Median Travel Time Research Articles

Mobile ECMO retrieval of patients during the COVID-19 pandemic.

ECMO support is particularly resource‐intensive and should be provided in highly specialized centers. Occasionally, ECMO needs to be initiated in non‐ECMO centers by mobile ECMO retrieval teams. Subsequently, patients must be transferred on ECMO to the ECMO center. We report single‐center data from out‐of‐center initiations of ECMO during the COVID‐19 pandemic. From March 2020 through February 2021, nine patients were connected to ECMO before transfer to our center. Median travel distance (IQR) from the referring hospital to our center was 66 km (20‐92), median land travel time (IQR) was 51 minutes (26‐92). Personal protective equipment was available for all team members and used throughout the missions. No infections of team members with SARS‐CoV‐2 occurred. Three patients survived until hospital discharge. Median duration of ECMO (IQR) was 18 days (2‐78) in survivors and 19 days (9‐42) in non‐survivors, respectively. Out‐of‐center initiation of ECMO during the COVID‐19 pandemic was feasible and safe for patients and staff.

Clinical, Operational, and Socioeconomic Analysis of EMS Bypass of the Closest Facility for Pediatric Asthma Patients

IntroductionPediatric hospital care is becoming increasingly regionalized, with fewer facilities providing inpatient care for common conditions such as asthma. That trend has major implications for emergency medical services (EMS) medical care and operations because EMS historically transports patients to the closest facility. This study describes EMS transport patterns of pediatric asthma patients in greater depth, including an analysis of facility bypass rates and the association of bypass with demographics and clinical outcomes.MethodsThis was a retrospective study of pediatric asthma patients ages 2–18 years transported by Lee County, FL EMS between March 1, 2018 – December 31, 2019. A priori, we defined bypass as greater than five minutes extra transport time. We performed geospatial analysis and mapping of EMS pediatric asthma encounters. We used the Pediatric Destination Tree (PDTree) project’s tiered approach to characterize receiving hospital facility pediatric capability. We analyzed incidence and characteristics of bypass, and bypass and non-bypass patient characteristics including demographics, emergency department (ED) clinical outcomes, and socioeconomic disadvantage (SED).ResultsFrom the study period, there were a total of 262 encounters meeting inclusion criteria, 254 (96.9%) of which could be geocoded to EMS incident and destination locations. Most encounters (72.8%) bypassed at least one facility, and the average number of facilities bypassed per encounter was 1.52. For all 185 bypass encounters, there was a median additional travel time of 13.5 minutes (interquartile range 7.5 – 17.5). Using the PDTree’s classification of pediatric capability of destination facilities, 172 of the 185 bypasses (93%) went to a Level I facility. Bypass incidence varied significantly by age, but not by minority status, asthma severity, or by the area deprivation index of the patient’s home address. Overall, the highest concentrations of EMS incidents tended to occur in areas of greater SED. With regard to ED outcomes, ED length of stay did not vary between bypass and non-bypass patients (P = 0.54), and neither did hospitalization (P = 0.80).ConclusionWe found high rates of bypass for pediatric EMS encounters for asthma exacerbations, and that bypass frequency was significantly higher in younger age groups. With national trends pointing toward increasing pediatric healthcare regionalization, bypass has significant implications for EMS operations.

Abstract 101: The Tanzanian Pediatric Cancer Network: A Comprehensive Evaluation of the Incidence of Presenting Patients and Access to Pediatric Cancer Care in Tanzania

Abstract Purpose: In sub-Saharan Africa, cancer treatment facilities are often scarce and centered in urban areas, which means many patients must travel long distances to reach cancer care. Still, little is known about how this distribution of cancer centers impacts patients' ability to access care. Methods: Records of all pediatric patients (<18yo) treated for cancer in Tanzania in 2019 through the Tanzanian Pediatric Cancer Network were reviewed. This network represents all 9 hospitals currently treating children with cancer in Tanzania. Demographic and diagnostic information was recorded. ArcGIS was used to estimate patients' travel times from home addresses to cancer care. AccessMod was used to calculate 4-hour access for the population. Results: In 2019, a total of 821 pediatric patients were diagnosed with cancer with an incidence of 1.42/100,000 population. The most common diagnoses were Wilms tumor (17.2%, n=134), retinoblastoma (16.8%, n=131), and acute lymphoblastic leukemia (15.9%, n=124). There was regional variation by diagnosis, with the Lake Zone having the highest incidence of Burkitt lymphoma (0.29; average 0.15) and retinoblastoma (0.5; average 0.33). Reported regional incidence inversely correlated to distance to cancer treatment facility. Median travel time to cancer care for patients was 4.55 hours. Currently 74.5% of the population lives within 4 hours of existing cancer care facilities, and it would require adding cancer care capacity to 8 additional facilities to reach 95% coverage. Conclusions: This is the first comprehensive evaluation of the incidence of presenting pediatric cancer patients in Tanzania. Only 18.8% of the estimated pediatric patients with cancer in Tanzania were treated in 2019. The reported incidence was lower in areas further away from treatment centers, suggesting the importance of increased access to cancer care facilities as part of a future national scale-up strategy. Citation Format: Luke Maillie, Alice Mutagonda, Lulu Chirande, Laiti Rehema, Shakilu Jumanne, Elton Meleki, Furaha Serventi, Julius Alloyce, Franco Afyusisye, Sebastian Sanchez, Matthew Sisk, Marie Jose Voeten, Ester Kawira, Heronima Joas, Trish Scanlan, Kristin Schroeder. The Tanzanian Pediatric Cancer Network: A Comprehensive Evaluation of the Incidence of Presenting Patients and Access to Pediatric Cancer Care in Tanzania [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 101.

Transformation of organic micropollutants along hyporheic flow in bedforms of river-simulating flumes

Urban streams receive increasing loads of organic micropollutants from treated wastewaters. A comprehensive understanding of the in-stream fate of micropollutants is thus of high interest for water quality management. Bedforms induce pumping effects considerably contributing to whole stream hyporheic exchange and are hotspots of biogeochemical turnover processes. However, little is known about the transformation of micropollutants in such structures. In the present study, we set up recirculating flumes to examine the transformation of a set of micropollutants along single flowpaths in two triangular bedforms. We sampled porewater from four locations in the bedforms over 78 days and analysed the resulting concentration curves using the results of a hydrodynamic model in combination with a reactive transport model accounting for advection, dispersion, first-order removal and retardation. The four porewater sampling locations were positioned on individual flowpaths with median solute travel times ranging from 11.5 to 43.3 h as shown in a hydrodynamic model previously. Highest stability was estimated for hydrochlorothiazide on all flowpaths. Lowest detectable half-lives were estimated for sotalol (0.7 h) and sitagliptin (0.2 h) along the shortest flowpath. Also, venlafaxine, acesulfame, bezafibrate, irbesartan, valsartan, ibuprofen and naproxen displayed lower half-lives at shorter flowpaths in the first bedform. However, the behavior of many compounds in the second bedform deviated from expectations, where particularly transformation products, e.g. valsartan acid, showed high concentrations. Flowpath-specific behavior as observed for metformin or flume-specific behavior as observed for metoprolol acid, for instance, was attributed to potential small-scale or flume-scale heterogeneity of microbial community compositions, respectively. The results of the study indicate that the shallow hyporheic flow field and the small-scale heterogeneity of the microbial community are major controlling factors for the transformation of relevant micropollutants in river sediments.

Needs Based Assessment of Trauma Systems 2, is it ready for primetime? A natural experiment testing its reliability.

Needs Based Assessment of Trauma Systems 2 (NBATS-2) attempts to predict the impact on patient volume and travel time for patients when a new trauma center (TC) is added to the system. The purpose of this study was to examine NBATS-2 predictive accuracy regarding expected volume and travel times of trauma patients at a newly designated TC and nearby legacy TCs when compared with actual data. Needs Based Assessment of Trauma Systems predictive model for volume of trauma patients at the new TC was run based on 25th, 50th, and 75th percentiles of both state and National Trauma Data Bank (NTDB) patients per 100 TC beds. This was compared with the actual number of trauma patients from the State Discharge Data set before (2011-2012) and after (2016-2017) designation of the TC. Analysis was then augmented using the geographic information system (ArcGIS) spatial modeling to characterize median travel times for actual trauma patients, before and after designation of the TC. Both state and NTDB 25th, 50th, and 75th percentiles resulted in significant overestimation of volume at the new TC in 2016. After another year of TC maturation (2017), overestimation decreased but was still present. The 25th percentile from state and NTDB data sets provided the most accurate predictions. For the legacy TCs, the model switched from under to overestimation as the state and NTDB percentiles increased. The geographic information system accurately showed patients traveling <40 minutes to a TC nearly doubled. Needs Based Assessment of Trauma Systems 2 provides an excellent template for state strategic planning; however, it overestimates new TC volume and under/overestimates volumes for legacy TCs depending on the state and NTDB percentiles used. This study shows that population density of the county in which the new or legacy TC is located should be considered when choosing the appropriate state or NTDB percentile. The geographic information system appropriately showed a decrease in trauma patient travel times after TC designation. Care Management, level V.

Multi-site practice and physician travel burden by oncology specialty.

e13513 Background: Having physicians who practice at multiple sites may increase patients’ access to care, but also burden physicians Gynecologic oncologists (GO) are increasingly practicing at more sites across a larger geographic area, but the degree to which medical oncologists (MO), surgical oncologists (SO) or radiation oncologists (RO) are also doing so is unknown. Methods: We conducted a retrospective, observational study using data from the 2020 Physician Compare National Dataset. We included GO, MO, SO, RO, as determined by self-reported specialty. Practice sites with incomplete street addresses were excluded. For each specialty, we calculated the number of practice sites per physician, geographic practice dispersion (median driving distance required to go to each practice site), and temporal practice dispersion (median travel time required to go to each practice site). We used linear regression to compare the number of practice sites, geographic practice dispersion, and temporal practice dispersion by specialty. Results: The number of physicians, mean number of practice sites, along with geographic and temporal practice dispersion by specialty are shown in the table. MO practiced at a smaller number of practice sites compared to GO (p&lt;0.001) and RO (p&lt;0.001). Compared to MO, SO had a smaller geographic dispersion (median driving distance 22 miles vs. 38 miles, p=&lt;0.001) and temporal practice dispersion (median driving time 27 minutes vs. 43 minutes, p&lt;0.001), whereas RO had a larger geographic dispersion (median RO driving distance 58 minutes, p&lt;0.001) and temporal practice dispersion (median RO driving time 63 minutes, p&lt;0.001). Conclusions: Oncologic specialties vary in the number of practice sites and practice dispersion per oncologist. In particular, GO and RO practice at more sites than MO, with MO practices more geographically concentrated than RO practices. While SO practice at a similar number of practice sites compared to MO, their practices are the most geographically concentrated. While these practice patterns may represent increased patient access to specialty oncology care, the impact on quality of care and physician wellness is unknown.[Table: see text]

Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county.

ObjectivesAn Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data.MethodsWe obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types.ResultsA total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26–35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race.ConclusionsDisparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.

Optimization for Locating Emergency Medical Service Facilities: A Case Study for Health Planning from China.

PurposeRational location of emergency medical service (EMS) facilities could improve access to EMS, and thus assist in saving patients’ lives and improving their health outcomes. A considerable amount of spatial optimization research has been devoted to the development of models to support location planning in the context of EMS, with extensive applications in policy making around the world. However, in China, studies on the location of EMS facilities have not been paid enough attention to, let alone their practical applications. This paper conducted location optimization for EMS facilities in Chengdu, one of the biggest cities in southwest China with more than 16.5 million population, aiming to optimize the EMS system by adding (upgrading) a minimum number of EMS facilities to achieve a given population coverage.MethodsLocation optimization was conducted according to regional health policy goal for the EMS system in Chengdu, China, 2017. The nearest-neighbor approach was used to calculate the shortest travel time based on geographical information system (GIS). The location set covering model was used to formulate the optimization problem under China’s context, and genetic algorithm (GA) was employed to determine the optimized locations.ResultsThe results showed that a minimum number of 55 new facilities were required to upgrade to EMS facilities to achieve the policy goal of 90% population coverage of EMS within 15 minutes. Access to EMS also improved substantially in terms of shortest travel time after facility upgrading. The weighted median shortest travel time to EMS facilities in Chengdu decreased by 14.57%, from 6.45 minutes to 5.51 minutes.ConclusionOur study showed that the solution could effectively achieve the policy goal of population coverage with a minimum number of new EMS facilities. Our findings would support evidence-based decision-making in future EMS planning in China.

Three‐Dimensional Distribution of Groundwater Residence Time Metrics in the Glaciated United States Using Metamodels Trained on General Numerical Simulation Models

AbstractResidence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as &lt;65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated United States is about 6,000 km3, or about 0.5% of globally stored young groundwater.

Evaluation of the Relationship Between Geographic Proximity and Treatment for People Referred to a Metropolitan Multidisciplinary Pain Clinic.

This study examined which patient characteristics are associated with traveling further to attend a metropolitan, publicly funded pain management service, and whether travel distance was associated with differences in treatment profile, duration, and percentage of appointments attended. Cross-sectional observational cohort study. Patients ≤70 years of age with a single referral between January 2014 and June 2018 who had not died within 12 months of their first appointment and who had a usual place of residence were included (N = 1,684; mean age = 47.2 years; 55.5% female). Travel distance was calculated with the HERE Routing API on the basis of historical travel times for each scheduled appointment. Median travel time was 27.5 minutes (Q1, Q3: 12.5, 46.2). Ordinal regression showed that women had 20% lower odds of traveling further, but people who were overweight or obese (odds ratio [OR] = 1.4-2.3), unemployed (OR = 1.27), or taking higher opioid dosages (OR = 1.79-2.82) had higher odds of traveling further. People traveling >60 minutes had fewer treatment minutes (median = 143 minutes) than people living within 15 minutes of the pain clinic (median = 440 minutes), and a smaller proportion of those traveling >60 minutes attended group programs vs. medical appointments only (n = 35, 17.0%) relative to those living within 15 minutes of their destination (n = 184, 32.6%). People living 16-30 minutes from the clinic missed the highest proportion of appointments. Although people traveling further for treatment may be seeking predominantly medical treatment, particularly opioid medications, the present findings highlight the need to further explore patient triage and program models of care to ensure that people living with persistent disabling pain can access the same level of care, regardless of where they live.

IAT-TiMeS: Intra-Arterial Thrombectomy Transfer Metric Study in Texas

ObjectiveWe aim to report intra-arterial thrombectomy transfer metrics for ischemic stroke patients that were transferred to hub hospitals for possible intra-arterial thrombectomy in multiple geographic regions throughout the state of Texas and to identify potential barriers and delays in the intra-arterial thrombectomy transfer process. MethodWe prospectively collected data from 8 participating Texas comprehensive stroke/thrombectomy capable centers from 7 major regions in the State of Texas. We collected baseline clinical and imaging data related to the pre-transfer evaluation, transfer metrics, and post-transfer clinical and imaging data. ResultsA total of 103 acute ischemic stroke patients suspected/confirmed to have large vessel occlusions between December 2016 to May 2019 that were transferred to hubs as possible intra-arterial thrombectomy candidates were enrolled. A total of 56 (54%) patients were sent from the spoke to the hub via ground ambulance with 47 (46%) patients traveling via air ambulance. The median spoke arrival to hub arrival time was 174 min, median spoke arrival to departure from spoke was 131 min, and median travel time was 39 min. The spoke arrival time to transfer initiation was 68 min. CT-perfusion obtained at the spoke and earlier initiation of transfer were statistically associated with shorter transfer times. ConclusionTransfer of intra-arterial thrombectomy patients in Texas may take over 4 h from spoke arrival to hub arrival. This time may be shortened by earlier transfer initiation and acceptance.

Rural representation of the surveillance, epidemiology, and end results database.

SEER data are widely used to study rural-urban disparities in cancer. However, no studies have directly assessed how well the rural areas covered by SEER represent the broader rural United States. Public data sources were used to calculate county level measures of sociodemographics, health behaviors, health access and all cause cancer incidence. Driving time from each census tract to nearest Commission on Cancer certified facility was calculated and analyzed in rural SEER and non-SEER areas. Rural SEER and non-SEER counties were similar with respect to the distribution of age, race, sex, poverty, health behaviors, provider density, and cancer screening. Overall cancer incidence was similar in rural SEER vs non-SEER counties. However, incidence for White, Hispanic, and Asian patients was higher in rural SEER vs non-SEER counties. Unadjusted median travel time was 53min (IQR 34-82) in rural SEER tracts and 54min (IQR 35-82) in rural non-SEER census tracts. Linear modeling showed shorter travel times across all levels of rurality in SEER vs non-SEER census tracts when controlling for region (Large Rural: 13.4min shorter in SEER areas 95% CI 9.1;17.6; Small Rural: 16.3min shorter 95% CI 9.1;23.6; Isolated Rural: 15.7min shorter 95% CI 9.9;21.6). The rural population covered by SEER data is comparable to the rural population in non-SEER areas. However, patients in rural SEER regions have shorter travel times to care than rural patients in non-SEER regions. This needs to be considered when using SEER-Medicare to study access to cancer care.

“In cities, it’s not far, but it takes long”: comparing estimated and replicated travel times to reach life-saving obstetric care in Lagos, Nigeria

BackgroundTravel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to...

Juvenile Chinook Salmon Survival, Travel Time, and Floodplain Use Relative to Riverine Channels in the Sacramento–San Joaquin River Delta

AbstractFloodplains provide multiple benefits to both resident and migratory fish species, including juvenile Chinook Salmon Oncorhynchus tshawytscha, but direct comparisons of survival during migration through a floodplain versus riverine routes are scarce. The Yolo Bypass is a broad floodplain of the Sacramento River that floods in about 30% of years in response to large, uncontrolled runoff events. We analyzed data from an acoustic telemetry study conducted in winter 2016 to estimate the proportion of tagged juvenile Chinook Salmon entrained from the Sacramento River into the Yolo Bypass and their spatial distribution within the Yolo Bypass. In addition, we compared survival and travel time of Chinook Salmon that migrated through the Yolo Bypass to those migrating via alternative non‐floodplain migration routes at varying stages of a flood event that activated the Yolo Bypass. We found that entrainment into the Yolo Bypass ranged from 1% to 80% among different release groups, with the highest entrainment coinciding with the peak of the March 2016 flooding event. Survival for Chinook Salmon migrating through the Yolo Bypass was similar to survival of those migrating through main‐stem migration routes. At the relatively high flows necessary to enable flooding of the Yolo Bypass, survival estimates varied little among release groups and migration routes. Furthermore, mean daily survival rates for Chinook Salmon migrating through the flooded Yolo Bypass were comparable to those of fish migrating through the other non‐floodplain routes. Median travel times remained relatively constant during various stages of flooding in the Yolo Bypass. This research should help managers to better understand the potential costs and benefits to floodplain restoration and routing of migrating Chinook Salmon into off‐channel habitat.

Categorising specimen referral delays for CD4 testing: How inter-laboratory distances and travel times impact turn-around time across a national laboratory service in South Africa.

BackgroundThe South African National Health Laboratory Service provides laboratory services for public sector health facilities, utilising a tiered laboratory model to refer samples for CD4 testing from 255 source laboratories into 43 testing laboratories.ObjectiveThe aim of this study was to determine the impact of distance on inter-laboratory referral time for public sector testing in South Africa in 2018.MethodsA retrospective cross-sectional study design analysed CD4 testing inter-laboratory turn-around time (TAT) data for 2018, that is laboratory-to-laboratory TAT from registration at the source to referral receipt at the testing laboratory. Google Maps was used to calculate inter-laboratory distances and travel times. Distances were categorised into four buckets, with the median and 75th percentile reported. Wilcoxon scores were used to assess significant differences in laboratory-to-laboratory TAT across the four distance categories.ResultsCD4 referrals from off-site source laboratories comprised 49% (n = 1 390 510) of national reporting. A positively skewed distribution of laboratory-to-laboratory TAT was noted, with a median travel time of 11 h (interquartile range: 7–17), within the stipulated 12 h target. Inter-laboratory distance categories of less than 100 km, 101–200 km, 201–300 km and more than 300 km (p < 0.0001) had 75th percentiles of 8 h, 17 h, 14 h and 27 h.ConclusionVariability in inter-laboratory TAT was noted for all inter-laboratory distances, especially those exceeding 300 km. The correlation between distance and laboratory-to-laboratory TAT suggests that interventions are required for distant laboratories.

Human Milk Expression, Storage, and Transport by Women Whose Infants Are Inpatients at a Tertiary Neonatal Unit in Melbourne, Australia: An Exploratory Study.

Expression and storage of mothers' own milk at home and its transportation to hospital neonatal units are a common practice worldwide when newborns are inpatients. Studies assessing adherence to hospital protocols and guidelines for this are not widely published. To explore the advice received and practices followed by mothers when expressing, storing, and transporting their milk from home to the hospital, with a substudy exploring the factors related to temperature maintenance of refrigerated milk at recommended values. Cross-sectional descriptive study at the neonatal intensive care unit of Mercy Hospital for Women, Melbourne, Australia. Mothers who were discharged home after birth of the infant, but whose infant(s) remained in the neonatal unit for 7 days or more participated. All participants completed a self-administered questionnaire. In the substudy, home refrigerator temperature and surface temperature of milk on arrival to the hospital were recorded. The questionnaire was completed by 100 mothers; 38 participated in the substudy. Median travel time from home to the hospital was 32 minutes (range, 2-135 minutes). Lactation consultants were the largest group providing information, with 44 participants (45%) identifying them as the primary information source. Knowledge about recommended refrigerator storage times for expressed milk was correct in 53 mothers (54%). Coolness of milk was better maintained when transported in an insulated food container than nonuse (surface temperature: mean 9.1°C vs 12.2°C; P = .007). Distance and travel duration were not correlated with temperature. More diligent monitoring of conditions under which mothers' own milk is transported to hospital is required, and the use of an insulated food container for refrigerated/frozen milk, even for a short duration, should be strongly recommended. Staff to be trained and better equipped to provide uniform, concise information on expressed human milk management to mothers. Further research to correlate factors associated with transporting human milk expressed at home and infant health outcome is needed.

Retention in care and distance between home and hospital in HIV patients of Buenos Aires City

Distance from patient's home to the hospital has been proposed as one of the limiting factors for patient's retention in care. Retrospective cohort study of HIV+ patients 18 years or older who had their first clinical visit between 2011 and 2013 at a reference center in Buenos Aires, Argentina. Patients were considered to be retained in care if they had>=1 clinical visit, laboratory markers (VL and/or CD4 count) and/or ARVs pick-up during the year after their first clinical visit. Each patient address's latitude-longitude was obtained using Google Maps® web service. Home-hospital distance and travel time were obtained with Google Maps Distance Matrix API® service. Of 1020 patients who started follow-up, 15 died and 158 were transferred to another site. Of the remaining, 816 (96.3%) had identifiable address in their electronic medical record. Median age at the time of the first visit was 33 (IQR 27-41) years, 654 (77.9%) patients were male. Median home-hospital distance was 10.3 (IQR 4.4-34.7) km and median travel time was 58.5 (IQR 35-102.5) minutes. 730 patients (89.5%; CI 87.1-91.5%) remained in follow-up after 1 year of their first visit. We didńt find association between travel time and home-hospital distance with retention in this population. In our study, distance between home and the care center was not associated with lower retention one year after first visit in adult HIV patients attending a public hospital.

Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis

BackgroundSevere acute respiratory syndrome coronavirus 2, the virus causing COVID-19, is rapidly spreading across sub-Saharan Africa. Hospital-based care for COVID-19 is often needed, particularly among older adults. However, a key barrier to accessing hospital care in sub-Saharan Africa is travel time to the nearest health-care facility. To inform the geographical targeting of additional health-care resources, we aimed to estimate travel time at a 1 km × 1 km resolution to the nearest hospital and to the nearest health-care facility of any type for adults aged 60 years and older in sub-Saharan Africa.MethodsWe assembled a dataset on the geolocation of health-care facilities, separately for hospitals and any type of health-care facility and including both private-sector and public-sector facilities, using data from the OpenStreetMap project and the Kenya Medical Research Institute–Wellcome Trust Programme. Population data at a 1 km × 1 km resolution were obtained from WorldPop. We estimated travel time to the nearest health-care facility for each 1 km × 1 km grid using a cost–distance algorithm.Findings9·6% (95% CI 5·2–16·9) of adults aged 60 years or older across sub-Saharan Africa had an estimated travel time to the nearest hospital of 6 h or longer, varying from 0·0% (0·0–3·7) in Burundi and The Gambia to 40·9% (31·8–50·7) in Sudan. For the nearest health-care facility of any type (whether primary, secondary, or tertiary care), 15·9% (95% CI 10·1–24·4) of adults aged 60 years or older across sub-Saharan Africa had an estimated travel time of 2 h or longer, ranging from 0·4% (0·0–4·4) in Burundi to 59·4% (50·1–69·0) in Sudan. Most countries in sub-Saharan Africa contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of 12 h or longer and to the nearest health-care facility of any type of 6 h or longer. The median travel time to the nearest hospital for the fifth of adults aged 60 years or older with the longest travel times was 348 min (IQR 240–576; equal to 5·8 h) for the entire population of sub-Saharan Africa, ranging from 41 min (34–54) in Burundi to 1655 min (1065–2440; equal to 27·6 h) in Gabon.InterpretationOur high-resolution maps of estimated travel times to both hospitals and health-care facilities of any type can be used by policy makers and non-governmental organisations to help target additional health-care resources, such as makeshift hospitals or transport programmes to existing health-care facilities, to older adults with the least physical access to care. In addition, this analysis shows the locations of population groups most likely to under-report COVID-19 symptoms because of low physical access to health-care facilities. Beyond the COVID-19 response, this study can inform the efforts of countries to improve physical access to care for conditions that are common among older adults in the region, such as chronic non-communicable diseases.FundingBill & Melinda Gates Foundation.

Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru.

To better estimate the travel time to the most proximate health care facility (HCF) and determine differences across heterogeneous land coverage types, this study explored the use of a novel cloud-based geospatial modeling approach. Geospatial data of 145,134 cities and villages and 8,067 HCF were gathered with land coverage types, roads and river networks, and digital elevation data to produce high-resolution (30 m) estimates of travel time to HCFs across Peru. This study estimated important variations in travel time to HCFs between urban and rural settings and major land coverage types in Peru. The median travel time to primary, secondary, and tertiary HCFs was 1.9-, 2.3-, and 2.2-fold higher in rural than urban settings, respectively. This study provides a new methodology to estimate the travel time to HCFs as a tool to enhance the understanding and characterization of the profiles of accessibility to HCFs in low- and middle-income countries.

Profile and complexity of travel medicine consultations in Chile: unicentric cross-sectional study

ObjectiveTo analyse the spectrum, vaccination needs and pretravel advice complexity of travellers presenting at a travel medicine clinic in Santiago, Chile.DesignCross-sectional study.SettingPretravel consultations in a private healthcare centre in Chile,...