of ELSA from wave 2 in 2004–2005 until wave 5 in 2010–2011. Wave 2 was the first wave to include questions on receipt of quality-indicated healthcare, and information was not collected on every variable in every wave. We studied five common and important long-term conditions: angina, diabetes, depression, osteoarthritis and cataract.

Effective treatment is freely available for all five conditions from the National Health Service. Variables We collected data on illness burden, self-reported medical diagnosis and treatment of angina, cataract, depression, diabetes and osteoarthritis. The illness burden for angina was defined as grade 2 on the Rose Angina scale (pain or discomfort in chest when walking at an ordinary pace on the level on most occasions or more often, which makes participant stop or slow down if occurs while walking, and which then goes away within 10 min, and which includes either sternum (any level), or left arm and left anterior Dacomitinib chest). Illness burden for diabetes was defined as a fasting glycosylated haemoglobin level of >7.5%.16 Illness burden for depression was defined as a score of 3 or more on the eight-item Centre for Epidemiologic Studies Depression Scale (CES-D). The application of these standardised scales in ELSA has been described previously.1 Illness burden for osteoarthritis was defined as self-reported pain in the hip or knee of 5 or more on a scale of 0–10.17 Illness burden for cataract was defined broadly as reporting poor vision or blindness.

22 23 The slope order of inequality consisted of wealth quintiles

22 23 The slope order of inequality consisted of wealth quintiles with values of 0.1, 0.3, 0.5, 0.7 and 0.9, that is, the midpoints of each quintile on a scale of zero (least NVP-BEZ235 structure wealthy) to one (most wealthy). The slope order of inequality was modelled as a continuous variable, so that the slope or coefficient of a logit linear regression line across all five quintiles represents the difference in outcome between the hypothetically wealthiest and least wealthy participant. Exponentiating this slope coefficient results in an

OR, which is the ratio of the odds of the outcome in the wealthiest compared with the least wealthy participant. This OR is also known as a relative index of inequality.22 Advantages of this method of quantifying inequality are that it includes all participants, instead of just comparing the highest and lowest quintiles, it accounts for the number of participants in each category and it provides a single overall measure of inequality. We included

all participants in the main cross-sectional analysis in order to compare the distribution of illness burden in the whole population with the distributions of diagnoses and treatments in the whole population. This meant that diagnosis was assessed even in those who did not meet the criteria for ‘illness burden’, and treatment was assessed

even in those with no diagnosis. For the subsidiary analysis using longitudinal data, we estimated the OR of receiving a diagnosis by a subsequent wave only for those who had met the criteria for ‘illness burden’ in a previous wave, and then the likelihood of receiving treatment only for those who had received a diagnosis in a previous wave. This was a subsidiary analysis as the number of participants that could be followed over time in this manner Cilengitide was small, particularly for treatment in angina and depression. Results The whole sample (n=12 765) was composed of participants aged 50 years or more who had responded to at least one wave of ELSA from 2004–2005 until 2010–2011. The response rate in 2004–2005 was 82%.24 25 In wave 5 (2010–2011), self-reported medical diagnosis for all five conditions increased as wealth decreased, for example, in depression from 4% in the wealthiest quintile to 11% in the poorest (table 1). There was little variation between the waves for each of the five conditions (table 2).

Community-based health check-up information was collected in the

Community-based health check-up information was collected in the Health Management Center of the First People’s Hospital of Shunde. The centres provided data for participants selleck compound who enrolled in their health check-up programmes conducted between January 2011 and December 2013. Participants

aged ≥35 years with complete data for the following characteristics were included in this study: age, sex, smoking/drinking habits, history of chronic diseases and treatment, family history of hypertension, height, weight, BP, fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), serum creatinine (Scr), blood urea nitrogen and serum uric acid (UA). BP measurement Although our data were based on retrospective analysis of community-based health check-up information, the protocol of BP measurement in our Health Management Center is carried out consistently since the foundation of the department. Participants were asked to avoid caffeinated beverages, smoking and exercise for at least 30 min,

and BP measurements were taken after the participants were allowed to rest quietly for at least 5 min. Three BP measurements (2 min between each) were obtained for each individual by trained nurses, who were part of the Health Management Center, with a mercury sphygmomanometer. The first and fifth Korotkoff sounds were recorded as SBP and DBP, respectively. During the measurements, the participants were seated with the arm supported at the level of the heart. The mean of three BP measurements was calculated and recorded. Definition of correlative risk factors The correlative risk factors estimated in our study included the following: (1) BP classification was based on the recommendations from the JNC 7.1 Optimal BP was defined as SBP <120 mm Hg and DBP <80 mm Hg. Hypertension was defined as SBP ≥140 mm Hg and/or DBP ≥90 mm Hg, or previously diagnosed as hypertension Carfilzomib and currently undergoing antihypertensive

treatment. Prehypertension was defined if individuals were not undergoing antihypertensive treatment and had an SBP of 120–139 mm Hg and/or DBP of 80–89 mm Hg. Prehypertension was further divided into low-range (SBP 120–129 and/or DBP 80–84 mm Hg) and high-range (SBP 130–139 mm Hg and/or DBP 85–89 mm Hg) subgroups. (2) Impaired glucose regulation was diagnosed based on FPG according to the American Diabetes Association criteria,10 including diabetes mellitus (DM; FPG ≥7.0 mmol/L) and impaired fasting glucose (IFG, FPG 5.6–6.9 mmol/L). Dyslipidaemia was defined as with a history of receiving antidyslipidaemia agents or TC ≥5.18 mmol/L, LDL-C ≥3.37 mmol/L, HDL-C <1.04 mmol/L and/or TG ≥1.

The impact assessment was further subcategorised into the impact

The impact assessment was further subcategorised into the impact on students (target selleck compound population of CBE), and the impact on others involved in CBE programmes. Table 1 Domains in Rossi, Lipsey and Freeman’s approach to programme evaluation Figure 1 Flow chart of search strategy used in systematic review. Abstraction of data was performed independently by reviewers SL and NT. Themes were also independently drawn from data analysis of the impact assessments on students. Disagreements between the two reviewers were resolved by arriving at a consensus. Results Current provision of community-based teaching in UK medical schools We were able to obtain information from the medical school websites

about the provision of community-based teaching in all 32 undergraduate medical schools, and this is outlined in table 2 and summarised in table 3. All undergraduate medical schools provided

some form of community-based teaching or placement. There was, however, variation in the structure, duration and time in the course when community teaching was delivered (see tables 2 and ​and3).3). CBE mainly took the form of clinical placements, patient studies and optional modules. The duration of community-based teaching or placements varied from half day visits to various community settings (as undertaken in schools such as Hull York, Newcastle, Nottingham and St George’s) to a year-long module on primary care and population medicine (as undertaken in Brighton & Sussex). Analysis of the varying formats of CBE (with the exclusion of Norwich, due to the lack of

year-by-year curriculum details) revealed that most medical schools (a total of 31) provide early exposure to general practice or community teaching. Twenty-eight medical schools (90.3%) provide community teaching from the first year of undergraduate medical education. By the end of the second year of preclinical education, students of 29 medical schools (93.5%) would have received some form of community-based teaching. Table 2 An outline of community-based teaching in undergraduate medical courses within the UK Table 3 Summary of findings from online survey The most popular form of community-based teaching within medical schools was general practice placements with 83.9% (26 schools from a total of 31) providing general practice placements within the first 2 years Brefeldin_A of study. Patient studies were the least common form of placements. These were defined as projects where students visited patients within the community or at home. Only 38.7% (12 schools) provided this format of community education at some point in their courses. Fourteen (45.2%) medical schools provided regular exposure to community teaching in every year or phase of the course. With regards to optional modules offered to students, only three of the medical schools offered them—9.7%.

