Novel hot and cold spots of mental hospital deserts across China using 2019-2022 antidepressant procurement data

doi:10.21203/rs.3.rs-4346667/v1

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Novel hot and cold spots of mental hospital deserts across China using 2019-2022 antidepressant procurement data

https://doi.org/10.21203/rs.3.rs-4346667/v1

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Depression imposes a significant disease burden globally, being considered the second leading cause of disability in China. Therefore, it is worth noting whether patients with depression have sufficient opportunities to access treatment during travel. The minimum-cost path analysis method was employed to calculate travel times from population points to the nearest hospitals providing psychiatric medications, and to distinguish population points as either within or outside hospital deserts. Lastly, Poisson spatial scan analysis was used to identify hot spots and cold spots of hospital deserts. This study elucidates the limited and unequal access to antidepressant medications in China, providing policymakers and stakeholders with valuable insights to formulate effective strategies.

Depression, a global prevalent mental illness, exerts a substantial impact on individual and societal well-being[1]. In China, depression is identified as the second leading cause of years lived with disability, contributing to diminished quality of life over an extended period[2]. In the aftermath of the COVID-19 pandemic, individuals are contending with unparalleled levels of stress and experiencing profound psychological distress[3]. The significance of access to effective treatment options, such as antidepressant medications, cannot be overstated in managing these conditions[4]. Examining the spatial accessibility of these medications enables the identification of regions potentially impeded by treatment barriers[5]. Consequently, this facilitates the development of targeted interventions aimed at enhancing accessibility and addressing existing disparities.

Previous studies shed light on the concerning issue of hospital deserts, defined as areas with limited or no access to medical institutions dispensing antidepressant medications[6]. These studies frequently delineate hospital deserts through the dual dimensions of distance and time[7]. In contrast, these desert areas often become focal points for national policy interventions. The analysis reveals a significant nationwide proportion of population points (over 60%) exceeding the 90-minute travel threshold, indicating substantial geographical barriers to accessing these medications. This limited accessibility can have detrimental consequences for individuals suffering from depression, potentially causing delays or hindrances in their treatment process.

Furthermore, spatial inequities in access to antidepressant medications across different regions and provinces has becoming an important topic for urban planning and public health[8]. Some regions with advanced economic development generally demonstrate superior accessibility compared to remote areas. However, even within regions boasting better average accessibility, noteworthy disparities persist. These findings emphasize the need for targeted interventions to rectify these discrepancies and guarantee equitable access to essential mental healthcare services throughout the nation[9].

Therefore, this study investigates the spatial accessibility of antidepressant medications in China across 356 cities within 31 provinces, spanning the period from 2019 to 2022. The primary objective is to assess the availability and distribution of these medications, identifying areas with limited access and potential disparities across different regions. The quantification of accessibility through travel time estimations and the identification of "hospital deserts" (areas exceeding a 15-minute travel threshold) will provide insights into geographical barriers.

Analyzing trends over the four years will help us comprehend potential improvements or challenges in accessibility. Additionally, examining regional and provincial disparities will highlight areas facing greater accessibility challenges and potential inequalities. Lastly, employing spatial scan statistics will pinpoint "hot spots" and "cold spots" of hospital deserts, enabling targeted interventions to enhance access and address existing disparities[10]. By comprehending the spatial distribution of antidepressant medications and identifying areas with limited or unequal access, this study aims to inform policymakers and stakeholders in developing strategies to ensure equitable access to essential mental healthcare services across China.

Study area

Our study encompassed 31 provincial units in mainland China, spanning four municipalities (Beijing, Shanghai, Tianjin, and Chongqing), five minority autonomous regions (Inner Mongolia, Xinjiang, Tibet, Guangxi, and Ningxia), and 22 provinces. According to the National Statistical Yearbook of 2023, our study area had a total area of 9.63 million square kilometers and a population exceeding 1.4 billion. Notably, our investigation incorporated 356 cities across this vast and diverse landscape.

In this study, adhering to the World Health Organization model list of essential medicines (2021) (WHO-ATC codes N06A or N06BA), we included procurement records of antidepressant medications from healthcare institutions. Some drugs were excluded due to their absence in the domestic market, leading to a lack of relevant data. Ultimately, 17 depressant drugs, covering SSRIs, SNRIs, TcAs, NaSSAs, NRIs and Melatonin Receptor Agonist, were selected representing the majority of frontline clinical antidepressant options in the country. Notably, 9 out of the 17 drugs were integrated into the VBP, constituting a 52% inclusion rate (Table 1).

Table 1

List of Antidepressant Medications and VBP Batches
Drug name	Type	VBP batch	VBP time	Common dosage forms
Escitalopram	SSRIs	1st	2019/9	Oral regular release dosage form
Paroxetine	SSRIs	1st	2019/9	Tablet
Fluoxetine	SSRIs	3rd	2020/8	Capsules
Citalopram	SSRIs	3rd	2020/8	Oral regular release dosage form
Sertraline	SSRIs	3rd	2020/8	Oral regular release dosage form
Duloxetine	SNRIs	4th	2021/2	Oral regular release dosage form
Venlafaxine	SNRIs	5th	2021/6	Oral sustained-release and controlled-release form
Flupentixol-melitracen	TcAs	7th	2022/7	Oral regular release dosage form
Mirtazapine	NaSSAs	8th	2023/4	Oral regular release dosage form
Clomipramine	TcAs	Not included	/	Injection
Fluvoxamine	SSRIs	Not included	/	Oral regular release dosage form
Amitriptyline	TcAs	Not included	/	Tablet
Mianserin	NaSSAs	Not included	/	Oral regular release dosage form
Doxepin	TcAs	Not included	/	Tablet, Capsules
Agomelatine	Melatonin Receptor Agonist	Not included	/	Oral regular release dosage form
Milnacipran	SNRIs	Not included	/	Oral regular release dosage form
Reboxetine	NRIs	Not included	/	Oral regular release dosage form

Data source

Various datasets were gathered to model spatial accessibility and identify hospital deserts. Drug procurement information was sourced from the China Pharmaceutical Association, offering comprehensive data spanning from 2019 to 2022 regarding national drug procurement. In the dataset, the antidepressant medications include 17 types: Escitalopram, Paroxetine, Fluoxetine, Sertraline, Citalopram, Duloxetine, Venlafaxine, Flupentixol-melitracen, Mirtazapine, Clomipramine, Fluvoxamine, Amitriptyline, Mianserin, Doxepin, Agomelatine, Milnacipran and Reboxetine. Population information was extracted from the websites of the National Statistical Bureau. Geographic data, including location and road traffic network details, was obtained from Gaode Location Service. Geographic administrative division data were obtained from Bigemap[11]. Population information is sourced from WorldPop[12], and the demographic data from this project is based on the national census conducted in 2020. Friction surfaces for motorized travel speeds are derived from the Malaria Atlas Project (eTable 1).

Spatial accessibility

A grid with a resolution of 1x1 km² was overlaid onto all 356 cities nationwide[13]. We assumed that individuals with mental disorders used motorized vehicles or public transit for travel, rather than walking[11]. Travel time from each population point to the nearest medical institution under motorized speed was employed as indicators defining spatial accessibility and hospital deserts[14]. In China, people predominantly rely on public rail transportation for commuting, which significantly saves travel time. The computation of travel time utilized Dijkstra's minimum-cost path algorithm[15]. The algorithm tested all possible routes from residential locations to nearby medical institutions, selecting the route with the shortest travel time. Additionally, we categorized travel time into 5 ranges (minutes): <15, 15–30, 30–60, 60–90, > 90, and summarized the population coverage within each range[13][16].

