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

Abstract

BackgroundAlthough 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.MethodsHousehold 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.ResultsThe 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).ConclusionsThis 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.

Urbanicity and Lifestyle Risk Factors for Cardiometabolic Diseases in Rural Uganda: A Cross-Sectional Study

“…The interrelation between SES and urbanicity is complex and beyond the scope of this study; however, directions of all associations observed are consistent with previous literature [11]–[16],[18],[40],[47]. Finally the generalisability of our results to other rural communities needs to be considered.…”

Association of Urbanicity With Psychosis in Low- and Middle-Income Countries

“…To further validate the WHS measure of urbanicity, we examined whether WHS-defined urban areas had higher rates of known transnational indicators of urban living (eg, percentage of jobs in agriculture, household television ownership, and household electricity), confirming that households identified as urban in the WHS conformed to common validated characteristics of urban areas (eTables 4 and 5 in the Supplement). 21 Outcomes: Psychotic Experiences and Psychotic Disorder All participants were asked questions about psychotic symptoms that came from the WHO Composite International Diagnostic Interview (CIDI), version 3.0. 22 The psychosis module…”

The Associations Between Urbanicity and Physical Activity and Sitting Time in Mexico

“…The study population was determined by those individuals with physical activity and sitting time (number of doctors per inhabitant, amount of medical units and access to health services). 12 For each participant, an urbanicity score was given according to their state. Two overall urbanicity indices were computed by summing the seven sub-scores where the Demographic sub-score was measured as population size (Overall urbanicity 1) and as population density (Overall urbanicity 2).…”

“…9 More recently, tools have been developed to provide a more comprehensive measurement of urbanicity, incorporating variables such as economic activity, communications, education, infrastructure and social services. 10,11,12 Features of the built environment such as the perceived proximity to shops and recreational facilities, street connectivity, and aesthetic qualities of a place are positively associated with physical activity. 13,14 A number of studies have also suggested that areas in which buildings are within close proximity to one another, and that facilitate walking between locations, are associated with greater physical activity.…”