Child health research and planning in Europe disadvantaged by major gaps and disparities in published statistics

Abstract Background Population data, such as mortality and morbidity statistics, are essential for many reasons, including giving context for research, supporting action on health determinants, formulation of evidence-based policy for health care and outcome evaluation. However, when considering children, it is difficult to find such data, despite children comprising one-fifth of the European population and being in a key formative life stage and dependent on societal support. Moreover, it would be expected that there should be confidence in the key child health data available, with little to no discrepancy between recognized health statistic databases. Methods This study explored the main health databases in or including Europe to collate child mortality data, for both all-cause and specific-cause mortality. Tables were constructed for comparison of values and rankings. Results The results show that there are major differences in reported mortality data between two prominent health statistic databases, difference in coding systems, and unannounced changes within one of the databases. Conclusions The lack of health data for children seems compounded by challenges to the trust and credibility, which are vital if these data are to have utility. Children and society are the losers, and resolution is needed as a priority.

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936Descriptive study of healthy life expectancy in all secondary medical areas in Japan

International Journal of Epidemiology ◽

10.1093/ije/dyab168.507 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

Author(s):

Toshiyuki Ojima ◽

Rikuya Hosokawa ◽

Tomoya Myojin ◽

Jun Aida ◽

Katsunori Kondo ◽

...

Keyword(s):

Life Expectancy ◽

Health Centre ◽

Population Data ◽

Vital Statistics ◽

Healthy Life Expectancy ◽

Mortality Data ◽

Term Care ◽

Public Health Centre ◽

Healthy Life ◽

Mortality And Morbidity

Abstract Background Healthy life expectancy (HLE) is an index combined with mortality and morbidity. Monitoring HLE is useful to assess and stimulate health promotion policies/programmes. Though HLE in national or prefectural areas have often been observed, further data in smaller areas are required. The aim of the study is to reveal descriptive features of HLE in secondary medical areas, that is almost same as public health centre jurisdictions and median of population is 214 thousand, in Japan. Methods HLE by gender in all 341 secondary medical areas were calculated using Sullivan method. Population data was used from resident registry. Mortality data was from vital statistics of total death in 2016-2018. Data of proportions of unhealthy people was from long-term care insurance data in 2017 using proportion of people with care level 2 (almost bed ridden level) or more severe. Finally, maps of HLE of all of Japan were drawn. Results Means (standard deviations, maximums, minimums, means of ranges of 95% confidence intervals) of HLE at birth are 79.21 (0.86, 81.36, 76.90, 0.92) and 83.75 (0.62, 85.45, 81.99, 0.80) years for males and females, respectively. Areas with short HLE were prevalent in Tohoku region (northern part), while that with long HLE in Chubu region (central part). Conclusions Descriptive features of HLE in smaller areas of all of Japan can be firstly clarified. Key messages Monitoring HLE in local areas would be feasible and useful in some countries. Precision of HLE of areas of these population size would be acceptable.

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Spatiotemporal Econometrics Models for Old Age Mortality in Europe

Mathematics ◽

10.3390/math9091061 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1061

Author(s):

Patricia Carracedo ◽

Ana Debón

Keyword(s):

Panel Data ◽

Goodness Of Fit ◽

Population Aging ◽

European Population ◽

Data Models ◽

European Countries ◽

Mortality Data ◽

Panel Data Models ◽

Estimation Of Parameters ◽

Advantages And Disadvantages

In the past decade, panel data models using time-series observations of several geographical units have become popular due to the availability of software able to implement them. The aim of this study is an updated comparison of estimation techniques between the implementations of spatiotemporal panel data models across MATLAB and R softwares in order to fit real mortality data. The case study used concerns the male and female mortality of the aged population of European countries. Mortality is quantified with the Comparative Mortality Figure, which is the most suitable statistic for comparing mortality by sex over space when detailed specific mortality is available for each studied population. The spatial dependence between the 26 European countries and their neighbors during 1995–2012 was confirmed through the Global Moran Index and the spatiotemporal panel data models. For this reason, it can be said that mortality in European population aging not only depends on differences in the health systems, which are subject to national discretion but also on supra-national developments. Finally, we conclude that although both programs seem similar, there are some differences in the estimation of parameters and goodness of fit measures being more reliable MATLAB. These differences have been justified by detailing the advantages and disadvantages of using each of them.

