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.

Objectives: To assess the impact on readers’ interpretation of the results reported in an abstract for a hypothetical clinical trial with 1) a statistically significant result, 2) spin, 3) both a statistically significant result and spin compared to 4) no spin and no statistically significant result. Participants: Health students and professionals from universities and health institutions in France and the UK. Interventions: Participants completed an online questionnaire using Likert scales and free text, after reading one of the four versions of an abstract about a hypothetical randomized trial evaluating “Naranex†and “Bulofil†(two hypothetical drugs) for chronic low back pain. The abstracts differed in a) reported result of “mean difference of 1.31 points (95%CI 0.08 to 2.54; p= 0.04)“ or “mean difference of 1.31 points (95%CI -0.08 to 2.70; p= 0.06)†and b) presence or absence of spin. The effect size for the trial’s primary outcome (pain disability score) was the same in each abst...

*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.

Abstract

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content

Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in all ten administrative regions in Ghana. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older were selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Ghana used a stratified, multistage cluster design. The sample was stratified by administrative region (Ashanti, Brong Ahafo, Central, Eastern, Greater Accra, Northern, Upper East, Upper West, Volta and Western) and type of locality (urban/rural) resulting in 20 strata and is nationally representative. The Census Enumerated Areas (CEA) of the 2000 Population and Housing Census was used as the sampling frame. A sample of 251 EAs was selected as the primary sampling units (PSU). One of the selected PSUs was not used. This was because the EA which was expected to be located at Korle Bu Teaching hospital cccould not be traced. The number of EAs to be selected from each strata was based on proportional allocation (determined by the number of EAs in each strata specified on the census frame). EAs were then selected from each stratum with probability proportional to size; the measure of size being the number of individuals aged 50 years or more in the EA. In each selected EA, a listing of the households was conducted to classify each household into the following mutually exclusive categories: (1) WHS/SAGE Wave 0 follow-up households with one or more members aged 50 years or more; (2) New households with one or more members aged 50 years or more; (3) WHS/SAGE Wave 0 follow-up households which did not include any members aged 50 years or more, but included residents aged 18-49; and, (4) New households which did not include any members aged 50 years or more, but included residents aged 18-49.

Twenty-four households were randomly selected from each selected EA. All WHS/SAGE Wave 0 follow-up 50-plus households were eligible for the household interview (one household respondent was selected). Twenty such households were selected. If this target number was not reached, then the balance was selected using systematic sampling from the new 50-plus households. All 50+ members of the household were eligible for the individual interview (multiple individual interviews possible in these households).

Stages of selection Strata: Region, Locality=20 PSU: EAs=235 surveyed SSU: Households=5269 surveyed TSU: Individual=5573 surveyed

Sampling deviation

One of the 251 selected PSUs was not used. This was because the EA which was expected to be located at Korle Bu Teaching hospital cccould not be traced.

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionniare was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into three local languanges: Akan, Ga and Twi. All SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=86% Cooperation rate=98%

Individual: Response rate=80% Cooperation rate=92%

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)