Examining inequalities in spatial access to national health insurance fund contracted facilities in Kenya.

BACKGROUND: Kenya aims to achieve universal health coverage (UHC) by 2030 and has selected the National Health Insurance Fund (NHIF) as the 'vehicle' to drive the UHC agenda. While there is some progress in moving the country towards UHC, the availability and accessibility to NHIF-contracted facilities may be a barrier to equitable access to care. We estimated the spatial access to NHIF-contracted facilities in Kenya to provide information to advance the UHC agenda in Kenya. METHODS: We merged NHIF-contracted facility data to the geocoded inventory of health facilities in Kenya to assign facility geospatial locations. We combined this database with covariates data including road network, elevation, land use, and travel barriers. We estimated the proportion of the population living within 60- and 120-minute travel time to an NHIF-contracted facility at a 1-x1-kilometer spatial resolution nationally and at county levels using the WHO AccessMod tool. RESULTS: We included a total of 3,858 NHIF-contracted facilities. Nationally, 81.4% and 89.6% of the population lived within 60- and 120-minute travel time to an NHIF-contracted facility respectively. At the county level, the proportion of the population living within 1-hour of travel time to an NHIF-contracted facility ranged from as low as 28.1% in Wajir county to 100% in Nyamira and Kisii counties. Overall, only four counties (Kiambu, Kisii, Nairobi and Nyamira) had met the target of having 100% of their population living within 1-hour (60 min) travel time to an NHIF-contracted facility. On average, it takes 209, 210 and 216 min to travel to an NHIF-contracted facility, outpatient and inpatient facilities respectively. At the county level, travel time to an NHIF-contracted facility ranged from 10 min in Vihiga County to 333 min in Garissa. CONCLUSION: Our study offers evidence of the spatial access estimates to NHIF-contracted facilities in Kenya that can inform contracting decisions by the social health insurer, especially focussing on marginalised counties where more facilities need to be contracted. Besides, this evidence will be crucial as the country gears towards accelerating progress towards achieving UHC using social health insurance as the strategy to drive the UHC agenda in Kenya.

A Concrete Core Void Imaging Approach and Parameter Analysis of Concrete-Filled Steel Tube Members Using Travel Time Tomography: Multi-Physics Simulations and Experimental Studies.

Concrete-filled steel tube (CFST) members have been widely used in civil engineering due to their advanced mechanical properties. However, internal defects such as the concrete core voids and interface debonding in CFST structures are likely to weaken their load-carrying capacity and stiffness, which affects the safety and serviceability. Visualizing the inner defects of the concrete cores in CFST members is a critical requirement and a challenging task due to the obvious difference in the material mechanical parameters of the concrete core and steel tube in CFST members. In this study, a curved ray theory-based travel time tomography (TTT) with a least square iterative linear inversion algorithm is first introduced to quantitatively identify and visualize the sizes and positions of the concrete core voids in CFST members. Secondly, a numerical investigation of the influence of different parameters on the inversion algorithm for the defect imaging of CFST members, including the effects of the model weighting matrix, weighting factor and grid size on the void's imaging quality and accuracy, is carried out. Finally, an experimental study on six CFST specimens with mimicked concrete core void defects is performed in a laboratory and the mimicked defects are visualized. The results demonstrate that TTT can identify the sizes and positions of the concrete core void defects in CFST members efficiently with the use of optimal parameters.

Impact of centralization of prostate cancer services on the choice of radical treatment.

