Changes in the thresholds of MetS components account for differences in prevalence rates of this diagnosis, chan- ging also the population attributable risk (PAR) of their components. Prevalence of MetS have been investigated for non-HIV-infected subjects [11,12], but are scarcely reported for the HIV-infected population  or com- pared to the general population . In addition, some components of MetS are particularly vulnerable to the effect of highly active antiretroviral therapy (HAART) [15,16], and it might be time-dependent. This study veri- fied the prevalence of metabolic syndrome according to the NCEP-ATPIII, IDF and the AHA/NHLBI criteria, and investigated the PAR of each component on prevalence rates among HIV-infected adults.
The aim of the present study is to deter- mine the relationship between socioeco- nomic, demographic, reproductive and health service factors and the likelihood of C-section. Furthermore, in this paper, we present estimates of adjusted population attributable risks (aPARs) for the selected risk factors identified in this study popula- tion. The aPAR is defined as the proportion of cases that can be related to a given risk factor (or a set of risk factors) and is useful in assessing its impact at the population level. To our knowledge, this analysis con- stitutes the first attempt at a comprehensive population attributable risk study of the risk factors for C-sections.
This study used a large population-based sample of Nor- wegian adults, and allowed for an appropriate follow-up time to assess diabetes incidence. We rigorously con- trolled for a range of clinical and sociodemographic covariates and exposures. We also used a formula for calculating population attributable risk that was valid in the presence of confounding and interaction, and pro- vided an intuitive presentation of these results. Instead of focusing on the potential proportion of diabetes cases that could be reduced by eliminating depression or anxi- ety in the population overall, the current analysis allows an examination of strata in which targeted treatment of these symptoms might be more beneficial. For example, of our study population who experienced symptoms of depression and anxiety at baseline, only 13.6% of these individuals developed diabetes during follow-up, yet they accounted for 19.2% of new cases. Given these findings and the current diabetes epidemic, research to deter- mine whether treating anxiety and depression in peo- ple exposed to other diabetes risk factors is effective in reducing the incidence rate may therefore prove to be useful .
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The estimated population attributable risk indicates that the risk factors identified explain approximately 40% of all cases of infection. The higher population attributable risk percent found for “ever use of sniffed drugs” (7.2%) than for “ever use of injected drugs” (4.3%) was probably due to the lower frequency of IDUs than of sniffed drug users in the studied population. Although we cannot evaluate to what extent there was an underreporting of injection drug use because of stigma and discrimination in our study, the frequencies we found (data not shown) seem consonant with the national survey conducted by the Brazilian Cen- ter of Information on Psychotropic drugs (CEBRID) and a study among hospitalized patients [34,35]. In relation to IDU, as the population attributable risk is related to the general population (injecting and non-injecting drug users), the attributable risk among the exposed (injecting drug users) will be greater and therefore the proportion of infection in that group that could be prevented by elimin- ating the exposure would be larger among IDUs.
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To our knowledge, this study is the ﬁ rst to provide the population attributable risk percentage (PAR%) data on different types of refractive errors in adult Asians. Data from this population-based study demonstrated the expected association between age and different types of refractive errors. Extremely low socioeconomic status, illiteracy, rural residence, smoking, ARM, and severity of nuclear opacity were signiﬁ cantly associated with myopia. Based on our results, smoking, hypertension, diabetes mellitus, and early AMD were identiﬁ ed as modiﬁ able risk factors, whereas age, nuclear cataract and AMD were identiﬁ ed as nonmodiﬁ able risk factors.
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We fit a series of clog-log discrete-time survival models to estimate smoking-based differences in US adult mortality risk. First, we fit a baseline model that estimates differences in mortality risks between current, former, and never smokers (reference category). Next, we fit a confounder model that estimates age-specific dif- ferences in mortality risks between current, former, and never smokers, adjusting for race/ethnicity and gender as categorical confounders of the smoking-mortality as- sociation. We also fit models separately for black and white men and women that estimate age-specific RRs for former and current smokers compared to never smokers (i.e., confounder-specific models to be used with the weighted-sum approach to calculate PAFs). Finally, we fit a bias model that refits the confounder model by ac- counting for cohort-based variation in mortality risk and age-related selection biases in the NHIS-LMF data.
