Before, During, and After the First Wave of COVID-19: Mortality
                    Analyses Reveal Relevant Trends in Germany and its States until June
                    2020

ABSTRACT Objective Well-established mortality ratio methodology can contribute to a fuller picture of the SARS-CoV-2/COVID-19 burden of disease by revealing trends and informing mitigation strategies. This work examines respective data from Germany by way of example. Methods Using monthly and weekly all-cause mortality data from January 2016 to June 2020 (published by the German Federal Statistical Institute) for all ages,<65 years and≥65 years, and specified for Germany’s federal states, we explored mortality as sequela of COVID-19. We analysed standardized mortality ratios (SMRs) comparing 2020 with 2016–2019 as reference years with a focus on trend detection. Results In Germany as a whole, elevated mortality in April (most pronounced for Bavaria) declined in May. The states of Hamburg and Bremen had increased SMRs in all months under study. In Mecklenburg-Western Pomerania, decreased SMRs in January turned monotonically to increased SMRs by June. Irrespective of age group, this trend was pronounced and significant. Conclusions Increased SMRs in Hamburg and Bremen must be interpreted with caution because of potential upward distortions due to a “catchment bias”. A pronounced excess mortality in April across Germany was confirmed and a hitherto undetected trend of increasing SMRs for Mecklenburg-Western Pomerania was revealed. To meet the pandemic challenge and to benefit from research based on data collected in standardized ways, national authorities should regularly conduct SMR analyses. For independent analyses, national authorities should also expedite publishing raw mortality and population data, including detailed information on age, sex, and cause of death, in the public domain.

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USE OF CODED MORTALITY DATA TO ASSESS AREA CANCER RATES: IMPACT OF RESIDENCE REPORTING AND CODING ERRORS

American Journal of Epidemiology ◽

10.1093/oxfordjournals.aje.a115780 ◽

1990 ◽

Vol 132 (supp1) ◽

pp. 178-182 ◽

Cited By ~ 8

Author(s):

ALLAN N. WILLIAMS ◽

REBECCA A. JOHNSON ◽

ALAN P. BENDER

Keyword(s):

Rural Areas ◽

Cancer Mortality ◽

Mortality Rates ◽

Mortality Data ◽

Standardized Mortality Ratio ◽

Death Certificates ◽

Total Cancer ◽

Mortality Ratio ◽

Cancer Rates ◽

Standardized Mortality Ratios

Abstract In spite of their limitations, mortality data are used in many epidemiologic and public health settings. In this investigation, the authors examined the extent to which community cancer mortality rates were affected by incorrect reporting or coding of residence on death certificates. Observed and expected cancer mortality for two adjacent communities in northern rural Minnesota for the periods 1970–1974 and 1980–1984 were obtained from computerized state mortality data. Using statewide rates to obtain expected values, standardized mortality ratios for total cancers for both periods combined were 138 for men (101 observed deaths) and 148 for women (86 observed deaths). These excesses were statistically significant (p < 0.05). However, after review of data from the actual death certificates, city maps, and information from city officials, 44 of the 187 total cancer deaths (24%) were found to have had an incorrectly reported or coded residence status. After removal of these cases, the standardized mortality ratio for total cancers for males went from 138 to 107, and for females the standardized mortality ratio went from 148 to 111. No standardized mortality ratios remained statistically significant These findings may have implications for those who use mortality data for assessing cancer rates in communities in rural areas.

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Risk factors for excess all-cause mortality during the first wave of the COVID-19 pandemic in England: A retrospective cohort study of primary care data

PLoS ONE ◽

10.1371/journal.pone.0260381 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0260381

Author(s):

Iain M. Carey ◽

Derek G. Cook ◽

Tess Harris ◽

Stephen DeWilde ◽

Umar A. R. Chaudhry ◽

...