OBJECTIVE: To assess the impact of centralization of prostate cancer surgery and radiotherapy services on the choice of prostate cancer treatment. PATIENTS AND METHODS: This national population-based study used linked cancer registry data and administrative hospital-level data for all 16 621 patients who were diagnosed between 1 January 2017 and 31 December 2018 with intermediate-risk prostate cancer and who underwent radical prostatectomy (RP) or radical radiation therapy (RT) in the English National Health Service (NHS). Travel times by car to treating centres were estimated using a geographic information system. We used logistic regression to assess the impact of the relative proximity of alternative treatment options on the type of treatment received, with adjustment for patient characteristics. RESULTS: Of the 78 NHS hospitals that provide RT or RP for prostate cancer, 41% provide both, 36% provide RT and 23% provide RP. Compared to patients who had both treatment options available at their nearest centre where overall 57% of patients received RT and 43% RP, patients were less likely to receive RT if their nearest centre offered RP only and the extra travel time to a hospital providing RT was >15 min (52% of patients received RT and 48% RP%, odds ratio [OR] 0.70 (0.58-0.85); P < 0.001). Conversely, patients were more likely to receive RT if their nearest centre offered RT and the extra travel time to a hospital providing RP was >15 min (63% of patients received RT and 37% RP, OR 1.23 (1.08-1.40); P < 0.001). There was a negligible impact on the type of treatment received if centres providing alternative treatment options were ≤15-min travel time from each other. CONCLUSION: The relative proximity of prostate cancer treatment options to a patient's residence is an independent predictor for the type of radical treatment received. Centralization policies for prostate cancer should not focus on one treatment modality but should consider all treatments to avoid a negative impact on treatment choice.

Urban-rural disparities in geographic accessibility to care for people living with HIV.

In the United States, people living with HIV (PLWH) in rural areas fare worse along the HIV care continuum than their urban counterparts; this may be due in part to limited geographic access to care. We estimated drive time to care for PLWH, focusing on urban-rural differences. Adult Medicaid enrollees living with HIV and their usual care clinicians were identified using administrative claims data from 14 states (Medicaid Analytic eXtract, 2009-2012). We used geographic network analysis to calculate one-way drive time from the enrollee's ZIP code tabulation area centroid to their clinician's practice address, then examined urban-rural differences using bivariate statistics. Additional analyses included altering the definition of rurality; examining subsamples based on the state of residence, services received, and clinician specialty; and adjusting for individual and county characteristics. Across n = 49,596 PLWH, median drive time to care was 12.8 min (interquartile range 26.3). Median drive time for rural enrollees (43.6 (82.0)) was nearly four times longer than for urban enrollees (11.9 (20.6) minutes, p < 0.0001), and drive times exceeded one hour for 38% of rural enrollees (versus 12% of urban, p < 0.0001). Urban-rural disparities remained in all additional analyses. Sustained efforts to circumvent limited geographic access to care are critical for rural areas.

Geographic accessibility and hospital competition for emergency blood transfusion services in Bungoma, Western Kenya.

BACKGROUND: Estimating accessibility gaps to essential health interventions helps to allocate and prioritize health resources. Access to blood transfusion represents an important emergency health requirement. Here, we develop geo-spatial models of accessibility and competition to blood transfusion services in Bungoma County, Western Kenya. METHODS: Hospitals providing blood transfusion services in Bungoma were identified from an up-dated geo-coded facility database. AccessMod was used to define care-seeker's travel times to the nearest blood transfusion service. A spatial accessibility index for each enumeration area (EA) was defined using modelled travel time, population demand, and supply available at the hospital, assuming a uniform risk of emergency occurrence in the county. To identify populations marginalized from transfusion services, the number of people outside 1-h travel time and those residing in EAs with low accessibility indexes were computed at the sub-county level. Competition between the transfusing hospitals was estimated using a spatial competition index which provided a measure of the level of attractiveness of each hospital. To understand whether highly competitive facilities had better capacity for blood transfusion services, a correlation test between the computed competition metric and the blood units received and transfused at the hospital was done. RESULTS: 15 hospitals in Bungoma county provide transfusion services, however these are unevenly distributed across the sub-counties. Average travel time to a blood transfusion centre in the county was 33 min and 5% of the population resided outside 1-h travel time. Based on the accessibility index, 38% of the EAs were classified to have low accessibility, representing 34% of the population, with one sub-county having the highest marginalized population. The computed competition index showed that hospitals in the urban areas had a spatial competitive advantage over those in rural areas. CONCLUSION: The modelled spatial accessibility has provided an improved understanding of health care gaps essential for health planning. Hospital competition has been illustrated to have some degree of influence in provision of health services hence should be considered as a significant external factor impacting the delivery, and re-design of available services.