Methods We analysed 10 691 site-years of vital registration data, 768 site-years of verbal autopsy data, and 361 site-years of mortality surveillance data using the Cause of Death Ensemble model to estimate tuberculosis mortality rates. We analysed all available age-specific and sex-specific data sources, including annual case notifications, prevalence surveys, and estimated cause-specific mortality, to generate internally consistent estimates of incidence, prevalence, and mortality using DisMod-MR 2.1, a Bayesian meta-regression tool. We assessed how observed tuberculosis incidence, prevalence, and mortality differed from expected trends as predicted by the Socio-demographic Index (SDI), a composite indicator based on income per capita, average years of schooling, and total fertility rate. We also estimated tuberculosis mortality and disability-adjusted life-years attributable to the independent effects of risk factors including smoking, alcohol use, and diabetes.
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Results. Outdoor air pollutants were associated with parent-reported perceived ambient air pollution. Physi- cian-diagnosed asthma was reported for 8.1% of the boys (1330 of 16 441) and 5.6% of the girls (894 of 16 056). The risk of physician-diagnosed asthma was significantly as- sociated with parental atopy and perceived ambient air pollution in both sexes. The presence of visible cock- roaches (odds ratio [OR]: 1.30; 95% confidence interval [CI]: 1.07–1.59), mold on walls at home (OR: 1.20; 95% CI: 1.01–1.41), and water damage (OR: 1.33; 95% CI: 1.02–1.70) were also associated with asthma in girls; however, only visible mold on walls at home was related to asthma in boys. Mutually adjusted analytical models produced sta- tistically significant associations between any indoor fac- tor and asthma in girls (OR: 1.24; 95% CI: 1.00 –1.56) but not in boys (OR: 1.04; 95% CI: 0.87–1.25). For all heredi- tary and environmental factors, the total population at- tributable risk was 44.31% in boys and 60.61% in girls.
This epidemiological study represents PAF of hypertension related to obesity in Iran. In this study, prevalence of obesity in different provinces of Iran was also extracted according to age and gender from Iranian Ministry of Health Non-Communicable Disease Risk Factor InfoBase in 2009. Risk factors for non- communicable diseases' surveillance have been executed since 2004 with five stages at the provincial level (2004- 2009). I its prototype in 2004, had 89,404 samples with a systematic approach and multi-stage cluster sampling method chosen from the whole country.
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One of the most important outcomes of placental malaria is LBW. The most common method of diagnosing placen- tal malaria remains thick blood smear of placental blood, but many studies have used PCR of placental blood. Prior studies correlating PCR+ and blood smear+ placental malaria with LBW have produced conflicting results [33,34]. Although PCR is more sensitive than placental blood smear for malaria detection, the clinical predictive value of placental malaria diagnosed by PCR remains unclear, especially in the setting of HIV . In this study, placental malaria determined by either PCR or smear of placental blood was associated with LBW. Further, these data suggest that placental malaria detected by blood smear may be the most clinically relevant definition. Spe- cifically, a statistically significant association between PCR+/smear- placental malaria and LBW was found only among HIV-uninfected women, although this study was underpowered to detect such an association among HIV- infected women. Indeed, the underlying mechanism lead- ing to PCR+/smear- placental malaria in this population receiving anti-folate prophylaxis remains unknown. PCR+/smear- placental malaria may be seen simply due to increased sensitivity of PCR compared to blood smear for identification of low-level infections. A more intriguing explanation is that PCR+/smear- placental malaria may represent past P. falciparum infection of the placenta with successful clearance of the parasite by either IPT-SP or daily TS. In this scenario, the placenta is exposed to active infection for a shorter period of time and, therefore, the harmful effects on the fetus are mitigated. If this explana- tion is true, the data here suggest that HIV-uninfected women on IPT-SP were more able to clear P. falciparum infection of the placenta as their gravidity increased. On the other hand, the HIV-infected multigravidae in this study were no more likely to clear infection than primi- gravidae, likely due to altered immune-recognition in the setting of HIV.
