The role of disease burden measures in future estimates of endemic waterborne disease

The 1996 Safe Drinking Water Act amendments require the US Environmental Protection Agency and the Centers for Disease Control and Prevention to develop a national estimate of the occurrence of waterborne infectious disease that is attributable to public drinking water systems in the United States. Much of the information for developing the national estimate will be derived from epidemiologic data, and the primary outcome of this effort will be an estimate of the number of cases of gastrointestinal illness. While quantifying the number of these cases provides some measure of waterborne disease impact, the usefulness of this measure may be limited because the full spectrum of societal impact also involves consideration of the additional effects of these diseases such as hospitalization costs and lost productivity. If decision-makers wish to compare the impact of waterborne infectious diseases to the impact of some other public health concern (e.g. to aid in resource allocation decisions), then a comparison of case numbers may prove inadequate. Case numbers alone do not provide sufficient information about the severity of different illnesses. Society may value the avoidance of a few cases of severely debilitating illness more than it values the avoidance of many cases of mild illness. In order to compare disparate public health concerns, "burden of disease" measures that incorporate indicators of disease severity, costs, or societal values may prove essential for some types of decisions. We describe epidemiologic measures of severity, quality adjusted life years (QALYs), disability adjusted life years (DALYs), willingness-to-pay, and cost-of-illness methods commonly used for burden of disease estimates, and discuss how some of these summary measures of burden might be used for waterborne disease estimates.     

The male predominance in the incidence of infectious diseases in children: a postulated explanation for disparities in the literature.

In children, a male predominance in the incidence of symptomatic disease has been reported for some infectious agents and not for others. Not only are the factors underlying these sex differences poorly understood, but it is also not clear why the differences are described only for selected infectious diseases. In this study of sex- and age-specific incidence of infectious diseases in children, a possible explanation for the inconsistencies in the literature was explored. The sex ratio in reported disease incidence in Israel during a period of about 20 years was examined for various viral and bacterial infections. In addition, an hypothetical mathematical model was developed which assumes increased susceptibility to infectious disease (such as in relative immune deficiency) in a proportion of males. In children aged under 4 years, a higher incidence among males was consistently observed for all diseases, and the sex ratio varied between 1.16 (95% confidence interval (CI): 1.13-1.18) for shigellosis to 1.98 (95% CI: 1.79-2.17) for viral meningitis. The highest ratios were associated with the diseases which tend to present asymptomatically most often, which is consistent with the predictions of the model. The male excess in symptomatic disease appears to be present for most infectious diseases and this should be taken into account in studies comparing observed disease incidence between groups with different sex ratios. The inconsistencies in reports on the excess male morbidity for infectious diseases may be due to variations in symptomatic to asymptomatic infection ratios.     

应用Meta分析有效估计人群归因危险度百分比的探讨

人群归因危险度百分比 (populationattributableriskproportion ,PARP)是总体人群中某种疾病归因于某种因素的暴露所引起的发病 (死亡 )占全部发病 (死亡 )的百分比 ,反映该因素所引起的发病 (死亡 )占全部发病 (死亡 )的比重。通过PARP可了解各危险因素对人群中某疾病的发病所产生的影响 ,亦即消除某危险因素后 ,所产生的对预防该疾病的效果将占有多大比重。它能够为卫生政策的制订提供依据 ,有着重要的公共卫生的实际意义。目前常用的估计PARP的方法有两种 :一种是利用全国人群抽样调查获得的人群总暴露率来估计 ;另一种是利用某地区…     

Detecting small-area similarities in the epidemiology of childhood acute lymphoblastic leukemia and diabetes mellitus, type 1: a Bayesian approach

