Title ed Assessing Equity in accessibility to haemodialysis services by automobile in Cali, Colombia: cross-sectional analyses using publicly available data. Luis Gabriel Cuervo,(1) Carmen Juliana Villamizar,(2) Lyda Osorio, (3) María Beatriz Ospina, (4) Diana E. Cuervo, (5) Daniel Cuervo, (6) María O. Bula, (7) Pablo Zapata, (6) Nancy J. Owens, (8) Janet Hatcher-Roberts, (9) Edith Alejandra Martín(10), Felipe Piquero,(11) Luis Fernando Pinilla, (12) Eliana Martínez-Herrera, (13) ‡ Ciro Jaramillo,(14) ‡ on behalf of the AMORE Project Collaboration*. iew (1) Corresponding author. PhD Candidate, Universitat Autònoma de Barcelona, Spain: LuisGabriel.Cuervo@autonoma.cat. Mobile +1 (240) 499-5506 (2) MPH Candidate, Johns Hopkins Bloomberg School of Public Health, Wolfe Street Building, W1015, Baltimore, Maryland, USA. (3) School of Public Health, Universidad del Valle, Colombia. (4) Faculty of Health Sciences, Queen’s University, Carruthers Hall 204, K7L 3N6, Kingston (ON), v Canada. (5) National Disability Board of Colombia. (6) IQuartil SAS, Cra 13A # 107A-47 Bogotá 110111, Colombia. re (7) Independent researcher, Bogotá, Colombia. (8) Independent content and communications consultant, Fairfax, VA, USA. (9) WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment for Health Equity, Bruyère Research Institute, University of Ottawa, K1N 5C8, Ottawa (ON), Canada (10) Colombian Association of Transplanted Athletes, Bogota, Colombia er (11) Patient advocate and author of an autopathography. (12) Universidad de la Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá. Chía, Cundinamarca, Colombia. (13) National Faculty of Public Health, Universidad de Antioquia, Medellín, Colombia. (14) School of Civil and Geomatic Engineering of the Universidad del Valle, Cali, Colombia. pe ‡ Senior authors * AMORE Project collaborators will be acknowledged following peer review by providing a supplemental file. A temporary list of collaborators is provided for reference https://padlet.com/luisgabrielcuervo/Colaboradores ot tn rin ep Pr This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Abstract ed Objectives: To obtain dynamic spatial-temporal measurements of driving times to haemodialysis services and estimate the benefits of adding new services. Design: Cross-sectional analyses of dynamic spatial-temporal accessibility, considering traffic congestion. A web-based platform integrated traffic analysis zones, public census and health services datasets, with Google Distance Matrix API big travel-time big data. Predictive and prescriptive analytics identified optimal locations iew for new haemodialysis services and estimated accessibility improvements. Setting and participants: Cali, Colombia (2020 population: 2.258 million) using geographic and disaggregated sociodemographic data from the adjusted 2018 Colombian census. Predicted travel times were obtained for 6–12 July and 23–29 November 2020. This study is part of a project measuring accessibility to health services. Primary and secondary outcomes: Percentage of residents within 20 minutes by car of a haemodialysis service at v peak- and free-flow traffic congestion. Locations where new services maximise accessibility and estimated improvements. Findings were disaggregated by sociodemographic characteristics, providing an equity re perspective. Results: Accessibility was lower in July, without COVID-19 traffic restrictions. Traffic congestion reduces accessibility to haemodialysis, hurting more less-educated people, residents of low-income housing and outlying neighbourhoods, and specific ethnic groups. er For 6-12 July, free-flow and peak-traffic accessibility were 95.2% and 45.0%, respectively; 19.9% at peak- traffic for the lowest-income stratum. Adding services in the Agua Blanca district and southern Cali would increase peak traffic accessibility to 70.2% and 62.4% for the lowest-income stratum. Accessibility for 23–29 November was better. Conclusions: New haemodialysis services in eastern Cali would improve accessibility and reduce inequities. pe Dynamic accessibility measures improve health services and land-use planning. RESEARCH IN CONTEXT • What is already known on this topic – Urban and health services planning do not yet systematically ot integrate dynamic accessibility measures, which are superior to travelled distance or static assessments. Typically, accessibility studies are sophisticated and geared towards specialised audiences. • This study adds: dynamic assessments of urban travel times, the impact of congestion on health equity and tn accessibility, and the conclusion that new haemodialysis services can improve accessibility for low- income populations and residents from outlying neighbourhoods of Cali. Findings are presented using cartography, simple graphs, and descriptive statistics that different stakeholders can share and interpret with peers and counterparts. • How this study might affect research, practice, or policy – This study shows the potential and value of rin integrating dynamic spatial-temporal accessibility measurements into land-use and health services planning and creating win-win situations. It offers a practical approach to obtaining actionable information and developing a new strategy to improve public policy. Information is suitable for seeking social appropriation of knowledge and integrated knowledge translation. ep Strengths and limitations of this study: • This study provides affordable, dynamic accessibility measurements for haemodialysis services for urban Cali, with an equity perspective. • The study uses a web-based platform with a person-centred design that communicates findings using basic descriptive statistics, graphics and cartography. Pr • Dynamic spatial-temporal analysis measurements account for traffic congestion and offer a proxy for travel costs (i.e., distance, travel cost, and time). • Models need to be retrained if conditions change. • Reducing inequities in accessibility requires adding new services accessible to all who need them. Page 2 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Introduction Background/rationale ed Accessibility to essential health services advances sustainable development goals (SDGs), improves health and well-being (SDG 3), reduces inequalities (SDG 10) and makes cities and communities more sustainable (SDG 11). Accessibility measures how easily and quickly people can reach a particular location, such as a health service facility. It is a dynamic attribute because it can change over time due to factors such as changes in transportation infrastructure, traffic patterns, and population density. This study measures accessibility in terms iew of the travel time from a residence to a health service location. This information helps us understand how long people need to travel to access healthcare and can be used to identify areas where access to healthcare may be limited.1,2 This study is part of the AMORE Project, a proof of concept evaluating new approaches to place-based dynamic spatial-temporal accessibility measures (DSTAM) to health services by integrating publicly available data. Using existing data facilitates fast, affordable assessments of DSTAM, and the AMORE Project seeks to deliver v granular information for an entire city