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Abstract

Benign prostatic hyperplasia (BPH) is highly prevalent among older men, yet its population-level assessment often depends on physician diagnosis, which may vary by healthcare access and reporting behavior. In China, substantial urban-rural differences in healthcare utilization raise questions about whether reported differences in BPH reflect true variation or diagnostic patterns. This study therefore examined urban-rural differences in self-reported physician-diagnosed BPH and associated factors using nationally representative data. We conducted a cross-sectional analysis of 8455 men aged 45 years and older using data from Wave 4 (2018) of the China Health and Retirement Longitudinal Study (CHARLS). BPH status was defined based on self-reported physician diagnosis. Urban-rural differences in reported BPH were compared, and multivariable logistic regression models were used to examine factors associated with reported BPH diagnosis. Of 8455 participants, the overall proportion reported a physician diagnosis of BPH was 11.9%, with subgroup-specific proportions of 18.4% in urban men and 10.1% in rural men. After adjustment for sociodemographic, lifestyle, and health-related variables, rural residence was associated with lower odds of reporting BPH (aOR 0.61, 95% CI 0.51-0.72). Several associations differed by residence, including a positive association between moderate physical activity and reported BPH in urban men, and inverse associations for longer sleep duration and regular alcohol consumption in rural men (P < .05). Urban men were more likely than rural men to report a physician diagnosis of BPH. Multiple sociodemographic and lifestyle factors were statistically associated with reported BPH, with some variation by residence. Given the cross-sectional design and self-reported measures, these findings reflect patterns of reporting and diagnosis rather than confirmed differences in underlying disease prevalence. Longitudinal studies using validated clinical assessments are needed to further clarify these relationships.

Keywords

benign prostatic hyperplasia lower urinary tract symptoms urban-rural differences self-reported diagnosis aging population cross-sectional study CHARLS

Introduction

According to the Seventh National Population Census, China had 264.02 million adults aged ≥60 years in 2020, accounting for 18.70% of the total population and representing a 5.44 percentage-point increase since 2010.¹ The overall male-to-female ratio in 2020 was 105.07/100 females (≈ 1.05),² and the issues indicate a rapid acceleration in population aging, particularly among elderly men. Benign prostatic hyperplasia (BPH), often accompanied by lower urinary tract symptoms (LUTS), is one of the most common urologic conditions affecting aging men.³ The primary symptoms include dysuria, increased urinary frequency, urgency, nocturia, urinary hesitancy, weak stream, and overflow incontinence. Previous epidemiological studies have consistently reported a marked age-related rise in BPH prevalence, increasing from 2.9% among men aged 40 to 49 years to nearly 70% among those older than 80 years.⁴

Urban-rural disparities are widely recognized as an important source of variation in population health.⁵ Evidence from Nigeria, for example, suggests that urban residents have a higher likelihood of reporting asthma and allergic rhinitis than rural residents.⁶ In China, older adults similarly experience pronounced urban-rural differences in mental health, depressive symptoms, and lifestyle behaviors such as alcohol consumption.^7,8 China’s long-standing dual urban-rural structure has resulted in substantial differences in education, socioeconomic status, cultural environment, lifestyle patterns, living conditions, and access to healthcare resources. These imbalances may influence how chronic diseases—including BPH—are recognized, diagnosed, or reported across populations. Despite growing attention to aging-related conditions, few studies have specifically examined whether the prevalence of self-reported BPH differs between urban and rural older men in China. Differences in health awareness, healthcare utilization, and diagnostic opportunity between the 2 settings may contribute to variations in reported BPH, yet this topic has not been systematically explored.