Hospital deserts

Hospital deserts were defined based on the travel time from each 1x1 km² population community to the nearest medical institution. In reference to the United States Department of Agriculture's (USDA's) definition of food deserts[17], a threshold of 15 minutes was employed[18]. Population-dense communities where the travel time, using motorized vehicle speed, exceeded 15 minutes were designated as hospital deserts. Next, we calculated the proportion of population points considered as hospital deserts for each province each year and computed the mean and SD. When calculating the proportion, population points where travel time could not be calculated due to insufficient procurement were considered as part of the hospital deserts. To explore the inequality within each province, we also calculated the SD of city proportions within each province[19].

Novel hot spots and cold spots of hospital deserts

We utilized city-based Poisson circular spatial scans in SaTScan version 10.0.1, initially designed for exploring spatial clustering of cancer incidence[20]. The total population points for each city were used as the basis, population points designated as hospital deserts were considered as the focal points of interest. Differentiation was conducted using the relative risk ratio (RRs), identifying clusters with RRs > 1 as hot spots for hospital deserts and those with RRs < 1 as cold spots.

Visualization and Statistical Software

ArcGIS V.10.8 was used for data visualization, R version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria) was used for statistical analyses, and SaTScan version 10.0.1 was employed for Poisson spatial scans.

Overall access to hospital provision of depressant drugs

Nationally, over the past four years, approximately 10.2% of population points had travel times within 15 minutes to the nearest medical institution supplying antidepressant drugs. About 9.4% of population points had travel times between 15 and 30 minutes, while approximately 12.1% had travel times between 30 and 60 minutes. For around 8.1% of population points, travel times ranged from 60 to 90 minutes. Approximately 60.2% of population points had travel times exceeding 90 minutes, and we categorized these as inaccessible areas in our study.

At the province level, based on the four-year average travel time across provinces, within the 0 to 15-minute range, Shanghai and Tianjin exhibit the highest proportion of population points (93.9%, 68.6%), while Qinghai and Tibet show the lowest (1.0%, 0.3%). Within the 15 to 30-minute range, Zhejiang and Liaoning have the highest proportion of population points (31.7%, 29.4%), while Xinjiang and Tibet exhibit the lowest proportion of population points (2.1%, 0.4%). Within the 30 to 60-minute range, the provinces with the highest proportion of population points are Liaoning and Shanxi (29.7, 27.1%), while Tibet and Shanghai have the lowest proportion of population points (1.1%, 0.5%). It is noteworthy that Shanghai has virtually no population points with travel times exceeding 60 minutes. Within the 60 to 90-minute range, Jilin and Ningxia exhibit the highest proportion of population points (19.4%, 16.4%), while excluding Shanghai, the lowest proportion is observed in Tianjin and Tibet (1.7%, 1.7%). We consider population points with travel times exceeding 90 minutes as geographically inaccessible. Tibet and Qinghai have the highest proportion in this category (95.5%, 90.0%), indicating significant barriers to spatial accessibility for antidepressant medications in these provinces (Table 2).

