Does the edge effect impact on the measure of spatial accessibility to healthcare providers?

Affordable Housing and Neighborhood Child Maltreatment Reports

Reports of child maltreatment vary by neighborhood characteristics, yet the influence of housing affordability is less understood. The current study examines the relationship between reports of suspected child maltreatment and the spatial distribution of affordable housing across 2,341 census tracts in Los Angeles County, California. Bayesian conditionally autoregressive model results indicate that neighborhoods where residents paid a greater share of their income in rent had fewer reports of suspected child maltreatment, while neighborhoods with a higher number of subsidized rental units had more reports. These findings suggest that higher cost neighborhoods provide supportive resources and amenities to families which may reduce risk of child maltreatment. Subsidized housing units are more likely to be located in high poverty, under resourced neighborhoods, thus undermining the benefits of these programs to families. These findings have implications for equitable housing policy that promotes inclusive communities as a primary prevention strategy for child maltreatment.

Patterns of Cancer-Related Healthcare Access across Pennsylvania: Analysis of Novel Census Tract-Level Indicators of Persistent Poverty

BACKGROUND

Persistent poverty census tracts have had ≥20% of the population living below the federal poverty line for 30+ years. We assessed the relationship between persistent poverty and cancer-related healthcare access across census tracts in Pennsylvania.

METHODS

We gathered publicly available census tract-level data on persistent poverty, rurality, and sociodemographic variables, as well as potential access to healthcare (i.e., prevalence of health insurance, last-year check-up), realized access to healthcare (i.e., prevalence of screening for cervical, breast, and colorectal cancers), and self-reported cancer diagnosis. We used multivariable spatial regression models to assess the relationships between persistent poverty and each healthcare access indicator.

RESULTS

Among Pennsylvania's census tracts, 2,789 (89.8%) were classified as non-persistent poverty, and 316 (10.2%) were classified as persistent poverty (113 did not have valid data on persistent poverty). Persistent poverty tracts had lower prevalence of health insurance [estimate = -1.70, standard error (SE) = 0.10], screening for cervical cancer (estimate = -4.00, SE = 0.17) and colorectal cancer (estimate = -3.13, SE = 0.20), and cancer diagnosis (estimate = -0.34, SE = 0.05), compared with non-persistent poverty tracts (all P < 0.001). However, persistent poverty tracts had higher prevalence of last-year check-up (estimate = 0.22, SE = 0.08) and screening for breast cancer (estimate = 0.56, SE = 0.15; both P < 0.01).

CONCLUSIONS

Relationships between persistent poverty and cancer-related healthcare access outcomes differed in direction and magnitude. Health promotion interventions should leverage data at fine-grained geographic units (e.g., census tracts) to motivate focus on communities or outcomes.

IMPACT

Future studies should extend these analyses to other states and outcomes to inform public health research and interventions to reduce geographic disparities.

Unmatched spatially stratified controls: A simulation study examining efficiency and precision using spatially-diverse controls and generalized additive models

Unmatched spatially stratified random sampling (SSRS) of non-cases selects geographically balanced controls by dividing the study area into spatial strata and randomly selecting controls from all non-cases within each stratum. The performance of SSRS control selection was evaluated in a case study spatial analysis of preterm birth in Massachusetts. In a simulation study, we fit generalized additive models using controls selected by SSRS or simple random sample (SRS) designs. We compared mean squared error (MSE), bias, relative efficiency (RE), and statistically significant map results to the model results with all non-cases. SSRS designs had lower average MSE (0.0042-0.0044) and higher RE (77-80%) compared to SRS designs (MSE: 0.0072-0.0073; RE across designs: 71%). SSRS map results were more consistent across simulations, reliably identifying statistically significant areas. SSRS designs improved efficiency by selecting controls that are geographically distributed, particularly from low population density areas, and may be more appropriate for spatial analyses.

Impact of traffic congestion on spatial access to healthcare services in Nairobi

Background

Geographic accessibility is an important determinant of healthcare utilization and is critical for achievement of universal health coverage. Despite the high disease burden and severe traffic congestion in many African cities, few studies have assessed how traffic congestion impacts geographical access to healthcare facilities and to health professionals in these settings. In this study, we assessed the impact of traffic congestion on access to healthcare facilities, and to the healthcare professionals across the healthcare facilities.

