Spatial Access To Health Care Research Articles

Identification and analysis of spatial access disparities related to primary healthcare in Batna City, Algeria.

The issue of reducing spatial disparities is one of the most pressing concerns for policymakers and planners, which consider a crucial focus in planning and public service, especially accessibility to healthcare. Accessibility and proximity are the principal keys to providing good public service. Therefore, a healthcare system that meets the requirements of availability and affordability will be useless if spatial accessibility is not provided equally to all demands (population). Many technics and methods exist to quantify accessibility, including the two-step floating catchment area (2SFCA) method, its widely used to measure healthcare accessibility based on the travel distance threshold. This research paper aims to use the 2SFCA method to measure the spatial healthcare accessibility in Batna City because the 2SFCA method offers to measure accessibility on both spatial and functional levels. The spatial level will consider the threshold distances between the health demand (population) and the health provider location (healthcare facilities); moreover, functional accessibility is measured based on facility to population ratio that will give a better overview of Batna's healthcare provider. As a result, the optimal threshold distance that offers balanced results between the spatial accessibility score and other WHO ratios will be a distance between 1000- and 1500-meters travel distance. In addition, the central census districts have a higher access score than the rest of the city's districts; most census districts that do not have accessibility (12% of the population) to healthcare facilities are concentrated in the southwest of Batna city.

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

BackgroundTimely 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.MethodsWe 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.ResultsWe 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.ConclusionsOur 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.

The Contributions of Population Distribution, Healthcare Resourcing, and Transportation Infrastructure to Spatial Accessibility of Health Care.

Population demand, healthcare resourcing, and transportation linkage are considered as major determinants of spatial access to health care. Temporal changes of the 3 determinants would result in gain or loss of spatial access to health care. As a remarkable milestone achieved by Targeted Poverty Reduction Project launched in China, the significant improvements in spatial access to health care served as an ideal context for investigating the relative contributions of these 3 determinants to the changes in spatial access to health care in a rural county. A national level poverty-stricken county, Chishui county from Guizhou province, China, was chosen as our study area. The enhanced two-step floating catchment area model and the chain substitution method were employed for analysis. The relative contributions of the 3 determinants demonstrated variations with villages. The relative contributions of healthcare resourcing were positive in all villages as indicated by sharp increases in healthcare resources. Population changes and transportation infrastructure expansion had both negative and positive effects on spatial access to health care for different villages. Decisionmakers should take into account the duration of travel time spent between where people live, where transport hubs are located, and where healthcare services are delivered in the process of formulating policies toward rural healthcare planning. For villages with poorly-established infrastructure, the optimization of population distribution and healthcare resourcing should be considered as the priority. A stronger marginal effect would be induced by transportation infrastructure expansion with increased spatial accessibility. This study provides empirical evidences to inform healthcare planning in low- and middle-income countries.

An informatics-driven intelligent system to improve healthcare accessibility for vulnerable populations

Broad disparities persist in access to healthcare and health outcomes in the United States, especially among vulnerable populations. An important step towards improving their healthcare access is to ensure “having access”, or “accessibility”. Drawing together the Behavioral Model of Health Services Use, social determinants of health (SDOH), and spatial healthcare accessibility literature, the research attempts to create an integrative accessibility measure to support the improvement of “having access” for vulnerable populations. A Vulnerable Population Healthcare Accessibility Framework (VPHAF) is proposed to systematically integrate vulnerable population characteristics, health behavior, customer satisfaction, and other SDOH factors to measure healthcare accessibility using advanced analytics. The qualitative and quantitative evaluation of VPHAF demonstrates that it enhances the existing spatial accessibility methods. An intelligent spatial decision support system (SDSS) instantiates the framework to support the decision making in improving healthcare accessibility for vulnerable populations in a regional healthcare plan. The system was evaluated by decision makers from a real-world organization as a useful tool with high usability. The intelligent SDSS enables the collection of SDOH data from multiple sources and uses analytics to measure healthcare accessibility focusing on characteristics of vulnerable populations. Policymakers and health plan providers can use the system to increase awareness of social risks and advocate for better policy decisions in creating equitable healthcare access and reducing health disparity.