Table 2

Proportion with different accessibilities, mean across provinces by year (%)
Province	Region	0–15 mins	15–30 mins	30–60 mins	60–90 mins	> 90 mins
Anhui	Central	0.261	0.284	0.263	0.090	0.085
Fujian	East	0.319	0.175	0.186	0.109	0.201
Gansu	West	0.052	0.077	0.132	0.089	0.638
Guangdong	East	0.333	0.186	0.201	0.086	0.166
Guangxi	West	0.202	0.095	0.150	0.085	0.286
Guizhou	West	0.119	0.154	0.256	0.147	0.267
Hainan	East	0.272	0.228	0.212	0.078	0.184
Hebei	East	0.404	0.250	0.200	0.106	0.109
Henan	Central	0.370	0.245	0.185	0.079	0.110
Heilongjiang	Northeast	0.039	0.066	0.143	0.123	0.615
Hubei	Central	0.197	0.121	0.171	0.097	0.239
Hunan	Central	0.186	0.213	0.253	0.118	0.200
Jilin	Northeast	0.063	0.117	0.239	0.194	0.408
Jiangsu	East	0.515	0.255	0.145	0.032	0.017
Jiangxi	Central	0.237	0.224	0.233	0.114	0.185
Liaoning	Northeast	0.239	0.294	0.297	0.134	0.091
Neimenggu	West	0.028	0.079	0.129	0.125	0.738
Ningxia	West	0.140	0.168	0.261	0.164	0.234
Qinghai	West	0.010	0.036	0.036	0.034	0.900
Shandong	East	0.438	0.291	0.176	0.047	0.022
Shanxi	Central	0.174	0.219	0.271	0.148	0.194
Shannxi	West	0.153	0.156	0.211	0.133	0.326
Sichuan	West	0.096	0.068	0.082	0.054	0.651
Xizang	West	0.003	0.004	0.011	0.017	0.955
Xinjiang	West	0.013	0.021	0.046	0.044	0.875
Yunnan	West	0.118	0.057	0.106	0.076	0.477
Zhejiang	East	0.388	0.317	0.238	0.101	0.079
Beijing	East	0.632	0.184	0.119	0.047	0.026
Shanghai	East	0.939	0.056	0.005	0.000	0.000
Chongqing	West	0.226	0.200	0.218	0.110	0.210
Tianjin	East	0.686	0.210	0.087	0.017	0.004

Hospital deserts and inequalities

Nationally, with the continuous investment in healthcare resources and the implementation of centralized procurement policies, the issue of hospital deserts for antidepressant drugs is gradually improving. Over the four-year period from 2019 to 2021, the nationwide proportion of hospital deserts has shown a decline, but there was a slight rebound in 2022 (90.9%, 89.5%, 89.4%, 89.6%). This could be attributed to the strict lockdown measures implemented by the government in 2022 in response to the COVID-19 pandemic.

At the region and province level, the nationwide situation of hospital deserts also reveals regional disparities and inequalities (Table 3). Horizontally, the proportion of hospital deserts are lowest in the eastern regions of the country, followed by the central and northeastern regions. In contrast, the western regions exhibit the highest proportion (Fig. 1 Mean and standard deviation of hospital deserts proportion by province). The western provinces with the highest proportions include Tibet, Qinghai, Xinjiang, Inner Mongolia, and Gansu (99.7%, 99.0%, 98.7%, 97.2%, 94.8%). In the northeastern region, the provinces with the poorest performance are Heilongjiang and Jilin (96.1%, 93.7%). Shanghai stands out with exceptional performance, where less than 1% of population points are considered as hospital deserts. It is noteworthy that the proportion in Hainan may be overestimated due to its small total population points (Fig. 1).

Table 3

Proportion of hospital deserts (%), mean and standard deviation across provinces by year
Province	Region	2019	2020	2021	2022	Mean	SD
Anhui	Central	0.760	0.728	0.731	0.739	0.739	0.015
Fujian	East	0.689	0.674	0.674	0.686	0.681	0.008
Gansu	West	0.951	0.949	0.947	0.945	0.948	0.003
Guangdong	East	0.689	0.662	0.659	0.657	0.667	0.015
Guangxi	West	0.898	0.760	0.766	0.768	0.798	0.067
Guizhou	West	0.900	0.885	0.876	0.864	0.881	0.015
Hainan	East	0.778	0.625	0.764	0.746	0.728	0.070
Hebei	East	0.571	0.570	0.624	0.620	0.596	0.030
Henan	Central	0.639	0.624	0.627	0.628	0.630	0.007
Heilongjiang	Northeast	0.963	0.959	0.960	0.962	0.961	0.002
Hubei	Central	0.896	0.739	0.747	0.830	0.803	0.074
Hunan	Central	0.833	0.815	0.803	0.804	0.814	0.014
Jilin	Northeast	0.938	0.937	0.937	0.937	0.937	0.001
Jiangsu	East	0.520	0.489	0.467	0.465	0.485	0.025
Jiangxi	Central	0.776	0.756	0.761	0.757	0.763	0.009
Liaoning	Northeast	0.738	0.765	0.767	0.776	0.761	0.017
Neimenggu	West	0.975	0.973	0.970	0.970	0.972	0.002
Ningxia	West	0.868	0.850	0.850	0.871	0.860	0.011
Qinghai	West	0.997	0.983	0.988	0.991	0.990	0.006
Shandong	East	0.609	0.558	0.545	0.536	0.562	0.033
Shanxi	Central	0.829	0.827	0.825	0.824	0.826	0.002
Shannxi	West	0.857	0.845	0.840	0.847	0.847	0.007
Sichuan	West	0.923	0.919	0.886	0.886	0.904	0.020
Xizang	West	0.998	0.997	0.998	0.997	0.997	0.000
Xinjiang	West	0.987	0.987	0.988	0.986	0.987	0.001
Yunnan	West	0.942	0.860	0.861	0.864	0.882	0.040
Zhejiang	East	0.526	0.640	0.640	0.641	0.612	0.057
Beijing	East	0.359	0.359	0.398	0.356	0.368	0.020
Shanghai	East	0.061	0.061	0.060	0.060	0.061	0.000
Chongqing	West	0.792	0.774	0.759	0.771	0.774	0.014
Tianjin	East	0.349	0.293	0.312	0.301	0.314	0.024

The inter-province variation was also observed at the within-province level, analyzing based on the SD within provinces, regions with better accessibility may also exhibit significant inequality. Provinces with higher levels of dispersion include Guangdong, Hebei, Jiangsu, Henan, and Fujian (Table 4). Among them, Guangdong province has consistently exhibited the highest inequality over the past four years (Fig. 2 Standard deviation of hospital deserts' proportion within province by year). The proportion of hospital deserts in the Pearl River Delta and Chaoshan regions is significantly lower than that in western Guangdong, northern Guangdong, and other areas (eFigure 1).

Table 4

Standard deviation (SD) of hospital deserts proportion within provinces by year
Province	Region	2019	2020	2021	2022
Anhui	Central	0.089	0.109	0.111	0.115
Fujian	East	0.185	0.184	0.186	0.190
Gansu	West	0.075	0.076	0.077	0.076
Guangdong	East	0.279	0.272	0.274	0.272
Guangxi	West	0.034	0.088	0.081	0.092
Guizhou	West	0.060	0.050	0.058	0.056
Hainan	East	0.127	0.159	0.115	0.120
Hebei	East	0.193	0.200	0.210	0.222
Henan	Central	0.191	0.189	0.186	0.188
Heilongjiang	Northeast	0.027	0.027	0.028	0.028
Hubei	Central	0.096	0.117	0.117	0.112
Hunan	Central	0.078	0.079	0.087	0.086
Jilin	Northeast	0.031	0.030	0.029	0.028
Jiangsu	East	0.223	0.218	0.203	0.207
Jiangxi	Central	0.114	0.120	0.120	0.106
Liaoning	Northeast	0.092	0.093	0.100	0.099
Neimenggu	West	0.107	0.108	0.108	0.109
Ningxia	West	0.094	0.098	0.103	0.103
Qinghai	West	0.032	0.096	0.075	0.062
Shandong	East	0.064	0.066	0.064	0.072
Shanxi	Central	0.041	0.044	0.043	0.042
Shannxi	West	0.122	0.120	0.118	0.116
Sichuan	West	0.118	0.121	0.146	0.135
Xizang	West	0.005	0.004	0.005	0.009
Xinjiang	West	0.083	0.083	0.083	0.040
Yunnan	West	0.039	0.075	0.069	0.066
Zhejiang	East	0.182	0.169	0.169	0.170

