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PLOS One logoLink to PLOS One
. 2026 May 7;21(5):e0348087. doi: 10.1371/journal.pone.0348087

Nationwide trends and regional variation in intravitreal injections and anti-VEGF agent use in Japan from 2017 to 2022: An analysis of the NDB Open Data

Yoshiaki Kabata 1,*, Ryo Terauchi 2, Tadashi Nakano 2
Editor: Jiro Kogo3
PMCID: PMC13152162  PMID: 42096458

Abstract

Intravitreal injections are an essential treatment modality for retinal vascular and macular diseases, yet nationwide evidence describing long-term trends, regional variation, drug composition, seasonality, and the impact of the COVID-19 pandemic in Japan remains limited. We conducted a nationwide retrospective observational study using aggregated National Database of Health Insurance Claims and Specific Health Checkups (NDB) Open Data for fiscal years (FYs) 2017–2022 (April–March). Total intravitreal injection procedures were identified using procedure code G016, and drug-specific counts of aflibercept, ranibizumab, brolucizumab, and faricimab were extracted using drug codes. Crude utilization rates (per 100,000 population) were calculated using official population estimates. Annual trends were assessed using linear regression, regional variation was summarized descriptively and compared across predefined geographic groupings, monthly patterns were evaluated using Friedman tests, and the pandemic impact was examined by comparing FY 2019 (pre-COVID), FYs 2020–2021 (COVID period), and FY 2022 (post-COVID). National total intravitreal injection procedures (G016) increased from 600,068 in FY 2017–936,715 in FY 2022 (anti-VEGF injections: 591,469–929,704), corresponding to an average increase of approximately 65,000 injections per year. The mean crude utilization rates rose from 478.6 ± 109.2 to 775.7 ± 143.1 per 100,000 population, while relative inter-prefectural variability decreased modestly (coefficient of variation 0.228 to 0.184). Aflibercept remained the predominant anti-VEGF agent, with diversification after the introduction of brolucizumab (FY 2020) and faricimab (FY 2022). Monthly volumes showed modest seasonality, with higher counts toward the end of the fiscal year. Injection volumes showed no marked decline during the pandemic; April–May 2020 totals (119,100 injections) were only 0.9% lower than the same months in 2019 (120,202 injections). These findings indicate sustained growth in intravitreal injection activity in Japan, persistent but modestly narrowing regional variation, and maintenance of injection services during COVID-19.

Introduction

Retinal vascular and macular diseases such as age-related macular degeneration (AMD), diabetic macular edema, macular edema secondary to retinal vein occlusion, and myopic choroidal neovascularization are major causes of blindness and severe visual impairment worldwide [1–5]. Intravitreal injections of anti–vascular endothelial growth factor (VEGF) agents have become the first-line treatment for these conditions, and numerous clinical trials and real-world studies have demonstrated their efficacy in improving visual outcomes and reducing the risk of blindness [6–9]. In Japan, a rapidly aging population is expected to increase the number of patients with AMD and other retinal diseases [10,11]. Understanding the demand for anti-VEGF therapy and its impact on healthcare resources is therefore critical from both clinical and policy perspectives [10–12].

In Japan, the anti-VEGF agents aflibercept and ranibizumab were the first to become available, followed by brolucizumab in 2020 and faricimab in 2022. These drugs differ in their pharmacokinetics, mechanisms of action, dosing intervals, and approved indications, and these differences may influence treatment strategies and clinical practice patterns. Anti-VEGF therapy usually requires long-term, repeated intravitreal injections, and various regimens including treat-and-extend and pro re nata (PRN) approaches are employed [10,11]. Consequently, the actual implementation of anti-VEGF therapy in a given region or institution reflects not only the number of patients but also treatment algorithms, healthcare delivery systems, and patients’ healthcare-seeking behavior.

Intravitreal anti-VEGF injections are mainly performed in the outpatient setting; therefore, a continued increase in the number of injections may pose a substantial burden on outpatient services and healthcare costs [13]. Because a large proportion of the treated population is elderly, regional differences in demographic structure and the distribution of ophthalmic resources may have a significant impact on access to anti-VEGF treatment [13,14]. Several studies from Japan and other countries have examined real-world anti-VEGF use based on single-center or multicenter registries, but their study populations and catchment areas have been limited [3,10–12]. As a result, there are still relatively few reports that describe nationwide temporal trends and regional variation in injection volumes. In addition, large-scale nationwide analyses of drug-specific utilization and inter-prefectural variation in Japan are limited.

In recent years, part of the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) has been released as open data, enabling nationwide evaluation of procedure-specific volumes. Previous studies using NDB Open Data have analyzed temporal trends and the impact of the coronavirus disease 2019 (COVID-19) pandemic on ophthalmic surgeries such as cataract extraction, vitrectomy, and glaucoma surgery [15]. However, nationwide analyses focusing specifically on intravitreal injections and anti-VEGF agent use remain scarce, particularly with respect to drug-specific volumes, prefecture-level crude utilization rates and their inter-prefectural disparities, regional variation in drug choice, and detailed monthly trends.

The COVID-19 pandemic since 2020 has led to reductions in elective surgeries and outpatient visits in many countries [16]. In ophthalmology, temporary declines in cataract and vitreoretinal surgery volumes have been reported [17–20]. For intravitreal injections, reduced visits due to concerns about infection risk and service restrictions at healthcare facilities might theoretically have decreased injection volumes; on the other hand, continued treatment is often necessary to maintain vision. In Japan, the extent to which intravitreal injection volumes changed before, during, and after the COVID-19 pandemic, including possible effects on monthly trends and seasonality, has not been sufficiently clarified.

Therefore, the aim of this study was to describe nationwide patterns of intravitreal injections and anti-VEGF agent use in Japan using NDB Open Data and official population statistics. Specifically, we examined, between fiscal years (FYs) 2017 and 2022: (1) annual trends in the national total number of intravitreal injections and the volumes of each anti-VEGF agent; (2) annual trends and inter-prefectural variation in crude utilization rates (per 100,000 population), with sensitivity analyses accounting for prefecture age structure; (3) regional variation in anti-VEGF drug choice as reflected by prefecture-level shares; (4) monthly changes and seasonality of national injection volumes; and (5) changes in national totals and crude utilization rates before, during, and after the COVID-19 pandemic. Our goal was to provide a nationwide overview of the provision of intravitreal injections and anti-VEGF therapy in Japan and to inform future discussions on resource allocation and service planning.

Methods

Study design and data source

This nationwide retrospective observational study was conducted using NDB Open Data, which are aggregated statistics derived from the National Database of Health Insurance Claims and Specific Health Checkups of Japan [21]. The study period covered FYs 2017–2022, with each fiscal year defined as April of one calendar year to March of the following year. Data were obtained from the 4th through 9th releases of NDB Open Data.

From the procedure-specific aggregated tables of NDB Open Data, we identified the procedural code for intravitreal injection (G016, “intravitreal injection”) and, using drug-specific codes, the counts of aflibercept, ranibizumab, brolucizumab, and faricimab. In this study, injections of these four agents were collectively defined as intravitreal anti-VEGF injections. The unit of aggregation was the prefecture in which the medical institution was located. For each prefecture and fiscal year, we extracted the number of anti-VEGF injections by care setting (outpatient or inpatient) and by drug.

In the Japanese medical fee schedule, intravitreal injections are reimbursed under procedure code G016. In the NDB Open Data, counts for G016 are aggregated irrespective of the injected drug. Therefore, the total numbers of intravitreal injections used in this study include injections of anti-VEGF agents as well as intravitreal corticosteroids and other intravitreal drugs. In contrast, the use of individual anti-VEGF agents (aflibercept, ranibizumab, brolucizumab, and faricimab) can be identified from drug-specific codes. Across FYs 2017–2022, anti-VEGF agent-specific drug counts accounted for 98.6%–99.3% of all G016 injections (non–anti-VEGF injections, 0.7%–1.4% per year), indicating that G016 serves as a reliable population-level proxy for anti-VEGF injection activity.

Population data were obtained from the official population estimates published by the Statistics Bureau of the Ministry of Internal Affairs and Communications for each fiscal year and prefecture [22]. These data were used to calculate crude utilization rates per 100,000 population, based on the total number of intravitreal injections (outpatient and inpatient combined, including anti-VEGF agents and intravitreal corticosteroids).

Outcome measures and analytic items

1 ) Annual trends in national totals and drug-specific volumes.

For each fiscal year from 2017 to 2022, we calculated

  • (1)

    the national total number of intravitreal injections (regardless of drug), and

  • (2)

    the national total number of intravitreal anti-VEGF injections and drug-specific volumes for aflibercept, ranibizumab, brolucizumab, and faricimab.

To evaluate annual trends, we constructed simple linear regression models with fiscal year as a continuous independent variable and either the national total number of injections or crude utilization rates as the dependent variable.

To describe changes in national drug shares among anti-VEGF agents, we calculated, for each fiscal year, the proportion of all intravitreal anti-VEGF injections accounted for by each agent and visualized these proportions using bar charts.

2 ) Trends and regional variation in crude utilization rates.

Using total intravitreal injection counts (outpatient and inpatient combined) from the NDB Open Data, including injections of both anti-VEGF agents and intravitreal corticosteroids, we calculated crude utilization rates (per 100,000 population) for each of the 47 prefectures. For each year, we summarized the distribution of crude utilization rates using the national mean, standard deviation (SD), minimum, maximum, maximum-to-minimum ratio, and coefficient of variation (CV). Annual changes in the national mean were evaluated using simple linear regression.

Because age structure differs substantially across prefectures and changed over the study period, we performed two sensitivity analyses to assess the impact of population aging. First, we recalculated prefecture-level rates using the population aged ≥75 years as the denominator (injections per 100,000 population aged ≥75 years) for FYs 2017 and 2022, as an index of injection volume relative to the size of the oldest age group. Second, we performed indirect age-standardization based on two age groups (<75 and ≥75 years): expected injection counts for each prefecture were derived by applying national age-specific injection rates to prefecture-specific age-group population counts; standardized ratios and indirectly age-standardized utilization rates were then computed.

Major metropolitan areas vs other prefectures.

Major metropolitan areas were defined as Tokyo, Kanagawa, Saitama, Chiba, Aichi, Osaka, Hyogo, and Fukuoka (8 prefectures). The remaining 39 prefectures were classified as non-metropolitan. These definitions were chosen a priori for pragmatic reasons and were not intended to represent formal medical service regions.

Eastern vs western Japan.

