An Exploration of the Physical and Mental Health Impact among a Diverse Population in the United Kingdom Experiencing Perimenopause and Menopause (MARIE UK-WP2a)

An Exploration of the Physical and Mental Health Impact among a Diverse Population in the United Kingdom Experiencing Perimenopause and Menopause (MARIE UK-WP2a) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Exploration of the Physical and Mental Health Impact among a Diverse Population in the United Kingdom Experiencing Perimenopause and Menopause (MARIE UK-WP2a) Gayathri Delanerolle, Jie Sun, Julie Taylor, Paula Briggs, Lucky Saraswat, and 35 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7471671/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Menopause is associated with diverse physical and mental symptoms, yet variation across menopausal stages and modes of onset remains poorly characterised in United Kingdom (UK) based populations. This study aimed to evaluate symptom burden and quality of life across perimenopausal, menopausal, and postmenopausal individuals, including those with natural and surgical menopause. Methods A mixed-methods, prospective cohort study was conducted to explore perimenopausal, menopausal and post-menopausal experience in cis women, transgender and LGBTQ + populations in England, Wales, Scotland and Northern Ireland using the digital XM Qualtrics platform with psychometric and clinical scales including the Hospital Anxiety and Depression Scale (HADS), Greene Climacteric Scale (GCS), Insomnia Severity Index (ISI), Burnout Assessment Tool (BAT), Numeric Pain Rating Scale (NPRS), Menopause Rating Scale (MRS), and health-related quality of life (HrQoL) assessments. Quantitative data were gathered following informed consent at two time points, and qualitative interviews were conducted in a selected sub-cohort. Results Among 845 baseline and 538 follow-up participants (median age 52), anxiety severity was significantly higher during perimenopause compared to post-menopause (baseline: p = 0.013; follow-up: p = 0.013), while depressive symptoms were markedly greater in those with surgical menopause, as shown by both HADS (baseline: p = 0.001; follow-up: p < 0.001) and GCS scores (baseline: p = 0.004; follow-up: p = 0.003). People experiencing surgical menopause displayed increased insomnia, back pain, vasomotor symptoms, and reduced quality of life. Cognitive symptoms, including forgetfulness and difficulty concentrating, were most pronounced among the perimenopausal cohort. Insomnia and burnout were moderately prevalent among employed people, especially when coexisting with conditions such as endometriosis. Regional differences were minimal, though participants from Scotland reported higher pain scores. Conclusion Symptom severity and variability is dependent on menopausal stage and mode of onset. The study findings highlight the need for targeted, stage-specific clinical pathways and workplace adaptations, particularly for those with complex medical histories such as endometriosis. A stratified, inclusive approach to menopause care is urgently required across policy and practice settings. Women's studies Figures Figure 1 Figure 2 Evidence before this study Limited understanding of the physical and mental health impact of the perimenopausal, menopausal and post-menopausal experience, and health outcomes have been reported in a UK based diverse population. Added value of this study The MARIE project is the first multi-work package (WP) initiative led by the United Kingdom (UK) to explore the experiences and health outcomes of cis women, transgender and LGBTQ+ populations experiencing natural, medical or surgical menopause. This study provides new knowledge and insight on symptom profiles in a diverse UK based population addressing critical evidence gaps and informing inclusive, culturally competent healthcare policies and practices. Implications of all the available evidence This study impacts policy reforms, research equity, equality and changes to clinical practice, healthcare professional training, NHS service design and public health messaging. Introduction Perimenopause, menopause, and post-menopause collectively affect an estimated 13 million people in the United Kingdom (UK), representing nearly one-third of the female population and an increasing number of transgender and gender-diverse individuals. 1 Menopause is clinically defined as the permanent cessation of menstruation for 12 consecutive months. 2 The perimenopausal phase may begin years earlier and is marked by fluctuating hormone levels and a wide range of symptoms, while post-menopause encompasses the years following the final menstrual period. Common symptoms include vasomotor disturbances such as hot flushes and night sweats, sleep disruption, low mood, anxiety, brain fog, memory lapses, joint and muscle pain, vaginal dryness, and reduced libido. 3 While some individuals experience mild or transient symptoms, others report debilitating impacts that can affect personal relationships, employment, and overall quality of life. The severity and duration of symptoms can vary widely due to biological, psychological, social, and cultural factors, yet this variability is often poorly captured in clinical assessments and service delivery. In the UK, current menopause care is primarily provided through general practice, with treatment options largely centred around hormone replacement therapy (HRT). 4 While HRT has been shown to be highly effective in relieving many menopausal symptoms and in preventing osteoporosis, access remains uneven 6 . Patient experiences highlight a concerning landscape where many individuals feel dismissed or misunderstood by healthcare professionals, particularly when presenting with psychological or non-specific symptoms. 