Spatial-temporal excess mortality patterns of the 1918-1919 influenza pandemic in Spain

Background: The impact of socio-demographic factors and baseline health on the mortality burden of seasonal and pandemic influenza remains debated. Here we analyzed the spatial-temporal mortality patterns of the 1918 influenza pandemic in Spain, one of the countries of Europe that experienced the highest mortality burden. Methods: We analyzed monthly death rates from respiratory diseases and all-causes across 49 provinces of Spain, including the Canary and Balearic Islands, during the period January-1915 to June-1919. We estimated the influenza-related excess death rates and risk of death relative to baseline mortality by pandemic wave and province. We then explored the association between pandemic excess mortality rates and health and socio-demographic factors, which included population size and age structure, population density, infant mortality rates, baseline death rates, and urbanization. Results: Our analysis revealed high geographic heterogeneity in pandemic mortality impact. We identified 3 pandemic waves of varying timing and intensity covering the period from Jan-1918 to Jun-1919, with the highest pandemic-related excess mortality rates occurring during the months of October-November 1918 across all Spanish provinces. Cumulative excess mortality rates followed a south-north gradient after controlling for demographic factors, with the North experiencing highest excess mortality rates. A model that included latitude, population density, and the proportion of children living in provinces explained about 40% of the geographic variability in cumulative excess death rates during 1918-19, but different factors explained mortality variation in each wave. Conclusions: A substantial fraction of the variability in excess mortality rates across Spanish provinces remained unexplained, which suggests that other unidentified factors such as comorbidities, climate and background immunity may have affected the 1918-19 pandemic mortality rates. Further archeo-epidemiological research should concentrate on identifying settings with combined availability of local historical mortality records and information on the prevalence of underlying risk factors, or patient-level clinical data, to further clarify the drivers of 1918 pandemic influenza mortality.

Long-term dynamics of death rates of emphysema, asthma, and pneumonia and improving air quality.

BACKGROUND: The respiratory tract is a major target of exposure to air pollutants, and respiratory diseases are associated with both short- and long-term exposures. We hypothesized that improved air quality in North Carolina was associated with reduced rates of death from respiratory diseases in local populations. MATERIALS AND METHODS: We analyzed the trends of emphysema, asthma, and pneumonia mortality and changes of the levels of ozone, sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matters (PM2.5 and PM10) using monthly data measurements from air-monitoring stations in North Carolina in 1993-2010. The log-linear model was used to evaluate associations between air-pollutant levels and age-adjusted death rates (per 100,000 of population) calculated for 5-year age-groups and for standard 2000 North Carolina population. The studied associations were adjusted by age group-specific smoking prevalence and seasonal fluctuations of disease-specific respiratory deaths. RESULTS: Decline in emphysema deaths was associated with decreasing levels of SO2 and CO in the air, decline in asthma deaths-with lower SO2, CO, and PM10 levels, and decline in pneumonia deaths-with lower levels of SO2. Sensitivity analyses were performed to study potential effects of the change from International Classification of Diseases (ICD)-9 to ICD-10 codes, the effects of air pollutants on mortality during summer and winter, the impact of approach when only the underlying causes of deaths were used, and when mortality and air-quality data were analyzed on the county level. In each case, the results of sensitivity analyses demonstrated stability. The importance of analysis of pneumonia as an underlying cause of death was also highlighted. CONCLUSION: Significant associations were observed between decreasing death rates of emphysema, asthma, and pneumonia and decreases in levels of ambient air pollutants in North Carolina.

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.

Differences in late fetal death rates in association with determinants of small for gestational age fetuses: population based cohort study

Objective: To examine differences in late fetal death rates in association with determinants of small for gestational age fetuses.

In vivo estimates of division and death rates of human T lymphocytes.