Methods

Using data on health facilities obtained from the Ministry of Health in Kenya, we mapped 944 primary, 94 secondary and four tertiary healthcare facilities in Nairobi County. We then used traffic probe data to identify areas within a 15-, 30- and 45-min drive from each health facility during peak and off-peak hours and calculated the proportion of the population with access to healthcare in the County. We employed a 2-step floating catchment area model to calculate the ratio of healthcare and healthcare professionals to population during these times.

Results

During peak hours, <70% of Nairobi's 4.1 million population was within a 30-min drive from a health facility. This increased to >75% during off-peak hours. In 45 min, the majority of the population had an accessibility index of one health facility accessible to more than 100 people (<0.01) for primary health care facilities, one to 10,000 people for secondary facilities, and two health facilities per 100,000 people for tertiary health facilities. Of people with access to health facilities, a sub-optimal ratio of <4.45 healthcare professionals per 1,000 people was observed in facilities offering primary and secondary healthcare during peak and off-peak hours.

Conclusion

Our study shows access to healthcare being negatively impacted by traffic congestion, highlighting the need for multisectoral collaborations between urban planners, health sector and policymakers to optimize health access for the city residents. Additionally, growing availability of traffic probe data in African cities should enable similar analysis and understanding of healthcare access for city residents in other countries on the continent.

Challenging terrains: socio-spatial analysis of Primary Health Care Access Disparities in West Virginia

Existing measures of health care access were inadequate for guiding policy decisions in West Virginia, as they identified the entire state as having limited access. To address this, we compiled a comprehensive database of primary health care providers and facilities in the state, developed a modified E2SFCA tool to measure spatial access in the context of West Virginia's rural and mountainous nature, and integrated this with an index of socio-economic barriers to access. The integrated index revealed that the rural areas, especially in the southern part of the state, have especially limited access to primary health care. 1. Introduction. An emerging public health issue which has been exacerbated by the COVID-19 pandemic, is that of healthcare deserts, which are places where basic affordable health care is not accessible for residents. This problem has become worse in rural areas as rural hospitals close. In these areas, including West Virginia, scattered populations suffer from limited access to primary healthcare services. Uneven geographic and socio-economic barriers to accessing primary health care are major contributing factors to these health disparities. West Virginia's unique rural and mountainous settlement patterns, aging population, and economic crisis over the past two decades have resulted in unequal access to the primary healthcare services for its residents. The rural nature of the state makes it difficult to maintain medical facilities accessible to much of the population, especially as rural hospitals have been closing, such as the one in Williamson, WV (Jarvie, 2020). The mountainous terrain slows down travel across winding roads, lengthening travel times to the nearest hospital, while an aging population has increased health care needs. Lastly, an economic crisis and higher poverty rate makes West Virginians less able to pay for health care. As a result, West Virginians are confronting a health crisis. According to a recent report by the West Virginia Health Statistics Center (2019), West Virginians rank first in the country for heart attacks, have the second-highest obesity rate and prevalence of mental health problems in the country, along with the fourth-highest rate of diabetes and fifth-highest rate of cancer. An issue faced by West Virginia's policymakers is the limitations of tools for identifying and assessing healthcare deserts, as they are poorly suited for the unique challenges in West Virginia. Academic research has not analyzed comprehensive primary healthcare accessibility in WV, although previous studies have focused on Appalachia (e.g., Behringer & Friedell 2006; Smith & Holloman, 2011; Elnicki et al., 1995; Donohoe et al., 2015, 2016a, 2016b), and others focus on access to more specialized services (Valvi et al., 2019; Donohoe, 2016a). Existing approaches to identify the healthcare deprived areas, such as Health Professional Shortage Areas (HPSA), are not suitable for guiding West Virginia policies, because every one of the 55 counties within the state has several HPSAs, which makes prioritizing resources difficult. The lack of easily accessible, comprehensive, and up-to-date physician and healthcare facility database creates additional difficulties. Physician license datasets were found to often include inconsistent, misleading, and out-of-date information. The last limitation of the HPSA designation is that it is based on zip code areas and census tracts, which are not ideal as zip code areas lack spatial context and much covariate data, while rural census tracts are too large to capture spatial variation of access. In this context, the WV HealthLink project was begun with joint effort with WV Rural Health Initiative (RHI) to fill gaps in research and support decision making for primary healthcare access in West Virginia. The goals of the projects are: (1) to help West Virginia's three medical schools provide specialized professional training in rural healthcare; (2) to address health disparities by investing in clinical projects in underserved areas; and (3) to retain health professionals in WV. In 2018, to support these goals, HealthLink was invited by the RHI's leadership to analyze disparities in primary health care access in West Virginia and develop tools for rural healthcare decision-making. These goals also create a comprehensive and up-to-date physician and facility database, new analysis tools, and new visualization tools for decision support. The goals of this paper are to assess the spatial and social accessibility of primary health care in West Virginia, and to understand spatial and social determinants that shape this access. To achieve these goals, this paper completes the following objectives: (1) define primary healthcare and access; (2) build an extensive and up-to-date primary healthcare database; (3) develop an assessment framework for WV; and (4) visualize the results for policy makers and practitioners. The structure of this paper is as follows. First, we describe three methodological problems encountered as we define primary health care access. Second, we present the methods used to resolve these problems, and conclude by presenting our modified enhanced two-step floating catchment area (E2FCA hereafter) approach and its results for WV. Our foci in this modification were improving the accuracy of the analysis regarding measuring distance, considering distance decay effect, and more precisely representing the location of supply and demand.

Spatial Access to Sexual Health Clinics Measured Through a Novel Accessibility Score in Toronto, Canada

BACKGROUND

Understanding spatial access to sexual health services will provide the foundation for future resource planning and allocation. The purpose of this study was to evaluate the potential geographic access to sexual health services in Toronto, Canada, by developing a novel accessibility index to sexual health clinics.

METHODS

We created an accessibility index using the 2-step floating catchment area method to quantify neighborhood-level access to sexual health clinics. The index assumed mixed modes of urban travel through walking and public transit, as well as through driving, and was estimated at the census tract level.

RESULTS

Census tracts were grouped into quantiles by the estimated accessibility score. Census tracts with higher accessibility scores were characterized as those with greater residential instability and lower dependency and ethnic concentration. The downtown core area has all census tracts categorized as medium, high, or very high (average [SD] score, 1.320 [0.312]), whereas the noncore area has 56.98%, 302 of 530 census tracts categorized as medium, high, or very high (average [SD] score, -0.105 [0.960]).

CONCLUSIONS

We demonstrated the benefit of using statistical methods to quantify the geographical access to sexual health services and identified neighborhoods with high and low levels of access. Findings from this study present an overview of the level of spatial access to sexual health clinics in Toronto based on clinic locations in 2018 and can be further used to characterize neighborhoods with a lower level of access and inform policy and planning decisions in the city.

Delineating natural catchment health districts with routinely collected health data from women's travel to give birth in Ghana

Background

Health service areas are essential for planning, policy and managing public health interventions. In this study, we delineate health service areas from routinely collected health data as a robust geographic basis for presenting access to maternal care indicators.

Methods

A zone design algorithm was adapted to delineate health service areas through a cross-sectional, ecological study design. Health sub-districts were merged into health service areas such that patient flows across boundaries were minimised. Delineated zones and existing administrative boundaries were used to provide estimates of access to maternal health services. We analysed secondary data comprising routinely collected health records from 32,921 women attending 27 hospitals to give birth, spatial demographic data, a service provision assessment on the quality of maternal healthcare and health sub-district boundaries from Eastern Region, Ghana.

Results

Clear patterns of cross border movement to give birth emerged from the analysis, but more women originated closer to the hospitals. After merging the 250 sub-districts in 33 districts, 11 health service areas were created. The minimum percent of internal flows of women giving birth within any health service area was 97.4%. Because the newly delineated boundaries are more “natural” and sensitive to observed flow patterns, when we calculated areal indicator estimates, they showed a marked improvement over the existing administrative boundaries, with the inclusion of a hospital in every health service area.

Conclusion

Health planning can be improved by using routine health data to delineate natural catchment health districts. In addition, data-driven geographic boundaries derived from public health events will improve areal health indicator estimates, planning and interventions.

Territorial Strategy of Medical Units for Addressing the First Wave of the COVID-19 Pandemic in the Metropolitan Area of Mexico City: Analysis of Mobility, Accessibility and Marginalization

BACKGROUND

The COVID-19 pandemic has caused an exponential increase in the demand for medical care worldwide. In Mexico, the COVID Medical Units (CMUs) conversion strategy was implemented.

OBJECTIVE

To evaluate the CMU coverage strategy in the Mexico City Metropolitan Area (MCMA) by territory.

MATERIALS

The CMU directory was used, as were COVID-19 infection and mobility statistics and Mexican 2020 census information at the urban geographic area scale. The degree of urban marginalization by geographic area was also considered.

METHOD

Using descriptive statistics and the calculation of a CMU accessibility index, population aggregates were counted based on coverage radii. In addition, two regression models are proposed to explain (1) the territorial and temporal trend of COVID-19 infections in the MCMA and (2) the mobility of the COVID-infected population visiting medical units.

RESULTS

The findings of the evaluation of the CMU strategy were (1) in the MCMA, COVID-19 followed a pattern of contagion from the urban center to the periphery; (2) given the growth in the number of cases and the overload of medical units, the population traveled greater distances to seek medical care; (3) after the CMU strategy was evaluated at the territory level, it was found that 9 out of 10 inhabitants had a CMU located approximately 7 km away; and (4) at the metropolitan level, the lowest level of accessibility to the CMU was recorded for the population with the highest levels of marginalization, i.e., those residing in the urban periphery.

Measuring hospital spatial accessibility using the enhanced two-step floating catchment area method to assess the impact of spatial accessibility to hospital and non-hospital care on the length of hospital stay

Background

Optimal healthcare access improves the health status and decreases health inequalities. Many studies demonstrated the importance of spatial access to healthcare facilities in health outcomes, particularly using the enhanced two-step floating catchment area (E2SFCA) method. The study objectives were to build a hospital facility access indicator at a fine geographic scale, and then to assess the impact of spatial accessibility to inpatient hospital and non-hospital care services on the length of hospital stay (LOS).

Methods

Data concerning older adults (≥75 years) living in the Nord administrative region of France were used. Hospital spatial accessibility was computed with the E2SFCA method, and the LOS score was calculated from the French national hospital activity and patient discharge database. The relationship between LOS and spatial accessibility to inpatient hospital care and to three non-hospital care types (general practitioners, physiotherapists, and home-visiting nurses) was analyzed with linear regression models.

Results

The mean number (standard deviation) of beds per 10,000 inhabitants was 19.0 (10.69) in Medical, Surgical and Obstetrics (MCO) facilities and 5.58 (2.19) in Postoperative and Rehabilitation Care (SSR) facilities, highlighting important variations within the region. Accessibility to hospital services was higher in large urban areas, despite the dense population and higher demand. In 2014, the mean LOS scores were 0.26 for MCO and 0.85 for SSR, but their geographical repartition was non-homogeneous. The linear regression analysis revealed a strong negative and significant association between LOS and non-hospital care accessibility.

Conclusions

This is the first study to measure spatial accessibility to inpatient hospital care in France using the E2SFCA method, and to investigate the relationship between healthcare utilization (LOS score) and spatial accessibility to inpatient hospital care facilities and three types of non-hospital care services. Our findings might help to make decisions about deploying additional beds and to identify the best locations for non-hospital care services. They might also contribute to improve access, and to ensure the best coordination and sustainability of inpatient and outpatient services, in order to better cover the population’s healthcare needs. International studies using multiple consensual indicators of healthcare outcomes and accessibility and sophisticated modeling methods are needed.

Assessing consistency among indices to measure socioeconomic barriers to health care access

Many places within rural America lack ready access to health care facilities. Barriers to access can be both spatial and non-spatial. Measurements of spatial access, such as the Enhanced Floating 2-Step Catchment Area and other floating catchment area measures, produce similar patterns of access. However, the extent to which different measurements of socioeconomic barriers to access correspond with each other has not been examined. Using West Virginia as a case study, we compute indices based upon the literature and measure the correlations among them. We find that all indices positively correlate with each other, although the strength of the correlation varies. Also, while there is broad agreement in the general spatial trends, such as fewer barriers in urban areas, and more barriers in the impoverished southwestern portion of the state, there are regions within the state that have more disagreement among the indices. These indices are to be used to support decision-making with respect to placement of rural residency students from medical schools within West Virginia to provide students with educational experiences as well as address health care inequalities within the state. The results indicate that for decisions and policies that address statewide trends, the choice of metric is not critical. However, when the decisions involve specific locations for receiving rural residents or opening clinics, the results can become more sensitive to the selection of the index. Therefore, for fine-grained policy decision-making, it is important that the chosen index best represents the processes under consideration.

