File S1 includes Appendix S1, Appendix S2, Appendix S3, Appendix S4. Appendix S1: Search terms used to identify studies of one year mortality on antiretroviral therapy. Appendix S2: Full citations for studies reviewed. Appendix S3: Illustration of a distribution used to impute CD4 count with bands. Appendix S4: CD4 coefficient (bottom) and model fit (F-statistic – top) for the relationship between one year mortality on ART and baseline CD4 count using varying assumptions about the amount of mortality among those lost to follow-up. (DOCX)

Objectives: Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting: Data taken from employees at 3 different industrial sites in Australia. Participants: 7915 observations were included. Materials and methods: The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results: CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the number of visits, and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured as compared to structured missingness. Discussion: Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusions: Researchers are encouraged to use CART and BRT models to explore and understand missing data.

*Predictors were identified using center-specific stepwise Cox regression in the derivation sample. Those factors being significantly (two-sided P-value

AimsRecent literature has posed sedentary behaviour as an independent entity to physical inactivity. This study investigated whether associations between sedentary behaviour and cardio-metabolic biomarkers remain when analyses are adjusted for total physical activity.MethodsCross-sectional analyses were undertaken on 4,618 adults from the 2003/04 and 2005/06 U.S. National Health and Nutrition Examination Survey. Minutes of sedentary behaviour and moderate-to-vigorous physical activity (MVPA), and total physical activity (total daily accelerometer counts minus counts accrued during sedentary minutes) were determined from accelerometry. Associations between sedentary behaviour and cardio-metabolic biomarkers were examined using linear regression.ResultsResults showed that sedentary behaviour was detrimentally associated with 8/11 cardio-metabolic biomarkers when adjusted for MVPA. However, when adjusted for total physical activity, the associations effectively disappeared, except for C-reactive protein, which showed a very small, favourable association (β = −0.06) and triglycerides, which showed a very small, detrimental association (β = 0.04). Standardised betas suggested that total physical activity was consistently, favourably associated with cardio-metabolic biomarkers (9/11 biomarkers, standardized β = 0.08–0.30) while sedentary behaviour was detrimentally associated with just 1 biomarker (standardized β = 0.12).ConclusionThere is virtually no association between sedentary behaviour and cardio-metabolic biomarkers once analyses are adjusted for total physical activity. This suggests that sedentary behaviour may not have health effects independent of physical activity.

1T-test for trend for continuous covariates with serum 25-hydroxyvitamin D entered as ordinal values.Chi-square test for dichotomous covariates.

aMissing BMI data from 5 participants; BMI = body mass index; NTD = New Taiwan dollar; ER = emergency room; IADL = Instrumental Activities of Daily Living; ADL = Activities of Daily Living.

Full citations given in Appendix S2 in File S1.*Denotes mean not median.

Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization is considering whether the outbreak should be assessed as a “potential public health emergency of international concern”, as was done for the COVID-19 and Ebola outbreaks in the past. During this time, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, a dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 102,452 tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 26th June 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 5 different files based on the timelines of the associated tweets. The following are the details of these dataset files

Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the associated Tweet IDs: May 7, 2022 to May 21, 2022) Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the associated Tweet IDs: May 21, 2022 to May 27, 2022) Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the associated Tweet IDs: May 27, 2022 to June 5, 2022) Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the associated Tweet IDs: June 5, 2022 to June 11, 2022) Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 33518, Date Range of the associated Tweet IDs: June 12, 2022 to June 26, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.

*Missing 4 BMI data in non long-term care user; Missing 2 BMI data in institution user.BMI: Body mass index; NTD: New Taiwan dollar; ER: emergency room; IADL: instrumental activities of daily living;ADL: activities of daily living.

Abbreviations: HR Hazard Ratio, CI confidence interval.1Chi-square test (type 3).2Adjusted for age at recruitment, sex (when all subjects are included), height, body mass index, current smoking, season of blood sampling and alcohol intake.3Additionally adjusted for current physical activity.

Factors associated to overweight in workers at multivariate modeling.

OR = odds ratio; BMI = body mass index; NTD = New Taiwan dollar; ER = emergency room;IADL = Instrumental Activities of Daily Living; ADL = Activities of Daily Living.

Abbreviations: CI confidence interval.1F-test (type 3).2Adjusted for age at recruitment, sex (when all subjects are included), height, body mass index, current smoking, season of blood sampling and alcohol intake.3Additionally adjusted for current physical activity.

Estimations of cancer risks reported from population (ecological) studies.

OR = odds ratio; BMI = body mass index; NTD = New Taiwan dollar; ER = emergency room; IADL = Instrumental Activities of Daily Living; ADL = Activities of Daily Living.

aWeight change z-score from birth to 6, 9 or 12 months in equation 1, 2 and 3 samples respectively.bGSCE = General Certificate of Secondary Education; A-level = Advanced level.cDichotomised Yes/No variable. Numbers are for Yes.dInfant BMI >91st centile and growth from birth to 2 years of age ≥1 centile band.Values are numbers (percentages) unless stated otherwise.

Diversity of Worksites and Occupations Represented by 126 Participants in 49 Worksites in 5 Heavy Industries.

Distribution of continuous variables in the study population. (DOCX)

Manuscripts identified by the systematic review and stratified by A1) occupational cohorts, A2) occupational risk factors, and B) ecological population-level studies.

Maternal mortality due to knowledge, attitude, resource and management.