24 In three of these studies, heights and weights were measured t

24 In three of these studies, heights and weights were measured to preset standards by trained investigators,12 17 24 whereas heights and weights of our adolescent participants were mostly self-reported. It is possible that in our study some of the larger participants, particularly from body image conscious countries or cultures, may have under-reported their weights. In a study evaluating the correlation of measured versus self-reported heights and weights in adolescents, Brener et al found that their study subjects tended to over-report their height by 2.7 inches (6.9 cm) on average, and to under-report their weight by 3.5 pounds (1.6 kg) on average, resulting in a BMI understated by 2.6 kg/m2 when compared to measured values. White

adolescents were most likely to over-report their height and female adolescents were more likely to under-report their weight.25 Similarly, Danubio et al26 found that height was over-estimated in boys and girls (2.1 and 2.8 cm, respectively), and that weight was understated (1.5 kg in boys and 1.9 kg in girls). Rasmussen et al27 reported that in the COMPASS study, boys and girls who wished to be leaner under-reported their weight and BMI more than participants who were satisfied with their body size. When we restricted our analysis

to measured height and weight data only, the association between higher fast-food consumption and lower BMI was no longer observed in male adolescents, but the association between higher rates of fast-food consumption and lower BMI persisted in female adolescents. We need to consider the likelihood that, owing to the perception of the negative effects of fast-food consumption, adolescents

who are overweight or obese are likely to have under-reported their actual fast-food consumption. In a review of validation studies on energy intake reporting in children and adolescents, Livingstone and Robson found an increase in under-reporting of energy intake as age and BMI increased, with 14%, 25% and 40% of energy intake under-reported in obese 6-year-olds, 10-year-olds and adolescents, respectively.28 Finally, it is possible that our results are influenced by a degree of reverse causation where those participants who are already overweight or obese are avoiding fast-foods in order to reduce their body weight. Fast-food consumption This study has shown that up to 25% of children worldwide consume fast-food frequently or very frequently, and this increases to over 50% in the adolescent age group. This is consistent with results of Dacomitinib previous studies, particularly those based in the USA and the UK.11 29 30 This study has also highlighted the unexpectedly high proportion of fast-food consumption in both age groups in many developing countries, for which data have not previously been available. In particular, high prevalence of fast-food consumption was observed in centres in Latin America and Asia, which was similar in magnitude to that observed in the USA and Western Europe.

Secondary outcome variables include the difference in the percent

Secondary outcome variables include the difference in the percentage of total energy intake as total, complex and simple CHO, proteins and fats between T2DM and non-T2DM participants, percentage of patients with T2DM who adhere to the diet plan, glycaemic control as per American Diabetes Association (ADA) criteria5 (glycated buy inhibitor hemoglobin (HbA1c) <7%, fasting blood glucose (FBG) between 70 and 130 mg/dL, postprandial blood glucose (PPBG) <180 mg/dL) and the utilisation pattern of antidiabetic drugs. Statistical analysis and evaluations It was assumed that at least 50% of the total energy intake

comes from CHO and at least 50% of the complex CHO intake comes from total CHO in T2DM participants. Thus, 385 T2DM participants were required to achieve an allowable error of 5% where the allowable error is half the width of a 95% CI. Taking missing data into consideration, we planned

to conduct the survey with a total of 400 participants in each group. All analyses were performed on the eligible participants. The primary descriptive analysis of the data was performed using basic summary statistics. Further descriptive measures such as n, mean, median, SD, first quartile (Q1), third quartile (Q3), minimum and maximum were calculated for continuous variables. Percentages were calculated based on non-missing values. Frequency and percentage were calculated for categorical variables. For continuous variables, the mean change was compared statistically between T2DM and non-T2DM groups using either the independent t test or the Mann-Whitney U test based on normality of the data. The tests were carried out at a 5% level of significance and a p value ≤0.05 was considered as significant. Other comparisons specified in the

