Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Analysis of the prevalence of and factors associated with overactive bladder in adult Korean women

  • So Young Kim ,

    Contributed equally to this work with: So Young Kim, Woojin Bang

    Roles Writing – original draft, Writing – review & editing

    Affiliation Department of Otorhinolaryngology-Head & Neck Surgery, CHA Bundang Medical Center, CHA University, Seongnam, Korea

  • Woojin Bang ,

    Contributed equally to this work with: So Young Kim, Woojin Bang

    Roles Formal analysis

    Affiliation Department of Urology, Hallym University College of Medicine, Seoul, Korea

  • Hyo Geun Choi

    Roles Conceptualization, Formal analysis, Funding acquisition, Writing – review & editing

    pupen@naver.com

    Affiliation Department of Otorhinolaryngology-Head & Neck Surgery, Hallym University College of Medicine, Seoul, Korea

Analysis of the prevalence of and factors associated with overactive bladder in adult Korean women

  • So Young Kim, 
  • Woojin Bang, 
  • Hyo Geun Choi
PLOS
x

Abstract

Background

Overactive bladder (OAB) is one of the most prevalent lower urinary tract conditions and has been suggested to be related to various factors. We assessed the prevalence of and factors associated with OAB in women based on a large cross-sectional, population-based study of adult Korean women.

Methods

The Korean community health survey (KCHS) of 2012 was reviewed, and 107,950 female participants aged 19 to 107 years were identified for inclusion in this study. The overactive bladder symptom score (OABSS) was used to define and classify OAB as mild, moderate, or severe. Numerous variables, including marital status; physical activity; education and income levels; type of occupation; body mass index (BMI); smoking; alcohol; sleep time; and medical history of hypertension, diabetes mellitus, hyperlipidemia, or cerebral stroke, were evaluated. The correlation of these variables with the prevalence of OAB was analyzed using simple and multiple logistic regression analyses with complex sampling.

Results

The results showed that 5.2% of adult women experienced OAB. Multiple regression analyses showed a significant correlation between the following variables and OAB: older age (adjusted odds ratio [AOR] = 1.44, 95% confidence interval [CI] = 1.39–1.50, P < 0.001 as 10 years older); married status (AOR = 0.83, 95%CI = 0.70–0.96, P = 0.016); lower level of income (AOR = 1.50, 95%CI = 1.34–1.68, P < 0.001); high BMI (AOR = 1.33, 95%CI = 1.23–1.44, P < 0.001); smoking (AOR = 1.24, 95%CI = 1.04–1.47, P < 0.001); long sleep time (AOR = 1.95, 95%CI = 1.69–2.26); and medical history of hypertension (AOR = 1.11, 95%CI = 1.03–1.21, P = 0.011), diabetes mellitus (AOR = 1.38, 95%CI = 1.25–1.53, P < 0.001), hyperlipidemia (AOR = 1.27, 95%CI = 1.16–1.39, P < 0.001), and cerebral stroke (AOR = 2.04, 95%CI = 1.73–2.41, P < 0.001). The level of stress showed a dose-dependent association with OAB (AOR [95%CI] = 3.28 [2.81–3.83] > 2.11 [1.91–2.33] >1.28 [1.16–1.41] for severe > moderate > some stress, respectively, P < 0.001).

Conclusion

The prevalence of OAB was approximately 5.2% among adult Korean women. Older age; high BMI; stress level; sleep duration; levels of income and education; marital status; smoking; and medical history of hypertension, diabetes mellitus, hyperlipidemia, and cerebral stroke were significantly related to OAB in women.

Introduction

Overactive bladder (OAB) is a condition characterized by lower urinary tract symptoms (LUTS), including an urgency of urination, regardless of accompanied urinary incontinence, frequency, and nocturia [1]. This condition is disruptive, compromises quality of life, and incurs a large economic burden [2,3].

The pathophysiological mechanism of OAB is complicated and still elusive. In both genders, bladder dysfunction and several urinary neurotrophins are the typical urodynamic finding of OAB [4]. Voiding is controlled by complex central and peripheral neural systems. Thus, numerous neurogenic or myogenic etiologic factors may influence overactive bladder [5,6]. Urethral hypersensation may also influence OAB, and several gender-specific differences related to OAB have been suggested. Innate or acquired anatomical and physiological differences in the lower urinary tract may explain gender differences in OAB [7]. The structure of the lower urinary tract is markedly different between women and men. For example, the external urethral sphincter is poorly developed in women. The expression and function of neurotransmitter receptors in the bladder and urethra and micturition patterns also differ between genders [8]. Many women experience childbirth, a series of processes including pregnancy, delivery through the vaginal canal, or caesarean section. In addition, acquired coping strategies for the LUTS are different between women and men [7]. The effects of stress on OAB were suggested to be more common in women [9]. Moreover, fluctuations of ovarian hormones should be considered when discussing OAB in women. Due to these multifactorial origins, therapeutics for OAB are unsatisfactory, with a low treatment outcome and considerable side effects [10].

The overall OAB prevalence has been estimated to be approximately 10–16.6% and is similar in both genders [1114]. The prevalence of OAB is generally considered comparable between males and females less than 40 years of age and then increases in both genders with increasing age [13,15]. Recently, we reported the prevalence and associated factors of OAB in Korean men [16]. However, gender differences exist in the specific symptom clusters and the clinical impacts of OAB [8]. Approximately half of OAB patients complain of multiple symptoms [12]. In women, storage LUTS are more prevalent than voiding and postmicturition symptoms [7]. For instance, women complain more of urge incontinence [12,14]. Some studies even estimated that OAB symptoms are significantly higher in women, especially OAB with urge incontinence (OAB wet) [17]. Stress urinary incontinence is the most common type in women, while other types of urinary incontinence are more common in men [12]. Furthermore, while resultant OAB symptoms may be similar, the underlying mechanisms of OAB were suggested to be diverse according to gender [13]. Pregnancy, vaginal delivery, body mass index (BMI), diabetes, cognitive impairment, and neurological disorders are predisposing factors for urinary incontinence in women [7]. However, few studies have been conducted to comprehensively review the numerous physical, psychological, and socio-economic variables related to OAB.

The present study was designed to estimate the prevalence of and factors associated with OAB in adult women over a wide range of ages. Using a large population-based study group, the reliability and statistical power were maximized. Furthermore, because multiple pathophysiological factors are involved, we considered numerous variables to investigate the factors associated with OAB.

Materials and methods

Study population and data collection

This study was approved by the Institutional Review Board of Korea Centers for Disease Control and Prevention (IRB No. 2012-07CON-01-2C). Written informed consent was obtained from all the participants prior to the survey.

This study was a cross-sectional study that utilized the data from the Korean Community Health Survey (KCHS), which was conducted in 2012. The data were collected by the Centers for Disease Control and Prevention of Korea. The survey gathered information through face-to-face, paper-assisted personal interviews between trained interviewers and respondents. The sample size of the KCHS was 900 subjects in each of 253 community units, including 16 metropolitan cities and provinces. Detailed description including total population and sampling household and participants were reported in KCHS website [16]. The KCHS used a two-stage sampling process. A sample area (tong/ban/ri) was selected in the first stage as a primary sample unit according to the number of households in the area using a probability proportional to the sampling method. In the second stage, the number of households in the selected sample tong/ban/ri was identified to create a household directory. Sample households were selected using systematic sampling methods. This process was used to ensure that the sample units were representative of the entire population [18]. For the sample to be statistically representative of the population, the data collected from the survey were weighted by statisticians based on the sample design (S1 file) [19].

Survey

A question related to marital status including common-law marriage was included in the survey. To measure physical activity, participants were asked for the number of days spent during the most recent week walking more than 10 minutes. Educational level was divided into 3 groups as follows: uneducated participants and participants who had graduated only from elementary or middle schools were assigned to the “low” education group; graduates of high school comprised the “middle” education group; and junior college graduates, college graduates, and participants in graduate school formed the “high” education group. Occupation was classified into 5 groups according to physical activity level as follows: manager, expert, specialist, clerk; service worker, salesperson; technician, mechanic, production worker, engineer; farmer, fisher, laborer, soldier; unemployed; and student [20]. Participants under 110 cm or 30 kg were excluded from this study. Using criteria for the Asia-Pacific region [21], three BMI (kg/m2) groups were generated as follows: low BMI, < 18.5; normal BMI, 18.5–25; and high BMI, ≥ 25. Using the methods recommended by the Organization for Economic Cooperation and Development [22] (i.e., dividing household income by the square root of the number of household members), monthly income was divided by the square root of the number of household members and categorized into 150 lowest (0–840), low-middle (848–1,717), upper-middle (1,732–2,683), and highest (2,687–151 24,000) quartiles. Smoking status was divided into 3 groups: non-smoker, past smoker, and current smoker. The past smokers who had quit smoking for less than 1 year were included in the current smoker group. Alcohol consumption was divided into the following three categories: none; ≤ 1 time a month; 2–4 times a month; and ≥ 5 times a month. Amount of sleep was divided into three groups as follows: ≤ 6 h per day, 7–8 h per day, and ≥ 9 h per day. Sleep hours were surveyed as one hour interval. Participants who slept less than 3 hours per night were excluded from this study. Patients were asked whether they usually felt no stress, some stress, moderate stress, or severe stress. The participants were asked about their histories of other comorbidities, such as hypertension, diabetes mellitus, hyperlipidemia, and cerebral stroke, and participants who reported a history of any of these diseases diagnosed by a medical doctor were recorded as positive.

The overactive bladder symptom score (OABSS), which was developed and validated in the Japanese population, was used in this study [23] (S1 Table). A score ≥ 2 for Question 3 “How often do you leak urine because you cannot defer the sudden desire to urinate?” and an OABSS total score ≥ 3 were defined as having an overactive bladder [24]. Overactive bladder was divided into 3 groups according to the following scores: mild, a total score ≤ 5; moderate, a total score of 6–11; severe, a total score ≥ 12.

Statistical analysis

The differences in the mean age and number of days walked/week between normal participants (controls) and overactive bladder participants were compared using linear regression analysis with complex sampling. The proportion differences in marriage, education level, occupation, income level, BMI group, smoking, alcohol consumption history, sleep hours, stress level, hypertension, diabetes mellitus, hyperlipidemia, and cerebral stroke history were compared using the chi-square test with Rao-Scott correction.

To identify associations between the related factors and overactive bladder, simple and multiple logistic regression analyses with complex sampling were used. The complex sampling weighting strategy is described in detail in S1 File. In multiple logistic regression, age, number of days walked/week, marriage, education level, occupation, income level, BMI group, smoking, alcohol consumption history, sleep hours, stress level, hypertension, diabetes mellitus, hyperlipidemia, and cerebral stroke were adjusted as cofounders. Two-tailed analyses were conducted, and P-values less than 0.05 were considered significant. The adjusted odds ratio (AOR) and 95% confidence interval (CI) for overactive bladder were calculated. All results are presented as weighted values. The results were statistically analyzed using SPSS ver. 21.0 (IBM, Armonk, NY, USA).

Results

Of a total of 126,023 female participants ranging from 19 to 107 years of age, we excluded the following participants from this study: participants who did not fill out the overactive bladder survey (366 participants); participants who did not indicate height, weight, or income record (17,149 participants); and participants who had incomplete data related to marital status, education level, occupation, smoking, alcohol consumption history, sleep hours, stress level, hypertension, diabetes mellitus, hyperlipidemia, and cerebral stroke (558 participants). Finally, 107,950 participants were included in this study (Fig 1).

thumbnail
Fig 1. A schematic illustration of participant selection in the present study.

Among a total of 126,023 participants, participants without a history of OAB (n = 366), without a BMI or income record (17,149), and with other incomplete data (558) were excluded. The data for the 107,950 participants from whom complete data were obtained were analyzed.

https://doi.org/10.1371/journal.pone.0185592.g001

Of the 107,950 participants, 6,814 (5.2%) complained of symptoms of OAB. The results showed that OAB increased with age, was lowest in participants aged 19–30 years (2.3%), and was highest in individuals aged 81+ years (25.3%). According to the severity of OAB, the proportions of patients with mild, moderate, and severe OAB were relatively comparable in the 19-30-year-old group through the 41-50-year-old group, and mild OAB was more frequent than moderate and severe OAB. However, moderate OAB steeply increased after 51 years of age (Fig 2, S2 Table). The average age of participants in the OAB group was 58.1 years, which was significantly higher than that of participants in the control group (45.1 years old) (P < 0.001) (Table 1). The OAB group showed significant differences compared with the control group with respect to number of days walked/week; marital status; education level; occupation; level of income; BMI; smoking and alcohol; sleep time; level of stress; and medical history of hypertension, diabetes mellitus, hyperlipidemia, and cerebral stroke (each variable, P < 0.001). All these factors were analyzed for an association with OAB.

thumbnail
Fig 2. The prevalence of OAB increased with age.

According to the severity of OAB, mild, moderate, and severe OAB were relatively comparable from 19–30 years of age through 41–50 years of age and steeply increased after 51 years of age.

https://doi.org/10.1371/journal.pone.0185592.g002

All the variables showed significant associations with OAB according to simple logistic regression analyses (all, P < 0.001) (Table 2). The prevalence of OAB was significantly increased in older participants (AOR = 1.44, 95% CI = 1.38–1.49, P < 0.001 as 10 years older). More number of days walked/week was significantly related to a decrease in OAB (AOR = 0.99, 95% CI = 0.98–1.00, P = 0.056). The high BMI group was significantly associated with OAB (AOR = 1.33, 95% CI = 1.23–1.44, P < 0.001), and smoking was also significantly related to OAB (AOR = 1.56, 95% CI = 1.28–1.91 for past smoker; AOR = 1.23, 95% CI = 1.03 − 1.46 for current smoker, P < 0.001). A medical history of hypertension (AOR = 1.12, 95%CI = 1.03–1.22, P = 0.009), diabetes mellitus (AOR = 1.38, 95% CI = 1.25–1.53, P < 0.001), hyperlipidemia (AOR = 1.28, 95% CI = 1.17–1.40, P < 0.001), and cerebral stroke (AOR = 2.03, 95% CI = 1.72–2.40, P < 0.001) significantly increased the prevalence of OAB. Compared with the unemployed and student groups, participants in all other types of occupations exhibited a significantly lower prevalence of OAB (P = 0.001). Long sleep time was significantly associated with OAB (AOR = 1.95, 95% CI = 1.69–2.25, P < 0.001).

thumbnail
Table 2. Odd ratios of possible risk factors for overactive bladder using simple and multiple logistic regression analysis with complex sampling.

https://doi.org/10.1371/journal.pone.0185592.t002

Level of stress was significantly associated with OAB in a dose-dependent manner (AOR [95% CI] = 3.31 [2.83–3.86] > 2.12 [1.92–2.35] >1.28 [1.16–1.41] for severe > moderate > some stress, respectively, P < 0.001). Subjects with a low education level showed an OAB prevalence that was 1.25-fold greater than that of participants with a high education level (95%CI = 1.09–1.43, P < 0.001). No significant difference was observed in the prevalence of OAB between individuals with middle and high education levels. The prevalence of OAB was significantly higher in the lowest income group (AOR = 1.49, 95% CI = 1.33–1.67) and low-middle income group (AOR = 1.13, 95% CI = 1.01–1.26) than the highest income group (P < 0.001).

Some variables showed different associations with OAB when analyzed using simple vs. multiple logistic regression analyses. Being underweight was significantly associated with OAB in the simple regression analysis (OR = 0.83, 95% CI = 0.72–0.94, P < 0.001). However, the statistical significance was not maintained in the multiple regression analysis. Alcohol consumption showed a negative association with OAB in the simple logistic regression analysis (P < 0.001), but no significant association existed in the multiple regression analysis. Although short sleep time showed a significant association with OAB in the simple logistic regression analysis (OR = 1.44, 95% CI = 1.34–1.54, P < 0.001), this variable was not significantly associated with OAB in the multiple regression analysis.

Marital status showed opposite results between the simple and multiple regression analyses. Married subjects had a significantly higher OAB prevalence according to the simple logistic regression analysis (OR = 2.69, 95% CI = 2.35–3.09, P < 0.001). However, they showed a significantly lower OAB prevalence after adjustment for other variables (AOR = 0.80, 95% CI = 0.67–0.94, P = 0.008).

Discussion

In the present study, 5.2% of adult women had OAB. This percentage is somewhat lower than that obtained in previous studies, which ranged from 10 to 16% in the ≥ 40 years population based on the self-reported questionnaire on frequency, urgency, and urge incontinence [13,14]. Because our study participants ranged from 19 to 107 years of age and the proportional random selection was conducted with complex sampling, a much younger population may have been included in the present study than in previous studies. In addition, we used the standardized OABSS questionnaire instead of a simple question to assess the presence of OAB symptoms. Several physical factors, such as advanced age; high BMI; and a medical history of hypertension, diabetes mellitus, hyperlipidemia, and cerebral stroke, were significantly associated with OAB according to the multiple logistic regression analyses. Additionally, socio-economic factors, including lower educational and income levels, occupational status of unemployed or student, and being unmarried, lifestyle characteristics of fewer number of days walked/week and smoking, and psychological factors of long sleep time and higher stress level were significantly associated with OAB in the multiple logistic regression analyses. Many of these associate factors of OAB including self-reported stress, obesity, lack of exercise, and lower socioeconomic status are all markers of poor tolerance of homeostatic challenges. These findings were in accordance with previous studies. However, by considering numerous variables, we identified some different results with other studies after adjusted confounders. For instance, marital status increased OAB in the simple logistic regression analysis, while it showed a significantly negative association with OAB after adjustment for other variables.

Aging processes accompany senescence and deregulation of bladder function by diminished muscular and neurological activities [5,6], especially in women who have experienced traumatic obstetric events, including childbirth, and changes in sex hormone levels, which are known to perturb micturition function through related receptors expressed in the lower urinary tract [25]. In this study, these changes during the menopausal period were represented by a marked increase in OAB after 51 years of age.

Obesity was suggested to be a factor that was significantly associated with OAB in several studies, including the present study [13,26]. This association between obesity and OAB can be explained by mechanical and neuroendocrine factors [27]. Adipose tissue can increase autonomic nervous activity, especially that of noradrenergic sympathetic nerves, via leptin production [28]. The resultant increased sympathetic activity causes urinary frequency.

These types of disturbances of the autonomic nervous system may also be related to OAB in the several medical comorbidities associated with obesity. Our results showed significant associations between OAB and a medical history of hypertension, diabetes mellitus, hyperlipidemia, and cerebral stroke. Similarly, previous studies also suggested that obesity-related comorbidities, including metabolic syndrome, diabetes mellitus, and obesity, were linked to OAB [2931]. In addition to the neural mechanisms, increased ischemia of the bladder in cardiovascular diseases can lead to the overactivity and structural changes of it [32].

In our study, stress showed significant dose-dependent relationships with OAB. Similarly, previous studies demonstrated that several psychological disorders, including anxiety, depression, and stress, are often accompanied by OAB [33,34]. These psychological associations can be explained by the fact that bladder function is regulated by various neural systems, including central nervous system pathways. [35]. A hormonal influence may be involved in OAB. [36,37]. Moreover, women are more likely to be distressed by frequency of urination than men [12]. They are more vulnerable to affective disorders, which may induce hormonal dysregulation. The significant association between OAB and long sleep time is presumed to be mediated by stress or other psychological factors that are related to inadequate sleep duration.

Fewer numbers of days walked/week was significantly associated with OAB. A sedentary posture may have detrimental effects on micturition, as suggested for sitting on the toilet and micturating without pelvic relaxation in women [7]. Immobility may result in a greater physical effort for micturition, which may increase the risk of incontinence [7]. In addition to these physical impacts of sedentary behavior, some psychological factors, such as anxiety and stress, may influence the prevalence of OAB in these groups [33,34].

In contrast to our results, a recent study reported that compared with unemployed women, working females were at a significantly higher risk of OAB [38]. They suggested that detrimental conditions in the work place, including poor hygiene, a dangerous job associated with accidents, uncomfortable posture, carrying heavy weights, and stress, may be attributed to an increase in OAB in working women [38]. However, we included students in the unemployed group and subdivided employed groups according to their job-related physical activities, while the previous study adopted a binary classification of employed versus unemployed. The prevalence of the OAB-related factors of sitting posture and stress was predicted to be high in the unemployed or student group due to exam preparation for university entrance or job searching. Furthermore, reverse causal relationships are possible between the unemployed and OAB. The symptoms of OAB were suggested to be associated with a decrease in work productivity and a higher unemployment proportion [3941]. Thus, physically compromising conditions, including OAB, could affect occupational status.

Similar to previous studies, a poor socio-economic status with lower education and income levels was related to OAB in this study [40,42]. Socioeconomic disparities in healthy behavior and health status may cause more OAB in lower socioeconomic conditions [43]. Inversely, it is also possible that being unemployed or having a physically compromised status because of OAB results in a low socio-economic status. Smoking was significantly related to OAB in the present study. Several previous studies also demonstrated the increased prevalence of OAB among smokers [4446]. Smoking may have detrimental effects on pelvic floor function, thereby inducing urinary incontinence [47]. Moreover, smoking was related to bladder overactivity through nicotinic acetylcholine receptor dysfunction in the peripheral and central nervous systems [48]. Moreover, an increase in the occurrence of OAB in women compared with men was associated with smoking [49]. The antagonistic effect of nicotine on estrogen in women may precipitate nicotine-associated bladder overactivity and incontinence [44,49].

Interestingly, unmarried status was significantly related to OAB in the present study. As mentioned previously, this finding was contradictory to the prediction from previous studies. Obstetrical events, such as pregnancy and vaginal delivery, are natural risk factors for urinary incontinence in women [7,50]. However, it was suggested that this increased vaginal delivery-related risk of OAB decreased over time following delivery [26]. Moreover, sex hormonal influence may be related to a decrease in OAB in married subjects. Micturition is known to be regulated by sex hormones based on evidence of the expression of estrogen-inducible progesterone in the female urethra [25]. Sex hormonal changes, such as those that occur during menopause, may be more common in unmarried individuals who have less exposure to sexual activity. Menopause was reported to occur earlier in unmarried, divorced, or widowed women [51].

The present study has several advantages over previous studies. We had a large population-based study group encompassing a broad age range. Additionally, the survey was conducted with a validated symptom score instrument. Not confined to a binary measure of the presence or absence of OAB symptoms, we investigated the degree of OAB symptoms. Information on a large number of potentially confounding lifestyle and medical factors was collected and considered. Importantly, we demonstrated the association of several female gender-specific factors with OAB. However, the present study was based on subjective self-reported OAB symptom scores, which lack objective measures for LUTS. The cross-sectional study design limited the interpretation of the results with respect to identifying causal relationships. A future study with a prospective study design will address the current limitations.

The prevalence of OAB among adult Korean women was approximately 6.3%. Numerous physical, psychological, and socio-economic factors were related to OAB. In accordance with previous studies, older age, high BMI, medical comorbidities, stress, smoking, and levels of income and education were positively related to OAB. In addition, inadequate and long sleep duration, types of occupations, especially unemployed or student status, and unmarried status were related to OAB.

Supporting information

S1 File. The analytic methods of weighting.

https://doi.org/10.1371/journal.pone.0185592.s001

(DOCX)

S1 Table. Overactive bladder symptom score.

https://doi.org/10.1371/journal.pone.0185592.s002

(DOCX)

S2 Table. Prevalence of OAB according to severity.

https://doi.org/10.1371/journal.pone.0185592.s003

(DOCX)

Acknowledgments

This work was supported in part by a research grant (NRF-2015-R1D1A1A01060860 and 2017R1C1B1007696) from the National Research Foundation (NRF) of Korea and a Research Grant funded by Hallym University Sacred Heart Hospital (HURF-2016-38).

References

  1. 1. Abrams P, Cardozo L, Fall M, Griffiths D, Rosier P, Ulmsten U, et al. The standardisation of terminology in lower urinary tract function: report from the standardisation sub-committee of the International Continence Society. Urology. 2003;61(1):37–49. pmid:12559262
  2. 2. Dmochowski RR, Newman DK. Impact of overactive bladder on women in the United States: results of a national survey. Curr Med Res Opin. 2007;23(1):65–76. pmid:17257467
  3. 3. Milsom I, Coyne KS, Nicholson S, Kvasz M, Chen CI, Wein AJ. Global prevalence and economic burden of urgency urinary incontinence: a systematic review. Eur Urol. 2014;65(1):79–95. pmid:24007713
  4. 4. Antunes-Lopes T, Carvalho-Barros S, Cruz CD, Cruz F, Martins-Silva C. Biomarkers in overactive bladder: a new objective and noninvasive tool? Adv Urol. 2011;2011:382431. pmid:21687625
  5. 5. de Groat WC. A neurologic basis for the overactive bladder. Urology. 1997;50(6A Suppl):36–52; discussion 3–6.
  6. 6. Kajioka S, Seki N, Shahab N, Yunoki T, Naito S. Etiology of overactive bladder and its therapeutic perspective—focusing on a myogenic basis for the overactive bladder. Fukuoka Igaku Zasshi. 2010;101(5):94–100. pmid:20845720
  7. 7. Bauer RM, Huebner W. Gender differences in bladder control: from babies to elderly. World J Urol. 2013;31(5):1081–5. pmid:23881351
  8. 8. Patra PB, Patra S. Sex differences in the physiology and pharmacology of the lower urinary tract. Curr Urol. 2013;6(4):179–88. pmid:24917740
  9. 9. Smith AL, Hantsoo L, Malykhina AP, File DW, Valentino R, Wein AJ, et al. Basal and stress-activated hypothalamic pituitary adrenal axis function in postmenopausal women with overactive bladder. Int Urogynecol J. 2016: doi: pmid:26942596
  10. 10. Brubaker L, Fanning K, Goldberg EL, Benner JS, Trocio JN, Bavendam T, et al. Predictors of discontinuing overactive bladder medications. BJU Int. 2010;105(9):1283–90. pmid:19912189
  11. 11. Irwin DE, Milsom I, Hunskaar S, Reilly K, Kopp Z, Herschorn S, et al. Population-based survey of urinary incontinence, overactive bladder, and other lower urinary tract symptoms in five countries: results of the EPIC study. Eur Urol. 2006;50(6):1306–14; discussion 14–5. pmid:17049716
  12. 12. Irwin DE, Abrams P, Milsom I, Kopp Z, Reilly K, Group ES. Understanding the elements of overactive bladder: questions raised by the EPIC study. BJU Int. 2008;101(11):1381–7. pmid:18336602
  13. 13. Stewart WF, Van Rooyen JB, Cundiff GW, Abrams P, Herzog AR, Corey R, et al. Prevalence and burden of overactive bladder in the United States. World J Urol. 2003;20(6):327–36. pmid:12811491
  14. 14. Milsom I, Abrams P, Cardozo L, Roberts RG, Thuroff J, Wein AJ. How widespread are the symptoms of an overactive bladder and how are they managed? A population-based prevalence study. BJU Int. 2001;87(9):760–6. pmid:11412210
  15. 15. Eapen RS, Radomski SB. Gender differences in overactive bladder. Can J Urol. 2016;23(1 Suppl 1):2–9.
  16. 16. Korea Centers for Disease Control and Prevention. The Community Health Survey 2013 [cited 2013 Oct 5]. Available from: http://chs.cdc.go.kr.
  17. 17. Kim SY, Bang W, Choi HG. Analysis of the prevalence and associated factors of overactive bladder in adult Korean men. PlosOne. 2017; 12(4):e0175641.
  18. 18. Rim H, Kim H, Lee K, Chang S, Hovell MF, Kim YT, et al. Validity of self-reported healthcare utilization data in the Community Health Survey in Korea. J Korean Med Sci. 2011;26(11):1409–14. pmid:22065895
  19. 19. Oh DH, Kim SA, Lee HY, Seo JY, Choi BY, Nam JH. Prevalence and correlates of depressive symptoms in korean adults: results of a 2009 korean community health survey. J Korean Med Sci. 2013;28(1):128–35. pmid:23341723
  20. 20. Korean standard occupation classification 2015 [cited 2014 November 20]. Available from: https://kssc.kostat.go.kr
  21. 21. Taskforce IO. The Asia-Pacific perspective: Redefining obesity and its treatment 2000 [cited 2014 November 20]. Available from: http://www.wpro.who.int/nutrition/documents/docs/Redefiningobesity.pdf.
  22. 22. Development OfEC-oa. What are equivalence scales? 2009 [cited 2014 November, 20]. Available from: http://www.oecd.org/eco/growth/OECD-Note-EquivalenceScales.pdf.
  23. 23. Homma Y, Yoshida M, Seki N, Yokoyama O, Kakizaki H, Gotoh M, et al. Symptom assessment tool for overactive bladder syndrome—overactive bladder symptom score. Urology. 2006;68(2):318–23. pmid:16904444
  24. 24. Jeong SJ, Homma Y, Oh SJ. Korean version of the overactive bladder symptom score questionnaire: translation and linguistic validation. Int Neurourol J. 2011;15: 135–142. pmid:22087422
  25. 25. Savolainen S, Santti R, Streng T, Gustafsson JA, Harkonen P, Makela S. Sex specific expression of progesterone receptor in mouse lower urinary tract. Mol Cell Endocrinol. 2005;230(1–2):17–21. pmid:15664447
  26. 26. Handa VL, Pierce CB, Munoz A, Blomquist JL. Longitudinal changes in overactive bladder and stress incontinence among parous women. Neurourol Urodyn. 2015;34(4):356–61. pmid:24633996
  27. 27. Dallosso HM, McGrother CW, Matthews RJ, Donaldson MM, Leicestershire MRCISG. The association of diet and other lifestyle factors with overactive bladder and stress incontinence: a longitudinal study in women. BJU Int. 2003;92(1):69–77. pmid:12823386