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The spatial epidemiology of alcohol-related mortality among the Czech adult population

European Journal of Public Health ◽

10.1093/eurpub/ckaa165.238 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

L Kazmer ◽

I Kulhanova ◽

M Lustigova

Keyword(s):

Local Community ◽

Spatial Epidemiology ◽

Adult Population ◽

Age Groups ◽

Health Determinants ◽

Mortality Data ◽

Annual Data ◽

Cross Sectional ◽

Inequalities In Health ◽

Related Mortality

Abstract Background In Czechia, alcohol-induced deaths account for a significant portion of preventable mortality. As inequalities in health are both socially and spatially determined, the paper aims at the detailed examination of socio-geographic inequalities of this phenomenon. Methods The 2011-2015 annual data on both ICD-10 cause-specific deaths (K70; F10; X45/64; Y15) and mid-year population were obtained from the official Czech registries - the data were cross-classified by gender, 5-year age-groups, and permanent residence (N = 6,302 small area spatial units). The selected socio-demographic indicators (education, unemployment, religious population) from the Czech 2011 Census were spatially merged to the mortality dataset. From the data on education and unemployment, composite deprivation index (DI) was derived. In the adult population aged 25+, the age-standardised mortality ratios (SMR) were computed for each of the spatial units, separately by genders. The SMRs were spatially modelled by the Besag-York-Mollié (BYM) autoregressive approach, applying a fully bayesian framework integrated within the INLA R-package. The study applied cross-sectional design and employed ecological regression conducted on observational data. Results Compared to the Czech average, the highest SMRs were located in the historical regions of Moravia [SMR=1.15; 95%CI: 1.11-1.19] and Silesia [SMR=1.59; 95%CI: 1.52-1.66]. The SMRs were significantly correlated with DI among males [Rel.Risk=1.15; 95%CI: 1.11-1.19], and with religiousness rate among females [Rel.Risk=0.83; 95%CI: 0.77-0.90]. Conclusions Significant socio-geographic inequalities were detected, particularly with respect to the Czech historical regions. Among males, higher mortality was associated with a structural deprivation. Among females, protective effect of religiousness rate was found to be significant. The results highlight an importance of both socially and spatially integrated efforts for public health promotion. Key messages The inequalities in health are both socially and spatially contextualised. The paper presents robust empirical evidence in favour of the proposition, as examined on alcohol-related mortality data. The health determinants may be gender sensitive. Males might be more responsive to a structural disadvantage. Among females, cultural factors related to a local community might be more relevant.

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Spatially explicit analyses of environmental and health data to determine past, emerging and future threats to child health

Journal of Paediatrics and Child Health ◽

10.1111/jpc.15822 ◽

2021 ◽

Vol 57 (11) ◽

pp. 1830-1834

Author(s):

Peter N Le Souëf ◽

Chitra M Saraswati ◽

Melinda Judge ◽

Corey JA Bradshaw

Keyword(s):

Child Health ◽

Health Data ◽

Spatially Explicit

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Duration of immunity against COVID-19 after vaccination in Indian subcontinent

10.21203/rs.3.rs-1260289/v1 ◽

2022 ◽

Author(s):

Apoorva Munigela ◽

Sasikala M ◽

Gujjarlapudi Deepika ◽

Anand V Kulkarni ◽

Krishna Vemula ◽

...

Keyword(s):

Neutralizing Antibodies ◽

Booster Dose ◽

Indian Subcontinent ◽

Neutralizing Antibody ◽

Health Concern ◽

Mortality Data ◽

Optimal Timing ◽

Cross Sectional ◽

Mortality And Morbidity ◽

Antibody Levels

Abstract Coronavirus disease (COVID-19) continues to be a major health concern leading to substantial mortality and morbidity across the world. Vaccination is effective in reducing the severity and associated mortality. Data pertaining to the duration of immunity, antibody waning and the optimal timing of booster dose administration is limited. In this cross-sectional study, we assessed the antibody levels in healthcare workers who were fully vaccinated after obtaining Institutional ethics committee approval and informed consent. Whole blood was collected and enumeration of S1/S2 neutralizing antibody levels was carried out using LIAISON SARS-COV-2 S1/S2 IgG assay. A total of 1636 individuals who were vaccinated with Covaxin or Covishield were included. Of these, 52% were males with a median age of 29 years. Diabetes and Hypertension was noted in 2.32% (38/1636) and 2.87% (47/1636) of the individuals. Spike neutralizing antibodies were below the detectable range (<15 AU/ml) in 6.0% (98/1636) of the individuals. Decline in neutralizing antibody was seen in 30% of the individuals above 40 years of age with comorbidities (diabetes and hypertension) after 6 months. These individuals may be prioritized for a booster dose at 6 months.

