Self-reported Travel Time Research Articles

Care seeking during pregnancy: testing the assumptions behind service delivery redesign for maternal and newborn health in rural Kenya.

A health systems reform known as Service Delivery Redesign (SDR) for maternal and newborn health seeks to make high-quality delivery care universal in Kakamega County, in western Kenya, by strengthening hospital-level care and making hospital deliveries the default option for pregnant women. Using a large prospective survey of new mothers in Kakamega County, we examine several key assumptions that underpin the SDR policy's theory of change. We analyse data on place of delivery, travel time and distance, out-of-pocket spending, and self-reported quality of care for 19 127 women prospectively enrolled during antenatal care (ANC) and surveyed two times after their delivery. We analyze changes in womens' delivery location preferences in recent years in Kakamega, and over the course of their most recent pregnancy. We also evaluate travel time, out-of-pocket expenditures and patient satisfaction for women who deliver in public hospitals vs primary health centres. We find substantial changes in delivery location at the population level over time and for individual women over the course of pregnancy. Facility delivery has increased from 50.4% in 2010 to 89.5% in 2019; 70% of respondents deliver at a different facility than their reported intention at ANC. Out-of-pocket delivery expenditures are on average 1351 Kenyan shillings (Ksh) in hospitals compared to 964 Ksh in PHC (primary health care)s (P < 0.01). Transport expenditures are 337 Ksh for PHC level deliveries vs 422 Ksh for hospitals (P < 0.01). Self-reported average travel time is 51 min (PHC delivery) vs 47 min (hospital delivery) (P = 0.78). The average distance to a delivery location is 15.1 km for PHC deliveries vs 15.2 km for hospitals (P = 0.99). There were no differences in overall patient-reported quality scores, while some subcomponents of quality favoured hospitals. These findings support several key assumptions of the SDR theory of change in Kakamega County, while also highlighting important challenges that should be addressed to increase the likelihood of successful implementation.

Evaluating the Connection Between Rural Travel Time and Health: A Cross-Sectional Analysis of Older Adults Living in the Northeast United States.

To characterize the impact of rural patients' travel time to obtain healthcare on their reported utilization of preventive healthcare services and personal health outcomes. Online survey data from rural adults ages 50+ years living in the Northeastern United States were collected from February to August 2021. Study measures included self-reported travel time to obtain healthcare, use of preventive healthcare, and health outcomes. The associations between travel time with use of preventive care and health outcomes were assessed using linear, Poisson, and logistic regression analyses controlling for demographic variables. Our study population included 1052 rural adults, with a mean travel time of 18.5 min (range: 0-60). Travel time was greater for racial/ethnic minority participants and for higher-income participants (both P < .05), but it was not associated with use of preventive healthcare. Greater travel time was associated with poorer mental health and more comorbidities, including cancer and diabetes (all P < .05). Travel time varied by patient demographic factors, and it was associated with mental health and comorbidities. There was no association between travel time and preventive care use, suggesting that other barriers likely contribute to suboptimal use of these services within rural communities. Further research is needed to elucidate the causal pathways linking travel time to mental health and comorbidities within rural communities, as increased travel may exacerbate intrarural health disparities.

Biases in self-reported travel behaviour: effects on self-reported travel distance, travel time and rain prevalence

Travel time and distance are key variables in evaluating transport planning decisions. However, people's perceptions and experiences often differ from objectively measured conditions. Therefore, this paper assesses the effect of different socio-demographic variables, travel habits, and mobility options on self-reported travel distance, travel time, and rain prevalence. Data from a survey on the potential use of e-bikes are analysed using logistic regression models. The most important conclusion from this paper is that travel distances in self-reported surveys are often overestimated, especially when these distances are short. Travel times are better estimated.