Keyword(s):

Risk Factors ◽

Primary Care ◽

Excess Mortality ◽

Total Mortality ◽

Individual Risk ◽

Mortality Data ◽

All Cause Mortality ◽

Specific Mortality ◽

Mortality Ratio ◽

The Impact

Background The COVID-19 pandemic’s first wave in England during spring 2020 resulted in an approximate 50% increase in all-cause mortality. Previously, risk factors such as age and ethnicity, were identified by studying COVID-related deaths only, but these were under-recorded during this period. Objective To use a large electronic primary care database to estimate the impact of risk factors (RFs) on excess mortality in England during the first wave, compared with the impact on total mortality during 2015–19. Methods Medical history, ethnicity, area-based deprivation and vital status data were extracted for an average of 4.8 million patients aged 30–104 years, for each year between 18-March and 19-May over a 6-year period (2015–2020). We used Poisson regression to model total mortality adjusting for age and sex, with interactions between each RF and period (pandemic vs. 2015–19). Total mortality during the pandemic was partitioned into "usual" and "excess" components, assuming 2015–19 rates represented "usual" mortality. The association of each RF with the 2020 "excess" component was derived as the excess mortality ratio (EMR), and compared with the usual mortality ratio (UMR). Results RFs where excess mortality was greatest and notably higher than usual were age >80, non-white ethnicity (e.g., black vs. white EMR = 2.50, 95%CI 1.97–3.18; compared to UMR = 0.92, 95%CI 0.85–1.00), BMI>40, dementia, learning disability, severe mental illness, place of residence (London, care-home, most deprived). By contrast, EMRs were comparable to UMRs for sex. Although some co-morbidities such as cancer produced EMRs significantly below their UMRs, the EMRs were still >1. In contrast current smoking has an EMR below 1 (EMR = 0.80, 95%CI 0.65–0.98) compared to its UMR = 1.64. Conclusions Studying risk factors for excess mortality during the pandemic highlighted differences from studying cause-specific mortality. Our approach illustrates a novel methodology for evaluating a pandemic’s impact by individual risk factor without requiring cause-specific mortality data.

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Pembangunan Web Service Data Masyarakat Menggunakan REST API dengan Access Token

Jurnal ULTIMA Computing ◽

10.31937/sk.v10i1.838 ◽

2018 ◽

Vol 10 (1) ◽

pp. 19-26 ◽

Cited By ~ 1

Author(s):

Muhammad Iqbal Perkasa ◽

Eko Budi Setiawan

Keyword(s):

Web Service ◽

Job Search ◽

Population Data ◽

The Public ◽

Public Data ◽

Community Data ◽

The Government ◽

Rest Api ◽

Service Application ◽

Service Data

Data is one of the most important things in this information and information technology era that evolving now. Currently, the government still has not used the public data maximally for administrative purposes. Utilization of this big population data is the creation of a web service application system with REST API where this data will be open and accessible to those who have access. One of the institutions that use this service is the Manpower and Transmigration Service where this system can make the Dinas staff more efficient to create and register job search cards using available community data. This application is able to provide and facilitate many parties, such as data administrators to monitor data usage, registration employee in input data, and people able to register independently. Index Terms—Web service, API, Rest api, People data

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Fruit and Vegetable Intake and All-Cause Mortality in a Chinese Population: The China Health and Nutrition Survey

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010342 ◽

2021 ◽

Vol 18 (1) ◽

pp. 342

Author(s):

Yuxuan Gu ◽

Yansu He ◽

Shahmir H. Ali ◽

Kaitlyn Harper ◽

Hengjin Dong ◽

...

Keyword(s):

Vegetable Intake ◽

Total Mortality ◽

Negative Association ◽

Mortality Data ◽

Sample Population ◽

Fruit And Vegetable ◽

Nutrition Survey ◽

Related Factors ◽

Health And Nutrition ◽

All Cause Mortality

This study was to investigate the association of long-term fruit and vegetable (FV) intake with all-cause mortality. We utilized data from the China Health and Nutrition Survey (CHNS), a prospective cohort study conducted in China. The sample population included 19,542 adult respondents with complete mortality data up to 31 December 2011. Cumulative FV intake was assessed by 3 day 24 h dietary recalls. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause mortality. Covariates included sociodemographic characteristics, lifestyle factors, health-related factors, and urban index. A total of 1409 deaths were observed during follow-up (median: 14 years). In the fully adjusted model, vegetable intake of the fourth quintile (327~408 g/day) had the greatest negative association with death compared to the lowest quintile (HR = 0.63, 95% CI: 0.53–0.76). Fruit intake of the fifth quintile (more than 126 g/day) had the highest negative association (HR = 0.24, 95% CI: 0.15–0.40) and increasing general FV intake were also negatively associated with all-cause mortality which demonstrated the greatest negative association in the amount of fourth quintile (HR = 0.59, 95% CI: 0.49–0.70) compared to the lowest quintile. To conclude, greater FV intake is associated with a reduced risk of total mortality for Chinese adults. High intake of fruit has a stronger negative association with mortality than differences in intake of vegetables. Our findings support recommendations to increase the intake of FV to promote overall longevity.