Eastern Japan comprised 18 prefectures (Hokkaido, Aomori, Iwate, Miyagi, Akita, Yamagata, Fukushima, Ibaraki, Tochigi, Gunma, Saitama, Chiba, Tokyo, Kanagawa, Niigata, Yamanashi, Nagano, and Shizuoka). The remaining 29 prefectures were classified as western Japan. This east–west classification was likewise a pragmatic geographic grouping rather than an official healthcare catchment-area definition.

For each fiscal year, we compared mean crude utilization rates between these regional groups. When distributions were consistent with normality, group differences were assessed using Welch’s t-test; otherwise, the Mann–Whitney U test was used. To compare prefecture-level values between FY 2017 and FY 2022, we used paired t-tests and Wilcoxon signed-rank tests.

3 ) Regional variation in drug selection (prefecture-level shares).

Using the table of prefecture-level drug shares, we calculated, for each fiscal year, the proportion of intravitreal anti-VEGF injections represented by aflibercept and ranibizumab in each prefecture. We summarized the distribution of these proportions using the median, interquartile range, minimum, maximum, and CV.

Regional variation in drug selection was summarized using prefecture-level shares for each anti-VEGF agent and tabulated for FYs 2017–2022 (Supplementary Table S1).

4 ) Monthly volumes and seasonality.

From the “monthly totals” table, we extracted national monthly totals of intravitreal injections for FYs 2019–2022 (April–March for each year). For each fiscal year, we plotted line graphs of monthly injection counts to examine temporal patterns. Using data from all four fiscal years combined, we calculated the mean injection count for each calendar month (April–March) to assess seasonality, with a particular focus on whether volumes tended to increase toward the end of the fiscal year (February–March).

To statistically evaluate monthly differences, we used the Friedman test, treating month (April–March) as the within-subject factor and year as the block. Kendall’s W was calculated as a measure of effect size.

5 ) Temporal patterns before, during, and after the COVID-19 pandemic.

To assess the impact of the COVID-19 pandemic, we classified the four fiscal years analyzed monthly as follows:

  • Pre-COVID period: FY 2019 (April 2019–March 2020).

  • COVID period: FYs 2020 and 2021 (April 2020–March 2022).

  • Post-COVID period: FY 2022 (April 2022–March 2023).

We adopted this three-period classification as a pragmatic summary of the overall impact of the pandemic, recognizing that infection waves and public health measures varied over time. We applied the Friedman test to monthly national totals from FYs 2019–2022, treating year as the within-subject factor and the 12 months as repeated measures. Pairwise comparisons between FY 2019 and each of FYs 2020, 2021, and 2022 were performed using the Wilcoxon signed-rank test and paired t-tests based on corresponding months.

We further compared the combined total number of injections in April–May 2020—the period of the first state of emergency in Japan—with the corresponding period in 2019 to assess short-term changes during the early phase of the pandemic.

For prefecture-level crude utilization rates, we defined, for each prefecture, the mean of FYs 2017–2019 as the “pre-COVID mean” and the mean of FYs 2020–2022 as the “post-COVID mean”. Differences between these two periods were evaluated using paired t-tests and Wilcoxon signed-rank tests.

Statistical analysis

Continuous variables are presented as mean ± SD or median (interquartile range), as appropriate. Annual trends were evaluated using simple linear regression with fiscal year as a continuous independent variable. Group comparisons were performed using Welch’s t-test when assumptions were met; otherwise, the Mann–Whitney U test was used. For repeated measures, paired t-tests or Wilcoxon signed-rank tests were applied, and differences across multiple repeated measures were assessed using the Friedman test with Kendall’s W as an effect size. Poisson generalized estimating equation (GEE) models were fitted with an exchangeable working correlation structure and a log(population) offset; robust (sandwich) standard errors were used to account for potential overdispersion. All statistical tests were two-sided, and p values < 0.05 were considered statistically significant using JMP version 16 (SAS Institute Inc., Cary, NC, USA). Regression slopes are reported with 95% confidence intervals. For Friedman tests, we report the test statistic (χ²) with degrees of freedom. P values < 0.001 are reported as p < 0.001. Because the analyses were primarily descriptive and hypothesis-generating, no formal adjustment was applied for multiple comparisons; p values should be interpreted accordingly.

Ethics statement

This retrospective study used only aggregated, publicly available data from the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) Open Data and official population estimates from the Statistics Bureau of Japan. The datasets used for this research were accessed on 23 November 2025 (NDB Open Data [21]) and 23 November 2025 (population estimates [22]). No author had access, during or after data collection, to any information that could identify individual participants, and it is not possible to identify specific individuals from the aggregated data provided in these sources. According to national guidelines and our institutional policies, analyses based solely on such publicly available, de-identified data do not require review by an institutional ethics committee or the obtainment of informed consent.

Results

1 ) Annual trends in national totals and drug-specific volumes

The national total number of intravitreal injections (procedure code G016) increased from 600,068 in FY 2017–671,768 in FY 2018, 742,913 in FY 2019, 789,476 in FY 2020, 853,396 in FY 2021, and 936,715 in FY 2022 (Table 1; Fig 1). Simple linear regression showed that the national total increased by an average of 65,000 injections per year (95% CI, 58,700–71,300; R² = 0.995, p < 0.001). Across FYs 2017–2022, anti-VEGF agent-specific drug counts accounted for 98.6%–99.3% of all G016 injections (non–anti-VEGF injections, 6,871–8,599 per year).

Table 1. National intravitreal injection procedures identified by G016 and agent-specific intravitreal anti-VEGF injection counts by fiscal year.

Fiscal year G016 Total Aflibercept Ranibizumab Brolucizumab Faricimab Anti-VEGF Sum Non-VEGF (%)
FY 2017 600,068 440,961 150,508 591,469 1.4
FY 2018 671,768 498,398 164,944 663,342 1.3
FY 2019 742,913 551,378 183,682 735,060 1.1
FY 2020 789,476 594,690 167,874 19,401 781,965 1.0
FY 2021 853,396 653,958 161,937 30,630 846,525 0.8
FY 2022 936,715 662,013 168,038 40,099 59,554 929,704 0.7

Non-VEGF (%) indicates the proportion of G016 intravitreal injection procedures not captured by the included anti-VEGF drug codes and may include intravitreal corticosteroids.

Fig 1. Annual trends in national intravitreal anti-VEGF injections, fiscal year 2017-2022.

Fig 1

Bars show the annual number of intravitreal injections for each anti-VEGF agent (aflibercept, ranibizumab, brolucizumab, and faricimab), and the solid line indicates the national total number of intravitreal anti-VEGF injections in each fiscal year (FY). Aflibercept and ranibizumab were available throughout the study period, brolucizumab was introduced in FY 2020, and faricimab in FY 2022.

By drug, aflibercept injections increased from 440,961 in FY 2017–662,013 in FY 2022. Ranibizumab injections rose from 150,508–168,038 over the same period, peaking at 183,682 injections in FY 2019. Brolucizumab was introduced in FY 2020, with 19,401 injections in FY 2020, 30,630 in FY 2021, and 40,099 in FY 2022. Faricimab became available in FY 2022, with 59,554 injections in its first year.

The proportions of each agent among all anti-VEGF injections were as follows: in FY 2017, aflibercept 74.6% and ranibizumab 25.4%; up to FY 2019, these two drugs accounted for almost 100% of anti-VEGF injections. In FY 2020, the shares were aflibercept 76.1%, ranibizumab 21.5%, and brolucizumab 2.5%; in FY 2021, 77.3%, 19.1%, and 3.6%, respectively. In FY 2022, the shares were aflibercept 71.2%, ranibizumab 18.1%, brolucizumab 4.3%, and faricimab 6.4% (Fig 1).

2 ) Trends and regional variation in crude utilization rates

The mean crude utilization rate of intravitreal injections increased from 478.6 ± 109.2 injections per 100,000 population (95% CI of the mean, 446.5–510.7) in FY 2017 to 775.7 ± 143.1 (733.7–817.7) in FY 2022 (Table 2).

Table 2. Crude utilization rates per 100,000 population across the 47 prefectures by fiscal year.

Fiscal year Mean ± SD 95% CI (lower) 95% CI (upper) Minimum Maximum Max/ Min ratio CV
2017 478.6 ± 109.2 446.5 510.7 199.2 848.1 4.258 0.228
2018 540.3 ± 120.6 504.9 575.8 249.2 959.5 3.851 0.223
2019 602.0 ± 125.5 565.2 638.8 306.9 1014.7 3.306 0.208
2020 647.1 ± 130.5 608.8 685.4 322.6 1032.1 3.199 0.202
2021 701.7 ± 131.1 663.2 740.2 369.8 1036.5 2.803 0.187
2022 775.7 ± 143.1 733.7 817.7 407.8 1128.5 2.767 0.184

Values are expressed as mean ± standard deviation (SD), 95% confidence interval (CI) of the mean, minimum, maximum, maximum-to-minimum ratio, and coefficient of variation (CV). Rates are based on the total number of intravitreal injections (outpatient and inpatient combined, including anti-VEGF agents and intravitreal corticosteroids).

Linear regression indicated that the mean rate increased by 57.6 intravitreal injections per 100,000 population per year (95% CI, 52.6–62.5; R² = 0.996, p < 0.001). In FY 2017, prefecture-level crude rates ranged from 199.2 intravitreal injections per 100,000 population in Okinawa to 848.1 in Nagasaki (max/min 4.3; CV 0.228). In FY 2022, the corresponding range was 407.8 (Okinawa) to 1,128.5 (Nagasaki) (max/min 2.8; CV 0.184). In a Poisson generalized estimating equation model with a population offset and repeated measures by prefecture, the estimated annual rate ratio was 1.093 (95% CI 1.085–1.101; p < 0.001).

The increase from FY 2017 to FY 2022, expressed as a percentage with FY 2017 set to 100, ranged from 133% to 205% across prefectures (Supplementary Table S2–S4).

When major metropolitan prefectures were compared with non-metropolitan prefectures, mean crude utilization rates were 457.8 ± 72.5 vs 482.8 ± 115.6 injections per 100,000 population in FY 2017 and 726.0 ± 102.3 vs 785.9 ± 149.1 in FY 2022. In a Poisson generalized estimating equation model with a population offset and repeated measures by prefecture, the metropolitan indicator was not associated with the injection rate (IRR 0.94; 95% CI 0.83–1.06; p = 0.32), and the time metropolitan interaction was not significant (p = 0.35), indicating similar annual growth in both groups.

