Search

Background: Research over the past decade has suggested that prenatal and early postnatal nutrition influence the risk of developing chronic degenerative diseases up to 60 years later. We now present evidence that risk of death from infectious diseases in young adulthood is similarly programmed by early life events., Methods: In three rural Gambian villages, affected by a marked annual seasonality in diet and disease, we have kept detailed demographic, anthropometric and health records since 1949. Fate was known with certainty for 3,162 individuals (2,059 alive/1,103 dead, most dying in childhood). For this case-control analysis of antecedent predictors of premature mortality, all adult deaths (n = 61) were paired with two randomly selected controls matched for sex and year of birth., Results: Mean age at death was 25 (SD: 8) years. Adult death was associated with a profound bias in month of birth with 49 cases born in the nutritionally-debilitating hungry season (Jul-Dec) versus 12 in the harvest season (Jan-Jun). Relative to harvest season the hazard ratio for early death in hungry-season births rose from 3.7 (for deaths >14.5 years, P = 0.000013) to 10.3 (for deaths >25 years, P = 0.00002). Anthropometric and haematological status at 18 months of age was identical in cases and controls, indicating an earlier origin to the defect. Most deaths for which cause was known had a definite or possible infectious aetiology; none were from degenerative diseases of affluence., Conclusions: Early life exposures, correlated with season of birth, strongly influence susceptibility to fatal infections in young adulthood. The evidence suggests that nutritionally-mediated intrauterine growth retardation may permanently impair the development of immune function.

Although 25% of US men indicate that they are trying to lose weight, the association between intentional weight loss and longevity in men is unknown. The authors analyzed prospective data from 49,337 overweight (initial body mass index > or =27) white men aged 40-64 years who, in 1959-1960, answered questions on weight change direction, amount, time interval, and intent. Vital status was determined in 1972. Proportional hazards regression estimated mortality rate ratios for men who intentionally lost weight compared with men with no weight change. Analyses were stratified by health status and adjusted for age, initial body mass index, smoking status, alcohol intake, education, physical activity, health history, and physical symptoms. Among men with no reported health conditions (n = 36,280), intentional weight loss was not associated with total, cardiovascular (CVD), or cancer mortality, but diabetes-associated mortality was increased 48% (95% confidence interval (CI) -7% to +133%) among those who lost 20 pounds (9.1 kg) or more; this increase was largely related to non-CVD mortality. Among men with reported health conditions (n = 13,057), intentional weight loss had no association with total or CVD mortality, but cancer mortality increased 25% (95% confidence interval -4% to +63%) among those who lost 20 pounds or more. Diabetes-associated mortality was reduced 32% (95% confidence interval -52% to -5%) among those who lost less than 20 pounds and 36% (95% confidence interval -49% to -20%) among those who lost more than 20 pounds. These results and those from our earlier study in women (Williamson et al., Am J Epidemiol 1995;141:1128-41) suggest that intentional weight loss may reduce the risk of dying from diabetes, but not from CVD. In observational studies, however, it is difficult to separate intentional weight loss from unintentional weight loss due to undiagnosed, underlying disease. Well-designed observational studies, as well as randomized controlled trials, are needed to determine whether intentional weight loss reduces CVD mortality.

The gender gap in life expectancy (GGLE) varies substantially in EU 28 Member States. This paper addresses the question of whether gender inequality affects the GGLE as well as life expectancy (LE) in both genders. We conducted an ecological study and used the gender inequality index (GII) developed by the United Nations as well as the gender difference in LE in 2015. We found a correlation between GGLE and GII (r2=0.180) and between GII and LE of 0.418 (women) and 0.430 (men). Gender equality policies are still necessary and will have an effect on women's as well as men's health.