Childhood acute lymphoblastic leukemia and diabetes mellitus, type 1, have common epidemiologic and etiologic features, including correlated international incidence and associations with infections. The authors examined whether the diseases' similar large-scale distributions are reflected in small geographic areas while also examining the influence of sociodemographic characteristics. Details of 299 children (0-14 years) with acute lymphoblastic leukemia and 1,551 children with diabetes diagnosed between 1986 and 1998 were extracted from two registers in Yorkshire, United Kingdom. Standardized incidence ratios across 532 electoral wards were compared using Poisson regression, confirming significant associations between population mixing and the geographic heterogeneity of both conditions. Bayesian methods analysis of spatial correlation between diseases by modeling a bivariate outcome based on their standardized incidence ratios was applied; spatial and heterogeneity components were included within a hierarchical random effects model. A positive correlation between diseases of 0.33 (95% credible interval: -0.20, 0.74) was observed, and this was reduced after control for population mixing (r = 0.18), population density (r = 0.14), and deprivation (r = 0.06). The Bayesian approach showed a modest but nonsignificant joint spatial correlation between diseases, only partially suggesting that the risk of both was associated within some electoral wards. With Bayesian methodology, population mixing remained significantly associated with both diseases. The links between diabetes and acute lymphoblastic leukemia observed for large regions are weaker for small areas. More powerful replications are needed for confirmation of these findings.     

Trends in cardiovascular disease mortality in industrialized countries since 1950

In most industrialized countries, mortality in general, and cardiovascular disease mortality in particular, have shown decreasing trends since around 1970, following stagnation or increases observed during the 1950s and 1960s. In some countries, however (e.g. in Eastern Europe), male mortality from cardiovascular diseases increased during recent years. The levels and trends of mortality from cardiovascular diseases vary considerably among countries. Measured in terms of age-standardized rates, the ratio between the highest and the lowest rates around 1985 was about 2 for total mortality but about 4 for all cardiovascular diseases combined. With further breakdowns the ratio was even greater, i.e. 4-5 for heart diseases and 6-7 for cerebrovascular disease. For ischaemic heart disease alone, the ratio reached as high as 10, though part of this wide range should be attributed to artefacts due to the varying diagnostic practices followed in different countries. The speed of mortality changes also differed among countries, ranging from a rapid decrease to a rapid increase. In general, the trends were much more favourable in females than in males. Consequently, sex differentials have been widened. The male/female ratio in mortality for ischaemic heart disease has now exceeded 3 in a number of countries. The ratio for cerebrovascular diseases, which used to be close to 1 in many countries in the early 1950s, has also increased, often reaching the level of 1.5 or higher. Differentials were observed also among different age groups in some countries. There seems to be a tendency for mortality change, either an increase or a decrease, to be quicker in younger age groups than in older ones. These varying levels and trends in cardiovascular disease mortality have no doubt been caused by a multitude of risk factors operating in each country, affecting the incidence of cardiovascular diseases and their prognosis. Much is already known about these risk factors and about the measures to be taken by the health services as well as by individuals for prevention and effective therapy. The considerable variation in mortality levels and trends observed among different countries points to the possibility for action by countries heavily affected by cardiovascular diseases. Mortality surveillance in each country and at the international level thus reveals how each country's situation and trends compare with others and provides a basis for action and further research. Progress in WHO's MONICA (MONItoring of trends and determinants in CArdiovascular disease) project will clarify various aspects of the role played by risk factors in different communities.     

基于甲状腺激素血清学多指标相关性对2型糖尿病患者血管并发症的诊断效能评价

目的探讨2型糖尿病(T2MD)患者甲状腺功能血清学多指标间的相关性,以及甲状腺激素水平对大血管和微血管病变的影响。方法回顾性研究2014年12月—2017年12月锦州市中心医院收治的276例T2MD患者,以是否患有亚临床甲减作为分组标准,将患者分为单纯2型糖尿病组(T2DM组)147例和2型糖尿病合并亚临床甲减组(T2DM+SCH组)129例,观察比较两组血管并发症发生率、甲状腺激素与血管并发症及生化指标的相关性;多因素logistic回归分析合并亚临床甲减的T2MD患者的血管病变危险因素。结果 (1)微血管病变与病程、TSH和SBP呈正相关(P0.05),与TT3、FT4和FT3呈负相关(P0.05);(2)大血管病变与病程、年龄、吸烟史、TSH、SBP、UA和LDL-C均呈正相关(P0.05);(3)有吸烟史的糖尿病合并亚临床甲减患者大血管病变发生率比非吸烟者增加1.63倍,TSH与合并亚临床甲减的T2DM患者大血管病变和微血管病变均存在相关性。结论甲状腺激素水平对T2MD患者血管病变存在直接影响,对糖尿病血管并发症具有诊断效能。     