An approach for accessibility assessment and vulnerability analysis of national multimodal transport systems.

A series of ambitious accessibility-oriented policies have been launched in the world, which center around improving accessibility via the national multimodal transport systems including road, airline, and railway. The national multimodal transport accessibility assessment is one of the "basic" analyses for the design and implementation of these policies, whereas existing national-scale accessibility studies either focus on single-mode or two-mode transport or ignore the schedule-dependent nature of railway and airline. This article models the integrated road, railway, airline, and walking transport as a four-layer network with the consideration of their interdependencies. An algorithm is then developed to accurately assess the travel time-based accessibility on the four-layer network with the consideration of the daily schedule of trains and flights. The proposed approach is applied to map accessibility to 363 cities in mainland China and analyze the optimal travel modes. In addition, this article investigates the travel time-based vulnerability of the national multimodal transport system in mainland China under the extreme storm recently occurred in Zhengzhou (July 2021). The findings in this work provide insightful suggestions for transport planners to design the national multimodal transport systems and for stakeholders to schedule travels.

More than time: travel time to the delivery ward and maternal outcomes - onset of labour, postpartum haemorrhage and obstetric anal sphincter injury.

BACKGROUND: Closing delivery units increases travel time for some women. Whether increased travel time is associated with maternal outcomes is important for understanding the consequences of such closures. Previous studies are limited in measuring travel time and restricted to the outcome of caesarean section. METHODS: Our population-based cohort includes data from the Swedish Pregnancy Register for women giving birth between 2014 and 2017 (N = 364,630). We estimated travel time from home to the delivery ward using coordinate pairs of actual addresses. The association between travel time and onset of labour was modelled using multinomial logistic regression, and logistic regression was used for the outcomes postpartum haemorrhage (PPH) and obstetric anal sphincter injury (OASIS). FINDINGS: Over three-quarters of women had ≤30 min travel time (median 13.9 min). Women who travelled ≥60 min arrived to care sooner and laboured there longer. Women with further to travel had increased adjusted odds ratio (aOR) of having an elective caesarean section (31-59 min aOR 1.11; 95% confidence interval [CI] 1.07-1.16; ≥60 min aOR 1.25; 95% CI 1.16-1.36) than spontaneous onset of labour. Women (at full term with spontaneous onset) living ≥60 min away had reduced odds of having a PPH (aOR 0.84; 95% CI 0.76-0.94) or OASIS (aOR 0.79; 95% CI 0.66-0.94). INTERPRETATION: Longer travel time increased the odds of elective caesarean section. Women with furthest to travel arrived sooner and spent more time in care; although they had a lower risk of PPH or OASIS, they also tended to be younger, have a higher body mass index and were Nordic born.

Equitable and effective vaccine access considering vaccine hesitancy and capacity constraints.

The COVID-19 pandemic continues to have an unprecedented impact on people's lives and the economy worldwide. Vaccines are the strongest evidence-based defense against the spread of the disease. The release of COVID-19 vaccines to the general public created policy challenges associated with how to best allocate vaccines among different sub-regions. In the United States, after vaccines became widely available for all eligible adults, policymakers faced objectives such as (i) achieving an equitable allocation to reduce populations' travel times to get vaccinated and (ii) effectively allocating vaccine doses to minimize waste and unmet need. This problem was further exacerbated by the underlying factors of population vaccine hesitancy and sub-regions' varying capacity levels to administer vaccines to eligible and willing populations. Although simple to implement, commonly used pro rata policies do not capture the complexities of this problem. We propose two alternatives to simple pro rata policies. The first alternative is based on a Mixed-Integer Linear Programming Model that minimizes the maximum travel duration of patients and aims to achieve an equitable and effective allocation of vaccines to sub-regions while considering capacity and vaccine hesitancy. A second alternative is a heuristic approach that may be more palatable for policymakers who (i) are not familiar with mathematical modeling, (ii) are reluctant to use black-box models, and (iii) prefer algorithms that are easy to understand and implement. We demonstrate the results of our model through a case study based on real data from the state of Alabama and show that substantial improvements in travel time-based equity are achievable through capacity improvements in a small subset of counties. We perform additional computational experiments that compare the proposed methods in terms of several metrics and demonstrate the promising performance of our model and proposed heuristic. We find that while our mathematical model can achieve equitable and effective vaccine allocation, the proposed heuristic performs better if the goal is to minimize average travel duration. Finally, we explore two model extensions that aim to (i) lower vaccine hesitancy by allocating vaccines, and (ii) prioritize vaccine access for certain high-risk sub-populations.