secondary variables were carried out similarly. As per recommendations of the National Institute of Nutrition6 (NIN) and Indian Consensus Guideline7 for Healthy Eating, a balanced diet should provide approximately 50–60% of total calories from CHO (preferably from complex CHO), approximately 10–15% calories from proteins, and approximately 20–30% calories from visible Batimastat and invisible fats. Data were stratified as per CHO consumption: below NIN recommendation (<50%), as per recommendation (50–60%), and above recommendation (>60%) to capture the natural distribution of patients within these stratifications. In addition, we also compared the findings with the WHO Expert group recommendations, that is, total CHO should provide 55–75% total energy and that free sugars should provide less than 10% energy.8 For categorical variables, the number and percentage of participants were considered. Continuous data are presented in this article as the mean and SD. Statistical evaluations were performed using the software SAS, V.9.1.3.

Side-effects scale:31 Side-effects scale will be completed as a c

Side-effects scale:31 Side-effects scale will be completed as a control check in medicated participants to ensure greater speed to diagnosis/medication normalisation is not off-set by greater side effects. SNAP-IV:32 The proportion of patients achieving selleck bio symptom normalisation assessed via the SNAP-IV. If the young person receives a QbTest on medication (Qb2), the timing on the 3-month SNAP-IV will be moved to coincide with Qb2 to provide a direct comparison of subjective (SNAP-IV) and objective (QbTest) measures. The SNAP-IV is a rating scale designed to assess ADHD symptoms. SDQ:30 The SDQ is a brief behavioural screening questionnaire

which can be used as part of a clinical assessment. C-GAS (Children’s Global Assessment

Scale33): Clinician opinion of patient outcome will be assessed via the C-GAS. The C-GAS is a 0–100 scale which that integrates psychological, social and academic functioning in children. EQ-5D-Y (EuroQol Five Dimensions Heath Questionnaire-Youth34): Child health-related quality of life will be assessed using the EQ-5D-Y. A resource collection profile tool will be used. It will encompass elements of a CSRI (Client Service Receipt Inventory35) often used in mental health studies but will be a specifically designed economic collection pro-forma for the purpose of this study. It will collect demographic details as well as information on all the services used by the child and family borne costs to be estimated. Indirect costs such as time lost from work incurred by the child’s parents or carers will further be recorded. This measure will enable a societal wide perspective for a cost-effectiveness analysis of the QbTest. The DAWBA29 QbTest22 SDQ30 Side-effects scale,31 SNAP-IV32 C-GAS33 EQ-5D-Y34 and CSRI35 all have established reliability, validity and history of use in

clinical and research settings. Feasibility and acceptability QbTest opinion questionnaire and interview: Clinician and patient opinion of the QbTest will be assessed via a questionnaire, developed by CLH and currently used to assess QbTest opinion in on-going studies at the Queens Medical Centre, Nottingham. This will provide information on the acceptability of QbTest in routine NHS settings. A subsample (n=20) of families and clinicians will be invited to participate in qualitative interviews to further AV-951 explore acceptability and feasibility of the QbTest. The subsample will be chosen at random from each participating site, using a random number generator. Table 1 displays a summary of measures, the informant and the time point of completion. All measures will have a 1-month window for completion, with the exception of the clinic pro-forma which must be completed during or just after the clinic appointment and the QbTest which must form part of the diagnostic or medication assessment.

However, 56 observational studies reported on eight main types of

However, 56 observational studies reported on eight main types of immediate medical harms (bleeding, shock, genital tissue swelling, fever, infections and problems with urination and wound healing) on 133 515 females of various ages and types of FGM/C. The rate of immediate complications varied greatly across the studies. There were strong indications of under-reporting selleckchem Imatinib Mesylate of immediate complications from the procedure, with some studies reporting that 90% of the girls undergoing FGM/C experienced no bleeding at all.30 64 However, representative studies (ie, where the participants can be assumed to represent the larger population) of moderate