When eastern Japan was compared with western Japan, mean crude utilization rates were 465.3 ± 100.1 vs 486.8 ± 115.4 injections per 100,000 population in FY 2017 and 745.3 ± 134.3 vs 794.6 ± 147.4 in FY 2022. In a Poisson generalized estimating equation model with a population offset and repeated measures by prefecture, the eastern Japan indicator was not associated with the injection rate (IRR 0.93; 95% CI 0.83–1.04; p = 0.21), and the time east interaction was not significant (p = 0.47).

Using indirect age standardization based on national age-specific rates (<75 and ≥75 years), variation across prefectures was also attenuated compared with crude utilization rates. In FY 2017, the max/min ratio decreased from 4.3 (crude) to 3.55 (indirect), and the CV decreased from 0.228 to 0.220. In FY 2022, the corresponding values were 2.8 to 2.29 and 0.184 to 0.172, indicating that age structure explains only a modest portion of between-prefecture variation. Observed-to-expected ratios ranged from 0.468 to 1.660 in FY 2017 and from 0.619 to 1.418 in FY 2022 (Supplementary Table S4).

Because prefecture-level injection counts were not available by age group, we conducted sensitivity analyses using the population aged ≥75 years as the denominator, as an index of injection volume relative to the size of the oldest age group. The prefecture-mean rate increased from 3210.6 ± 721.5 to 4681.4 ± 802.1 (injections per 100,000 population aged ≥75 years) from FY 2017 to FY 2022. In FY 2017, rates ranged from 1659.6 to 5176.6 (max/min 3.1; CV 0.225), and in FY 2022 from 2769.6 to 6435.1 (max/min 2.3; CV 0.171). Prefecture rankings were broadly consistent between the total-population and ≥75-year denominators (Spearman’s ρ = 0.786 for FY 2017 and 0.768 for FY 2022; both p < 0.001) (Supplementary Tables S2–S3).

3 ) Regional variation in drug selection (prefecture-level shares)

Between FY 2017 and FY 2022, the mean prefecture-level share of aflibercept among anti-VEGF injections decreased slightly from 73% to 70%, with a range of 48–90% in FY 2017 and 55–82% in FY 2022 (mean change −3 percentage points). In contrast, the mean share of ranibizumab decreased more clearly from 21% (range 0–43%) to 14% (0–29%; mean change −7 percentage points). Aflibercept shares increased by ≥10 percentage points in a few prefectures such as Yamagata and Oita, but decreased by ≥10 percentage points in others including Wakayama and Kochi. Ranibizumab shares declined markedly in several prefectures that had high baseline use, such as Yamagata, Yamanashi, Tokushima, Kagawa, Ehime, and Oita, whereas new or increased use of ranibizumab was observed in a small number of prefectures, including Saga and Okinawa. Overall, by FY 2022 aflibercept accounted for around 70% of anti-VEGF injections in most prefectures, while ranibizumab was generally used in 20% or fewer of such injections. Detailed yearly values for each prefecture from FY 2017 to FY 2022 are provided in Supplementary Table S1.

4 ) Monthly volumes and seasonality

National monthly totals for intravitreal injections during FYs 2019–2022 ranged from 59,129–64,273 injections per month in FY 2019, 58,105–74,737 in FY 2020, 66,253–78,890 in FY 2021, and 73,778–85,241 in FY 2022 (Fig 2).

Fig 2. Monthly number of intravitreal injections from fiscal year 2019 to 2022.

Fig 2

Line graph showing the number of intravitreal injections per month nationwide, from April to the following March, for each fiscal year (2019–2022). Overall monthly counts increased year by year, with broadly similar seasonal patterns and modest peaks toward the end of each fiscal year. The dip in May corresponds to Golden Week, Japan’s consecutive national holidays.

Across the four fiscal years, the mean monthly value was highest in March at approximately 76,000 injections and lowest in May at approximately 65,000; the remaining months were generally in the range of 67,000–73,000 injections. The Friedman test using four years of monthly data showed significant differences among months (χ² = 30.5, df = 11, p = 0.001), with Kendall’s W = 0.69. The mean monthly numbers of injections were 61,909 in FY 2019, 65,790 in FY 2020, 71,116 in FY 2021, and 78,060 in FY 2022, indicating a monotonic increase over time.

5 ) Temporal patterns before, during, and after the COVID-19 pandemic

When monthly totals from FYs 2019–2022 were analyzed as repeated measures, the Friedman test demonstrated significant differences among fiscal years (χ² = 34.9, df = 3, p < 0.001), with a large effect size (Kendall’s W = 0.97). In Wilcoxon signed-rank tests comparing corresponding months, monthly injection counts in FY 2020, FY 2021, and FY 2022 were all significantly higher than in FY 2019, with median differences of +3.3 × 10³ injections/month (p = 0.005), + 9.2 × 10³ (p < 0.001), and +1.5 × 10⁴ (p < 0.001), respectively.

The mean monthly injection volumes for FY 2019, the combined FYs 2020–2021 (COVID period), and FY 2022 (post-COVID period) were 61,909, 68,453, and 78,060, respectively. Relative to FY 2019 (set as 1.00), the corresponding values were 1.11 and 1.26. The combined total for April–May 2020, during the first state of emergency, was 119,100 injections, representing a 0.9% decrease from the 120,202 injections in April–May 2019.

Discussion

In this nationwide analysis using NDB Open Data, we characterized annual trends, regional variation, drug selection patterns, monthly changes, and the impact of the COVID-19 pandemic on intravitreal injections in Japan between FYs 2017 and 2022. Total intravitreal injections billed under procedure code G016 increased by approximately 57% (600,068 in FY 2017–936,715 in FY 2022), and agent-specific anti-VEGF injections increased from 591,469–929,704 over the same period. The mean crude utilization rate per 100,000 population across the 47 prefectures increased by about 1.6-fold, and a sensitivity analysis using the population aged ≥75 years as the denominator showed similar temporal increases and a comparable pattern of inter-prefectural variation. Although substantial absolute differences in rates were observed among prefectures, both the coefficient of variation and the maximum-to-minimum ratio decreased slightly, suggesting a modest reduction in relative regional variability. Aflibercept remained the predominant anti-VEGF agent throughout the study period, while the introduction of brolucizumab and faricimab contributed to gradual diversification of drug use. Monthly data showed limited seasonality, with slightly higher volumes toward the end of the fiscal year and lower volumes around major holidays, and national injection volumes were largely maintained during the COVID-19 pandemic.

These findings are broadly consistent with previous Japanese data showing sustained growth in anti-VEGF treatment. Hashimoto and colleagues, using the DeSC claims database, reported that sex- and age-adjusted rates of intravitreal anti-VEGF injections in Japan approximately doubled between 2014 and 2020, that patients aged ≥70 years accounted for about 80% of injections, and that neovascular age-related macular degeneration was the leading indication with aflibercept representing roughly 80% of injections [12]. These observations align with our finding that aflibercept accounted for around 70% of anti-VEGF injections at the national level and that overall intravitreal injection activity continued to expand through FY 2022. Compared with the DeSC database [12], which covers specific insurers and provides detailed patient-level information, NDB Open Data encompass virtually all insured residents and allow population-based prefecture-level comparisons, thereby complementing previous work by extending the observation window, providing national benchmarks, and including newer agents such as faricimab.

Internationally, our results fit into a global pattern of increasing anti-VEGF treatment activity. Large claims-based studies from the United States and Europe have shown steady year-on-year growth in intravitreal anti-VEGF injections, with treatment rates reaching several hundred injections per 100,000 population [13,14,23–25]. At the same time, our data highlight important system-level differences in drug selection. In many Western healthcare systems, bevacizumab dominates anti-VEGF use because of its low cost and established off-label use, whereas ranibizumab and aflibercept account for smaller proportions. In contrast, bevacizumab is rarely used intravitreally in Japan because it is not approved and not routinely reimbursed for ophthalmic indications, and the market is essentially shared among aflibercept, ranibizumab, brolucizumab, and faricimab [13,14]. Both the present study and previous Japanese claims analyses indicate that aflibercept consistently accounts for the majority of anti-VEGF injections, with ranibizumab generally below 20% by the end of the study period [12]. This dominance of aflibercept likely reflects differences in regulatory approvals, reimbursement policies, pricing structures, and perceived clinical advantages for specific subtypes such as polypoidal choroidal vasculopathy. Faricimab achieved 6.4% market share within its first year of approval (FY 2022), indicating rapid adoption. This may reflect its bispecific mechanism targeting both VEGF-A and angiopoietin-2 (Ang-2) [26,27], and the potential for extended treatment intervals of up to 16 weeks demonstrated in pivotal trials, which align well with treat-and-extend protocols widely used in Japan. In contrast, brolucizumab uptake remained modest (4.3% by FY 2022) despite approval in 2020, likely reflecting clinician caution following post-marketing reports of intraocular inflammation and retinal vasculitis [28,29].

The pronounced inter-prefectural variation in crude utilization rates observed in our study parallels geographic heterogeneity reported in other countries [23]. Although relative variability decreased modestly over time, absolute differences remained large, with FY 2022 rates ranging from approximately 400 to over 1,000 injections per 100,000 population. Importantly, a sensitivity analysis using the population aged ≥75 years as the denominator showed a broadly similar distribution, suggesting that the observed regional differences are not explained solely by variation in the size of older populations. Simple dichotomies such as major metropolitan versus non-metropolitan prefectures or eastern versus western Japan did not yield statistically significant differences in mean rates, suggesting that broad geographic categories alone cannot adequately explain regional variation. More granular factors—such as the distribution of high-volume providers and tertiary centers, referral networks, cross-prefectural patient flow, and regional disease burden—are likely to be more important determinants of access and treatment intensity, but could not be directly assessed using NDB Open Data.

An important methodological feature of this study is that total intravitreal injections were identified using procedure code G016, which does not distinguish between anti-VEGF agents and other intravitreal drugs such as corticosteroids. To address this limitation, we summarized both total G016 procedures and agent-specific anti-VEGF injections identified using drug codes. Across FYs 2017–2022, agent-specific anti-VEGF counts captured 98.6%–99.3% of all G016 procedures, and the residual proportion not captured by the included anti-VEGF codes was small (0.7%–1.4%), which may include intravitreal corticosteroids. These findings support the use of G016 as a reasonable proxy for overall intravitreal injection activity at the population level, while emphasizing that drug-specific analyses should rely on agent codes and that modest regional or temporal differences in non-VEGF use cannot be fully excluded.

Our analysis of monthly injection volumes showed modest but statistically significant differences across months, with lower counts in February and May and higher counts in March. These fluctuations were small compared with the overall upward secular trend, indicating that seasonality in intravitreal injection activity is limited. Factors such as the shorter calendar month and winter conditions in February, which may reduce outpatient visits among elderly patients, and holiday-related scheduling changes during Golden Week in late April to early May may partly explain the lower volumes in these months. Conversely, higher volumes in March may reflect efforts by providers and patients to complete planned treatments before the end of the fiscal year and reimbursement cycle.

With regard to the COVID-19 pandemic, our analysis cannot isolate a causal pandemic effect because we lack a counterfactual scenario (i.e., what would have happened without COVID-19). The observed year-over-year increases primarily reflect the underlying secular trend in intravitreal injection utilization. However, the continuation of this growth trend—rather than a decline or plateau—suggests that intravitreal injection services were maintained as essential care. Whereas Wada et al. reported that cataract extraction declined by 15.5% and vitrectomy by 13.2% during the first COVID-19 wave (April–May 2020 vs. 2019) using the same NDB Open Data, our data show that intravitreal injection volumes decreased by only 0.9% over the same period. Several international studies have described substantial reductions in anti-VEGF injection visits and treatment delays during the first wave of the pandemic [28,30–34]. In contrast, our national-level analysis showed that monthly intravitreal injection counts in FYs 2020, 2021, and 2022 were all significantly higher than in FY 2019, and even during April–May 2020—the period of the first state of emergency in Japan—the total number of injections was only 0.9% lower than in the corresponding months of 2019. Together with previous NDB-based studies demonstrating sharp but transient declines in cataract and vitreoretinal surgery volumes, these results suggest that intravitreal injections were prioritized and maintained as an essential service. This pattern is consistent with intravitreal injections being relatively preserved as time-sensitive care, given that delaying anti-VEGF therapy may lead to irreversible visual loss. However, the drivers of this difference cannot be determined from aggregated claims data, and temporary delays, regimen modifications, or shifts in drug choice at the individual or facility level cannot be excluded.

From a health-policy perspective, this study underscores the growing and sustained demand for intravitreal injections in an aging society and the need to ensure adequate outpatient capacity and efficient care pathways. The large inter-prefectural differences in crude utilization rates, despite some reduction in relative variability, suggest that access to intravitreal therapy may remain uneven and that structural factors such as travel distance to hospitals and organizational capacity are likely to be relevant. Aligning resource allocation with regional needs, strengthening referral networks, and optimizing injection clinic workflows will be important to sustain high-quality care as the number of eligible patients continues to increase. Future work linking administrative data with disease-specific and facility-level information will be essential to clarify how regional service structures, drug selection patterns, and treatment intensity per patient translate into visual outcomes and long-term healthcare resource use.

This study has several limitations. First, NDB Open Data provide only aggregated counts at the prefectural level; patient-level information, including diagnoses, laterality, and visual outcomes, is not available. As a result, the unit of analysis was the number of injections rather than the number of patients, and we could not distinguish between increases in the number of treated patients and increases in injections per patient. Second, aggregation is based on the location of medical institutions rather than patients’ residences, so cross-prefectural referrals and visits to high-volume tertiary centers may have influenced estimates of regional variation. Third, we evaluated crude utilization rates per 100,000 population; because age-specific injection counts by prefecture are not available in NDB Open Data, formal age-standardization across prefectures was not feasible, and we therefore performed a sensitivity analysis using the population aged ≥75 years as the denominator. Fourth, our statistical analyses were primarily descriptive and used simple regression and nonparametric tests; we did not apply formal corrections for multiple comparisons, and prefecture-level inferential results should be interpreted cautiously as exploratory. Finally, the regional groupings used in this study were pragmatic and do not necessarily correspond to formal healthcare catchment areas; more granular spatial analyses will be needed to better understand the determinants of regional variation.

Conclusion

Using nationwide NDB Open Data, intravitreal injection procedures in Japan increased substantially from FY 2017 to FY 2022. Agent-specific anti-VEGF injections accounted for more than 98% of procedures billed under G016 throughout the study period and increased in parallel with the total G016 volume. Crude utilization rates per 100,000 population rose nationally, and the overall pattern was similar when rates were calculated per 100,000 population aged ≥75 years, while relative inter-prefectural variability narrowed modestly. Aflibercept remained the dominant agent, with gradual diversification after the introduction of brolucizumab and faricimab. Monthly patterns suggested limited seasonality with fiscal year-end and holiday-related fluctuations. National injection volumes were largely preserved during the COVID-19 pandemic, consistent with intravitreal therapy being prioritized as time-sensitive outpatient care.

Supporting information

S1 Table. Prefecture-level yearly shares of aflibercept and ranibizumab among intravitreal anti-VEGF injections in Japan, FY2017–FY2022.

(XLSX)

pone.0348087.s001.xlsx (12KB, xlsx)
S2 Table. Prefecture-level intravitreal injection utilization in FY2017 and FY2022 using crude and ≥75-year population denominators.

Crude utilization rates were calculated as the number of intravitreal injection procedures (G016) per 100,000 total population. Rates using the ≥ 75-year denominator were calculated as injections per 100,000 population aged ≥75 years, as an index of injection volume relative to the size of the oldest age group. The percentage change represents the relative change in injection counts from FY2017 to FY2022 for each prefecture.

(XLSX)

pone.0348087.s002.xlsx (12KB, xlsx)
S3 Table. Indirect age standardization of intravitreal injection utilization using two age groups (<75 and ≥75 years) in FY2017 and FY2022.

Expected injection counts for each prefecture were derived by applying national age-specific injection rates (<75 and ≥75 years) to prefecture-specific age-group population counts. Observed-to-expected ratios (standardized utilization ratios; observed/expected) with 95% confidence intervals and indirectly age-standardized utilization rates (per 100,000 population) are shown for FY2017 and FY2022.

(XLSX)

pone.0348087.s003.xlsx (13.6KB, xlsx)
S4 Table. Summary of between-prefecture variation in crude,  ≥75-year population–based, and indirectly age-standardized intravitreal injection utilization (FY2017 vs FY2022).

For each fiscal year (FY2017 and FY2022), the distribution of prefecture-level utilization metrics was summarized using the mean, standard deviation (SD), minimum, maximum, maximum-to-minimum ratio, and coefficient of variation (CV = SD/mean) across the 47 prefectures. Metrics include crude utilization rates per 100,000 total population, utilization rates per 100,000 population aged ≥75 years, and indirectly age-standardized utilization rates based on two age groups (<75 and ≥75 years).

(XLSX)

pone.0348087.s004.xlsx (9.4KB, xlsx)

Acknowledgments

Not applicable.

Data Availability

All derived datasets and analysis tables generated for this study have been deposited on Figshare (DOI: https://doi.org/10.6084/m9.figshare.31223410). The original source data are publicly available as aggregated tables from Japan’s National Database of Health Insurance Claims and Specific Health Checkups (NDB) Open Data and official population statistics; this figshare record contains the processed tables and outputs needed to reproduce the results. All analyses were conducted using these publicly accessible sources, and no individual-level data were used.

Funding Statement

The author(s) received no specific funding for this work.

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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-->5. Review Comments to the Author

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Reviewer #1: This manuscript sets out to report the changing pattern in IVT anti-VEGF use in Japan.

The findings are interesting. A comparison to world trends (data from elsewhere) would be interesting, and contextualise the trends in the data provided here.

Reviewer #2: 1. Is the manuscript technically sound, and do the data support the conclusions? The study pulls from a big national dataset and uses basic descriptive stats. The main story—more injections over time, aflibercept leading the pack, only small regional differences, and barely any dip during COVID—makes sense and mostly lines up with the claims data. Still, there are some real issues. The big one: the authors treat the G016 code as if it only means anti-VEGF injections, but that code also covers steroids like Ozurdex and triamcinolone. If steroid use changed over time or varied by region, the trends they show could be off. I’m not saying the whole analysis falls apart, but calling G016 purely anti-VEGF is just too blunt, and honestly, a bit sloppy. Also, the claim that injections were “resilient” during COVID needs more clinical context. Of course retina specialists kept doing these procedures—they’re critical. So, the dataset and methods work for a broad overview, but the authors need to tone down some conclusions and be upfront about the limitations. As long as readers pay attention to the caveats, the data do support increasing IVI use with some regional variation.

2. Has the statistical analysis been performed appropriately and rigorously? Not really. The authors mostly lean on simple linear regressions, correlations, and Friedman/Kendall tests. They throw out a bunch of p-values (often <0.001) for trends, but leave out effect sizes and confidence intervals. Since these are count data over time and across regions, you’d expect something more sophisticated. A mixed-effects Poisson regression (with prefecture as a random effect and time as a predictor), or maybe a time-series approach like seasonal decomposition, would do a better job handling autocorrelation and overdispersion. At the very least, they should report the actual trend slopes (like change in injections per year) with 95% confidence intervals, and adjust for multiple comparisons. Right now, they present a bunch of “significant” findings (east vs. west, before vs. after COVID, seasonal effects) but don’t mention multiplicity at all. Simple linear fits and correlation coefficients (like “r=0.239” for specialist density) get treated as if they’re rock solid, but weak effects like that really need a solid model, or they might just be noise. The analysis just feels too basic for PLOS ONE—some hierarchical modeling, proper uncertainty, and clear confounder adjustments would really help.

3. Did the authors make all the underlying data fully available? Only partly. They do mention that the NDB Open Data and population stats are public, so technically, you can access the basic numbers. But they don’t share their analytic code or any of the datasets they created from the raw data. That’s a big problem. PLOS ONE wants full reproducibility, which means the authors should upload their analysis scripts (R, Python, whatever they used) and any tables they produced (monthly counts, rates, that sort of thing) to a public repository with a DOI. Just saying “the data are public” isn’t enough—you can’t actually repeat their work without the code. On top of that, they don’t even specify which NDB release they used (was it the 7th or 8th?), and that detail matters because results change with new data. This is a pretty serious gap. “Data underlying findings” covers the whole pipeline, and right now, we don’t have that. The authors need to upload their scripts, processed data, and clearly state which data release they used. Otherwise, it’s not fully transparent.