Disparities in Travel-Related Barriers to Accessing Health Care From the 2017 National Household Travel Survey

Geographic access, including mode of transportation, to health care facilities remains understudied. To identify sociodemographic factors associated with public vs private transportation use to access health care and identify the respondent, trip, and community factors associated with longer distance and time traveled for health care visits. This cross-sectional study used data from the 2017 National Household Travel Survey, including 16 760 trips or a nationally weighted estimate of 5 550 527 364 trips to seek care in the United States. Households that completed the recruitment and retrieval survey for all members aged 5 years and older were included. Data were analyzed between June and August 2022. Mode of transportation (private vs public transportation) used to seek care. Survey-weighted multivariable logistic regression models were used to identify factors associated with public vs private transportation and self-reported distance and travel time. Then, for each income category, an interaction term of race and ethnicity with type of transportation was used to estimate the specific increase in travel burden associated with using public transportation compared a private vehicle for each race category. The sample included 12 092 households and 15 063 respondents (8500 respondents [56.4%] aged 51-75 years; 8930 [59.3%] females) who had trips for medical care, of whom 1028 respondents (6.9%) were Hispanic, 1164 respondents (7.8%) were non-Hispanic Black, and 11 957 respondents (79.7%) were non-Hispanic White. Factors associated with public transportation use included non-Hispanic Black race (compared with non-Hispanic White: adjusted odds ratio [aOR], 3.54 [95% CI, 1.90-6.61]; P < .001) and household income less than $25 000 (compared with ≥$100 000: aOR, 7.16 [95% CI, 3.50-14.68]; P < .001). The additional travel time associated with use of public transportation compared with private vehicle use varied by race and household income, with non-Hispanic Black respondents with income of $25 000 to $49 999 experiencing higher burden associated with public transportation (mean difference, 81.9 [95% CI, 48.5-115.3] minutes) than non-Hispanic White respondents with similar income (mean difference, 25.5 [95% CI, 17.5-33.5] minutes; P < .001). These findings suggest that certain racial, ethnic, and socioeconomically disadvantaged populations rely on public transportation to seek health care and that reducing delays associated with public transportation could improve care for these patients.

Comparing modelled with self-reported travel time and the used versus the nearest facility: modelling geographic accessibility to family planning outlets in Kenya

IntroductionGeographic access to family planning (FP) services has been characterised through a variety of proximity metrics. However, there is little evidence on the validity of women’s self-reported compared with modelled...

An assessment of geographical access and factors influencing travel time to emergency obstetric care in the urban state of Lagos, Nigeria.

Previous efforts to estimate the travel time to comprehensive emergency obstetric care (CEmOC) in low- and middle-income countries (LMICs) have either been based on spatial models or self-reported travel time, both with known inaccuracies. The study objectives were to estimate more realistic travel times for pregnant women in emergency situations using Google Maps, determine system-level factors that influence travel time and use these estimates to assess CEmOC geographical accessibility and coverage in Lagos state, Nigeria. Data on demographics, obstetric history and travel to CEmOC facilities of pregnant women with an obstetric emergency, who presented between 1st November 2018 and 31st December 2019 at a public CEmOC facility were collected from hospital records. Estimated travel times were individually extracted from Google Maps for the period of the day of travel. Bivariate and multivariate analyses were used to test associations between travel and health system-related factors with reaching the facility >60 minutes. Mean travel times were compared and geographical coverage mapped to identify ‘hotspots’ of predominantly >60 minutes travel to facilities. For the 4005 pregnant women with traceable journeys, travel time ranges were 2–240 minutes (without referral) and 7–320 minutes (with referral). Total travel time was within the 60 and 120 minute benchmark for 80 and 96% of women, respectively. The period of the day of travel and having been referred were significantly associated with travelling >60 minutes. Many pregnant women living in the central cities and remote towns typically travelled to CEmOC facilities around them. We identified four hotspots from which pregnant women travelled >60 minutes to facilities. Mean travel time and distance to reach tertiary referral hospitals were significantly higher than the secondary facilities. Our findings suggest that actions taken to address gaps need to be contextualized. Our approach provides a useful guide for stakeholders seeking to comprehensively explore geographical inequities in CEmOC access within urban/peri-urban LMIC settings.