人口老龄化所致健康影响研究进展

目的 概括人口老龄化所致健康影响研究的最新进展，为未来开展相关研究提供参考。方法 系统检索PubMed、Web of Science、Embase、Cochrane Library、中国知网和万方数据2009-2019年发表的文献。按纳入和排除标准筛选文献，并提取基本信息和主要研究结果。结果 本研究共纳入符合要求的文献65篇。44.6%的已发表文献采用死亡数/率作为健康效应评估指标；66.2%的文献集中于单一国家，如：中国、英国和美国等；60.0%的文献研究了老龄化对单一疾病的健康影响，如：糖尿病、肺癌和冠心病等；67.7%的文献采用分解法量化人口老龄化的健康效应；15.4%的文献模拟了干预措施对平衡人口老龄化影响的效果。大多数研究发现人口老龄化导致一些疾病的负担增加，如：癌症、心血管和痴呆等，但有研究报道人口老龄化导致新生儿病、疟疾等疾病的负担减少。发表文献采用的归因方法不尽一致。多个常用方法对研究假设、归因顺序和对照组的选择敏感，造成研究结果缺乏可比性和稳定性。结论 目前缺乏针对全球和不同疾病的人口老龄化健康效应的系统研究。既往归因研究所采用方法不尽相同，研究结果受所采用方法的局限性影响，造成归因结果不稳定。建议未来采用稳定的人口老龄化健康效应归因方法对人口老龄化所致的全球疾病负担开展系统研究。     

Effective reproduction numbers are commonly overestimated early in a disease outbreak

Reproduction numbers estimated from disease incidence data can give public health authorities valuable information about the progression and likely size of a disease outbreak. Here, we show that methods for estimating effective reproduction numbers commonly give overestimates early in an outbreak. This is due to many factors including the nature of outbreaks that are used for estimation, incorrectly accounting for imported cases and outbreaks arising in subpopulations with higher transmission rates. Awareness of this bias is necessary to correctly interpret estimates from early disease outbreak data.     

Statistical issues in life course epidemiology

There is growing recognition that the risk of many diseases in later life, such as type 2 diabetes or breast cancer, is affected by adult as well as early-life variables, including those operating prior to conception and during the prenatal period. Most of these risk factors are correlated because of common biologic and/or social pathways, while some are intrinsically ordered over time. The study of how they jointly influence later ("distal") disease outcomes is referred to as life course epidemiology. This area of research raises several issues relevant to the current debate on causal inference in epidemiology. The authors give a brief overview of the main analytical and practical problems and consider a range of modeling approaches, their differences determined by the degree with which associations present (or presumed) among the correlated explanatory variables are explicitly acknowledged. Standard multiple regression (i.e., conditional) models are compared with joint models where more than one outcome is specified. Issues arising from measurement error and missing data are addressed. Examples from two cohorts in the United Kingdom are used to illustrate alternative modeling strategies. The authors conclude that more than one analytical approach should be adopted to gain more insight into the underlying mechanisms.     

What can go wrong when ignoring correlation bounds in the use of generalized estimating equations

The analysis of repeated measure or clustered data is often complicated by the presence of correlation. Further complications arise for discrete responses, where the marginal probability‐dependent Fr'echet bounds impose feasibility limits on the correlation that are often more restrictive than the positive definite range. Some popular statistical methods, such as generalized estimating equations (GEE), ignore these bounds, and as such can generate erroneous estimates and lead to incorrect inferential results. In this paper, we discuss two alternative strategies: (i) using QIC to select a data‐driven correlation value within the Fréchet bounds, and (ii) the use of likelihood‐based latent variable modeling, such as multivariate probit, to get around the problem all together. We provide two examples of the repercussions of incorrectly using existing GEE software in the presence of correlated binary responses. Copyright © 2010 John Wiley & Sons, Ltd.     