Spatial variation and inequities in antenatal care coverage in Kenya, Uganda and mainland Tanzania using model-based geostatistics: a socioeconomic and geographical accessibility lens.

BACKGROUND: Pregnant women in sub-Saharan Africa (SSA) experience the highest levels of maternal mortality and stillbirths due to predominantly avoidable causes. Antenatal care (ANC) can prevent, detect, alleviate, or manage these causes. While eight ANC contacts are now recommended, coverage of the previous minimum of four visits (ANC4+) remains low and inequitable in SSA. METHODS: We modelled ANC4+ coverage and likelihood of attaining district-level target coverage of 70% across three equity stratifiers (household wealth, maternal education, and travel time to the nearest health facility) based on data from malaria indicator surveys in Kenya (2020), Uganda (2018/19) and Tanzania (2017). Geostatistical models were fitted to predict ANC4+ coverage and compute exceedance probability for target coverage. The number of pregnant women without ANC4+ were computed. Prediction was at 3 km spatial resolution and aggregated at national and district -level for sub-national planning. RESULTS: About six in ten women reported ANC4+ visits, meaning that approximately 3 million women in the three countries had 20,000 women having

Pentavalent vaccination in Kenya: coverage and geographical accessibility to health facilities using data from a community demographic and health surveillance system in Kilifi County.

BACKGROUND: There is substantial evidence that immunization is one of the most significant and cost-effective pillars of preventive and promotive health interventions. Effective childhood immunization coverage is thus essential in stemming persistent childhood illnesses. The third dose of pentavalent vaccine for children is an important indicator for assessing performance of the immunisation programme because it mirrors the completeness of a child's immunisation schedule. Spatial access to an immunizing health facility, especially in sub-Sahara African (SSA) countries, is a significant determinant of Pentavalent 3 vaccination coverage, as the vaccine is mainly administered during routine immunisation schedules at health facilities. Rural areas and densely populated informal settlements are most affected by poor access to healthcare services. We therefore sought to determine vaccination coverage of Pentavalent 3, estimate the travel time to health facilities offering immunisation services, and explore its effect on immunisation coverage in one of the predominantly rural counties on the coast of Kenya. METHODS: We used longitudinal survey data from the health demographic surveillance system implemented in Kaloleni and Rabai Sub-counties in Kenya. To compute the geographical accessibility, we used coordinates of health facilities offering immunisation services, information on land cover, digital elevation models, and road networks of the study area. We then fitted a hierarchical Bayesian multivariable model to explore the effect of travel time on pentavalent vaccine coverage adjusting for confounding factors identified a priori. RESULTS: Overall coverage of pentavalent vaccine was at 77.3%. The median travel time to a health facility was 41 min (IQR = 18-65) and a total of 1266 (28.5%) children lived more than one-hour of travel-time to a health facility. Geographical access to health facilities significantly affected pentavalent vaccination coverage, with travel times of more than one hour being significantly associated with reduced odds of vaccination (AOR = 0.84 (95% CI 0.74 - 0.94). CONCLUSION: Increased travel time significantly affects immunization in this rural community. Improving road networks, establishing new health centres and/or stepping up health outreach activities that include vaccinations in hard-to-reach areas within the county could improve immunisation coverage. These data may be useful in guiding the local department of health on appropriate location of planned immunization centres.

Association of travel time with mental health service use in primary health care according to contact type - a register-based study in Kainuu, Finland.

