National, regional and global mortality due to alcoholic cardiomyopathy in 2015

Objectives(1) A comprehensive mortality assessment of alcoholic cardiomyopathy (ACM) and (2) examination of under-reporting using vital statistics data.MethodsA modelling study estimated sex-specific mortality rates for each country, which were subsequently aggregated by region and globally. Input data on ACM mortality were obtained from death registries for n=91 countries. For n=99 countries, mortality estimates were predicted using aggregate alcohol data from WHO publications. Descriptive additional analyses illustrated the scope of under-reporting.ResultsIn 2015, there were an estimated 25 997 (95% CI 17 385 to 49 096) global deaths from ACM. This translates into 6.3% (95% CI 4.2% to 11.9%) of all global deaths from cardiomyopathy being caused by alcohol. There were large regional variations with regard to mortality burden. While the majority of ACM deaths were found in Russia (19 749 deaths, 76.0% of all ACM deaths), for about one-third of countries (n=57) less than one ACM death was found. Under-reporting was identified for nearly every second country with civil registration data. Overall, two out of three global ACM deaths might be misclassified.ConclusionsThe variation of ACM mortality burden is greater than for other alcohol-attributable diseases, and partly may be the result of stigma and lack of detection. Misclassification of ACM fatalities is a systematic phenomenon, which may be caused by low resources, lacking standards and stigma associated with alcohol-use disorders. Clinical management may be improved by including routine alcohol assessments. This could contribute to decrease misclassifications and to provide the best available treatment for affected patients.

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Monitoring progress with national and subnational health goals by integrating verbal autopsy and medically certified cause of death data

BMJ Global Health ◽

10.1136/bmjgh-2021-005387 ◽

2021 ◽

Vol 6 (5) ◽

pp. e005387

Author(s):

Tim Adair ◽

Sonja Firth ◽

Tint Pa Pa Phyo ◽

Khin Sandar Bo ◽

Alan D Lopez

Keyword(s):

Cause Of Death ◽

Verbal Autopsy ◽

Integration Method ◽

Mortality Rates ◽

Vital Statistics ◽

Death Registration ◽

Chronic Respiratory Diseases ◽

Health Goals ◽

Specific Mortality ◽

Four Causes

IntroductionThe measurement of progress towards many Sustainable Development Goals (SDG) and other health goals requires accurate and timely all-cause and cause of death (COD) data. However, existing guidance to countries to calculate these indicators is inadequate for populations with incomplete death registration and poor-quality COD data. We introduce a replicable method to estimate national and subnational cause-specific mortality rates (and hence many such indicators) where death registration is incomplete by integrating data from Medical Certificates of Cause of Death (MCCOD) for hospital deaths with routine verbal autopsy (VA) for community deaths.MethodsThe integration method calculates population-level cause-specific mortality fractions (CSMFs) from the CSMFs of MCCODs and VAs weighted by estimated deaths in hospitals and the community. Estimated deaths are calculated by applying the empirical completeness method to incomplete death registration/reporting. The resultant cause-specific mortality rates are used to estimate SDG Indicator 23: mortality between ages 30 and 70 years from cardiovascular diseases, cancers, chronic respiratory diseases and diabetes. We demonstrate the method using nationally representative data in Myanmar, comprising over 42 000 VAs and 7600 MCCODs.ResultsIn Myanmar in 2019, 89% of deaths were estimated to occur in the community. VAs comprised an estimated 70% of community deaths. Both the proportion of deaths in the community and CSMFs for the four causes increased with older age. We estimated that the probability of dying from any of the four causes between 30 and 70 years was 0.265 for men and 0.216 for women. This indicator is 50% higher if based on CSMFs from the integration of data sources than on MCCOD data from hospitals.ConclusionThis integration method facilitates country authorities to use their data to monitor progress with national and subnational health goals, rather than rely on estimates made by external organisations. The method is particularly relevant given the increasing application of routine VA in country Civil Registration and Vital Statistics systems.