Despite extensive research on BPH and aging, it remains unclear whether urban-rural differences exist in the reporting of physician-diagnosed BPH among older men in China, particularly within a nationally representative framework. The China Health and Retirement Longitudinal Study (CHARLS) offers a unique opportunity to address this gap. CHARLS is a nationally representative longitudinal survey of adults aged ≥45 years, covering 150 districts and 450 villages or urban communities across China, and including 17 708 respondents from 10 257 households in its baseline wave.⁹ The survey employs rigorous multistage stratified sampling and adheres to international Health and Retirement Study (HRS) standards to ensure data quality and representativeness.⁹ Using the 2018 CHARLS data, this study compares the prevalence of self-reported physician-diagnosed BPH between urban and rural elderly men in China. In addition, it examines sociodemographic and lifestyle factors associated with BPH within each residence subgroup. Given the cross-sectional nature of the data and reliance on self-report, the findings represent patterns of association rather than causal relationships or confirmed disease prevalence.

Methods

Ethics and Data Source

This study used publicly available data from the China Health and Retirement Longitudinal Study (CHARLS) Wave 4 (2018). Ethical approval for all CHARLS waves was granted by the Institutional Review Board of XXXX (IRB00001052-11015 for the main household survey; IRB00001052-11014 for biomarker collection).¹⁰ All analyses were conducted using de-identified, publicly accessible data obtained from the CHARLS project website.

Study Design and Participants

We conducted a cross-sectional analysis using CHARLS 2018 data. BPH status was identified using questionnaire item DA029, which asks: “Have you ever been diagnosed with a prostate illness such as prostate hyperplasia (excluding prostate cancer)?” Among 9301 male respondents, 846 had missing responses for DA029, leaving 8455 men for analysis. Of these, 1140 reported a physician diagnosis of BPH.

The variables included in the analysis were: residential location (urban or rural), age, education level, marital status, religious affiliation, life satisfaction, self-rated health satisfaction, sleep duration, chronic diseases status, ethnic minority status, alcohol consumption, and physical activity level. CHARLS survey modules have been validated and widely used in prior epidemiologic and aging studies.¹¹

Lifestyle Variables

Alcohol Consumption: categorized as regular (>1/month), occasional (<1/month), or never.

Sleep Duration: self-reported nightly hours, categorized as ≤5, 6 to 7, or ≥7 hours.

Physical Activity: categorized as none, light, moderate, or intensive based on the frequency of moderate activity.

Statistical Analysis

Analyses were conducted using R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were summarized as counts (percentages) and compared using χ² tests. Univariate and multivariate logistic regression models were fitted to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Statistical significance was defined as P < .05.

Because this study used all eligible male respondents from the nationally representative CHARLS 2018 wave, the analytic sample size was determined by the structure of the dataset rather than by an a priori calculation. To address sample adequacy, we conducted a post hoc assessment using the standard formula for estimating a single proportion. The assumed proportion (≈ 12%) was based on prior CHARLS-based national estimates reporting LUTS/BPH around 10.7% to 12.0%.^12,13 Assuming a proportion of 12% for self-reported physician-diagnosed BPH, a 95% confidence level (Z = 1.96), and a margin of error of 0.02, the minimum required sample size was ~1014. The final analytic sample (n = 8455) substantially exceeded this threshold, indicating that the study had sufficient precision for descriptive estimates and multivariable modeling.

Handling of Missing Data

Missingness in the primary outcome variable (DA029) and relevant covariates was extremely low (<0.5%). Therefore, complete-case analysis was used for the main models, excluding 32 observations with missing data. Given the minimal proportion of missingness, such exclusions are unlikely to introduce meaningful bias, and characteristics of excluded respondents did not differ materially from those included in the analysis. Because of the very low level of missing data, complex imputation was not required for the primary analysis.

Sensitivity Analyses and Model Diagnostics

Sensitivity Analyses: To evaluate robustness to alternative missing-data assumptions, we conducted multiple imputation by chained equations (20 imputations), which is within recommended ranges for low-level missingness.

Model Diagnostics: Multicollinearity was assessed using variance inflation factors (VIF). Model fit was evaluated using the Hosmer-Lemeshow goodness-of-fit test. The events-per-variable (EPV) was 43.7, exceeding the recommended minimum of 10 and confirming sufficient sample size for stable estimates (Supplementary File 1).