Modeling Health Seeking Behavior Based on Location-Based Service Data: A Case Study of Shenzhen, China

Understanding residents’ health seeking behavior is crucial for the planning and utilization of healthcare resources. With the support of emerging location-based service (LBS) data, this study proposes a framework for inferring health seeking trips, measuring observed spatial accessibility to healthcare, and interpreting the determinants of health seeking behavior. Taking Shenzhen, China as a case study, a supply–demand ratio calculation method based on observed data is developed to explore basic patterns of health seeking, while health seeking behavior is described using a spatial analysis framework based on the Huff model. A total of 95,379 health seeking trips were identified, and their analysis revealed obvious differences between observed and potential spatial accessibility. In addition to the traditional distance decay effect and number of doctors, the results showed health seeking behavior to be determined by hospital characteristics such as hospital scale, service quality, and popularity. Furthermore, this study also identified differences in health seeking behavior between subgroups with different ages, incomes, and education levels. The findings highlight the need to incorporate actual health seeking behavior when measuring the spatial accessibility of healthcare and planning healthcare resources. The framework and methods proposed in this study can be applied to other contexts and other types of public facilities.

Spatial Healthcare Accessibility: A District-Level Analysis of Travel for Outpatient Diabetology in Czechia.

Assessments of regional differences in the accessibility and capacity of health services often rely on indicators based on data from the permanent residents of a given region. However, a patient does not always use health services in their place of residence. The objective of this article is to evaluate the influence of spatial healthcare accessibility on regional differences in the provision and take-up of health services, using outpatient diabetology in Czechia as a case study. The analysis is grounded in monitoring the differences in the patient’s place of residence and the location of the healthcare provided. Anonymized individual data of the largest Czech health insurance company for 2019 are used (366,537 patients, 2,481,129 medical procedures). The data are aggregated at the district level (LAU 1). It has been identified that regions where patients travel outside their area of residence to access more than half of their healthcare needs are mostly in local/regional centres. Moreover, these patients increase the number of medical services provided in local/regional centres, often by more than 20%, which has been reflected in greater healthcare capacity in these centres. To assess regional differences, it is important to take the spatial healthcare accessibility into account and also consider why patients travel for healthcare. Reasons could be the insufficient local capacity, varied quality of health services or individual factors. In such cases, healthcare actors (health insurance companies, local government etc.) should respond to the situation and take appropriate action to reduce these dissimilarities.

The capitalization of spatial healthcare accessibility into house prices in Taiwan: an application of spatial quantile regression

Purpose Population ageing is fast becoming a major social concern across the globe. This ageing trend unavoidably fuels elders’ demand for healthcare services. As the main users of health care service, whether the healthcare is geographically approachable in local areas is more imperative to senior residents with restricted mobility. This paper proposes to examine the effect of elders’ healthcare accessibility on property prices of Taipei Metropolis, Taiwan. Design/methodology/approach Luo and Qi’s (2009) enhanced two-step floating catchment area method – taking both healthcare demand and supply into account – was used to measure three types of healthcare services: “physician-to-elder ratio”, “hospital bed-to-elder ratio” and “ambulance-to-elder ratio”. Spatial quantile regression (SQR) model was then used to examine the spatial effect of healthcare accessibility on different property price ranges. Findings The “physician-to-elder ratio” and “hospital bed-to-elder ratio” demonstrated expected consistent positive effects across all quantiles of property prices (p < 0.01) in SQR, and its effects aggravated as the quantiles of property prices rose. The “ambulance-to-elder ratio” demonstrated a non-linear influence on property prices (i.e. a negative effect on lowest quantile prices but a positive on higher quantile prices) possibly due to the semi-obnoxious characteristic of the ambulance. That is, residents living in lower priced neighbourhoods may dislike ambulances’ annoying sound of sirens (i.e. ambulances’ disamenity), while residents living in higher valued neighbourhoods may on the contrary appreciate ambulances’ healthcare services (i.e. amenity). Practical implications These findings are expected to offer some insights for government’s policies in providing elders in their later years with good residential quality and easy access to healthcare resource. Originality/value This paper is one of the few studies that consider the capitalization of the spatial healthcare accessibility to elders into property prices. In this ageing trend across the globe, although all the accessibility to medical resources should be equally critical, the application of spatial quantile regression revealed residents’ inconsistent tendency against semi-obnoxious ambulances. It provides a different perspective in defining the importance of healthcare accessibility in neighbourhoods.