Efficiency of fine scale and spatial regression in modelling associations between healthcare service spatial accessibility and their utilization

BACKGROUND

Healthcare accessibility, a key public health issue, includes potential (spatial accessibility) and realized access (healthcare utilization) dimensions. Moreover, the assessment of healthcare service potential access and utilization should take into account the care provided by primary and secondary services. Previous studies on the relationship between healthcare spatial accessibility and utilization often used conventional statistical methods without addressing the scale effect and spatial processes. This study investigated the impact of spatial accessibility to primary and secondary healthcare services on length of hospital stay (LOS), and the efficiency of using a geospatial approach to model this relationship.

METHODS

This study focused on the ≥ 75-year-old population of the Nord administrative region of France. Inpatient hospital spatial accessibility was computed with the E2SFCA method, and then the LOS was calculated from the French national hospital activity and patient discharge database. Ordinary least squares (OLS), spatial autoregressive (SAR), and geographically weighted regression (GWR) were used to analyse the relationship between LOS and spatial accessibility to inpatient hospital care and to three primary care service types (general practitioners, physiotherapists, and home-visiting nurses). Each model performance was assessed with measures of goodness of fit. Spatial statistical methods to reduce or eliminate spatial autocorrelation in the residuals were also explored.

RESULTS

GWR performed best (highest R2 and lowest Akaike information criterion). Depending on global model (OLS and SAR), LOS was negatively associated with spatial accessibility to general practitioners and physiotherapists. GWR highlighted local patterns of spatial variation in LOS estimates. The distribution of areas in which LOS was positively or negatively associated with spatial accessibility varied when considering accessibility to general practitioners and physiotherapists.

CONCLUSIONS

Our findings suggest that spatial regressions could be useful for analysing the relationship between healthcare spatial accessibility and utilization. In our case study, hospitalization of elderly people was shorter in areas with better accessibility to general practitioners and physiotherapists. This may be related to the presence of effective community healthcare services. GWR performed better than LOS and SAR. The identification by GWR of how these relationships vary spatially could bring important information for public healthcare policies, hospital decision-making, and healthcare resource allocation.

Geospatial patterns of comorbidity prevalence among people with osteoarthritis in Alberta Canada

BACKGROUND

Knowledge of geospatial pattern in comorbidities prevalence is critical to an understanding of the local health needs among people with osteoarthritis (OA). It provides valuable information for targeting optimal OA treatment and management at the local level. However, there is, at present, limited evidence about the geospatial pattern of comorbidity prevalence in Alberta, Canada.

METHODS

Five administrative health datasets were linked to identify OA cases and comorbidities using validated case definitions. We explored the geospatial pattern in comorbidity prevalence at two standard geographic areas levels defined by the Alberta Health Services: descriptive analysis at rural-urban continuum level; spatial analysis (global Moran's I, hot spot analysis, cluster and outlier analysis) at the local geographic area (LGA) level. We compared area-level indicators in comorbidities hotspots to those in the rest of Alberta (non-hotspots).

RESULTS

Among 359,638 OA cases in 2013, approximately 60% of people resided in Metro and Urban areas, compared to 2% in Rural Remote areas. All comorbidity groups exhibited statistically significant spatial autocorrelation (hypertension: Moran's I index 0.24, z score 4.61). Comorbidity hotspots, except depression, were located primarily in Rural and Rural Remote areas. Depression was more prevalent in Metro (Edmonton-Abbottsfield: 194 cases per 1000 population, 95%CI 192-195) and Urban LGAs (Lethbridge-North: 169, 95%CI 168-171) compared to Rural areas (Fox Creek: 65, 95%CI 63-68). Comorbidities hotspots included a higher percentage of First Nations or Inuit people. People with OA living in hotspots had lower socioeconomic status and less access to care compared to non-hotspots.

CONCLUSIONS

The findings highlight notable rural-urban disparities in comorbidities prevalence among people with OA in Alberta, Canada. Our study provides valuable evidence for policy and decision makers to design programs that ensure patients with OA receive optimal health management tailored to their local needs and a reduction in current OA health disparities.

Impact of nonphysician providers on spatial accessibility to primary care in Iowa

OBJECTIVE

To assess the impact of nonphysician providers on measures of spatial access to primary care in Iowa, a state where physician assistants and advanced practice registered nurses are considered primary care providers.