We present data on the decay, after radiotherapy, of naive and memory human T lymphocytes with stable chromosome damage. These data are analyzed in conjunction with existing data on the decay of naive and memory T lymphocytes with unstable chromosome damage and older data on unsorted lymphocytes. The analyses yield in vivo estimates for some life-history parameters of human T lymphocytes. Best estimates of proliferation rates have naive lymphocytes dividing once every 3.5 years and memory lymphocytes dividing once every 22 weeks. It appears that memory lymphocytes can revert to the naive phenotype, but only, on average, after 3.5 years in the memory class. The lymphocytes with stable chromosome damage decay very slowly, yielding surprisingly low estimates of their death rate. The estimated parameters are used in a simple mathematical model of the population dynamics of undamaged naive and memory lymphocytes. We use this model to illustrate that it is possible for the unprimed subset of a constantly stimulated clone to stay small, even when there is a large population of specific primed cells reverting to the unprimed state.

Statistical methodology for a comparison of the individual characteristics and death rates of disabled-worker beneficiaries in 1972 and 1985 /

"September 1992."

Social inequalities in male mortality, and in male mortality from smoking: indirect estimation from national death rates in England and Wales, Poland, and North America

Background There are substantial social inequalities in adult male mortality in many countries. Smoking is often more prevalent among men of lower social class, education, or income. The contribution of smoking to these social inequalities in mortality remains uncertain. Methods The contribution of smoking to adult mortality in a population can be estimated indirectly from disease-specific death rates in that population (using absolute lung cancer rates to indicate proportions due to smoking of mortality from certain other diseases). We applied these methods to 1996 death rates at ages 35-69 years in men in three different social strata in four countries, based on a total of 0.6 million deaths. The highest and lowest social strata were based on social class (professional vs unskilled manual) in England and Wales, neighbourhood income (top vs bottom quintile) in urban Canada, and completed years of education (more than vs less than 12 years) in the USA and Poland. Results In each country, there was about a two-fold difference between the highest and the lowest social strata in overall risks of dying among men aged 35-69 years (England and Wales 21% vs 43%, USA 20% vs 37%, Canada 21% vs 34%, Poland 26% vs 50%: four-country mean 22% vs 41%, four-country mean absolute difference 19%). More than half of this difference in mortality between the top and bottom social strata involved differences in risks of being killed at age 35-69 years by smoking (England and Wales 4% vs 19%, USA 4% vs 15%, Canada 6% vs 13%, Poland 5% vs 22%: four-country mean 5% vs 17%, four-country mean absolute difference 12%). Smoking-attributed mortality accounted for nearly half of total male mortality in the lowest social stratum of each country. Conclusion In these populations, most, but not all, of the substantial social inequalities in adult male mortality during the 1990s were due to the effects of smoking. Widespread cessation of smoking could eventually halve the absolute differences between these social strata in the risk of premature death.

Distinguishing between mean-field, moment dynamics and stochastic descriptions of birth-death-movement processes

Mathematical descriptions of birth–death–movement processes are often calibrated to measurements from cell biology experiments to quantify tissue growth rates. Here we describe and analyze a discrete model of a birth–death-movement process applied to a typical two–dimensional cell biology experiment. We present three different descriptions of the system: (i) a standard mean–field description which neglects correlation effects and clustering; (ii) a moment dynamics description which approximately incorporates correlation and clustering effects, and; (iii) averaged data from repeated discrete simulations which directly incorporates correlation and clustering effects. Comparing these three descriptions indicates that the mean–field and moment dynamics approaches are valid only for certain parameter regimes, and that both these descriptions fail to make accurate predictions of the system for sufficiently fast birth and death rates where the effects of spatial correlations and clustering are sufficiently strong. Without any method to distinguish between the parameter regimes where these three descriptions are valid, it is possible that either the mean–field or moment dynamics model could be calibrated to experimental data under inappropriate conditions, leading to errors in parameter estimation. In this work we demonstrate that a simple measurement of agent clustering and correlation, based on coordination number data, provides an indirect measure of agent correlation and clustering effects, and can therefore be used to make a distinction between the validity of the different descriptions of the birth–death–movement process.

