Geographic accessibility to board-certified dental anesthesiology specialists in Japan: visualizing "medical deserts" in dental anesthesia

Abstract

Background

Systemic management by board-certified dental anesthesiology specialists from the Japanese Dental Society of Anesthesiology plays an essential role in ensuring the safety, comfort, and quality of dental care. However, these specialists are most commonly located in urban areas. This study provides a nationwide analysis of geographic accessibility, identifying "medical deserts" (operationally defined as travel time ≥ 120 min) in dental anesthesia by calculating actual travel times to facilities with dental anesthesiology specialists across Japan.

Methods

Data from 355 dental anesthesiology specialists and 1,878 municipalities were analyzed. Travel times from each municipal office to the nearest facility with a dental anesthesiology specialist were calculated using the Google Maps Directions Application Programming Interface. The Dental Anesthesia Rurality Grade (DARG) was defined as follows: Grade A (accessible, < 60 min), Grade B (intermediate, 60 ≤ 120 min), and Grade C (rural, ≥ 120 min). A Strategic Priority Index (SPI) was developed to identify Strategic Priority Areas (SPAs) where interventions may have greater population-level impacts.

Results

Grade A status encompassed 87.9% of the population, but occupied only 48.1% of the land area. Conversely, 11.8% of the municipalities (n = 221) were classified as Grade C, representing 20.1% of the land area. A significant positive correlation was found between travel time and aging rate (r = 0.61, P < 0.001), suggesting a pattern consistent with the Inverse Care Law in dental anesthesia. The SPI suggested candidate SPAs such as those in Matsuyama and Hakodate.

Conclusions

There are major differences in the geographic accessibility of facilities with affiliated dental anesthesiology specialists across different regions in Japan. Classification by DARG and detailed analysis using the SPI from this study could serve as a helpful model for evaluating geographic access and guiding future resource planning in areas with geographic limitations and an aging population.

INTRODUCTION

Systemic management by board-certified dental anesthesiology specialists from the Japanese Dental Society of Anesthesiology (JDSA) plays an essential role in ensuring the safety, comfort, and quality of dental care. For patients with intellectual disabilities, autism spectrum disorders, or complex systemic comorbidities, dental treatment conducted under systemic management, including intravenous sedation and general anesthesia, is essential to ensure both safety and procedural efficiency [1,2,3]. However, the structural challenge of specialist resources across both medical and dental disciplines, specifically the concentration of practices in urban areas resulting in limited access to said resources in rural or remote regions, is a universal issue in Japan and many other countries [4,5,6,7]. In particular, the "accessibility paradox"—in which the most vulnerable patients requiring dental anesthesia face the greatest barriers to reaching dental anesthesiology specialists—is a social obstacle that requires urgent resolution [7]. As Japan is a global leader in population aging, structural imbalances in its dental care delivery system serve as a bellwether for other nations experiencing similar demographic trends [8].

Prior studies have reported significant statistical imbalances in the distribution of various dental specialists in Japan. In particular, dental anesthesiology specialists are extremely concentrated in urban university hospitals [6]. However, existing evaluations have largely been limited to quantitative "supply-side evaluations" based on administrative units, while the nuances of "actual travel time" as a measure of geographic accessibility faced by patients have not yet been visualized. In the field of dental anesthesia, accessibility is not merely a matter of convenience, it is also serves as a critical clinical indicator that determines the feasibility of rapid reconsultation for postoperative complications and safe same-day discharge after ambulatory general anesthesia or intravenous sedation. As such, a risk assessment based on the "actual travel time" from the patient's residence is required, rather than a uniform evaluation based on the 'supply quantity' of dental anesthesiology specialists within traditional administrative units (municipalities). Furthermore, a detailed analysis of the mismatch between potential medical needs (aging rates) and actual accessibility in dental anesthesia, which may reflect the Inverse Care Law [9], currently remains insufficient.

The present study aimed to define a unique index, the Dental Anesthesia Rurality Grade (DARG), based on actual travel times estimated using a geographic information system, to quantitatively identify "medical deserts" (travel time ≥ 120 min) for dental anesthesia and investigate the relationship between accessibility and medical needs across all municipalities in Japan. As a nationwide geographic accessibility analysis, by introducing the Strategic Priority Index (SPI), which applies the concept of total travel costs from urban planning, we aimed to provide evidence-based perspectives to address the global challenge of allocating limited dental anesthesia resources fairly and efficiently.