4. Is the manuscript clear and written in standard English? For the most part, yes, but it’s a little rough around the edges. The main story comes through, and the organization mostly makes sense. Still, the writing feels uneven. Some sentences are awkward or choppy (especially around line 143, where the verb tense jumps around), and there’s inconsistent hyphen usage—sometimes it’s “anti–VEGF,” other times “anti-VEGF.” Typos slip through too (they wrote “statisitical” at one point). The Introduction reads like standard boilerplate and could use some tightening. Get to the point faster—highlight what makes Japan unique (high PCV rates, older population) instead of generic AMD background. Some abbreviations (like FY and NDB) show up in figure legends without being spelled out, which could confuse readers who aren’t specialists. An international audience might get tripped up by the minor language issues and formatting quirks. So, yes, it’s understandable, but it needs another proofreading pass—fix grammar, polish the writing, clear up the abbreviations, and definitely check that verb tense around line 143 (or maybe it’s 145). Clean it up before publishing.

5. Review Comments to the Author

5.1 Major Comments

G016 Code and Intravitreal Steroids: The main issue here is that the authors keep using the G016 procedure code as if it only means anti-VEGF injections, but that’s not true. G016 also includes intravitreal steroids like Ozurdex and triamcinolone. This isn’t just a technicality—it matters. In Japan, doctors use steroids pretty often, especially for tough cases of DME or RVO. Plus, during COVID, some switched to longer-acting steroids to stretch out visit intervals. The manuscript mentions steroids once, almost in passing, then goes back to treating G016 like it’s just a stand-in for anti-VEGF. That’s misleading. The authors need to fix this, and there are a few ways to do it. If they have enough data, they should subtract out an estimate for steroid injections from the G016 total, or at least run a sensitivity analysis—look at the trends using only the agent-specific anti-VEGF codes and compare those numbers to the G016 totals. If the use of steroids has changed over time, that could throw off the whole story about “resilience” or steady growth in anti-VEGF use. At the very least, the authors should call out this limitation plainly in the discussion. Right now, they’re using a very rough tool and not admitting that the numbers are “contaminated” by non-VEGF drugs, which makes the results hard to trust. They need to clarify what share of G016 is really non-VEGF—maybe even show a small table or graph—or re-run the main results just with VEGF-only data.

Faricimab Uptake and Agent Market Dynamics: The manuscript barely touches on Faricimab’s entry, but the numbers show it made a big splash—almost 60,000 injections in 2022, grabbing 6.4% of the market right away. That’s not a minor detail; it’s a major shift. So why did doctors in Japan jump on Faricimab so fast? Most retina specialists would say it’s because of the drug’s bispecific action (VEGF-A and Ang-2) and its longer-lasting effects, which fit perfectly with the popular treat-and-extend approach for exudative AMD or PCV. Meanwhile, Brolucizumab barely grew, probably because of safety concerns like retinal vasculitis and intraocular inflammation—problems that the manuscript barely acknowledges. The authors need to talk about this. Add a line or two comparing Faricimab’s rapid rise to Brolucizumab’s sluggish uptake, and spell out the clinical reasons behind these trends. It would help to mention when each drug was approved in Japan, any changes in their labels or safety warnings, and the thinking in the retina community. For example, a short paragraph like, “Faricimab’s dual mechanism and longer durability likely explain its quick adoption in 2022, while Brolucizumab uptake stalled because of ongoing safety worries,” would give readers real context. Right now, the manuscript just says there’s “diversification after introduction of Faricimab” without explaining why doctors changed their habits. It would also help to include a simple table showing each drug’s market share by year. This isn’t just background; understanding how and why doctors switch agents is actually central to what this paper is trying to do.

COVID-19 “Resilience” Needs Clinical Context: It’s surprising to see that IVI volumes in April–May 2020 dropped just 0.9% compared to 2019. But there’s more to this story. Ophthalmology clinics made a clear choice: they kept intravitreal injections going during lockdowns because skipping anti-VEGF treatments can lead to permanent vision loss. Meanwhile, elective surgeries like cataract and some glaucoma procedures dropped off a cliff. The authors need to connect their numbers to this reality. For example, just say it: “Retina clinics treated intravitreal injections as essential during the pandemic and kept patients on schedule.” If there are Japanese guidelines or local reports about ophthalmology care during the state of emergency, cite them. It would help to compare this with the well-known ~10% drop in cataract/vitrectomy volumes (as one reviewer pointed out). Right now, saying volumes “did not decline” makes it sound like injections just happened to hold steady. Make it clear: “These data show clinicians made a deliberate effort to prioritize IVIs and prevent irreversible vision loss—something seen in other countries too.” In short, explain the clinical reasoning: MNV injections continued because patients simply can’t afford to miss them, even in a pandemic.

Regional Analysis and Specialist Distribution: The regional comparison in the paper—mapping injection rates against ophthalmologist density—feels too basic. The analysis lumps all board-certified ophthalmologists together, but not everyone injects. Some focus on glaucoma, retina, pediatrics, and so on. Referral patterns muddy the picture too. Big university hospitals often pull in patients from nearby prefectures, so their numbers look inflated, while neighboring areas look artificially low. For example, Tokyo’s injection rate includes people from all over Kanto, while a rural region might send their patients elsewhere. The authors need to acknowledge this “centrality bias.” If data on retina specialists is available, use that instead of the total number of ophthalmologists—or at least mention this limitation. Also, clarify that NDB data shows where providers are based, not where patients live, so the maps reflect where injections are billed, not patient residence. It’s no surprise the correlation is weak (r≈0.24); counting ophthalmologists alone is too crude a measure. At the very least, discuss these issues: advanced referral hospitals in certain prefectures probably draw patients from a wider region. Without this context, saying “injection volume is not simply determined by ophthalmologist count” doesn’t add much. This section needs real context about how clinics are distributed, how patients travel, and which doctors actually do the injections—retina specialists at large centers versus generalists elsewhere.

Seasonal Trends (Fiscal Year and Golden Week): The monthly ups and downs you see—those little spikes in March and the drop-off in late April and early May—aren’t random. In Japan, March marks the end of the fiscal year, so clinics rush to use up their budgets, and patients try to squeeze in treatments before any new rules or changes kick in. Then Golden Week hits in late April and early May, and everything slows down because a lot of clinics close for a few days. These patterns aren’t about biology suddenly changing in March; it’s just how the calendar works in Japan. The Results or Discussion section should say this up front: something like, “March peaks match fiscal year-end budget use, and the drop in April–May lines up with Golden Week holidays, which is a known pattern in Japanese hospitals.” Even adding a small “Golden Week” note on the figures would make things clearer. If you leave this unexplained—or just say volumes are “slightly higher at the end of the fiscal year”—readers who don’t know Japan will be lost.

Statistical Analysis and Reporting: The methods lean too much on basic tests. Running simple linear regressions across calendar years (and reporting just “p<0.001”) misses the point that you’re dealing with count data that varies by time and region. I expected at least a mention of models that fit the data better, like mixed-effects Poisson with random effects for prefectures, or time-series decomposition (like STL) to pull apart trend and seasonality. It would help to plot time trends with confidence bands, not just lines with stars—show the uncertainty. Effect sizes (like annual change in injections per 100,000, with 95% confidence intervals) should be front and center, maybe in a table. Right now, p-values seem tacked on. Also, there are a lot of comparisons (east vs. west, fiscal periods, etc.), but no correction for multiple testing. The paper would be stronger with at least a false discovery rate or Bonferroni correction, and you should mention you did it. And please, just write “p < 0.001” instead of “p = 8.2×10^-6.” Bottom line: The methods do the job for a general overview, but with a big dataset like this, I expected more. Even if you don’t redo everything, at least spell out the limitations—like, “We used simple regressions because of data aggregation, but future work could use more advanced models”—and make sure all the reported numbers (like slopes and confidence intervals) are easy to find.

Age Standardization: Japan’s population got a lot older between 2017 and 2022, and each prefecture has its own age makeup. The authors use “population-adjusted” rates per 100,000, but these are just crude rates—they don’t account for how many people are elderly. An older population always drives up AMD/VEGF indications, so without age-standardization, any comparisons over time or across regions are misleading. A prefecture with more seniors will naturally show higher crude IVI rates, but that doesn’t mean doctors there are doing things differently. At minimum, the authors should include age-stratified rates (like under 70 vs 70 and above), or better yet, directly age-standardized rates based on the 2019 Japan standard or WHO standard, maybe in the supplement. They need to recalculate key variability measures—coefficient of variation, min/max ratio—using age-adjusted data. I get that the NDB is anonymized, but age-group data should be available. Skipping this step is a big oversight, especially since “super-aging” Japan gets a mention in the introduction. Bottom line: adjust your rates for aging.

Reproducibility and Data Transparency: The manuscript needs to spell out exactly how the analysis was run. List every code used—not just G016—and say which NDB release you used (7th or 8th Open Data?). Replication depends on it. More importantly, deposit your analysis scripts (R, Python, JMP—whatever you used) and any intermediate tables (monthly injection counts, rates, etc.) in a public repository like OSF, Zenodo, or a GitHub repo with a DOI. Right now, the Data Availability statement just says “publicly available data,” but that doesn’t actually let anyone reproduce your study. PLOS ONE policy requires full reproducibility. This isn’t optional—without it, the paper doesn’t meet publication standards. Also, make sure to confirm compliance with NDB usage terms and address any small-cell suppression issues, even though aggregates probably avoid those. The point is simple: someone else should be able to rerun your entire workflow, from raw data to final figures.

5.2 Minor Comments

• Abstract: The statement “0.9% lower in April–May 2020 vs 2019” lacks context and leaves readers guessing. Include the actual numbers or expected volumes for those months so it’s clear whether 0.9% is meaningful or negligible.

• Introduction: The opening paragraphs get lost discussing general AMD/VEGF benefits. Trim the generic background and focus right away on Japan’s unique context—high PCV prevalence, an aging population, and the novelty of using the NDB. The intro feels weak right now. Spell out the specific knowledge gap from the start.

• Lines 143–145 (Methods): The tense jumps between past and present when describing data sources. Keep it consistent.

• Methods: You say you ran Friedman tests for seasonality, but only report “p<0.001.” Include the test statistic and degrees of freedom—something like “χ²(df)=X, p=Y”—so readers can see the full results.

• Figure legends: Define any abbreviations the first time they appear in captions (e.g., FY, NDB). Spell out “FY2022” as “fiscal year 2022,” etc., for clarity.

• Figure 4: Change the Y-axis label to “Injections per 100,000 population.” Mention “FY2022” in the title or axis label. Add small tick marks or annotations for fiscal year end and Golden Week on the time-series plots so readers can quickly spot those dips and spikes.