Changes of mortality and causes of death from 1970 to 2005 and decomposition analysis in Shandong province [山东省1970-2005年人群死亡率极死因变化与差别分解分析]

Objective To describe the trend of overall mortality and major causes of death in Shandong population from 1970 to 2005,and to quantitatively estimate the influential factors. Methods Trends of overall mortality and major causes of death were described using indicators such as mortality rates and age-adjusted death rates by comparing three large-scale mortality surveys in Shandong province. Difference decomposing method was applied to estimate the contribution of demographic and non-demographic factors for the change of mortality. Results The total mortality had had a slight change since 1970s,but had increased since 1990s.However,both the mortality rates of age-adjusted and age-specific decreased significantly. The mortality of Group Ⅰ diseases including infectious diseases as well maternal and perinatal diseases decreased drastically. By contrast, the mortality of non-communicable chronic diseases (NCDs)including cardiovascular diseases(CVDs),cancer and injuries increased. The sustentation of recent overall mortality was caused by the interaction of demographic and non-demographic factors which worked oppositely. Non-demographic factors were responsible for the decrease of Group Ⅰ disease and the increase of injuries. With respect to the increase of NCDs as a whole. Demographic factors might take the full responsibility and the non-demographic factors were the opposite force to reduce the mortality. Nevertheless, for the increase of some leading NCD diseases as CVDs and cancer, the increase was mainly due to non-demographic rather than demographic factors. Conclusion Through the interaction of the aggravation of ageing population and the enhancement of non-demographic effect, the overall mortality in Shandong would maintain a balance or slightly rise in the coming years. Group Ⅰ diseases in Shandong had been effectively under control. Strategies focusing on disease control and prevention should be transferred to chronic diseases, especially leading NCDs, such as CVDs and cancer.

Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm

Background Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under ‘normal’ conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data. Methods Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series. Results The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors. Conclusions The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.

Global, regional, and national age–sex specifi c all-cause and cause-specifi c mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Background Up-to-date evidence on levels and trends for age-sex-specific all-cause and cause-specific mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specific all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specific causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65·3 years (UI 65·0–65·6) in 1990, to 71·5 years (UI 71·0–71·9) in 2013, while the number of deaths increased from 47·5 million (UI 46·8–48·2) to 54·9 million (UI 53·6–56·3) over the same interval. Global progress masked variation by age and sex: for children, average absolute differences between countries decreased but relative differences increased. For women aged 25–39 years and older than 75 years and for men aged 20–49 years and 65 years and older, both absolute and relative differences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10·7%, from 4·3 million deaths in 1990 to 4·8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specific mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.

Assessment of the underreporting rate of death cause data in Shandong province using capture-mark-recapture method [应用捕获再捕获方法评估山东省死因登记漏报率水平]

To evaluate the underreporting rate of death -cause data in Shandong province during 2012 to 2013 by capture -mark -recapture method and to provide the base for health strategy. Methods All counties were divided into 5 stratifications according the death rates of 2012, and 14 counties were selected, then 3 towns or streets were selected in each country, 10 villages or neighborhood committees were selected in each town （street）. The death data collected from security bureau and civil affairs bureau were compared with the reporting death data from the National Cause of Death Surveillance, and the underreporting rate was calculated. Results In present study, 6 929 death cases were collected, it was found that 1 556 cases were underreported. The death cases estimated by CMR method were 6 227 cases （95%CI: 7 593-7 651）, and the average underreporting rate was 23.15%. There were significantly differences between different stratifications （P<0.01）. The underreporting rate in 0-4 years old group was 56.93%, the male underreporting rate was 22.31% and the female underreporting rate was 24.09%. There was no significant difference between male and female groups （P>0.05）. Conclusion There is an obvious underreport in the cause of death surveillance of Shandong province, and the underreporting rates are different among the 5 stratifications. The underreporting rate is higher in 0-4 years old group, and the investigation of the death cause surveillance for young residents is not perfect in some countries. The investigation quality of the death cause surveillance should be improved, increasing the integrity of the report data and adjusting the mortalities in different stratifications for obtaining a accurate mortality in Shandong province.