Geospatial assessment of primary healthcare centres in Jeddah, Saudi Arabia.

Objectives:To assess the level of satisfaction expressed for services rendered in the public health sector to clients who were seen at outpatient departments (OPDs) and primary healthcare (PHC) facilities in Al-Qassim Health Cluster in Saudi Arabia.Methods:This study used a comparative cross-sectional study design, and data were obtained as secondary data from The Patient’s Experience Platform. Data analysis was carried out using Stata (version 16), and the significance level was 5%.Results:In comparing the mean satisfaction in 9 domains, statistically significantly higher means for the PHC facilities than the OPDs were seen across 7 domains: patients navigating health facilities (p=0.008), nursing services (p<0.001), physicians’ services (p=0.004), radiological services (p<0.001), personal issues (p=0.003), the pharmacy (p=0.005), and general experience (p=0.036).Conclusion:This study’s findings are significant. They indicate that client satisfaction with services at PHC facilities was significantly higher than with services at hospital OPDs in Al-Qassim region. This underscores the need to improve the quality of services provided at OPDs to bring them to the same level as those provided by PHC facilities. Policymakers and implementers should, therefore, sustain efforts to make OPDs and PHC facilities more desirable for clients.

Accurate disease diagnosis relies on a well-organized and reliable laboratory system. This study assesses the quality of laboratory services in Tanzania based on the nationwide Star Rating Assessment (SRA) of Primary Healthcare (PHC) facilities conducted in 2017/18. This cross-sectional study utilized secondary data from all the country's PHC facilities stored in the SRA database. Laboratory service quality was assessed by aggregating scores as percentages of the maximum achievable score across various indicators: dedicated laboratory department/room, adequate equipment, staffing levels, adherence to testing protocols, establishment of turnaround times, internal and external quality controls, and safety and supplies management. Scores equal to or exceeding 80% were deemed compliant. Multiple linear regression was used to determine the influence of facility characteristics (level, ownership, location, staffing) on quality scores, with statistical significance set at p < 0.05. The study included 6,663 PHC facilities (85.9% dispensaries, 11% health centers, 3.2% hospital-level-1), with the majority being public (82.3% vs. 17.7%) and located in rural areas (77.1% vs. 22.9%). On average, facilities scored 30.8% (SD = 35.7), and only 26.6% met staffing requirements. Compliance with quality standards was higher in private (63% vs. 19%, p<0.001) and urban facilities (62% vs. 16%, p<0.001). More than half of the facilities did not meet either of the eight quality indicators. Quality was positively linked to staffing compliance (Beta = 5.770) but negatively impacted by dispensaries (Beta = -6.342), rural locations (Beta = -0.945), and public ownership (Beta = -1.459). A score of 30% falls significantly short of the national target of 80%. Improving laboratory staffing levels at PHC facilities could improve the quality of laboratory services, especially in public facilities that are based in rural areas. There is a need to further strengthen laboratory services in PHC facilities to ensure the quality of laboratory services and clients' satisfaction.

Accurate disease diagnosis relies on a well-organized and reliable laboratory system. This study assesses the quality of laboratory services in Tanzania based on the nationwide Star Rating Assessment (SRA) of Primary Healthcare (PHC) facilities conducted in 2017/18. This cross-sectional study utilized secondary data from all the country’s PHC facilities stored in the SRA database. Laboratory service quality was assessed by aggregating scores as percentages of the maximum achievable score across various indicators: dedicated laboratory department/room, adequate equipment, staffing levels, adherence to testing protocols, establishment of turnaround times, internal and external quality controls, and safety and supplies management. Scores equal to or exceeding 80% were deemed compliant. Multiple linear regression was used to determine the influence of facility characteristics (level, ownership, location, staffing) on quality scores, with statistical significance set at p < 0.05. The study included 6,663 PHC facilities (85.9% dispensaries, 11% health centers, 3.2% hospital-level-1), with the majority being public (82.3% vs. 17.7%) and located in rural areas (77.1% vs. 22.9%). On average, facilities scored 30.8% (SD = 35.7), and only 26.6% met staffing requirements. Compliance with quality standards was higher in private (63% vs. 19%, p<0.001) and urban facilities (62% vs. 16%, p<0.001). More than half of the facilities did not meet either of the eight quality indicators. Quality was positively linked to staffing compliance (Beta = 5.770) but negatively impacted by dispensaries (Beta = -6.342), rural locations (Beta = -0.945), and public ownership (Beta = -1.459). A score of 30% falls significantly short of the national target of 80%. Improving laboratory staffing levels at PHC facilities could improve the quality of laboratory services, especially in public facilities that are based in rural areas. There is a need to further strengthen laboratory services in PHC facilities to ensure the quality of laboratory services and clients’ satisfaction.