• Maps (Figs. 2–3): Add the class intervals or color legend details (quantiles, etc.) so the shading thresholds are obvious.

• Table 1: Add confidence intervals for the annual mean rates, not just mean±SD. Mean±SD only describes the data; CIs show how precise those estimates are.

• Drug Market Share: Instead of hiding the agent percentages in the text, use a simple table or bullet list:

FY2022: Aflibercept 71.2%, Ranibizumab 18.1%, Faricimab 6.4%, Brolucizumab 4.3%.

This format also sets up a discussion about why Faricimab’s share climbed so fast.

• Abbreviations: Spell out “PCV” (polypoidal choroidal vasculopathy) the first time you mention it, especially since it helps explain aflibercept’s dominance in Japan.

• Hyphenation/Spelling: Make sure “anti-VEGF” is written the same way throughout (some spots have an en-dash). “Board-certified ophthalmologists” needs a hyphen. Also, check for inconsistent spacing around parentheses and symbols.

• “Statistical” typo: I noticed “statisitical” somewhere—run a global search and fix it.

• Number formatting: Pick one style for thousands (e.g., “1 093.0” with a space or comma) and stick with it throughout to avoid confusion.

• Discussion: The paper ends abruptly without any global perspective. Add at least one sentence comparing Japan’s trends to those in countries like South Korea or Australia (claims-based studies are out there) to put the findings in an international context. As a reviewer put it, “Who ends a discussion without mentioning Korea?!”

• Data Policy statement: Specify whether you used the 7th or 8th NDB Open Data release. This detail helps others reproduce your work.

• English usage: The manuscript is mostly clear, but it needs a careful grammar and typo check. Examples: don’t write “p < 0.001 (0.000)”—just use “p < 0.001.” Avoid redundant phrases like “very very significant”—one “very” is enough. Double-check subject–verb agreement.

• Order of Discussion: I agree with another reviewer—discussing policy and regulation in the middle of the discussion feels awkward. Cover limitations first, then finish with broader implications for a smoother flow.

• Supplement: If possible, include a short checklist (like STROBE) in the supplement to show you covered key observational design elements (data sources, variables, bias, etc.). Even with aggregated data, this level of transparency helps.

Overall, the paper offers valuable insights, but the narrative needs tightening and the methods section needs more substance. Tackle these points to make the manuscript much stronger and ready for PLOS ONE.

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Reviewer #2: No

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PLoS One. 2026 May 7;21(5):e0348087. doi: 10.1371/journal.pone.0348087.r002

Author response to Decision Letter 1


2 Feb 2026

Response to Reviewers

Manuscript ID: PONE-D-25-67681

Title: Nationwide trends and regional variation in intravitreal injections and anti-VEGF agent use in Japan from 2017 to 2022: an analysis of the NDB Open Data

Journal: PLOS ONE

We thank the Academic Editor and reviewers for their constructive comments. We have revised the manuscript accordingly. Line numbers below refer to the revised with Track Changes manuscript.

Summary of major revisions

• Clarified that procedure code G016 captures intravitreal injection procedures and may include non–anti-VEGF drugs (e.g., corticosteroids), and added Table 1 reporting annual G016 totals alongside agent-specific anti-VEGF counts and the proportion not captured by anti-VEGF drug codes.

• Added age-structure sensitivity analyses: ≥75-year denominator rates and indirect age-standardization using two age groups (<75, ≥75), with results in Supplementary Tables S2–S4.

• Strengthened statistical reporting by emphasizing effect sizes and 95% CIs, adding a Poisson GEE model for prefecture-level repeated measures, reporting Friedman χ²/df, and standardizing p-value reporting.

• Expanded clinical interpretation of COVID-19 findings, noting the time-sensitive nature of anti-VEGF therapy and prioritization to prevent irreversible visual loss.

• Added clinical context for drug-market dynamics, including faricimab uptake.

• Removed map figures and replaced geographic displays with tables/supplementary materials to comply with CC BY requirements.

Responses to Journal Requirements

1. PLOS ONE style requirements / file naming

We revised the manuscript formatting and file naming to conform with PLOS ONE requirements.

2. Funding text in Acknowledgments

We removed funding-related text from the manuscript. “The authors received no specific funding for this work.” include cover letter.

3. Data repository (acceptable public repository)

We updated the Data Availability Statement to indicate deposition of the minimal processed datasets, intermediate tables, and analysis scripts in figshare, and we will provide the DOI/URL in the submission system (Lines 605-614).

4. Copyrighted map images

We removed the map figures and replaced them with tabular summaries and supplementary tables (Results section; Supplementary Tables S2–S4).

Reviewer #1

Comment 1

A comparison to world trends would contextualize the trends reported here.

Response: We agree and added a brief international comparison in the Discussion, noting that several countries reported short-term declines in intravitreal injection visits during the first pandemic wave, whereas our national analysis showed minimal short-term reduction and continued growth overall.

Changes made: Discussion, Lines 438-442 and 471-478.

Reviewer #2

Comment 2.1

G016 code includes intravitreal steroids; the manuscript should not treat G016 as anti-VEGF only.

Response: We clarified throughout that G016 captures intravitreal injection procedures irrespective of drug class and may include intravitreal corticosteroids. To address potential non-anti-VEGF “contamination,” we added a national-level sensitivity comparison by separating agent-specific anti-VEGF injections using drug codes and reporting the proportion of G016 not captured by these anti-VEGF codes (non-VEGF%). We also expanded the Discussion to clarify interpretive limits of prefecture-level crude G016 rates and to acknowledge that time- or region-specific changes in non-VEGF use cannot be fully evaluated in the Open Data.

Changes made: Results Lines 257-263; Table 1 and its legend; Discussion Lines 513-521.

FY G016 Total Anti-VEGF Sum Difference Non-VEGF %

2017 600,068 591,469 8,599 1.4%

2018 671,768 663,342 8,426 1.3%

2019 742,913 735,060 7,853 1.1%

2020 789,476 781,965 7,511 1.0%

2021 853,396 846,525 6,871 0.8%

2022 936,715 929,704 7,011 0.7%

Comment 2.2

Faricimab uptake and agent market dynamics; brolucizumab safety concerns should be discussed.

Response: We added clinical context describing likely reasons for rapid faricimab uptake (durability/dual-pathway mechanism supporting treat-and-extend) and slower brolucizumab uptake (safety concerns, including intraocular inflammation/retinal vasculitis) following its introduction. "Faricimab achieved 6.4% market share within its first year of approval (FY 2022), indicating rapid adoption. This may reflect its bispecific mechanism targeting both VEGF-A and angiopoietin-2 (Ang-2), and the potential for extended treatment intervals of up to 16 weeks demonstrated in pivotal trials, which align well with treat-and-extend protocols widely used in Japan for neovascular AMD and polypoidal choroidal vasculopathy. In contrast, brolucizumab uptake remained modest (4.3% by FY 2022) despite approval in 2020, likely reflecting clinician caution following post-marketing reports of intraocular inflammation and retinal vasculitis."

Changes made: Discussion Lines 471-478.

Comment 2.3

COVID-19 “resilience” needs clinical context (time-sensitive care and risk of irreversible visual loss).

Response: We revised the Discussion to explicitly state that intravitreal anti-VEGF therapy is time-sensitive and that delaying treatment may cause irreversible visual loss, which likely contributed to prioritization of injections during the state of emergency. We also added the absolute April–May counts to contextualize the 0.9% change.

Changes made: Discussion Lines 541-546.

Comment 2.4

Regional analysis and specialist distribution; referral/centrality bias; location of billing vs residence.

Response: We removed the specialist-density analysis (due to interpretability concerns with aggregated prefecture-level data). We expanded the limitations to clarify that prefecture-level counts reflect the location of medical institutions submitting claims, not patient residence, and that referral patterns and cross-prefecture care may influence observed regional variation.

Changes made: Specialist-density analysis removed; Limitations Lines 579-580.

Comment 2.5

Seasonal trends (fiscal year and Golden Week) should be discussed.

Response: We added an explanation that the late-fiscal-year rise and the dip around late April–early May are consistent with the Japanese fiscal calendar and Golden Week holidays, which can affect clinic schedules and appointment timing.

Changes made: Discussion Lines 438–442.

Comment 2.6

Statistical analysis and reporting (effect sizes/CI; count-data models; multiple comparisons; p-value formatting).

Response: To keep changes limited while addressing this concern, we (i) reported slopes and 95% confidence intervals for annual trends; (ii) added a Poisson GEE model with a log(population) offset for repeated prefecture-level observations, reporting IRRs with 95% CIs for regional indicators and their time interactions; (iii) reported Friedman χ² statistics and degrees of freedom; and (iv) standardized p-value reporting (e.g., p < 0.001 where applicable). Regarding multiplicity, we clarified that analyses were primarily descriptive/exploratory; therefore, we did not apply formal multiple-testing adjustments, and we interpret p-values cautiously while emphasizing effect sizes and confidence intervals.

Changes made: Statistical analysis Lines 238-242.

Comment 2.7

Age standardization is needed for time and regional comparisons.

Response: We added two sensitivity approaches addressing age structure: (i) recalculation of rates using the ≥75-year population as the denominator (FY2017 and FY2022), and (ii) indirect age-standardization using two age groups (<75, ≥75) to compute SIRs and indirectly standardized rates. These results are provided in Supplementary Tables S2–S4 and summarized in the Results.

Changes made: Methods Lines 153–162; Results Lines 354–377; Supplementary Tables S2–S4.

Comment 2.8

Reproducibility and data transparency (share scripts/derived tables; specify NDB release).

Response: We specified the NDB Open Data releases used (4th–9th releases, corresponding to FY2017–FY2022) and updated the Data Availability Statement to provide the processed datasets, intermediate tables, and analysis scripts in figshare to enable full reproduction.

Changes made: Methods Lines 110.

Minor comments

• Abstract: The statement “0.9% lower in April–May 2020 vs 2019” lacks context and leaves readers guessing. Include the actual numbers or expected volumes for those months so it’s clear whether 0.9% is meaningful or negligible.

Response: April–May 2020 totals (119,100 injections) were 0.9% lower than the same months in 2019 (120,202 injections). We added specific figures.

• Introduction: The opening paragraphs get lost discussing general AMD/VEGF benefits. Trim the generic background and focus right away on Japan’s unique context—high PCV prevalence, an aging population, and the novelty of using the NDB. The intro feels weak right now. Spell out the specific knowledge gap from the start.