Novel hot spots and cold spots of hospital deserts

Nationwide, the overall number of hospital desert hotspots has generally decreased year by year, although there was a slight rebound from 2021 to 2022. In 2019, there were a total of 16 hot spots clusters nationwide with a non-zero radius and the highest RR was 1.18 (Fig. 3 Hospital desert' hot spots by year) (The clustering with a radius of 0 is not displayed in the graph). Among the 16 hot spots, 14 were located around provinces in the western regions, such as Xinjiang, Tibet, Qinghai, and Inner Mongolia. One hotspot was around Heilongjiang Province in the northeastern region, and another one was near Henan Province in the central region. In 2020, the number of hot spots with a non-zero radius decreased to 13 and the highest RR was 1.21. In addition to the regions with more hotspot clusters such as Xinjiang, Tibet, Qinghai, and Inner Mongolia, in this year, there were also hotspot clusters in provinces like Gansu, Yunnan, and Sichuan. In 2021, the nationwide count of hospital deserts saw a significant decrease, with only 4 remaining with the highest RR 1.21, located in the western region, Sichuan, Gansu, and northern Inner Mongolia. In 2022, the number of hotspots increased to 6, with one hotspot added near Yunnan and another near Liaoning (Fig. 3).

Regarding cold spots, in 2019, there were a total of 8 clusters of hospital deserts cold spots with a non-zero radius nationwide, mostly concentrated in the eastern regions such as Jiangsu, Zhejiang, Beijing, Guangdong, and Shandong. A smaller portion of cold spots was concentrated around provincial capitals in the central and northeastern regions (Fig. 4 Hospital desert' cold spots by year). In both 2020 and 2021, the number of cold spots remained at 9, but in 2022, it decreased to 6. This reduction was due to several small-radius clusters in the Yunnan-Guizhou-Sichuan region merging into one large-radius cluster.

Key findings and their implications

This study conducted a comprehensive analysis of spatial accessibility to antidepressant medications across 356 cities in China from 2019 to 2022, concurrently calculating the proportion of hospital deserts. Our findings reveal significant spatial disparities in access, with over 60% of the population residing in areas exceeding the 90-minute travel time threshold to the nearest medical institution dispensing these medications. This highlights the substantial geographical barriers faced by individuals seeking treatment for depression in these regions, potentially delaying or hindering their access to essential mental healthcare.

In terms of provincial hospital desert proportions, the eastern regions, notably exemplified by provinces like Shanghai, exhibit superior accessibility compared to the western regions (Fig. 1). Furthermore, this study identified the unequal distribution of hospital deserts within provinces. Despite low hospital desert proportions in certain provinces, such as Guangdong and Jiangsu, there exists significant intra-provincial urban imbalance (Fig. 2). In Guangdong province, hospital deserts are more concentrated in the northern and western cities, while the densely populated and economically developed Pearl River Delta region experiences very few hospital deserts (eFigure 1).

Additionally, there has been a significant reduction in the hot spots of hospital deserts, with a slight rebound observed from 2021 to 2022. As of 2022, stubborn hot spots persist, notably in western Chinese provinces, northeastern Inner Mongolia, northwestern Heilongjiang, western Liaoning, northern Yunnan, and southern Shaanxi (Fig. 3). These regions seem to demand more effective interventions for enhanced spatial accessibility. These findings underscore the need for targeted interventions to address these disparities and ensure equitable access to essential mental healthcare services across the entire nation. Moreover, our findings align with previous studies highlighting spatial disparities in access to healthcare services in China.

National Volume-Based Procurement

National Volume-Based Procurement (VBP) is a policy implemented to improve efficiency and affordability in the healthcare system. While its primary goal is to reduce overall healthcare costs, the impact on access and inequality deserves careful consideration. Here's a breakdown of how VBP price changes might affect inequality: VBP aims to negotiate lower prices for medications by leveraging bulk purchasing power. This can directly reduce the out-of-pocket costs for patients, especially for those without comprehensive health insurance. Improved affordability might encourage individuals to seek treatment, leading to better health outcomes. Further, lower prices through VBP could incentivize healthcare providers to stock a wider range of essential medications. This could benefit patients in underserved areas where access to specific medications might have been limited due to cost constraints.

