Correlation matrix for covariates tabulated in the analysis (left) and principal components analysis results (right).

Scree plot (right) for total variance explained for included variables in principal components analysis (CKD, Diabetes, Stroke, HHD). CKD, Chronic Kidney Disease. DM, Diabetes mellitus. HHD, Hypertensive Heart Disease, HIV, Human Immunodeficiency Virus. KMO, Kaiser-Meyer-Olkin test. BST, Bartlett’s Sphericity Test.</p

Simple univariate linear regression models for the correlation between covariates and OPD Adjusted (OPD divided by fertility rate).

x-axis expressed as proportion (between 0 and 1) except for GDP per capita (expressed as $US). P-values calculated for regression model. Y-axis transformed to the logit scale. Note that the y-axis intervals are not evenly spaced. Orphans per death adjusted = orphans per death divided by national average total fertility rate. Solid lines represent regression prediction, grey bands represent 95$US.</p

Kernel density functions for OPD by GDP category (Left) and boxplots of OPD by GDP category (right).

GDP categories calculated as countries with a GDP below the median value in the study data, and those countries with a GDP above the median value. Median value = US $12,939 per capita.</p

Multiple linear regression model for OPD adjusted with a logit link function.

Multiple linear regression model for OPD adjusted with a logit link function.</p

Association between 2^nd dose vaccination coverage and OPD by region.

Correlation coefficient and p-value for univariate linear regression models displayed in text in main panels. Gray bands represent 95% confidence intervals. Vaccination data as of 1st December 2021.</p

Distribution of orphans per death by country.

Countries organized by WHO region.</p

DataSheet1_The Impact of Multimorbidity on All-Cause Mortality: A Longitudinal Study of 87,151 Thai Adults.docx

Objectives: To investigate associations between multimorbidity, socio-demographic and health behaviour factors, and their interactions (multimorbidity and these factors) with all-cause mortality among Thai adults.Methods: Associations between multimorbidity (coexistence of two + chronic diseases) and mortality between 2005 and 2019 were investigated among Thai Cohort Study (TCS) participants (n = 87,151). Kaplan-Meier survival curves estimated and compared survival times. Multivariate Cox proportional hazards models examined associations between risk factors, and interactions between multimorbidity, these factors, and survival.Results: 1,958 cohort members died between 2005 and 2019. The risk of death was 43% higher for multimorbid people. In multivariate Cox proportional hazard models, multimorbidity/number of chronic conditions, age, long sleep duration, smoking and drinking were all independent factors that increased mortality risk. Women, urbanizers, university education, over 20,000-baht personal monthly income and soybean products consumption lowered risk. The interactions between multimorbidity and these variables (except for female, urbanizers and soybeans intake) also had significant (p Conclusion: The results emphasise the importance of healthy lifestyle and reduced intake of alcohol and tobacco, in reducing premature mortality, especially when suffering from multimorbidity.</p

Fully adjusted models of any traffic injury, motorcycle (m.c.) injury, and car crash injury.

<p>* Number of study participants with injury / number of study participants exposed.</p><p>Fully adjusted models of any traffic injury, motorcycle (m.c.) injury, and car crash injury.</p

Ratio of registered motorcycles / passenger cars in Thailand between 2004 and 2012; ratio of new driving licences issued for motorcycles / automobiles.

<p>Based on source data: Road Transport Thailand—AJTP Information Center [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120617#pone.0120617.ref007" target="_blank">7</a>]</p

Association between injury and SF-8 health scores, Thai Cohort Study 2009.

<p>* Physical and Mental Component Summary scores were produced by multivariate linear regression fitting injury frequency to SF-8 summary health outcomes after adjusting for age-sex categories, household income, urban-rural residence, health-risk behaviours (smoking, alcohol drinking), and doctor-reported chronic conditions.</p><p>** Score differences for each injury frequency express the deviance from the reference scores for the ‘never’ group. For example, cohort members with one traffic injury reported a PCS score of 48.4, or 1.39 lower than the ‘never’ group (49.8).</p