Retail availability and legal purchases of dried flower in Canada post-legalization

ObjectiveRetail availability of cannabis has the potential to influence demand for both legal and illegal cannabis. The aims of the study were to: 1) estimate the percentage of consumers who report purchasing dried flower legally; and 2) examine the association between purchasing dried flower legally and retail availability, where retail availability was represented as: a) Canadian province; b) ‘objective’ retail proximity; and c) self-reported retail proximity. MethodsData come from Canadian respondents in Wave 2 of the International Cannabis Policy Study (ICPS) conducted one year after non-medical cannabis legalization in September-October 2019. Respondents were 18+ years in Alberta/Quebec and 19+ years elsewhere and had purchased any dried flower in the past year (n = 2506). Respondents were recruited through commercial online panels. Weighted binary logistic regression models examined likelihood of purchasing dried flower legally. ResultsOverall, 47.7 % of past-year dried flower purchasers reported last purchasing dried flower legally, with variation across provinces (range = 40.5 %–81.2 %). Likelihood of purchasing dried flower legally was greater among those who lived closer to a legal retail store based on Euclidean distance (<3 km vs. > 10 km: AOR = 1.56, 95 % CI: 1.20,2.02), and who had shorter self-reported travel time to a retail store (<5 min. vs. > 15 min.: AOR = 2.24, 95 % CI: 1.56,3.21). ConclusionOne year after legalization, retail availability was associated with last purchasing dried flower legally among past-year dried flower purchasers. To our knowledge, the current study is among the first to examine the legality of purchase source used for dried flower and retail availability of cannabis in Canada post-legalization.

Validating the Global Surgery Geographical Accessibility Indicator: Differences in Modeled Versus Patient-Reported Travel Times

BackgroundSince long travel times to reach health facilities are associated with worse outcomes, geographic accessibility is one of the six core global surgery indicators; this corresponds to the second of the “Three Delays Framework,” namely “delay in reaching a health facility.” Most attempts to estimate this indicator have been based on geographical information systems (GIS) algorithms. The aim of our study was to compare GIS derived estimates to self-reported travel times for patients traveling to a district hospital in rural Rwanda for emergency obstetric care.MethodsOur study includes 664 women who traveled to undergo a Cesarean delivery in Kirehe, Rwanda. We compared self-reported travel time from home to the hospital (excluding waiting time) with GIS estimated travel times, which were computed using the World Health Organization tool AccessMod, using linear regression.ResultsThe majority of patients used multiple modes of transportation (walking = 48.5%, public transport = 74.2%, private transport = 2.9%, and ambulance 70.6%). Self-reported times were longer than GIS estimates by a factor of 1.49 (95% CI 1.40–1.57). Concordance was higher when the GIS model took into account that all patients in Rwanda are referred via their health center (β = 1.12; 95% CI 1.05–1.18).ConclusionsTo our knowledge, in this largest to date GIS validation study for geographical access to healthcare in low- and middle-income countries, a standard GIS model was found to significantly underestimate real travel time, which likely is in part because it does not model the actual route patients are travelling. Therefore, previous studies of 2-h access to surgery will need to be interpreted with caution, and future studies should take local travelling conditions into account.

Is the closest health facility the one used in pregnancy care-seeking? A cross-sectional comparative analysis of self-reported and modelled geographical access to maternal care in Mozambique, India and Pakistan