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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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Planting the S.E.E.D.S of Indigenous Population Health Data Linkage

International Journal for Population Data Science ◽

10.23889/ijpds.v5i5.1626 ◽

2020 ◽

Vol 5 (5) ◽

Author(s):

Robyn K Rowe ◽

Jennifer D Walker

Keyword(s):

Population Health ◽

Indigenous Peoples ◽

Health Care Policy ◽

Indigenous Population ◽

Data Linkage ◽

Indigenous Health ◽

Population Data ◽

Health Data ◽

Indigenous Populations ◽

Data Stewardship

IntroductionThe increasing accessibility of data through digitization and linkage has resulted in Indigenous and allied individuals, scholars, practitioners, and data users recognizing a need to advance ways that assert Indigenous sovereignty and governance within data environments. Advances are being talked about around the world for how Indigenous data is collected, used, stored, shared, linked, and analysed. Objectives and ApproachDuring the International Population Data Linkage Network Conference in September of 2018, two sessions were hosted and led by international collaborators that focused on regional Indigenous health data linkage. Notes, discussions, and artistic contributions gathered from the conference led to collaborative efforts to highlight the common approaches to Indigenous data linkage, as discussed internationally. This presentation will share the braided culmination of these discussions and offer S.E.E.D.S as a set of guiding Indigenous data linkage principles. ResultsS.E.E.D.S emerges as a living and expanding set of guiding principles that: 1) prioritizes Indigenous Peoples’ right to Self-determination; 2) makes space for Indigenous Peoples to Exercise sovereignty; 3) adheres to Ethical protocols; 4) acknowledges and respects Data stewardship and governance, and; 5) works to Support reconciliation between Indigenous Peoples and settler states. S.E.E.D.S aims to centre and advance Indigenous-driven population data linkage and research while weaving together common global approaches to Indigenous data linkage. Conclusion / ImplicationsEach of the five elements of S.E.E.D.S interweave and need to be enacted together to create a positive Indigenous data linkage environment. When implemented together, the primary goals of the S.E.E.D.S Principles is to guide positive Indigenous population health data linkage in an effort to create more meaningful research approaches through improved Indigenous-based research processes. The implementation of these principles can, in turn, lead to better measurements of health progress that are critical to enhancing health care policy and improving health and wellness outcomes for Indigenous populations.

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Modeling Polypharmacy Side Effects with Graph Convolutional Networks

10.1101/258814 ◽

2018 ◽

Cited By ~ 6

Author(s):

Marinka Zitnik ◽

Monica Agrawal ◽

Jure Leskovec

Keyword(s):

Side Effects ◽

Drug Interactions ◽

Protein Interactions ◽

Side Effect ◽

Population Data ◽

Model Parameters ◽

Mortality And Morbidity ◽

Convolutional Networks ◽

Important Challenge ◽

Complex Relationships

AbstractMotivation: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.Results: Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.Availability: Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.Contact:[email protected]

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Quantification of mortality and morbidity in general population of heavily-industrialized city of Abadan: Effect of long-term exposure

Journal of Air Pollution and Health ◽

10.18502/japh.v5i3.5390 ◽

2021 ◽

Author(s):

Akram Ghorbanian ◽

Ahmad Jonidi Jafari ◽

Abbas Shahsavani ◽

Ali Abdolahnejad ◽

Majid Kermani ◽

...

Keyword(s):

Air Pollution ◽

Mortality Data ◽

Air Pollution Control ◽

Medical Sciences ◽

Mortality And Morbidity ◽

Khuzestan Province ◽

Vice Chancellor ◽

One Year ◽

The One

Introduction: In the 21st century, air pollution has become a global and environmental challenge. The increase in cases of illness and mortality due to air pollution is not hidden from anyone. Therefore, in this study, we estimated the mortality rate due to cause by air pollution agents (PM2.5) in the southernmost city of Khuzestan province (Abadan city) at 2018-2019. Materials and methods: To estimate the mortality duo to air pollution, data related to PM2.5 particles daily concentrations was received from the Abadan Environmental Protection Organization. The average 24-h concentrations of PM2.5 were calculated using Excel. Then, mortality data were obtained from the Vice Chancellor for Health, Abadan University of Medical Sciences. Finally, by AirQ+ software, each of the mortality in 2018-2019 in Abadan was estimated. Results: The obtained data indicated that the concentration of PM2.5 particles within the one-year period was higher than the value set by WHO guideline and EPA standard. Which caused the citizens of Abadan to be exposed to PM2.5 more than 8.23 times than the guidelines of the WHO and 5.34 times more than the standard of the EPA. The output of the model used in this study was as follows: natural mortality (462 cases, AP: 38.25%), mortality duo to LC (6 cases, AP: 32.18%), mortality duo to COPD (8 cases, AP: 26.64%), mortality duo to Stroke (86 cases, AP: 71.26%), mortality duo to IHD (183 cases, AP: 68.34%) and mortality duo to ALRI (2 cases, AP: 32.9%). Conclusion: Planning appropriate strategies of air pollution control to reduce exposure and attributable mortalities is important and necessary.

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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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