Introduction: Verbal autopsy (VA) is a method that determines the cause of death by interviewing a relative of the deceased about the events occurring before the death, in regions where medical certification of cause of death is incomplete. This paper aims to review the ethical standards reported in peer-reviewed VA studies., Methods: A systematic review of Medline and Ovid was conducted by two independent researchers. Data were extracted and analysed for articles based on three key areas: Institutional Review Board (IRB) clearance and consenting process; data collection and management procedures, including: time between death and interview; training and education of interviewer, confidentiality of data and data security; and declarations of funding and conflict of interest., Results: The review identified 802 articles, of which 288 were included. The review found that 48% all the studies reported having IRB clearance or obtaining consent of participants. The interviewer training and education levels were reported in 62% and 21% of the articles, respectively. Confidentiality of data was reported for 14% of all studies, 18% did not report the type of respondent interviewed and 51% reported time between death and the interview for the VA. Data security was reported in 8% of all studies. Funding was declared in 63% of all studies and conflict of interest in 42%. Reporting of all these variables increased over time., Conclusions: The results of this systematic review show that although there has been an increase in ethical reporting for VA studies, there still remains a large gap in reporting., (© The Author 2017; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association)

Context/problem: Comparative analysis of relative and absolute mortality differentials between socioeconomic groups are now available. These show consistently increasing relative increases in mortality differentials but complex trends in absolute mortality differentials., Objective: This paper provides an analysis of relative and absolute trends in mortality by cause of death and socioeconomic group (SEG) from 1984 to 2008 among men and active women aged 30-64 years in Ireland and compares these results with recent European and US studies to give an overview of trends., Methods: This paper uses mortality data from the Irish Central Statistics Office from 1984 to 2008 to calculate standardized death rates by age, sex, socioeconomic status and cause of death showing trends in SEG inequalities in mortality in Ireland. These show which specific causes of death are driving all-cause mortality trends., Results: SEG differentials in all-cause mortality among men and women have been increasing since the 1980s. Some of this increase reflects larger falls in cardiovascular causes among advantaged groups, but the trend is largely accounted for by increasing inequalities in mortality in digestive, neoplasm and external causes of deaths., Conclusions: These findings are in line with international findings that show that socioeconomic differentials in digestive, neoplasm and external cause deaths are driving general socioeconomic differentials in all-cause mortality. External cause deaths may have been influenced by levels of economic activity, particularly in construction, during the economic boom among manual workers. Furthermore, deaths from digestive diseases during the 1990s and 2000s may well be the result of increases in liver disease associated with excessive alcohol consumption., (© The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.)

Although there is strong evidence that short-term exposure to particulate matter is associated with health risks, less is known about whether some subpopulations face higher risks. We identified 108 papers published after 1995 and summarized the scientific evidence regarding effect modification of associations between short-term exposure to particulate matter and the risk of death or hospitalization. We performed a meta-analysis of estimated mortality associations by age and sex. We found strong, consistent evidence that the elderly experience higher risk of particular matter--associated hospitalization and death, weak evidence that women have higher risks of hospitalization and death, and suggestive evidence that those with lower education, income, or employment status have higher risk of death. Meta-analysis showed a statistically higher risk of death of 0.64% (95% confidence interval (CI): 0.50, 0.78) for older populations compared with 0.34% (95% CI: 0.25, 0.42) for younger populations per 10 μg/m3 increase of particulate matter with aerodynamic diameter ≤10 μm. Women had a slightly higher risk of death of 0.55% (95% CI: 0.41, 0.70) compared with 0.50% (95% CI: 0.34, 0.54) for men, but these 2 risks were not statistically different. Our synthesis on modifiers for risks associated with particulate matter can aid the design of air quality policies and suggest directions for future research. Studies of biological mechanisms could be informed by evidence of differential risks by population, such as by sex and preexisting conditions.

