Dynamic accessibility by car to tertiary care emergency services in Cali, Colombia, in 2020: cross-sectional equity analyses using travel time big data from a Google API

Abstract

ObjectivesTo test a new approach to characterise accessibility to tertiary care emergency health services in urban Cali and assess the links between accessibility and sociodemographic factors relevant to health equity.DesignThe impact of traffic congestion on accessibility to tertiary care emergency departments was studied with an equity perspective, using a web-based digital platform that integrated publicly available digital data, including sociodemographic characteristics of the population and places of residence with travel times.Setting and participantsCali, Colombia (population 2.258 million in 2020) using geographic and sociodemographic data. The study used predicted travel times downloaded for a week in July 2020 and a week in November 2020.Primary and secondary outcomesThe share of the population within a 15 min journey by car from the place of residence to the tertiary care emergency department with the shortest journey (ie, 15 min accessibility rate (15mAR)) at peak-traffic congestion hours. Sociodemographic characteristics were disaggregated for equity analyses. A time-series bivariate analysis explored accessibility rates versus housing stratification.ResultsTraffic congestion sharply reduces accessibility to tertiary emergency care (eg, 15mAR was 36.8% during peak-traffic hours vs 84.4% during free-flow hours for the week of 6–12 July 2020). Traffic congestion sharply reduces accessibility to tertiary emergency care. The greatest impact fell on specific ethnic groups, people with less educational attainment and those living in low-income households or on the periphery of Cali (15mAR: 8.1% peak traffic vs 51% free-flow traffic). These populations face longer average travel times to health services than the average population.ConclusionsThese findings suggest that health services and land use planning should prioritise travel times over travel distance and integrate them into urban planning. Existing technology and data can reveal inequities by integrating sociodemographic data with accurate travel times to health services estimates, providing the basis for valuable indicators.

Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders

“…Reducing inequities in the geographic accessibility of such services is critical to achieving this objective 37 – 42 and, in turn, depends on detailed knowledge regarding variations in the geospatial distribution of mental health disorders and elucidation of the factors responsible for such variations. Within Latin America, geospatial methodology has been used to quantify geographic accessibility to different forms of healthcare 39 , 43 , but not to mental healthcare.…”

“…Recent studies in Brazil and Peru have used geospatial analyses to document the relationship between inequalities in factors such as education levels, income, employment, and migration and geographic variation in the frequency of mental illness parameters (suicide and depressive symptomatology, respectively) 53 , 54 . By incorporating information on sociodemographic factors that is available at a detailed level throughout Colombia (e.g., each residence has an assigned socioeconomic status indicator, from 1 [lowest] to 6 [highest], which determines utility rates 39 ), future studies, using sources such as Colombia’s National Department of Statistics census microdata 23 , could provide a more comprehensive equity perspective by integrating disaggregated georeferenced population sociodemographic open data.…”

Assessing the effects of the large‐scale road construction on the ethnic disparities of accessibility in Tibet from 2010 to 2020

“…From the existing research on the disparity of accessibility between different ethnic groups, one many of the conclusions of relevant research pointed to an unideal situation, that is, people of the ethnic minority groups are more likely to suffer from the loss caused by the unfair accessibility though there were still many mixed findings and uncertainty (Hu, 2019). For example, racial/ethnic minorities are consistently underrepresented among visitors compared to Whites to US national park (Xiao et al, 2018); White-majority neighbourhoods have much better healthcare and green space accessibility than both Black-majority and Hispanic-majority neighborhoods, while Indian, Hindu and Sikh groups were found to have limited access to greenspace in the city (Comber et al, 2008;Liu et al, 2021Liu et al, , 2022; For traffic congestion, the greatest impact fell on specific ethnic groups who also faces longer average travel times to health services (Cuervo et al, 2022). For the reason, some researchers have uncovered the inequality caused by disparity of resource distributions across different regions and ethnic groups (Altonji & Blank, 1999;Zenk et al, 2005).…”

Dynamic Geographical Accessibility Assessments to Improve Health Equity: Protocol for a Test Case in Cali, Colombia (Evaluaciones Dinámicas De Accesibilidad Geográfica Para Mejorar La Equidad: Prueba En Cali, Colombia: Protocolo De Investigación)

“…Figure 4 Comparing accessibility by socioeconomic stratum, tertiary care emergencies. 96 Electronic copy available at: https://ssrn.com/abstract=4175407…”

“…This version of the protocol was completed and prepared for publication following the approval by ethics review committees, and includes a graph published in Figure 4 from the first published reports. 96 The Follow-up Commission of the Doctoral Program on Biomedical Research Methodology and Public Health of the Universitat Autònoma de Barcelona reviewed the study in September 2020 and 2021.…”