西北地区人间布鲁氏菌病发病率与自然和社会因素相关性

  目的  描述西北地区布鲁氏菌病(简称布病)的流行特征,探索其发病率与自然和社会因素的相关性。  方法  收集全国2004—2015年布病监测数据、西北5省2004—2015年布病报告数据及自然和社会因素资料,描述布病的时间分布特征,采用主成分回归分析探索各因素对发病率的影响。  结果  2004—2015年间西北5省布病发病率逐年升高,且整体高于全国水平。每年5—8月为发病高峰。牧业总产值、地区生产总值、牛肉牛奶产量与布病发病率呈正相关,牛期末数量与布病发病率呈负相关。  结论  西北地区布病发生率较全国同期水平高,地区生产总值、牧业总产值、牛肉牛奶产量等社会因素对发病率有影响,春夏回暖季尽早防控,控制养殖规模,对于控制布病疫情具有重要意义。     

Disease prevalence estimations based on contact registrations in general practice

This paper describes how to estimate the prevalence of chronic diseases in a population using data from contact registrations in general practice with a limited time length. Instead of using only total numbers of observed patients adjusted for the length of the observation period, we propose the use of (i) the time of the first contact of patients, (ii) the joint total numbers of patients and contacts, and (iii) the sets of patients in distinct time intervals, to generate prevalence rate estimates. The three new prevalence rate estimators have been developed assuming either a homogeneous or a parameterized heterogeneous patient population. Systematic and stochastic components of the estimators have been analysed by cross-validation for five chronic diseases using data from the Dutch 'Study on Chronic Conditions'. The results show that the first two estimators work well for diseases with a relatively structured visiting behaviour, such as hypertension and diabetes mellitus, assuming a time-constant contact rate and homogeneous patient population. For diseases such as ischaemic heart disease, chronic non-specific respiratory diseases and osteoarthritis, that do not satisfy these assumptions, the methods generally result in underestimations.     

Refining estimates of major depression incidence and episode duration in Canada using a Monte Carlo Markov model.

BACKGROUND: Serial period prevalence estimates for recurrent diseases such as major depression are available more frequently than fully detailed longitudinal data, but it is difficult to estimate incidence and episode duration from such data. Incidence and episode duration are critical decision modeling parameters for recurrent diseases. OBJECTIVES: To reduce bias that would otherwise occur in national incidence and duration-of-episode estimates for major depressive episodes deriving from studies using serial period prevalence data and to illustrate a methodological approach for the estimation of incidence from such studies. METHODS: Monte Carlo simulation was applied to a Markov process describing incidence and recovery from major depressive episodes. RESULTS: The annual incidence and episode duration were found to be 3.1% and 17.1 weeks, respectively. These estimates are expected to be less subject to bias than those generated without modeling. CONCLUSIONS: These results highlight the usefulness of Markov models for analysis of longitudinal data. The methods described here may be useful for decision modeling and may be generalizable to other chronic diseases.     

Handling of baseline measurements in the analysis of crossover trials

Different analytic approaches for modeling baseline data in crossover trials were compared based on the efficiency in estimating treatment effects. Jointly modeling baseline and post-baseline data is recommended to best utilize baseline data. It results in the most significant gain in efficiency when data are strongly correlated within the same period but weakly correlated between different periods. Its performance remains comparable to the best of various other modeling methods under small within period correlation or large between period correlation. We also examined the use of baseline data in modeling carryover effect. We noted that to model carryover effect in crossover trial generally would lead to a much less efficient estimator and much more sensitive inference.     

Relationships among diet, physical activity and other lifestyle factors and debilitating diseases in the elderly

Diet and physical activity are two major lifestyle factors that play a role in the prevention or management of debilitating conditions affecting older people. Both under- and overnutrition predispose to diseases. Low sodium and high potassium intakes, as well as the consumption of fruits and vegetables are associated with a reduction of hypertension and diseases arising from hypertension such as stroke and dementia. Dietary patterns (consumption of quantity and types of fats, cholesterol, vegetable oils, fish) are important in the prevention of coronary heart disease. Calcium and vitamin D intakes are important factors in the development of osteoporosis, while various dietary factors have been linked to the development of cancer. Physical activity is important in the prevention of functional decline and increased survival, reduced incidence of falls and fractures, and has various cardiovascular health benefits. Apart from prevention of diseases, exercise also has an important role in improving function in some chronic diseases such as heart failure or chronic obstructive pulmonary disease. Both diet and exercise interact, so that public health recommendations often take the form of lifestyle modification advice in the prevention of disease and disability.     

A geostatistical approach to the analysis of pattern in rare disease.