Background: Assessing the causes of death across all regions of the world requires a framework for integrating, and analysing, the fragmentary information that is available on numbers of deaths and their cause distributions. This paper provides an overview of the met and methods used by the World Health Organization to develop global-, regional- and country-level estimates of mortality for a comprehensive set of causes, and provides an overview of global and regional levels and patterns of causes of death for the year 2004. The paper also examines some of the data gaps, uncertainties and limitations in the resulting mortality estimates., Sources of Data: Deaths for 136 disease and injury causes were estimated from available death registration data (111 countries), sample death registration data (India and China), and for the remaining countries from census and survey information, and cause-of-death models. Population-based epidemiological studies and notifications systems also contributed to estimating mortality for 21 of these causes (representing 28% of deaths globally, 58% in Africa)., Areas of Agreement: Ischaemic heart disease and cerebrovascular disease are the leading causes of death, followed by lower respiratory infections, chronic obstructive pulmonary disease and diarrhoeal diseases. AIDS and TB are the sixth and seventh most common causes of death, respectively, lower than in previous estimates. One-half of all child deaths are from four preventable and treatable communicable diseases. Globally, around 6 in 10 deaths are from non-communicable diseases, 3 from communicable diseases and 1 from injuries. Injury mortality is highest in South-East Asia, Latin America and the Eastern Mediterranean region. These results illustrate continuing huge disparities in risks and causes of death across the world., Areas of Controversy: Global mortality analyses of the type reported here have been criticized for making estimates of mortality for regions with limited, incomplete and uncertain data. Estimates presented here use a range of techniques depending on the type and quality of evidence. Better evidence on levels of adult mortality is needed for African countries., Growing Points: Considerable gaps and deficiencies remain in the information available on causes of death. Nine of 10 deaths in 2004 occurred in low- and middle-income countries, reinforcing the fundamental importance of improving mortality statistics as a measure of health status in the developing world. Acknowledging the controversies around use of incomplete and uncertain data, systematic assessments and synthesis of the available evidence will continue to provide important inputs for global health planning., Areas Timely for Developing Research: Innovative methods involving sample registration, and the use of verbal autopsy questionnaires in surveys, are needed to address these gaps. Research on strategies to improve comparability of cause-of-death certification and coding practices across countries is also a high priority.

Death rarely results from only one cause, and it can be caused by a variety of factors. Multiple cause-of-death data files can list as many as 20 contributing causes of death in addition to the reported underlying cause of death. Analysis of multiple cause-of-death data can provide information on associations between causes of death, revealing common combinations of events or conditions which lead to death. Additionally, physicians report the causal train of events through which they believe that different conditions or events may have led to each other and ultimately caused death. In this paper, the authors discuss methods used in studying associations between reported causes of death and in investigating commonly reported causal pathways between events or conditions listed on the death certificate.

Background: Reliable information on causes of death is a fundamental component of health development strategies, yet globally only about one-third of countries have access to such information. For countries currently without adequate mortality reporting systems there are useful models other than resource-intensive population-wide medical certification. Sample-based mortality surveillance is one such approach. This paper provides methods for addressing appropriate sample size considerations in relation to mortality surveillance, with particular reference to situations in which prior information on mortality is lacking., Methods: The feasibility of model-based approaches for predicting the expected mortality structure and cause composition is demonstrated for populations in which only limited empirical data is available. An algorithm approach is then provided to derive the minimum person-years of observation needed to generate robust estimates for the rarest cause of interest in three hypothetical populations, each representing different levels of health development., Results: Modelled life expectancies at birth and cause of death structures were within expected ranges based on published estimates for countries at comparable levels of health development. Total person-years of observation required in each population could be more than halved by limiting the set of age, sex, and cause groups regarded as 'of interest'., Discussion: The methods proposed are consistent with the philosophy of establishing priorities across broad clusters of causes for which the public health response implications are similar. The examples provided illustrate the options available when considering the design of mortality surveillance for population health monitoring purposes.

Objective: To determine how often 'old age' was given as a cause of death in patients presenting for cremation, with particular reference to their characteristics and the medical conditions known to the certifying doctor at the time of death., Design: Prospective review of all cremation papers presented to one crematorium over a two-year period., Setting: An English crematorium serving a population of about 250,000., Main Outcome Measures: Deaths certified as being associated with 'old age' and those solely registered as due to this cause. Demographics of the patients and whether further enquiries by the medical referee revealed un-notified co-morbid conditions., Results: Of 4300 cremation papers studied, 300 (7%) deaths were said to be associated with old age, of which 98 were to old age alone. Simple further enquiry discovered between one and six unrecorded co-morbid conditions in at least two-thirds of those certified as dying solely from old age., Implications: If this experience were reflected throughout the country, it would suggest significant underestimation of the incidence of medical conditions, with resultant effects on national and international health policy and investment.