BackgroundTravel time to care is known to influence uptake of health services. Generally, pregnant women who take longer to transit to health facilities are the least likely to deliver in facilities. It is not clear if modelled access predicts fairly the vulnerability in women seeking maternal care across different spatial settings.ObjectivesThis cross-sectional analysis aimed to (i) compare travel times to care as modelled in a GIS environment with self-reported travel times by women seeking maternal care in Community Level Interventions for Pre-eclampsia: Mozambique, India and Pakistan; and (ii) investigate the assumption that women would seek care at the closest health facility.MethodsWomen were interviewed to obtain estimated travel times to health facilities (R). Travel time to the closest facility was also modelled (P) (closest facility tool (ArcGIS)) and time to facility where care was sought estimated (A) (route network layer finder (ArcGIS)). Bland–Altman analysis compared spatial variation in differences between modelled and self-reported travel times. Variations between travel times to the nearest facility (P) with modelled travel times to the actual facilities accessed (A) were analysed. Log-transformed data comparison graphs for medians, with box plots superimposed distributions were used.ResultsModelled geographical access (P) is generally lower than self-reported access (R), but there is a geography to this relationship. In India and Pakistan, potential access (P) compared fairly with self-reported travel times (R) [P (H0: Mean difference = 0)] < .001, limits of agreement: [− 273.81; 56.40] and [− 264.10; 94.25] respectively. In Mozambique, mean differences between the two measures of access were significantly different from 0 [P (H0: Mean difference = 0) = 0.31, limits of agreement: [− 187.26; 199.96]].ConclusionModelling access successfully predict potential vulnerability in populations. Differences between modelled (P) and self-reported travel times (R) are partially a result of women not seeking care at their closest facilities. Modelling access should not be viewed through a geographically static lens. Modelling assumptions are likely modified by spatio-temporal and/or socio-cultural settings. Geographical stratification of access reveals disproportionate variations in differences emphasizing the varied nature of assumptions across spatial settings.Trial registration ClinicalTrials.gov, NCT01911494. Registered 30 July 2013, https://clinicaltrials.gov/ct2/show/NCT01911494

Over- and Under-Estimation of Travel Time on Commute Trips: GPS vs. Self-Reporting

The underlying structure of road networks (e.g., circuity, relative discontinuity) contributes to the travel time perception of travelers. This study considers additional factors (e.g., arrival flexibility, access to traffic information) and tests nonlinearities linking perception of travel time. These factors are linked to four categories according to time perception research in psychology: temporal relevance, temporal uncertainty, and temporal expectancies; task complexity, absorption, and attentional deployment; and affective elements. This study estimates the relationship on data collected from commuters recruited from a previous GPS-based study in the Minneapolis-St. Paul region consisting of trips from home to work and back. For these work trips, the subjects’ self-reported travel times and the subjects’ travel times measured by GPS devices were collected. The results indicate that nonlinearities are present for road network attributes. Furthermore, the additional factors (e.g., arrival flexibility, access to traffic information) influence the travel time perception of travelers.

Uncovering the influence of commuters’ perception on the reliability ratio

The dominant method for measuring values of travel time savings (VOT), and values of travel time reliability (VOR) is discrete choice modeling. Studies using revealed preference have tended to use travel times measured by devices such as loop detectors, and thus the perception error of travelers has been largely ignored. In this study, the influence of commuters’ perception error is investigated on data collected of commuters recruited from previous research. The subjects’ self-reported travel times from surveys, and the subjects’ travel times measured by GPS devices were collected. The results indicate that the subjects reliability ratio is greater than 1 in the models with self- reported travel times. In contrast, subjects reliability ratio is smaller than 1 in the models with travel times as measured by GPS devices.

Incidence and Outcomes after Out-of-Hospital Medical Emergencies in Gambia: A Case for the Integration of Prehospital Care and Emergency Medical Services in Primary Health Care.