The incidences of human disease vary from place to place, and some show distinct patterns. Patterns in rare diseases, such as childhood cancer, are not readily discernible, however, and this makes it difficult to relate their distribution to factors in the environment in seeking possible causes of the disease. Provided cases are geographically indexed, data on a disease can be analysed geostatistically. Variograms can be computed to determine the strength and spatial scale of any pattern, and to summarize the variation, and then the risk of developing the disease can be estimated by kriging. This has been done for the incidence rates of childhood cancer from 1980 to 1984 in the West Midlands Health Authority Region of England. A novel modification was made to compute the variogram of the risk from that of the frequencies, taking into account the binomial nature of the data. The incidence of the disease appears patchy. The results show that this patchiness is spatially correlated and not purely random. The risk appears greatest in the rural south west of the Region and in some of the suburbs around the conurbation. This approach using geostatistics seems very promising and will be developed further as more data for this and other diseases become available.     

Finding factors influencing risk: comparing Bayesian stochastic search and standard variable selection methods applied to logistic regression models of cases and controls

When modeling the risk of a disease, the very act of selecting the factors to be included can heavily impact the results. This study compares the performance of several variable selection techniques applied to logistic regression. We performed realistic simulation studies to compare five methods of variable selection: (1) a confidence interval (CI) approach for significant coefficients, (2) backward selection, (3) forward selection, (4) stepwise selection, and (5) Bayesian stochastic search variable selection (SSVS) using both informed and uniformed priors. We defined our simulated diseases mimicking odds ratios for cancer risk found in the literature for environmental factors, such as smoking; dietary risk factors, such as fiber; genetic risk factors, such as XPD; and interactions. We modeled the distribution of our covariates, including correlation, after the reported empirical distributions of these risk factors. We also used a null data set to calibrate the priors of the Bayesian method and evaluate its sensitivity. Of the standard methods (95 per cent CI, backward, forward, and stepwise selection) the CI approach resulted in the highest average per cent of correct associations and the lowest average per cent of incorrect associations. SSVS with an informed prior had a higher average per cent of correct associations and a lower average per cent of incorrect associations than the CI approach. This study shows that the Bayesian methods offer a way to use prior information to both increase power and decrease false-positive results when selecting factors to model complex disease risk.     

HLA and disease

Association of HLA and diseases is well known. Several population studies are available suggesting evidence of association of HLAs in more than 40 diseases. HLA found across various populations vary widely. Some of the reasons attributed for such variation are occurrence of social stratification based on geography, language and religion, consequences of founder effect, racial admixture or selection pressure due to environmental factors. Hence certain HLA alleles that are predominantly associated with disease susceptibility or resistance in one population may or may not show any association in other populations for the same disease. Despite of these limitations, HLA associations are widely studied across the populations worldwide and are found to be important in prediction of disease susceptibility, resistance and of evolutionary maintenance of genetic diversity. This review consolidates the HLA data on some prominent autoimmune and infectious diseases among various ethnic groups and attempts to pinpoint differences in Indian and other population.     

Genomic interactions with disease and nutrition

The putative influence of genomic factors on the responsiveness to nutrient intake is a newly developed field of research. As well, there is growing interest for determining the interactions between nutrient, inflammation and aging and the possible impact on lifespan and disease development. Inflammation adversely affects health in many diseases with an inflammatory basis, such as atherosclerosis, obesity and type 2 diabetes mellitus. The metabolic effects of inflammation are mediated by pro-inflammatory cytokines. Metabolic effects include insulin insensitivity, hyperlipidemia, muscle protein loss and oxidant stress. Aging is also characterized by an increase in inflammatory stress and contains some of the hallmarks of inflammatory disease. It is also a phase of life when inflammatory diseases rise in incidence. Evidence is accumulating that the individual level of cytokine production is influenced by single nucleotide polymorphisms (SNPs) in cytokine genes. The combination of SNPs might control the relative level of inflammatory stress following inflammatory stimuli and diseases. These genomic characteristics might therefore influence lifespan, morbidity and mortality in diseases with an infectious or inflammatory basis.Recent studies indicate that genotypic factors may influence the effectiveness of such immunonutrients as anti-oxidants and n-3 polyunsaturated fatty acids. A better understanding of this aspect of nutrient gene interactions and of the genomic factors which influence the intensity of inflammation in disease will help in the targeting of nutritional therapy.