Interaction Tests: To examine whether associations between lifestyle factors and BPH varied by residence, interaction terms (residence × lifestyle variables) were added to the multivariate model. Most interaction terms were not statistically significant (P > .05), indicating broadly similar associations across urban and rural settings. Significant interactions were observed for “no alcohol consumption × rural residence” (β = .39, P = .013) and selected categories of life satisfaction (P = .024-.042), suggesting limited effect modification by residence.

Results

Participant Characteristics and Distribution of Self-Reported BPH Diagnosis

A total of 8455 male participants were included in the analysis, of whom 21.5% resided in urban areas and 78.5% in rural areas (Table 1). Across both settings, ~60% were aged 45 to 65 years, and 25.8% were aged 65 to 75 years. Most participants (73.4%) had completed junior high school or below.

Table 1.

Participants and Prevalence of BPH According to Urban and Rural Areas.

Variables	Urban (N = 1817)			Rural (N = 6638)
Variables	BPH	Normal	P value	BPH	Normal	P value
Age (y)			<.001			<.001
≤55	68	516		120	1850
56-65	89	497		205	1905
66-75	106	306		223	1518
≥76	70	147		120	618
Minorities			.447			.029
Han	334	1476		670	5901
Ethnic minorities	0	7		1	66
Education			.001			<.001
No formal education	6	53		60	769
Middle school and below	174	899		517	4616
High and vocational school	104	395		87	547
College degree and above	50	136		7	35
Marital status			.269			.895
Married	303	1356		595	5319
Separated divorced and widowed	31	118		65	562
Never married	0	9		11	86
Religious beliefs			.831			.042
Yes	16	78		64	434
No	318	1405		607	5533
Drinking			.065			<.001
Drink more than once a month	146	692		245	2808
Drink but less than once a month	49	152		71	481
None of these	139	639		355	2675
Life satisfaction			.298			<.001
Not at all satisfied	4	32		27	116
Not very satisfied	19	78		62	404
Somewhat satisfied	201	775		354	3048
Very satisfied	73	358		162	1654
Completely satisfied	15	80		22	290
Health satisfaction			<.001			<.001
Not at all satisfied	19	57		68	293
Not very satisfied	77	192		177	943
Somewhat satisfied	165	741		277	2643
Very satisfied	43	273		85	1393
Completely satisfied	8	60		20	240
Sleep duration (h)			.01			<.001
≤5	101	355		231	1611
5-7	109	452		157	1466
≥7	124	676		283	2890
Chronic disease			<.001			<.001
Yes	130	861		233	3514
No	204	622		438	2453
Physical activity			.035			.006
None	20	109		76	581
Light	140	612		203	1626
Moderate	122	447		144	1131
Intensive	52	315		248	2627

Educational attainment differed markedly by residence: 10.2% of urban men had a college degree or higher, compared with 0.6% in rural areas. Nearly 90% of participants in both groups were married, and more than 90% reported no religious affiliation. Patterns of alcohol consumption and satisfaction with life or health were broadly similar between urban and rural older men. Light physical activity was more common in urban participants (41%), whereas heavy physical activity was more frequent in rural participants (43%).

Overall, 11.9% of participants reported having been diagnosed with BPH by a physician. When stratified by residence, the proportion was 18.4% among urban men and 10.1% among rural men (Table 1).

Residence-specific comparisons showed that in urban men, BPH self-reported differed significantly by age, educational level, health satisfaction, sleep duration, chronic disease status, and physical activity (P < .05). In rural men, significant differences were also observed across ethnic minority status, religious affiliation, alcohol consumption, life satisfaction, and physical activity (P < .05).

Factors Associated With BPH in Elderly Men

After adjusting for sociodemographic, lifestyle, and health-related variables, rural residence remained associated with lower odds of self-reported BPH compared with urban residence (aOR 0.61, 95% CI 0.51-0.72; Table 2).

Table 2.

Determinants of Prostate Status in the Elderly by Binary Logistic Regression (n = 8455).