Background: Verbal autopsy (VA) is an indirect method of ascertaining cause of death from information about symptoms and signs obtained from bereaved relatives. This method has been used in several settings to assess cause-specific mortality. However, cause-specific mortality estimates obtained by VA are susceptible to bias due to misclassification of causes of death. One way of overcoming this limitation of VA is to adjust the crude VA estimate of cause-specific mortality fractions (CSMF) using the sensitivity and specificity of the VA tool. This paper explores the application of sensitivity and specificity of VA data obtained from a hospital-based validation study for adjusting the effect of misclassification error in VA data obtained from a demographic surveillance system., Method: Data from a multi-centre validation study of 796 adult VA, conducted in Tanzania, Ethiopia and Ghana, were used to explore the effect of distribution of causes of death in the validation study population and the pattern of misclassification on the sensitivity and specificity of VA. VA estimates of CSMF for six causes (acute febrile illness, diarrhoeal diseases, TB/AIDS, cardiovascular disorders, direct maternal causes and injures) were obtained from a demographic surveillance system in Morogoro Rural District in Tanzania. These were adjusted for misclassification error by using sensitivity and specificity values of VA obtained from the validation study in a model proposed for correcting the effect of misclassification error in morbidity prevalence surveys., Results: Sensitivity and specificity of VA differed between the three validation study sites depending on the distribution of causes of death. These differences were explained by variations in the level and pattern of misclassification between sites. When these estimates of sensitivity and specificity were applied to data from the demographic surveillance system with a comparable structure of causes of death the difference between crude and adjusted VA estimates of CSMF ranged from 3 to 83%., Conclusion: Estimates of sensitivity and specificity obtained from hospital-based validation studies must be used cautiously as a de facto 'gold standard' for adjusting the misclassification error in CSMF derived from VA. It is not possible to use sensitivity and specificity estimates derived from a location-specific validation study to adjust for misclassification in VA data from populations with substantially different patterns of cause-specific mortality.

Background: The Seven Countries Study has shown that population mortality rates for various chronic diseases are related to diet and smoking. This paper addresses the associations between diet, smoking and 25-year all-cause mortality., Methods: Baseline surveys were carried out between 1958 and 1964 on 12,763 middle-aged men constituting 16 cohorts in seven countries. In 1987/88 equivalent food composites representing the average food intake of each cohort at baseline were collected and chemically analysed in one central laboratory. During 25 years of follow-up 5973 men died and age-adjusted population mortality rates were calculated for each cohort., Results: Multivariate linear regression analyses showed that the population intake of saturated fat and the prevalence of smoking were positively associated with population all-cause mortality rates. Population vitamin C intake was inversely associated with all-cause mortality. It was calculated that a reduction in saturated fat intake of 5% of energy, a 20 mg/d increase in vitamin C and a 10% decrease in the prevalence of smokers may decrease the 25-year all-cause population mortality rate by 12.4% (95% CI: 5.6, 19.4%) at an average population all-cause mortality rate of 45%., Conclusion: At the population level saturated fat, vitamin C and cigarette smoking are important determinants of all-cause mortality.

Background: The CONSENSUS trial was the first study to show prognostic improvement by an ACE inhibitor. Patients in NYHA class IV heart failure were treated with enalapril or placebo. After study completion (average 183 days) all patients were offered open-label enalapril therapy. This paper reports on the survival at the 10-year follow up of the patients randomized in the CONSENSUS trial., Methods: All 35 participating centres in CONSENSUS I were asked to complete a questionnaire on the survival status at 1 November 1996 of patients randomized in CONSENSUS., Results: At 10-year follow up, one patient was lost to follow-up. Five patients, all in the enalapril group, were long-term survivors (P = 0.004). Averaged over the duration of the trial (double-blind plus open-label extension) the risk reduction was 30% (P = 0.008), with a 95% confidence interval of 11% to 46%. At the end of the double-blind study period, mortality was considerably higher among patients who did not receive open ACE inhibitor therapy compared to those who did., Conclusion: After a treatment period of, on average, 6 months, enalapril was shown to be effective. The effect was sustained for at least 4 years i.e. for another 3.5 years. The present follow-up is the first heart failure trial where the full life-cycle has been followed from randomization. In severe heart failure, mortality is significantly reduced by enalapril. On average, the beneficial effect is maintained for several years and overall survival time is prolonged by 50% (from 521 to 781 days).