with an equity perspective.1–3 An earlier report assessed accessibility in urban Cali to tertiary care emergency services, which are essential to all residents.4 This study assesses re accessibility to haemodialysis services. It identifies the approximate location(s) where new services would place most of the urban population of Cali within 20 minutes of a haemodialysis service by car and the population in the catchment area of those locations. This study thus provides a proxy measurement and new data to inform health services planning and a strategy for reducing inequities in accessibility.1 Haemodialysis is an expensive, life-saving treatment usually administered three times weekly for about four er hours. Regular use and adherence usually improve the quality of life of people with end-stage renal disease (ESRD).5–7 Roughly three of every four people requiring kidney replacement therapy in Colombia are in haemodialysis. There are 516 patients per million population (PMP) in haemodialysis and 103 new PMP yearly.8 Home haemodialysis is not widely available in Colombia,9 and travel time to haemodialysis might be associated with higher mortality rates and lower health-related quality of life.10 People in outlying areas of Cali pe seeking haemodialysis have described the challenges they face in video testimonies.11 Colombia offers universal health coverage through a two-tier health services system and special regimes that cover insured and uninsured populations.12 Nonetheless, travel times and their associated direct and indirect costs represent access barriers to care. In 2019, the Colombian Constitutional Court ruled that the national health insurance program must cover transportation costs, but Colombian insurers still need to comply with this order fully.13 ot The direct and indirect costs of frequent commutes to haemodialysis can impoverish patients and their families. A family member usually accompanies the patient to haemodialysis sessions, often missing work or the chance to seek a new job or a promotion.14,15 We did not find an analysis or assessment of the financial burdens of tn haemodialysis, but obtained personal accounts of how haemodialysis can impact patient and family finances, including video testimony of patients from outlying areas of Cali.11 Haemodialysis causes changes that can impair physical capacity and mobility, and patients need endurance training to counter these changes, but that training is not a standard practice.16,17 Such mobility issues add to the challenges of long journeys. rin DSTAM account for traffic congestion delays and are becoming available for cities where traffic congestion impact accessibility and health equity.2,18–21 Unlike most accessibility studies, which use sophisticated methods requiring specialised expertise, the AMORE Project follows knowledge translation good practices by engaging a broad range of stakeholders with a person-centred strategy. That approach aims to translate integrated knowledge and deliver information that stakeholders can communicate to their peers and the public. 1,22–26 ep DSTAM can inform health services and land-use planning from an equity perspective. In this project, DSTAM rely on simple metrics like time-to-destination to monitor and address accessibility, health equity, and social justice factors.2 This study assessed accessibility by car to haemodialysis services in Cali, the third-largest city in Colombia by Pr population and the largest in the Colombian Pacific region. A 20-minute travel-time threshold was set to predict the effects of adding new services in one or two new locations that would optimise accessibility. 1 An interface of the web-based AMORE Platform is available for readers to test assumptions, modify parameters, and draw conclusions.22,27,28 This study advances the global research priorities for urban health. 29 Page 3 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Objectives This study characterises geographical accessibility to haemodialysis services in urban Cali and its relationship ed with sociodemographic factors and health equity. It also suggests locations where new services would maximise accessibility if all patients could use them and predicts how much accessibility would increase. This study also benchmarks potential improvements that could be reassessed as conditions change and addresses these questions: • What accessibility to haemodialysis did urban Cali dwellers have in 2020, depending on their sociodemographic characteristics? iew • Did accessibility vary according to socioeconomic status in urban Cali? • What improvement would result from strategically adding new haemodialysis services in one or two new neighbourhoods in Cali? Methods: Study Population and setting The rationale and methods for this study are published.1–4 This study assesses accessibility by car to the service v with the shortest travel time among the 11 haemodialysis services, which together had 370 haemodialysis chairs in 2020. According to an international traffic scorecard, Cali was the fifth-most congested city in South America re and 76th globally.30 It has been a magnet for migration, and it was an epicentre for social unrest in 2021, during the COVID-19 pandemic.31–33 Traffic congestion fluctuated widely in 2020 due to COVID-19 pandemic-related quarantines, stay-at-home orders, and traffic restrictions, such as those implemented in November.4,34–36 ESRD has increased in Cali in recent years, affecting 128 per 100,000 people in 2020. Rates increase with age, especially after age 30. In 2020, 1545 people underwent haemodialysis in Cali, with the vast majority being er adults.37,38 Patients typically travel by car (private or for hire) with a family member to haemodialysis appointments. In Colombia, insurers contract with health-providing institutions (IPS in Spanish), and these contracts define which health services patients access. We did not match patients with insurance and provider contracts. pe About 75% of Cali households rely on the informal economy, mainly the most vulnerable. Unemployment rates in 2020 were 17.3% for men, 24% for women, 24.2% for young men (15-28 years), and 34.3% for young women. About 11.7% of the population lives in multidimensional poverty, and 82.2% of urban Cali residents have health insurance. 39,40 Targeted sites/participants ot We identified all 11 haemodialysis services in Cali using the Ministry of Health Special Registry of Health Services Providers (REPS in Spanish), which remained unchanged between July 2020 and January 2021. These services are all located along a corridor in north-central Cali that follows a major street, Calle Quinta, and appear on Map 1.41 tn Map 1 Location of haemodialysis units in Cali, 2020 (order in which REPS displayed them) 41 Study design We did two cross-sectional analyses by integrating public data sources in the web-based AMORE Platform, which displays DSTAM for each Traffic Analysis Zone (TAZ) to the TAZ hosting the haemodialysis services rin with the shortest travel time. TAZs are geographic units commonly used in transportation planning and are usually built on census blocks. Travel times are typically short within a TAZ, but populations are not evenly distributed in TAZs. Therefore, travel times from each of 507 TAZs in Cali were measured from the population- adjusted geometric centroids. 