Variables	OR (95% CI)	P value
Residence
Urban	1
Rural	0.61 (0.51-0.72)	.001
Age (y)
≤55	1
56-65	1.53 (1.26-1.87)	.001
66-75	2.44 (2.00-2.98)	.001
≥76	3.52 (2.77-4.48)	.001
Education
No formal education	1
Middle school and below	1.83 (1.40-2.43)	.001
High and vocational school	2.87 (2.09-3.98)	.001
College degree and above	3.85 (2.50-5.92)	.001
Drinking
Drink more than once a month	1
Drink but less than once a month	1.49 (1.18-1.87)	.001
None of these	1.11 (0.95-1.29)	.182
Life satisfaction
Not at all satisfied	1
Not very satisfied	0.89 (0.56-1.45)	.638
Somewhat satisfied	0.89 (0.59-1.39)	.610
Very satisfied	0.95 (0.61-1.50)	.812
Completely satisfied	0.82 (0.47-1.43)	.477
Health satisfaction
Not at all satisfied	1
Not very satisfied	0.95 (0.72-1.28)	.749
Somewhat satisfied	0.57 (0.43-0.75)	.001
Very satisfied	0.37 (0.26-0.51)	.001
Completely satisfied	0.56 (0.34-0.90)	.020
Sleep duration (h)
≤5	1
5-7	0.87 (0.72-1.04)	.132
≥7	0.77 (0.66-0.91)	.002
Chronic disease
No	1
Yes	0.47 (0.41-0.54)	.001
Physical activity
None	1
Light	1.14 (0.89-1.48)	.315
Moderate	1.38 (1.05-1.81)	.021
Intensive	1.17 (0.90-1.53)	.260

Note. OR (95% CI)—fully adjusted model.

Several variables showed significant associations with BPH (P values ranging from <.001 to .021):

Age: BPH odds increased progressively with age:

56 to 65 years (aOR 1.53, 95% CI 1.26-1.87).

66 to 75 years (aOR 2.44, 95% CI 2.00-2.98).

≥76 years (aOR 3.52, 95% CI 2.77-4.48).

Education: Compared with no formal education, higher education levels were associated with progressively higher odds of BPH:

Middle school or below (aOR 1.83).

High school/vocational education (aOR 2.87).

College or above (aOR 3.85).

Alcohol Consumption: Occasional drinkers (<1/month) had higher odds of BPH than regular drinkers (>1/month; aOR 1.49, 95% CI 1.18-1.87).

Health Satisfaction: Greater health satisfaction was inversely associated with BPH; individuals somewhat, very, or completely satisfied had significantly lower odds (aOR range 0.37-0.57).

Sleep Duration: Sleeping ≥7 hours was associated with lower odds of BPH compared with ≤5 hours (aOR 0.77, 95% CI 0.66-0.91).

Chronic Disease: Individuals with chronic diseases were less likely to report BPH (aOR 0.47, 95% CI 0.41-0.54).

Physical Activity: Moderate physical activity was positively associated with BPH (aOR 1.38, 95% CI 1.05-1.81).

Among lifestyle factors, occasional drinking and moderate physical activity were the strongest positive correlates of BPH, whereas sleep duration ≥7 hours showed the strongest inverse association.

Urban-Rural Differences in Associated Factors

Stratified analyses revealed both shared and setting-specific patterns (Table 3).

Table 3.

Determinants and Their Effects on the Prostate Among Elderly Men in Urban and Rural Areas by Logistic Regression.