The Gambia is going through a rapid epidemiologic transition with a dual disease burden of infections and non-communicable diseases occurring at the same time. Acute, time-sensitive, medical emergencies such as trauma, obstetric emergencies, respiratory failure, and stroke are leading causes of morbidity and mortality among adults in the country.ProblemData on medical emergency care and outcomes are lacking in The Gambia. Data on self-reported medical emergencies among adults in a selection of Gambian communities are presented in this report. A total of 320 individuals were surveyed from 34 communities in the greater Banjul area of The Gambia using a survey instrument estimating the incidence of acute medical emergencies in an adult population. Self-reported travel time to a health facility during medical emergencies and patterns of health-seeking behavior with regard to type of facility visited and barriers to accessing emergency care, including cost and medical insurance coverage, are presented in this report. Of the 320 individuals surveyed, 262 agreed to participate resulting in a response rate of 82%. Fifty-two percent of respondents reported an acute medical emergency in the preceding year that required urgent evaluation at a health facility. The most common facility visited during such emergencies was a health center. Eighty-seven percent of respondents reported a travel time of less than one hour during medical emergencies. Out-of-pocket cost of medications accounted for the highest expenditure during emergencies. There was a low awareness and willingness to subscribe to health insurance among individuals surveyed. There is a high incidence of acute medical emergencies among adults in The Gambia which are associated with adverse outcomes due to a combination of poor health literacy, high out-of-pocket expenditures on medications, and poor access to timely prehospital emergency care. There is an urgent need to develop prehospital acute care and Emergency Medical Services (EMS) in the primary health sector as part of a strategy to reduce mortality and morbidity in the country. TourayS, SanyangB, ZandrowG, TourayI. Incidence and outcomes after out-of-hospital medical emergencies in Gambia: a case for the integration of prehospital care and Emergency Medical Services in primary health care. Prehosp Disaster Med. 2018;33(6):650-657.

Comparing potential spatial access with self-reported travel times and cost analysis to haemodialysis facilities in North-eastern Iran

End-stage renal disease patients regularly need haemodialysis three times a week. Their poor access to haemodialysis facilities is significantly associated with a high mortality rate. The present cross-sectional study aimed to measure the potential spatial access to dialysis services at a small area level (census tract level) in North Khorasan Province, Iran. The patients were interviewed to obtain their travel information. The two-step floating catchment area (2SFCA) method was used to measure the spatial accessibility of patients to the dialysis centres. The capacity of the dialysis centre was defined as the number of active dialysis facilities in each centre and the haemodialysis patients in each area were considered as the users of dialysis services. The travel cost from each patient's residence to the haemodialysis facilities was visualized by the Kriging interpolation algorithm in the study area. Spatial accessibility to the dialysis centre was poor in the northern part of the study area. Fortunately, there were not many haemodialysis patients in that area. Patients' travel costs were high in the northern areas compared to the rest of study area. We observed a statistically significant reverse correlation between the self-reported travel time and computed spatial accessibility (-0.570, P value <0.01, two-tailed spearman test). This study supports the notion that the 2SFCA method could be associated with revealed access time to dialysis facilities, especially in low traffic and in flat areas such as northern Khorasan. The mapping of patients' distribution and interpolated travel cost to the haemodialysis facilities could help policymakers to allocate health resources to the areas where the need is greater.

Quantifying errors in travel time and cost by latent variables

Travel time and travel cost are key variables for explaining travel behaviour and deriving the value of time. However, a general problem in transport modelling is that these variables are subject to measurement errors in transport network models. In this paper we show how to assess the magnitude of the measurement errors in travel time and travel cost by latent variables, in a large-scale travel demand model. The case study for Stockholm commuters shows that assuming multiplicative measurement errors for travel time and cost result in a better fit than additive ones, and that parameter estimates of the choice model are impacted by some of the key modelling assumptions. Moreover, our results suggest that measurement errors in our dataset are larger for the travel cost than for the travel time, and that measurement errors are larger in self-reported travel time than software-calculated travel time for car-driver and car-passenger, and of similar magnitude for public transport. Among self-reported travel times, car-passenger has the largest errors, followed by car-driver and public transport, and for the software-calculated times, public transport exhibits larger errors than car. These errors, if not corrected, lead to biases in measures derived from the models, such as elasticities and values of travel time.