and high methodological quality indicated that the most common immediate complications were: excessive bleeding (median 32%, range 5–62%), urine retention (median 31%, range 8–53%), genital tissue swelling (median 15%, range 2–27%), problems with wound healing (13%) and pain (11%).30 73 74 Girls generally suffered more than one immediate complication. We identified three clinical reports on deaths directly attributed to FGM/C.75–77 Fourteen studies reported the number of events for different types of FGM/C separately, allowing us to estimate differences in risk

across exposure groups.30 45 64 65 78–87 Our results indicated that there might be a greater risk of immediate harms with FGM/C type III relative to types I–II. We found few, and small, differences in risk of immediate complications with FGM/C types I–II compared to type IV (generally ‘nick’). Genitourinary problems With respect to the genitourinary sequelae of FGM/C, reported years and sometimes decades following the procedure, we identified 17 comparative studies.21 27 29–33 37 43 45 46 54 55 57 60 64 65 In total, the studies included 38 390

women. The most frequently measured outcomes were genital tissue damage, vaginal discharge and itching, urological complications and infections. Many sequelae were examined in only one or a few studies and/or they were relatively rare events, such as keloids and abscesses. Analyses were thus often unable to establish whether there were statistically significant differences between the groups being compared and the CIs were wide. As a result, there was insufficient information available from the studies to assess difference Drug_discovery in risk relative to FGM/C exposure. The results were inconclusive with respect to: scarring, keloids, abscesses, fistulae, damaged tissue (perineum, anal sphincter), disfigurement, vaginal obstruction and cysts. According to four cross-sectional studies (n=3657), there was a trend for a greater risk of vaginal discharge and itching with FGM/C (adjusted ORs (AOR) from 0.94 to 2.81).33 54 57 60 Urological long-term complications were reported in four comparative studies (n=3611), none of which could establish a statistically significant difference, either in unadjusted analyses (RRs from 0.85 to 1.78) or in adjusted analyses (AORs from 0.80 to 1.

This reinforces the importance of the AQMAs set up within the

This reinforces the importance of the AQMAs set up within the

county in response to high-NOx levels. Road traffic is a large contributor to air pollution selleck chemical within Warwickshire. Diesel engines are responsible for a large part of the NOx component of these emissions. An unexpected result also appeared within our analysis. Particulate matter air pollution became significantly negatively correlated with heart failure morbidity and also mortality when incorporated into our model, with all risk factors taken into account and controlled for in this study. This would imply some sort of unexpected ‘protective influence’ from Pm air pollution on heart failure patients. This clearly contradicts our expectations and is at odds with

a wealth of existing evidence that indicates that Pm air pollution contributes risk to and exacerbates cardiovascular disease.4 We offer a possible explanation for this based on the following four observations: The aforementioned negative correlation of Pm air pollution with heart failure morbidity and mortality in our model. Pm air pollution actually varied very little across the county compared to the other types of air pollution. All types of air pollution tended to decrease in rural areas, but Pm tended to decrease much less compared to the other components of air pollution. Consequently, in rural areas of the county where most types of air pollution are significantly lower, Pm pollution was relatively higher compared to NOx, Benzene and SO2. There seems to be a high risk of heart failure deaths in urban centres (particularly Nuneaton, Bedworth, Warwick, Royal Leamington Spa and Kenilworth), higher than can be explained by our model. Conversely, there seems to be a particularly low risk of heart failure deaths in some rural areas within the western part of Stratford-on-Avon, lower than can be explained by our model. A possible hypothesis based on these observations is that there is an additional factor influencing

the morbidity and mortality of heart failure not looked at in this study, namely the urban/rural nature of a patient’s living environment. It could be the case that living in an urban environment contributed risk Dacomitinib and living in a rural area provided protection against heart failure morbidity and mortality. This would be an effect in addition to any increase in air pollution or social deprivation within urban settings compared to rural settings. This could certainly be plausible in principle, with people in rural areas perhaps doing more physical activity, eating more healthily, etc. If this were the case it would explain the excess deaths in urban centres found in this study. It could also be responsible for the unexpected protective factor attributed to Pm air pollution in our analysis.