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Assessing mortality registration in Kerala: the MARANAM study

Genus ◽

10.1186/s41118-021-00149-z ◽

2022 ◽

Vol 78 (1) ◽

Author(s):

Aashish Gupta ◽

Sneha Sarah Mani

Keyword(s):

Vital Statistics ◽

Middle Income ◽

Registration System ◽

Individual Level ◽

Civil Registration ◽

Middle Income Countries ◽

Registration Data ◽

Life Expectancy At Birth ◽

Sample Registration System ◽

Low And Middle Income

AbstractComplete or improving civil registration systems in sub-national areas in low- and middle-income countries provide several opportunities to better understand population health and its determinants. In this article, we provide an assessment of vital statistics in Kerala, India. Kerala is home to more than 33 million people and is a comparatively low-mortality context. We use individual-level vital registration data on more than 2.8 million deaths between 2006 and 2017 from the Kerala MARANAM (Mortality and Registration Assessment and Monitoring) Study. Comparing age-specific mortality rates from the Civil Registration System (CRS) to those from the Sample Registration System (SRS), we do not find evidence that the CRS underestimates mortality. Instead, CRS rates are smoother across ages and less variable across periods. In particular, the CRS records higher death rates than the SRS for ages, where mortality is usually low and for women. Using these data, we provide the first set of annual sex-specific life tables for any state in India. We find that life expectancy at birth was 77.9 years for women in 2017 and 71.4 years for men. Although Kerala is unique in many ways, our findings strengthen the case for more careful attention to mortality records within low- and middle-income countries, and for their better dissemination by government agencies.

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Comparability of Mortality Estimates from Social Surveys and Vital Statistics Data in the United States

10.31235/osf.io/x9f5y ◽

2019 ◽

Author(s):

Dustin C Brown ◽

Joseph Lariscy ◽

Lucie Kalousova

Keyword(s):

Mortality Risk ◽

General Social Survey ◽

The United States ◽

Mortality Rates ◽

Vital Statistics ◽

National Death Index ◽

Social Survey ◽

Social Surveys ◽

Cox Proportional Hazard Models ◽

Mortality Estimates

Social surveys prospectively linked with death records provide invaluable opportunities for the study of the relationship between social and economic circumstances and mortality. Although survey-linked mortality files play a prominent role in U.S. health disparities research, it is unclear how well mortality estimates from these datasets align with one another and whether they are comparable with U.S. vital statistics data. We conduct the first study that systematically compares mortality estimates from several widely-used survey-linked mortality files and U.S. vital statistics data. Our results show that mortality rates and life expectancies from the National Health Interview Survey Linked Mortality Files, Health and Retirement Study, Americans’ Changing Lives study, and U.S. vital statistics data are similar. Mortality rates are slightly lower and life expectancies are slightly higher in these linked datasets relative to vital statistics data. Compared with vital statistics and other survey-linked datasets, General Social Survey-National Death Index life expectancy estimates are much lower at younger adult ages and much higher at older adult ages. Cox proportional hazard models regressing all-cause mortality risk on age, gender, race, educational attainment, and marital status conceal the issues with the General Social Survey-National Death Index that are observed in our comparison of absolute measures of mortality risk. We provide recommendations for researchers who use survey-linked mortality files.

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Annual tabulation programme of vital statistics compiled from civil registration data

Statistical Papers (Ser. M) - Principles and recommendations for a vital statistics system ◽

10.18356/ecc59ee2-en ◽

2014 ◽

pp. 153-200 ◽

Cited By ~ 1

Author(s):

Keyword(s):

Vital Statistics ◽

Civil Registration ◽

Registration Data

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Mortality from Alcoholic Cardiomyopathy: Exploring the Gap between Estimated and Civil Registry Data

Journal of Clinical Medicine ◽

10.3390/jcm8081137 ◽

2019 ◽

Vol 8 (8) ◽

pp. 1137 ◽

Cited By ~ 3

Author(s):

Jakob Manthey ◽

Jürgen Rehm

Keyword(s):