BACKGROUND: The study aim was to analyse how mental health services are used in different parts of the Kainuu region in Finland and whether travel time to primary health care services is associated with the use of different contact types (in-person visits, remote contacts, home visits). METHODS: The study population included adults who had used mental health services under primary health care (N = 7643) between 2015 and 2019. The travel times to the nearest health centre in a municipality were estimated as the population-weighted average drive time in postal code areas. The Kruskal-Wallis test and pairwise comparisons with Dunn-Bonferroni post hoc tests were used to assess the differences in mental health service use between health centre areas. A negative binomial regression was performed for the travel time categories using different contact types of mental health service use as outcomes. Models were adjusted for gender, age, number of mental health diseases and the nearest health centre in the municipality. RESULTS: Distance was negatively associated with mental health service use in health centre in-person visits and in home visits. In the adjusted models, there were 36% fewer in-person visits and 83% fewer home visits in distances further than 30 min, and 67% fewer home visits in a travel time distance of 15-30 min compared with 15 min travel time distance from a health centre. In the adjusted model, in remote contacts, the incidence rate ratios increased with distance, but the association was not statistically significant. CONCLUSIONS: The present study revealed significant differences in mental health service use in relation to travel time and contact type, indicating possible problems in providing services to distant areas. Long travel times can pose a barrier, especially for home care and in-person visits. Remote contacts may partly compensate for the barrier effects of long travel times in mental health services. Especially with conditions that call for the continuation and regularity of care, enabling factors, such as travel time, may be important.

National travel distances for emergency care.

BACKGROUND: Most emergency department (ED) patients arrive by their own transport and, for various reasons, may not choose the nearest ED. How far patients travel for ED treatment may reflect both patients' access to care and severity of illness. In this study, we aimed to examine the travel distance and travel time between a patient's home and ED they visited and investigate how these distances/times vary by patient and hospital characteristics. METHODS: We randomly sampled and collected data from 14,812 patients discharged to the community (DTC) between January and March 2016 from 50 hospital-based EDs nationwide. We geocoded and calculated the distance and travel time between patient and hospital-based ED addresses, examined the travel distances/ times between patients' home and the ED they visited, and used mixed-effects regression models to investigate how these distances/times vary by patient and hospital characteristics. RESULTS: Patients travelled an average of 8.0 (SD = 10.9) miles and 17.3 (SD = 18.0) driving minutes to the ED. Patients travelled significantly farther to avoid EDs in lower performing hospitals (p < 0.01) and in the West (p < 0.05) and Midwest (p < 0.05). Patients travelled farther when visiting EDs in rural areas. Younger patients travelled farther than older patients. CONCLUSIONS: Understanding how far patients are willing to travel is indicative of whether patient populations have adequate access to ED services. By showing that patients travel farther to avoid a low-performing hospital, we provide evidence that DTC patients likely do exercise some choice among EDs, indicating some market incentives for higher-quality care, even for some ED admissions. Understanding these issues will help policymakers better define access to ED care and assist in directing quality improvement efforts. To our knowledge, our study is the most comprehensive nationwide characterization of patient travel for ED treatment to date.

Use of faecal immunochemical tests common in patients with suspected colorectal cancer but unrelated to travel distance to secondary care: a population-based study from Swedish primary care.