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Association of mushroom consumption with all-cause and cause-specific mortality among American adults: prospective cohort study findings from NHANES III

Nutrition Journal ◽

10.1186/s12937-021-00691-8 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Djibril M. Ba ◽

Xiang Gao ◽

Joshua Muscat ◽

Laila Al-Shaar ◽

Vernon Chinchilli ◽

...

Keyword(s):

Lower Risk ◽

Proportional Hazards ◽

Total Mortality ◽

Cox Proportional Hazards ◽

Dietary Factors ◽

Mortality Data ◽

Nhanes Iii ◽

Dietary Recall ◽

All Cause Mortality ◽

Specific Mortality

Abstract Background Whether mushroom consumption, which is rich in several bioactive compounds, including the crucial antioxidants ergothioneine and glutathione, is inversely associated with low all-cause and cause-specific mortality remains uncertain. This study aimed to prospectively investigate the association between mushroom consumption and all-cause and cause-specific mortality risk. Methods Longitudinal analyses of participants from the Third National Health and Nutrition Examination Survey (NHANES III) extant data (1988–1994). Mushroom intake was assessed by a single 24-h dietary recall using the US Department of Agriculture food codes for recipe foods. All-cause and cause-specific mortality were assessed in all participants linked to the National Death Index mortality data (1988–2015). We used Cox proportional hazards regression models to calculate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (95% CIs) for all-cause and cause-specific mortality. Results Among 15,546 participants included in the current analysis, the mean (SE) age was 44.3 (0.5) years. During a mean (SD) follow-up duration of 19.5 (7.4) years , a total of 5826 deaths were documented. Participants who reported consuming mushrooms had lower risk of all-cause mortality compared with those without mushroom intake (adjusted hazard ratio (HR) = 0.84; 95% CI: 0.73–0.98) after adjusting for demographic, major lifestyle factors, overall diet quality, and other dietary factors including total energy. When cause-specific mortality was examined, we did not observe any statistically significant associations with mushroom consumption. Consuming 1-serving of mushrooms per day instead of 1-serving of processed or red meats was associated with lower risk of all-cause mortality (adjusted HR = 0.65; 95% CI: 0.50–0.84). We also observed a dose-response relationship between higher mushroom consumption and lower risk of all-cause mortality (P-trend = 0.03). Conclusion Mushroom consumption was associated with a lower risk of total mortality in this nationally representative sample of US adults.

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Trends in hospitalization and in-hospital mortality of patients with heart failure in Spain. A population-based study (2003–2015)

European Heart Journal ◽

10.1093/ehjci/ehaa946.1187 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

J.L Bonilla Palomas ◽

M.P Anguita-Sanchez ◽

F.J Elola ◽

J.L Bernal ◽

C Fernandez-Perez ◽

...

Keyword(s):