METHODS

1. Data collection and study population

This study utilized secondary data publicly available from Japanese government statistics and academic websites and was approved by the Clinical Research Ethics Committee of the Nagasaki University Hospital (Approval No. 26052210). Although this study was a geographic information analysis based exclusively on publicly available data, the protocol was officially reviewed and approved by the committee in accordance with the institutional guidelines. Furthermore, permission for secondary use of the dental anesthesiology specialist registry data was obtained from the JDSA. Data on 371 dental anesthesiology specialists, including names, affiliations, and addresses, were obtained from a list of dental anesthesiology specialists on the JDSA website [10] as of January 13, 2026.

2. Data consolidation and geographic processing

To ensure an accurate geographic accessibility analysis, we applied a standardized process for the dental anesthesiology specialist dataset. The steps are as follows:

- Facility identification: Each dental anesthesiology specialist was linked to their main clinical facility.

- Affiliation consolidation: For the 91 dental anesthesiology specialists linked to educational or research institutions, facility information was unified with related academic hospitals for consistent geographic representation.

- Handling of uncertain data: Every entry was verified using institutional websites and address databases. During this, 14 individuals with unknown affiliate names or locations were excluded. Of the eight individuals with unknown locations, seven were identified through web searches, and one was excluded. In addition, one individual from a non-medical corporation was excluded. In total, 16 individuals were excluded because of unverifiable clinical practice sites, making up 4.3% (16/371) of the initial dataset.

- Access point definition: A total of 177 facilities in Japan were identified as having affiliated dental anesthesiology specialists, encompassing 355 specialists nationwide. After updating the location information for one relocated university hospital, we analyzed these 177 facilities and defined each unique facility location as an "access point" (Fig. 1, Supplementary Table 1).

Fig. 1. Flowchart of the study population and facility selection process. A total of 371 dental anesthesiology specialists were initially assessed for eligibility. After applying the exclusion criteria, 355 dental anesthesiology specialists were included in the final analysis, including 177 facilities with affiliated dental anesthesiology specialists across Japan.

3. Calculation of geographic accessibility

To obtain precise location information for the 177 identified facilities, addresses were converted into latitude and longitude coordinates (WGS84) using the Google Maps Geocoding Application Programming Interface (API; Google LLC, CA, USA) (Supplementary Table 1). Similarly, the coordinates of 1,878 municipal offices across Japan were obtained as representative points (centroids) for patient residences (Supplementary Table 2). Travel times were calculated as the time required to travel from municipal offices (used as residence proxies) to the nearest dental anesthesiology specialist facility, ensuring consistent 'origin-to-destination' measurement. To improve the computational efficiency of this nationwide analysis, we first identified the three nearest facilities using a straight-line distance (Euclidean distance). Subsequently, we calculated the actual travel times using the road network. Calculating all-to-all road network travel times for a nationwide dataset is costly; therefore, we used a two-step process. First, the three closest facilities based on straight-line distances were extracted from each municipal office. Second, the actual travel time and distance for these three candidates were calculated using Google Maps Directions API [11,12]. The travel mode was set to "Driving" to reflect the limited availability of public transportation in rural areas. For routes where land-only travel was impossible, such as islands or coastal regions, the shortest route via regular ferry services was calculated based on the API specifications. Air routes were excluded as they are not standard means of transportation for routine dental care. In cases where a municipal office and a facility were located in close proximity, the API occasionally assigned both to the same coordinates, resulting in a travel time of 0 min; these were accepted as valid values indicating "immediate accessibility." The facility with the shortest travel time was identified as that nearest to a dental anesthesiology specialist. This value was used as the geographic accessibility index for each municipality.

To standardize and reproduce geographic accessibility metrics, all queries using the Google Maps Directions API were conducted without incorporating the real-time or predicted traffic conditions, by omitting the departure time parameter. This approach estimates the basic travel times across a road network, avoiding temporary or localized traffic fluctuations. The basic maritime travel times for remote island routes were calculated using standard route distances and cruising speeds from the API's default database. As the specific departure times were not used, these ferry calculations did not include daily schedules or port waiting times, resulting in the shortest transit duration.