42 The 11 haemodialysis services in Cali are concentrated in six TAZs. ep Data for the cross-sectional analyses was obtained from the web-based AMORE Platform (https://www.iquartil.net/proyectoAMORE), hosted by iQuartil SAS and developed under the leadership of the principal investigator (LGC) and analytics adviser (DC). The AMORE Project explores whether dynamic travel times to specific services can be easily analysed using public data sources. The platform shows the populations within reach of the service with the shortest travel time Pr and disaggregates sociodemographic variables to provide an equity perspective. The AMORE Platform integrates these data sources: Page 4 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 • 2018 National Census Data for Cali, obtained from the official public databases of the Colombian National Department of Statistics (DANE in Spanish), and adjusted to 2020.43,44 • ed The administrative divisions of Cali were obtained from government sources: the IDESC Geoportal, Traffic Analysis Zones (TAZ), and census block sectors. 45,46 • Google’s Distance Matrix API. For this test case assessment of urban Cali, data were downloaded on July 3, for the week of 6 – 12 July 2021. Predicted times were downloaded on 27 October for the week of 23 – 29 November 2020. • The 11 haemodialysis services in Cali were identified using the REPS registry.41 These services were iew in six TAZs, with one TAZ hosting four services and another TAZ hosting three. Data for this case study were a subset of the data used by the AMORE Project to measure accessibility to 30 health services: 14 tertiary care emergency departments, 11 haemodialysis services, and five radiotherapy services. These 30 services were in 15 TAZs, with one TAZ hosting seven services, another TAZ four, six TAZ hosting two, and seven hosting a single service. Had the project focused on haemodialysis only, the eleven services would have been studied by assessing travel times to six TAZs. v Measuring DSTAM for the three types of services (tertiary care emergencies, haemodialysis, and radiotherapy), using traffic congestion clusters and TAZs brought efficiencies and reduced travel time data download cost and re complexity. Measurements were reduced from 43.1 million data points (507 origins to 506 destinations for 168 hours weekly) to a sample of 68,445 (507 origins to 15 destinations for 9 traffic congestion clusters) for each week analysed.1,4 Using predictive analytics based on the sample, an estimation was made for 2.3 million travel times (507 origins to 506 destinations in 9 traffic congestion clusters) for each week analysed. Variables er The main study outcome was the proportion of urban dwellers likely to reach a haemodialysis service within 20 minutes by car. Data were disaggregated by age, self-reported sex, education level attained, literacy, marital status, and household economic stratum based on electricity bill stratification. Other study outcomes include 10- minute accessibility intervals, up to an hour, by household economic stratification. pe Travel-time thresholds were based on expert advice from contributors to the AMORE Project collaborative group, including patient advocates. We found no international standards, and we only assessed travel time by car (private and for hire), which will likely be unevenly distributed across socioeconomic groups. Data are displayed using graphs with disaggregated figures and colour-coded choropleth maps, presenting travel times and population density for each TAZ. For the predictive and prescriptive analyses, one or two TAZs ot where new services would optimise accessibility were identified, and results were disaggregated by sociodemographic characteristics. Figures 1 and 2 are screenshots from AMORE Platform interfaces showing the variables reported for the situational and predictive analyses. tn Traffic clusters range from free-flow (level 1) to peak traffic (level 9).1,4 Figure 1. AMORE Platform interfaces with situational and predictive analyses, 6–12 July 2020 Figure 2. AMORE Platform interfaces with situational and predictive analyses, 23–29 Nov 2020 rin These data are also displayed in colour-coded choropleth maps presented in 3D, with each choropleth representing a TAZ and their height representing its population density. These maps reveal the relationship between population density and geographic location with travel times. The colour code should help stakeholders and sector leaders agree on a common goal, such as “painting the city green” or putting all people within a 20- minute travel-time threshold from a service. ep Figure 3. Choropleth maps comparing accessibility if services were added in new TAZs, for 6-12 July 2020 Figure 4. Choropleth maps comparing accessibility if services were added in new TAZs, for 23-29 November 2020 Household socioeconomic stratification in Colombia has an incremental range from 1 to 6, with six representing Pr the highest-income household group. Household socioeconomic stratum was categorised into Low (levels 1 and 2), middle (3 and 4), and high (5 and 6). Earlier publications have described the definition of traffic levels and subgroup variation measurement.3,4,42 Page 5 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Results / Outcomes Cali has substantial traffic congestion and remains congested through most of the day and evening, even on ed weekends. Traffic level 8, the second highest, accounts for 40/168 (24%) hours of the week. Traffic clusters are skewed, with most activity hours having high-traffic congestion levels. Figures 1 and 2 are screenshots from AMORE Platform interfaces that display the variables and disaggregated data for the situational and predictive analyses for the July and November assessments. These analyses include baseline data and estimate accessibility after adding services in one or two additional TAZs. iew Figure 3, Figure 4, and Supplemental File 1 illustrate the impact of traffic congestion on accessibility under real and predicted scenarios. These results point to the locations where new services would maximise urban accessibility and provide overall and disaggregated estimates and variations for the July and November assessments. Supplemental File 1. Traffic congestion and accessibility to haemodialysis, Cali 6-12 July 2020. Animation v Population characteristics are presented in Table 1. About half of Cali residents live in low-income housing, 40% live in middle-income households, and about 9% live in high-income housing, including domestic workers. re The southern end of Cali is inhabited mainly by high-income households living in villas. The choropleth map shows that most of the population and the most densely populated areas are towards the periphery of Cali and far from haemodialysis services. Travel-time comparisons between 6–12 July and 23–29 November 2020 show that traffic was notably lighter in November, when COVID-19 traffic restrictions were in place. The effect of these measures varied throughout city sectors.er The images and data revealed that services are concentrated, and most of the population incurs long journeys, particularly the poorest and those