VBP negotiations might prioritize cost savings over product diversity. This could lead to the delisting of less profitable medications, even if they are important for specific patient populations. This could disproportionately affect patients with less common conditions who rely on these medications. The benefits of VBP might not be evenly distributed across the country. Urban areas with more bargaining power and higher patient volume might see greater cost reductions compared to rural areas. This could exacerbate existing disparities in access to affordable healthcare.

Intervention measures

While this study highlights the concerning issue of limited geographic accessibility to essential mental health drugs, several potential solutions offer promising avenues for improvement. Telehealth and online platforms can bridge physical distance by enabling virtual consultations and facilitating remote prescriptions. Mobile clinics and outreach programs can directly deliver services and medications to underserved communities. Strengthening rural healthcare infrastructure by establishing clinics and incentivizing healthcare professionals is crucial for long-term sustainability. Additionally, community-based interventions involving trained personnel and awareness campaigns can address local needs and reduce stigma. Utilizing existing logistics networks and optimizing supply chain management can ensure efficient and reliable medication distribution. Finally, policy measures, collaboration, and knowledge sharing can create a supportive ecosystem for improving accessibility. Implementing a combination of these strategies, tailored to specific contexts, holds the potential to create a more equitable landscape where individuals, regardless of location, have access to the essential mental health drugs they need.

Limitations and improvements

This study has limitations that warrant further exploration. First, our analysis relies on travel time estimations, which may not fully capture the true accessibility challenges faced by individuals. Factors like public transportation availability, individual limitations, and stigma associated with mental health could further impede access. Second, our model cannot account for variations in resources within healthcare institutions, such as differences in the number of doctors and hospital grades. Third, our model computes travel time on a city-wide basis, making it unable to address issues associated with healthcare across different cities. Finally, the study focuses solely on antidepressant medications. Investigating the accessibility of other mental health resources, such as therapists and specialized mental health facilities, would provide a more comprehensive picture of mental healthcare accessibility in China.

Future research holds great potential to address the limitations of this study. By incorporating qualitative data, such as interviews or focus groups, we can gain valuable insights into the lived experiences of individuals navigating access to mental healthcare services. This can reveal the real-world challenges beyond travel time estimations, including transportation barriers, personal limitations, and even stigma associated with mental health. Additionally, expanding the scope to encompass additional resources beyond medications, like therapists and specialized facilities, will provide a more comprehensive picture of China's mental healthcare accessibility landscape. Finally, delving into the underlying factors contributing to the observed disparities is crucial. This could involve investigating areas like healthcare resource allocation, infrastructure development, and social determinants of health, ultimately informing strategies to address the root causes of unequal access.

This study sheds light on the concerning issue of limited and unequal access to antidepressant medications in China. By providing a comprehensive analysis of spatial accessibility and identifying regional and provincial disparities, the study aims to inform policymakers and stakeholders in developing strategies to improve access to mental healthcare services and bridge existing inequalities. Further research is needed to gain deeper insights into the lived experiences of individuals and explore potential interventions to ensure equitable access to essential mental healthcare services across China.

VBP: National Volume-Based Procurement; USDA: United States Department of Agriculture; SD: Standard deviation; RRs: Relative risk ratio

Author Contribution

Aoming Xue and Qingyuan Xue contributed equally to this work and share first authorship.

Data availability

The authors do not have permission to share data at present. The data of this project will be made public, but the right of using and interpreting belongs to the big project team.

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Novel hot and cold spots of mental hospital deserts across China using 2019-2022 antidepressant procurement data

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Abstract

Figures

Introduction

Methods

Study area

Data source

Spatial accessibility

Hospital deserts

Novel hot spots and cold spots of hospital deserts

Visualization and Statistical Software

Results

Overall access to hospital provision of depressant drugs

Hospital deserts and inequalities

Novel hot spots and cold spots of hospital deserts

Discussion

Key findings and their implications

National Volume-Based Procurement

Intervention measures

Limitations and improvements

Conclusion

Abbreviations

Declarations

Author Contribution

Data availability

References

Additional Declarations

Supplementary Files

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