Response: We couldn't reduce it much because we need to cite the literature later.

• Lines 143–145 (Methods): The tense jumps between past and present when describing data sources. Keep it consistent.

Response: We revised.

• Methods: You say you ran Friedman tests for seasonality, but only report “p<0.001.” Include the test statistic and degrees of freedom—something like “χ²(df)=X, p=Y”—so readers can see the full results.

Response: We revised.

• Figure legends: Define any abbreviations the first time they appear in captions (e.g., FY, NDB). Spell out “FY2022” as “fiscal year 2022,” etc., for clarity.

Response: This figure has been deleted. We have also checked the other figures.

• Figure 4: Change the Y-axis label to “Injections per 100,000 population.” Mention “FY2022” in the title or axis label. Add small tick marks or annotations for fiscal year end and Golden Week on the time-series plots so readers can quickly spot those dips and spikes.

Response: This figure has been deleted.

• Maps (Figs. 2–3): Add the class intervals or color legend details (quantiles, etc.) so the shading thresholds are obvious.

Response: This map has been deleted.

• Table 1: Add confidence intervals for the annual mean rates, not just mean±SD. Mean±SD only describes the data; CIs show how precise those estimates are.

Response:

• Drug Market Share: Instead of hiding the agent percentages in the text, use a simple table or bullet list:

FY2022: Aflibercept 71.2%, Ranibizumab 18.1%, Faricimab 6.4%, Brolucizumab 4.3%.

This format also sets up a discussion about why Faricimab’s share climbed so fast.

Response:

• Abbreviations: Spell out “PCV” (polypoidal choroidal vasculopathy) the first time you mention it, especially since it helps explain aflibercept’s dominance in Japan.

Response:

• Hyphenation/Spelling: Make sure “anti-VEGF” is written the same way throughout (some spots have an en-dash). “Board-certified ophthalmologists” needs a hyphen. Also, check for inconsistent spacing around parentheses and symbols.

Response: We also checked the other parts.

• “Statistical” typo: I noticed “statisitical” somewhere—run a global search and fix it.

Response: We revised.

• Number formatting: Pick one style for thousands (e.g., “1 093.0” with a space or comma) and stick with it throughout to avoid confusion.

Response: We also checked the other parts.

• Discussion: The paper ends abruptly without any global perspective. Add at least one sentence comparing Japan’s trends to those in countries like South Korea or Australia (claims-based studies are out there) to put the findings in an international context. As a reviewer put it, “Who ends a discussion without mentioning Korea?!”

Response: 34. Kim J-G, Kim YC, Kang KT. Impact of Delayed Intravitreal Anti-Vascular Endothelial Growth Factor (VEGF) Therapy Due to the Coronavirus Disease Pandemic on the Prognosis of Patients with Neovascular Age-Related Macular Degeneration. J Clin Med. 2022;11: 2321. doi:10.3390/jcm11092321 Added new references.

• Data Policy statement: Specify whether you used the 7th or 8th NDB Open Data release. This detail helps others reproduce your work.

Response: We specified the NDB Open Data releases used (4th–9th releases, corresponding to FY2017–FY2022) and updated the Data Availability Statement to provide the processed datasets, intermediate tables, and analysis scripts in figshare to enable full reproduction.

Changes made: Methods Lines 110.

• English usage: The manuscript is mostly clear, but it needs a careful grammar and typo check. Examples: don’t write “p < 0.001 (0.000)”—just use “p < 0.001.” Avoid redundant phrases like “very very significant”—one “very” is enough. Double-check subject–verb agreement.

Response: p < 0.001 was standardized.

• Order of Discussion: I agree with another reviewer—discussing policy and regulation in the middle of the discussion feels awkward. Cover limitations first, then finish with broader implications for a smoother flow.

Response: The discussion section has been revised.

We believe these revisions have substantially strengthened the manuscript. We thank the reviewers again for their thorough and constructive feedback.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0348087.s006.docx (25.9KB, docx)

Decision Letter 1

Jiro Kogo

23 Mar 2026

<div>PONE-D-25-67681R1-->-->Nationwide trends and regional variation in intravitreal injections and anti-VEGF agent use in Japan from 2017 to 2022: an analysis of the NDB Open Data-->-->PLOS One

Dear Dr. Kabata,

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: PLOS ONE Review Form

1) Technical soundness & data support for conclusions?

Yes, with some minor reservations. The G016 reconciliation and the GEE both support the main claims—national growth, slight narrowing of inter-prefecture variability, stable IVI volumes during early COVID, and more market diversity with faricimab. Keep the focus on effect sizes and continue to remind readers that the prefecture contrasts are descriptive, not causal, to avoid the ecological fallacy.

2) Statistical analysis performed appropriately and rigorously?

For the most part, yes. Adding GEE with a log(population) offset, reporting slopes with 95% confidence intervals, and standardizing the p-values puts the analysis on a solid foundation for PLOS ONE. Make sure to state the GEE working correlation structure and provide comments on dispersion diagnostics. After that, the statistics appear fine.

3) Data availability?

Appears compliant. There is a Figshare DOI for the derived tables and scripts, and the main sources (NDB Open Data and Statistics Bureau) are public. Double-check that the Figshare record actually contains the code and a README that enables exact reproduction of all figures and tables.

4) Manuscript clarity and standard English?

Mostly, yes, but a careful final proof is needed. Remove any remaining tracked change artifacts, make terminology consistent (choose between crude utilization and incidence), correct hyphenation and thousands separators, and address a few tense shifts (see Line 143 and likely 145). Figure labels and captions should also align with the revised text.

Major Comments

1. Core methodological fix (G016 ≠ anti VEGF only): You’ve clearly stated that G016 covers steroids and other intravitreal drugs, and you included an annual reconciliation showing anti VEGF codes account for 98.6–99.3% of all G016 procedures (with non-VEGF at just 0.7–1.4%). That’s the main fix I wanted—definitely keep it. But your text keeps switching between “incidence,” “population adjusted rates,” and “crude utilization.” Pick one: use “crude utilization rate (per 100,000)” everywhere, and save “incidence” for actual patient-level disease events. Also, when you first introduce G016, work the non-VEGF percentage into the main text (not just Table 1), so readers immediately see why G016 works as a population-level proxy.

2. Age structure—substantially better, but a few notes. You added two sensitivity analyses (≥75 year denominator and indirect age standardization with two age bands). That’s a real improvement, and your results show that age adjustment narrows the range between prefectures. Leave the SIRs and ≥75 denominator results in the supplement. In the main Results, add one clear sentence summarizing how much the max/min and CV shrink after standardization—just surface the numbers. If there’s no way to access further age strata, say so once, clearly, and move on. Don’t expand the Methods again.

3. Statistical framing—now works for PLOS ONE, just needs two tweaks. You added a Poisson GEE with a log(population) offset and cluster-robust SEs by prefecture, which is the right call. The time IRR ~1.093/year gives a direct effect size. Please also add (i) the assumed working correlation (exchangeable or independent), and (ii) a one-line comment on over- or under-dispersion diagnostics (even if GEE robust SEs handle it). No need to go further. Your choice to skip multiplicity adjustments is fine, since you already warn readers.

4. COVID-19 interpretation—clinically grounded now; just make the contrast sharper. You backed up the “resilience” story with absolute April–May counts (119,100 vs 120,202; −0.9%) and explained the urgency of IVI for MNV/PCV—good additions. Spell out the contrast in one tight sentence: injections stayed steady while cataract and vitrectomy volumes dropped. Anchor this with prior NDB-based surgical declines—don’t just imply it, state it directly in the Discussion.

5. Drug market dynamics—context is there; give readers a quick view. You explained the rapid uptake of faricimab (VEGF A/Ang 2, T&E durability) versus the caution with brolucizumab (IOI/retinal vasculitis). Add a mini table—just one row—in the Results or Supplement summarizing FY2022 market share: Aflibercept 71.2%, Ranibizumab 18.1%, Faricimab 6.4%, Brolucizumab 4.3%. This saves readers from digging through the Discussion for numbers. It’s a quick fix and makes things clearer.

6. Regional analysis—good restraint after pulling the specialist density figure; avoid ecological overreach. You dropped the scatter/ρ analysis and reframed the east–west/metro contrasts using the GEE, which works. Keep highlighting the limitation that NDB aggregates by billing location, not residence, when you talk about regional variation and centrality bias. It’s mentioned now—make sure it appears once in the first paragraph of Limitations.

7. Data sharing and reproducibility—meets policy on paper, but double-check the payload. You listed a Figshare DOI (10.6084/m9.figshare.31223410) and say the derived tables and scripts are included. Before resubmission, make sure the DOI works, scripts actually reproduce all tables/figures from the raw NDB Open Data plus population files, and the README files specify NDB release numbers (4th–9th) and access dates (already in the manuscript). This is key for PLOS ONE compliance.

Minor Comments

• Language and residual artifacts — clear out the leftover tracked changes.

I’m still seeing obvious editing scars in the R1 file—words mashed together like “servicetreatment” and “injectionsinjection,” awkward phrases like “population adjustedbased,” a random “Fig 52” label, and a few sentences that got mangled when edits clashed. Proofread the Abstract and early Methods carefully. Line 143 flips tense; double-check line 145 too. This looks sloppy. Please fix it.

• Terminology uniformity.

Stick with “crude utilization rate (per 100,000 population)” throughout. Drop “incidence” unless you’re actually modeling patient-level events. Update the Fig 1 caption to match your new wording—it still says “anti VEGF injections,” but the y-axis shows anti VEGF agents plus a solid line. Make sure the caption and axis titles line up and are clear.

• Figures and deletions.

You removed the maps for CC BY compliance—good. Make absolutely sure there are no lingering references to heatmaps or map legends in the text or figure captions. A few slipped through in the tracked changes. Also, annotate Golden Week in Fig 2 with a subtle band; readers outside Japan need that cue. Just check the monthly panel and add it.

• Friedman reporting.

You now include χ² and degrees of freedom—keep that. If your software gives Kendall’s W confidence bounds, add them; if not, don’t worry about it. Minor point.

• House style details.

Standardize p values as p < 0.001 where relevant. Make hyphenation consistent (anti VEGF, board certified), and use the same thousands separator everywhere. You mentioned this in your response letter—make sure the R1 file is fully updated.

• International context—now present, keep it brief.

You added a short paragraph and cited Korea—good. Don’t expand further. The focus is Japan; keep the global perspective tight.