Automobile dependence and physical inactivity: Insights from the California Household Travel Survey

Abstract Background Auto-dependence has been linked to the physical inactivity epidemic across U.S. cities, resulting in unprecedented increases in incidences of obesity, cardiovascular diseases, depression, etc. The search for strategies to pull an overwhelming majority of auto-dependents out of their sedentary lifestyles by encouraging them to use transit, walk and bike continues to challenge planners and policy-makers. Methods We use the 2012–13 California Household Travel Survey data for analyzing the auto-dependence and physical inactivity connection. We select a sample of employed individuals with access to car in urban California, and classify them as discretionary transit riders (N=390), active auto-dependents (N=1287), or sedentary auto-dependents (N=8754) based on their self-reported travel mode use and time spent in physical activity over a 24-h period. We investigate factors that are associated with significantly high physical activity among some auto-dependents relative to the sedentary majority. We also revisit the transit-physical activity connection, and explore conditions that make transit use unfeasible for some active auto-dependents. Results Discretionary transit use is associated with higher physical activity. However, there is large variation in physical activity within auto-dependents; significantly higher physical activity is associated with factors such as higher income, flexible work schedule, shorter work hours, and mixed land use. Kids, inflexibility of work schedule, low residential density, lack of pedestrian and bicycling friendly street design, and long distance to transit stops prohibit otherwise active auto-dependents from choosing transit. Employment sector influences both physical activity and choice of transit. Conclusion To get sedentary auto-dependents out of endemic physical inactivity, our research indicates the need for targeting lower-incomes, incentivizing employers to provide flexible work hours, and to continue dense, mixed-use developments that make active travel feasible. In addition, to get active auto-dependents to use transit, transit managers must focus on retaining immigrant riders and non-Hispanic Asians, and attracting people with children.

Haemodialysis services in the northeastern region of Iran.

Chronic kidney disease is growing and the current estimated global prevalence exceeds 13%. As the use of haemodialysis machines for patients with end stage renal disease increases survival considerably, it is critical to plan correctly for the allocation of these machines. This study aimed to develop a geographical information systems (GIS)-based approach to predict the need for this service in the northeastern region of Iran taking into account where patients live and where haemodialysis is the most needed and identifying areas with poor access to haemodialysis centres. Patients were interviewed to obtain self-reported actual travel time and the inverse distance-weighting algorithm was used to determine access in each area. The prediction is based on the domestic growth rate for haemodialysis services and the estimated active hours of machine use for the next five years. We estimate that six new haemodialysis machines are required in northeastern Iran at the present time with 50 machines required over the next five years. Ashkhane City was identified to have the least access to haemodialysis centres in the study area. Our GIS-based model can be used to investigate not only the need for new haemodialysis machines but also to examine geographic disparities in the allocation of haemodialysis centres and to identify areas most in need of this service. It is important that policymakers consider both spatial and non-spatial dimensions of access to enable better allocation of haemodialysis services ensuring they are targeted to reach those in need.

Travel patterns during pregnancy: comparison between Global Positioning System (GPS) tracking and questionnaire data