Beyond the map: evidencing the spatial dimension of health inequalities

BackgroundSpatial inequalities in health result from different exposures to health risk factors according to the features of geographical contexts, in terms of physical environment, social deprivation, and health care accessibility. Using a common geographical referential, which combines indices measuring these contextual features, could improve the comparability of studies and the understanding of the spatial dimension of health inequalities.MethodsWe developed the Geographical Classification for Health studies (GeoClasH) to distinguish French municipalities according to their ability to influence health outcomes. Ten contextual scores measuring physical and social environment as well as spatial accessibility of health care have been computed and combined to classify French municipalities through a K-means clustering. Age-standardized mortality rates according to the clusters of this classification have been calculated to assess its effectiveness.ResultsSignificant lower mortality rates compared to the mainland France population were found in the Wealthy Metropolitan Areas (SMR = 0.868, 95% CI 0.863–0.873) and in the Residential Outskirts (SMR = 0.971, 95% CI 0.964–0.978), while significant excess mortality were found for Precarious Population Districts (SMR = 1.037, 95% CI 1.035–1.039), Agricultural and Industrial Plains (SMR = 1.066, 95% CI 1.063–1.070) and Rural Margins (SMR = 1.042, 95% CI 1.037–1.047).ConclusionsOur results evidence the comprehensive contribution of the geographical context in the constitution of health inequalities. To our knowledge, GeoClasH is the first nationwide classification that combines social, environmental and health care access scores at the municipality scale. It can therefore be used as a proxy to assess the geographical context of the individuals in public health studies.

Improving the spatial accessibility of healthcare in North Kivu, Democratic Republic of Congo

Accessibility to healthcare has a direct bearing on the overall well-being of the population. Poor access to healthcare has serious consequences particularly in low and low-middle income countries (LMIC) in sub-Saharan Africa. The lack of detailed and up-to-date spatial data and health information in these regions further challenges both the accurate assessment of spatial accessibility and the determination of optimal locations of healthcare facilities that would improve health service planning. In the present study, we proposed a systematic approach to assess the spatial accessibility to healthcare and to identify optimal locations for additional healthcare facilities based on the accessibility measures. Results from a raster-based accessibility measurement showed that majority of population could not reach the nearest hospitals within 2-hours; only 25%, 50%, and 44% of population reached the nearest hospital within 2-hours under walking, motor and, bus travel scenarios, respectively. Our results also showed that the five newly proposed hospitals whose optimal locations were determined using a location-allocation model could potentially increase 11.41%, 8.29%, and 8.95% of additional population coverage for the three travel scenarios. The proposed health system evaluation approach and the health care planning based on open-source data derived from remote sensing and crowdsourcing and the spatial modeling approach has the potential to be useful in LMIC to improve overall population health.

Spatial Interaction Model for Healthcare Accessibility: What Scale Has to Do with It

This manuscript develops a theoretical spatial interaction model using the entropy approach to relax the assumption of the deterministic utility function. The spatial healthcare accessibility improves as the demand for healthcare increases or the opportunity cost of traveling to and from healthcare providers decreases. The empirical application used different spatial econometric techniques and multilevel modeling to evaluate the spatial distribution of existing hospitals in Texas and their social and economic correlates. To control for spatial autocorrelation, spatial autoregressive regression models were estimated, and geographically weighted regression models examined potential spatial non-stationarity. The multilevel modeling controlled for spatial autocorrelation and also allowed local variation and spatial non-stationarity. The empirical analysis showed that healthcare accessibility was not stationary in Texas in 2015, with areas of poor accessibility in rural and peripheral areas in Texas, when using hospitals’ location and county data. The model of spatial interaction applied to healthcare accessibility can be used to evaluate policies aiming at the provision of health services, such as closures of hospitals and capacity increases.

A Rational Agent Model for the Spatial Accessibility of Primary Health Care

Accurate modeling of the spatial accessibility of health care is critical to measuring and responding to physician shortages. We develop a new model in which patients choose the primary care location that minimizes their combined accessibility and availability costs. This model offers several advantages with respect to existing access frameworks. It incorporates feedback between patient decisions and endogenizes the trade-off between travel times and congestion at the point of care. It allows for patients to seek care from their home or workplace and can account for multiple travel modes. Our open-sourced implementation scales efficiently to large areas and fine spatial granularity. Using distributed computing, we calculate travel times for this model at the census tract level for the entire United States, and we also make this resource available. We compare the results to those from existing primary care access models. Key Words: floating catchment areas, primary health care, rational agent models, spatial accessibility.