It is well known that equity assessment of the spatial distribution of primary health care facilities (PHCFs) is significant to optimize the allocation of health care resources and enhance the ability to react to public health emergencies, yet there is much discussion about the poor effectiveness of assessment for many cities due to the limitations of the insufficient ability of traditional data to reflect residents' demands, etc. In many cities where PHCFs are inequity distributed, this is urgently needed for the government. Using Fuzhou City, China as a case study, we propose a comprehensive method for assessing the equity that consisting of two frameworks based on the Geographic Information System. The first framework is assessing resident demand based on daily demand and potential demand, and the second is assessing PHCFs supply based on road impedance. This method combines an index system based on multi-source data and a spatial matching analysis between resident demand and PHCFs supply based on the supply-demand disparity index. The demand degree for the PHCFs and the supply degree of accessing the PHCFs of different residential areas differ to great extents. The equity of the spatial distribution of PHCFs shows well overall, but there is still a lack of equity in local areas. The number of the residential areas with the lowest equity, insufficient supply and oversupply accounted for 10.31% and 39.00% respectively; the former is mainly gathered in a concentrated form at the edge and in a scattered form inside, and the latter is distributed in the interior mostly in a concentrated manner. The key findings highlight several aspects of improving the layout of PHCFs such as strengthen an in-depth analysis on residents' demand. This study contributes to a more accurate equity assessment, and further improves the layout of health care facilities.

BackgroundIt is well known that equity assessment of the spatial distribution of primary health care facilities (PHCFs) is significant to optimize the allocation of health care resources and enhance the ability to react to public health emergencies, yet there is much discussion about the poor effectiveness of assessment for many cities due to the limitations of the insufficient ability of traditional data to reflect residents’ demands, etc. In many cities where PHCFs are inequity distributed, this is urgently needed for the government.MethodsUsing Fuzhou City, China as a case study, we propose a comprehensive method for assessing the equity that consisting of two frameworks based on the Geographic Information System. The first framework is assessing resident demand based on daily demand and potential demand, and the second is assessing PHCFs supply based on road impedance. This method combines an index system based on multi-source data and a spatial matching analysis between resident demand and PHCFs supply based on the supply-demand disparity index.ResultsThe demand degree for the PHCFs and the supply degree of accessing the PHCFs of different residential areas differ to great extents. The equity of the spatial distribution of PHCFs shows well overall, but there is still a lack of equity in local areas. The number of the residential areas with the lowest equity, insufficient supply and oversupply accounted for 10.31% and 39.00% respectively; the former is mainly gathered in a concentrated form at the edge and in a scattered form inside, and the latter is distributed in the interior mostly in a concentrated manner.ConclusionThe key findings highlight several aspects of improving the layout of PHCFs such as strengthen an in-depth analysis on residents’ demand. This study contributes to a more accurate equity assessment, and further improves the layout of health care facilities.

From service capacity to spatial equity: Exploring a multi-stage decision-making approach for optimizing elderly-care facility distribution in the city centre of Tianjin, China

Access to adequate healthcare systems is seen as a fundamental human right. Therefore, healthcare services must be distributed and accessiblein the most efficient way possible to those who need them the most. Primary healthcarecentersare the backbone of any healthcare system as they provide essential healthcare services to the general population. Inequalities in the distribution of primary healthcare centers can lead to disparities in healthcare access and outcomes. This study aimed to assess the equity of primary healthcare centers distribution, we analyzed and evaluated the allocation of primary healthcare centers in Saudi Arabia from 2017 to 2021. This cross-sectional study utilized data from the Health Statistical Yearbook published by the Ministry of Health, Saudi Arabia, during the period of 2017-2021. The number of primary healthcarecenters per 10,000 population was calculated for the 20 health regions. We used the Gini indexto measure inequalityin the distribution of primary healthcarecenters. The Pearson coefficient was calculated to assess the correlation between the number ofprimary healthcarecenters and the population in each health region. The overall ratio of primary healthcare centers to population decreased from 0.72 to 0.62 between 2017 and 2021. The Gini index showed relative equality in the distribution of primary healthcare centers from 2017 to 2021 with values between 0.2 and 0.3. There was a positivecorrelation between the population and the primary healthcare centers in Jeddah, Tabouk, and the Northern Region. However, in Riyadh, Makkah, Taif, Madinah, Qaseem, Eastern Region, Al-Ahsa, Aseer, Hail, Jazan, Najran, Al-Baha, and Al-Jouf, the correlation was found to be negative. From 2017 to 2021, primary healthcare facilities are distributed fairly throughout20 health regions of Saudi Arabia.However, there are still some disparities between provinces, and efforts must be made to ensure that primary healthcare centers are distributed equitably across the country to improve healthcare access and outcomes for all.