BackgroundMaternal exposures to traffic-related air pollution have been associated with adverse pregnancy outcomes. Exposures to traffic-related air pollutants are strongly influenced by time spent near traffic. However, little is known about women’s travel activities during pregnancy and whether questionnaire-based data can provide reliable information on travel patterns during pregnancy.ObjectivesExamine women’s in-vehicle travel behavior during pregnancy and examine the difference in travel data collected by questionnaire and global positioning system (GPS) and their potential for exposure error.MethodsWe measured work-related travel patterns in 56 pregnant women using a questionnaire and one-week GPS tracking three times during pregnancy (<20 weeks, 20–30 weeks, and >30 weeks of gestation). We compared self-reported activities with GPS-derived trip distance and duration, and examined potentially influential factors that may contribute to differences. We also described in-vehicle travel behavior by pregnancy periods and influences of demographic and personal factors on daily travel times. Finally, we estimated personal exposure to particle-bound polycyclic aromatic hydrocarbon (PB-PAH) and examined the magnitude of exposure misclassification using self-reported vs. GPS travel data.ResultsSubjects overestimated both trip duration and trip distance compared to the GPS data. We observed moderately high correlations between self-reported and GPS-recorded travel distance (home to work trips: r = 0.88; work to home trips: r = 0.80). Better agreement was observed between the GPS and the self-reported travel time for home to work trips (r = 0.77) than work to home trips (r = 0.64). The subjects on average spent 69 and 93 minutes traveling in vehicles daily based on the GPS and self-reported data, respectively. Longer daily travel time was observed among participants in early pregnancy, and during certain pregnancy periods in women with higher education attainment, higher income, and no children. When comparing self-reported vs. GPS data, we found that estimated personal exposure to PB-PAH did not differ remarkably at the population level, but the difference was large at an individual level.ConclusionSelf-reported home-to-work data overestimated both trip duration and trip distance compared to GPS data. Significant differences in PAH exposure estimates were observed at individual level using self-reported vs. GPS data, which has important implications in air pollution epidemiological studies.

The challenge of global water access monitoring: evaluating straight-line distance versus self-reported travel time among rural households in Mozambique

Support is growing for the incorporation of fetching time and/or distance considerations in the definition of access to improved water supply used for global monitoring. Current efforts typically rely on self-reported distance and/or travel time data that have been shown to be unreliable. To date, however, there has been no head-to-head comparison of such indicators with other possible distance/time metrics. This study provides such a comparison. We examine the association between both straight-line distance and self-reported one-way travel time with measured route distances to water sources for 1,103 households in Nampula province, Mozambique. We find straight-line, or Euclidean, distance to be a good proxy for route distance (R(2) = 0.98), while self-reported travel time is a poor proxy (R(2) = 0.12). We also apply a variety of time- and distance-based indicators proposed in the literature to our sample data, finding that the share of households classified as having versus lacking access would differ by more than 70 percentage points depending on the particular indicator employed. This work highlights the importance of the ongoing debate regarding valid, reliable, and feasible strategies for monitoring progress in the provision of improved water supply services.

GPS-measured distance to clinic, but not self-reported transportation factors, are associated with missed HIV clinic visits in rural Uganda

Studies of the association between transportation barriers and HIV-related health outcomes have shown both positive and negative effects, possibly because a reliable, validated measure of transportation barriers has not been identified. Prospective cohort study of HIV-infected patients in rural Uganda. Participants were enrolled from the HIV clinic at the regional referral hospital in Mbarara, Uganda as part of the Uganda AIDS Rural Treatment Outcomes (UARTO) Study. We collected the following measures of transportation barriers to HIV clinic: global positioning systems (GPS)-tracked distance measured by driving participants to their homes along their typical route; straight-line GPS distance from clinic to home, calculated with the Great Circle Formula; self-reported travel time; and self-reported travel cost. We assessed inter-measure agreement using linear regression, correlation coefficients and κ statistics (by measure quartile) and validated measures by fitting linear regression models to estimate associations with days late for clinic visits. One hundred and eighty-eight participants were tracked with GPS. Seventy-six percent were women, with a median age of 40 years and median CD4 cell count of 193 cells/μl. We found a high correlation between GPS-based distance measures (β=0.74, P<0.001, R²=0.92, κ=0.73), but little correlation between GPS-based and self-reported measures (all R²≤0.4). GPS-based measures were associated with days late to clinic (P<0.001); but neither self-reported measure was associated (P>0.85). GPS-measured distance to clinic is associated with HIV clinic absenteeism and should be prioritized over self-reported measures to optimally risk-stratify patients accessing care in rural, resource-limited settings.

Latent temporal preferences: An application to airline travel

An essential element of demand modeling in the airline industry is the representation of time of day demand—the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that accounts for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive. The strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution.