Gender disparities in bladder cancer: A population-based study on life expectancy and health spending in Asia

Yi Hong Li; Yen-Chuan Ou; Min Che Tung; Yi Sheng Lin; Ya Chu Yang; Ying Ming Chiu; Chao Yu Hsu

doi:10.1371/journal.pone.0323803

. 2025 Jun 4;20(6):e0323803. doi: 10.1371/journal.pone.0323803

Gender disparities in bladder cancer: A population-based study on life expectancy and health spending in Asia

Yi Hong Li ¹, Yen-Chuan Ou ^1,^*, Min Che Tung ^1,^*, Yi Sheng Lin ^1,², Ya Chu Yang ³, Ying Ming Chiu ^3,^4,⁵, Chao Yu Hsu ¹

Editor: Mazyar Zahir⁶

PMCID: PMC12136307 PMID: 40465631

Abstract

Background

The aim of this study was to elucidate the disparities in life expectancy, loss-of-life expectancy, and lifetime medical expenditure between sexes in patients with bladder cancer.

Methods

In this retrospective study, we used three Taiwanese databases to analyze the data of patients diagnosed with bladder cancer between 2008 and 2019. Patients aged <30 years or >90 years were excluded. Survival and lifetime costs were estimated using the Kaplan–Meier and semiparametric methods. Subgroup analyses were performed to examine the effects of cancer stage, age, and factors such as hemodialysis on patient outcomes and costs.

Results

This study included 30,390 new diagnoses of bladder cancer. Disparities in loss-of-life expectancy between men and women were observed in both non-muscle-invasive bladder cancer (3.17 [0.55] years for men vs. 7.14 [0.76] years for women) and muscle-invasive bladder cancer (8.86 [0.43] years for men vs. 10.64 [0.63] years for women). Carcinoma in situ revealed its profound impact, with the associated loss-of-life expectancy mirroring those of advanced stages (combined sex carcinoma in situ: 8.58 years, stage 2 men: 9.48 years, stage 2 women: 9.53 years). The cost per life-year showed a marked difference, especially for non-muscle-invasive bladder cancer ($4,631 for men vs. $7,636 for women) and muscle-invasive bladder cancer ($6,033 for men vs. $7,753 for women). Hemodialysis accounted for a significant portion of these costs, with hemodialysis rates of 4.6% in men and 18.5% in women.

Conclusions

Women have a higher prevalence of high-grade histopathology and an extended duration of hemodialysis, culminating in inferior outcomes in non-muscle-invasive bladder cancer and muscle-invasive bladder cancer and augmented costs, compared with men. The role of hemodialysis and the carcinoma in situ stage highlights the need for vigilant monitoring and early aggressive treatment strategies.

Introduction

Rapid advancements in bladder cancer treatments, including various chemotherapies and immunotherapies, incorporation of robotic surgery, and improvements in overall care quality, have presumably led to escalated associated costs. The prevalence of bladder cancer is significantly higher in men than in women, with a sex ratio of approximately 3:1–4:1 [1]; this difference has been attributed to higher smoking rates and exposure to occupational chemicals among men. However, this ratio is not mirrored in mortality rates. Most studies have reported higher mortality and morbidity rates in women than in men, associated with delayed diagnoses, later-stage presentations, longer referral times to urologists, and compromised treatment quality [2]. Some studies have similarly reported a higher incidence of nonurothelial cell carcinoma in women than in men [3,4]. Thus, extensive research has highlighted a noticeable sex disparity in the outcomes of bladder cancer treatments, with women often experiencing worse treatment outcomes than men [5]. Moreover, some studies suggest that factors such as hormones, gene mutations [6], and immunobiology may influence treatment responses, emphasizing the multifactorial nature of gender disparities in bladder cancer.

The exploration of these varied factors emphasizes the urgent need for more comprehensive and inclusive research methodologies to better understand the differences in bladder cancer prevalence and outcomes between the sexes. Therefore, the aim of this study was to estimate the life expectancy (LE), loss of LE, and lifetime medical expenditures of patients with bladder cancer after the disease diagnosis to evaluate the current gender disparities in Asian populations and identify vulnerable groups.

Materials and methods

Materials

This study was approved by the Institutional Review Board of Tungs’ Taichung Metro Harbor Hospital (Taichung, Taiwan; approval number, 110034). This study was conducted in accordance with the ethical standards established in the 1964 Declaration of Helsinki and its later amendments. This study was conducted by Health and Welfare Data Center (Taipei City, Taiwan) and all individual information data were de-identified. Informed consent could not be obtained from the study population. We used three databases provided by the Health and Welfare Data Science Center (Taipei, Taiwan), which are linked to each other. Data used in this study were accessed on 06/10/2021 for research purposes. First, we used the Taiwan Cancer Registry database to identify patients diagnosed with bladder cancer (International Classification of Diseases-O-3 code: C67) between 01/01/2008 and 31/12/2019. The authors did not have access to information that could identify individual participants during or after data collection. This database includes information on the cancer site, diagnosis date, pathological grade, muscle invasion, sex, and cancer stage. The National Health Insurance Research Database (NHIRD) was subsequently used to extract information on the medical expenditures and comorbidities of each patient; this database covers >99.6% of the Taiwanese population, representing a large source of data for nationwide research [7]. Finally, we used the National Mortality Registry data to confirm the survival status of the patients. Patients aged <30 years or >90 years were excluded. Specifically, patients aged 18–30 years were excluded from the study due to the small sample size of fewer than 100 cases, which would make statistical stratification unreliable and limit the feasibility of meaningful subgroup analysis. Additionally, patients aged over 90 years were excluded, as the average life expectancy in Taiwan is 76.94 years for men and 83.74 years for women [8]. These exclusions were deemed necessary to maintain the scientific rigor and interpretability of the findings.

Statistical analysis

Each patient with bladder cancer was tracked from the date of cancer diagnosis until death or the end of the follow-up period, which was December 31, 2019. The survival function was generated using the Kaplan–Meier method until the end of the follow-up period. To extrapolate the survival function beyond the follow-up limit, the semiparametric survival extrapolation method proposed by Hwang et al. [9] was used. This method has been previously applied to various diseases, such as rheumatoid arthritis, psoriasis, stroke, and type 1 diabetes. The detailed steps are included in in Supplement S1 Text. To validate our extrapolation method, we compared life expectancy estimates extrapolated from the first 6 years with 12-year Kaplan–Meier estimates (S1 Table). In addition to the semiparametric extrapolation, we performed a multivariable Cox regression analysis to assess the independent effects of sex, age group, pathological grade, and cancer stage on overall survival. This model provided hazard ratios (HRs) with 95% confidence intervals and p-values for each factor, clarifying the statistical significance of individual components.

Hwang et al. [9] also proposed a method for estimating lifetime medical costs. Medical expenditure data were collected for each patient with bladder cancer after the cancer diagnosis. The monthly average healthcare cost was computed by summing the costs for all patients in a given month and dividing it by the number of surviving cases in that same month. Given the assumption that medical costs would increase before death, the average healthcare costs for the months preceding death were weighted to estimate the average cost function. The average cost function was subsequently multiplied by the corresponding monthly survival probability, and the costs for all months were summed to obtain lifetime medical costs. For consistency, the cost per life-year (CPLY) was derived by dividing the lifetime medical costs by the discounted LE (at a rate of 3% per year). All currency values were expressed in United Sates dollars (USD) in 2019 (1 USD = 30.93 New Taiwan dollars [NTD]). The R package iSQoL2 was used to estimate LE and lifetime costs.

Results

A total of 30,390 new patients with bladder cancer diagnosed between 2008 and 2019 were identified, with men comprising 70% of the population. During the follow-up period, the composition of medical expenditures differed between males and females. In females, the distribution of costs was as follows: medications (14.9%), laboratory tests and imaging studies (34.1%), dialysis (39.7%), and medical consumables (4.4%). In contrast, the corresponding proportions in males were medications (19.2%), laboratory tests and imaging studies (52.0%), dialysis (16.1%), and medical consumables (4.4%). Table 1 and Fig 1 summarize LE, loss of LE, lifetime medical costs, and CPLY, all stratified, based on sex and age at diagnosis. The data revealed that the younger the age group, regardless of sex, the greater was the loss of LE. Female patients with bladder cancer across all age groups had a lower LE than did their male counterparts. Additionally, women experienced a higher loss of LE than men, with nearly a twofold difference in every age group. When considering lifetime medical costs, women generally incurred higher costs than men, with the exception of the 80–89 age group. The CPLY for women peaked in the 30–59 age group ($8,308) and decreases with age, whereas for men, it peaked in the 80–89 age group ($5,758) and increased with age. The primary reason for the higher medical costs among women is the significantly higher proportion of women receiving long-term dialysis, compared with men. A more detailed explanation of this reason will follow in a later section.

Table 1. Estimation of life expectancy, loss of life expectancy, lifetime cost (US) and cost per life-year for bladder cancer, stratified by sex and age at diagnosis.

sex	age	n	no. of death(%)	Age (mean±SD), years	LE (SE), years	Loss-of-LE (SE), years	Lifetime cost (SE), dollars^a	Cost per life-year, dollars^a
Male
	30-59	4981	1115(22.39)	52.29 ± 6.37	21.60 (1.31)	7.50 (1.30)	81,563(4,032)	4,489
	60-69	5645	1688(29.90)	64.99 ± 2.87	12.81 (0.46)	6.20 (0.47)	64,659(2,098)	5,383
	70-79	6228	2928(47.01)	75.12 ± 2.86	8.27 (0.21)	3.69 (0.21)	43,412(1,064)	5,399
	80-89	4438	2932(66.07)	84.21 ± 2.73	4.67 (0.11)	2.35 (0.11)	26,738(627)	5,758
Female
	30-59	1801	460(25.54)	52.86 ± 6.24	19.99 (2.15)	13.66 (2.17)	141,602(10,171)	8,308
	60-69	2403	748(31.13)	65.17 ± 2.86	12.35 (1.11)	10.16 (1.11)	95,821(5,512)	8,233
	70-79	2983	1474(49.41)	75.11 ± 2.77	7.67 (0.26)	6.72 (0.27)	52,548(1,683)	7,023
	80-89	1911	1319(69.02)	84.06 ± 2.66	4.07 (0.19)	4.22 (0.18)	25,500(1,136)	6,312

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SD, standard deviation. SE, standard error of mean.

^a$1 dollar (US) = $30.93 dollars (New Taiwan).

The black lines represent sex-, age-, calendar year-matched referents simulated from the national vital statistics of Taiwan. The red dashed lines represent the real-world data from bladder cancer cohort and the blue dashed lines represent extrapolation from this bladder cancer cohort. The area between the black line and the red/blue dashed line represents the loss-of-life expectancy (loss-of-LE).

When the data were analyzed, based on the pathological grade of the disease, the loss of LE for patients with high-grade bladder cancer was approximately 2 years longer than that for patients with low-grade bladder cancer in men [mean (standard error of the mean): 6.12 (0.39) years vs. 4.28 (1.05) years] and in women [8.40 (0.52) years vs. 6.61 (1.48) years]. CPLY was higher in patients with high-grade bladder cancer than in patients with low-grade bladder cancer, with the highest cost observed in women with high-grade bladder cancer ($8,710) (Table 2).

Table 2. Estimation of life expectancy, loss of life expectancy, lifetime cost (USD) and cost per life year for bladder cancer, stratified by sex and pathologic grading.

sex	grade	n	no. of death(%)	Age (mean±SD), years	LE (SE), years	Loss-of-LE (SE), years	Lifetime cost (SE), dollars^a	Cost per life year, dollars^a
Male
	Low	6032	1651(27.37)	67.42 ± 12.65	13.64 (1.06)	4.28 (1.05)	54,756(2,709)	4,316
	High	13231	5816(43.96)	69.71 ± 11.58	10.17 (0.37)	6.12 (0.39)	52,170(1,408)	5,532
Female
	Low	2011	554(27.55)	68.28 ± 11.74	13.89 (1.49)	6.61 (1.48)	70,127(5,175)	5,437
	High	5988	2783(46.48)	70.51 ± 10.99	10.23 (0.51)	8.40 (0.52)	81,487(2,920)	8,710

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SD, standard deviation. SE, standard error of mean.

^a$1 dollar (US) = $30.93 dollars (New Taiwan).

Within the same cancer stage, women experienced a higher loss of LE than did men. This was especially notable in stage 1, where the difference in loss of LE between women and men was the largest [7.69 (1.00) years vs. 3.84 (0.77) years]. The CPLY for men gradually increased with advancing stages; however, for women, the CPLY remained similar from stages 0–3. Both sexes had the highest CPLY at stage 4 of the disease. Stage 0IS, also known as a flat tumor or carcinoma in situ (CIS), was separate from stage 0A. To ensure statistical reliability and minimize potential bias due to sample size imbalance, we combined both genders for analysis. Specifically, during stage stratification, there were 193 females and 341 males in the 0IS stage. Given the relatively small female sample size, combining both genders allowed for more reliable results and better accounted for significant differences between groups. Stage 0IS had a substantial impact on LE, with a loss of LE as high as 9.26 (1.00) years. This was significantly more than that of stage 0A and even more than that of the stage 1 and 2 groups (Table 3 and S1 Fig in the Supplement).

Table 3. Estimation of life expectancy, loss of life expectancy, lifetime cost (USD) and cost per life year for bladder cancer, stratified by sex and cancer stage.

sex	stage	n	no. of death(%)	no. of male (%)	Age (mean±SD), years	LE (SE), years	Loss-of-LE (SE), years	Lifetime cost (SE), dollars^a	Cost per life year, dollars^a
	0IS	534	184(34.46)	341 (63.86)	68.91 ± 10.67	8.58 (0.88)	9.26 (1.00)	60,275(5,592)	7,132
Male
	0A	5303	1238(23.35)		67.79 ± 12.35	15.08 (1.09)	2.61 (1.09)	62,769(4,763)	4,594
	1	6674	2122(31.8)		68.75 ± 11.85	13.09 (0.79)	3.84 (0.77)	55,394(1,979)	4,613
	2	2972	1442(48.52)		69.88 ± 11.86	9.48 (0.72)	6.70 (0.73)	47,565(2,182)	5,394
	3	1446	885(61.2)		70.63 ± 11.40	6.95 (0.59)	8.69 (0.62)	40,282(1,775)	6,239
	4	1379	1174(85.13)		70.30 ± 11.86	2.87 (0.48)	13.00 (0.50)	27,305(1,741)	9,935
Female
	0A	2123	518(24.4)		68.22 ± 11.14	14.10 (1.58)	6.37 (1.60)	93,090(7,595)	7,131
	1	2769	979(35.36)		69.50 ± 11.08	11.74 (0.96)	7.69 (1.00)	86,985(5,267)	7,952
	2	1340	688(51.34)		71.85 ± 10.88	9.53 (0.84)	8.06 (0.88)	66,565(4,282)	7,658
	3	601	391(65.06)		72.88 ± 11.45	6.05 (0.97)	10.79 (1.05)	44,086(4,549)	7,708
	4	728	640(87.91)		71.83 ± 11.36	2.55 (0.61)	15.06 (0.70)	25,050(2,680)	10,452

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SD, standard deviation. SE, standard error of mean.

^a$1 dollar (US) = $30.93 dollars (New Taiwan).

Based on whether the cancer was muscle-invasive, the cancer stage was divided into stages 0–1 and stages 2–4. The difference in loss of LE between stages 0–1 and stages 2–4 was greater in women [7.14 (0.76) years vs. 10.64 (0.63) years] than in men [3.17 (0.55) years vs. 8.86 (0.43) years]. Furthermore, the difference in CPLY was higher in women (stages 0–1 and stages 2–4: $7,636 vs. $7,753) than in men ($4,631 vs. $6,033) (Table 4). We subsequently examined the relationship between cancer stage and age groups. In females, MIBC was more prevalent than in males for those aged above 70, whereas NMIBC was more common in females than in males for those aged below 70 (S2 Fig).

Table 4. Estimation of life expectancy, loss of life expectancy, lifetime cost (USD) and cost per life year for bladder cancer, stratified by sex and cancer stage.

sex	stage	n	no. of death(%)	Age (mean±SD), years	LE (SE), years	Loss-of-LE (SE), years	Lifetime cost (SE), dollars^a	Cost per life-year, dollars^a
Male
	0-1	12318	3481(28.26)	68.33 ± 12.06	14.08 (0.55)	3.17 (0.55)	59,269(2,308)	4,631
	2-4	5797	3501(60.39)	70.16 ± 11.75	7.16 (0.40)	8.86 (0.43)	40,468(1,314)	6,033
Female
	0-1	5085	1560(30.68)	68.96 ± 11.08	12.73 (0.75)	7.14 (0.76)	90,242(3,887)	7,636
	2-4	2669	1719(64.41)	72.08 ± 11.15	6.79 (0.63)	10.64 (0.63)	48,533(2,731)	7,753

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SD, standard deviation. SE, standard error of mean.

^a$1 dollar (US) = $30.93 dollars (New Taiwan).

To validate our extrapolation method, we compared life expectancy estimates extrapolated from the first 6 years with 12-year Kaplan–Meier estimates (S1 Table). The relative bias was minimal, ranging from –6.85% to 1.85% in males and from –2.14% to 1.80% in females, demonstrating good agreement between extrapolated and observed estimates. These findings support the robustness of our approach despite inherent uncertainties. To elucidate prognostic factors, we conducted a multivariable Cox regression including sex, age, tumor grade, and cancer stage (S2 Table). After adjustment, male sex was associated with lower mortality (HR 0.90, 95% CI 0.87–0.94, p < 0.01). Compared to patients aged 30–59, those aged 60–69, 70–79, and 80–89 had HRs of 1.49, 2.50, and 4.62, respectively (all p < 0.01). High-grade tumors (HR 1.52) and advanced-stage disease (HR 2.72) also significantly increased mortality risk (p < 0.01).

We analyzed the cost components to understand the variability in the CPLY. The primary driver of higher medical costs among women was their significantly greater proportion of undergoing dialysis, compared with men, with a ratio of 22.02% to 7.39%. A subgroup analysis examining the timing of hemodialysis before and after a bladder cancer diagnosis showed that a larger percentage of women had already been on hemodialysis (84.1%), compared with men (62.7%). Furthermore, in the female cohort, younger patients comprised the majority of patients on dialysis before being diagnosed with bladder cancer, with 90.71% aged 30–59 years. This trend was not as evident in the male cohort, in which approximately 60% of all age groups were on dialysis.

To better understand the cost burden of dialysis, we analyzed the actual dialysis costs and total medical costs for both dialysis and non-dialysis patients during follow-up. We calculated the proportion of dialysis costs relative to total medical costs by summing the dialysis expenses for all dialysis patients and dividing this total by the overall medical costs for all patients. Unlike the CPLY, which was estimated using extrapolation, this analysis presents observed dialysis costs as they occurred in real-time.

Our findings indicate that female patients in the 30–59 age group accounted for the highest proportion of dialysis costs, with this age group contributing 49.98% of dialysis costs relative to total medical costs. This proportion decreased steadily with increasing age, reaching 19.53% in the 80–89 age group. A similar downward trend was observed in male patients; however, the proportions were significantly lower. Among men, dialysis costs constituted 20.75% of total medical expenses in the 30–59 age group, decreasing to 8.26% in the 80–89 age group. Across all age groups, dialysis accounted for 39.68% of total costs in females compared to just 16.1% in males.

Additionally, women with different disease stages showed a prolonged duration of hemodialysis. In both sexes, patients with more advanced disease stages underwent hemodialysis for a longer period than that of their counterparts (Table 5 and Fig 2).

Table 5. Number of bladder cancer patients receiving long-term dialysis, stratified by sex, age at diagnosis, and proportion of dialysis costs during follow-up.

	age	Number of Patients (n)	Dialysis Patients (n, %)	Dialysis Before Dx (n, %)	Dialysis After Dx (n, %)	Dialysis costs/ Total medical costs (%) during follow-up (million USD)
Male
	30-59	4981	385(7.73)	245(63.64)	140(36.36)	27.62/133.06(20.75)
	60-69	5645	510(9.03)	322(63.14)	188(36.86)	28.82/150.65(19.13)
	70-79	6228	484(7.77)	294(60.74)	190(39.26)	22.76/164.4(13.85)
	80-89	4438	194(4.37)	126(64.95)	68(35.05)	7.43/89.98(8.26)
	All	21292	1573(7.39)	987(62.75)	586(37.25)	86.63/538.1(16.1)
Female
	30-59	1801	538(29.87)	488(90.71)	50(9.29)	43.52/87.08(49.98)
	60-69	2403	664(27.63)	585(88.10)	79(11.90)	44.49/98.8(45.03)
	70-79	2983	603(20.21)	462(76.62)	141(23.38)	30.67/93.85(32.68)
	80-89	1911	198(10.36)	150(75.76)	48(24.24)	7.45/38.15(19.53)
	All	9098	2003(22.02)	1685(84.12)	318(15.88)	126.13/317.89(39.68)

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The x-axis represents the timeline of initiating hemodialysis, with 0 denoting the time of bladder cancer diagnosis. Negative values indicate hemodialysis initiation before the bladder cancer diagnosis, while positive values indicate hemodialysis initiation after the bladder cancer diagnosis. For instance, 1 indicates hemodialysis initiation within 1 year after the bladder cancer diagnosis, and >5 indicates hemodialysis initiation more than 5 years after the bladder cancer diagnosis.

Discussion

Over the past decade, the sex ratio for bladder cancer diagnosis in Taiwan has been 2.4:1 [10]. This ratio is notably lower than the ratios of 3:1–4:1 frequently cited in previous studies. Sex disparities in bladder cancer outcomes have been extensively researched and can be attributed to various factors, including smoking habits, hormonal influences, delays in diagnosis upon initial presentation, postponed referral to urologists, and differences in adherence to clinical guidelines [11]. Women are often diagnosed at more advanced stages of the disease, which further exacerbates this disparity. Nevertheless, in our study, the proportion of women was marginally higher than that of men (34.4% vs. 32.0%).

Cancer stage: non-muscle-invasive bladder cancer

In both sexes, a significant proportion of patients are diagnosed at stages Tis to T1, and are classified as presenting non-muscle-invasive bladder cancer (NMIBC). A substantial body of the literature posits that within this NMIBC cohort, women generally have worse prognosis than men. Our findings corroborate this assertion because female patients have more than twice as much loss of LE as their male counterparts (7.14 years vs. 3.17 years). Some investigations have highlighted that women often present with high-grade disease on histopathological examinations [11]. Consistent with this finding, our study revealed the predominant presence of high-grade disease in women. Moreover, meta-analyses that did not separate data by grade (Tis to T1) reported higher recurrence rates, poorer responses to Bacillus Calmette-Guerin (BCG) therapy, and higher cancer-specific mortality in women than in men [12].

However, it is essential to recognize the existence of contradictory findings. Some studies have contended that sex discrepancies in NMIBC are negligible but become prominent in muscle-invasive bladder cancer (MIBC) [13]. Adding to this discourse, one study posited that even minor delays in treating NMIBC could escalate the risks of disease progression and recurrence [14]. This finding complements the existing literature that underscores the detrimental effects of treatment decisions, particularly in women.

CIS, also referred to as Tis in our study and categorized as stage 0, often has a less favorable prognosis, particularly in patients who do not respond well to BCG treatment [15]. CIS has a pronounced proclivity for the recurrence and progression of MIBC. The standard treatment for CIS involves transurethral resection of the bladder tumor, followed by intravesical BCG therapy. For patients unresponsive to this initial line of treatment, more intensive alternatives, such as radical cystectomy (RC) or bladder-sparing therapy, are advised. Given their complexity, these interventions naturally result in heightened loss of LE and associated costs.

Although most studies have focused on concurrent (or concomitant) CIS, patients with primary CIS appear to have worse outcomes [16], a finding specifically addressed in our study. The resultant LE and costs were comparable to those observed for stage 2 bladder cancer (LE for combined CIS is 8.58 years, whereas it is 9.48 years for men with stage 2 and 9.53 years for women with stage 2). Hence, our findings contradict those of previous studies and draw parallels between CIS and other categorizations, underscoring the need for vigilant monitoring and aggressive treatment of patients with CIS.

In our subgroup analysis, which examined the interplay between cancer stage and age, we observed a notable increase in aggressive stage diagnoses among female patients aged >70 years. Male patients aged <70 years were conversely more likely to be diagnosed with MIBC at the initial presentation. This finding challenges the widely held belief that a late diagnosis is solely responsible for more advanced diseases in women. We propose that factors such as improved clinical access, employer-sponsored health checks, and greater health awareness, particularly among younger individuals, may have had a role in this shift in the epidemiological pattern.

Cancer stage: muscle-invasive bladder cancer

RC is the standard treatment of choice for resectable MIBC. The bulk of the literature covers post-RC treatment efficacy and the responses to neoadjuvant and adjuvant chemotherapies. Of note, a meta-analysis consistently suggested that female individuals had worse cancer-specific survival (CSS), overall survival (OS), and disease-free survival than did male individuals [17]. An interesting finding is that, although the meta-analysis included 10 studies centered on Asian populations, no significant differences were found in the subgroup analyses of CSS, OS, and disease-free survival. Upon closer inspection, these 10 studies primarily involved populations from Korea and Japan, with some being compromised in terms of quality, particularly in the analysis of OS. Given this context, we believe that our study provides valuable insights, particularly by addressing gaps in Asian demographics and offering new perspectives.

Our data indicated that, although women experience a more pronounced loss of LE than did men, the disparity is less pronounced than in MIBC. In particular, women faced a loss of LE of 10.64 years, compared with 8.86 years for men. Upon age stratification, we detected an encouraging trend toward a reduced MIBC incidence in younger women. However, these slight variations do not fully explain the increased loss of LE in the female demographic. We unfortunately could not quantify the percentage of patients who underwent RC or their subsequent chemotherapy responses.

The rationale behind sex disparities in post-RC patient outcomes remains a topic of considerable debate. Numerous meta-analyses have indicated that women generally have poorer CSS, OS, and recurrence rates than do men [18]. Although some reports have attributed this factor to women often being diagnosed at a more advanced stage or encountering diagnostic delays, the reasons remain speculative. A potential contributing factor can be differing treatment patterns, with men being more likely than women to receive curative treatment [19]. However, in contrast to these findings, another study found no significant sex differences in treatment quality [20], although women tended to start treatment earlier than men. On a different note, a meta-analysis explored the diminishing gap, positing that women receiving neoadjuvant chemotherapy coupled with RC often experienced superior results in terms of recurrence and CSS, although there was no discernible difference between RC with adjuvant chemotherapy [21]. A similar trend was observed in another observational study, further supporting these findings [22].

Cost per life-year

Among women, although the average CPLY was significantly higher than that for men, the costs for stages Ta to 3 remained relatively consistent, with only stage 4 incurring the highest costs for both sexes. This finding differs from that of a population study in Germany [23], which showed that high-risk NMIBC incurred the highest cumulative follow-up costs, unlike MIBC. In that study, follow-up using computed tomography urography was the most costly, followed by cystoscopy.

While previous studies with similar designs have consistently shown an increasing trend in costs with age for both males and females [24,25], our study reveals a contrasting pattern in females. Specifically, male patients in our study follow the expected trend of rising CPLY with age, whereas female patients demonstrate an opposite pattern. Examining the breakdown of costs within Taiwan’s National Health Insurance, we found that hemodialysis dominated expenses for both sexes across all age groups. Further analysis of the proportion of dialysis costs relative to total medical expenses indicated a similar trend in both genders, with the highest costs observed in the 30–59 age group. However, the proportion of all age groups was significantly lower in males (16.1%) compared to females (39.68%), highlighting a notable gender disparity in dialysis-related financial burden.

The substantial cost of established hemodialysis significantly influences overall expenditure, particularly for women in the 30–59 age group. The higher proportion of dialysis costs in women may be attributed to several factors, including a greater prevalence of long-term hemodialysis and a higher likelihood of starting dialysis before bladder cancer diagnosis (84.1% in females vs. 62.7% in males). Younger women in particular (90.71% aged 30–59 years) were already on dialysis prior to their cancer diagnosis. This cumulative cost of long-term dialysis could explain the pronounced impact on their CPLY.

Although our data did not directly indicate this preference, our clinical experience suggests that Taiwanese patients frequently opt for trimodal therapy over RC. One study [26] explored the cost difference between trimodal therapy and RC, revealing that the former had higher expenses within a 1-year follow-up period. To determine which treatment is more cost-intensive and understand the potential influence of sex differences, a more extensive investigation over an extended timeframe may be required.

Influence of hemodialysis

Hemodialysis has profound implications for patient survival, comorbidities, and financial burden. It appears to be associated with a higher mortality rate than that of certain cancers [27]. Our data revealed that patients with advanced disease had a greater prevalence of hemodialysis. We propose that hemodialysis suggests an elevated risk of developing bladder cancer and indicates progression to more advanced stages. A study from Taiwan demonstrated that patients with chronic kidney disease (CKD) on dialysis were at a heightened risk of genitourinary cancers, with bladder cancer being notably prevalent [28]. Of interest, women had a higher hazard ratio than did men (13.94 vs. 4.9), with younger age groups being particularly at risk. These findings align with our observation of higher dialysis rates among women and suggest that CKD and prolonged hemodialysis may disproportionately affect female patients. Numerous studies have scrutinized the impact of CKD on oncological outcomes after RC [29,30]. These studies have uniformly reported reduced cancer-specific and OS rates. Another study identified adverse pathological features along with increased rates of transfusion and readmission [30]. In patients with end-stage renal disease, undergoing RC is associated with substantial complications and mortality [31]. Moreover, patients with CKD continue to exhibit an elevated incidence of complications when transurethral resection of the bladder tumor is performed for NMIBC [32].

Prolonged hemodialysis also poses risks such as cardiovascular diseases, infections, and other morbidities. This logically accounts for the higher costs associated with younger patients who, despite having comparable stage distributions, bear more expenses and experience greater loss of LE due to the predominant prevalence of hemodialysis. Additionally, although cisplatin-eligible patients generally experience better therapeutic outcomes, patients on dialysis or who have a glomerular filtration rate <60 mL/min are typically excluded from standard treatment protocols. This exclusion may explain the higher dialysis rates among female patients because a smaller proportion is eligible for standard therapies.

To the best of our knowledge, this study is the first to provide stage-specific stratification of bladder cancer outcomes. Sex disparities in these outcomes can be attributed to various factors. Two recent population studies from Norway and the Netherlands showed that although female patients with bladder cancer had higher mortality rates at diagnosis than did their male counterparts, this pattern shifted in favor after 2 years [33,34]. This trend suggests an indirect possibility that women may be diagnosed at more advanced disease stages, evidenced by a male-to-female ratio in MIBC of 29%:31% (p < 0.001), because of potential delays in urologist referrals. Our findings indicated a notably higher prevalence of MIBC in female patients than in male patients, with a male-to-female ratio of 32% to 34.4%. Within the Tis stage, women initially had worse outcomes did than men, but by the 6th year, this trend was reversed in favor of women (S3 Fig). However, in all other stages, women consistently had worse outcomes than that of men.

Clinical implications for high-risk populations

We introduce a novel concept regarding the impact of hemodialysis on the diagnosis of bladder cancer. Young female patients undergoing hemodialysis often face unique diagnostic challenges owing to anuria or oliguria. These patients are at a particularly high risk of poor bladder cancer outcomes, which raises concerns about the adequacy of current diagnostic and screening protocols. Although routine cystoscopy for bladder cancer screening is not widely recommended in the general population, we propose that early detection strategies may be beneficial for this high-risk group. Targeted screening, such as periodic cystoscopy, may improve outcomes by enabling earlier detection and treatment in young females on hemodialysis, in whom diagnosis is frequently delayed owing to their clinical presentation.

Limitations

This study has some limitations. We were unable to fully account for various factors, including smoking, occupational exposure to chemicals, underlying diseases, socioeconomic status, and insurance status, all of which may influence mortality outcomes. However, we attempted to minimize these differences by matching the study population with the general population based on age, sex, and year of diagnosis. Additionally, the Monte Carlo-based reference group may not capture key factors (e.g., comorbidities, socioeconomic status), potentially biasing comparisons. Moreover, our model assumes patient homogeneity for the included variables. In reality, variations in treatment, healthcare access, and other factors may introduce residual confounding. Regarding medical costs, self-paid medications and caregiving expenses are not covered by Taiwan’s National Health Insurance, were not included in our cost analyses. This omission could underestimate the financial burden of the disease on individuals and their families. With respect to race, as the National Health Insurance covers >99.9% of the Taiwanese population, the representation of white, black, and other racial groups is minimal. Some studies have reported similar mortality rates between Caucasian and Asian populations, whereas black patients tend to have worse outcomes, possibly due to more advanced stages and higher-grade disease at diagnosis [35]. Additionally, reliance on database records poses limitations, such as the potential for incomplete or outdated data, errors in data entry or collection, and biases, because the data may not be fully representative of the entire population. These factors may affect the comprehensiveness and accuracy of our findings. Nonetheless, we have taken stringent measures to validate our data and have openly discussed these limitations to ensure transparency and maintain the integrity of our study conclusions.

Conclusions

Our study examines sex disparities in bladder cancer outcomes, highlighting the marked differences in medical implications and costs. In particular, younger women face heightened risks with an increased loss of LE and costs. Contrary to prior assumptions, delayed diagnoses are not the primary reason for advanced disease in women, especially because younger women have fewer advanced cases. However, women have worse outcomes in both NMIBC and MIBC, likely driven by high-grade histopathology and prolonged hemodialysis. Aggressive follow-up and early surgical interventions, especially for CIS, are crucial, because CIS outcomes often resemble those of more advanced stages. A pivotal finding is the under-recognized effect of hemodialysis on outcomes, which is associated with higher comorbidity and limited treatment options. Our study underscores the need for prompt and accurate diagnosis, especially in women, and suggests improvements in treatment approaches. Despite some data omissions, this study offers key insights for healthcare professionals and stresses the importance of addressing sex disparities in bladder cancer outcomes.

Supporting information

S1 Text. The semiparametric survival extrapolation method used in this study involves three main steps.

(DOCX)

pone.0323803.s001.docx^{(14.9KB, docx)}

S1 Table. Validation of extrapolated life expectancy estimates in bladder cancer patients: comparison of 6-year follow-up extrapolations with 12-year Kaplan–Meier estimates.^a.

(DOCX)

pone.0323803.s002.docx^{(16.1KB, docx)}

S2 Table. Multivariable cox proportional hazards regression analysis of overall survival in bladder cancer patients.

(DOCX)

pone.0323803.s003.docx^{(16KB, docx)}

S1 Fig. Comparison of life expectancy (LE) loss estimates across multiple cohorts.

For female bladder cancer patients diagnosed at stage 1, their life expectancy after diagnosis is 11.74 years, which is 1.35 years shorter than that of male bladder cancer patients diagnosed at the same stage (whose life expectancy after diagnosis is 13.09 years). Life expectancy loss refers to the difference between the life expectancy in the study cohort and that of a reference population matched by age, sex, and calendar year, simulated using life tables. A comparison of life expectancy loss, or the difference in life expectancy loss (7.69 − 3.84 = 3.85), represents a difference-in-differences adjusted for potential confounding factors. Values are presented as mean ± SEM.

(DOCX)

pone.0323803.s004.docx^{(389.8KB, docx)}

S2 Fig. Stacked bar chart illustrates the distribution of NMIBC, MIBC across age groups for males and females.

Both genders exhibited a similar trend, with the percentage of NMIBC decreasing and the percentage of MIBC increasing in older age groups. The proportion of cases classified as unknown remained consistent across all age groups and genders. In age groups below 70, females demonstrated a comparable or slightly higher percentage of NMIBC compared to males, whereas in age groups above 70, the percentage of NMIBC was significantly lower in females. Conversely, the percentage of MIBC showed an opposite pattern, with higher proportions observed in females in the older age groups.

(JPG)

pone.0323803.s005.jpg^{(60.8KB, jpg)}

S3 Fig. Long term survival after bladder cancer diagnosis, stratified by sex and stage.

(DOCX)

pone.0323803.s006.docx^{(49.2KB, docx)}

Acknowledgments

Not applicable.

Abbreviations

CIS: carcinoma in situ
CKD: chronic kidney disease
CPLY: Cost per life-year
ESRD: end-stage renal disease
LE: life expectancy
Loss-of-LE: Loss-of-life expectancy
MIBC: muscle-invasive bladder cancer
NHIRD: National Health Insurance Research Database
NMIBC: non-muscle-invasive bladder cancer
NTD: new Taiwan dollar
RC: radical cystectomy

Data Availability

This study is based on third-party data obtained from the National Health Insurance Research Database (NHIRD), which has been transferred to the Health and Welfare Data Science Center (HWDC), Ministry of Health and Welfare, Taiwan. The NHIRD contains de-identified healthcare data and is not publicly available due to government regulations. Access to the dataset requires formal application and approval from the HWDC. Interested researchers may apply for data access via the official website (https://dep.mohw.gov.tw/DOS/cp-5119-59201-113.html). The authors confirm that they do not have the right to share the raw data and that others can access the data in the same manner as the authors. The authors had no special privileges in obtaining the dataset. Proper citation and acknowledgment of the NHIRD as the data source are required when using the dataset.

Funding Statement

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

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PLoS One. 2025 Jun 4;20(6):e0323803. doi: 10.1371/journal.pone.0323803.r001

Author response to Decision Letter 0

11 Nov 2024

PLoS One. doi: 10.1371/journal.pone.0323803.r002

Decision Letter 0

Mazyar Zahir

5 Jan 2025

PONE-D-24-48193Gender disparities in bladder cancer: A population-based study on life expectancy and health spending in AsiaPLOS ONE

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Reviewer #2: Yes

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Reviewer #1: Dear Author

Thank you for your retrospective cross-sectional study.

1: Was all the information you needed available in the patient file?

2: Why did you exclude all patients under the age of 30 from the study?

Reviewer #2: Thank you for the opportunity to review this manuscript, and congratulations to the authors for conducting this study. The study from Taiwan, based on database exploration, compared life expectancy and medical expenditure between males and females, revealing interesting findings in their population.

Here are some major issues worth addressing to improve the presentation of the work:

1. The reason for excluding individuals under 30 and over 90 years of age from the study should be added.

2. Some issues in the discussion need further elaboration. For example, the following finding requires more explanation: The CPLY for women peaked in the 30–59 age group ($8,308) and decreased with age, whereas for men, it peaked in the 80–89 age group ($5,758) and increased with age.

3. Why didn’t you divide males and females with 0IS in Table 3A? Throughout the manuscript, 0IS was not stratified by sex.

4. The primary driver of higher medical costs was introduced as hemodialysis. However, there is no information on how much of the costs were due to dialysis and the role of other expenditures. Providing this information would clarify it for the readers.

5. In the discussion section, you mentioned, “In our subgroup analysis, which examined the interplay between cancer stage and age, we observed a notable increase in aggressive stage diagnoses among female patients aged >70 years.” More clarification is suggested regarding the interplay between cancer stage and age for each sex in the results section.

Additionally, here are some minor issues suggested for improvement:

1. In the results section of the abstract, it seems that “male” and “female” have been used incorrectly in this phrase: (3.17 [0.55] years for women vs. 7.14 [0.76] years for men).

2. Using MeSH terms is recommended for keywords.

3. Please recheck the numbers in the tables. For example, the percentages of the number of deaths in Table 3 seem incorrect.

4. In the discussion section, you mentioned, “CIS, also referred to as Tis in our study and categorized as stage 1, often…”. Doesn’t CIS categorize as stage 0?

**********

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Reviewer #2: No

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PLoS One. 2025 Jun 4;20(6):e0323803. doi: 10.1371/journal.pone.0323803.r003

Author response to Decision Letter 1

6 Feb 2025

Reviewer #1:

Dear Author

Thank you for your retrospective cross-sectional study.

1: Was all the information you needed available in the patient file?

Answer:

Thank you for your question. As noted in the "Limitation" section of the manuscript, certain factors—such as smoking habits, occupational exposure to chemicals, socioeconomic status, and insurance status—could not be directly obtained due to the inherent constraints of retrospective studies and database-based research. Additionally, self-paid medications and caregiving expenses, which are not covered by Taiwan’s National Health Insurance, could not be included in the analysis. While these limitations exist, we mitigated potential biases by matching our study population with the general population based on age, sex, and year of diagnosis. This limitation has been explicitly discussed in the manuscript to provide transparency regarding the scope and boundaries of our findings (page 29-30, line 459 to 477 in tracked changes file; page 28-29, line 445 to 463 in without tracked changes file). We also added an explanation in the text that such results might overlook the actual economic burden that patients and their families have to bear.

2: Why did you exclude all patients under the age of 30 from the study?

Answer:

Thank you for your comment. Patients aged <30 years was excluded for the following reasons: Patients aged 18–30 years were excluded because the number of cases in this age group was fewer than 100, making statistical stratification unreliable and limiting the potential for meaningful subgroup analysis. This exclusion was necessary to ensure the scientific rigor and interpretability of the study findings. This explanation has been added to the "Materials and Methods" section of the manuscript (page 7, line 104 to 110 in tracked changes file; page 7, line 103 to 109 in without tracked changes file).

Reviewer #2:

Thank you for the opportunity to review this manuscript, and congratulations to the authors for conducting this study. The study from Taiwan, based on database exploration, compared life expectancy and medical expenditure between males and females, revealing interesting findings in their population.

Here are some major issues worth addressing to improve the presentation of the work:

1. The reason for excluding individuals under 30 and over 90 years of age from the study should be added.

Answer:

Thank you for your comment. Patients aged <30 years or >90 years were excluded for the following reasons: Patients aged 18–30 years were excluded because the number of cases in this age group was fewer than 100, making statistical stratification unreliable and limiting the potential for meaningful subgroup analysis. Patients aged >90 years were excluded due to shorter average life expectancy relative to the Taiwanese population(76.94 years for men and 83.74 years for women). These exclusions were necessary to ensure the scientific rigor and interpretability of the study findings. This explanation has been added to the "Materials and Methods" section of the manuscript (page 7, line 104 to 110 in tracked changes file; page 7, line 103 to 109 in without tracked changes file).

Answer:

Thank you for your insightful comments. After re-examining the data, we found that dialysis significantly contributes to total medical costs.

In males, CPLY increases with age, which aligns with trends observed in previous studies indicating that healthcare needs and related complications tend to accumulate in older age groups.

In contrast, the CPLY trend in females follows an inverse pattern compared to males. Our analysis revealed that the highest proportion of dialysis costs relative to total expenditures is observed in the 30–59 age group, accounting for 49.98% of the total. Notably, while the total hemodialysis costs for male patients amount to 27.62 million USD, female patients incur a significantly higher total of 43.52 million USD.

Since our dialysis costs are based on 'known' expenses during the current tracking period, whereas CPLY estimates future costs based on these data, the 30-59 age group—having the most documented dialysis cases—contributes to the highest projected costs. This trend aligns with the observed cost patterns across the population.

The gender disparity observed in dialysis costs underscores the importance of considering both the long-term financial burden of dialysis and the population's age structure when interpreting cost patterns. We have revised the results and discussion sections to reflect this additional context and appreciate the opportunity to clarify these points (page 16-17 , line 231 to 245 and revised Table 4 in tracked changes file; page 16 to 17, line 230 to 247 and revised Table 4 in without tracked changes file).

3. Why didn’t you divide males and females with 0IS in Table 3A? Throughout the manuscript, 0IS was not stratified by sex.

Answer:

Thank you for this observation. We did not stratify stage 0IS by sex in Table 3A because the female sample size (n=193) was relatively small compared to female in other stage. To ensure statistical reliability and avoid potential bias due to sample size imbalance, we combined both genders for analysis. Additionally, in the revised manuscript, we have clarified this point (page 13, line 192 to 197 in tracked changes file; page 13, line 191 to 195 in without tracked changes file). This approach allowed us to provide more robust and interpretable results while accounting for significant differences between groups.. This approach also highlights the notably higher mortality and CPLY observed in this subgroup, which we believe underscores the significance of this finding regardless of sex.

Answer:

Thank you for your insightful comment. We appreciate the opportunity to clarify the composition of medical expenditures, particularly the contribution of dialysis costs relative to other components.

During the follow-up period, the distribution of medical expenses varied between males and females. In females, the cost composition was as follows: medications (14.9%), laboratory tests and imaging studies (34.1%), dialysis (39.7%), and medical consumables (4.4%). In contrast, the corresponding distribution in males was medications (19.2%), laboratory tests and imaging studies (52.0%), dialysis (16.1%), and medical consumables (4.4%) (page 9, line 137 to 143 in tracked changes file; page 9, line 136 to 142 in without tracked changes file).

These findings highlight that dialysis accounted for a substantially larger proportion of total medical costs in females compared to males. This gender disparity suggests that differences in dialysis utilization may be a key factor influencing overall healthcare expenditures.

We have incorporated these details into the revised manuscript to improve clarity and address this concern.

Answer:

Thank you for your insightful comment. To address your concern, we have added further clarification in the results section to elaborate on the interplay between cancer stage and age for each sex. Specifically, we have highlighted the trends observed in our subgroup analysis, where a notable increase in the proportion of aggressive stage diagnoses (MIBC) was evident among female patients aged >70 years.

Additionally, we have included a new Fig S3. to visually illustrate the trends in NMIBC, MIBC, and unknown stage distributions across different age groups for both sexes. This figure complements the text and provides a clear comparison of how cancer stage proportions change with age for males and females(page 14-15, line 211 to 214 in tracked changes file; page 14, line 210 to 213 and new Fig S3 in without tracked changes file).

Additionally, here are some minor issues suggested for improvement:

1. In the results section of the abstract, it seems that “male” and “female” have been used incorrectly in this phrase: (3.17 [0.55] years for women vs. 7.14 [0.76] years for men).

Answer:

We acknowledge that there was an error in the labeling of the data for men and women. This misprint has been corrected in the revised manuscript

2. Using MeSH terms is recommended for keywords.

Answer:

We have updated the keywords to use appropriate MeSH terms as recommended.

3. Please recheck the numbers in the tables. For example, the percentages of the number of deaths in Table 3 seem incorrect.

Answer:

We have re‐calculated the percentages in Table 3 and confirmed that the values are correct. No changes were necessary based on our recalculations.

4. In the discussion section, you mentioned, “CIS, also referred to as Tis in our study and categorized as stage 1, often…”. Doesn’t CIS categorize as stage 0?

Answer:

We agree that CIS (Carcinoma In Situ) should be categorized as stage 0 rather than stage 1. This misprint has been corrected in the revised version.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0323803.s008.docx^{(25.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0323803.r004

Decision Letter 1

Mazyar Zahir

25 Feb 2025

PONE-D-24-48193R1Gender disparities in bladder cancer: A population-based study on life expectancy and health spending in AsiaPLOS ONE

Dear Dr. Ou,

Please submit your revised manuscript by Apr 11 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mazyar Zahir

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: No

Reviewer #2: (No Response)

Reviewer #3: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: (No Response)

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: (No Response)

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6. Review Comments to the Author

Reviewer #1: Dear Author

Thank you for your revision .

The items requested by me from the authors have been answered correctly and completely.

Reviewer #2: The authors have properly addressed the comments, except for one. The death percentages in Table 2 appear to be incorrect. For instance, the percentage of deaths for low-grade females is listed as 159%. Other percentages in this table also seem incorrect, such as 1238 out of 6032 being reported as 74.98%. Please re-check the numbers in this table.

Reviewer #3: - While the study uses advanced statistical techniques, it does not report p-values for the individual components and comparisons within the semiparametric method. Calculating a single p-value for the entire extrapolation process is challenging due to the complexity and multi-step nature of the method. However, it is recommended to report p-values for individual components, such as the comparison of survival rates and covariates using Cox proportional hazards models, to provide a clearer understanding of the statistical significance of the findings.

- The method assumes homogeneity in the patient data concerning the variables incorporated within the model. Variability in treatment, access to healthcare, and other patient-specific factors may not be comprehensively considered and ought to be recognized as a limitation.

- Extending the survival curve month by month involves extrapolating beyond the observed data, introducing uncertainties. This approach assumes that the trends observed in the initial months will continue, which might not always be the case. Including sensitivity analyses or validating the model with external data could strengthen the findings.

- The reference group created using the Monte Carlo method based on life tables may not fully capture all variables affecting the general population's survival, such as comorbidities or socio-economic factors. This could lead to biased comparisons and should be discussed as a potential limitation.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

PLoS One. 2025 Jun 4;20(6):e0323803. doi: 10.1371/journal.pone.0323803.r005

Author response to Decision Letter 2

17 Mar 2025

Reviewer #1:

Dear Author

Thank you for your revision. The items requested by me from the authors have been answered correctly and completely.

Answer:

We sincerely appreciate your time and effort in reviewing our manuscript. We are grateful for your positive feedback and for acknowledging that we have adequately addressed your previous comments. As per your assessment, no additional revisions were required. Nonetheless, we have carefully re-reviewed the entire manuscript to ensure clarity, accuracy, and completeness, and we have made further refinements based on feedback from the other reviewers to enhance the overall quality of our work.

Thank you again for your constructive review and support.

Reviewer #2:

The authors have properly addressed the comments, except for one. The death percentages in Table 2 appear to be incorrect. For instance, the percentage of deaths for low-grade females is listed as 159%. Other percentages in this table also seem incorrect, such as 1238 out of 6032 being reported as 74.98%. Please re-check the numbers in this table.

Answer:

We appreciate your careful review and for highlighting the discrepancies in Table 2. Upon re-examining our calculations, we discovered that errors arose from a misalignment in data extraction and an incorrect denominator for percentage calculations. We have thoroughly revised Table 2 (pages 12–13, revised Table 2) so that all reported values accurately reflect the proportion of deaths in each cohort. The corrected percentages now fall within expected ranges. Additionally, we reviewed all other tables and data sources to ensure consistency and accuracy throughout the manuscript.

Thank you again for your constructive feedback, which has significantly improved the precision of our study.

Reviewer #3:

1. While the study uses advanced statistical techniques, it does not report p-values for the individual components and comparisons within the semiparametric method. Calculating a single p-value for the entire extrapolation process is challenging due to the complexity and multi-step nature of the method. However, it is recommended to report p-values for individual components, such as the comparison of survival rates and covariates using Cox proportional hazards models, to provide a clearer understanding of the statistical significance of the findings.

Answer:

Thank you for your valuable feedback. In response, we have performed a multivariable Cox regression analysis including sex, age, pathological grade, and cancer stage (pages 8, lines 119–123). This analysis provides hazard ratios with 95% confidence intervals and p-values for each variable—for example, male sex is associated with a lower risk of death (HR 0.90, 95% CI 0.87–0.94, p < 0.01), while increasing age, high-grade tumors, and advanced-stage disease significantly elevate mortality risk (all p < 0.01). These results are detailed in the newly added Supplement Table S5 and summarized in the Results section (pages 16, lines 226–232, and new S2 Table). We believe this additional analysis enhances the statistical rigor and clarity of our findings.

2. The method assumes homogeneity in the patient data concerning the variables incorporated within the model. Variability in treatment, access to healthcare, and other patient-specific factors may not be comprehensively considered and ought to be recognized as a limitation.

Answer:

Thank you for this important comment. We acknowledge that our model assumes patient homogeneity for the included variables. In reality, variations in treatment, healthcare access, and other patient-specific factors may introduce residual confounding. We have revised the Limitations section to include the following statement (page 29, lines 458–460):

"Moreover, our model assumes patient homogeneity for the included variables. In reality, variations in treatment, healthcare access, and other factors may introduce residual confounding."

We believe this revision clearly recognizes the inherent heterogeneity in the patient population and enhances the transparency of our study.

3. Extending the survival curve month by month involves extrapolating beyond the observed data, introducing uncertainties. This approach assumes that the trends observed in the initial months will continue, which might not always be the case. Including sensitivity analyses or validating the model with external data could strengthen the findings.

Answer:

Thank you for your insightful comment. In response, we have inserted a sentence in the Methods section (page 8, lines 117–119; page 16, lines 221–226) describing a sensitivity analysis in which we compared life expectancy estimates extrapolated from the first 6 years of follow-up with 12-year Kaplan–Meier estimates (new Supplement S1 Table). The analysis revealed minimal relative bias, thereby supporting the robustness of our extrapolation approach despite inherent uncertainties. We hope this revision adequately addresses your concern.

4. The reference group created using the Monte Carlo method based on life tables may not fully capture all variables affecting the general population's survival, such as comorbidities or socio-economic factors. This could lead to biased comparisons and should be discussed as a potential limitation.

Answer:

Thank you for your valuable comment regarding our reference group. We recognize that using a Monte Carlo simulation based on life tables may not capture key factors—such as comorbidities and socioeconomic status—that affect the general population’s survival, potentially biasing comparisons. In response, we have revised the Limitations section (page 29, lines 457–458) to include the following statement:

"Additionally, the Monte Carlo-based reference group may not capture key factors (e.g., comorbidities, socioeconomic status), potentially biasing comparisons."

We believe this addition enhances the transparency of our study by clearly acknowledging this limitation.

Attachment

Submitted filename: Reviewer response.docx

pone.0323803.s009.docx^{(18.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0323803.r006

Decision Letter 2

Mazyar Zahir

15 Apr 2025

<p>Gender disparities in bladder cancer: A population-based study on life expectancy and health spending in Asia

PONE-D-24-48193R2

Dear Dr. Ou,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Mazyar Zahir, MD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: (No Response)

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: (No Response)

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: (No Response)

Reviewer #3: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: (No Response)

Reviewer #3: (No Response)

**********

6. Review Comments to the Author

Reviewer #2: (No Response)

Reviewer #3: The items I previously requested from the authors have been addressed in a timely manner. I appreciate their responsiveness and the clarity of their answers.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #2: No

Reviewer #3: Yes: Nasrin Borumandnia

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PLoS One. doi: 10.1371/journal.pone.0323803.r007

Acceptance letter

Mazyar Zahir

PONE-D-24-48193R2

PLOS ONE

Dear Dr. Ou,

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.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Mazyar Zahir

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Text. The semiparametric survival extrapolation method used in this study involves three main steps.

(DOCX)

pone.0323803.s001.docx^{(14.9KB, docx)}

S1 Table. Validation of extrapolated life expectancy estimates in bladder cancer patients: comparison of 6-year follow-up extrapolations with 12-year Kaplan–Meier estimates.^a.

(DOCX)

pone.0323803.s002.docx^{(16.1KB, docx)}

S2 Table. Multivariable cox proportional hazards regression analysis of overall survival in bladder cancer patients.

(DOCX)

pone.0323803.s003.docx^{(16KB, docx)}

S1 Fig. Comparison of life expectancy (LE) loss estimates across multiple cohorts.

(DOCX)

pone.0323803.s004.docx^{(389.8KB, docx)}

S2 Fig. Stacked bar chart illustrates the distribution of NMIBC, MIBC across age groups for males and females.

(JPG)

pone.0323803.s005.jpg^{(60.8KB, jpg)}

S3 Fig. Long term survival after bladder cancer diagnosis, stratified by sex and stage.

(DOCX)

pone.0323803.s006.docx^{(49.2KB, docx)}

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Submitted filename: Response to Reviewers.docx

pone.0323803.s008.docx^{(25.8KB, docx)}

Attachment

Submitted filename: Reviewer response.docx

pone.0323803.s009.docx^{(18.4KB, docx)}

Data Availability Statement

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PERMALINK

Gender disparities in bladder cancer: A population-based study on life expectancy and health spending in Asia

Yi Hong Li

Yen-Chuan Ou

Min Che Tung

Yi Sheng Lin

Ya Chu Yang

Ying Ming Chiu

Chao Yu Hsu

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Materials

Statistical analysis

Results

Table 1. Estimation of life expectancy, loss of life expectancy, lifetime cost (US) and cost per life-year for bladder cancer, stratified by sex and age at diagnosis.

Fig 1. Life expectancy (LE) and expected loss of life expectancy for bladder cancer, stratified by sex and age at diagnosis.

Table 2. Estimation of life expectancy, loss of life expectancy, lifetime cost (USD) and cost per life year for bladder cancer, stratified by sex and pathologic grading.

Table 3. Estimation of life expectancy, loss of life expectancy, lifetime cost (USD) and cost per life year for bladder cancer, stratified by sex and cancer stage.

Table 4. Estimation of life expectancy, loss of life expectancy, lifetime cost (USD) and cost per life year for bladder cancer, stratified by sex and cancer stage.

Table 5. Number of bladder cancer patients receiving long-term dialysis, stratified by sex, age at diagnosis, and proportion of dialysis costs during follow-up.

Fig 2. The cumulative proportion of hemodialysis initiation in bladder cancer patients over time, stratified by sex and stage.

Discussion

Cancer stage: non-muscle-invasive bladder cancer

Cancer stage: muscle-invasive bladder cancer

Cost per life-year

Influence of hemodialysis

Clinical implications for high-risk populations

Limitations

Conclusions

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Decision Letter 0

Mazyar Zahir

Roles

Author response to Decision Letter 1

Decision Letter 1

Mazyar Zahir

Roles

Author response to Decision Letter 2

Decision Letter 2

Mazyar Zahir

Roles

Acceptance letter

Mazyar Zahir

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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