4. Definition of the DARG

In the present study, for geographic screening, we operationally defined a ‘medical desert’ as an area in which the one-way travel time was 120 min or more. This serves as a framework for identifying regions with significant geographical requirements. The DARG was used to classify municipalities based on administrative and clinical considerations.

- Grade A (Accessible): Travel time < 60 minutes - emergency transport or reconsultation is feasible to ensure standard medical safety.

- Grade B (Intermediate): Travel time 60-120 minutes - day-trip treatment is possible; however, the physical burden increases, necessitating strict preoperative evaluation and regional backup.

- Grade C (Rural): Travel time ≥120 min - one-way travel exceeding 2 h is operationally defined as a "medical desert" for geographic screening, as same-day return following anesthesia may be logistically difficult and clinically burdensome.

Thresholds were set by combining the administrative standards with clinical considerations. For the 60-minute travel time, we used the standard from the 'Medical Care Plan' by the Ministry of Health, Labour and Welfare [13], which applies to wide-area medical zones. For the 120-minute threshold, we defined this as a means of identifying areas in critical need. Although the 'Safety Standards for Ambulatory Anesthesia' [14] stress the importance of quick access in emergencies, they do not give a specific time limit. In the present study, we defined a one-way travel time of over 120 min as the threshold for 'medical deserts.' This indicates a significant deviation from the expected prompt care, indicating regions with severe geographic inequality.

5. Socio-demographic analysis

To evaluate the impact of rurality through the lenses of efficiency, equity, and potential medical needs, we conducted a multi-faceted analysis using the "Standardized Dataset for Statistical Education (SSDSE-A-2025)" [15] provided by the National Statistics Center of Japan.

1) Population and area imbalance

The proportion of the total population and land area occupied by each DARG grade was calculated to compare the population covered (efficiency) and geographical extent of the underserved areas (equity).

2) SPI

To quantify the negative social impact of limited access, we developed a unique SPI based on the concept of “aggregate travel costs in urban planning” [16]. Areas with high SPI values were identified as Strategic Priority Areas (SPAs) where interventions may have greater population-level impacts. SPI was calculated as follows:

SPI = Travel time to the nearest facility with a dental anesthesiology specialist (min) × Total population of the municipality

3) Evaluation of potential dental anesthesia needs

The aging rate, used as a proxy for potential dental anesthesia needs, was calculated as follows:

Aging rate (%) = (Population aged 65 and over / Total population) × 100

For municipalities with missing data due to evacuation orders (e.g., Fukushima Prefecture), the prefectural average (36.5%) was used. The correlation between travel time and the aging rate was evaluated to assess whether the observed pattern was consistent with the Inverse Care Law [9].

6. Statistical analysis

Continuous variables are presented as the median and interquartile range [IQR]. The Kruskal-Wallis test, followed by Dunn’s multiple comparison test, was applied to compare the grades. Spearman’s rank correlation coefficient (r) was calculated to evaluate the relationships between variables. All statistical analyses were conducted using GraphPad Prism 9.5.1 (GraphPad Software, San Diego, CA, USA). Statistical significance was set at P < 0.01.

RESULTS

1. Overview of geographic accessibility

After data cleaning, 1,878 municipalities (including the administrative wards of designated cities) were included in the analysis. Across all municipalities, the median travel time to the nearest facility with a dental anesthesiology specialist was 41.3 [22.0–76.2] min, while the median travel distance was 35.1 [11.7–74.4] km (Table 1, Supplementary Table 2). The minimum travel time was 0 min, and the maximum was 797.6 min (approximately 13.3 h), owing to geographic constraints such as remote islands (Supplementary Table 2), revealing significant regional disparities in access to dental anesthesia services.

Table 1. Summary of municipal characteristics and accessibility metrics by dental anesthesia rurality grade (DARG).