from minority ethnic groups. Placement of new services to optimise equitable accessibility pe The optimal location for maximising accessibility changed between neighbouring TAZs between July and November but remained close and within minutes. For July, the best TAZ was in the Alirio Mora Beltrán neighbourhood; for November, it was in the neighbouring Marroquin III TAZ. Both TAZs are in the eastern Agua Blanca district (Figure 5). If services were added in two TAZs, the TAZ initially identified would remain the same. The second recommended TAZ was for Parcelaciones del Pance in July and San Joaquín in November; these southern neighbourhoods are adjacent (Figure 5). ot Figure 5. TAZs where new services optimise accessibility Adding new haemodialysis service(s) to the recommended locations — in Alirio Mora Beltran or Marroquin III TAZ in the eastern district of Agua Blanca, with access to all patients — would greatly improve accessibility for tn the entire city. In this scenario, we estimated that accessibility for July 2020 rose from 45.0% to 70.2% for July 2020 and from 69.7% to 91.4% for November. These results levelled accessibility specifically by benefiting the lowest-income residents of Cali. For example, in the July estimates, accessibility for the lowest-income group (i.e., stratum 1) rose from 11.4% to 64.9%. Populations living in the southernmost high-income suburban areas benefited the least from the new service. rin Adding services in the southern Pance neighbourhood would reduce differences for most subgroups, raising urban accessibility to 75.1% in July and 97.2% in November. Both results represent dramatic improvements over baseline assessments. Applying the city’s prevalence of haemodialysis (128 per 100,000 people) to July’s estimations shows that ep adding services in Alirio Mora Beltrán would put 700,472 people within the 20-minute threshold of those services, with an estimated 897 requiring haemodialysis; the figure could be higher considering that ESRD is more prevalent in low-income communities. Adding services in Parcelaciones del Pance would put an additional 122,665 people within the 20-minute Pr threshold, which could result in an estimated 157 requiring haemodialysis. The two services would bring a population of 823,137 within the 20-minute threshold, and about 1054 require haemodialysis. While these estimations are simplistic and a proper one more would have more nuances, they give an insight into the opportunities and possibilities of refining these tests case to guide decision making. Page 6 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 The strategic location of new services will specially benefit populations in outlying areas and vulnerable groups such low-income households, some ethnic groups, and people whose education attainment isn’t high. It will also ed likely reduce the workload and need for night schedules for existing services. Choropleth maps show that offering haemodialysis in the eastern district of Agua Blanca would make haemodialysis accessible to densely populated areas of the city. These additions would nearly double overall urban accessibility and level accessibility rates in sociodemographic groups, reducing most inequities (Figure 3 and Figure 4; Table 2 and Table 3). iew Table 1. 20-minute accessibility to haemodialysis in Cali by socio-demographic group The 10-minute interval assessments by housing socioeconomic stratum (Figure 6) also revealed links between income and accessibility, except for the highest-income households living in south-end villas. For these households, accessibility would only improve if services were added in the thinly populated south end of the v city. Differences between income groups were noticeable throughout and worsened with traffic congestion. Adding services in the proposed TAZs reduced inequities even though these persisted for the shorter 10-minute journeys (Figure 1, Figure 2, Figure 6). re The analysis found that during peak traffic congestion and at most hours of the day (traffic levels 8 and 9), most people in the lowest-income stratum could not reach a haemodialysis service within 20 minutes. This was true in both the July and November assessments (11.4% and 39.6%, respectively). Data indicates that accessibility is an access barrier for low-income households, specifically in areas with high er population density and outlying urban sectors (Supplemental File 1 and 2). The situation is worse than the study found because we did not consider service restrictions providers impose, which further reduce accessibility. Data for the predicted accessibility to the haemodialysis service with the shortest travel times are provided in pe Table 2 (absolute figures) and Table 3 (relative subgroup proportions). Haemodialysis services are concentrated in areas distant from most of the population. Even during the early morning (5 AM) or late evening (11 PM) on weekdays, almost one-fifth of the population is beyond the 20-min travel threshold, including some of the poorest people and those living in outlying areas. The 20-minute threshold used in this study was deemed sound and appropriate for context by collaborative ot group members, including patient representatives. We found no benchmarks for travel times to haemodialysis. Time is a continuous variable; an arbitrary threshold was chosen. An interactive interface enables stakeholders, including decision-makers, to test different assumptions and variables, assess the resulting data, draw conclusions and challenge current thinking.22 The haemodialysis sections of the AMORE Platform will be tn available at https://www.iquartil.net/proyectoAMORE/ for at least two years from the date of this publication. Discussion This proof-of-concept study focuses on urban Cali, Colombia, and similar studies could include entire metropolitan areas and their broader service catchment areas. rin The studied scenarios considered transportation by car, commonly used and deemed convenient by those seeking haemodialysis services.5 Future studies could explore other means of transportation, link services with insurance, and integrate prevalence and incidence data or appointment availability to provide more refined information. Future studies could also determine availability with the shortest travel time and expand coverage to suburban and rural areas or other communities accessing these services. These objectives, and examining ep different health services, are beyond the scope of this report. In terms of cost, if each travel time measurement were charged at 0.08 USD, obtaining measurement for every origin-destination combination in 168 hours of a week would be 3.45 million USD. The efficiencies introduced by clustering and predictive modelling would reduce this cost to 5476 USD. Pr Other studies have shown that socioeconomic characteristics and distance are associated with accessibility. This study provides accurate granular measurements, identifies travel time as a barrier to access, and corroborates that those most in need generally pay a higher share of their family income to access services.47–51 Page 7 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 The most vulnerable populations are most likely to require haemodialysis and face higher direct and indirect costs, even if national insurance covers the treatment (Figure 1, Figure 2, and Figure 6). Moreover, insurance ed agreements likely restrict access to some haemodialysis services offering the shortest travel times. This report does not include an economic analysis, but low-income families might rely on just one or two minimum wages. Frequent long trips can hurt these families by consuming most of the minimum wage in direct travel costs. This burden will likely affect families in outlying areas of Cali, where much of the population lives and some of the poorest residents concentrate. Low-income families are likely to live with three or fewer minimum wages and can easily incur catastrophic health costs, well above 15% of their family income. 