Pearson Correlation ◽

Age Distribution ◽

Age Groups ◽

Correlation Coefficients ◽

Alcohol Exposure ◽

The Elderly ◽

Mortality Rates ◽

Mortality Data ◽

Alcoholic Cardiomyopathy ◽

Mortality Estimates

Background: Based on civil registries, 26,000 people died from alcoholic cardiomyopathy (ACM) in 2015 globally. In the Global Burden of Disease (GBD) 2017 study, garbage coded deaths were redistributed to ACM, resulting in substantially higher ACM mortality estimates (96,669 deaths, 95% confidence interval: 82,812–97,507). We aimed to explore the gap between civil registry and GBD mortality data, accounting for alcohol exposure as a cause of ACM. Methods: ACM mortality rates were obtained from civil registries and GBD for n = 77 countries. The relationship between registered and estimated mortality rates was assessed by sex and age groups, using Pearson correlation coefficients, in addition to comparing mortality rates with population alcohol exposure—the underlying cause of ACM. Results: Among people aged 65 years or older, civil registry mortality rates of ACM decreased markedly whereas GBD mortality rates increased. The widening gap of registered and estimated mortality rates in the elderly is reflected in a decrease of correlations. The age distribution of alcohol exposure is more consistent with the distribution of civil registry rather than GBD mortality rates. Conclusions: Among older adults, GBD mortality estimates of ACM seem implausible and are inconsistent with alcohol exposure. The garbage code redistribution algorithm should include alcohol exposure for ACM and other alcohol-attributable diseases.

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Socioeconomic Disparities in All-Cause and Cause-Specific Mortality Rates among Municipalities in Japan, 1999–2019

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17249213 ◽

2020 ◽

Vol 17 (24) ◽

pp. 9213

Author(s):

Tasuku Okui

Keyword(s):

Mortality Rates ◽

Vital Statistics ◽

Socioeconomic Disparities ◽

Standardized Mortality Ratio ◽

Low Ses ◽

All Cause Mortality ◽

Specific Mortality ◽

Cause Of Deaths ◽

Mortality Ratio ◽

The Difference

Differences in all-cause and cause-specific mortality rates depending on municipal socioeconomic status (SES) in Japan have not been revealed over the last 20 years. This study exposes the difference in 1999 and 2019 using the Vital Statistics. All of the municipalities were grouped into five quintiles based on their SES, and standardized mortality ratio (SMR) of each municipal quintile compared with all of Japan was calculated for all-cause mortality and representative cause of deaths. As a result, although SMR for all-cause mortality for women tended to be lower in low SES quintiles in 1999, the reverse phenomenon was observed in 2019. Additionally, although SMR for all-cause of mortality for men was the lowest in the highest SES quintiles already in 1999, the difference in the SMR for all-cause mortality rates between the lowest and highest SES quintiles increased in 2019. The improvement of the SMR in the highest SES quintile and the deterioration in the lowest was also observed in representative types of cancer, heart disease, stroke, pneumonia, liver disease, and renal failure for men and women. Therefore, this study indicates a disparity in mortality depending on municipal SES enlarged in the last 20 years.

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Routes to improved mortality monitoring: Evidence from the Kerala MARANAM Study

10.31235/osf.io/jachq ◽

2020 ◽

Author(s):

Aashish Gupta ◽

Sneha Mani

Keyword(s):

Developing Countries ◽

Mortality Rates ◽

Vital Statistics ◽

Vital Registration ◽

Middle Income ◽

Registration System ◽

Individual Level ◽

Registration Data ◽

Life Expectancy At Birth ◽

Sample Registration System

Despite having universal mortality registration, vital registration systems in many regions of contemporary developing countries do not receive adequate attention. Using individual-level vital-registration data on more than 2.8 million deaths between 2006 and 2017 from the Kerala MARANAM (Mortality and Registration Assessment and Monitoring) Study, we examine completeness of vital statistics and reliability of mortality rates estimated using them. Our findings show that age-specific mortality rates obtained from vital statistics system in Kerala, a comparatively low-mortality context in a low- and middle-income setting, are more reliable than the ones estimated by India's Sample Registration System. This is particularly true for ages where mortality is low, and for women. Using these data we provide the first set of annual sex-specific life-tables for any state in India. We find that life expectancy at birth was 77.9 years for women in 2017, and 71.4 years for men. Although Kerala is unique in many ways, our findings strengthen the case for more attention to mortality records within developing countries, and for their better dissemination by government agencies.