Heart Failure ◽

Mortality Rate ◽

Hospital Mortality ◽

National Health System ◽

Admission Rate ◽

Population Data ◽

Principal Diagnosis ◽

Data Set ◽

Discharge Rates ◽

Mortality Ratio

Abstract Background Heart failure (HF) is one of the most pressing current public health concerns. However, in Spain there is a lack of population data. Purpose To investigate trends in HF hospitalization and in-hospital mortality rates. Methods We conducted a retrospective observational study of patients discharged with the principal diagnosis of HF from The National Health System' acute hospitals during 2003–2015. The source of the data was the Minimum Basic Data Set of the Ministry of Health, Consumer and Social Welfare. We analyzed trends in hospital discharge rates for HF (discharge rates were weighted by age and gender) an in-hospital mortality. The risk-standardized in-hospital mortality ratio (RSMR) was defined as the ratio between predicted mortality (which individually considers the performance of the hospital where the patient is attended) and expected mortality (which considers a standard performance according to the average of all hospitals) multiplied by the crude rate of mortality. RSMR was calculated using a risk adjustment multilevel logistic regression models developed by the Medicare and Medicaid Services. Temporal trend during the observed period was modelled using Poisson regression analysis with year as the only independent variable. In this model, the incidence rate ratio (IRR) and their 95% confidence intervals (95% CI) was calculated. Results A total of 1 254 830 episodes of HF were selected. Throughout 2003–2015 the number of hospital discharges with principal diagnosis of HF increased by 61% (IRR: 1.04; CI: 1.03–1.04; p<0.001), meanwhile the crude mortality rate and the mean length of stay (LOS) diminished significantly (IRR: 0.99; CI: 0.98–1; and IRR: 1.04; CI: 0.99–0.99; p<0.001, for both). Discharge rates weighted by age and sex showed a statistically significant increase during the period (IRR: 1.03; CI: 1.03–1.03; p<0.001); however, whereas discharge rates increased significantly in older groups of age (≥75 years old) (IRR: 1–1.02; p<0.001) they diminished in younger groups of age (45–74 years old) (IRR: 0.99; p<0.001 and there was not a significant trend in the discharge rates for the group of 35–44 years old (Figure). The risk-standardized in-hospital mortality ratio did not significantly change throughout 2003–2015 (IRR: 0.997; CI: 0.992–1; p=0.32), however the risk-standardized LOS ratio diminished from 1.07 in 2003 to 0.97 in 2015 (IRR: 0.98: IC: 0.98–0.99; p<0.001). Conclusions From 2003 to 2015, HF admission rate increased significantly in Spain as a consequence of the sustained increase of hospitalization in the population over 75. The crude in-hospital mortality rate diminished significantly for the same period, but the risk-standardized in-hospital mortality ratio did not significantly change. Figure 1 Funding Acknowledgement Type of funding source: None

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Excess Mortality During the COVID-19 Pandemic in Philadelphia

American Journal of Public Health ◽

10.2105/ajph.2021.306285 ◽

2021 ◽

pp. e1-e6

Author(s):

Megan Todd ◽

Meagan Pharis ◽

Sam P. Gulino ◽

Jessica M. Robbins ◽

Cheryl Bettigole

Keyword(s):

Public Health ◽

Excess Mortality ◽

Poisson Model ◽

People Of Color ◽

Mortality Data ◽

Registration System ◽

Vital Registration System ◽

All Cause Mortality ◽

Race Ethnicity ◽

Excess Deaths

Objectives. To estimate excess all-cause mortality in Philadelphia, Pennsylvania, during the COVID-19 pandemic and understand the distribution of excess mortality in the population. Methods. With a Poisson model trained on recent historical data from the Pennsylvania vital registration system, we estimated expected weekly mortality in 2020. We compared these estimates with observed mortality to estimate excess mortality. We further examined the distribution of excess mortality by age, sex, and race/ethnicity. Results. There were an estimated 3550 excess deaths between March 22, 2020, and January 2, 2021, a 32% increase above expectations. Only 77% of excess deaths (n=2725) were attributed to COVID-19 on the death certiﬁcate. Excess mortality was disproportionately high among older adults and people of color. Sex differences varied by race/ethnicity. Conclusions. Excess deaths during the pandemic were not fully explained by COVID-19 mortality; ofﬁcial counts signiﬁcantly undercount the true death toll. Far from being a great equalizer, the COVID-19 pandemic has exacerbated preexisting disparities in mortality by race/ethnicity. Public Health Implications. Mortality data must be disaggregated by age, sex, and race/ethnicity to accurately understand disparities among groups. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e6. https://doi.org/10.2105/AJPH.2021.306285 )

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Hospital mortality under surgical care

Annals of The Royal College of Surgeons of England ◽

10.1308/003588411x563411 ◽

2011 ◽

Vol 93 (3) ◽

pp. 193-200 ◽

Cited By ~ 4

Author(s):

O Aziz ◽

D Fink ◽

L Hobbs L ◽

G Williams ◽

TC Holme

Keyword(s):