52,53 iew Patient stories show that getting to services is challenging and physically demanding for patients living in outlying city areas. One haemodialysis patient explained, “I spent four hours in haemodialysis. Many other patients would spend hours just commuting to and from haemodialysis, whether on their car or shared rides arranged by health providers. Long travel times added to treatment duration, taking most of the time patients and companions had for other activities.” (Personal communication, Felipe Piquero, November 2022).5,11 The situation was worse and riskier for those relying on public transportation. v Key results Accessibility figures varied substantially between the July and November assessments, but the ideal locations re for new haemodialysis services did not. These assessments suggest the need for adding haemodialysis services accessible to all people in the eastern Agua Blanca district. These locations in eastern Cali were consistent when independent assessments explored adding services in one or two TAZs. A potential application of this data is prioritising transportation or subsidising travel costs for people with longer travel times and vulnerable populations; data could be used to determine subsidy rates.54 er Traffic congestion consistently burdened people in outlying areas of Cali, including most low-income populations. Traffic congestion remains high and impacts accessibility most of the day (Figure 7). Readers can explore different assumptions in the interactive platform.4,22 pe Limitations While all people should be within a short travel time of service, travel time to the nearest service alone does not guarantee accessibility. Haemodialysis patient services are covered by health insurance, but the extent of coverage depends on agreements with service providers, and coverage only sometimes includes transportation. The ideal scenario would be for all patients to access a service in each of the six TAZs hosting these services. Services would also need the capacity to absorb the demand. The geographic, ethnic, and socioeconomic ot distribution of haemodialysis patients in Cali is unknown to the research team.38 Bias The study shows travel times to the TAZ hosting the haemodialysis service(s) with the shortest travel times. tn Because accessibility to services sometimes depends on insurance affiliation, findings will likely show a favourable result despite services being concentrated in six TAZs. Some patients may not be eligible to use the services in the TAZ with the shortest travel time. We measured travel times twice during the COVID-19 pandemic, and variations are likely due to changes in rin travel restrictions and stay-at-home orders in effect in November 2020. Both assessments were done in weeks with no holidays. This study does not adjust the socioeconomic stratum for low-paid, domestic homeworkers who live and work in middle or high-income households. ep Travel time between TAZs follows diverse patterns and directions throughout the day. For example, commuting impacts TAZs differently. Traffic clusters were sorted in incremental order for each TAZ, and it was found that the sorting switched the order of contiguous clusters in fifteen cases. This has a minimal impact as variations between adjacent clusters are small. Pr The concentration of the eleven units in the central west end of the city (near Calle Quinta, see Map 1 or Platform) might mitigate differences in service arrangements, and refining those measurements is beyond the scope of this report. This study also did not assess appointment availability in services, a refinement that could be developed for the AMORE Platform. Page 8 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Haemodialysis travel times varied substantially during the COVID-19 pandemic, and it is still unknown how this influenced Google Distance Matrix algorithms.55 Empirical and anecdotal reports suggest they remained ed accurate.56 Poor road conditions can add to travel times and are not evenly distributed throughout the city.57 Impactful road conditions and infrastructure changes, for example, road enhancements and new traffic light corridors controlled by artificial intelligence, might justify reassessing travel times. iew Table 2. Predicted accessibility to the haemodialysis service with the shortest travel times (absolute figures) Table 3. Predicted accessibility to the haemodialysis service with the shortest travel times (relative figures) Interpretation Accessibility varied substantially between July and November (45.0% vs 69.7%), probably due to traffic restrictions during the COVID-19 pandemic in November. 4 This variation added 557,713 people into the 20- minute threshold, primarily benefitting low- and middle-income populations. Accessibility during peak traffic v times rose 31.3% for low-income households (19.9% to 51.2%) and 21.0% for middle-income households (68.9% to 89.9%). Accessibility for high-income households rose just 6.2% (from 72.0% to 78.2%), perhaps re because most families live close to services or in south-end villas that will still face long trips. Subgroup improvements by other sociodemographic characteristics varied between 18% and 29%, with two exceptions. First, accessibility for people with graduate degrees rose 13.2%, reflecting November traffic restrictions and overlap in geographic distribution with high-income households. Second, accessibility rose 79.6% for the small Palenque people population concentrating around a cluster of TAZs.4 er Considering that the cost of displacement is a function of distance, time, and expenditure, Figure 6 suggests, as presented in some sophisticated time-space maps and the evolving perspective of time-space geography, that strategically adding new services could “shrink the city” by putting such health services within a short travel pe time of the population.58 We suggest updating the data to see how they evolve as the COVID-19 pandemic recedes. We recommend that planning authorities who make decisions using this study rely on the July assessment, which will likely reflect everyday conditions before travel restrictions. Nonetheless, the areas in which new services would optimise accessibility appear to remain consistent. Updating data and checking the models would be ideal, and this unfunded proof of concept indicates that these steps are affordable for a city. ot If planning authorities must decide, we recommend adding sufficient services to cope with demand, first in the most