Variables	Urban		Rural
Variables	OR	95% CI	OR	95% CI
Age (y)
≤55	1		1
56-65	1.41	0.99-2.01	1.58**	1.25-2.02
66-75	3.03**	2.11-4.36	2.24**	1.76-2.85
≥76	4.40**	2.87-6.77	3.25**	2.43-4.36
Education
No formal education	1		1
Middle school and below	2.20	0.97-5.95	1.82**	1.37-2.45
High and vocational school	3.37*	1.45-9.26	2.87**	1.99-4.18
College degree and above	4.52*	1.87-12.75	3.05*	1.16-7.12
Drinking
Drink more than once a month	1		1
Drink but less than once a month	1.24	0.84-1.83	1.62*	1.20-2.15
None of these	0.83	0.62-1.10	1.25*	1.05-1.50
Life satisfaction
Not at all satisfied	1		1
Not very satisfied	1.45	0.50-4.87	0.79	0.47-1.35
Somewhat satisfied	1.53	0.59-4.76	0.78	0.49-1.27
Very satisfied	1.44	0.54-4.62	0.86	0.53-1.44
Completely satisfied	1.53	0.49-5.44	0.68	0.36-1.31
Health satisfaction
Not at all satisfied	1		1
Not very satisfied	1.19	0.65-2.22	0.91	0.66-1.26
Somewhat satisfied	0.66	0.37-1.21	0.54**	0.40-0.75
Very satisfied	0.49*	0.25-0.98	0.33**	0.23-0.49
Completely satisfied	0.57	0.21-1.46	0.57	0.32-0.99
Sleep duration (h)
≤5	1		1
5-7	0.97	0.70-1.35	0.81	0.64-1.01
≥7	0.75	0.55-1.03	0.78*	0.64-0.94
Chronic disease
No	1		1
Yes	0.52**	0.40-0.68	0.45**	0.38-0.53
Physical activity
None	1		1
Light	1.32	0.77-2.36	1.10	0.82-1.48
Moderate	1.81*	1.04-3.28	1.21	0.89-1.66
Intensive	1.47	0.80-2.79	1.09	0.81-1.47

P <.05. **P < .001.

Urban Men

Moderate physical activity had the strongest association with BPH (aOR 1.81, 95% CI 1.04-3.28).

Higher age and higher education remained consistently associated with greater odds.

High health satisfaction was associated with lower odds (aOR 0.49).

Rural Men

Sleep duration ≥7 hours was inversely associated with BPH (aOR 0.78, 95% CI 0.64-0.94).

Compared with regular drinkers, occasional (aOR 1.62) and never-drinkers (aOR 1.25) had higher odds.

Age and education showed patterns similar to urban men.

Health Satisfaction Remained Inversely Associated

Overall, the dominant lifestyle correlates differed by residence: moderate physical activity in urban men versus alcohol consumption patterns and sleep duration in rural men.

Sensitivity Analyses and Model Diagnostics

Sensitivity Analyses: Multiple imputation (20 imputations) yielded effect estimates that were directionally consistent with complete-case results, with all changes <5%, indicating stable findings (Supplementary File 2).

Multicollinearity: VIF values ranged from 1.03 to 1.83, indicating no meaningful multicollinearity.

Model Fit: Hosmer-Lemeshow test showed good calibration (χ² = 6.586, P = .5819).

Sample Adequacy: EPV for the main model was 43.7, exceeding recommended thresholds for logistic regression (Supplementary File 1).

Discussion

Building on these findings, this study highlights how urban-rural context may shape patterns of BPH diagnosis and reporting among older men. Several sociodemographic and lifestyle variables showed statistically significant associations with reported BPH diagnosis, and some patterns differed between urban and rural settings. Because BPH status and covariates were self-reported and cross-sectional, the observed associations should be interpreted as patterns of correlation rather than evidence of causal relationships or true differences in biological disease burden.

Interpretation of Urban-Rural Differences

A consistently higher proportion of urban men reported a physician diagnosis of BPH. After multivariable adjustment, rural residence remained associated with lower odds of reporting BPH. Given the large, well-documented disparities in healthcare access, diagnostic opportunities, health literacy, and routine health examinations between urban and rural residents in China,¹⁴ these patterns likely reflect differences in diagnosis and detection, rather than underlying biological differences. Prior studies have similarly shown that urban adults have higher rates of outpatient visits, specialist consultations, and chronic disease detection than rural adults, which may elevate the likelihood of receiving a formal diagnosis. Therefore, the findings should be understood primarily as differences in diagnosed BPH rather than differences in clinical prevalence.

Sociodemographic Correlates

Age

The strong positive association between age and reported BPH diagnosis aligns with previous epidemiologic studies showing that urinary symptoms and clinically detected prostatic enlargement become more common with advancing age.¹⁵ Although our data cannot establish temporal sequencing, the association is consistent with well-known age-related changes in prostate physiology.

Education

Higher educational attainment was associated with greater odds of reporting BPH in both urban and rural settings. Previous research suggests that individuals with higher education often have greater health awareness, more frequent healthcare utilization, and a lower threshold for seeking medical care for urinary symptoms.^16,17 Conversely, men with lower education may under-recognize symptoms, delay care, or face financial and structural barriers to diagnosis. Thus, the observed association may reflect differential awareness, help-seeking, and diagnostic access, rather than socioeconomic status exerting a biological effect.

Health Satisfaction

Lower self-rated health satisfaction was associated with higher odds of reporting BPH, consistent with studies showing a relationship between urinary symptoms and poorer subjective well-being or psychological distress.¹⁸ Because both measures were self-reported, shared method effects and overall health perception may partly explain this association.

Chronic Disease and Reporting Patterns

Interestingly, the presence of any chronic disease was inversely associated with reported BPH. This contrasts with literature linking specific chronic conditions—such as metabolic syndrome, diabetes, cardiovascular disease, chronic obstructive pulmonary disease (COPD), and non-alcoholic fatty liver disease—to urologic symptoms or prostate enlargement.^19
-23 Several explanations are plausible:

Competing Health Priorities: Men with multimorbidity may prioritize management of more symptomatic or serious illnesses, potentially underreporting mild urinary symptoms.

Symptom Misattribution: Older adults may interpret LUTS as part of normal aging rather than a condition requiring clinical attention.^24,25

Healthcare Pathway Bias: Patients already engaged in treatment for major chronic conditions may have fewer opportunities or incentives to seek urologic evaluation.

Residual Confounding: Factors unavailable in CHARLS, such as body mass index (BMI), income, or health insurance type, may influence both chronic disease burden and likelihood of receiving a BPH diagnosis.

Because the chronic disease measure was broad and not disease-specific, this inverse association should be interpreted cautiously and warrants further study using objective clinical measures.

Lifestyle Factors: Alcohol, Sleep, and Physical Activity

Alcohol Consumption

Regular alcohol consumption showed an inverse association with reported BPH, consistent with evidence suggesting that moderate, routine alcohol intake may correlate with lower odds of diagnosed BPH in some populations.^26,27 However, this association was not present in urban men and may differ according to drinking patterns, social context, and diagnostic behavior. Rural older men in China have been reported to drink more consistently and in higher quantities,²⁸ which may partly explain the rural-specific association.

Sleep Duration

Longer sleep duration was associated with lower odds of reporting BPH. Prior research has proposed potential links involving circadian hormonal regulation, autonomic nervous system activity, and symptom severity.^29
-31 Although mechanistic pathways cannot be inferred here, insufficient sleep may correlate with poorer overall health status or stress, which could shape symptom perception and reporting. Sleep patterns also vary across urban-rural settings due to differences in workload, economic conditions, and living environment.³²

Physical Activity

Moderate physical activity was positively associated with reported BPH in urban men. This finding differs from earlier studies reporting either null or inverse associations with vigorous activity.^33
-36 One interpretation is that “moderate physical activity” in urban settings may capture health-seeking individuals who are more engaged with preventive care and may undergo more frequent diagnosis. Conversely, in rural areas, moderate or heavy physical activity may reflect labor-related activity not associated with healthcare utilization. These findings underscore the contextual nature of self-reported physical activity and suggest possible diagnostic or reporting differences rather than biological effects.

Roles of Additional Covariates

Marital status, religion, and life satisfaction were included as contextual variables because they may influence health behaviors, support networks, and healthcare utilization.^37
-40 However, none were significantly associated with reported BPH after adjustment, suggesting that any effects—if present—may be indirect or mediated through factors such as education, health status, or socioeconomic conditions.

Methodological Considerations, Sensitivity Analyses, and Robustness

Sensitivity analyses using multiple imputation yielded findings consistent with complete-case models, and model diagnostics (VIF, Hosmer-Lemeshow, EPV) suggested adequate specification, no multicollinearity, and sufficient statistical power. These results support the overall robustness of the observed associations. However, because both exposure and outcome variables were self-reported and measured concurrently, reverse causation and shared reporting biases remain possible explanations for several associations.

Measurement Validity and Implications

BPH status was based on self-reported physician diagnosis, without clinical verification through prostate volume measurement, prostate-specific antigen (PSA) testing, or International Prostate Symptom Score (IPSS) scoring. Studies in China indicate that self-reported chronic diseases have moderate agreement with clinical assessments but may underestimate true prevalence.^41
-43 Recent CHARLS-based analyses using the same BPH definition have reported comparable patterns.^44,45 However, because both BPH diagnosis and lifestyle behaviors were self-reported, recall bias, social desirability bias, and differential awareness between urban and rural residents may influence the findings.¹³ These limitations highlight the need for future studies incorporating objective diagnostic measures.

Limitations and Future Directions

This analysis is subject to several limitations. First, all key variables—including BPH diagnosis—were self-reported, potentially introducing misclassification and under-detection, especially in rural areas. Second, the cross-sectional design precludes temporal inference; the observed associations may reflect reverse causation or diagnostic patterns rather than underlying physiological relationships. Third, important confounders such as BMI, income, and healthcare access indicators were not available. Future longitudinal studies incorporating clinical BPH measures and detailed health-system variables are needed to clarify these associations.

Conclusion

In this nationally representative sample of Chinese adults aged 45 years and older, urban men reported a higher proportion of physician-diagnosed BPH than their rural counterparts. Several sociodemographic, lifestyle, and health-related factors showed statistically significant associations with reported BPH status, with some patterns differing by residence. Because both the outcome and exposures were self-reported and assessed cross-sectionally, these findings reflect observed associations rather than estimates of underlying disease burden or causal effects. Future studies incorporating objective clinical assessments and longitudinal follow-up are needed to validate these patterns and to better understand the contextual, behavioral, and healthcare-related factors that may influence BPH diagnosis in different populations.

Supplemental Material

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Supplemental material, sj-xlsx-4-inq-10.1177_00469580261422684 for Urban-Rural Differences in Self-Reported Physician-Diagnosed BPH and Associated Factors Among Older Men: A Cross-Sectional Analysis of a National Survey by Qingyang Meng, Chao Lv, Xinze Xia, Zexuan Lv, Wenhui Lai, Yangyang Wu, Kaikai Lv, Shuai Huang, Zhenjun Luo, Xiaowei Hao, Tao Song, Mingyue Zhao and Qing Yuan in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

We acknowledge the China Health and Retirement Longitudinal Study (CHARLS) team and all participants for their contributions to the survey data used in this study.

ORCID iD

Chao Lv

Ethical Considerations

This study used data from the China Health and Retirement Longitudinal Study (CHARLS). Ethical approval for CHARLS was obtained from the Institutional Review Board of Peking University (IRB00001052-11015 and IRB00001052-11014).

Author Contributions

Qing Yuan and Mingyue Zhao designed and supervised the study. Qingyang Meng and Chao Lv contributed equally to data analysis and manuscript drafting. The remaining authors assisted with data collection, statistical analysis, and critical revision. All authors approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Key Research and Development Program of China (grant nos 2023YFC3605305, 2022YFC3602900, and 2022YFC3602905). But they had no role in the design and conduct of the study, and decision to this manuscript writing and submission.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All data were stored in the National Institute of Development, Peking University, China, and can be accessed on the CHARLS project website ().

Supplemental Material

Supplemental material for this article is available online.

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