Relative risks (RRs) for each cancer site associated with obesity and physical inactivity were evaluated using reported epidemio- logical studies in Korea. We conducted a comprehensive literature search for studies published in English or Korean before August 2012 in PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) and KoreaMed (http://www.koreamed.org/SearchBasic.php) using the search keywords ‘‘Korea,’’ ‘‘obesity,’’ ‘‘overweight,’’ ‘‘excess weight,’’ ‘‘physical activity,’’ ‘‘physical inactivity,’’ and ‘‘cancer.’’ Inclusion criteria for literature search was that the study should be epidemiological studies conducted on the Korean population and provided the information to estimate the RRs or ORs for cancer incidence. Additional citations were identified from the reference lists of the resulting articles and with information provided by cancer experts in Korea. When there were multiple reports of the same study, the publication with the longest follow-up period or the largest event numbers was selected for the estimation of pooled RRs. When necessary, we obtained additional data through personal communication with the author(s) of the published articles . The initial literature search identified 30 studies on obesity and cancer, but many of these were excluded from the final analysis by following exclusion criteria: risk estimates and/or precise information (e.g., standard errors, 95% confidence intervals) were not available or the classification of overweight and obesity was different (19 studies); and multiple results were reported from the same study population (4 studies). Finally seven studies on obesity were included in the meta-analysis [14–20]. For physical activity, seven studies were initially identified, four of which were excluded because the classification of physical activity was rather different (for example, only defined as ‘‘yes’’ and ‘‘no’’), and three studies [16,21,22] were used for the final evaluation of RRs (Tables S1, S2). Out of nine studies used for excess body weight and physical activity, two studies were cohort studies and seven studies were case-control studies. When the outcome is a rare event, RRs can be estimated by ORs. As we dealt with cancer incidences that are very rare, we did not separate the estimates from cohort studies and those from case-control studies when performing meta-analyses. Because we found no reported exam- ination of the association between colorectal cancer and physical activity in Korean women, we used the estimated risk value obtained from men.
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Background: The attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data. Methods: Using simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier’s estimator, one semiparametric based on Cox’s model, and one parametric based on the piecewise constant hazards model, as well as one simpler method based on estimated exposure prevalence at baseline and Cox’s model hazard ratio. We considered a fixed binary exposure with varying exposure probabilities and strengths of association, and generated event times from a proportional hazards model with constant or monotonic (decreasing or increasing) Weibull baseline hazard, as well as from a nonproportional hazards model. We simulated 1,000 independent samples of size 1,000 or 10,000. The methods were compared in terms of mean bias, mean estimated standard error, empirical standard deviation and 95% confidence interval coverage probability at four equally spaced time points.
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ture review, the proportion of each condition that would be influenced by the addition of one egg daily was esti- mated. A second PubMed search was conducted to iden- tify publications estimating the costs of the conditions of interest. Abstracts were reviewed to identify publications with original data that assessed the cost of illness; reviews and comparative cost studies were excluded. The estimates were reviewed to determine the type of costs presented (e.g., direct medical vs. lost productivity), the population considered (e.g., Medicare-eligible vs. younger patients), and the year in which costs were estimated (in order to inflate to a common year).
The years of life lost by an individual as a result of his or her obesity have been estimated in several studies (Fontaine et al. 2003; Prospective Studies Collaboration 2009). In this paper, we are asking a question about population health rather than individual health: how many years of life are forfeited, on average, by members of a population as a result of the level of obesity in that population. Answering this question involves combining the prevalence of obesity in a population with the risks of mortality for people in a particular BMI category in order to estimate the effects of obesity on age- specific mortality rates. Estimates of the impact of obesity on a population’s level of life expectancy are uncommon; an exception is Olshansky et al., whose effort was limited to the US (Olshansky et al. 2005). Yet, these estimates are important as they provide a basis for conducting cross-national comparisons, which can be used to determine why some countries achieve better health outcomes than others.
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There was strong evidence for an association be- tween the nongastrointestinal Charlson Index and upper GIB after adjusting for all measured risk factors (single comorbidity adjusted OR ⫽ 1.43; 95% CI: 1.35–1.52; mul- tiple or severe comorbidity adjusted OR ⫽ 2.26; 95% CI: 2.14 –2.38; P ⬍ .001 likelihood ratio test). Table 2 shows the adjusted ORs from the final model for each exposure. We found the largest association with a bleed was with a previous Mallory-Weiss syndrome, which reflects the in- herent risk of bleeding in recurrent vomitters. The vari- ables for angiodysplasia and dialysis had the highest vari- ance inflation factors, 1.48 and 2.35, respectively. As both of these were less than the a priori threshold of 5, all exposures were included in the final conditional logistic regression model.
