The Inequality Mirror: Using a Student Survey to Teach Social Stratification

Social inequality is a central theme in sociology study plans (both in research and education), but it is often one of the most difficult topics to teach. This article presents an innovative student-centered strategy for teaching social inequality that uses a survey to collect data on students’ socioeconomic characteristics and perceptions of inequality. To stimulate reflection and discussion on the social mechanisms that reproduce inequality, this information is subsequently presented to them in conjunction with a comparative analysis to general population data. The exercise seeks to make social inequality less abstract for students by involving them in the research process and by using data relative to their own lives and families. Ultimately, the strategy boosts students’ sociological imagination and their capacity for critical thinking by encouraging them to see the connections between individual biographies and broader social forces.

SARS-CoV-2 infection and COVID-19 vaccination rates in pregnant women in Scotland

Population-level data on COVID-19 vaccine uptake in pregnancy and SARS-CoV-2 infection outcomes are lacking. We describe COVID-19 vaccine uptake and SARS-CoV-2 infection in pregnant women in Scotland, using whole-population data from a national, prospective cohort. Between the start of a COVID-19 vaccine program in Scotland, on 8 December 2020 and 31 October 2021, 25,917 COVID-19 vaccinations were given to 18,457 pregnant women. Vaccine coverage was substantially lower in pregnant women than in the general female population of 18−44 years; 32.3% of women giving birth in October 2021 had two doses of vaccine compared to 77.4% in all women. The extended perinatal mortality rate for women who gave birth within 28 d of a COVID-19 diagnosis was 22.6 per 1,000 births (95% CI 12.9−38.5; pandemic background rate 5.6 per 1,000 births; 452 out of 80,456; 95% CI 5.1−6.2). Overall, 77.4% (3,833 out of 4,950; 95% CI 76.2−78.6) of SARS-CoV-2 infections, 90.9% (748 out of 823; 95% CI 88.7−92.7) of SARS-CoV-2 associated with hospital admission and 98% (102 out of 104; 95% CI 92.5−99.7) of SARS-CoV-2 associated with critical care admission, as well as all baby deaths, occurred in pregnant women who were unvaccinated at the time of COVID-19 diagnosis. Addressing low vaccine uptake rates in pregnant women is imperative to protect the health of women and babies in the ongoing pandemic.

Measurement of Physical Fitness and 24/7 Physical Activity, Standing, Sedentary Behavior, and Time in Bed in Working-Age Finns: Study Protocol for FINFIT 2021

Background: Population studies gathering measured data on fitness and physical behavior, covering physical activity, standing, sedentary behavior, and time in bed, are scarce. This article describes the protocol of the FINFIT 2021 study that measures fitness and physical behavior in a population-based sample of adults and analyzes their associations and dose–response relationships with several health indicators. Methods: The study comprises a stratified random sample of 20–69-year-old men and women (n = 16,500) from seven city-centered regions in Finland. Physical behavior is measured 24/7 by tri-axial accelerometry and analyzed with validated MAD-APE algorithms. Health and fitness examinations include fasting blood samples, measurements of blood pressure, anthropometry, and health-related fitness. Domains of health, functioning, well-being, and socio-demographics are assessed by a questionnaire. The data are being collected between September 2021 and February 2022. Discussion: The study provides population data on physical fitness and physical behavior 24/7. Physical behavior patterns by intensity and duration on an hour-by-hour basis will be provided. In the future, the baseline data will be assessed against prospective register-based data on incident diseases, healthcare utilization, sickness absence, premature retirement, and death. A similar study will be conducted every fourth year with a new random population sample.