6 Ethnic minority populations frequently encounter culturally insensitive care and language barriers, while trans and non-binary individuals often report a lack of recognition of their menopausal needs altogether. Medical or surgical menopause resulting from interventions such as chemotherapy, hysterectomy, or gender-affirming treatments is further under-researched and poorly supported in mainstream practice. 5 These shortcomings are compounded by socioeconomic inequities, where access to information and services constrained by cost and location. Despite growing awareness and national policy interest, including the publication of the National Institute of Care and Excellence (NICE) guideline NG23 and the establishment of the UK Menopause Taskforce, several challenges remain. 6 The evidence base informing current guidelines is limited in diversity, with most studies focused on white, middle-class, cis-women from high-income settings. Long-term outcomes data, especially regarding the safety and efficacy of newer HRT formulations or interventions in minoritised populations, are still lacking. Additionally, public health messaging and workplace policies often fail to account for intersectional identities or the full spectrum of menopausal experience, resulting in fragmented support. 7 In research, funding for menopause remains disproportionately low in comparison to other health conditions affecting similar populations. 8 Existing studies frequently exclude or underrepresent individuals undergoing early menopause, those with chronic conditions, and LGBTQ + individuals. The psychosocial and occupational impacts of menopause have received relatively little structured investigation, despite clear indications that these domains are crucial to wellbeing and productivity. 9 Rationale for the MARIE Project In light of the clinical and research gaps, we developed the MARIE 10 project to meet the unmet needs to explore the experiences of perimenopause, menopause, and post-menopause across cis-women, transgender, and LGBTQ + populations. By incorporating natural, medical, and surgical menopause within a diverse participant sample, MARIE aims to generate inclusive, actionable insights to inform equitable healthcare policies, culturally competent practice, and patient-centred interventions. Methods This study aimed to evaluate the impact of mental and physical health by way of symptom burden, psychosocial functioning and quality of life across perimenopausal, menopausal, and postmenopausal individuals, including those with natural and surgical menopause. Ethics approval The study was approved by Health Research Authority and Health and Care Research Wales Approval (22/EE/0158). Design A mixed-methods, explanatory sequential design was developed to gather quantitative and qualitative data using digital approaches. All participants were allocated an anonymous ID and identifiable material were not recorded. Eligibility All participants that experienced perimenopausal, menopausal or post-menopausal symptoms over the age of 18 years, living in England, Wales, Scotland and Northern Ireland and willing to provide informed consent were included. To ensure equity of access to healthcare, individuals with learning disabilities were excluded from the study, provided they were able to provide informed consent to participate. Recruitment Participants were recruited through a range of digital and community-based platforms, such as Facebook, X (formerly Twitter), LinkedIn, and NHS Trust websites. We also utilised NHS app push notifications via primary care and collaborated with gynaecology clinics in acute hospitals, NHS Trust’s internal communication streams as well as NHS psychological therapies services (IAPT). Additionally, places of worship such as Hindu and Buddhist temples and mosques supported recruitment by signposting individuals to the study. Study information with an online link and QR code was provided where the participant information sheet and consent forms were made available. From the quantitative sample (n = 1043), a sub-group (n = 50) of participants that provided consent were purposively selected to take part in the qualitative interviews. Data collection All participants provided informed consent using the XM platform before completing clinically validated questionnaires of Hospital Anxiety and Depression Scale (HADS), Greene Climacteric Scale (GCS), Health-related quality of life (HrQoL), Menopausal rating scale (MRS), Quebec Pain Disability Scale (QPDS), Numeric pain rating scale (NPRS) and the Insomnia Severity Index (ISI). These were completed at baseline (day 0) and day-30. The aims, outcomes and outcome measures are demonstrated in Table 1 . Participants were able to withdraw at any time, and data were anonymised in accordance with GDPR regulations. Table 1 demonstrates aims, outcomes, outcome measures and questionnaire dimensions Aims Outcome Outcome measure Construct Analytical rationale To determine the mental health impact due to Menopause Mental health impact Cognitive impact HADS Insomnia Severity Index Scale Topic guide for the qualitative interview Equity issues Moderator To determine the challenges associated with menopause Mental health impact Cognitive impact The Menopausal rating scale (MRS) To assess menopausal symptom scale Outcome measure To determine the change in symptoms Psychological impact Cognitive impact Greene Climacteric Scale (GCS) Burn out due to menopausal symptoms and/or employment Outcome measure To determine the quality of life among menopausal women Quality of life Health related quality of life (HRQoL) Psychological impact - Anxiety & Depression Outcome measure To determine lower back pain Pain disability Quebec Pain