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Analytical formulae for confidence intervals of attri- butable risk measures are not easily produced , and this also applies to the extended versions developed here. Although approximated estimators have been proposed [15,16], in this context the most straightforward approach is to rely on interval estimation obtained empirically through Monte Carlo simulations [17,18]. Basically, we take random samples η (j) of the original parameters η of the cross-basis in Eq. (3) from the assumed multi- variate normal distribution with point estimate η ˆ and (co)variance matrix V ( η) ˆ derived from the regression model. These samples η (j) are used to compute β x, (j) for = 0 , . . . , L and each intensity x, empirically reconstructing
Like studies elsewhere [25, 26, 28], the CRC risk in men associated with alcohol consumption in the current study was higher than women. This may be due to gender difference in practices that men had a relatively larger spectrum of alcohol intake and this allowed easier identifi- cation of an association, or that there may be hormone- related differences in alcohol metabolism . Possible explanations for the lower PAF for alcohol consumption in Malaysia compared to other countries such as Korea and UK may be due to (i) a true low consumption of alco- hol as it is not an acceptable cultural norm amongst Malays (i.e. the Islamic faith prohibits alcohol consump- tion), (ii) the use of self-reported alcohol consump- tion, and/or (iii) whether a large proportion of the Malaysian population had slow action of enzyme
In our study, the impact of population ageing on the hospitalisation costs for CVD was measured using the method that was adapted from the principles of the com- ponent decomposition method used in studies in Australia and Korea [12,13]. The previous studies, the change in healthcare expenditure was attributable to the impact of ageing, the growth of population over time, change in pro- portion of people using healthcare services and change in average cost per episode [12,13]. Similar to the studies, the change in total costs of hospitalisation for CVD over a period of time were attributable to following components: population growth, ageing of the population, the increase in total number of episodes of hospitalisations and the increase in average cost per episode. Steps to calculate proportion of contribution of each component to the change in total costs of hospitalisation for CVD between 1993/94 and 2004/05 were described in detail below.
risk of preterm labour or delivery, fetal distress or death, and low birth weight off-spring. Low birth weight remains an important cause for infant and child mortality. In a few meta-analysis researchers have found a significant association between abuse and low birth weight. The relation between adverse outcome of pregnancy and abuse during pregnancy may occur through direct and indirect mechanisms. Direct mechanisms involve trauma to the pregnant abdomen leading to premature labour, rupture of membranes, placental abruption or a ruptured uterus. Abuse during pregnancy has been associated with low socioeconomic status, poor maternal weight gain, anaemia, an unhealthy diet, sexually transmitted diseases and psychological morbidity 5, 50, 52, 53 .These variables, as well as stress and lack of social support, have been identified as risk factors for low birth weight. It has been argued that if the health risks associated with abuse are sequelae of violence, then abuse may be a previously unrecognized cause of low birth weight. Even these are preliminary findings and the ideal situation is that little is known about the possible effect of violence against women on the survival of their offspring. According to a case control study done in Ghana to examine the risk factors for child mortality, an increased odd of death was found if the child’s father beat the mother, among other causes 54 .Further research is needed on the influence of violence on women and its influence on infant and child mortality 6 .
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For the other three types of harm, published continuous risk function curves were not available and we developed a method to derive our own continuous risk function curves from the broader data that were available. Under this method we developed two-part linear risk functions whereby the risk is flat from zero consumption up to a particular threshold and then rises linearly as consumption increases. For each harm and age/sex subgroup it was therefore necessary to decide on an appropriate threshold after which the risk function begins to rise, and then to estimate the slope of the rising straight line risk function beyond that threshold to fit the available observed data. The central idea is that we know the distribution of alcohol consumption in England (from the GHS), we have or can derive estimates of alcohol attributable fraction (AAF), and therefore it is possible to fit a linear risk function that implies the same AAF as that observed. For acute health harms that are partially attributable to alcohol (e.g. fatal road traffic accidents), published evidence did exist on the alcohol attributable fraction (AAF) (e.g. 37% of fatal road traffic accidents for men aged 25 – 34 are attributable to alcohol) (Jones et al., 2008). We assumed RR is a function of peak daily consumption, with RR=1 below a threshold of 3 units for women and 4 for men, and then estimated the slope of the risk function beyond the threshold by fitting the slope using ordinary least-squares regression to minimise the difference between the implied predicted AAF (the implied risk for those GHS samples with consumption above zero divided by the implied risk for the whole subgroup) and actual observed AAF for the age/sex subgroup.
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