Costs of administration, travelling, and productivity losses associated with hospital administration of multiple myeloma drugs in Finland

Aim: To estimate the drug administration, travelling, and productivity costs associated with infusion or subcutaneous proteasome inhibitor (PI) treatments (specifically carfilzomib and bortezomib) for multiple myeloma (MM) patients in Finland.Materials and methods: Price tariffs of Finnish hospital districts are used as the basis of invoicing sent to healthcare service payers. A review of these price tariff lists was conducted and obtained data analysed to estimate the mean unit cost of PI administration visit. Travelling costs stratified by areas with different population densities were assessed, based on the national travelling reimbursement register data maintained by the Social Insurance Institution of Finland. Productivity costs due to time spent on administration visits and travelling were estimated based on an expert interview and a spatial healthcare accessibility analysis.Results: Nineteen (95%) of the Finnish hospital districts were included in the review. Relevant unit cost information was found for 15 (75%) of the districts. The mean PI administration cost alone was 270€ (95% CI = 189€–351€) per administration and increased to 371€ when travelling costs were included. Productivity costs added, the mean PI administration costs totalled 405€ for bortezomib and 437€ for carfilzomib.Limitations: The costing rationale of price tariffs may vary between hospital districts. Productivity costs were estimated conservatively, due to lack of data.Conclusions: The administration of intravenous or subcutaneous PIs to treat MM in healthcare facilities causes significant and potentially avoidable healthcare, travelling, and indirect costs, and they should be included in all health economic evaluations (HEEs). As the cost estimates utilized in this study represent most of central hospitals in the country, they provide useful information for future HEEs. A broader conclusion is that novel oral medications, such as the first oral PI, have a significant potential for reducing administration-related costs of subcutaneous or intravenous PIs.

Spatial accessibility analysis of healthcare service centers in Gamo Gofa Zone, Ethiopia through Geospatial technique

The population of Ethiopia has been facing a high rate of morbidity and mortality. Most of the people live in rural areas where healthcare service is detrimental and very poor and their accessibility to these services is abysmally low. Spatial healthcare accessibility is determined by the availability and location of healthcare facilities as well as the location of people. It can be measured by distance traveled and traveling time. This study was carried out to analyze the distribution and spatial accessibility of healthcare service centers by using Geospatial technologies in Gamo Gofa zone of SNNP (Southern Nations and Nationalities People) region, Ethiopia. Satellite data, DEM (Digital Elevation Model), road network, population data and healthcare service centers location data was used for further analysis by using AccessMod extension 4.0 to Arc GIS 9.3.1 software. 30- and 60-min traveling time were considered to analyze the accessibility by means of walking and motor vehicle as the mode of transportation. It was found that 12.62%, 36.60%, 29.10% and 59% peoples of the total population were able to access the healthcare service centers by means of 30-min walking, 60-min walking, 30-min walking and motor vehicle and 60-min walking and motor vehicle respectively. Health and disease management is key to social, cultural and economic development of any society so this approach offers considerable benefit.

Cross-border spatial accessibility of health care in the North-East Department of Haiti

BackgroundThe geographical accessibility of health services is an important issue especially in developing countries and even more for those sharing a border as for Haiti and the Dominican Republic. During the last 2 decades, numerous studies have explored the potential spatial access to health services within a whole country or metropolitan area. However, the impacts of the border on the access to health resources between two countries have been less explored. The aim of this paper is to measure the impact of the border on the accessibility to health services for Haitian people living close to the Haitian-Dominican border.MethodsTo do this, the widely employed enhanced two-step floating catchment area (E2SFCA) method is applied. Four scenarios simulate different levels of openness of the border. Statistical analysis are conducted to assess the differences and variation in the E2SFCA results. A linear regression model is also used to predict the accessibility to health care services according to the mentioned scenarios.ResultsThe results show that the health professional-to-population accessibility ratio is higher for the Haitian side when the border is open than when it is closed, suggesting an important border impact on Haitians’ access to health care resources. On the other hand, when the border is closed, the potential accessibility for health services is higher for the Dominicans.ConclusionThe openness of the border has a great impact on the spatial accessibility to health care for the population living next to the border and those living nearby a road network in good conditions. Those findings therefore point to the need for effective and efficient trans-border cooperation between health authorities and health facilities. Future research is necessary to explore the determinants of cross-border health care and offers an insight on the spatial revealed access which could lead to a better understanding of the patients’ behavior.

Designing utilization-based spatial healthcare accessibility decision support systems: A case of a regional health plan

In the U.S., myriad healthcare reforms have begun to show some positive effects on enabling “potential access”. One facet of healthcare access, “having access”, which is the availability and accessibility of health services for the surrounding populations, has not been adequately addressed. Research regarding “having access” is presently championed by a family of methods called Floating Catchment Area (FCA). However, existing scholarship is limited in integrating non-spatial factors within the FCA methods. In this research, we propose a novel utilization-based framework as the first attempt to adopt the Behavioral Model of Health Services Use as a theoretical lens to integrate non-spatial factors in spatial healthcare accessibility research. The framework employs a unique approach to derive categorical and factor weights for different population subgroup's healthcare needs using predictive analytics. The proposed framework is evaluated using a case study of a regional health plan. A Spatial Decision Support System (SDSS) instantiates the framework and enables decision makers to explore physician shortage areas. The SDSS validates the practicality of the proposed utilization-based framework and subsequently allows other FCA methods to be implemented in real-world applications.