Variables	Overall	Grade A	Grade B	Grade C
Definition (travel time)	-	< 60 min	60 – < 120 min	≥ 120 min
No. of municipalities (n)	1,878	1,239	418	221
Percentage (%)	100	66	22.2	11.8
Total population (n)	28,067.5 [8,540.25-76,645.0]	47,625 [17,018.0-109,513.0]	10,778.5 [4,436.3-27,918.8]	6,294.0 [2,980.5-15,172.5]
Population share (%)	100	87.9	8.7	3.4
Land area (km2)	108.5 [39.4-255.6]	70.8 [27.5-185.2]	200.7 [102.4-363.1]	253.6 [96.5-504.2]
Land area share (%)	100	48.1	31.8	20.1
Travel time (min)	41.3 [22.0-76.2]	28.0 [16.3-41.1]	81.3 [69.0-98.2]	169.3 [141.7-224.6]
Travel distance (km)	35.1 [11.7-74.4]	17.5 [7.0-34.8]	79.8 [65.1-99.1]	182.8 [144.3-227.6]
Older adults (n, > = 65 years)	8,136 [2,967.0 - 19,192.0]	12,023 [5,208.3 - 26,225.0]	3,968.5 [1,779.5 - 10,344.2]	2,517.0 [1,247.5 - 6,095.5]
Aging rate (%)	33.6 [27.8-39.5]	30.1 [25.9-36.1]	39.0 [34.5–43.4]	39.4 [35.1–43.5]
SPI	1,054,563 [445,089–2,382,698]	1,062,454 [452,867–2,349,564]	904,362 [365,432–2,358,511]	1,274,534 [634,293–2,623,496]

Continuous variables are expressed as medians [interquartile ranges].

SPI, strategic priority index.

2. Classification results based on DARG

Municipalities were classified into three grades based on their calculated travel times (Table 1 and Fig. 2).

Fig. 2. Geographic distribution of the Dental Anesthesia Rurality Grade (DARG) in Japan. The black circles indicate the locations of 177 facilities with dental anesthesiology specialists. The color-coded areas represent the DARG classifications: Grade A (green, <60 min), Grade B (yellow, 60 – < 120 min), and Grade C (red, ≥ 120 min).

- Grade A (accessible): This group comprises 1,239 municipalities (66.0%). The median travel time was 28.0 [16.3–41.1] min, indicating high accessibility to dental anesthesiology specialists.

- Grade B (Intermediate): This group comprises 418 municipalities (22.2%). The median travel time was 81.3 [69.0–98.2] min; although day-trip consultations are feasible, these areas present a substantial burden.

- Grade C (Rural): This group comprises 221 municipalities (11.8%). The median travel time was 169.3 [141.7–224.6] min, with a maximum value of approximately 13 h. These areas represent "medical deserts" for dental anesthesia, where receiving ambulatory general anesthesia or sedation management is both clinically and logistically difficult.

3. Disparity between population coverage and land area share

An analysis of population coverage versus land area share across grades revealed a marked imbalance (Table 1, Fig. 3, Supplementary Table 2). Grade A covers 87.9% of the total population, but occupies only 48.1% of land area. Conversely, Grade C accounts for a mere 3.4% of the population share, but spans 20.1% of the national land area (Table 1, Fig. 3, Supplementary Table 2). This highlights that while the current centralized model is "efficient" in terms of population size, it leaves over one-fifth of the nation's land as a medical desert in dental anesthesia.

Fig. 3. Disparity between population coverage and land area share by the Dental Anesthesia Rurality Grades (DARGs). The comparison highlights the concentration of the population in Grade A areas (87.9%) versus the expansive land area covered by Grade C "medical deserts" (20.1%).

4. Identification of SPAs

The relationship between municipal population size and travel time was visualized using a bubble chart (Fig. 4). Overall, a trend was observed whereby municipalities with smaller populations tended to have longer travel times (r = -0.57, P < 0.001). Based on this relationship, Table 2 presents the top-ranked municipalities according to the Strategic Priority Index SPI (Supplementary Table 2). Municipalities such as Matsuyama City (population: approximately 511,000; travel time: 186.4 min) and Oita City (population: approximately 475,000; travel time: 106.4 min) were identified as "mismatch areas," defined as urban centers with large populations that nonetheless face substantial barriers to specialized dental anesthesia care. These were extracted as SPAs where the social benefit of the intervention (e.g., resource allocation or facility establishment) would be maximized.

Fig. 4. Relationship between municipal population and travel time to the nearest facility with a dental anesthesiology specialist. The municipal population is presented on a logarithmic scale. The vertical dashed lines represent the DARG thresholds at 60 and 120 min. A significant negative correlation was observed (r = -0.57, P < 0.001). Numbers 1–10 indicate the top 10 high-priority areas for effective strategic intervention. DARG, Dental Anesthesia Rurality Grade.

Table 2. Top 10 high-priority areas for effective strategic intervention.

Rank	Prefecture	Municipality	Total population (n)	Travel time (min)	Grade	SPI
1st	Ehime	Matsuyama	511,000	186.4	C	95,280,000
2nd	Hokkaido	Hakodate	251,000	233	C	58,500,000
3rd	Oita	Oita	475,000	106.4	B	50,600,000
4th	Hokkaido	Kushiro	165,000	248.2	C	40,970,000
5th	Akita	Akita	307,000	128.9	C	39,650,000
6th	Aomori	Aomori	275,000	144.1	C	39,650,000
7th	Kochi	Kochi	326,000	110.6	B	36,110,000
8th	Kagoshima	Amami	41,000	747.3	C	30,930,000
9th	Nagano	Nagano	372,000	74.7	B	27,840,000
10th	Hokkaido	Obihiro	166,000	159.5	C	26,560,000

It is important to note that SPI is calculated as a product of travel time and population. Therefore, high-population Grade B municipalities (e.g., Oita City) may rank highly as Strategic Priority Areas because of the large number of individuals affected, even if the absolute travel time is shorter than that in the Grade C regions.

SPI, Strategic Priority Index.

5. Correlation between potential dental anesthesia needs and geographic barriers

The relationship between geographic accessibility and potential dental anesthesia needs, represented by the aging rate (Supplementary Table 2), was evaluated. The median aging rate increased significantly as accessibility decreased (Table 1, Fig. 5; P < 0.001). Post-hoc pairwise comparisons revealed significant differences between grades A (30.1 [25.9–36.1]) and B (39.0 [34.5–43.4], P < 0.001), as well as between grades A and C (39.4 [35.1–43.5], P < 0.001; Fig. 5). A significant positive correlation was found between the travel time and aging rate (r = 0.61, P < 0.001; Fig. 6). This indicates a pattern consistent with the “Inverse Care Law” in dental anesthesia in Japan; specifically, populations with the highest potential needs tend to face the longest travel times to reach dental anesthesiology specialists.

Fig. 5. Comparison of aging rates among the different Dental Anesthesia Rurality Grade (DARG) classifications. The horizontal line within each box represents the median, and the lower and upper boundaries of the box indicate the 25th and 75th percentiles (interquartile range), respectively. The whiskers extend to the 5th and 95th percentiles. ^*Significant differences (P < 0.001 vs. Grade A) were observed for both Grades B and C.

Fig. 6. Correlation between municipal aging rate and travel time to the nearest facility with a dental anesthesiology specialist. Municipalities with higher aging rates tend to face longer travel times to reach the nearest dental anesthesiology specialist facility (r = 0.61, P < 0.001).

DISCUSSION

1. Reality of medical deserts in dental anesthesia

The results of this study revealed that only 177 facilities in Japan have affiliated dental anesthesiology specialists. However, it should be noted that these facilities represent practice locations with affiliated specialists, which may not necessarily equate to open capacity for external referrals or routine outpatient anesthesia care. Nevertheless, more than 10% of all municipalities (n = 221, 11.8%) were classified as Grade C (rural), in which travel time exceeded 120 min (Table 1, Fig. 2). While the fact that Grade A covers approximately 88% of the total population indicates that centralizing dental anesthesiology specialist resources in urban areas is efficient for maintaining high levels of clinical expertise (Fig. 3), more than 20% of Japan’s total land area remains a "medical desert" (Grade C, travel time 120 min) regarding specialized care in dental anesthesia. This finding suggests a significant lack of geographical equity (Fig. 2 and 3).

In the present study, a one-way travel time of 120 min was defined as the operational threshold, beyond which prompt emergency consultation or post-procedure re-evaluation becomes logistically difficult. Our analysis showed that, in certain remote areas, the maximum travel time reached 797.6 min (approximately 13.3 h), highlighting extreme regional disparities. Such prolonged travel times imply that a standard clinical model of dental anesthesia, safe same-day discharge after general anesthesia or intravenous sedation, is practically unattainable in these areas. Furthermore, even in Grade B areas, the actual barriers to care may be higher in regions with limited transportation or severe weather, potentially creating additional "hidden" medical deserts.

Okawa & Hirata [6] statistically demonstrated the extreme concentration of dental anesthesiology specialists in urban university hospitals using the Gini coefficient, characterizing it as a quantitative maldistribution. This finding is critical for a macro-level understanding of resource distribution in Japan. However, counting the "number" of dental anesthesiology specialists in large areas does not fully demonstrate the physical and time costs (accessibility) patients face when seeking care. The primary strength of this study is its detailed calculation of the actual travel time using the Google Maps API for all 1,878 municipalities. While earlier studies examined a fixed number of dental anesthesiology specialists, this study demonstrates the real-time aspect of how long it takes for a patient to reach a dental anesthesiology specialist. In dental anesthesia, especially for outpatient general anesthesia and sedation, travel time is not only about convenience; it is also a key clinical factor that affects the ability to handle postoperative complications quickly and ensure safe discharge. By focusing on "time" instead of "numbers," the present study adds to existing research and offers a new view that better matches clinical reality.

Our previous study used a nationwide claims database to show the overall trends in dental anesthesia services across Japan [3]. While this data effectively shows the national trends in "volume," it hides the detailed reality of "accessibility," such as the travel burden on patients. This study's municipal-level analysis highlighted the ongoing presence of local "medical deserts (Grade C)," particularly in large areas like Hokkaido. We believe that combining geographical perspectives with broad statistics will help to identify hidden barriers to care, contributing to true health equity.

2. Structural barriers for the most vulnerable populations

The median travel time of 169.3 min in Grade C areas not only indicates a lack of convenience, but further highlights the structural exclusion of specific patient populations, including the elderly in areas with high potential needs and patients with complex disabilities, from dental anesthesia services. Prolonged travel and environmental changes are direct risk factors for patients with intellectual disabilities or autism spectrum disorders, which can trigger psychological agitation or panic. Similarly, in patients with complex systemic diseases, the physical and physiological stress of travel may compromise perioperative safety. This creates an "accessibility paradox" in which the most vulnerable patients requiring high-level systemic management face the greatest clinical risks during transit, potentially forcing them to forego necessary care [1,7].

Furthermore, the present study demonstrates a pattern consistent with the “Inverse Care Law” [9] in dental anesthesia. Grade C areas, which faced the greatest access barriers, also exhibited significantly higher aging rates (Fig. 5; Table 1). This "double burden"—in which the high-risk elderly individuals who most require systemic management are physically furthest from dental anesthesiology specialist resources (Fig. 6)—may serve as a warning for aging societies worldwide. However, it is important to note that this association may also be influenced by broader, multifaceted regional dynamics, such as rural depopulation, shifting population densities, and baseline characteristics of the regional healthcare infrastructure.

3. Validity of the evaluation: significance of time-based assessment

In the study of rural healthcare in Japan, Kaneko et al. [17] developed the "Rurality Index for Japan (RIJ)." While the RIJ is an excellent comprehensive index for primary care accessibility, dental anesthesia specifically requires strict temporal constraints to ensure safety, including emergency responsiveness and the feasibility of same-day discharge. Therefore, rather than using a multidimensional index, the present study adopted the spatial barrier measurement proposed by Guagliardo [18] using actual travel time as a direct indicator of clinical risk. To incorporate clinical weighting into these time categories, we defined DARG based on established medical and social standards. These thresholds should be periodically re-evaluated in clinical practice, and social norms should continue to evolve.

4. Evidence-based resource optimization via SPI

The SPI introduced in the present study is a special metric that measures the social benefit of an intervention using the "aggregate travel cost" concept of urban planning [16]. It is important to clarify that SPI is the first screening tool to help policymakers find areas where geographic barriers have the greatest impact. While SPI offers a way to prioritize this, it is not a proven decision-making tool and does not consider the complex factors of dental anesthesia needs, such as local disability rates, facility capacity, or specific referral paths. Consequently, areas identified as SPAs should be interpreted as candidates for further granular investigation into local clinical demands and socioeconomic characteristics rather than as definitive targets for immediate resource allocation. It is important to note that SPI is calculated as a product of travel time and population. Therefore, high-population Grade B municipalities (e.g., Oita City) may rank highly as SPAs because of the large number of individuals affected, even if the absolute travel time is shorter than that in Grade C regions.