Application of Univariate Probability Distributions Fitting With Monte Carlo Simulation

In this study, we present a univariate probability distribution through application of the three Sub and Super Exponential heavier-longer and lighter-shorter tails fitting. This univariate family includes the Lognormal, Gamma and Weibull distribution, the adequacy of the distribution tails is obtained by adequate Fitting Tests and descriptive Criterion. It emphasizes on tail values and is independent of the number of intervals. In this regards the time series analysis for the last three centuries of the logarithm population data sets over to Karachi region (from1729 to1946 and from 1951 to 2018) is used, which contains irregular and regular length and peaks, That peaks /tails fitting is attained by methods for validation and normality tests and defined by stochastic depiction. In other hand, Weibull and Lognormal distribution tails are found as heavier distribution by two validation tests (Maximum Likelihood Estimation and probability of correct selection), In the final section, the univariate probability distributions are used to Monte Carlo simulation for generating the actual population data, it indicates that the heavy-tailed Lognormal and Weibull distributions are also fitted contract than the more commonly seen lighter tailed Gamma distribution. So, the Monte Carlo Simulation performs the appropriate Lognormal and Weibull distributions for irregular and regular data and generate data values (298 and 69) from duration of 1729 to 2020 and 1951 to 2020. Copyright(c) The Author

Fear and Attitude towards SARS-CoV-2 (COVID-19) Infection in Spanish Population during the Period of Confinement

In January 2020, the WHO classified SARS-CoV-2 infection as a public health emergency and it was declared a pandemic on 11 March 2020. The media warned about the danger of infection, fuelling the population’s fear of the new situation and increasing the perception of risk. This fear can cause behaviour that will determine the course of the pandemic and, therefore, the purpose of this study was to analyse the fear of infection from COVID-19 among the Spanish population during the state of emergency. A cross-sectional, descriptive observational study was conducted with 16,372 participants. Data on sociodemographic factors, health factors, risk perception and fear were collected through an online survey. Level of fear is associated with older age, a lower level of education, having a person infected with SARS-CoV-2 in the immediate surroundings and living with and belonging to the most socioeconomically vulnerable group of people. Risk perception is associated with increased preventive behaviour. This paper provides relevant information for the public health sector since it contributes first-hand knowledge of population data that is highly useful in terms of prevention. Understanding the experiences of people in this pandemic helps to create more effective future intervention strategies in terms of planning and management for crisis situations.

Identifying best modelling practices for tobacco control policy simulations: a systematic review and a novel quality assessment framework

BackgroundPolicy simulation models (PSMs) have been used extensively to shape health policies before real-world implementation and evaluate post-implementation impact. This systematic review aimed to examine best practices, identify common pitfalls in tobacco control PSMs and propose a modelling quality assessment framework.MethodsWe searched five databases to identify eligible publications from July 2013 to August 2019. We additionally included papers from Feirman et al for studies before July 2013. Tobacco control PSMs that project tobacco use and tobacco-related outcomes from smoking policies were included. We extracted model inputs, structure and outputs data for models used in two or more included papers. Using our proposed quality assessment framework, we scored these models on population representativeness, policy effectiveness evidence, simulated smoking histories, included smoking-related diseases, exposure-outcome lag time, transparency, sensitivity analysis, validation and equity.FindingsWe found 146 eligible papers and 25 distinct models. Most models used population data from public or administrative registries, and all performed sensitivity analysis. However, smoking behaviour was commonly modelled into crude categories of smoking status. Eight models only presented overall changes in mortality rather than explicitly considering smoking-related diseases. Only four models reported impacts on health inequalities, and none offered the source code. Overall, the higher scored models achieved higher citation rates.ConclusionsWhile fragments of good practices were widespread across the reviewed PSMs, only a few included a ‘critical mass’ of the good practices specified in our quality assessment framework. This framework might, therefore, potentially serve as a benchmark and support sharing of good modelling practices.