In this paper, the authors report on the design of a population-based case-control study of family history as a risk factor for coronary heart disease (CHD). They studied the characteristics of subjects who completed a detailed family history questionnaire in 1992-1994 as well as the accuracy of recall of family history in order to quantify both selection and recall biases. Coronary disease cases were enrolled through the Newcastle MONICA Project (Monitoring Trends and Determinants in Cardiovascular Disease), which registered all suspected heart attacks and sudden cardiac deaths in the Lower Hunter region of New South Wales, Australia, between August 1984 and March 1994. Controls were selected at random from the New South Wales electoral roll. The response rate was 76% in cases and 62% in controls; the major factor associated with participation in the study was perceived family history of CHD, more so in the control series than in the case series. Accuracy was determined by comparing information obtained from the proband with that recorded on death certificates. In first-degree relatives, sensitivity of CHD recall was 85% (95% confidence interval (CI) 74-92%) in cases and 95% (95% CI 84-99%) in controls, while specificity was 59% (95% CI 49-69%) and 74% (95% CI 65-82%), respectively. The net bias in both selection and recall is toward the null and hence the comparisons provide a conservative estimate of risk of CHD associated with a positive family history.

Quantification of the excess mortality from all causes associated with obesity remains controversial. In this paper, 6,193 obese patients, those with a body mass index (weight (kg)/height (m)2 (BMI)) range of 25-74 kg/m2, recruited from 1961 to 1994 in Düsseldorf, Germany, were followed for a mean time of 14 (standard deviation, 8.2) years, yielding 87,179 observed patient-years. During the study period, 1,028 patients (16.6%) died. The entire cohort was grouped into approximate quartiles according to BMI: group 1, BMI from 25 to < 32; group 2, BMI from 32 to < 36; group 3, BMI from 36 to < 40; group 4, BMI > or = 40 kg/m2. The following risk ratios were estimated by means of Cox proportional hazards models using the lowest BMI group as reference category: group 2 for men, 1.02 (95% confidence interval 0.76-1.37); for women, 1.23 (95% confidence interval 0.96-1.58); group 3 for men, 1.50 (95% confidence interval 1.09-2.06); for women, 1.33 (95% confidence interval 1.03-1.73); and group 4 for men, 2.10 (95% confidence interval 1.53-2.88); for women, 2.25 (95% confidence interval 1.78-2.84). The following standardized mortality ratios were calculated by using the respective geographic area (the Federal State of North Rhine Westphalia) as reference population: group 1 for men, 1.26 (95% confidence interval 0.98-1.61); for women, 1.00 (95% confidence interval 0.81-1.23); group 2 for men, 1.31 (95% confidence interval 1.09-1.57); for women, 1.20 (95% confidence interval 1.02-1.40); group 3 for men, 1.92 (95% confidence interval 1.53-2.38); for women, 1.27 (95% confidence interval 1.07-1.50); and group 4 for men, 3.05 (95% confidence interval 2.47-3.73); for women, 2.31 (95% confidence interval 2.04-2.60). In addition to age, sex, and BMI, Cox proportional hazards models revealed systolic blood pressure, glucose intolerance, diabetes, and smoking as significant independent mortality risk factors, whereas cholesterol was not significant. In this prospective study of a large cohort of obese persons, morbid obesity (BMI of > or = 40 kg/m2) was a strong predictor of premature death. Excess mortality risks associated with gross obesity (BMI from 32 to < 40 kg/m2) were considerably lower than hitherto assumed; moderate degrees of obesity (BMI from 25 to < 32 kg/m2) were not significantly associated with excess mortality.

Background: Verbal autopsies (VA) are increasingly being used in developing countries to determine causes of death, but little attention is generally given to the misclassification effects of the VA. This paper considers the effect of misclassification on the estimation of differences in cause-specific mortality rates between two populations., Methods: The bias in the percentage difference in cause-specific mortality between two populations has been explored under two different models: i) assuming that mortality from all other causes does not differ between the two populations; ii) allowing for a difference in mortality from all other causes. The bias is described in terms of the sensitivity and specificity of the VA diagnosis and the proportion of mortality due to the cause of interest. Methods for adjustment of sample size and adjusting the estimate of effect are also explored., Results: The results are illustrated for a range of plausible values for these parameters. The bias is more extreme as both sensitivity and specificity fall, and is particularly affected even by a small loss of specificity. The bias also increases as the proportion of all deaths due to the cause of interest decreases, and is affected by the size of the true change in mortality due to the cause of interest relative to the change in mortality from other causes. Calculations from existing data suggest prohibitively large sample sizes may often be required to detect important differences in cause-specific mortality rates in studies using existing VA., Conclusions: Highly specific VA tools are needed before observed differences in cause-specific mortality can be interpreted. Loss of power due to misclassification may obscure real differences in cause-specific mortality.