  28. 28. Shen J, Tanida M, Niijima A, Nagai K. In vivo effects of leptin on autonomic nerve activity and lipolysis in rats. Neurosci Lett. 2007;416(2):193–7. pmid:17306457
  29. 29. Uzun H, Yilmaz A, Kemik A, Zorba OU, Kalkan M. Association of insulin resistance with overactive bladder in female patients. Int Neurourol J. 2012;16(4):181–6. pmid:23346484
  30. 30. Hill SR, Fayyad AM, Jones GR. Diabetes mellitus and female lower urinary tract symptoms: a review. Neurourol Urodyn. 2008;27(5):362–7. pmid:18041770
  31. 31. Wang R, Lefevre R, Hacker MR, Golen TH. Diabetes, glycemic control, and urinary incontinence in women. Female Pelvic Med Reconstr Surg. 2015;21(5):293–7. pmid:26313496
  32. 32. Azadzoi KM, Tarcan T, Kozlowski R, Krane RJ, Siroky MB. Overactivity and structural changes in the chronically ischemic bladder. J Urol. 1999;162(5):1768–78. pmid:10524933
  33. 33. Milsom I, Kaplan SA, Coyne KS, Sexton CC, Kopp ZS. Effect of bothersome overactive bladder symptoms on health-related quality of life, anxiety, depression, and treatment seeking in the United States: results from EpiLUTS. Urology. 2012;80(1):90–6. pmid:22748867
  34. 34. Kinsey D, Pretorius S, Glover L, Alexander T. The psychological impact of overactive bladder: a systematic review. J Health Psychol. 2016;21(1):69–81. pmid:24591118
  35. 35. Largo RH, Molinari L, von Siebenthal K, Wolfensberger U. Development of bladder and bowel control: significance of prematurity, perinatal risk factors, psychomotor development and gender. Eur J Pediatr. 1999;158(2):115–22. pmid:10048607
  36. 36. Valentino RJ, Wood SK, Wein AJ, Zderic SA. The bladder-brain connection: putative role of corticotropin-releasing factor. Nat Rev Urol. 2011;8(1):19–28. pmid:21135878
  37. 37. Wood SK, Baez MA, Bhatnagar S, Valentino RJ. Social stress-induced bladder dysfunction: potential role of corticotropin-releasing factor. Am J Physiol Regul Integr Comp Physiol. 2009;296(5):R1671–8. pmid:19279290
  38. 38. Kim Y, Kwak Y. Urinary incontinence in women in relation to occupational status. Women Health. 2016:1–18.
  39. 39. Coyne KS, Sexton CC, Thompson CL, Clemens JQ, Chen CI, Bavendam T, et al. Impact of overactive bladder on work productivity. Urology. 2012;80(1):97–103. pmid:22748868
  40. 40. Coyne KS, Sexton CC, Kopp ZS, Ebel-Bitoun C, Milsom I, Chapple C. The impact of overactive bladder on mental health, work productivity and health-related quality of life in the UK and Sweden: results from EpiLUTS. BJU Int. 2011;108(9):1459–71. pmid:21371240
  41. 41. Sexton CC, Coyne KS, Vats V, Kopp ZS, Irwin DE, Wagner TH. Impact of overactive bladder on work productivity in the United States: results from EpiLUTS. Am J Manag Care. 2009;15(4 Suppl):S98–S107.
  42. 42. Wang Y, Xu K, Hu H, Zhang X, Wang X, Na Y, et al. Prevalence, risk factors, and impact on health related quality of life of overactive bladder in China. Neurourol Urodyn. 2011;30(8):1448–55. pmid:21826714
  43. 43. Kim HJ, Ruger JP. Socioeconomic disparities in behavioral risk factors and health outcomes by gender in the Republic of Korea. BMC Public Health. 2010;10: 195. pmid:20398324
  44. 44. Madhu C, Enki D, Drake MJ, Hashim H. The functional effects of cigarette smoking in women on the lower urinary tract. Urol Int. 2015;95(4):478–82. pmid:26452108
  45. 45. Hirayama A, Torimoto K, Mastusita C, Okamoto N, Morikawa M, Tanaka N, et al. Risk factors for new-onset overactive bladder in older subjects: results of the Fujiwara-kyo study. Urology. 2012;80(1):71–6. pmid:22626577
  46. 46. de Boer TA, Slieker-ten Hove MC, Burger CW, Vierhout ME. The prevalence and risk factors of overactive bladder symptoms and its relation to pelvic organ prolapse symptoms in a general female population. Int Urogynecol J. 2011;22(5):569–75. pmid:21104400
  47. 47. Tampakoudis P, Tantanassis T, Grimbizis G, Papaletsos M, Mantalenakis S. Cigarette smoking and urinary incontinence in women—a new calculative method of estimating the exposure to smoke. Eur J Obstet Gynecol Reprod Biol. 1995;63(1):27–30. pmid:8674561
  48. 48. Masuda H, Hayashi Y, Chancellor MB, Kihara K, de Groat WC, de Miguel F, et al. Roles of peripheral and central nicotinic receptors in the micturition reflex in rats. J Urol. 2006;176(1):374–9. pmid:16753446
  49. 49. Maserejian NN, Kupelian V, Miyasato G, McVary KT, McKinlay JB. Are physical activity, smoking and alcohol consumption associated with lower urinary tract symptoms in men or women? Results from a population based observational study. J Urol. 2012;188(2):490–5. pmid:22704109
  50. 50. Findik RB, Unluer AN, Sahin E, Bozkurt OF, Karakaya J, Unsal A. Urinary incontinence in women and its relation with pregnancy, mode of delivery, connective tissue disease and other factors. Adv Clin Exp Med. 2012;21: 207–213. pmid:23214285
  51. 51. Gold EB, Bromberger J, Crawford S, Samuels S, Greendale GA, Harlow SD, et al. Factors associated with age at natural menopause in a multiethnic sample of midlife women. Am J Epidemiol. 2001;153(9):865–74. pmid:11323317