Disability Scale (QPDS) Psychological impact - Sleep Quality Outcome measure To determine the wellbeing challenges whilst working Workforce performance Burnout Assessment Tool (BAT-12) To understand and assess menopausal symptoms and their severity during the three phases, the Greene Climacteric scale Outcome measure To determine vasomotor symptoms Quality of life The Menopausal rating scale (MRS) and Greene Climacteric Scale (GCS) To understand and assess lower back pain Outcome measure Statistical analysis plan Demographic characteristics were presented as frequencies for categorical variables and means with standard deviations (SD) for continuous variables. To compare the differences in numerical variables between the baseline and follow-up, a t-test was employed. Categorical variables were evaluated using Pearson's chi-square test or Fisher's exact test, as appropriate for cell frequency distributions. Statistical analyses were completed using R version 4.4.3. Two-tailed p-values < 0.05 were considered statistically significant. Participants who failed to respond to any item within a given scale were excluded from the corresponding analysis. For scales with partial responses, missing item-level responses in assessment scales were addressed through multiple imputation implemented via the 'mice' package in R. The procedure employed predictive mean matching with 5 iterations, generating five complete datasets to preserve the statistical validity of subsequent inferences while accounting for missing-at-random assumptions. Thematic analysis A sub-set of participants were invited to complete a qualitative interview using a semi-structured topics guide. A contextual analysis was conducted to report the healthcare services experience. Inter-stage Menopausal Comparisons Differences across menopausal stages were analysed using either ANOVA or the Kruskal-Wallis test, depending on whether the variables conformed to a normal distribution. Normality assumptions were formally evaluated using the Shapiro-Wilk test. Post-hoc pairwise comparisons for significant results employed Scheffé's method (ANOVA) or Dunn's test with Holm correction (Kruskal-Wallis). For categorical variables, the chi-square test or Fisher's exact test was utilised for comparison. Subgroup analysis Two subgroup analyses were performed using geographical location and menopausal status (supplement-1). The analysis was adjusted for differential distributions of menopausal stages among subgroups. For continuous outcomes, analysis of covariance (ANCOVA) was used to compare adjusted marginal means with 95% confidence intervals (CI), controlling for menopausal stage as a covariate. Statistically significant ANCOVA results were indicated between-subgroup differences after menopausal stage adjustment. Categorical outcomes were analysed using Cochran-Mantel-Haenszel (CMH) tests to assess subgroup-outcome associations while maintaining menopausal stage stratification. Results Cohort, data were collected from 1043 participants at baseline and 602 participants during follow-up (Fig. 1). Among these, 198 participants at baseline and 64 participants at follow-up did not respond to any assessment scales. Consequently, the final samples for analysis included 845 individuals at baseline and 538 individuals at follow-up. The cohort comprised 845 baseline and 538 follow-up participants, with a median age of 52 years (IQR 48–55). Significant inter-stage differences in anxiety severity were observed (baseline: p = 0.012; follow-up: p = 0.017), with perimenopause demonstrating elevated anxiety level versus post-menopause (baseline: p = 0.013; follow-up: p = 0.013). Additionally, both HADS Depressed (baseline: p = 0.001; follow-up: p < 0.001) and GCS Depressed (baseline: p = 0.004; follow-up: p = 0.003) demonstrated significantly higher depressive severity in surgical menopause compared to natural menopause across baseline and follow-up assessments. Participant characteristics Table 2 presents the demographic characteristics of the UK cohort, comprising 845 baseline participants and 538 follow-up completers. The UK-based menopausal women demonstrated a median age of 52 years (IQR: 48–55), with median menarche onset at 13 years (IQR: 12–14) and median menopause commencement at 47 years (IQR: 43–50). Majority of the sample at both time points were White (94.0% at baseline; 96.8% at follow-up), with minimal representation from Asian, Black, and Mixed/Other ethnic groups. Asian participants comprised only 1.4% at baseline and dropped to 0.7% at follow-up; similarly, Black participants constituted 2.3% at baseline and also fell to 0.7% at follow-up. The representation of individuals from Mixed or Other ethnic backgrounds remained low but slightly more stable (2.3–1.7%). Stratification by England (n = 553, 65.7%), Scotland (n = 247, 29.3%), Wales (n = 16, 1.9%), Northern Ireland (n = 8, 1.0%) and other (n = 18, 2.1%), with follow-up retention indicated proportional representation across regions (England: n = 351, 65.4%; Scotland: n = 157, 29.2%; Wales: n = 10, 1.9%; Northern Ireland: n = 7, 1.3%, Other: n = 12, 2.2%). Menopausal stage classification showed baseline distribution of 328 Perimenopause (39.0%), 299 menopause (35.5%), and 190 Post-menopause (22.6%), with follow-up cohorts demonstrating comparable phase distribution: 188 Perimenopausal (35.2%), 191 Menopausal (35.8%), and 149 post-menopausal (27.9%) participants. The menopause status at baseline showed 12 medical (1.5%), 711 natural (86.1%), and 103 surgical (12.4%) cases, whereas follow-up contained 8 medical (1.5%), 446 natural (83.0%), and 83 surgical (15.5%) menopausal statuses. Table 2 indicates the population characteristics of the sample. Bold values indicate statistical