impactful recommended location, using the July estimates, which seem to reflect typical conditions in Cali. However, a better option is to update the assessments as the COVID-19 pandemic recedes and updated census tn data become available in 2023. The overall recommendation to add service(s) accessible to all patients in eastern Cali, near the Alirio Mora Beltrán neighbourhood, might not change. Implementing that recommendation would probably double accessibility and reduce inequities. Generalisability Our findings show that it is possible to obtain data on accessibility to health services, such as haemodialysis in rin Colombia, and deliver it in user-accessible formats for relevant stakeholders and diverse audiences to share. It is likely also possible in other countries with digitised census and services data and where travel-time estimates are available. Multiple travel-time data providers are likely available for other health services in congested cities in Latin America and beyond. ep DSTAM might not be as valuable in uncongested areas, such as most rural settings, where travel-time variations are slight. The study received no external funding and involved a multistakeholder, multidisciplinary group that includes local data scientists and academics. It can likely be replicated with the funding and resources available to cities. Available data provide a general assessment of accessibility. For services such as tertiary care emergencies Pr accessible and relevant to the entire population, findings can be easily generalised. There is room for refinement by adding layers such as the incidence of ESRD and the availability of haemodialysis chairs. Further improvements require details on insurance agreements, access to providers, and Page 9 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 the distribution of insurance coverage in population groups. Still, the recommendation of our study to add services that all citizens can access in the recommended areas might hold. Adding existing public and private ed infrastructure layers suitable to host new services is another possible refinement. It might be a sound idea to repeat these studies regularly as infrastructure, traffic conditions, and the population change; there has been significant migration to Cali. Findings from the AMORE Project suggest that strategically adding new services can dramatically improve iew accessibility and synergically advance SDG 3, SDG 10, and SDG 11 in Cali. This test case shows the value of using available data to measure and monitor dynamic geographical accessibility to health services in cities and regions. This information can be useful in achieving the United Nations 2030 Agenda goals, including Goal 3: Good Health and Well-Being (ensuring everyone has access to quality healthcare services). This test case provides inputs for specific indicators, such as coverage for essential health services (3.8.1, the percentage of the population with access to essential health services). This test case v shows how dynamic geographical accessibility can be used to help understand and improve access to healthcare. This test case also highlights the potential for using dynamic geographical accessibility data to support re democratic urban planning processes and promote the participation of civil society (SDG indicator 11.3.2). City planners and decision-makers who understand the travel times to different locations and their effects, can make more informed decisions about placing healthcare facilities. They can involve the community in these decisions by providing them with data about accessibility and health equity. Community involvement can help ensure that city planners and decision-makers consider the needs and concerns of residents and can lead to more equitable distribution of resources.59 Other information er Funding The authors have not declared a specific grant for this research from any funding agency in public, commercial pe or not-for-profit sectors. Universidad del Valle supports Lyda Osorio’s academic contribution. Disclaimer The depiction of boundaries on included maps does not imply the expression of any opinion on the part of publishers concerning the legal status of any country, territory, jurisdiction or area or its authorities. This map is provided without any warranty, either express or implied. Maps included in this report were obtained from ot Mapbox© through PowerBi, and Map 1 was obtained using Google Maps©. The views expressed in this article do not necessarily represent the decisions or policies of the author’s employers, including PAHO/WHO and Egis Consulting. Reproductions of this article should not include any suggestion that PAHO/WHO or Egis Consulting endorsed this research or is endorsing any specific tn organisation, service, or product. Ethical considerations This observational study on quality improvement for health services planning does not involve human subjects research. It integrates anonymised coded secondary data sources obtained from publicly available open rin records. No identifiable private information was used in the study. The Doctoral Programme on Methodology of Biomedical Research and Public Health at the Department of Paediatrics, Obstetrics & Gynaecology and Preventative Medicine at the Universitat Autònoma de Barcelona has provided oversight of the project. Contributors to this study are members of the AMORE Project Collaborative Group and public servants in their official capacity; those who approved are listed in the acknowledgements. The protocol is cited in this article ep and available from the institutional repository of the Universitat Autònoma de Barcelona (https://ddd.uab.cat/record/266860?ln=ca).1 Contributions and acknowledgements Authors and contributors Pr Acknowledgements We thank Stephen Volante, CT, an American Translators Association member, for his editing support. iQuartil SAS hosts the AMORE Platform, and we are most thankful to Pablo Zapata and Lizardo Vanegas for improvements to the AMORE Project website. The development of the AMORE Platform used for this study Page 10 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 was led by Luis Gabriel Cuervo, Daniel Cuervo, Luis Fernando Pinilla, and Pablo Zapata. An early prototype of the AMORE Platform was developed under the guidance of the principal investigator (LGC) by Team 33 of the 2020 cohort of the Data Science for All – Correlation Once certificate training program.1,60,61 AMORE Project ed contributors Jaime Miranda and Freddy Enrique Ágredo provided valuable comments on the preprint version of this manuscript.62 References iew 1. Cuervo LG, Jaramillo C, Cuervo D, Martínez Herrera E, Hatcher-Roberts J, Pinilla LF, et al. 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] [Internet]. Rochester, NY; 2022 [cited 2022 Jul 29]. Available from: http://dx.doi.org/10.2139/ssrn.4175407 v 2. Cuervo LG, Martínez-Herrera E, Cuervo D, Jaramillo C. Improving equity using dynamic geographic accessibility data for urban health services planning. 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Choropleth maps comparing accessibility if services were added in new TAZs, for 23-29 November 2020 iew Figure 5. TAZs where new services optimise accessibility Table 1. 