Will affordability policy transcend climate change? A new lens to re-examine equitable access to healthcare in the San Francisco Bay Area

Equal spatial access to healthcare is a primary public policy concern. The debate on access to healthcare facilities, particularly for low-income individuals, has increasingly drawn attention to the interactions between equity, accessibility, and healthcare. However, little is known about the joint effects of affordability issues and environmental constraints. Therefore, this study aimed to re-examine the equity in spatial accessibility of healthcare in the San Francisco Bay Area from a new angle. The study evaluated the potential impacts of climate change on the spatial access to healthcare through network analysis including service area and location-allocation methods. A great loss of accessibility to healthcare occurs owing to financial barriers aggravated by failure of transportation networks induced by climate change. In particular, the consequences of climate change were found to be a serious cause of inequality in spatial access to healthcare for disadvantaged people. In order to secure equitable healthcare in the Bay Area, it is recommended that more efforts be put into geographically redistributing public health resources to deliberately deliver equitable healthcare, particularly to the disadvantaged population. In addition, the government policy intervention on affordability, introduced to improve healthcare equity, is essential for those encountering financial barriers during climate change. The findings will help decision makers and planners to rethink how to enhance equitable access to healthcare, either through the allocation of healthcare facilities planning or through public policy on affordability.

Combining Transportation Network Models with Kernel Density Methods to Measure the Relative Spatial Accessibility of Pediatric Primary Care Services in Jefferson County, Kentucky

When considering access to healthcare, the question of whether the provider is available and accessible must be answered before the question of cost. Most contemporary techniques of estimating the spatial accessibility of healthcare cannot simultaneously take into account transportation times and the spatially distributed service capacity of healthcare providers. This paper creates the Comprehensive Spatial Accessibility Rank (CSAR) model to calculate a relative estimate of spatial accessibility that can account for both public and private transportation and the spatial distribution of a service provider's capacity for service in relation to the distribution of a study area's population. It uses data for Jefferson County, Kentucky to show how the CSAR model could be used to identify possible disparities in accessing pediatric primary healthcare services. The CSAR model is able to detect relative disparities between defined subpopulations and or geographic regions, allowing for the comparison of the effects of physical infrastructure in access.

A commuter-based two-step floating catchment area method for measuring spatial accessibility of daycare centers

This paper puts forward a commuter-based version of the two-step floating catchment area (2SFCA) method, which has gained acceptance in studies on spatial health care accessibility. Current implementations of the 2SFCA method are static in that they consider centroid-based night-time representations of the population. The proposed enhancement to the 2SFCA approach addresses this limitation by accounting for trip-chaining behavior. The presented method is illustrated in a case study of accessibility of daycare centers in the province East Flanders in Belgium. The results show significant spatial differences in accessibility between the original and commuter-based version of the 2SFCA (CB2SFCA). They highlight the importance of giving heed to more complex travel behavior in cases where the need for detailed accessibility calculations is apparent.

Estimating Proximity to Care

Spatial accessibility of healthcare may be measured by proximity of patient residence to health services, typically in driving distance or driving time. Precise driving distances and times are rarely available. Although straight line distances between zipcode centroids and between precise address locations are used as proxy measures for distance to care, the accuracy of these measures has received little study. Among a cohort of Medicare beneficiaries, actual driving distances and times between patient residence and clinic were obtained from commercial software (MapQuest). We used a split-sample design to build and validate linear regression models that predict actual driving distances and times from estimated distances between zipcode centroids and between precise residential and hospital locations, adjusting for urban/suburban/rural residential status. On average, predicted driving distances and times were larger than actual values. Zipcode centroid distances alone predicted longer driving distances than observed values: rural +19% (3.2 miles), suburban +23% (3.7 miles), and urban +27% (2.0 miles). Predicted time was 36% (9.4 min) longer in rural, 32% (6.8 min) longer in suburban, and 38% (4.7 min) longer in urban areas than observed values. Including urban/suburban/rural categorization of residence improved the accuracy of predicted driving distance and time for suburban and urban areas but diminished accuracy for rural areas. Similar trends were observed for distance estimates from precise locations. Distances between zipcode centroids and precise residential/hospital locations provide reasonable estimates of driving distance and time for epidemiologic research. Estimates are improved for suburban and urban residences when data are augmented by urban categorization.