DATA SOURCES

2017 Iowa Health Professions Inventory (Carver College of Medicine), and minor civil division (MCD) level population data for Iowa from the American Community Survey.

STUDY DESIGN

We used a constrained optimization model to probabilistically allocate patient populations to nearby (within a 30-minute drive) primary care providers. We compared the results (across 10 000 scenarios) using only primary care physicians with those including nonphysician providers (NPPs). We analyze results by rurality and compare findings with current health professional shortage areas.

DATA COLLECTION/EXTRACTION METHODS

Physicians and NPPs practicing in primary care in 2017 were extracted from the Iowa Health Professions Inventory.

PRINCIPAL FINDINGS

Considering only primary care physicians, the average unallocated population for primary care was 222 109 (7 percent of Iowa's population). Most of the unallocated population (86 percent) was in rural areas with low population density (< 50/square mile). The addition of NPPs to the primary care workforce reduced unallocated population by 65 percent to 78 252 (2.5 percent of Iowa's population). Despite the majority of NPPs being located in urban areas, most of the improvement in spatial accessibility (78 percent) is associated with sparsely populated rural areas.

CONCLUSIONS

The inclusion of nonphysician providers greatly reduces but does not eliminate all areas of inadequate spatial access to primary care.

Urban and rural differences in geographical accessibility to inpatient palliative and end-of-life (PEoLC) facilities and place of death: a national population-based study in England, UK

BACKGROUND

Little is known about the role of geographic access to inpatient palliative and end of life care (PEoLC) facilities in place of death and how geographic access varies by settlement (urban and rural). This study aims to fill this evidence gap.

METHODS

Individual-level death data in 2014 (N = 430,467, aged 25 +) were extracted from the Office for National Statistics (ONS) death registry and linked to the ONS postcode directory file to derive settlement of the deceased. Drive times from patients' place of residence to nearest inpatient PEoLC facilities were used as a proxy estimate of geographic access. A modified Poisson regression was used to examine the association between geographic access to PEoLC facilities and place of death, adjusting for patients' socio-demographic and clinical characteristics. Two models were developed to evaluate the association between geographic access to inpatient PEoLC facilities and place of death. Model 1 compared access to hospice, for hospice deaths versus home deaths, and Model 2 compared access to hospitals, for hospital deaths versus home deaths. The magnitude of association was measured using adjusted prevalence ratios (APRs).

RESULTS

We found an inverse association between drive time to hospice and hospice deaths (Model 1), with a dose-response relationship. Patients who lived more than 10 min away from inpatient PEoLC facilities in rural areas (Model 1: APR range 0.49-0.80; Model 2: APR range 0.79-0.98) and urban areas (Model 1: APR range 0.50-0.83; Model 2: APR range 0.98-0.99) were less likely to die there, compared to those who lived closer (i.e. ≤ 10 min drive time). The effects were larger in rural areas compared to urban areas.

CONCLUSION

Geographic access to inpatient PEoLC facilities is associated with where people die, with a stronger association seen for patients who lived in rural areas. The findings highlight the need for the formulation of end of life care policies/strategies that consider differences in settlements types. Findings should feed into local end of life policies and strategies of both developed and developing countries to improve equity in health care delivery for those approaching the end of life.

Urban and Rural Disparities in Hospital Utilization among Indonesian Adults

BACKGROUND

Equal access to healthcare facilities, patient's satisfaction, and respect for the desire of the patient were recognized as the basic principles of each of the health care system. Each person must be given the opportunity to access health services in accordance with the requirements of their health. We aimed to prove the existence of disparities hospital utilization based on the category of urban-rural areas.

METHODS

The research used the 2013 Indonesian Basic Health Survey (RKD) as analysis material, that was designed a cross-sectional survey. With the multi-stage cluster random sampling method, 722,329 respondents were obtained. Data were analyzed using Multinomial Logistic Regression tests.

RESULTS

The results showed adults living in urban were likely to use hospital outpatient facilities 1.246 times higher than adults living in rural areas (OR 1.246; 95% CI 1.026 - 1.030). The likelihood of utilizing at the same time outpatient and inpatient facilities at 1.134 times higher in adults living in urban than those in rural areas (OR 1.134; 95% CI 1.025 - 1.255). While for the category of hospital inpatient utilization, there was no significant difference.

CONCLUSION

There was a disparity in hospital utilization between urban-rural areas. Urban show better utilization than rural areas in outpatient and at the same time the use of inpatient care.