20-minute accessibility to haemodialysis in Cali by socio-demographic group Table 2. Predicted accessibility to the haemodialysis service with the shortest travel times (absolute figures) Table 3. Predicted accessibility to the haemodialysis service with the shortest travel times (relative figures) v Figure 6. Traffic variations and their effect (July vs November 2020) 6 – 12 July vs 23 – 29 November 2020 re Figure 7. characteristic accessibility during day hours, traffic congestion level Supplemental File 1. Traffic congestion and accessibility to haemodialysis, Cali 6-12 July 2020. Animation Supplemental File 2. Choropleth maps comparing accessibility 6-12 July and changes as services are added in recommended locations er pe ot tn rin ep Pr Page 16 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 41 Map 1 Location of haemodialysis units in Cali, 2020 (order in which REPS displayed them) ed v iew re er pe ot tn rin ep Pr Page 17 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Figure 1. AMORE Platform interfaces with situational and predictive analyses, 6–12 July 2020 ed v iew re er pe ot tn rin ep Pr Page 18 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Figure 2. AMORE Platform interfaces with situational and predictive analyses, 23–29 Nov 2020 ed v iew re er pe ot tn rin ep Pr Page 19 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Figure 3. Choropleth maps comparing accessibility if services were added in new TAZs, for 6-12 July 2020 ed v iew re er pe ot tn rin ep Pr Page 20 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Figure 4. Choropleth maps comparing accessibility if services were added in new TAZs, for 23-29 November 2020 ed v iew re er pe ot tn rin ep Pr Page 21 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Figure 5. TAZs where new services optimise accessibility ed v iew re er pe ot tn rin ep Pr Page 22 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Table 1. 20-minute accessibility to haemodialysis in Cali by socio-demographic group ed Situational analysis 20 min Nov 2020 Total Accessibility Accessibility Subgroup accessibility to the nearest Jul 2020 (#) Variation (#) % (#) Population July November variation hemodialysis service (using Total Pop) 1,017,011 1,574,724 557,713 2,258,823 45.0% 69.7% Socio-economic stratum iew Low 221,296 568,115 346,819 1,109,549 49.1% 19.9% 51.2% 31.3% Middle 645,076 841,224 196,148 935,699 41.4% 68.9% 89.9% 21.0% High 147,229 160,082 12,853 204,589 9.1% 72.0% 78.2% 6.3% N.D. 3,410 5,303 1,893 8,986 0.4% 37.9% 59.0% 21.1% Ethnicity Afrodescendent 85,481 174,224 88,743 325,865 14.4% 26.2% 53.5% 27.2% Rrom (nomadic) 40 58 18 102 0.0% 39.2% 56.9% 17.6% Indigenous 4,339 7,394 3,055 11,112 0.5% 39.0% 66.5% 27.5% Islander/Raizal 214 276 62 382 0.0% 56.0% 72.3% 16.2% v Other (Caucasian, Mestizo) 910,535 1,369,069 458,534 1,890,491 83.7% 48.2% 72.4% 24.3% Palenque 41 231 190 245 0.0% 16.7% 94.3% 77.6% N.D. 16,361 23,472 7,111 30,626 1.4% 53.4% 76.6% 23.2% re Educational level Graduate degree 48,919 57,641 8,722 72,441 3.2% 67.5% 79.6% 12.0% Bachelor's Degree 187,250 240,652 53,402 295,319 13.1% 63.4% 81.5% 18.1% Technical 123,846 187,591 63,745 244,160 10.8% 50.7% 76.8% 26.1% Middle 254,015 409,594 155,579 608,429 26.9% 41.7% 67.3% 25.6% High School 132,875 222,293 er 89,418 337,065 14.9% 39.4% 65.9% 26.5% Primary 178,303 304,450 126,147 468,206 20.7% 38.1% 65.0% 26.9% Pre-school 13,559 23,360 9,801 36,294 1.6% 37.4% 64.4% 27.0% No data 78,244 129,143 50,899 196,909 8.7% 39.7% 65.6% 25.8% Literacy Literate 935,369 1,436,134 500,765 2,043,041 90.4% 45.8% 70.3% 24.5% pe No literacy 21,864 40,417 18,553 66,383 2.9% 32.9% 60.9% 27.9% N.A. 44,609 76,938 32,329 121,140 5.4% 36.8% 63.5% 26.7% N.D. 15,169 21,235 6,066 28,259 1.3% 53.7% 75.1% 21.5% Gender/Sex Fem 547,601 844,077 296,476 1,208,617 53.5% 45.3% 69.8% 24.5% Masc 469,410 730,647 261,237 1,050,206 46.5% 44.7% 69.6% 24.9% Civil status Single 370,444 575,999 205,555 821,536 36.4% 45.1% 70.1% 25.0% ot Married or cohabitation 403,517 624,355 220,838 896,958 39.7% 45.0% 69.6% 24.6% Divorced or separated 81,711 118,628 36,917 163,980 7.3% 49.8% 72.3% 22.5% Widow 52,705 73,457 20,752 95,611 4.2% 55.1% 76.8% 21.7% N.A. 94,566 162,468 67,902 254,492 11.3% 37.2% 63.8% 26.7% tn N.D. 14,068 19,817 5,749 26,246 1.2% 53.6% 75.5% 21.9% Age 0-4 44,609 76,938 32,329 121,140 5.4% 36.8% 63.5% 26.7% 0-14 150,093 256,909 106,816 400,527 17.7% 37.5% 64.1% 26.7% 5-14 105,484 179,971 74,487 279,387 12.4% 37.8% 64.4% 26.7% 15-24 147,838 242,147 94,309 363,311 16.1% 40.7% 66.7% 26.0% 15-59 657,107 1,027,559 370,452 1,482,069 65.6% 44.3% 69.3% 25.0% rin 15-64 716,344 1,111,425 395,081 1,595,016 70.6% 44.9% 69.7% 24.8% 60+ 209,811 290,256 80,445 376,227 16.7% 55.8% 77.1% 21.4% 65+ 150,574 206,390 55,816 263,280 11.7% 57.2% 78.4% 21.2% 80+ 40,103 52,080 11,977 64,100 2.8% 62.6% 81.2% 18.7% ep Pr Page 23 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Table 2. Predicted accessibility to the haemodialysis service with the shortest travel times (absolute figures) ed Predicted 20 min Benefit of Variation + Benefit of Add 1 Add 2 Add 1 Variation +1 accessibility to the nearest Jul 2020 (#) Variation Variation the second Nov 2020 (#) Add 2 Services 2 serv. Nov the second Service Services Service service Nov hemodialysis service, by service vs. baseline service subgroup (#) 1,017,011 1,585,437 568,426 1,696,205 679,194 110,768 1,574,724 2,063,889 489,165 2,194,907 620,183 131,018 Socio-economic stratum iew Low 221,296 692,535 471,239 708,285 486,989 15,750 568,115 1,012,179 444,064 1,054,841 486,726 42,662 Middle 645,076 738,967 93,891 787,189 142,113 48,222 841,224 883,232 42,008 934,581 93,357 51,349 High 147,229 147,714 485 194,064 46,835 46,350 160,082 160,448 366 197,011 36,929 36,563 N.D. 3,410 6,221 2,811 6,667 3,257 446 5,303 8,030 2,727 8,474 3,171 444 Ethnicity Afrodescendent 85,481 226,665 141,184 235,681 150,200 9,016 174,224 309,773 135,549 320,945 146,721 11,172 Rrom (nomadic) 40 70 30 75 35 5 58 88 30 93 35 5 Indigenous 4,339 7,093 2,754 7,661 3,322 568 7,394 10,023 2,629 10,830 3,436 807 Islander/Raizal 214 287 73 308 94 21 276 339 63 366 90 27 Other (Caucasian, Mestizo) 910,535 1,329,587 419,052 1,429,331 518,796 99,744 1,369,069 1,715,776 346,707 1,833,419 464,350 117,643 Palenque 41 58 59 1 231 v 17 18 244 13 245 14 1 N.D. 16,361 21,677 5,316 23,090 6,729 1,413 23,472 27,646 4,174 29,009 5,537 1,363 Educational level Graduate degree 48,919 51,673 2,754 64,274 15,355 12,601 57,641 59,250 1,609 70,683 13,042 11,433 re Bachelor Degree 187,250 214,500 27,250 246,213 58,963 31,713 240,652 259,026 18,374 290,113 49,461 31,087 Technical 123,846 173,029 49,183 183,949 60,103 10,920 187,591 225,703 38,112 238,756 51,165 13,053 Middle 254,015 429,219 175,204 449,999 195,984 20,780 409,594 561,317 151,723 589,778 180,184 28,461 High School 132,875 234,812 101,937 245,256 112,381 10,444 222,293 312,505 90,212 326,680 104,387 14,175 Primary 178,303 323,554 145,251 337,279 158,976 13,725 304,450 433,804 129,354 453,532 149,082 19,728 Pre-school 13,559 24,185 10,626 25,912 12,353 1,727 23,360 32,893 9,533 35,018 11,658 2,125 No data 78,244 134,465 56,221 143,323 65,079 8,858 129,143 179,391 50,248 190,347 61,204 10,956 Literacy er Literate 935,369 1,439,178 503,809 1,540,840 