Surgical Care ◽

Hospital Performance ◽

General Surgeon ◽

Mortality Data ◽

Co Morbidity ◽

Specific Mortality ◽

Mortality Ratio ◽

Case Mix Adjustment ◽

Surrogate Measure ◽

Morbidity Index

INTRODUCTION The ‘hospital standardised mortality ratio’ (HSMR) has been used in England since 1999 to measure NHS hospital performance. Large variations in reported HSMR between English hospitals have recently led to heavy criticism of their use as a surrogate measure of hospital performance. This paper aims to review the mortality data for a consultant general surgeon contributed by his NHS trust over a 3-year period as part of the trust's HSMR calculation and evaluate the accuracy of coding the diagnoses and covariates for case mix adjustment. SUBJECTS AND METHODS The Dr Foster Intelligence database was interrogated to extract the NHS trust's HSMR benchmark data on inpatient mortality for the surgeon from 1 April 2006 to 31 March 2009 and compared to the hospital notes. RESULTS 30 patients were identified of whom 12 had no evidence of being managed by the surgeon. This represents a potential 40% inaccuracy rate in designating consultant responsibility. The remaining 18 patients could be separated into ‘operative’ (11 patients) and ‘non-operative’ (7 patients) groups. Only 27% in the operative group and 43% of the non-operative mortality group respectively had a Charlson co-morbidity index recorded despite 94% of the cases having significant co-morbidities CONCLUSIONS Highlighting crude and inaccurate clinician-specific mortality data when only 1-5% of deaths under surgical care may be associated with avoidable adverse events seems potentially irresponsible.

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Demographic Losses Due To Alcohol-Related Mortality in Lithuania: Results Obtained Through Combining Death Records with the 2011 Population Census Data

Lietuvos statistikos darbai ◽

10.15388/ljs.2014.13890 ◽

2014 ◽

Vol 53 (1) ◽

pp. 15-23

Author(s):

Daumantas Stumbrys ◽

Domantas Jasilionis ◽

Dalia Ambrozaitienė ◽

Vlada Stankūnienė

Keyword(s):

Marital Status ◽

Mortality Risk ◽

Census Data ◽

Population Data ◽

Mortality Data ◽

Population Census ◽

The Republic ◽

The Individual ◽

Education Groups ◽

Related Mortality

This paper presents the results of a study on sociodemographic mortality differentials in Lithuania based on censuslinked mortality data. Population data come from the individual records of the 2011 Population and Housing Census of the Republic of Lithuania. The results of the research demonstrate that education and marital status are very strong predictors of alcohol-related mortality. Among males aged 30 and older, the alcohol-related mortality risk in non-married groups is up to 3.4 times as high as in the group of married males. The alcohol-related mortality risk in lower-education groups is up to 3.7 times as high as in the group of those with higher education. The findings of the study suggest that the elimination of educational differences would allow avoiding 55.7 %, the elimination of marital status differences – 40.2 %, the elimination of ethnic group differences – 11.1 % of alcohol-related deaths.

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‘Trend in premature mortality from four major NCDs in Nanjing, China, 2007–2018’

BMC Public Health ◽

10.1186/s12889-021-12018-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Huafeng Yang ◽

Yali Fu ◽

Xin Hong ◽

Hao Yu ◽

Weiwei Wang ◽

...

Keyword(s):

Rural Areas ◽

Urban Areas ◽

Respiratory Diseases ◽

Premature Mortality ◽

Population Data ◽

Integrated Approach ◽

Mortality Data ◽

Relative Reduction ◽

Mortality Trends ◽

Chronic Respiratory Diseases

Abstract Background This study aims to analyze the trends of premature mortality caused from four major non-communicable diseases (NCDs), namely cardiovascular disease (CVD), cancer, chronic respiratory diseases, and diabetes in Nanjing between 2007 and 2018 and project the ability to achieve the “Healthy China 2030” reduction target. Methods Mortality data of four major NCDs for the period 2007–2018 were extracted from the Death Information Registration and Management System of Chinese Center for Disease Control and Prevention. Population data for Nanjing were provided by the Nanjing Bureau of Public Security. The premature mortality was calculated using the life table method. Joinpoint regression model was used to estimate the average annual percent changes (AAPC) in mortality trends. Results From 2007 to 2018, the premature mortality from four major NCDs combined in Nanjing decreased from 15.5 to 9.5%, with the AAPC value at − 4.3% (95% CI [− 5.2% to − 3.4%]). Overall, it can potentially achieve the target, with a relative reduction 28.6%. The premature mortality from cancer, CVD, chronic respiratory diseases and diabetes all decreased, with AAPC values at − 4.2, − 5.0%, − 5.9% and − 1.6% respectively. A relative reduction of 40.6 and 41.2% in females and in rural areas, but only 21.0 and 12.8% in males and in urban areas were projected. Conclusion An integrated approach should be taken focusing on the modifiable risk factors across different sectors and disciplines in Nanjing. The prevention and treatment of cancers, diabetes, male and rural areas NCDs should be enhanced.

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