significance (§ indicates t test, ‡ indicates Chi-squared test, † indicates Fisher’s exact test) Baseline (n = 845) Follow-up (n = 538) P-value Age Mean(± SD) 51.12(± 5.97) 51.30(± 5.69) 0.573§ Median(IQR) 52(48–55) 52(48–55) Age of First Period Mean(± SD) 12.81(± 1.70) 12.88(± 1.71) 0.472§ Median(IQR) 13(12–14) 13(12–14) Age at Menopause Onset Mean(± SD) 46.13(± 5.31) 46.21(± 5.36) 0.810§ Median(IQR) 47(43–50) 47(43–50) Ethnicity Asian 12(1.4%) 4(0.7%) Black 19(2.3%) 4(0.7%) Mixed/Other 19(2.3%) 9(1.7%) White 787(94.0%) 520(96.8%) 0.080† Gender Woman 902 564 Man 4 0 Prefer not to say 2 0 Transgender 9 6 Relationship Status Cohabiting 120(14.3%) 66(12.4%) Divorced 72(8.6%) 51(9.6%) Married 518(61.6%) 518(61.6%) Other 36(4.3%) 19(3.6%) Single 93(11.1%) 64(12.1%) Widowed 2(0.2%) 0(0.0%) 0.751† Currently Reside England 553(65.7%) 351(65.4%) Northern Ireland 8(1.0%) 7(1.3%) Other 18(2.1%) 12(2.2%) Scotland 247(29.3%) 157(29.2%) Wales 16(1.9%) 10(1.9%) 0.982‡ Educational Qualification A-level, higher grade or equivalent 165(19.6%) 118(22.0%) GCSE, O’Level, standard grade or equivalent 124(14.8%) 61(11.4%) No formal educational qualifications 6(0.7%) 1(0.2%) Postgraduate (e.g., MA or PhD) or equivalent 239(28.5%) 157(29.2%) Undergraduate (e.g., BA or BSc) or equivalent 306(36.4%) 200(37.2%) 0.245† Employment Status Cared for a family member 8(1.0%) 5(0.9%) Employed casually 18(2.2%) 9(1.7%) Employed full-time 500(60.5%) 321(60.8%) Employed part-time 227(27.4%) 147(27.8%) Home maker 28(3.4%) 17(3.2%) Retired 20(2.4%) 11(2.1%) Unemployed 26(3.1%) 18(3.4%) 0.996‡ Menopausal Stage Menopause 299(35.5%) 191(35.8%) Perimenopause 328(39.0%) 188(35.2%) Post-menopause 190(22.6%) 149(27.9%) Pre-menopause 25(3.0%) 6(1.1%) 0.019‡ Menopausal status Medical menopause 12(1.4%) 8(1.5%) Natural menopause 711(86.1%) 446(83.0%) Surgical menopause 103(12.5%) 83(15.5%) 0.290‡ Taking Prostap or Zoladex Yes 7(0.8%) 2(0.4%) No 820(99.2%) 536(99.6%) 0.496† Premature Ovarian Failure Diagnosis Yes 23(2.8%) 15(2.8%) No 802(97.2%) 522(97.2%) 0.999‡ Had A Hysterectomy Yes 92(11.1%) 73(13.6%) No 735(88.9%) 464(86.4%) 0.200‡ Age at different menopausal stages As shown in Table 2 , the median age across menopausal stages were quantified as follows: perimenopausal women demonstrated 49 years (IQR 46–51), menopausal stage 53 years (IQR 50–55), and postmenopausal stage 56 years (IQR 52–59). And there were statistically significant differences in age among different menopausal stages (Kruskal-Wallis test, p < 0.001, Table 2 – 3 ). Table 3 ​Age at different menopausal stages. Bold values indicate statistical significance (Kruskal-Wallis test). Each Menopausal stage Perimenopause Menopause Post-menopause P-value Baseline Age Mean(± SD) Median(IQR) n = 328 48.72(± 3.95) 49(46–51) n = 299 51.89(± 5.64) 53(50–55) n = 190 54.78(± 6.50) 55(52–59) < 0.001* Follow-up Age Mean(± SD) Median(IQR) n = 188 48.58 (± 3.79) 49(46–51) n = 191 51.64(± 5.42) 53(50–55) n = 149 54.59(± 6.11) 56(52–59) < 0.001* Multiscale Assessment Outcomes As shown in Table 4 , HADS indicate that half of the menopausal women exhibited abnormal anxiety levels (baseline: n = 471, 55.7%; follow-up: n = 272, 50.6%), with over 20% categorized as borderline abnormal anxiety (baseline: n = 200, 23.7%; follow-up: n = 128, 23.8%). For depressive symptoms, 30.3% (n = 256) and 28.4% (n = 153) of participants met criteria for abnormal depression at baseline and follow-up, respectively, while borderline abnormal depression was observed in 27.6% (n = 233) and 26.8% (n = 144) of cases across the two time points. Table 4 Post-hoc pairwise comparisons for age at different menopausal stages. Bold values indicate statistically significant pairwise differences (Dunn test with Holm correction) P-value of Pairwise comparisons Perimenopause vs. Menopause Perimenopause vs. Post-menopause Menopause vs. Post-menopause Age Baseline Follow-up < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 The psychological domain of GCS yielded baseline scores of 16.54 (± 5.84) and follow-up scores of 16.06 (± 5.85), while the physiological domain showed 6.68 (± 4.11) at baseline and 6.74 (± 4.08) at follow-up. Vasomotor domain scores decreased marginally from 2.47 (± 1.81) at baseline to 2.26 (± 1.67) at follow-up. Within the GCS Psychological subscales, clinically significant anxiety was observed in 348 participants (41.5%) at baseline, and 192 individuals (36.1%) at follow-up. Similarly, the Depressed subscales identified 280 cases (33.4%) meeting clinical thresholds for depression at baseline, with a follow-up prevalence of 166 participants (31.2%). ISI scores indicate a comparable distribution of insomnia severity between the two groups, with no significant difference in mean scores (13.78 ± 6.70 vs 13.60 ± 6.65; p = 0.623). Similarly, the proportions of participants across insomnia severity categories (no, sub-threshold, moderate, severe) were not statistically different ( p = 0.527). NPRS showed a slightly higher mean pain score in one group (3.87 ± 2.29) compared to the other (3.79 ± 2.07), although the difference in pain severity categories reached statistical significance ( p = 0.028), suggesting differences in how participants reported pain intensity. BAT scores across all four domains showed no significant differences between groups ( p > 0.2 for all subscales). MRS and HrQoL scores were also similar between the groups (MRS: 20.22 ± 7.69 vs 20.00 ± 7.46, p = 0.603; HrQoL: 10.38 ± 6.37 vs 10.75 ± 5.79, p = 0.259). QPDS) scores and severity levels showed no significant differences (mean: 16.64 ± 17.33 vs 16.63 ± 17.84, p = 0.992; categorical distribution p = 0.667). Overall, there were no substantial differences between groups across most domains, except for pain severity, which showed a statistically significant variation (Table 5 ). Table 5 : Participants' scale scores where Bold values indicate statistical significance ( § indicates t test, ‡ indicates Chi-squared test, † indicates Fisher’s exact test) Scales Baseline Follow-up P-value HADS HADS HADS(Anxious) HADS(Depressed) HADS Anxious levels Normal Borderline abnormal Abnormal HADS Depressed levels Normal Borderline abnormal Abnormal 19.41(±8.03) 11.10(±4.55) 8.31(±4.35) 174(20.6%) 200(23.7%) 471(55.7%) 356(42.1%) 233(27.6%) 256(30.3%) 18.85(±7.93) 10.64(±4.51) 8.21(±4.31) 138(25.7%) 128(23.8%) 272(50.6%) 241(44.8%) 144(26.8%) 153(28.4%) 0.205§ 0.068§ 0.674§ 0.068‡ 0.606‡ GCS GCS GCS(Anxious) GCS(Depressed) GCS(Psychological) GCS(Physiological) GCS(Vasomotor) GCS Anxious levels Normal Clinically Anxious GCS Depressed levels Normal Clinically Depressed 27.62(±10.07) 8.73(±3.12) 7.81(±3.31) 16.54(±5.84) 6.68(±4.11) 2.47(±1.81) 491(58.5%) 348(41.5%) 559(66.6%) 280(33.4%) 27.01 (±9.77) 8.56(±3.10) 7.51(±3.35) 16.06(±5.85) 6.74(±4.08) 2.26(±1.67) 340(63.9%) 192(36.1%) 366(68.8%) 166(31.2%) 0.263§ 0.324§ 0.101§ 0.144§ 0.800§ 0.029§ 0.053‡ 0.437‡ ISI ISI ISI levels No insomnia Sub-threshold insomnia Moderate insomnia Severe insomnia 13.78(±6.70) 154(19.1%) 293(36.3%) 234(29.0%) 127(15.7%) 13.60(±6.65) 108(20.4%) 183(34.5%) 167(31.5%) 72(13.6%) 0.623§ 0.527‡ NPRS NPRS NPRS levels No pain Mild pain Moderate pain Severe pain 3.87(±2.29) 46(6.2%) 308(41.3%) 293(39.3%) 98(13.2%) 3.79(±2.07) 16(3.3%) 235(48.2%) 174(35.7%) 63(12.9%) 0.549§ 0.028‡ BAT BAT BAT(Exhaustion) BAT(Mental distance) BAT(Cognitive impairment) BAT(Emotional impairment) 3.25 (±0.79) 3.53 (±0.93) 2.83 (±1.04) 3.39 (±0.90) 3.00 (±0.94) 3.22 (±0.79) 3.51 (±0.89) 2.80 (±1.06) 3.37 (±0.90) 2.94 (±0.99) 0.424§ 0.600§ 0.651§ 0.609§ 0.227§ MRS MRS 20.22 (±7.69) 20.00 (±7.46) 0.603§ HrQoL HrQoL 10.38 (±6.37) 10.75 (±5.79) 0.259§ QPDS QPDS QBDS levels Mild Moderate Severe Very Severe Extreme 16.64(±17.33) 377(45.9%) 293(35.7%) 110(13.4%) 36(4.4%) 5(0.6%) 16.63(±17.84) 253(47.8%) 177(33.5%) 74(14.0%) 19(4.4%) 6(1.1%) 0.992§ 0.667† Scale Outcomes across Menopausal Stages HADS revealed significant differences in anxiety levels across menopausal stages at both baseline and follow-up (Chi-squared test: baseline p = 0.010; follow-up p = 0.025). Post-hoc analyses confirmed that perimenopausal women exhibited significantly higher anxiety than postmenopausal women (baseline p = 0.010; follow-up p = 0.025). Significant differences in total GCS scores were observed across baseline p < 0.001 and follow-up p = 0.007. Post-hoc comparisons indicated menopausal women scored higher than post-menopausal women at both timepoints (p < 0.010; follow-up p = 0.047). Although the scores of Anxious (baseline: p = 0.010, follow-up: p = 0.027) and Depressed (baseline: p = 0.002, follow-up: p = 0.002) on the indicated significant differences among different menopausal stages, depression at follow-up was statistically significant (p = 0.011). The differences in GCS Physiological domain among different menopausal stages were significant (baseline: p = 0.003, follow-up: p < 0.001). Post-hoc pairwise comparisons showed a higher p-value during perimenopause than post-menopause score (baseline: p = 0.029, follow-up: p < 0.001). BAT demonstrated significant burnout variations across menopausal stages at baseline (p = 0.017) and follow-up (p = 0.018). Post-hoc analyses showed severe burnout among perimenopausal women in comparison to post-menopausal women (baseline p = 0.031; follow-up p = 0.020). Further details are provided in Tables 6 , 7 , and 8 . Table 6 : Scale scores across different menopausal stages (baseline data) Bold values indicate statistical significance (* indicates Kruskal-Wallis test, ^ indicates Anova test, ‡ indicates Chi-squared test, † indicates Fisher’s exact test) Scales Each Menopausal stage Perimenopause Menopause Post-menopause P-value HADS HADS HADS(Anxious) HADS(Depressed) HADS Anxious levels Normal Borderline abnormal Abnormal HADS Depressed levels Normal Borderline abnormal Abnormal 19.71 (±7.84) 11.38 (±4.44) 8.33(±4.26) 58 (17.7%) 70 (21.3%) 200 (61.0%) 136 (41.5%) 98 (29.9%) 94 (28.7%) 20.00 (±7.99) 11.40 (±4.57) 8.65(±4.30) 55 (18.4%) 78 (26.1%) 166 (55.5%) 119 (39.8%) 77 (25.8%) 103 (34.4%) 18.14 (±8.26) 10.31 (±4.53) 7.84 (±4.61) 54 (28.4%) 46 (24.2%) 90 (47.4%) 90 (47.4%) 47 (24.7%) 53 (27.9%) 0.032^ 0.012* 0.098* 0.010‡ 0.242‡ GCS GCS GCS(Anxious) GCS(Depressed) GCS(Psychological) GCS(Physiological) GCS(Vasomotor) GCS Anxious levels Normal Clinically Anxious GCS Depressed levels Normal Clinically Depressed 27.65 (±9.65) 8.86 (±3.03) 7.98 (±3.12) 16.85 (±5.55) 6.75 (±4.13) 2.23 (±1.64) 189 (58.0%) 137 (42.0%) 220 (67.5%) 106 (32.5%) 29.18 (±9.84) 9.02 (±3.03) 8.12 (±3.18) 17.14 (±5.60) 7.13 (±4.12) 2.86 (±1.97) 169 (56.9%) 128 (43.1%) 186 (62.6%) 111 (37.4%) 25.46 (±10.20) 8.22 (±3.28) 7.03 (±3.51) 15.25 (±6.25) 5.81 (±3.85) 2.32 (±1.69) 114 (60.6%) 74 (39.4%) 136 (72.3%) 52 (27.7%) <0.001^ 0.039* 0.002* 0.005* 0.003* <0.001* 0.715‡ 0.082‡ ISI ISI ISI levels No insomnia Sub-threshold insomnia Moderate insomnia Severe insomnia 13.34 (±6.64) 64 (20.6%) 114 (36.8%) 89 (28.7%) 43 (13.9%) 14.71 (±6.44) 45 (15.6%) 99 (34.3%) 95 (32.9%) 50 (17.3%) 13.28 (±7.10) 39 (21.2%) 71 (38.6%) 44 (23.9%) 30 (16.3%) 0.020* 0.267‡ NPRS NPRS NPRS levels No pain Mild pain Moderate pain Severe pain 3.81 (±2.30) 20 (6.8%) 115 (39.1%) 123 (41.8%) 36 (12.2%) 3.89 (±2.14) 11 (4.2%) 118 (45.4%) 100 (38.5%) 31 (11.9%) 3.93 (±2.39) 10 (5.9%) 69 (40.6%) 65 (38.2%) 26 (15.3%) 0.908* 0.599‡ BAT BAT BAT(Exhaustion) BAT(Mental distance) BAT(Cognitive impairment) BAT(Emotional impairment) 3.30 (±0.78) 3.58 (±0.90) 2.91 (±1.01) 3.43 (±0.88) 3.04 (±0.91) 3.32 (±0.76) 3.65 (±0.89) 2.85 (±1.03) 3.44 (±0.91) 3.06 (±0.95) 3.11 (±0.86) 3.34 (±1.02) 2.69 (±1.11) 3.33 (±0.93) 2.85 (±1.00) 0.017* 0.006* 0.069* 0.364* 0.068* MRS MRS 19.75 (±7.18) 21.44 (±7.69) 19.54 (±7.92) 0.006^ HrQoL HrQoL 10.17 (±6.43) 10.69 (±6.36) 10.65 (±6.23) 0.181* QPDS QPDS QBDS levels Mild Moderate Severe Very Severe Extreme 14.93 (±15.64) 149 (47%) 124 (39.1%) 33 (10.4%) 10 (3.2%) 1 (0.3%) 18.28 (±18.33) 129 (43.9%) 97 (33%) 50 (17%) 16 (5.4%) 2 (0.7%) 17.04 (±18.61) 87 (47.3%) 63 (34.2%) 22 (12%) 10 (5.4%) 2 (1.1%) 0.142* 0.179† Table 7 Post-hoc pairwise comparisons for Scale scores across different menopausal stages Scales Participants P-value of Pairwise comparisons Perimenopause vs. Menopause Perimenopause vs. Post-menopause Menopause vs. Post-menopause HADS HADS HADS(Anxious) Baseline Baseline Follow-up 0.902 0.722 0.158 0.098 0.013 0.013 0.041 0.026 0.246 GCS GCS GCS(Anxious) GCS(Depressed) GCS(Psychological) GCS(Physiological) GCS(Vasomotor) Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline 0.154 0.839 0.607 0.780 0.533 0.442 0.569 0.773 0.224 0.047 < 0.001 0.053 0.011 0.080 0.038 0.007 0.002 0.015 0.006 0.029 < 0.001 0.634 < 0.001 0.047 0.041 0.049 0.002 0.014 0.006 0.040 0.002 0.075 0.011 ISI ISI Baseline 0.042 0.856 0.042 BAT BAT BAT(Exhaustion) BAT(Emotional impairment) Baseline Follow-up Baseline Follow-up 0.973 0.561 0.282 0.281 0.031 0.020 0.044 0.013 0.021 0.057 0.004 0.124 MRS MRS Baseline 0.021 0.956 0.027 Bold values indicate statistically significant differences (Scheffé's method for ANOVA; Dunn's test with Holm correction for Kruskal-Wallis). Employed women experiencing insomnia may face compounded health burdens, particularly when sleep disturbances co-occur with pain and psychological distress. The study highlights moderate to severe levels of back pain and burnout across menopausal stages, which, in employed individuals, may reduce productivity, affect mental health, and contribute to work-related stress. Psychological symptoms such as anxiety and depression more prevalent in perimenopausal and surgically menopausal women can further disrupt sleep quality, creating a cyclical relationship between mental distress, pain perception, and fatigue. For those with coexisting conditions like endometriosis, which itself is associated with chronic pelvic and back pain, this burden is amplified. Women undergoing medical menopause due to conditions such as endometriosis may experience abrupt hormonal shifts, leading to more intense vasomotor symptoms, mood instability, and pain exacerbation. These intersecting challenges can severely impact functioning in the workplace and daily life, underscoring the need for comprehensive, multidisciplinary management. Interventions must address pain relief, sleep support, and psychological care, especially in occupational settings, to improve overall quality of life and prevent long-term disability. Symptom severity Symptom severity varied markedly across menopausal stages and types (Fig. 2). Perimenopausal participants reported the highest levels of anxiety and cognitive disruption, including difficulty concentrating and forgetfulness, as measured by HADS and the Burnout Assessment Tool. Women undergoing surgical menopause consistently demonstrated the most severe depressive symptoms, sleep disturbance, vasomotor complaints, and pain, particularly back pain, across both timepoints. The Greene Climacteric Scale revealed greater psychological symptom burden in perimenopausal individuals, while the MRS and ISI highlighted heightened somatic and sleep-related difficulties in the surgical menopause group. In contrast, postmenopausal participants reported significantly lower symptom severity across most domains. Despite this, quality of life scores remained moderately impaired in all groups, with a greater burden observed in those experiencing complex or medically induced menopausal transitions. Notably, employed women with pre-existing conditions such as endometriosis experienced amplified symptoms across psychological, cognitive, and somatic domains, particularly when undergoing medical menopause. Recurrent & Refractory Endometriosis with or without subfertility have enormous impact on variability of menopausal symptoms. These patterns underscore the heterogeneous nature of menopausal symptomatology and the need for stratified support. Table 8 Symptom Profile by Menopausal Stage Natural Menopause Symptom Domain Perimenopause Menopause Post - menopause Anxiety High Moderate Low Depression Moderate Moderate Low Cognitive Symptoms Moderate Moderate Mild Sleep Disturbance Moderate Moderate Mild Pain Mild Moderate Mild Vasomotor Symptoms Mild Moderate Mild Psychological Distress High High Low Burnout Moderate Moderate Low Quality of Life Low Moderate Stable Surgical Menopause Anxiety n/a High n/a Depression n/a High n/a Cognitive Symptoms n/a Moderate n/a Sleep Disturbance n/a Moderate n/a Pain n/a Moderate n/a Vasomotor Symptoms n/a Moderate n/a Psychological Distress n/a High n/a Burnout n/a Moderate n/a Quality of Life n/a Low n/a Medical Menopause Anxiety n/a High n/a Depression n/a High n/a Cognitive Symptoms n/a High n/a Sleep Disturbance n/a High n/a Pain n/a High n/a Vasomotor Symptoms n/a High n/a Psychological Distress n/a High n/a Burnout n/a High n/a Quality of Life n/a Low n/a Contextual summary from qualitative interviews Widespread dissatisfaction with NHS GP services among women navigating menopause, with 76% (n = 38) expressing discontent and many turning to private care due to barriers in access and quality of care. Participants strongly advocated for GP-level menopause training and the integration of menopause clinics into primary care, highlighting structural deficits in current provision. Limited access to specialist gynaecologists, especially for those experiencing complex or surgical menopause, points to systemic bottlenecks and unmet informational needs. Delays in diagnosis and treatment were frequently attributed to inadequate GP knowledge and fragmented commissioning of specialist services. Furthermore, the underutilisation of non-hormonal treatments, despite their relevance for certain individuals, reflects a lack of inclusive care pathways. Policy-level insights demonstrated overwhelming support for multidisciplinary teams (84%) and educational resources (88%), signalling a collective call for systemic reform. These findings underscore the urgency for a nationally coordinated, patient-centred women’s health strategy embedded within NHS structures to address long-standing gaps in menopause care. Workplace impact Critical gaps in workplace support for menopausal individuals were identified with 64% (n = 32) reporting a negative impact on their ability to work and only 36% (n = 18) receiving any form of support. Limited access to formal policies with only 22% reporting a menopause policy. Approximately 28% confirmed flexible working was on offer. Alarmingly, some participants had left employment entirely due to menopause-related difficulties, raising concerns around workforce retention and economic inequality. Poor coping was strongly associated with absence of support structures, with 30% (n = 15) reporting inadequate coping, particularly where no policies or flexibility were in place. Conversely, access to supportive measures was linked to better workplace outcomes, reinforcing the importance of adaptation. These findings point to an urgent need for menopause-inclusive occupational health frameworks, mandatory workplace policies, and gender-sensitive human-reso strategies to promote wellbeing and equity for menopausal staff and ensure sustained participation in the labour market. Table 9 is the contextual overview from qualitative interviews conducted among 50 participants ID Primary care Acute care Policy Not happy with the GP Happy with the GP Using a private GP due to issues with NHS GP Barriers with GP Practice Would want the GPs to be trained in menopause GP practice should have menopause clinic Under the care of a NHS gynaecologist Under the care of a private gynaecologist Had a radical hysterectomy and would like more information Educational material should be provided A multidisciplinary team dedicated to menopause will be good I would like to see a womens health policy I would like to see a menopause friendly policy 1 x x x x 2 x x x 3 x x x x 4 x x x x 5 x x x x 6 x x x 7 x x x x 8 x x x x 9 x x x 10 x x x 11 x x x x 12 x x x 13 x x 14 x x 15 x x 16 x x x x 17 x x x x 18 x x 19 x x x x x x 20 x x x x x 21 x x x 22 x x x x x x 23 x x x x x x x 24 x x x 25 x x x 26 x x x x x x x 27 x x x x 28 x x x x x x x x 29 x x x x x x x x 30 x x x x x x x x 31 x 32 x x x x x x x x 33 x x x x x x x x 34 x x x x x x x x 35 x x x x x x 36 x x x x x x x x 37 x x x x x x x x x 38 x x x x x x x x 39 x x 40 x x x x x x x x 41 x x x x x x 42 x x x x x x x 43 x x x x x x x x x 44 x x x x x x x 45 x x x 46 x x x x x x x 47 x x x x x x x x x 48 x x x x x x x x 49 x x x x x x x x 50 x x x x x x x x Table 10 indicates the impact in the work-place based on qualitative interviews (n=50) Impact in the work place Negatively impacted work Support provided by the work place Menopause policy available at work Flexible working available at work Coping at work Not coping well at work 1 x 2 x 3 x 4 x 5 x x 6 x 7 x x 8 x x 9 x 10 x 11 x 12 x 13 x x 14 x x 15 x x 16 x x 17 x x x 18 x x x 19 x x x 20 x x 21 x 22 x x x x 23 x x 24 x 25 x x 26 x 27 x x x x x x 28 x x x x x 29 x x x x x 30 x x 31 x 32 Participant Not working 33 x x x 34 x x 35 Participant Not working ; left due to menopause 36 Participant Not working 37 x x x 38 Participant Not working; left due to menopause 39 x x x x 40 x x x x x 41 x x x x x 42 Participant Not working; left due to menopause 43 x x x x 44 x x 45 x x x 46 x x x 47 x 48 x x x x 49 x 50 x x Discussion This UK-based cohort study provides a rich overview of the lived experience of menopause through a multidimensional lens, encompassing psychological, physiological, and quality of life indicators. Significant differences emerged across menopausal stages and statuses, with perimenopausal participants experiencing higher anxiety than their postmenopausal counterparts, and women with surgical menopause demonstrating greater severity of depressive symptoms, pain, vasomotor disturbances, and insomnia than those undergoing natural menopause. These outcomes were consistently observed across the HADS, GCS, ISI, NPRS and BAT. Additionally, regional comparisons revealed that women in Scotland reported higher pain levels compared to their counterparts in England, although most other symptom scores did not differ significantly by geography after adjustment for menopausal stage. Greater cognitive impairment among perimenopausal women compared to post-menopausal counterparts suggests the need for early recognition and supportive interventions during this transitional stages. Overall, the study reflects substantial variability in menopausal symptoms and outcomes, highlighting the interplay between biological, medical, and contextual factors. This has been utilised to co-create the MARiE intervention toolkit. Clinically significant interpretation of these findings indicates a marked disparity in symptom severity between women experiencing surgical and natural menopause. Surgical menopause was associated with significantly higher depression and burnout scores, more severe vasomotor and physiological symptoms, and a reduced health-related quality of life. These differences remained statistically significant