605,471 101,662 1,436,134 1,867,076 430,942 1,986,355 550,221 119,279 No literacy 21,864 44,390 22,526 46,272 24,408 1,882 40,417 61,347 20,930 64,213 23,796 2,866 N.A. 44,609 81,605 36,996 87,318 42,709 5,713 76,938 110,033 33,095 117,422 40,484 7,389 N.D. 15,169 20,264 5,095 21,775 6,606 1,511 21,235 25,433 4,198 26,917 5,682 1,484 Gender/Sex Fem 547,601 849,850 302,249 910,080 362,479 60,230 844,077 1,104,162 260,085 1,175,110 331,033 70,948 Masc 469,410 735,587 266,177 786,125 316,715 50,538 730,647 959,727 229,080 1,019,797 289,150 60,070 pe Civil status - - - Single 370,444 576,988 206,544 615,450 245,006 38,462 575,999 754,020 178,021 799,355 223,356 45,335 Married or cohabitation 403,517 625,534 222,017 673,554 270,037 48,020 624,355 813,993 189,638 870,343 245,988 56,350 Divorced or separated 81,711 120,329 38,618 127,626 45,915 7,297 118,628 151,450 32,822 160,193 41,565 8,743 Widow 52,705 2,171 - 50,534 75,848 23,143 73,677 73,457 89,402 15,945 93,550 20,093 4,148 N.A. 94,566 171,688 77,122 183,663 89,097 11,975 162,468 231,388 68,920 246,543 84,075 15,155 N.D. 14,068 18,727 4,659 20,064 5,996 1,337 19,817 23,636 3,819 24,923 5,106 1,287 Age - - 0-4 44,609 81,605 36,996 87,318 42,709 5,713 76,938 110,033 33,095 117,422 40,484 7,389 0-14 150,093 270,764 120,671 289,257 139,164 18,493 256,909 364,761 107,852 387,896 130,987 23,135 ot 5-14 105,484 189,159 83,675 201,939 96,455 12,780 179,971 254,728 74,757 270,474 90,503 15,746 15-24 147,838 249,309 101,471 266,631 118,793 17,322 242,147 331,487 89,340 352,301 110,154 20,814 15-59 657,107 1,031,825 374,718 1,696,205 1,039,098 664,380 1,027,559 1,349,753 322,194 1,439,686 412,127 89,933 15-64 716,344 1,115,142 398,798 1,196,118 479,774 80,976 1,111,425 1,453,704 342,279 1,549,551 438,126 95,847 60+ 209,811 282,848 73,037 299,656 89,845 16,808 290,256 349,375 59,119 367,325 77,069 17,950 65+ 150,574 199,531 48,957 210,830 60,256 11,299 206,390 245,424 39,034 257,460 51,070 12,036 tn 80+ 40,103 50,570 10,467 52,893 12,790 2,323 52,080 60,472 8,392 62,987 10,907 2,515 rin ep Pr Page 24 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Table 3. Predicted accessibility to the haemodialysis service with the shortest travel times (relative figures) ed Benefit of Benefit of Predicted 20 min Variation Variation + 2 Jul 2020 (% Add 1 Add 2 the Add 1 Variation +1 Add 2 the Subgroup's accessibility to the nearest Variation adding 2 Nov 2020 (%) serv. Nov vs. subgroup) Service Services second Service service Nov Services second population hemodialysis service, by services baseline service service subgroup (%) Socio-economic stratum Low 19.9% 62.4% 42.5% 63.8% 43.9% 1.4% 51.2% 91.2% 40.0% 95.1% 43.9% 3.8% 1,109,549 Middle 68.9% 79.0% 10.0% 84.1% 15.2% 5.2% 89.9% 94.4% 4.5% 99.9% 10.0% 5.5% 935,699 iew High 72.0% 72.2% 0.2% 94.9% 22.9% 22.7% 78.2% 78.4% 0.2% 96.3% 18.1% 17.9% 204,589 N.D. 37.9% 69.2% 31.3% 74.2% 36.2% 5.0% 59.0% 89.4% 30.3% 94.3% 35.3% 4.9% 8,986 Ethnicity Afrodescendent 26.2% 69.6% 43.3% 72.3% 46.1% 2.8% 53.5% 95.1% 41.6% 98.5% 45.0% 3.4% 325,865 Rrom (nomadic) 39.2% 68.6% 29.4% 73.5% 34.3% 4.9% 56.9% 86.3% 29.4% 91.2% 34.3% 4.9% 102 Indigenous 39.0% 63.8% 24.8% 68.9% 29.9% 5.1% 66.5% 90.2% 23.7% 97.5% 30.9% 7.3% 11,112 Islander/Raizal 56.0% 75.1% 19.1% 80.6% 24.6% 5.5% 72.3% 88.7% 16.5% 95.8% 23.6% 7.1% 382 Other (Caucasian, Mestizo) 48.2% 70.3% 22.2% 75.6% 27.4% 5.3% 72.4% 90.8% 18.3% 97.0% 24.6% 6.2% 1,890,491 Palenque 16.7% 23.7% 6.9% 24.1% 7.3% 0.4% 94.3% 99.6% 5.3% 100.0% 5.7% 0.4% 245 N.D. 53.4% 70.8% 17.4% 75.4% 22.0% 4.6% 76.6% 90.3% 13.6% 94.7% 18.1% 4.5% 30,626 Educational level 0.0% v Graduate degree 67.5% 71.3% 3.8% 88.7% 21.2% 17.4% 79.6% 81.8% 2.2% 97.6% 18.0% 15.8% 72,441 Bachelor Degree 63.4% 72.6% 9.2% 83.4% 20.0% 10.7% 81.5% 87.7% 6.2% 98.2% 16.7% 10.5% 295,319 Technical 50.7% 70.9% 20.1% 75.3% 24.6% 4.5% 76.8% 92.4% 15.6% 97.8% 21.0% 5.3% 244,160 Middle 41.7% 70.5% 28.8% 74.0% 32.2% 3.4% 67.3% 92.3% 24.9% 96.9% 29.6% 4.7% 608,429 re High School 39.4% 69.7% 30.2% 72.8% 33.3% 3.1% 65.9% 92.7% 26.8% 96.9% 31.0% 4.2% 337,065 Primary 38.1% 69.1% 31.0% 72.0% 34.0% 2.9% 65.0% 92.7% 27.6% 96.9% 31.8% 4.2% 468,206 Pre-school 37.4% 66.6% 29.3% 71.4% 34.0% 4.8% 64.4% 90.6% 26.3% 96.5% 32.1% 5.9% 36,294 No data 39.7% 68.3% 28.6% 72.8% 33.1% 4.5% 65.6% 91.1% 25.5% 96.7% 31.1% 5.6% 196,909 Literacy Literate 45.8% 70.4% 24.7% 75.4% 29.6% 5.0% 70.3% 91.4% 21.1% 97.2% 26.9% 5.8% 2,043,041 No literacy 32.9% 66.9% 33.9% 69.7% 36.8% 2.8% 60.9% 92.4% 31.5% 96.7% 35.8% 4.3% 66,383 N.A. 36.8% 67.4% 30.5% 72.1% 35.3% 4.7% 63.5% 90.8% 27.3% 96.9% 33.4% 6.1% 121,140 N.D. 53.7% 71.7% 18.0% 77.1% er 23.4% 5.3% 75.1% 90.0% 14.9% 95.3% 20.1% 5.3% 28,259 Gender/Sex Fem 45.3% 70.3% 25.0% 75.3% 30.0% 5.0% 69.8% 91.4% 21.5% 97.2% 27.4% 5.9% 1,208,617 Masc 44.7% 70.0% 25.3% 74.9% 30.2% 4.8% 69.6% 91.4% 21.8% 97.1% 27.5% 5.7% 1,050,206 Civil status 0.0% Single 45.1% 70.2% 25.1% 74.9% 29.8% 4.7% 70.1% 91.8% 21.7% 97.3% 27.2% 5.5% 821,536 Married or cohabitation 45.0% 69.7% 24.8% 75.1% 30.1% 5.4% 69.6% 90.8% 21.1% 97.0% 27.4% 6.3% 896,958 pe Divorced or separated 49.8% 73.4% 23.6% 77.8% 28.0% 4.4% 72.3% 92.4% 20.0% 97.7% 25.3% 5.3% 163,980 Widow 55.1% 2.3% -52.9% 79.3% 24.2% 77.1% 76.8% 93.5% 16.7% 97.8% 21.0% 4.3% 95,611 N.A. 37.2% 67.5% 30.3% 72.2% 35.0% 4.7% 63.8% 90.9% 27.1% 96.9% 33.0% 6.0% 254,492 N.D. 53.6% 71.4% 17.8% 76.4% 22.8% 5.1% 75.5% 90.1% 14.6% 95.0% 19.5% 4.9% 26,246 Age 0-4 36.8% 67.4% 30.5% 72.1% 35.3% 4.7% 63.5% 90.8% 27.3% 96.9% 33.4% 6.1% 121,140 0-14 37.5% 67.6% 30.1% 72.2% 34.7% 4.6% 64.1% 91.1% 26.9% 96.8% 32.7% 5.8% 400,527 5-14 37.8% 67.7% 29.9% 72.3% 34.5% 4.6% 64.4% 91.2% 26.8% 96.8% 32.4% 5.6% 279,387 15-24 40.7% 68.6% 27.9% 73.4% 32.7% 4.8% 66.7% 91.2% 24.6% 97.0% 30.3% 5.7% 363,311 15-59 44.3% 69.6% 25.3% 114.4% 70.1% 44.8% 69.3% 91.1% 21.7% 97.1% 27.8% 6.1% 1,482,069 15-64 44.9% 69.9% 75.0% 5.1% 69.7% 91.1% 97.1% 6.0% 1,595,016 ot 25.0% 30.1% 21.5% 27.5% 60+ 55.8% 75.2% 19.4% 79.6% 23.9% 4.5% 77.1% 92.9% 15.7% 97.6% 20.5% 4.8% 376,227 65+ 57.2% 75.8% 18.6% 80.1% 22.9% 4.3% 78.4% 93.2% 14.8% 97.8% 19.4% 4.6% 263,280 80+ 62.6% 78.9% 16.3% 82.5% 20.0% 3.6% 81.2% 94.3% 13.1% 98.3% 17.0% 3.9% 64,100 tn rin ep Pr Page 25 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Figure 6. Traffic variations and their effect (July vs November 2020) 6 – 12 July vs 23 – 29 November 2020 ed i ew e v r r ee t p n o in t p r r e P This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Figure 7. characteristic accessibility during day hours, traffic congestion level ed i ew e v r r ee t p n o in t p r r e P Page 27 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 ed i ew e v r Supplemental File 1. Traffic congestion and accessibility to haemodialysis, Cali 6-12 July 2020. Animation r https://figshare.com/s/38142a4a4958b6b054e5 ee This file is a GIF or MP4. Its DOI: 10.6084/m9.figshare.21699773 will be activated before publication and replace this temporary link: t p n o in t p r r e P Page 28 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562 Supplemental File 2. Choropleth maps comparing accessibility 6-12 July and changes as services are added in recommended locations ed i ew e v r r ee t p n o in t p r r e P Page 29 of 29 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4299562