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans, but limited data has made identification of causal proteins in other ancestries challenging. Here we present a multi-ancestry proteome-wide MR analysis pipeline based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the causal effects of 1,545 proteins on eight complex diseases in up to 32,658 individuals of African ancestries and 1.22 million individuals of European ancestries. We identified 45 and seven protein-disease pairs with MR and genetic colocalization evidence in the two ancestries respectively. 15 protein-disease pairs showed evidence of differential effects between males and females. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence of an effect in both ancestries, seven pairs with European-specific effects and seven with African-specific effects. Integrating these MR signals with observational and clinical trial evidence, we were able to evaluate the efficacy of one existing drug, identify seven drug repurposing opportunities and predict seven novel effects of proteins on diseases. Our results highlight the value of proteome-wide MR in informing the generalisability of drug targets across ancestries and illustrate the value of multi-cohort and biobank meta-analysis of genetic data for drug development.

Temporal and Spatial Evolution of Climate Comfort and Population Exposure in Guangdong Province in the Last Half Century

Regional Climatic Comfort Index (CCI) deteriorated significantly due to the climate change and anthropogenic interference. Knowledge regarding the long-term temporal dynamics of CCI in typical regions should be strengthened. In this study, we analyze the temporal and spatial evolution of CCI from 1969 to 2018 in Guangdong Province, based on meteorological indicators, including heat, humidity, wind and cloth loading etc.. Additionally, the population exposure to climate unconformity was examined since 1990 with the help of population data. Our study found that: (1) the warming and humidifying of the summer climate served as the main driving force for the continuous deterioration of CCI, with the comfortable days decreased by 1.06d/10a and the extremely muggy days increased by 2.83d/10a; (2) spatially, the lowest climate comfortability concentrated in southwestern Guangdong with more than 50 uncomfortable days each year, while the climate comfortability in northeastern Guangdong tends to deteriorated whit higher rate, which can reach as high as 6d/10a; (3) in summer, the population exposure to uncomfortable climate highly centralized in the Pearl River Delta, Shantou, Jieyang, and the surrounding areas, and both area and population exposure showed increasing trends. Particularly, Shenzhen held the highest growth rate of population exposure with an increase rate of 2.94 million/10a; (4) although the discomfort distribution and deterioration rate vary across the province, the spatial heterogeneity of comfortability is diminishing in Guangdong Province. This study will provide scientific reference for regional urban planning, thermal environment improvement, local resident health risk analysis, and key strategy implementation, etc.

Journey of Data Collection: My Experience Narrative of Working with Destitute Children Residing at Children’s Home

This article represents a narrative description of my data collection journey and the experience of working with children residing at Child Care Institution (CCI) like children’s home. It outlines my experience in a creative language and also draws attention to the challenges I faced, be it seeking permission from the concerned authorities or visiting the children’s home to actually working with the children. The procedures to access these children are quite lengthy and strenuous. Overall, this article highlights my experiences as a researcher, working with the children, the lessons I learnt and dealing with challenges imposed by COVID-19. In conclusion, through this experience article, I intend to make my fellow researchers aware of the procedures and challenges involved in dealing with this population, data collection process, which could benefit them to prepare accordingly, and to recommend to the caretakers and stakeholders the need for research in this area and promote activities to enhance mental health conditions.

ANALYSIS OF VISIBLE INFRARED IMAGING RADIOMETER SUITE CAPABILITY FOR POPULATION ESTIMATION ON JAVA ISLAND

Population data, despite their significance, are often missing or difficult to access, especially in cities/regencies not belonging to the metropolitan areas or centers of various human activities. This hinders practices that are contingent on their availability. In this study, population estimation was carried out using nighttime light imagery generated by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. The variable illuminated area was integrated with the population data using linear regression based on an allometric formula so as to produce a regression value, correlation coefficient (r), and coefficient of determination (r2). The average r2 between the illuminated area and the total population was 0.86, indicating a strong correlation between the two variables. Validation using samples of population estimates from three different years yielded an average error of 73% for each city and 7% for the entire study area. The estimation results for the number of residents per city/regency cannot be used as population data due to the high percent error, but for the population on a larger regional scale, in this case, the island of Java, they have a much smaller percent error and can be used as an initial picture of the total population.