Inequity in accessibility to urban parks in environmental gentrification areas based on Multi-G3SFCA: A case study of Wuhan’s main urban districts

Adjusting for quality of care in primary health care utilization and benefits in Bihar, India: A benefit incidence analysis

Study objective: To develop a quantitative methodology to optimally site new primary health care facilities so as to achieve the maximum population level increase in accessibility to care. The study...

Introduction: Hospital bed density is a key indicator of healthcare system capacity. In Saudi Arabia, total hospital bed density is lower than the global benchmark. This study aims to examine national trends and regional variations in bed density per 1,000 population in Ministry of Health (MOH) hospitals across Saudi Arabia. Methods: This retrospective study analyzed secondary data from the Saudi Open Data Platform on bed density in MOH hospitals across all regions of Saudi Arabia between 2018 and 2022. Other public and private hospitals are excluded from this analysis. The data were analyzed using descriptive statistics, trend analysis, the Lorenz curve, and the Gini index. Results: Between 2018 and 2022, population growth of approximately 6.6% outpaced the increase in bed capacity in MOH hospitals (4.1%), resulting in a slight decline in MOH bed density nationally from 1.45 to 1.41 beds per 1,000 population, with periodic fluctuations. The national distribution of beds in MOH hospitals showed relative equality, with a Gini index ranging from 0.208 in 2018 to 0.211 in 2022. However, marked regional disparities were observed. At MOH hospitals, bed density was 1.0–1.3 beds per 1,000 population in large regions, including Riyadh, Makkah, and the Eastern Province, compared with 3.1–3.9 beds per 1,000 population in smaller regions, including Al-Jouf, Al-Bahah, and the Northern Borders. Similar patterns were observed for beds in intensive care units and emergency departments. Of the 287 MOH hospitals, 221 (77.0%) were community-level facilities with fewer than 200 beds, and 66 (23.0%) were large hospitals with more than 200 beds. Nearly two-thirds (60.6%) of the large hospitals were located in Riyadh, Makkah, and the Eastern Province. Conclusion: Although the national allocation of beds in MOH hospitals appears relatively equitable, bed density per 1,000 population has declined slightly due to population growth, with substantial regional disparities. Continuous monitoring of bed-density indicators, strategic planning of hospital capacity, and strengthening of community-based services are required to reduce pressure on inpatient services, support equitable access, and align with Saudi Vision 2030 and the national healthcare system goals.

Wet markets serve as critical infrastructure for access to fresh food for urban residents in China, playing a vital role in daily life and public well-being. However, their accessibility is often shaped by disparities between urban cores and rapidly expanding peripheral districts, raising concerns over spatial equity in the urban food environment. This study investigates these disparities in Shanghai by comparing wet market accessibility in Putuo district (urban core) and Minhang district (periphery). Accessibility is measured using the Gaussian-enhanced two-step floating catchment area (2SFCA) method, incorporating travel time data from the Baidu Map API for multiple transportation modes. The Gini coefficient is further employed to evaluate the equity of accessibility distribution. The results reveal a notable disparity: residents in the periphery (Minhang) experience a higher average level of accessibility, but their access is distributed significantly less equitably compared to those in the traditional urban core (Putuo). These findings underscore a critical trade-off between development efficiency and spatial equity, highlighting the need for targeted planning strategies and policies to address spatial inequalities in fresh food access in rapidly transforming cities.

ABSTRACTEnsuring healthy lives and promoting well-being for all ages is the 3rd Sustainable Development Goal (SDG). Inequality in access to health care remains one of the primary challenges in achieving the goal. With the ever-increasing expansion of urban areas and population growth, there is a need to regularly examine the pattern of accessibility of basic amenities across regions, States and urban areas. This study examined geographic access to Primary Health Care Facilities (PHCF) in Nigeria using the combination of open data and geospatial analysis techniques. Thus, showcasing an approach can be replicated across different regions in Sub-Saharan Africa due to issues of information gap. Data on elevation, location of health care facilities, population and network data were utilised. The result shows that PHCF aggregate at certain locations, e.g. major urban agglomerations, and transit route leading to these places. High concentrations are found in the capital city. The average travel time to the nearest PHCF is about 14 min (Standard Deviation ±13.30 min) while the maximum is about 2 hours. Pockets of low accessibility areas exist across the Akwa Ibom State in the Niger Delta region of Nigeria. There is an indication that most places have good geographic access. Across the 1787 settlements identified in our dataset, 98.3% are with good access (<30 min), 27 settlements are located in the poor access class (31–60 min), while two settlements are within the very poor access class (>60 min). Geographic access is not the main limiting factor to health care access in the region. Therefore, computation of access to health care should take into consideration other dimensions of accessibility, to create a robust measure which will support effective and efficient health care planning and delivery.

BackgroundAchieving fair access to healthcare and improving population health are crucial in all settings. Properly staffed and fairly distributed primary health care (PHC) facilities are prerequisites to ensure accessible healthcare services. Nevertheless, availability and accessibility issues are common public health concerns, especially in under-resourced countries including Ethiopia. Measuring inequalities in accessibility of healthcare resources guide policy decisions to improve PHC services and ultimately achieving universal health coverage (UHC).PurposeTo assess availability and measure magnitude and trend of inequalities in accessibility of health centre-based PHC resources in Ethiopia during 2015 to 2017.MethodsWe conducted a cross-sectional population-based analysis of district-level data collected from 16th December 2017 until 24th May 2018. Afar, Dire-Dawa, and Tigray regions were purposefully included in the study to represent the four pastoralist/semi-pastoralist, three urban and four agrarian regions in Ethiopia, respectively. We used ratios, different inequality indices and Gini decomposition techniques to characterise the inequalities.ResultsIn 2017, median of health centres (HCs) per 15,000 inhabitants and their Gini indices (GIs) for Afar, Dire-Dawa, and Tigray were 0.781, 0.566, 0.591 vs. 0.237, 0.280, 0.216 respectively. Median overall skilled health workers (SHWs) per 10,000 inhabitants were 5.250, 7.539, and 6.246, respectively. These accounted for 11.80%, 16.94% and 14.04% of the WHO target of 44.5 to achieve SDGs. The corresponding GIs for the regions were 0.347, 0.186 and 0.175. Despite a higher overall SHWs inequality in the urban districts of Tigray (GI = 0.301), only Tigray showed significant inequality reductions in GHE (p < 0.001) and in all categories of SHWs (p < 0.05).ConclusionsOur analysis provided a clear picture of availability and inequalities in PHC resources across three regions in Ethiopia. Identifying contributing factors to low densities and high inequalities of SHWs may help improve PHC services nationwide, along with pathway towards UHC.

Insufficient vaccination coverage among healthcare workers (HCWs) contributes to nosocomial outbreaks and avoidable deaths. This study assesses vaccine hesitancy among HCWs in primary healthcare facilities and identifies contributing factors in Najran City, Saudi Arabia. A cross-sectional study was conducted in August 2023 among healthcare workers (HCWs) at various healthcare facilities providing primary care. The study utilized a questionnaire developed in accordance with World Health Organization` (WHO) and Centers for Disease Control and Prevention (CDC) guidelines, covering sociodemographic and medical characteristics, vaccination profiles (19 items), and evidence of vaccination status. A multistage sampling technique was employed to obtain a representative sample, involving random selection of HCWs from various categories (physicians, pharmacists, dentists, nurses, laboratory technicians, radiology technicians, and paramedics) and clustering willing participants from each facility type. Among the 591healthcare workers (HCWs), 57.7% were male and 45.8% were aged 31-40 years. Vaccination compliance rates varied, with the highest rates observed for the third COVID-19 dose (86.3%), hepatitis B (81.2%), and influenza (78.3%), while the lowest rate was for Tdap during pregnancy (27.7%). Immunization documentation was primarily evidenced through vaccine records (67.5%), lab tests (51.8%), and disease confirmation (36.9%). In this study 70.9% partially, 19.1% fully vaccinated, and 10% unvaccinated. Regarding immunization documentation, 57% was partial, 15.7% complete, and 27.2% incomplete. Significant predictors of being unvaccinated included male sex (p=0.001), Saudi nationality (p=0.030), and technician/paramedic profession (p<0.001). Additionally, HCWs experiencing burnout were more likely to be unvaccinated (p=0.006). Multivariate analysis revealed that technicians/paramedics were 2.74 times more likely to be unvaccinated than physicians (AOR=2.743; 95% CI=1.140-6.600; p=0.024). This study highlights significant gaps in vaccination coverage among HCWs, working at primary healthcare facilities with only 19.1% fully vaccinated. Factors such as male gender and burnout contribute to vaccine hesitancy. To address these gaps, future directions such as targeted educational interventions, burnout mitigation, mobile vaccination units, and awareness campaigns are recommended. Additionally, supportive measures to manage burnout are crucial for improving vaccination rates and protecting public health.