Using the SPI (Tables 1 and 2, Supplementary Table 2) and a bubble chart (Fig. 4), we identified SPAs such as Matsuyama and Hakodate. These are "mismatch areas" where poor access affects many people, increasing the social impact of accessibility barriers. Therefore, focused actions in these areas, such as evaluating the local clinical capacity, permanently placing dental anesthesiology specialists, and establishing regional referral networks, could help address these accessibility barriers. Prior research has suggested that such focused resource allocation is effective [5], and reducing regional differences remains the main goal of Japanese dental health policy [8]. The SPI could be a useful tool for making decisions to enhance the population-level impact through targeted interventions.

Maintaining the current city-focused model makes it difficult to improve access for vulnerable rural patients. To address this, we need to shift to a "mobile health delivery" model that does not rely on patients traveling long distances. This includes remote preoperative assessments using ICT, mobile dental anesthesia teams, and strong coordination with local dental practitioners. This data-driven approach could help health policymakers and dental anesthesia societies worldwide create evidence-based strategies for the best use of dental anesthesiology specialists. However, the implementation of these interventions presents significant challenges. Setting up mobile dental anesthesia teams requires coordinated financial support from national and local governments, along with collaborative frameworks between regional dental associations and academic societies, to ensure safety and quality standards. Therefore, these interventions should be considered long-term strategic goals that require gradual investment in local health infrastructure and policy integration, rather than immediate, universally applicable solutions.

Our findings may be beneficial not only for formulating health policies, but also for dental anesthesiology specialists in clinical practice. First, understanding the geographic accessibility of a patient’s residence could assist in perioperative risk assessment. Second, recognizing "medical deserts" and SPAs serves as a strategic reference for dental anesthesiology specialists to identify regions in which their skills are most critically needed when planning their career paths or choosing practice locations.

5. Limitations

The present study had several limitations. First, for our geographic accessibility metrics, travel times were calculated assuming no traffic and ignoring peak-hour congestion, seasonal weather, and real-time ferry schedules. Thus, our estimates show that the minimum travel time and actual barriers to clinical access were likely to be higher. In addition, our two-step facility selection process, which first narrowed candidates using the straight-line (Euclidean) distance before calculating the road travel time, might occasionally miss the quickest facility in complex geographic contexts (e.g., coastal regions or mountainous terrain). However, given the extreme concentration and limited total number of dental anesthesiology specialist facilities nationwide (177 facilities across 1,878 municipalities), candidate facilities in the surrounding regions are geographically sparse. Consequently, the likelihood that a facility outside the three nearest Euclidean distances would yield a shorter road network travel time is negligible, suggesting that the magnitude of any potential approximation error is minimal. In addition, using municipal office coordinates as population proxies is practical for nationwide analysis but may be inaccurate in large or diverse municipalities.

Second, in our data processing and statistical approach, 16 dental anesthesiology specialists (4.3% of the initial dataset) were excluded because of unverifiable practice sites, and we assumed municipal independence without modeling spatial autocorrelation. Future studies should use more comprehensive datasets and spatial regression models (e.g., Spatial Lag or Error Models) to improve robustness. Additionally, we assumed private vehicle travel, which may have underestimated the burden on patients using public transportation.

Third, for clinical and policy interpretations, the aging rate is a rough measure of the demand for dental anesthesia, which is influenced by disability prevalence, systemic comorbidities, and socioeconomic factors. We assessed geographic reachability, but did not consider facility-level human or physical capacity. The present study also lacks data on facility capacity, number of dental anesthesiology specialists per facility, actual case volumes for sedation or general anesthesia, facility wait times, and detailed patient referral patterns, all of which may influence service availability. Therefore, our findings should not be viewed as identifying the areas with the'greatest medical needs.' Instead, the identified SPAs show regions where specialized dental anesthesia care is most at risk of being unequal, requiring further detailed investigation of local clinical demands and infrastructure.

In conclusion, the present study showed major geographic differences in dental anesthesia and possible disparities that reflect the 'Inverse Care Law' when using the aging rate as a measure of potential needs. About 10% of municipalities are "medical deserts" in terms of geographic access to specialist-affiliated facilities, according to our operational definition. The classification by DARG and detailed analysis using the SPI in this study may offer a helpful model for evaluating geographic access and guiding future resource planning in areas with geographic limits and fast-aging populations. Future research should include various data, such as socioeconomic factors and clinical capacity, to identify areas with the highest clinical needs.

DATA AVAILABILITY

All the key data supporting the findings of this study are provided in the article or its supplementary file. The underlying datasets and statistical analysis files (GraphPad Prism 9.5.1) are available from the corresponding author upon request.