The development and validation of an urbanicity scale in a multi-country study

Background : Although urban residence is consistently identified as one of the primary correlates of non-communicable disease in low- and middle-income countries, it is not clear why or how urban settings predispose individuals and populations to non-communicable disease (NCD), or how this relationship could be modified to slow the spread of NCD. The urban–rural dichotomy used in most population health research lacks the nuance and specificity necessary to understand the complex relationship between urbanicity and NCD risk. Previous studies have developed and validated quantitative tools to measure urbanicity continuously along several dimensions but all have been isolated to a single country. The purposes of this study were 1) To assess the feasibility and validity of a multi-country urbanicity scale; 2) To report some of the considerations that arise in applying such a scale in different countries; and, 3) To assess how this scale compares with previously validated scales of urbanicity. Methods : Household and community-level data from the Young Lives longitudinal study of childhood poverty in 59 communities in Ethiopia, India and Peru collected in 2006/2007 were used. Household-level data include parents’ occupations and education level, household possessions and access to resources. Community-level data include population size, availability of health facilities and types of roads. Variables were selected for inclusion in the urbanicity scale based on inspection of the data and a review of literature on urbanicity and health. Seven domains were constructed within the scale: Population Size, Economic Activity, Built Environment, Communication, Education, Diversity and Health Services. Results : The scale ranged from 11 to 61 (mean 35) with significant between country differences in mean urbanicity; Ethiopia (30.7), India (33.2), Peru (39.4). Construct validity was supported by factor analysis and high corrected item-scale correlations suggest good internal consistency. High agreement was observed between this scale and a dichotomized version of the urbanicity scale (Kappa 0.76; Spearman’s rank-correlation coefficient 0.84 (p < 0.0001). Linear regression of socioeconomic indicators on the urbanicity scale supported construct validity in all three countries (p < 0.05). Conclusions : This study demonstrates and validates a robust multidimensional, multi-country urbanicity scale. It is an important step on the path to creating a tool to assess complex processes like urbanization. This scale provides the means to understand which elements of urbanization have the greatest impact on health

Structural equation modeling in medical research: a primer

<p>Abstract</p> <p>Background</p> <p>Structural equation modeling (SEM) is a set of statistical techniques used to measure and analyze the relationships of observed and latent variables. Similar but more powerful than regression analyses, it examines linear causal relationships among variables, while simultaneously accounting for measurement error. The purpose of the present paper is to explicate SEM to medical and health sciences researchers and exemplify their application.</p> <p>Findings</p> <p>To facilitate its use we provide a series of steps for applying SEM to research problems. We then present three examples of how SEM has been utilized in medical and health sciences research.</p> <p>Conclusion</p> <p>When many considerations are given to research planning, SEM can provide a new perspective on analyzing data and potential for advancing research in medical and health sciences.</p

Interaction between Education and Household Wealth on the Risk of Obesity in Women in Egypt

Obesity is a growing problem in lower income countries particularly among women. There are few studies exploring individual socioeconomic status indicators in depth. This study examines the interaction of education and wealth in relation to obesity, hypothesising that education protects against the obesogenic effect of wealth

Education is associated with lower levels of abdominal obesity in women with a non-agricultural occupation: an interaction study using China's Four Provinces survey.

The prevalence of obesity is increasing rapidly in low- and middle-income countries (LMICs) as their populations become exposed to obesogenic environments. The transition from an agrarian to an industrial and service-based economy results in important lifestyle changes. Yet different socioeconomic groups may experience and respond to these changes differently. Investigating the socioeconomic distribution of obesity in LMICs is key to understanding the causes of obesity but the field is limited by the scarcity of data and a uni-dimensional approach to socioeconomic status (SES). This study splits socioeconomic status into two dimensions to investigate how educated women may have lower levels of obesity in a context where labour market opportunities have shifted away from agriculture to other forms of employment. The Four Provinces Study in China 2008/09 is a household-based community survey of 4,314 people aged ≥60  years (2,465 women). It was used to investigate an interaction between education (none/any) and occupation (agricultural/non-agricultural) on high-risk central obesity defined as a waist circumference ≥80 cm. An interaction term between education and occupation was incorporated in a multivariate logistic regression model, and the estimates adjusted for age, parity, urban/rural residence and health behaviours (smoking, alcohol, meat and fruit & vegetable consumption). Complete case analyses were undertaken and results confirmed using multiple imputation to impute missing data. An interaction between occupation and education was present (P = 0.02). In the group with no education, the odds of central obesity in the sedentary occupation group were more than double those of the agricultural occupation group even after taking age group and parity into account (OR; 95%CI: 2.21; 1.52, 3.21), while in the group with any education there was no evidence of such a relationship (OR; 95%CI: 1.25; 0.92, 1.70). Health behaviours appeared to account for some of the association. These findings suggest that education may have a protective role in women against the higher odds of obesity associated with occupational shifts in middle-income countries, and that investment in women's education may present an important long term investment in obesity prevention. Further research could elucidate the mechanisms behind this association

The association of birth order with later body mass index and blood pressure: a comparison between prospective cohort studies from the United Kingdom and Brazil

Published online 29 October 2013Previous studies have found greater adiposity and cardiovascular risk in first born children. The causality of this association is not clear. Examining the association in diverse populations may lead to improved insight.We examine the association between birth order and body mass index (BMI), systolic and diastolic blood pressure (SBP/DBP) in the 2004 Pelotas cohort from southern Brazil and the Avon Longitudinal Study of Parents and Children (ALSPAC) from Bristol, south-west England, restricting analysis to families with two children in order to remove confounding by family size.No consistent differences in BMI, SBP or DBP were observed comparing first and second born children. Within the Pelotas 2004 cohort, first born females were thinner, with lower SBP and DBP; for example, mean difference in SBP comparing first with second born was -0.979 (95% confidence interval -2.901 to 0.943). In ALSPAC, first born females had higher BMI, SBP and DBP. In both cohorts, associations tended to be in the opposite direction in males, although no statistical evidence for gender interactions was found.The findings do not support an association between birth order and BMI or blood pressure. Differences to previous studies may be explained by differences in populations and/or confounding by family size in previous studies.L D Howe, P C Hallal, A Matijasevich, J C Wells, I S Santos, A J D Barros, D A Lawlor, C G Victora and G D Smit

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE: To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES: PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION: Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS: 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity