Global maps of travel time to healthcare facilities

Effects of the Implementation of Transport-Driven Poverty Alleviation Policy on Health Care-Seeking Behavior and Medical Expenditure Among Older People in Rural Areas: Quasi-Experimental Study

BACKGROUND

Improving the rural residents' accessibility to and affordability of health care is recognized as a common target globally. The Health in All Policies approach, from the Declaration of Helsinki to the United Nations' Decade Of Healthy Ageing, strengthened the far-reaching effect of large-scale public policies on health care-seeking behavior; however, the effects of national transport policy on health care-seeking behavior is unclear.

OBJECTIVE

This quasi-experimental study aimed to examine the effects of the implementation of transport-driven poverty alleviation (TPA) policy on health care-seeking behavior and medical expenditure among older adults in rural areas and the mechanism underlying these effects.

METHODS

We designed a quasi-experiment to estimate the effects of TPA policy implementation on health care-seeking behavior and medical expenditure among older adults in rural areas through a difference-in-differences (DID) analysis based on data from the China Health and Retirement Longitudinal Study in 2011, 2013, 2015, and 2018. The underlying mechanism was analyzed and effect modification patterns were further investigated by poor households, health status, and age.

RESULTS

Our findings validated a positive contribution of TPA policy on health care-seeking behavior among older adults in rural areas. After the implementation of TPA policy, the number of inpatient visits increased by annually 0.35 times per person, outpatient medical expenditure increased by 192% per month, and inpatient medical expenditure increased by 57% annually compared with those of older adults in rural areas without the implementation of TPA policy. Further, there was a significant modification effect, with a positive effect among poor households, healthier older adults, and those aged 60-80 years. Additionally, the policy improved the patients' capabilities to seek long-distance care (β=23.16, 95% CI -0.99 to 45.31) and high-level hospitals (β=.08, 95% CI -0.02 to 0.13), and increased individual income to acquire more medical services (β=4.57, 95% CI -4.46 to 4.68).

CONCLUSIONS

These findings validate the positive contribution of TPA policy on health care-seeking behavior among older adults in rural areas; however, the medical expenditure incurred was also high. Concerted efforts are needed to address health care-seeking dilemmas in rural areas, and attention must be paid to curbing medical expenditure growth for older adults in rural areas during TPA policy implementation.

Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemic

BACKGROUND

Timely access to healthcare is essential but measuring access is challenging. Prior research focused on analyzing potential travel times to healthcare under optimal mobility scenarios that do not incorporate direct observations of human mobility, potentially underestimating the barriers to receiving care for many populations.

METHODS

We introduce an approach for measuring accessibility by utilizing travel times to healthcare facilities from aggregated and anonymized smartphone Location History data. We measure these revealed travel times to healthcare facilities in over 100 countries and juxtapose our findings with potential (optimal) travel times estimated using Google Maps directions. We then quantify changes in revealed accessibility associated with the COVID-19 pandemic.

RESULTS

We find that revealed travel time differs substantially from potential travel time; in all but 4 countries this difference exceeds 30 minutes, and in 49 countries it exceeds 60 minutes. Substantial variation in revealed healthcare accessibility is observed and correlates with life expectancy (⍴=-0.70) and infant mortality (⍴=0.59), with this association remaining significant after adjusting for potential accessibility and wealth. The COVID-19 pandemic altered the patterns of healthcare access, especially for populations dependent on public transportation.

CONCLUSIONS

Our metrics based on empirical data indicate that revealed travel times exceed potential travel times in many regions. During COVID-19, inequitable accessibility was exacerbated. In conjunction with other relevant data, these findings provide a resource to help public health policymakers identify underserved populations and promote health equity by formulating policies and directing resources towards areas and populations most in need.

Association between mental health workforce supply and clusters of high and low rates of youth suicide: An Australian study using suicide mortality data from 2016 to 2020

OBJECTIVE

To examine the association between mental health workforce supply and spatial clusters of high versus low incidence of youth suicide.

METHODS

A cross-sectional analysis of spatial suicide clusters in young Australians (aged 10-25) from 2016 to 2020 was conducted using the scan statistic and suicide data from the National Coronial Information System. Mental health workforce was extracted from the 2020 National Health Workforce Dataset by local government areas. The Geographic Index of Relative Supply was used to estimate low and moderate-to-high mental health workforce supply for clusters characterised by a high and low incidence of suicide (termed suicide hotspots and coldspots, respectively). Univariate and multivariate logistic regression was used to determine the association between suicide clusters and a range of sociodemographic characteristics including mental health workforce supply.

RESULTS

Eight suicide hotspots and two suicide coldspots were identified. The multivariate analysis showed low mental health workforce supply was associated with increased odds of being involved in a suicide hotspot (adjusted odds ratio = 8.29; 95% confidence interval = 5.20-13.60), followed by residential remoteness (adjusted odds ratio = 2.85; 95% confidence interval = 1.68-4.89), and illicit drug consumption (adjusted odds ratio = 1.97; 1.24-3.11). Both coldspot clusters occurred in areas with moderate-to-high mental health workforce supply.

CONCLUSION

Findings highlight the potential risk and protective roles that mental health workforce supply may play in the spatial distributions of youth suicide clusters. These findings have important implications for the provision of postvention and the prevention of suicide clusters.

Hide Your Distance: Privacy Risks and Protection in Spatial Accessibility Analysis

Measuring spatial accessibility to healthcare resources and facilities has long been an important problem in public health. For example, during disease outbreaks, sharing spatial accessibility data such as individual travel distances to health facilities is vital to policy making and designing effective interventions. However, sharing these data may raise privacy concerns, as information about individual data contributors (e.g., health status and residential address) may be disclosed. In this work, we investigate those unintended information leakage in spatial accessibility analysis. Specifically, we are interested in understanding whether sharing data for spatial accessibility computations may disclose individual participation (i.e., membership inference) and personal identifiable information (i.e., address inference). Furthermore, we propose two provably private algorithms that mitigate those privacy risks. The evaluation is conducted with real population and healthcare facilities data from Mecklenburg county, NC and Nashville, TN. Compared to state-of-the-art privacy practices, our methods effectively reduce the risks of membership and address disclosure, while providing useful data for spatial accessibility analysis.

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.

Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots

The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers-landscape change, host distribution, and human exposure-associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N = 9) or transboundary (N = 10). High-risk areas were mainly closer (11-20%) rather than far ( < 1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals and not livestock as secondary hosts. China (N = 2) and Indonesia (N = 1) had clusters with the highest risk. Our findings can help stakeholders in land use planning, integrating healthcare implementation and One Health actions.

A geospatial database of close-to-reality travel times to obstetric emergency care in 15 Nigerian conurbations

Travel time estimation accounting for on-the-ground realities between the location where a need for emergency obstetric care (EmOC) arises and the health facility capable of providing EmOC is essential for improving pregnancy outcomes. Current understanding of travel time to care is inadequate in many urban areas of Africa, where short distances obscure long travel times and travel times can vary by time of day and road conditions. Here, we describe a database of travel times to comprehensive EmOC facilities in the 15 most populated extended urban areas of Nigeria. The travel times from cells of approximately 0.6 × 0.6 km to facilities were derived from Google Maps Platform's internal Directions Application Programming Interface, which incorporates traffic considerations to provide closer-to-reality travel time estimates. Computations were done to the first, second and third nearest public or private facilities. Travel time for eight traffic scenarios (including peak and non-peak periods) and number of facilities within specific time thresholds were estimated. The database offers a plethora of opportunities for research and planning towards improving EmOC accessibility.

The potential of a multimodal digital care program in addressing healthcare inequities in musculoskeletal pain management

Digital interventions have emerged as a solution for time and geographical barriers, however, their potential to target other social determinants of health is largely unexplored. In this post-hoc analysis, we report the impact of social deprivation on engagement and clinical outcomes after a completely remote multimodal musculoskeletal (MSK) digital care program managed by a culturally-sensitive clinical team. Patients were stratified in five categories according to their social deprivation index, and cross-referenced with their race/ethnicity, rurality and distance to healthcare facilities. From a total of 12,062 patients from all U.S. states, 8569 completed the program. Higher social deprivation was associated with greater baseline disease burden. We observed that all categories reported pain improvements (ranging from -2.0 95%CI -2.1, -1.9 to -2.1 95%CI -2.3, -1.9, p < 0.001) without intergroup differences in mean changes or responder rates (from 59.9% (420/701) to 66.6% (780/1172), p = 0.067), alongside reduction in analgesic consumption. We observed significant improvements in mental health and productivity across all categories, with productivity and non-work-related functional recovery being greater within the most deprived group. Engagement was high but varied slightly across categories. Together these findings highlight the importance of a patient-centered digital care program as a tool to address health inequities in musculoskeletal pain management. The idea of investigating social deprivation within a digital program provides a foundation for future work in this field to identify areas of improvement.

A geospatial analysis of accessibility and availability to implement the primary healthcare roadmap in Ethiopia

BACKGROUND

Primary healthcare (PHC) is a crucial strategy for achieving universal health coverage. Ethiopia is working to improve its primary healthcare system through the Optimization of Health Extension Program (OHEP), which aims to increase accessibility, availability and performance of health professionals and services. Measuring current accessibility of healthcare facilities and workforce availability is essential for the success of the OHEP and achieving universal health coverage in the country.

METHODS

In this study we use an innovative mixed geospatial approach to assess the accessibility and availability of health professionals and services to provide evidence-based recommendations for the implementation of the OHEP. We examined travel times to health facilities, referral times between health posts and health centers, geographical coverage, and the availability and density of health workers relative to the population.

RESULTS

Our findings show that the accessibility and availability of health services in Somali region of Ethiopia is generally low, with 65% of the population being unable to reach a health center or a health post within 1 h walking and referral times exceeding 4 h walking on average. The density of the health workforce is low across Somali region, with no health center being adequately staffed as per national guidelines.

CONCLUSIONS

Improving accessibility and addressing healthcare worker scarcity are challenges for implementing the primary care roadmap in Ethiopia. Upgrading health posts and centers, providing comprehensive services, and training healthcare workers are crucial. Effective outreach strategies are also needed to bridge the gap and improve accessibility and availability.

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Urban network analytics has become an essential tool for understanding and modeling the intricate complexity of cities. We introduce the Urbanity data repository to nurture this growing research field, offering a comprehensive, open spatial network resource spanning 50 major cities in 29 countries worldwide. Our workflow enhances OpenStreetMap networks with 40 + high-resolution indicators from open global sources such as street view imagery, building morphology, urban population, and points of interest, catering to a diverse range of applications across multiple fields. We extract streetscape semantic features from more than four million street view images using computer vision. The dataset's strength lies in its thorough processing and validation at every stage, ensuring data quality and consistency through automated and manual checks. Accompanying the dataset is an interactive, web-based dashboard we developed which facilitates data access to even non-technical stakeholders. Urbanity aids various GeoAI and city comparative analyses, underscoring the growing importance of urban network analytics research.

Managing Musculoskeletal Pain in Older Adults Through a Digital Care Solution: Secondary Analysis of a Prospective Clinical Study

BACKGROUND

Aging is closely associated with an increased prevalence of musculoskeletal conditions. Digital musculoskeletal care interventions emerged to deliver timely and proper rehabilitation; however, older adults frequently face specific barriers and concerns with digital care programs (DCPs).

OBJECTIVE

This study aims to investigate whether known barriers and concerns of older adults impacted their participation in or engagement with a DCP or the observed clinical outcomes in comparison with younger individuals.

METHODS

We conducted a secondary analysis of a single-arm investigation assessing the recovery of patients with musculoskeletal conditions following a DCP for up to 12 weeks. Patients were categorized according to age: ≤44 years old (young adults), 45-64 years old (middle-aged adults), and ≥65 years old (older adults). DCP access and engagement were evaluated by assessing starting proportions, completion rates, ability to perform exercises autonomously, assistance requests, communication with their physical therapist, and program satisfaction. Clinical outcomes included change between baseline and program end for pain (including response rate to a minimal clinically important difference of 30%), analgesic usage, mental health, work productivity, and non-work-related activity impairment.

RESULTS

Of 16,229 patients, 12,082 started the program: 38.3% (n=4629) were young adults, 55.7% (n=6726) were middle-aged adults, and 6% (n=727) were older adults. Older patients were more likely to start the intervention and to complete the program compared to young adults (odds ratio [OR] 1.72, 95% CI 1.45-2.06; P<.001 and OR 2.40, 95% CI 1.97-2.92; P<.001, respectively) and middle-aged adults (OR 1.22, 95% CI 1.03-1.45; P=.03 and OR 1.38, 95% CI 1.14-1.68; P=.001, respectively). Whereas older patients requested more technical assistance and exhibited a slower learning curve in exercise performance, their engagement was higher, as reflected by higher adherence to both exercise and education pieces. Older patients interacted more with the physical therapist (mean 12.6, SD 18.4 vs mean 10.7, SD 14.7 of young adults) and showed higher satisfaction scores (mean 8.7, SD 1.9). Significant improvements were observed in all clinical outcomes and were similar between groups, including pain response rates (young adults: 949/1516, 62.6%; middle-aged adults: 1848/2834, 65.2%; and older adults: 241/387, 62.3%; P=.17).

CONCLUSIONS

Older adults showed high adherence, engagement, and satisfaction with the DCP, which were greater than in their younger counterparts, together with significant clinical improvements in all studied outcomes. This suggests DCPs can successfully address and overcome some of the barriers surrounding the participation and adequacy of digital models in the older adult population.

Serological evaluation of risk factors for exposure to malaria in a pre-elimination setting in Malaysian Borneo

Malaysia has reported no indigenous cases of P. falciparum and P. vivax for over 3 years. When transmission reaches such low levels, it is important to understand the individuals and locations where exposure risks are high, as they may be at greater risk in the case of a resurgence of transmission. Serology is a useful tool in low transmission settings, providing insight into exposure over longer durations than PCR or RDT. We ran blood samples from a 2015 population-based survey in northern Sabah, Malaysian Borneo on a multiplex bead assay. Using supervised machine learning methods, we characterised recent and historic exposure to Plasmodium falciparum and P. vivax and found recent exposure to P. falciparum to be very low, with exposure to both species increasing with age. We performed a risk-factor assessment on environmental, behavioural, demographic and household factors, and identified forest activity and longer travel times to healthcare as common risk-factors for exposure to P. falciparum and P. vivax. In addition, we used remote-sensing derived data and geostatistical models to assess environmental and spatial associations with exposure. We created predictive maps of exposure to recent P. falciparum in the study area and showed 3 clear foci of exposure. This study provides useful insight into the environmental, spatial and demographic risk factors for P. falciparum and P. vivax at a period of low transmission in Malaysian Borneo. The findings would be valuable in the case of resurgence of human malarias in the region.

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework

OBJECTIVES

To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs).

METHODS

A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake.

RESULTS

A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives.

CONCLUSIONS

We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

Updating estimates of Plasmodium knowlesi malaria risk in response to changing land use patterns across Southeast Asia

Background

Plasmodium knowlesi is a zoonotic parasite that causes malaria in humans. The pathogen has a natural host reservoir in certain macaque species and is transmitted to humans via mosquitoes of the Anopheles Leucosphyrus Group. The risk of human P. knowlesi infection varies across Southeast Asia and is dependent upon environmental factors. Understanding this geographic variation in risk is important both for enabling appropriate diagnosis and treatment of the disease and for improving the planning and evaluation of malaria elimination. However, the data available on P. knowlesi occurrence are biased towards regions with greater surveillance and sampling effort. Predicting the spatial variation in risk of P. knowlesi malaria requires methods that can both incorporate environmental risk factors and account for spatial bias in detection.

Methods & Results

We extend and apply an environmental niche modelling framework as implemented by a previous mapping study of P. knowlesi transmission risk which included data up to 2015. We reviewed the literature from October 2015 through to March 2020 and identified 264 new records of P. knowlesi, with a total of 524 occurrences included in the current study following consolidation with the 2015 study. The modelling framework used in the 2015 study was extended, with changes including the addition of new covariates to capture the effect of deforestation and urbanisation on P. knowlesi transmission.

Discussion

Our map of P. knowlesi relative transmission suitability estimates that the risk posed by the pathogen is highest in Malaysia and Indonesia, with localised areas of high risk also predicted in the Greater Mekong Subregion, The Philippines and Northeast India. These results highlight areas of priority for P. knowlesi surveillance and prospective sampling to address the challenge the disease poses to malaria elimination planning.

Rethinking access to care: A spatial-economic analysis of the potential impact of pharmacy closures in the United States

Data chronicling the geo-locations of all 61,589 pharmacies in the U.S. (from the Homeland Infrastructure Foundation-Level Data (HIFLD) Open Data interface, updated on April 2018) across 215,836 census block groups were combined with Medically Underserved Areas (MUAs) information, and the Centers for Disease Control and Prevention's Social Vulnerability Index (CDC-SVI). Geospatial techniques were applied to calculate the distance between the center of each census block and the nearest pharmacy. We then modeled the expected additional travel distance if the nearest pharmacy to the center of a census block closed and estimated additional travel costs, CO2 emissions, and lost labor productivity costs associated with the additional travel. Our findings revealed that MUA residents have almost two times greater travel distances to pharmacies than non-MUAs (4,269 m (2.65 mi) vs. 2,388 m (1.48 mi)), and this disparity is exaggerated with pharmacy closures (107% increase in travel distance in MUAs vs. 75% increase in travel distance in non-MUAs). Similarly, individuals living in MUAs experience significantly greater average annual economic costs than non-MUAs ($34,834 ± $668 vs. $22,720 ± $326). Our findings suggest the need for additional regulations to ensure populations are not disproportionately affected by these closures and that there is a significant throughput with community stakeholders before any pharmacy decides to close.

Impacts on Human Movement in Australian Cities Related to the COVID-19 Pandemic

No studies have yet examined high-resolution shifts in the spatial patterns of human movement in Australia throughout 2020 and 2021, a period coincident with the repeated enactment and removal of varied governmental restrictions aimed at reducing community transmission of SARS-CoV-2. We compared overlapping timeseries of COVID-19 pandemic-related restrictions, epidemiological data on cases and vaccination rates, and high-resolution human movement data to characterize population-level responses to the pandemic in Australian cities. We found that restrictions on human movement and/or mandatory business closures reduced the average population-level weekly movement volumes in cities, as measured by aggregated travel time, by almost half. Of the movements that continued to occur, long movements reduced more dramatically than short movements, likely indicating that people stayed closer to home. We also found that the repeated lockdowns did not reduce their impact on human movement, but the effect of the restrictions on human movement waned as the duration of restrictions increased. Lastly, we found that after restrictions ceased, the subsequent surge in SARS-CoV-2 transmission coincided with a substantial, non-mandated drop in human movement volume. These findings have implications for public health policy makers when faced with anticipating responses to restrictions during future emergency situations.

Boundary effects on topological characteristics of urban road networks

Urban road networks (URNs), as simplified views and important components of cities, have different structures, resulting in varying levels of transport efficiency, accessibility, resilience, and many socio-economic indicators. Thus, topological characteristics of URNs have received great attention in the literature, while existing studies have used various boundaries to extract URNs for analysis. This naturally leads to the question of whether topological patterns concluded using small-size boundaries keep consistent with those uncovered using commonly adopted administrative boundaries or daily travel range-based boundaries. This paper conducts a large-scale empirical analysis to reveal the boundary effects on 22 topological metrics of URNs across 363 cities in mainland China. Statistical results show that boundaries have negligible effects on the average node degree, edge density, orientation entropy of road segments, and the eccentricity for the shortest or fastest routes, while other metrics including the clustering coefficient, proportion of high-level road segments, and average edge length together with route-related metrics such as average angular deviation show significant differences between road networks extracted using different boundaries. In addition, the high-centrality components identified using varied boundaries show significant differences in terms of their locations, with only 21%-28% of high-centrality nodes overlapping between the road networks extracted using administrative and daily travel range-based boundaries. These findings provide useful insights to assist urban planning and better predict the influence of a road network structure on the movement of people and the flow of socio-economic activities, particularly in the context of rapid urbanization and the ever-increasing sprawl of road networks.

Change in Geographic Accessibility to Dental Clinics Affects Access to Care

Access to dental clinics is a feature of the neighborhood service environment that may influence oral health care utilization. However, residential selection poses a challenge to causal inference. By studying the involuntary relocation of survivors of the 2011 Great East Japan Earthquake and Tsunami (GEJE), we examined the association between changes in geographic distance to dental clinics and dental visits. Longitudinal data from a cohort of older residents in Iwanuma City directly impacted by the GEJE were analyzed in this study. The baseline survey was conducted in 2010, 7 mo before the occurrence of GEJE, and a follow-up was conducted in 2016. Using Poisson regression models, we estimated the incidence rate ratios (IRR) and 95% confidence intervals (CIs) for the uptake of denture use (as a proxy for dental visits) according to changes in distance from the nearest dental clinic to their house. Age at baseline, housing damage by the disaster, deteriorating economic conditions, and worsened physical activity were used as confounders. Among the 1,098 participants who had not worn dentures before the GEJE, 495 were men (45.1%), with a mean ± SD age at baseline of 74.0 ± 6.9 y. During the 6-year follow-up, 372 (33.9%) participants initiated denture use. Compared to those who experienced a large increase in distance to dental clinics (>370.0-6,299.1 m), a large decrease in distance to dental clinics (>429.0-5,382.6 m) was associated with a marginally significantly higher initiation of denture use among disaster survivors (IRR = 1.28; 95% CI, 0.99-1.66). The experience of major housing damage was independently associated with higher initiation of denture use (IRR = 1.77; 95% CI, 1.47-2.14). Improved geographic access to dental clinics may increase dental visits of disaster survivors. Further studies in non-disaster-affected areas are needed to generalize these findings.

Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic

An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.

Current patterns of trauma center proliferation have not led to proportionate improvements in access to care or mortality after injury: An ecologic study

BACKGROUND

Timely access to high-level (I/II) trauma centers (HLTCs) is essential to minimize mortality after injury. Over the last 15 years, there has been a proliferation of HLTC nationally. The current study evaluates the impact of additional HLTC on population access and injury mortality.

METHODS

A geocoded list of HLTC, with year designated, was obtained from the American Trauma Society, and 60-minute travel time polygons were created using OpenStreetMap data. Census block group population centroids, county population centroids, and American Communities Survey data from 2005 and 2020 were integrated. Age-adjusted nonoverdose injury mortality was obtained from CDC Wide-ranging Online Data for Epidemiologic Research and the Robert Wood Johnson Foundation. Geographically weighted regression models were used to identify independent predictors of HLTC access and injury mortality.

RESULTS

Over the 15-year (2005-2020) study period, the number of HLTC increased by 31.0% (445 to 583), while population access to HLTC increased by 6.9% (77.5-84.4%). Despite this increase, access was unchanged in 83.1% of counties, with a median change in access of 0.0% (interquartile range, 0.0-1.1%). Population-level age-adjusted injury mortality rates increased by 5.39 per 100,000 population during this time (60.72 to 66.11 per 100,000). Geographically weighted regression controlling for population demography and health indicators found higher median income and higher population density to be positively associated with majority (≥50%) HLTC population coverage and negatively associated with county-level nonoverdose mortality.

CONCLUSION

Over the past 15 years, the number of HLTC increased 31%, while population access to HLTC increased only 6.9%. High-level (I/II) trauma center designation is likely driven by factors other than population need. To optimize efficiency and decrease potential oversupply, the designation process should include population level metrics. Geographic information system methodology can be an effective tool to assess optimal placement.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level IV.

Geographic pair-matching in large-scale cluster randomized trials

Custer randomized trials are often used to study large-scale public health interventions. In large trials, even small improvements in statistical efficiency can have profound impacts on the required sample size and cost. Pair matched randomization is one strategy with potential to increase trial efficiency, but to our knowledge there have been no empirical evaluations of pair-matching in large-scale, epidemiologic field trials. Location integrates many socio-demographic and environmental characteristics into a single feature. Here, we show that geographic pair-matching leads to substantial gains in statistical efficiency for 14 child health outcomes that span growth, development, and infectious disease through a re-analysis of two large-scale trials of nutritional and environmental interventions in Bangladesh and Kenya. We estimate relative efficiencies ≥1.1 for all outcomes assessed and relative efficiencies regularly exceed 2.0, meaning an unmatched trial would have needed to enroll at least twice as many clusters to achieve the same level of precision as the geographically pair-matched design. We also show that geographically pair-matched designs enable estimation of fine-scale, spatially varying effect heterogeneity under minimal assumptions. Our results demonstrate broad, substantial benefits of geographic pair-matching in large-scale, cluster randomized trials.

Impact of the Early COVID-19 Pandemic on Burn Care: A Multi-National Study

This qualitative study documents and analyzes COVID-19's impacts on burn care in twelve nations. Burn care saw valuable improvements in surgical techniques, skin grafting, and other acute treatments during the decades before the COVID-19 pandemic which increased severely burned patients' survival rates and quality of life. Detailed interviews, conducted in the 12 nations reveal that the COVID-19 pandemic greatly affected the delivery of acute and rehabilitation services for burn patients. Resources have been suctioned away from non-COVID healthcare and burn care has not been spared. Acute, post-acute rehabilitation, and mental health services have all suffered. Weak preparation has deeply burdened health care services in most nations, resulting in lower access to care. Access problems have accelerated innovations like telehealth in many nations. The spread of misinformation through social and traditional media has contributed to the varied responses to COVID-19. This compounded problems in health care delivery. Burn care providers delivered services for survivors during extremely difficult circumstances by continuing to furnish acute and long-term services for patients with complex disease. Emphasis on future pandemic preparedness will be vital because they undermine all aspects of burn care and patient outcomes. As the COVID-19 pandemic continues to unfold around the world, it will be important to continue to monitor and analyze the adaptability of nations, health care systems, and burn care providers.

Spatial Accessibility Evaluation and Location Optimization of Primary Healthcare in China: A Case Study of Shenzhen

The unbalanced allocation of healthcare resources is a major challenge that hinders access to healthcare. Taking Shenzhen as an example, this study aimed to enhance equity in obtaining healthcare services, through measuring and visualizing the spatial accessibility of community healthcare centers (CHC), and optimizing CHC geospatial allocation. We used the number of health technicians per 10,000 to represent the CHC's service capacity, combined with resident points and census data to calculate the population the CHC needs to carry, and then analyzed the accessibility based on the Gaussian two-step floating catchment area method. In 2020, five regions in Shenzhen had better spatial accessibility scores: Nanshan (0.250), Luohu (0.246), Futian (0.244), Dapeng (0.226), and Yantian (0.196). The spatial accessibility of CHCs shows a gradual decrease from the city center to the edge, which is affected by economic and topographic factors. With the support of the maximal covering location problem model, we selected up to 567 candidate locations for the new CHC, which could improve Shenzhen's accessibility score from 0.189 to 0.361 and increase the coverage population by 63.46% within a 15-min impedance. By introducing spatial techniques and maps, this study provides (a) new evidence for promoting equitable access to primary healthcare services in Shenzhen and (b) a foundation for improving the accessibility of public service facilities in other areas.

An archetypes approach to malaria intervention impact mapping: a new framework and example application

BACKGROUND

As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored.

METHODS

First, dimensionality reduction and clustering techniques were applied to rasterized geospatial environmental and mosquito covariates to find archetypal malaria transmission patterns. Next, mechanistic models were run on a representative site from each archetype to assess intervention impact. Finally, these mechanistic results were reprojected onto each pixel to generate full maps of intervention impact. The example configuration used ERA5 and Malaria Atlas Project covariates, singular value decomposition, k-means clustering, and the Institute for Disease Modeling's EMOD model to explore a range of three-year malaria interventions primarily focused on vector control and case management.

RESULTS

Rainfall, temperature, and mosquito abundance layers were clustered into ten transmission archetypes with distinct properties. Example intervention impact curves and maps highlighted archetype-specific variation in efficacy of vector control interventions. A sensitivity analysis showed that the procedure for selecting representative sites to simulate worked well in all but one archetype.

CONCLUSION

This paper introduces a novel methodology which combines the richness of spatiotemporal mapping with the rigor of mechanistic modeling to create a multi-purpose infrastructure for answering a broad range of important questions in the malaria policy space. It is flexible and adaptable to a range of input covariates, mechanistic models, and mapping strategies and can be adapted to the modelers' setting of choice.

CLUMondo-BNU for simulating land system changes based on many-to-many demand-supply relationships with adaptive conversion orders

Land resources are fundamentally important to human society, and their transition from one macroscopic state to another is a vital driving force of environment and climate change locally and globally. Thus, many efforts have been devoted to the simulations of land changes. Among all spatially explicit simulation models, CLUMondo is the only one that simulates land changes by incorporating the multifunctionality of a land system and allows the establishment of many-to-many demand-supply relationships. In this study, we first investigated the source code of CLUMondo, providing a complete, detailed mechanism of this model. We found that the featured function of CLUMondo-balancing demands and supplies in a many-to-many mode-relies on a parameter called conversion order. The setting of this parameter is a manual process and requires expert knowledge, which is not feasible for users without an understanding of the whole, detailed mechanism. Therefore, the second contribution of this study is the development of an automatic method for adaptively determining conversion orders. Comparative experiments demonstrated the validity and effectiveness of the proposed automated method. We revised the source code of CLUMondo to incorporate the proposed automated method, resulting in CLUMondo-BNU v1.0. This study facilitates the application of CLUMondo and helps to exploit its full potential.

Fractional Dynamics Foster Deep Learning of COPD Stage Prediction

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages-from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.

Premature mortality attributable to NO2 exposure in cities and the role of built environment: A global analysis

BACKGROUND

Environmental risks accumulate in cities, including polluted air and health disparities, but these risks can be reduced through scientific city planning. The purpose of this study was to investigate the global burden of premature mortality attributable to NO₂ exposure in urban areas and the role of the built environment in this regard.

METHODS

An approach based on health impact assessment was used to estimate the premature mortality burdens associated with NO₂ exposure in 13,169 urban areas around the world using globally gridded NO₂ and population estimates, baseline mortality, and epidemiologically derived exposure-response functions. We used the most recent WHO recommended value (i.e.,10 μg/m³) as a counterfactual concentration. Finally, the relationship between the characteristics of the built environment at the city level and the burden of NO₂-related mortality was evaluated.

RESULTS

Worldwide, 549,715(95%CI: 276204-815,023) cases of death attributable to NO₂ exposure in urban areas could be prevented if compliance with the latest WHO guideline, accounting for 2.7 % (95%CI:1.4 %-4.0 %) of total mortalities in 2019. Across cities around the world, the age-standardized mortality rate (per 100,000 people) attributable to NO₂ exposure ranged from 51.3 (95%CI:25.8-76.0) in Central Asia to 3.4(95%CI: 1.7-5.1) in Oceania. Although there was a significant decrease in premature mortality attributable to NO₂ exposure globally, considerable regional heterogeneity exists, with cities in Central Asia and Andean Latin America in particular exhibiting an upward trend. Further, we discovered a positive association between population density and street connectivity with mortality attributable to NO₂. While the increase in green and blue space were significantly associated with a lower NO₂-associated mortality.

CONCLUSION

The findings of this study provided a comprehensive understanding of the premature mortality burden due to NO₂ in cities throughout the world and the role that urban planning policies can play in reducing the health burden associated with air pollution.

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis

Background

The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors.

Methods

Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted.

Findings

Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance.

Interpretation

Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures.

Funding

NASA's Group on Earth Observations Work Programme (16-GEO16-0047).

Determinants of maternal healthcare utilisation among pregnant women in Southern Ethiopia: a multi-level analysis

BACKGROUND

Despite efforts to make maternal health care services available in rural Ethiopia, utilisation status remains low. Therefore, this study aimed to assess maternal health care services' status and determinants in rural Ethiopia.

METHODS

The study used quasi-experimental pre- and post-comparison baseline data. A pretested, semi-structured, interviewer-administered questionnaire was used to collect data. A multilevel, mixed-effects logistic regression was used to identify individual and communal level factors associated with utilisation of antenatal care (ANC), skilled birth attendance (SBA), and postnatal care (PNC). The adjusted odds ratio (AOR) and corresponding 95% confidence intervals (CI) were estimated with a p-value of less than 0.05, indicating statistical significance.

RESULTS

Seven hundred and twenty-seven pregnant women participated, with a response rate of 99.3%. Four hundred and sixty-one (63.4%) of the women visited ANC services, while 46.5% (CI: 42-50%) of births were attended by SBA, and 33.4% (CI: 30-36%) had received PNC. Women who reported that their pregnancy was planned (aOR = 3.9; 95% CI: 1.8-8.3) and were aware of pregnancy danger signs (aOR = 6.8; 95% CI: 3.8-12) had a higher likelihood of attending ANC services. Among the cluster-level factors, women who lived in lowlands (aOR = 4.1; 95% CI: 1.1-14) and had easy access to transportation (aOR = 1.9; 95% CI: 1.1-3.7) had higher odds of visiting ANC services. Moreover, women who were employed (aOR = 3.1; 95% CI: 1.3-7.3) and attended ANC (aOR = 3.3; 95% CI: 1.8-5.9) were more likely to have SBA at delivery. The likelihood of being attended by SBA during delivery was positively correlated with shorter travel distances (aOR = 2.9; 95% CI: 1.4-5.8) and ease of access to transportation (aOR = 10; 95% CI: 3.6-29) to the closest healthcare facilities. Being a midland resident (aOR = 4.7; 95% CI: 1.7-13) and having SBA during delivery (aOR = 2.1; 95% CI: 1.2-3.50) increased the likelihood of attending PNC service.

CONCLUSIONS

Overall, maternal health service utilisation is low in the study area compared with the recommended standards. Women's educational status, awareness of danger signs, and pregnancy planning from individual-level factors and being a lowland resident, short travel distance to health facilities from the cluster-level factors play a crucial role in utilising maternal health care services. Working on women's empowerment, promotion of contraceptive methods to avoid unintended pregnancy, and improving access to health care services, particularly in highland areas, are recommended to improve maternal health service utilisation.

Epidemiological characterization of lonomism in South America

Lonomism is the envenomation caused by accidental contact with Lonomia caterpillars, and this is a public health priority in South America since it is relatively common but remains underestimated. This study presents an updated and integrative analysis of reliable global data from 1960 to 2020 on the epidemiology of this envenomation in the whole continent. A total of 13,186 notifications were recorded for South America: 13,056 (99.01%) in Brazil, 49 (0.37%) in Argentina, 43 (0.33%) in Colombia, 29 (0.22%) in Venezuela, 6 (0.05%) in Peru and 3 (0.02%) in French Guiana. The number of new locations reporting accidents has increased since the first formally notifications at the middle of the last century. Most of these locations are situated in Brazil, the pioneer in deeply researching the Lonomia genus and its impacts in public health. Most victims were males, in the economically productive age group, and accidental contact occurred more frequently in the upper limbs while doing activities outdoors in urban/rural areas. Regarding the determinants of clinical outcome, countries where patients received delayed medical care were associated with a higher number of moderate and/or severe cases, as well as a higher chance of progressing to death. Additionally, the longer the delay for accessing medical care, the higher the risk of developing systemic complications, thereby increasing the need for specific treatment. Our study also shows that accidents globally occurred more frequently in the summer months and in the beginning of autumn. Although having identified several shortcomings in the documents selected and verified that notifications are strongly biased by public policies adopted by each South American country, this study provides the first comprehensive assessment of lonomism in South America, demonstrating the increase in the number of accidents, and their expansion to other areas of the continent over time. Our findings will undoubtedly help guide the appropriate allocation of resources and implementation of prevention strategies and define priority criteria for epidemiological surveillance of this neglected tropical disease.

Estimated travel time and staffing constraints to accessing the Ethiopian health care system: A two-step floating catchment area analysis

Background

Despite large investments in the public health care system, disparities in health outcomes persist between lower- and upper-income individuals, as well as rural vs urban dwellers in Ethiopia. Evidence from Ethiopia and other low- and middle-income countries suggests that challenges in health care access may contribute to poverty in these settings.

Methods

We employed a two-step floating catchment area to estimate variations in spatial access to health care and in staffing levels at health care facilities. We estimated the average travel time from the population centers of administrative areas and adjusted them with provider-to-population ratios. To test hypotheses about the role of travel time vs staffing, we applied Spearman's rank tests to these two variables against the access score to assess the significance of observed variations.

Results

Among Ethiopia's 11 first-level administrative units, Addis Ababa, Dire Dawa, and Harari had the best access scores. Regions with the lowest access scores were generally poorer and more rural/pastoral. Approximately 18% of the country did not have access to a public health care facility within a two-hour walk. Our results suggest that spatial access and staffing issues both contribute to access challenges.

Conclusion

Investments both in new health facilities and staffing in existing facilities will be necessary to improve health care access within Ethiopia. Because rural and low-income areas are more likely to have poor access, future strategies for expanding and strengthening the health care system should strongly emphasize equity and the role of improved access in reducing poverty.

Travel scenario workshops for geographical accessibility modeling of health services: A transdisciplinary evaluation study

Introduction

Limited geographical access to quality Emergency Obstetric and Newborn Care (EmONC) is a major driver of high maternal mortality. Geographic access to EmONC facilities is identified by the global community as a critical issue for reducing maternal mortality and is proposed as a global indicator by the Ending Preventable Maternal Mortality (EPMM) initiative. Geographic accessibility models can provide insight into the population that lacks adequate access and on the optimal distribution of facilities and resources. Travel scenarios (i.e., modes and speed of transport) used to compute geographical access to healthcare are a key input to these models and should approximate reality as much as possible. This study explores strategies to optimize and harmonize knowledge elicitation practices for developing travel scenarios.

Methods

Knowledge elicitation practices for travel scenario workshops (TSW) were studied in 14 African and South-Asian countries where the United Nations Population Fund supported ministries of health and governments in strengthening networks of EmONC facilities. This was done through a mixed methods evaluation study following a transdisciplinary approach, applying the four phases of the Interactive Learning and Action methodology: exploration, in-depth, integration, and prioritization and action planning. Data was collected in November 2020-June 2021 and involved scoping activities, stakeholder identification, semi-structured interviews (N = 9), an evaluation survey (N = 31), and two co-creating focus group discussions (N = 8).

Results

Estimating realistic travel speeds and limited time for the workshop were considered as the largest barriers. The identified opportunities were inclusively prioritized, whereby preparation; a favorable composition of attendees; validation practices; and evaluation were anticipated to be the most promising improvement strategies, explaining their central place on the co-developed initial standard operating procedure (SOP) for future TSWs. Mostly extensive preparation-both on the side of the organization and the attendees-was anticipated to address nearly all of the identified TSW challenges.

Conclusion

This study showed that the different identified stakeholders had contradicting, complementing and overlapping ideas about strategies to optimize and harmonize TSWs. Yet, an initial SOP was inclusively developed, emphasizing practices for before, during and after each TSW. This SOP is not only relevant in the context of the UNFPA EmONC development approach, but also for monitoring the newly launched EPMM indicator and even in the broader field of geographic accessibility modeling.

Still in the shadows: a national study of acute mental health unit location across New Zealand hospitals

BACKGROUND

This study aimed to explore the location of acute mental health inpatient units in general hospitals by mapping their location relative to hospital facilities and community facilities and to compare their proximity to hospital facilities with that of general medical acute units.

METHODS

We obtained Google maps and hospital site maps for all New Zealand public hospitals. Geographic data were analysed and mental health units' locations in relation to hospital facilities and public amenities were mapped. Radar plots were constructed comparing acute medical and mental health units' locations in relation to hospital facilities.

RESULTS

Twenty-two mental health units were identified. They were located predominantly at the periphery of hospital campuses, but also at a distance from community facilities. Compared to acute medical units, mental health units were almost universally located further from shared hospital facilities - with distances approximately three times further to reach the main hospital entrance (2.7 times distance), the nearest public café (3.4 times), the emergency department (2.4 times), and medical imaging (3.3 times).

CONCLUSION

Despite the reforms of the 20th Century, mental health units still appear to occupy a liminal space; neither fully integrated into the hospital, nor part of the community. The findings warrant further investigation to understand the impact of these structural factors on parity of health care provision between mental and physical health care and the ability of mental health care services to support recovery.

Leaving no disease behind: The roadmap to securing universal health security and what this means for the surveillance of infectious diseases in Ghana as a precedent for sub-Saharan Africa

INTRODUCTION

Ghana is the first country in sub-Saharan Africa (SSA) to aim for universal health coverage (UHC). Based on Ghana's UHC system, the accessibility and distribution of healthcare facilities were evaluated for 2020. Projecting into 2030, this study aimed at providing geographical information data for guiding future policies on siting required healthcare facilities. Ghana as a precedent for SSA was evaluated and proposed to "leave no disease behind" in the surveillance of infectious diseases (IDs). This is to reinforce the sustainable development goals (SDG) 3 agenda on health that underpins monitoring equity in "leaving no one behind."

METHODS

Geospatial accessibility, travel time data, and algorithms were employed to evaluate the universality and accessibility of healthcare facilities, and their future projections to meet UHC by 2030. Healthcare facilities as surveillance sites were compared to community-based surveillance to identify which would be more applicable as a surveillance system to leave no disease behind in Ghana.

FINDINGS

Ghana has 93.8%, 6.1% and 0.1% as primary, secondary and tertiary healthcare facilities respectively. It has 26.1% of healthcare facilities remaining to meet the SDG 3 health target by 2030. In terms of providing quality healthcare, 29.3% and 67.2% of the additional required healthcare facilities for optimal allocation and achieving the UHC target need to be secondary and tertiary respectively. In assessing the broad spectrum of IDs studied from 2000 to 2020, an average of 226 IDs were endemic or potentially endemic to Ghana. The majority of the studies carried out to identify these IDs were done through community-based surveillance.

CONCLUSION

Establishing community-based surveillance sites to leave no disease behind and also providing the required healthcare facilities to reinforce leaving no one behind will enhance the universal health security of Ghana as a precedent for SSA.

Geographic barriers to care persist at the community healthcare level: Evidence from rural Madagascar

Geographic distance is a critical barrier to healthcare access, particularly for rural communities with poor transportation infrastructure who rely on non-motorized transportation. There is broad consensus on the importance of community health workers (CHWs) to reduce the effects of geographic isolation on healthcare access. Due to a lack of fine-scale spatial data and individual patient records, little is known about the precise effects of CHWs on removing geographic barriers at this level of the healthcare system. Relying on a high-quality, crowd-sourced dataset that includes all paths and buildings in the area, we explored the impact of geographic distance from CHWs on the use of CHW services for children under 5 years in the rural district of Ifanadiana, southeastern Madagascar from 2018-2021. We then used this analysis to determine key features of an optimal geographic design of the CHW system, specifically optimizing a single CHW location or installing additional CHW sites. We found that consultation rates by CHWs decreased with increasing distance patients travel to the CHW by approximately 28.1% per km. The optimization exercise revealed that the majority of CHW sites (50/80) were already in an optimal location or shared an optimal location with a primary health clinic. Relocating the remaining CHW sites based on a geographic optimum was predicted to increase consultation rates by only 7.4%. On the other hand, adding a second CHW site was predicted to increase consultation rates by 31.5%, with a larger effect in more geographically dispersed catchments. Geographic distance remains a barrier at the level of the CHW, but optimizing CHW site location based on geography alone will not result in large gains in consultation rates. Rather, alternative strategies, such as the creation of additional CHW sites or the implementation of proactive care, should be considered.

Mapping national, regional and local prevalence of hypertension and diabetes in Ethiopia using geospatial analysis

OBJECTIVES

This study aimed to map the national, regional and local prevalence of hypertension and diabetes in Ethiopia.

DESIGN AND SETTING

Nationwide cross-sectional survey in Ethiopia combined with georeferenced ecological level data from publicly available sources.

PARTICIPANTS

9801 participants aged between 15 and 69 years.

PRIMARY OUTCOME MEASURES

Prevalence of hypertension and diabetes were collected using the WHO's STEPS survey approach. Bayesian model-based geostatistical techniques were used to estimate hypertension and diabetes prevalence at national, regional and pixel levels (1×1 km²) with corresponding 95% credible intervals (95% CrIs).

RESULTS

The national prevalence was 19.2% (95% CI: 18.4 to 20.0) for hypertension and 2.8% (95% CI: 2.4 to 3.1) for diabetes. Substantial variation was observed in the prevalence of these diseases at subnational levels, with the highest prevalence of hypertension observed in Addis Ababa (30.6%) and diabetes in Somali region (8.7%). Spatial overlap of high hypertension and diabetes prevalence was observed in some regions such as the Southern Nations, Nationalities and People's region and Addis Ababa. Population density (number of people/km²) was positively associated with the prevalence of hypertension (β: 0.015; 95% CrI: 0.003-0.027) and diabetes (β: 0.046; 95% CrI: 0.020-0.069); whereas altitude in kilometres was negatively associated with the prevalence of diabetes (β: -0.374; 95% CrI: -0.711 to -0.044).

CONCLUSIONS

Spatial clustering of hypertension and diabetes was observed at subnational and local levels in Ethiopia, which was significantly associated with population density and altitude. The variation at the subnational level illustrates the need to include environmental drivers in future NCDs burden estimation. Thus, targeted and integrated interventions in high-risk areas might reduce the burden of hypertension and diabetes in Ethiopia.

Proximity of Water Wells to Public Water Testing Facilities in Alberta Using Drive Times

Approximately 10% of Albertans rely on well water for domestic purposes. The responsibility of water testing and stewardship is left to private well owners. Few well water owners conduct routine testing of their well water supplies. Drive times to public water testing facilities may be an important factor limiting a well owner's ability to conduct routine water testing. The objective of this study is to describe the proximity of water wells, using drive times, to public water testing facilities and describe the availability of facilities based on hours of operation. Using network analysis, we determined the proportion of a sample of wells within 3 estimated drive times of public water testing facilities. 5872 wells were included in the sample. One hundred and seven water testing facilities were mapped within the province. Of the 5872 wells mapped, 89% were located within 30 minutes of a water testing facility, 15% were located within 0 to 10 minutes of a water testing facility, 48% were located between 10 and 20 minutes of a water testing facility and 37% were located within 20 to 30 minutes of a water testing facility. Further analysis revealed that access to water testing facilities may be influenced by the hours of operation of the facilities.

Spatial accessibility to health facilities in Sub-Saharan Africa: comparing existing models with survey-based perceived accessibility

Background

Mapping geographical accessibility to health services is essential to improve access to public health in sub-Saharan Africa. Different methods exist to estimate geographical accessibility, but little is known about the ability of these methods to represent the experienced accessibility of the population, and about the added-value of sophisticated and data-demanding methods over simpler ones. Here we compare the most commonly used methods to survey-based perceived accessibility in different geographical settings.

Methods

Modelled accessibility maps are computed for 12 selected sub-Saharan African countries using four methods: Euclidean distance, cost-distance considering walking and motorized speed, and Kernel density. All methods are based on open and large-scale datasets to allow replication. Correlation coefficients are computed between the four modelled accessibility indexes and the perceived accessibility index extracted from Demographic and Health Surveys (DHS), and compared across different socio-geographical contexts (rural and urban, population with or without access to motorized transports, per country).

Results

Our analysis suggests that, at medium spatial resolution and using globally-consistent input datasets, the use of sophisticated and data-demanding methods is difficult to justify as their added value over a simple Euclidian distance method is not clear. We also highlight that all modelled accessibilities are better correlated with perceived accessibility in rural than urban contexts and for population who do not have access to motorized transportation.

Conclusions

This paper should guide researchers in the public health domain for knowing strengths and limits of different methods to evaluate disparities in health services accessibility. We suggest that using cost-distance accessibility maps over Euclidean distance is not always relevant, especially when based on low resolution and/or non-exhaustive geographical datasets, which is often the case in low- and middle-income countries.

Spatial modeling of vaccine deserts as barriers to controlling SARS-CoV-2

BACKGROUND

COVID-19 vaccine distribution is at risk of further propagating the inequities of COVID-19, which in the United States (US) has disproportionately impacted the elderly, people of color, and the medically vulnerable. We sought to measure if the disparities seen in the geographic distribution of other COVID-19 healthcare resources were also present during the initial rollout of the COVID-19 vaccine.

METHODS

Using a comprehensive COVID-19 vaccine database (VaccineFinder), we built an empirically parameterized spatial model of access to essential resources that incorporated vaccine supply, time-willing-to-travel for vaccination, and previous vaccination across the US. We then identified vaccine deserts-US Census tracts with localized, geographic barriers to vaccine-associated herd immunity. We link our model results with Census data and two high-resolution surveys to understand the distribution and determinates of spatially accessibility to the COVID-19 vaccine.

RESULTS

We find that in early 2021, vaccine deserts were home to over 30 million people, >10% of the US population. Vaccine deserts were concentrated in rural locations and communities with a higher percentage of medically vulnerable populations. We also find that in locations of similar urbanicity, early vaccination distribution disadvantaged neighborhoods with more people of color and older aged residents.

CONCLUSION

Given sufficient vaccine supply, data-driven vaccine distribution to vaccine deserts may improve immunization rates and help control COVID-19.

Incorporating Exercise Efficiency to Evaluate the Accessibility and Capacity of Medical Resources in Tibet, China

Accessibility and capacity of medical resources are key for the health care and emergency response, while the efficiency of the medical resources is very much limited by hypoxia in Tibet, China. Through introducing exercise efficiency, this study explores the accessibility of township residence to county-ship medical resources in Tibet using weighted mean travel time (WMT), and evaluates the medical capacity accordingly. The results show that: 1) the average travel time of township residence to county-level hospital is around 2 h by motor vehicle in Tibet. More than half of the population can not reach the county-ship hospital within 1 h, 33.24% of the population can not reach within 2 h, and 3.75% of the population can not reach within 6 h. 2) When considering the catchment of the medical resources and the population size, the WMT of the county-ship medical resources ranges from 0.25 h to 10.92 h. 3) After adjusted by travel time and exercise efficiency, the county-ship medical capacity became more unequal, with 38 out of 74 counties could not meet the national guideline of 1.8 medical beds per 1000. 4) In total, there are 17 counties with good WMT and sufficient medical resources, while 13 counties having very high WMT and low capacity of medical resources in Tibet. In the end, suggestions on medical resources relocation and to improve the capacity are provided. This study provides a method to incorporate exercise efficiency to access the accessibility and evaluate medical capacity that can be applied in high altitude ranges.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material is available in the online version of this article at 10.1007/s11769-022-1321-1.

Estimates of the number and distribution of zero-dose and under-immunised children across remote-rural, urban, and conflict-affected settings in low and middle-income countries

While there has been great success in increasing the coverage of new childhood vaccines globally, expanding routine immunization to reliably reach all children and communities has proven more challenging in many low- and middle-income countries. Achieving this requires vaccination strategies and interventions that identify and target those unvaccinated, guided by the most current and detailed data regarding their size and spatial distribution. Through the integration and harmonisation of a range of geospatial data sets, including population, vaccination coverage, travel-time, settlement type, and conflict locations. We estimated the numbers of children un- or under-vaccinated for measles and diphtheria-tetanus-pertussis, within remote-rural, urban, and conflict-affected locations. We explored how these numbers vary both nationally and sub-nationally, and assessed what proportions of children these categories captured, for 99 lower- and middle-income countries, for which data was available. We found that substantial heterogeneities exist both between and within countries. Of the total 14,030,486 children unvaccinated for DTP1, over 11% (1,656,757) of un- or under-vaccinated children were in remote-rural areas, more than 28% (2,849,671 and 1,129,915) in urban and peri-urban areas, and up to 60% in other settings, with nearly 40% found to be within 1-hour of the nearest town or city (though outside of urban/peri-urban areas). Of the total number of those unvaccinated, we estimated between 6% and 15% (826,976 to 2,068,785) to be in conflict-affected locations, based on either broad or narrow definitions of conflict. Our estimates provide insights into the inequalities in vaccination coverage, with the distributions of those unvaccinated varying significantly by country, region, and district. We demonstrate the need for further inquiry and characterisation of those unvaccinated, the thresholds used to define these, and for more country-specific and targeted approaches to defining such populations in the strategies and interventions used to reach them.

Improving the efficiency of scale-up and deployment of community health workers in Mali: A geospatial analysis

Optimising the scale and deployment of community health workers (CHWs) is important for maximizing geographical accessibility of integrated primary health care (PHC) services. Yet little is known about approaches for doing so. We used geospatial analysis to model optimised scale-up and deployment of CHWs in Mali, to inform strategic and operational planning by the Ministry of Health and Social Development. Accessibility catchments were modelled based on travel time, accounting for barriers to movement. We compared geographic coverage of the estimated population, under-five deaths, and plasmodium falciparum (Pf) malaria cases across different hypothetical optimised CHW networks and identified surpluses and deficits of CHWs compared to the existing CHW network. A network of 15 843 CHW, if optimally deployed, would ensure that 77.3% of the population beyond 5 km of the CSCom (community health centre) and CSRef (referral health facility) network would be within a 30-minute walk of a CHW. The same network would cover an estimated 59.5% of U5 deaths and 58.5% of Pf malaria cases. As an intermediary step, an optimised network of 4 500 CHW, primarily filling deficits of CHW in the regions of Kayes, Koulikoro, Sikasso, and Ségou would ensure geographic coverage for 31.3% of the estimated population. There were no important differences in geographic coverage percentage when prioritizing CHW scale-up and deployment based on the estimated population, U5 deaths, or Pf malaria cases. Our geospatial analysis provides useful information to policymakers and planners in Mali for optimising the scale-up and deployment of CHW and, in turn, for maximizing the value-for-money of resources of investment in CHWs in the context of the country's health sector reform. Countries with similar interests in optimising the scale and deployment of their CHW workforce may look to Mali as an exemplar model from which to learn.

Spatial Analysis of Schistosomiasis in Hunan and Jiangxi Provinces in the People's Republic of China

Understanding the spatial distribution of schistosome infection is critical for tailoring preventive measures to control and eliminate schistosomiasis. This study used spatial analysis to determine risk factors that may impact Schistosoma japonicum infection and predict risk in Hunan and Jiangxi Provinces in the People's Republic of China. The study employed survey data collected in Hunan and Jiangxi in 2016. Independent variable data were obtained from publicly available sources. Bayesian-based geostatistics was used to build models with covariate fixed effects and spatial random effects to identify factors associated with the spatial distribution of infection. Prevalence of schistosomiasis was higher in Hunan (12.8%) than Jiangxi (2.6%). Spatial distribution of schistosomiasis varied at pixel level (0.1 × 0.1 km), and was significantly associated with distance to nearest waterbody (km, β = -1.158; 95% credible interval [CrI]: -2.104, -0.116) in Hunan and temperature (°C, β = -4.359; 95% CrI: -9.641, -0.055) in Jiangxi. The spatial distribution of schistosomiasis in Hunan and Jiangxi varied substantially and was significantly associated with distance to nearest waterbody. Prevalence of schistosomiasis decreased with increasing distance to nearest waterbody in Hunan, indicating that schistosomiasis control should target individuals in close proximity to open water sources as they are at highest risk of infection.

Assessing road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019

BACKGROUND

The ability of disaster response, preparedness, and mitigation efforts to assess the loss of physical accessibility to health facilities and to identify impacted populations is key in reducing the humanitarian consequences of disasters. Recent studies use either network- or raster-based approaches to measure accessibility in respect to travel time. Our analysis compares a raster- and a network- based approach that both build on open data with respect to their ability to assess the loss of accessibility due to a severe flood event. As our analysis uses open access data, the approach should be transferable to other flood-prone sites to support decision-makers in the preparation of disaster mitigation and preparedness plans.

METHODS

Our study is based on the flood events following Cyclone Idai in Mozambique in 2019 and uses both raster- and network-based approaches to compare accessibility to health sites under normal conditions to the aftermath of the cyclone to assess the loss of accessibility. Part of the assessment is a modified centrality indicator, which identifies the specific use of the road network for the population to reach health facilities.

RESULTS

Results for the raster- and the network-based approaches differed by about 300,000 inhabitants (~ 800,000 to ~ 500,000) losing accessibility to healthcare sites. The discrepancy was related to the incomplete mapping of road networks and affected the network-based approach to a higher degree. The modified centrality indicator allowed us to identify road segments that were most likely to suffer from flooding and to highlight potential backup roads in disaster settings.

CONCLUSIONS

The different results obtained between the raster- and network-based methods indicate the importance of data quality assessments in addition to accessibility assessments as well as the importance of fostering mapping campaigns in large parts of the Global South. Data quality is therefore a key parameter when deciding which method is best suited for local conditions. Another important aspect is the required spatial resolution of the results. Identification of critical segments of the road network provides essential information to prepare for potential disasters.

Fiscal autonomy of subnational governments and equity in healthcare resource allocation: Evidence from China

Objectives

Promoting equity in healthcare resource allocation (EHRA) has become a critical political agenda of governments at all levels since the ambitious Universal Health Coverage was launched in China in 2009, while the role of an important institutional variable-fiscal autonomy of subnational governments-is often overlooked. The present study was designed to determine the effect of FASG on EHRA and its potential mechanism of action and heterogeneity characteristics to provide empirical support for the research field expansion and relative policies making of EHRA.

Methods

From the start, we utilized the Theil index and the entropy method to calculate the EHRA index of 22 provinces (2011-2020) based on the medical resource data of 287 prefecture-level cities. Furthermore, we used the two-way fixed effects model (FE) to identify and analyze the impact of FASG on EHRA and then used three robustness test strategies and two-stage least squares (2SLS) regression to verify the reliability of the conclusions and deal with potential endogeneity problems, respectively. At last, we extend the baseline regression model and obtain the two-way FE threshold model for conducting heterogeneity analysis, which makes us verify whether the baseline model has nonlinear characteristics.

Results

The static value and the trend of interannual changes in the EHRA values in different provinces are both very different. The regression results of the two-way FE model show that FASG has a significant positive impact on EHRA, and the corresponding estimated coefficient is - 0.0849 (P < 0.01). Moreover, this promotion effect can be reflected through two channels: enhancing the intensity of government health expenditure (IGHE) and optimizing the allocation of human resources for health (AHRH). At last, under the different economic and demographic constraints, the impact of FASG on EHRA has nonlinear characteristics, i.e., after crossing a specific threshold of per capita DGP (PGDP) and population density (PD), the promotion effect is reduced until it is not statistically significant, while after crossing a particular threshold of dependency ratio (DR), the promotion effect is further strengthened and still statistically significant.

Conclusions

FASG plays an essential role in promoting EHRA, which shows that subnational governments need to attach great importance to the construction of fiscal capability in the allocation of health care resources, effectively improve the equity of medical and health fiscal expenditures, and promote the sustainable improvement of the level of EHRA.

The Role of Government-run Insurance in Primary Health Care Utilization: A Cross-Sectional Study in Papua Region, Indonesia, in 2018

Health development in the Papua region often lags behind other areas of Indonesia. The study aims to analyze the role of government-run insurance in primary health care utilization in the Papua region, Indonesia. The study examined 17,879 Papuan. The study used primary health care utilization as an outcome variable and health insurance ownership as an exposure variable. The study also employed nine control variables: province, residence, age, gender, marital status, education, employment, wealth, and travel time to primary health care. The research employed data using binary logistic regression in the final analysis. The results show that Papuans with government-run insurance were three times more likely to utilize primary health care than uninsured Papuans (AOR 3.081; 95% CI 3.026-3.137). Meanwhile, Papuan with private-run insurance were 0.133 times less likely to utilize primary health care than uninsured Papuans (AOR 0.133; 95% CI 0.109-0.164). Moreover, Papuans who have two types of health insurances (government-run and private-run) were 1.5 times more likely to utilize the primary health care than uninsured Papuan (AOR 1.513; 95% CI 1.393-1.644). The study concluded that government-run insurance increases the chance of primary health care utilization in the Papua region, Indonesia. Government-run insurance has the most prominent role compared to other health insurance categories.

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

Background

Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels.

Methods

Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min).

Results

Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels.

Conclusions

The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.

Distance is “a big problem”: a geographic analysis of reported and modelled proximity to maternal health services in Ghana

Background

Geographic barriers to healthcare are associated with adverse maternal health outcomes. Modelling travel times using georeferenced data is becoming common in quantifying physical access. Multiple Demographic and Health Surveys ask women about distance-related problems accessing healthcare, but responses have not been evaluated against modelled travel times. This cross-sectional study aims to compare reported and modelled distance by socio-demographic characteristics and evaluate their relationship with skilled birth attendance. Also, we assess the socio-demographic factors associated with self-reported distance problems in accessing healthcare.

Methods

Distance problems and socio-demographic characteristics reported by 2210 women via the 2017 Ghana Maternal Health Survey were included in analysis. Geospatial methods were used to model travel time to the nearest health facility using roads, rivers, land cover, travel speeds, cluster locations and health facility locations. Logistic regressions were used to predict skilled birth attendance and self-reported distance problems.

Results

Women reporting distance challenges accessing healthcare had significantly longer travel times to the nearest health facility. Poverty significantly increased the odds of reporting challenges with distance. In contrast, living in urban areas and being registered with health insurance reduced the odds of reporting distance challenges. Women with a skilled attendant at birth, four or more skilled antenatal appointments and timely skilled postnatal care had shorter travel times to the nearest health facility. Generally, less educated, poor, rural women registered with health insurance had longer travel times to their nearest health facility. After adjusting for socio-demographic characteristics, the following factors increased the odds of skilled birth attendance: wealth, health insurance, higher education, living in urban areas, and completing four or more antenatal care appointments.

Conclusion

Studies relying on modelled travel times to nearest facility should recognise the differential impact of geographic access to healthcare on poor rural women. Physical access to maternal health care should be scaled up in rural areas and utilisation increased by improving livelihoods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-04998-0.

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Deaths and property damage from floods have increased drastically in the past two decades due to various reasons such as increased populations, unplanned developments, and climate change. Such losses from floods can be reduced by issuing timely early warnings and through effective response mechanisms based on situational intelligence during emerging flood situations. This paper presents the outcome of a literature review that was conducted to identify the types and sources of the intelligence required for flood warning and response processes as well as the technology solutions that can be used for offering such intelligence. Twenty-seven different types of intelligence are presented together with the technologies that can be used to extract such intelligence. Furthermore, a conceptual architecture that illustrates how relevant technology solutions can be used to extract intelligence at various stages of a flood cycle for decision-making in issuing early warnings and planning responses is presented.