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Generating age-specific mortality statistics from incomplete death registration data: two applications of the empirical completeness method

Population Health Metrics ◽

10.1186/s12963-021-00262-3 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Tim Adair ◽

Alan D Lopez

Keyword(s):

Incomplete Data ◽

Adult Mortality ◽

Death Registration ◽

Average Correlation ◽

Male Adult ◽

Mortality Statistics ◽

Registration Data ◽

Specific Mortality ◽

Capture Recapture ◽

Mortality Estimates

Abstract Background The study aims to assess two approaches that apply the empirical completeness method to generate age-specific mortality statistics from incomplete death registration systems. Methods We use the empirical completeness method to calculate all-age death registration completeness, which is used with a model life table to generate mortality statistics and age-specific completeness using (1) the conventional method and (2) the equivalent deaths method. The results are compared with a capture-recapture (C-RC) study and three alternative mortality estimates for Brazilian states, and C-RC studies in Thailand, Oman and Vietnam, which independently estimate the level and age pattern of mortality or completeness. Results The empirical completeness method produces similar estimates of all-age completeness of registration to the C-RC studies. Compared with C-RC studies, at 15-59 years, the conventional method’s estimates of mortality and completeness are more concordant, while at 60-84 years the equivalent death method’s estimates are closer. Estimates of life expectancy from the two approaches each have similar concordance with the C-RC studies. For male adult mortality in Brazilian states, there is relatively strong average correlation of this study's estimates with three alternative estimates. Conclusions The two approaches produce mortality statistics from incomplete data that are mostly concordant with C-RC studies, and can be most usefully applied to subnational populations.

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Mortality estimates for WHO SEAR countries: problems and prospects

BMJ Global Health ◽

10.1136/bmjgh-2021-007177 ◽

2021 ◽

Vol 6 (11) ◽

pp. e007177

Author(s):

Chalapati Rao ◽

Kanitta Bundhamcharoen ◽

Matthew Kelly ◽

Viroj Tangcharoensathien

Keyword(s):

East Asia ◽

Empirical Data ◽

Relative Difference ◽

Vital Statistics ◽

Mortality Data ◽

Data Series ◽

South East Asia ◽

Mortality Statistics ◽

Specific Mortality ◽

Mortality Estimates

Cause-specific mortality estimates for 11 countries located in the WHO’s South East Asia Region (WHO SEAR) are generated periodically by the Global Burden of Disease (GBD) and the WHO Global Health Estimates (GHE) analyses. A comparison of GBD and GHE estimates for 2019 for 11 specific causes of epidemiological importance to South East Asia was undertaken. An index of relative difference (RD) between the estimated numbers of deaths by sex for each cause from the two sources for each country was calculated, and categorised as marginal (RD=±0%–9%), moderate (RD=±10%–19%), high (RD=±20%–39%) and extreme (RD>±40%). The comparison identified that the RD was >10% in two-thirds of all instances. The RD was ‘high’ or ‘extreme’ for deaths from tuberculosis, diarrhoea, road injuries and suicide for most SEAR countries, and for deaths from most of the 11 causes in Bangladesh, DPR Korea, Myanmar, Nepal and Sri Lanka. For all WHO SEAR countries, mortality estimates from both sources are based on statistical models developed from an international historical cause-specific mortality data series that included very limited empirical data from the region. Also, there is no scientific rationale available to justify the reliability of one set of estimates over the other. The characteristics of national mortality statistics systems for each WHO SEAR country were analysed, to understand the reasons for weaknesses in empirical data. The systems analysis identified specific limitations in structure, organisation and implementation that affect data completeness, validity of causes of death and vital statistics production, which vary across countries. Therefore, customised national strategies are required to strengthen mortality statistics systems to meet immediate and long-term data needs for health policy and research, and reduce dependence on current unreliable modelled estimates.

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