Reviewer #3: 1. Abstract:Results of the impact of the COVID-19 were not mentioned.

2. Table2: Although the results of crude rate are shown in Table 2, it is better to show the age-standardized value in the main text.

3. Results: 5)impact of the COVID-19 pandemic: The analysis method might not be appropriate. The current analysis simply compared the values of the three periods, and the results indicated that the time trend effect exists. This is not an effect of the COVID-19 effect. It is better to change the analysis method or the title of the analysis.

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Reviewer #2: No

Reviewer #3: No

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PLoS One. 2026 May 7;21(5):e0348087. doi: 10.1371/journal.pone.0348087.r004

Author response to Decision Letter 2


28 Mar 2026

Manuscript: Nationwide trends and regional variation in intravitreal injections and anti-VEGF agent use in Japan from 2017 to 2022: an analysis of the NDB Open Data

Journal Requirements

Reference list review: We have reviewed the reference list for completeness and accuracy. No retracted papers are cited. All references have been verified.

Reviewer #2

Major Comment 1

Comment: Pick one term: use "crude utilization rate (per 100,000)" everywhere, and save "incidence" for actual patient-level disease events. Also, when you first introduce G016, work the non-VEGF percentage into the main text.

Response: Thank you for this important point. We have made the following changes:

1. Standardized terminology throughout the manuscript, using "crude utilization rate (per 100,000 population)" consistently.

2. Removed all instances of "incidence." Specifically, "incidence rate ratio" was changed to "rate ratio," and "Standardized incidence ratios" was changed to "Observed-to-expected ratios."

3. In the Methods section where G016 is first introduced, we have added: "Across FYs 2017–2022, anti-VEGF agent-specific drug counts accounted for 98.6%–99.3% of all G016 injections (non–anti-VEGF injections, 0.7%–1.4% per year), indicating that G016 serves as a reliable population-level proxy for anti-VEGF injection activity."

Major Comment 2

Comment: Add one clear sentence summarizing how much the max/min and CV shrink after standardization.

Response: We have added the following summary sentence to the Results section: "After indirect age standardization, the max/min ratio decreased from 4.3 to 3.55 in FY 2017 and from 2.8 to 2.29 in FY 2022, while the CV decreased from 0.228 to 0.220 and from 0.184 to 0.172, respectively, indicating that age structure explains only a modest portion of between-prefecture variation."

Major Comment 3

Comment: Add (i) the assumed working correlation (exchangeable or independent), and (ii) a one-line comment on over- or under-dispersion diagnostics.

Response: We have added the following to the Statistical Analysis section: "Poisson generalized estimating equation (GEE) models were fitted with an exchangeable working correlation structure and a log(population) offset; robust (sandwich) standard errors were used to account for potential overdispersion."

Major Comment 4

Comment: Spell out the contrast in one tight sentence: injections stayed steady while cataract and vitrectomy volumes dropped. Anchor this with prior NDB-based surgical declines—don't just imply it, state it directly in the Discussion.

Response: We have added the following explicit contrast to the Discussion: "Whereas Wada et al. reported that cataract extraction declined by 15.5% and vitrectomy by 13.2% during the first COVID-19 wave (April–May 2020 vs. 2019) using the same NDB Open Data, our data show that intravitreal injection volumes decreased by only 0.9% over the same period."

Major Comment 5

Comment: Add a mini table summarizing FY2022 market share: Aflibercept 71.2%, Ranibizumab 18.1%, Faricimab 6.4%, Brolucizumab 4.3%.

Response: These market share figures are already presented in Table 1 and stated explicitly in the Results text: "In FY 2022, the shares were aflibercept 71.2%, ranibizumab 18.1%, brolucizumab 4.3%, and faricimab 6.4%." We believe this presentation is clear and avoids redundancy.

Major Comment 6

Comment: Make sure the limitation that NDB aggregates by billing location, not residence, appears once in the first paragraph of Limitations.

Response: This limitation appears as the second sentence in the Limitations section: "Second, aggregation is based on the location of medical institutions rather than patients' residences, so cross-prefectural referrals and visits to high-volume tertiary centers may have influenced estimates of regional variation."

Major Comment 7

Comment: Before resubmission, make sure the DOI works, scripts actually reproduce all tables/figures, and the README specifies NDB release numbers and access dates.

Response: We have verified that the Figshare DOI (10.6084/m9.figshare.31223410) is active and publicly accessible. The repository contains all derived data tables, analysis scripts, and a README file specifying NDB Open Data release numbers (4th–9th) and access dates (23 November 2025).

Minor Comments

Language and residual artifacts: We have carefully proofread the manuscript to remove all tracked change artifacts.

Comment: Update the Fig 1 caption to match your new wording—it still says "anti VEGF injections," but the y-axis shows anti VEGF agents plus a solid line.

Response: We confirm that the y-axis label ("The number of intravitreal anti-VEGF injections") and the caption ("the national total number of intravitreal anti-VEGF injections") are consistent. The bars represent drug-specific counts for each anti-VEGF agent, and the solid line indicates the sum of all four agents.

Figures: For Figure 2 (monthly patterns), we have added a note in the Figure 2 and figure legend: “Golden Week” and "The dip in May corresponds to Golden Week, Japan's consecutive national holidays."

Friedman reporting: χ² and degrees of freedom are reported. Kendall's W = 0.97 is retained as the effect size.

House style: P values are standardized as p < 0.001 where applicable. Hyphenation and thousands separators are consistent throughout.

Reviewer #3

Comment 1

Comment: Results of the impact of the COVID-19 were not mentioned.

Response: The COVID-19 findings are included in the Abstract: "Injection volumes showed no marked decline during the pandemic; April–May 2020 totals (119,100 injections) were only 0.9% lower than the same months in 2019 (120,202 injections)."

Comment 2

Comment: Although the results of crude rate are shown in Table 2, it is better to show the age-standardized value in the main text.

Response: The age-standardized findings are presented in the Results section: "In FY 2017, the max/min ratio decreased from 4.3 (crude) to 3.55 (indirect), and the CV decreased from 0.228 to 0.220. In FY 2022, the corresponding values were 2.8 to 2.29 and 0.184 to 0.172." Full details remain in Supplementary Tables S3–S4.

Comment 3

Comment: The analysis method might not be appropriate. The current analysis simply compared the values of the three periods, and the results indicated that the time trend effect exists. This is not an effect of the COVID-19 effect. It is better to change the analysis method or the title of the analysis.

Response: We agree with the reviewer. We have changed the section title from "Impact of the COVID-19 pandemic" to "Temporal patterns before, during, and after the COVID-19 pandemic" in both the Methods and Results sections. We have also revised the Discussion to acknowledge that our analysis cannot isolate a causal pandemic effect because we lack a counterfactual scenario, and that the observed increases primarily reflect the underlying secular trend. To provide context, we now cite Wada et al., who reported that cataract (−15.5%) and vitrectomy (−13.2%) volumes declined sharply during the same period using the same NDB data, whereas intravitreal injections decreased by only 0.9%.

We thank both reviewers for their constructive feedback, which has substantially improved the manuscript.

Attachment

Submitted filename: Response_to_Reviewers.docx

pone.0348087.s007.docx (20.1KB, docx)

Decision Letter 2

Jiro Kogo

13 Apr 2026

<p>Nationwide trends and regional variation in intravitreal injections and anti-VEGF agent use in Japan from 2017 to 2022: an analysis of the NDB Open Data

PONE-D-25-67681R2

Dear Dr. Kabata

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Jiro Kogo

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

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Reviewer #3: All comments have been addressed

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Reviewer #3: Yes

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Reviewer #3: Yes

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #3: Yes

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Reviewer #3: Yes

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Reviewer #3: Thank you for the revision. The authors addressed all of my comments, and I have no additonal comments.

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Reviewer #3: No

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Acceptance letter

Jiro Kogo

PONE-D-25-67681R2

PLOS One

Dear Dr. Kabata,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Prefecture-level yearly shares of aflibercept and ranibizumab among intravitreal anti-VEGF injections in Japan, FY2017–FY2022.

    (XLSX)

    pone.0348087.s001.xlsx (12KB, xlsx)
    S2 Table. Prefecture-level intravitreal injection utilization in FY2017 and FY2022 using crude and ≥75-year population denominators.

    Crude utilization rates were calculated as the number of intravitreal injection procedures (G016) per 100,000 total population. Rates using the ≥ 75-year denominator were calculated as injections per 100,000 population aged ≥75 years, as an index of injection volume relative to the size of the oldest age group. The percentage change represents the relative change in injection counts from FY2017 to FY2022 for each prefecture.

    (XLSX)

    pone.0348087.s002.xlsx (12KB, xlsx)
    S3 Table. Indirect age standardization of intravitreal injection utilization using two age groups (<75 and ≥75 years) in FY2017 and FY2022.

    Expected injection counts for each prefecture were derived by applying national age-specific injection rates (<75 and ≥75 years) to prefecture-specific age-group population counts. Observed-to-expected ratios (standardized utilization ratios; observed/expected) with 95% confidence intervals and indirectly age-standardized utilization rates (per 100,000 population) are shown for FY2017 and FY2022.

    (XLSX)

    pone.0348087.s003.xlsx (13.6KB, xlsx)
    S4 Table. Summary of between-prefecture variation in crude,  ≥75-year population–based, and indirectly age-standardized intravitreal injection utilization (FY2017 vs FY2022).

    For each fiscal year (FY2017 and FY2022), the distribution of prefecture-level utilization metrics was summarized using the mean, standard deviation (SD), minimum, maximum, maximum-to-minimum ratio, and coefficient of variation (CV = SD/mean) across the 47 prefectures. Metrics include crude utilization rates per 100,000 total population, utilization rates per 100,000 population aged ≥75 years, and indirectly age-standardized utilization rates based on two age groups (<75 and ≥75 years).

    (XLSX)

    pone.0348087.s004.xlsx (9.4KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0348087.s006.docx (25.9KB, docx)
    Attachment

    Submitted filename: Response_to_Reviewers.docx

    pone.0348087.s007.docx (20.1KB, docx)

    Data Availability Statement

    All derived datasets and analysis tables generated for this study have been deposited on Figshare (DOI: https://doi.org/10.6084/m9.figshare.31223410). The original source data are publicly available as aggregated tables from Japan’s National Database of Health Insurance Claims and Specific Health Checkups (NDB) Open Data and official population statistics; this figshare record contains the processed tables and outputs needed to reproduce the results. All analyses were conducted using these publicly accessible sources, and no individual-level data were used.


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