BACKGROUND: Evidence is increasing for the use of faecal immunochemical tests (FITs) for occult blood as diagnostic tools when colorectal cancer can be suspected. FITs have been used for this purpose in Swedish primary care since around 2005 despite absence of supporting guidelines. To our knowledge, the extent of this use has not been studied. OBJECTIVE: To investigate the use of FITs as diagnostic tools, and if the use was related to patient age, sex and travel time from primary care to diagnostic facilities in secondary care. DESIGN: Population-based retrospective study using data from electronic health records. SETTING AND SUBJECTS: Patients ≥18 years that provided FITs in primary care in five Swedish health care regions during 2015. Driving times from their primary care centres to secondary care were calculated. MAIN OUTCOME MEASURES: The proportion of patients that provided FITs was calculated for each region, different age intervals and grouped driving times. RESULTS: 18,913 patients provided FITs. The proportion of listed patients in the five regions that provided FITs increased with age: 0.86-1.2% for ages <65 years, 3.6-4.1% for ages 65-79 years and 3.8-6.1% for ages ≥80 years. Differences between the regions were small. There was no overall correlation between the proportion of patients that provided FITs and driving time to secondary care. CONCLUSION: FITs were used extensively in Swedish primary care with a higher use in older age groups. There was no tendency towards a higher use of FITs at primary care centres with longer driving times to secondary care.Key PointsEvidence is increasing for the use of faecal immunochemical tests (FITs) as diagnostic tools when colorectal cancer can be suspected. We investigated the use of FITs in Sweden.FITs were used extensively in primary care especially in older age groups.There were small differences in the use of FITs between five studied health care regions.There was no tendency towards a higher use of FITs at primary care centres with longer driving times to diagnostic facilities in secondary care.

Near-Surface Seismic Measurements in Gravel Pit, over Highway Tunnel and Underground Tubes with Ground Truth Information as an Open Data Set.

In this article, we describe in detail three seismic measurement campaigns based on refraction methods that we conducted at different sites in Bavaria, Germany. The measured data is published as an open data set. The particularity of this data set lies in its available ground truth information about each measurement site. Acquiring seismic data from sites with ground truth information is important for validation of seismic inversion algorithms. Since near-surface seismic field data with ground truth information is rather limited, we anticipate this data set to be a valuable contribution to the research community. For the measurements, three sites have been selected: (1) a gravel pit with a ground water layer, (2) a site above a highway tunnel and (3) a surface over underground tubes. The measurements have been conducted using line arrays of geophones, the Geode Seismograph from Geometrics Inc. and hammer strikes as seismic source. To obtain inversion results a travel time tomography based on first-arrivals within the software SeisImager is used. The inversion results show that we are able to image the ground water layer in the gravel pit, the highway tunnel and partly features of underground tubes. Furthermore, the results coincide with available ground truth information about the measurement sites. This paper summarizes the measurement campaigns and the respective data sets obtained through these campaigns. The data have been published by the authors as an open data set under the license CC BY 4.0 on figshare to make it available to the research community for validation of seismic data processing and inversion techniques.

First Arrival Picking of Zero-Phase Seismic Data by Hilbert Envelope Empirical Half Window (HEEH) Method.

First arrival travel time picking is an important step in many seismic data-processing applications. Most first arrival picking methods search for a sudden jump in seismic energy at trace onsets, which is clearly appropriate for minimum-phase data. This paper proposes a method for the first arrival picking of non-minimum phase data based on complex trace analysis. The Hilbert integral transform generates a complex seismic trace, followed by extraction of the envelope. The first arrival identification introduces an outlier detection method that uses the widely used three-sigma rule of thumb, which is commonly used in most software algorithms to identify outliers. The proposed method ultimately generates logical windows of ones (at the locations of outliers) and zeros (elsewhere). The first arrival is selected in the middle of the first outlier window. Testing the proposed method on zero-phase synthetic data with added 10% and 20% random noise, the method detected the true first arrivals accurately. Furthermore, tests on real Vibroseis data showed that the method recognizes the first arrivals with 67% accuracy within 20 milliseconds of their corresponding arrival times manually picked by an experienced geophysicist.

A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression.

Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages-initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50), mean absolute error (12.22±1.35) and the coefficient of determination (0.99784±0.00019). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data.

The travel time prediction of vehicles is an important part of intelligent expressways. It can not only provide the vehicle distribution trend of each section for the expressway management department to assist the fine management of the expressway, but it can also provide owners with dynamic and accurate travel time prediction services to assist the owners to formulate more reasonable travel plans. However, there are still some problems in the current travel time prediction research (e.g., different types of vehicles are not processed separately, the proximity of the road network is not considered, and the capture of important information in the spatial-temporal perspective is not considered in depth). In this paper, we propose a Multi-View Travel Time Prediction (MVPPT) model. First, the travel times of different types of vehicles of each section in the expressway are analyzed, and the main differences in the travel times of different types of vehicles are obtained. Second, multiple travel time features are constructed, which include a novel spatial proximity feature. On this basis, we use CNN to capture the spatial correlation and the spatial attention mechanism to capture key information, the BiLSTM to capture the time correlation of time series, and the time attention mechanism capture key time information. Experiments on large-scale real traffic data demonstrate the effectiveness of our proposal over state-of-the-art methods.

Is city-level travel time by car associated with individual obesity or diabetes in Latin American cities? Evidence from 178 cities in the SALURBAL project.

There is growing evidence that longer travel time by private car poses physical and mental risks. Individual-level obesity and diabetes, two of the main public health challenges in low- and middle-income contexts, could be associated to city-level travel times by car. We used individual obesity and diabetes data from national health surveys from individuals in 178 Latin American cities, compiled and harmonized by the SALURBAL project. We calculated city-level travel times by car using the Google Maps Distance Matrix API. We estimated associations between peak hour city-level travel time by car and obesity and diabetes using multilevel logistic regression models, while adjusting for individual characteristics and other city-level covariates. In our study we did not observe a relationship between city-level peak-hour travel time by car and individual obesity and diabetes, as reported in previous research for individual time spent in vehicles in high-income settings. Our results suggest that this relationship may be more complex in Latin America compared to other settings, especially considering that cities in the region are characterized by high degrees of population density and compactness and by a higher prevalence of walking and public transportation use.

Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak.

After the widespread impact of the COVID-19 pandemic, all public transport, including urban rail transit, inevitably adopted a vigorous physical-distancing policy to prevent the disease from spreading among passengers. Adoption of this measure resulted in a substantial reduction in train service capability and required control of the risk contact exposure duration. Thus, this paper proposes the Skip-Stop Strategy Patterns (3S-P) decision-support model to incorporate social distancing constraints in train operations. The 3S-P model is a two-stage, multi-objective optimization model for scheduling train skip-stop patterns to satisfy the study's two main objectives of minimizing the average passenger travel time and unserved passengers. In the proposed model, the first optimization identifies the optimal train skip-stop patterns, while the second assigns these patterns to establish an hourly train schedule. The paper's case study uses data from the Bangkok Mass Transit System (BTS) SkyTrain Silom Line in Bangkok, Thailand and considers the 0.5, 1, 1.5, and 2 m social distancing schemes. The results reveal that the optimal train skip-stop patterns are superior to the all-stop alternative with, on average, a 13.4% faster travel time at the same level of unserved passengers. Furthermore, the non-dominated schedules from the second optimization decrease the numbers of unserved passengers given equal average passenger travel times.

Risk of Coronavirus Disease 2019 Transmission in Train Passengers: an Epidemiological and Modeling Study.

BACKGROUND: Train travel is a common mode of public transport across the globe; however, the risk of coronavirus disease 2019 (COVID-19) transmission among individual train passengers remains unclear. METHODS: We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2334 index patients and 72 093 close contacts who had co-travel times of 0-8 hours from 19 December 2019 through 6 March 2020 in China. We analyzed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time. RESULTS: The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI], 5.3%-19.0%), with a mean of 0.32% (95% CI, .29%-.37%). Passengers in seats on the same row (including the adjacent passengers to the index patient) as the index patient had an average attack rate of 1.5% (95% CI, 1.3%-1.8%), higher than that in other rows (0.14% [95% CI, .11%-.17%]), with a relative risk (RR) of 11.2 (95% CI, 8.6-14.6). Travelers adjacent to the index patient had the highest attack rate (3.5% [95% CI, 2.9%-4.3%]) of COVID-19 infection (RR, 18.0 [95% CI, 13.9-23.4]) among all seats. The attack rate decreased with increasing distance, but increased with increasing co-travel time. The attack rate increased on average by 0.15% (P = .005) per hour of co-travel; for passengers in adjacent seats, this increase was 1.3% (P = .008), the highest among all seats considered. CONCLUSIONS: COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when traveling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.