Background: Verbal autopsy (VA) is an indirect method for estimating cause-specific mortality. In most previous studies, cause of death has been assigned from verbal autopsy data using expert algorithms or by physician review. Both of these methods may have poor validity. In addition, physician review is time consuming and has to be carried out by doctors. A range of methods exist for deriving classification rules from data. Such rules are quick and simple to apply and in many situations perform as well as experts., Methods: This paper has two aims. First, it considers the advantages and disadvantages of the three main methods for deriving classification rules empirically; (a) linear and other discriminant techniques, (b) probability density estimation and (c) decision trees and rule-based methods. Second, it reviews the factors which need to be taken into account when choosing a classification method for assigning cause of death from VA data., Results: Four main factors influence the choice of classification method: (a) the purpose for which a classifier is being developed, (b) the number of validated causes of death assigned to each case, (c) the characteristics of the VA data and (d) the need for a classifier to be comprehensible. When the objective is to estimate mortality from a single cause of death, logistic regression should be used. When the objective is to determine patterns of mortality, the choice of method will depend on the above factors in ways which are elaborated in the paper., Conclusion: Choice of classification method for assigning cause of death needs to be considered when designing a VA validation study. Comparison of the performance of classifiers derived using different methods requires a large VA dataset, which is not currently available.

Background: Verbal autopsy (VA) studies are important for measuring cause-specific mortality in areas where medical certification of cause of death is uncommon. This paper explores the effects of misclassification errors on the results of verbal autopsy studies, and recommends ways to take misclassification errors into account in the interpretation of results., Methods: Mathematical formulae are derived for determining the size and direction of the error in cause-specific mortality estimates based on VA studies caused by misclassification. The levels of sensitivity and specificity found in currently available validation studies for childhood VA are examined., Results: There can be substantial errors in the estimates of the cause-specific mortality fraction derived from VA studies. The cause-specific mortality fraction itself has an important influence on the size of the error for given levels of sensitivity and specificity, and when the cause-specific mortality fraction is small, the size of the error depends more on specificity than on sensitivity., Conclusion: Despite its drawbacks VA seems to be the most promising way of establishing cause of death when most deaths take place at home without medical attention. However, more validation studies on standardized instruments are required in order to collect information about sensitivity and specificity and subsequently improve the design of the instrument. At the same time, analysts need to take misclassification errors into consideration in ways outlined in this paper.

Projection disease incidence, prevalence, and net morbidity is often needed when individuals are likely to die, either disease free or after the disease has developed. Examples of this include remission of cancer or heart disease in elderly people who can die from these or other causes and occurrence of a particular acquired immune deficiency syndrome illness in human immunodeficiency virus type 1 (HIV-1) disease. Death is not an ancillary event but, rather, indicates either and end to disease morbidity or an end to risk to ever develop the disease. Thus, time to disease survival analyses that censor disease-free individuals at death can produce misleading results. The paper describes several useful quantifications of disease and death for this setting. A paradigm that utilizes Kaplan-Meier functions to estimate these quantities is introduced. The approach anchors on a four-stage disease/death model: stage A, living without disease; stage B, dead without ever developing disease; stage C, developed the disease and living; and stage D, dead after developing the disease. An application is made to projecting cytomegalovirus disease in a cohort of HIV-1-infected users of zidovudine and Pneumocystis prophylaxis from the Multicenter AIDS Cohort Study (MACS) during 1989-1993. At 3 years after a CD4+ count below 100/microliters, a man had an 18.7%, 46.3%, 5.3% or 29.9% chance, respectively, to be in stage A, B, C, or D. This man, on average, had 0.28 years of cytomegalovirus morbidity during these 3 years.