after controlling for menopausal stage, indicating that they are not simply a function of timing but are intrinsically linked to the abrupt hormonal changes following oophorectomy or hysterectomy. In contrast, the more gradual hormonal transition in natural menopause may allow for greater physiological adaptation, which could explain the comparatively lower symptom burden. The results also suggest that perimenopause, though often overlooked, is a period of significant vulnerability, especially with respect to anxiety and psychological distress. These insights emphasise the need for stratified care and early intervention strategies that account for both the type and timing of menopause. The study demonstrates a bidirectional relationship between mental and physical health, particularly the cognitive and affective consequences of abrupt hormonal shifts in surgical menopause although this is insufficiently explored in current guidelines. While the British Menopause Society (BMS) 11 promotes individualised care, operational guidance remains primarily focused on vasomotor and genitourinary symptoms, often underemphasising cognitive, psychological, and regional contextual differences. Notably, the study’s findings of elevated pain levels in Scottish women and the consistent cognitive burden in perimenopause point to geographic and temporal factors that NICE 12 and BMS documents do not currently reflect. This critical comparison reveals that although current UK guidelines provide a foundational framework for menopause management, they do not adequately capture the stratified, intersectional, and psychosocial complexities highlighted by the MARIE WP2a UK-cohort. The findings argue for urgent guideline refinement, integrating tailored mental health support, early interventions for perimenopausal women, and explicit attention to surgical menopause as a distinct clinical trajectory requiring targeted care pathways. The most striking finding was the consistently poorer outcomes among women who experienced surgical menopause, which had a cascading effect across multiple dimensions of wellbeing. This group reported higher levels of depression, insomnia, pain, and vasomotor symptoms, alongside reduced quality of life and increased burnout collectively reflecting a more intense and sustained symptom experience. For individuals undergoing surgical menopause, these findings carry significant personal and clinical relevance. They may face an elevated risk of psychological distress and somatic burden without adequate support. For many, the procedure is medically necessary, yet the lack of structured, long-term support services post-surgery can compound health challenges. This evidence underscores the importance of proactive education, shared decision-making, and the provision of tailored support pathways, including mental health services and hormone replacement therapy, where clinically appropriate. Women who undergo surgical menopause must not be treated as a homogenous group; rather, they require targeted support to mitigate these elevated risks. Given the unique situations and the real-world impact that emerges from this cohort study, it's clear that the considerations of shared-decision making and personalisation of care processes should be non-negotiable. The NHS in England endorses and promotes shared-decision making as one of the core pillars of clinical care, especially for long-term conditions like menopause where essential phrases such as ‘hormone therapy’ and ‘non-hormonal strategies’ require more than just clinical approval-they must reflect the individual's values, preferences, and lived experiences 13 . As with several critical aspects of women's health, the World Health Organization (WHO) underscores the importance of shared decision making as a fundamental quality care principle at its viscid centre which is equally impacted by social, biological, and psychological dimensions 14 . In addition, the UK Women’s Health Strategy for England outlines concerning gaps in the healthcare systems with respect to women's lifespan issues, specifically mentioning menopause where women often feel "dismissed" or “unsupported” by the system 15 . Our findings echo this with an emphasis on a need to improve psychological relief during perimenopause. The Royal College of Obstetricians and Gynaecologists (RCOG) advocates for an improved integrated, multi-disciplinary approach with more physical, mental, and sexual health components and calls for a shift from symptom-based care to a more personalised, holistic care model 16 . This includes screening for cognitive and emotional impact, especially in those with surgical menopause, and the provision of menopause education at primary and specialist care levels. Migrant women are of particular concern regarding health inequalities because they are likely hidden in data and excluded from the provision of services 17 . The UK Migrant Health Guide recognises menopause as an emerging health issue, noting that culturally stigmatised disorders, coupled with the need for primary care, result in poor access to treatment and care which, ultimately, leads to the poor health outcomes due to over-stretched primary care services 17 . Culturally sensitive care is needed not only for ethical reasons but also for effective clinical practice. Policy makers need to design stratified health care systems for migrant minorities, women with less socioeconomic status and ethnic underclasses, taking into account cultural beliefs and the interpretation of symptoms, and health seeking behaviours and pathways. In this context, studies such as the MARIE project are positioned perfectly to close this gap by incorporating stratified, multicultural, multi-perspectival data into menopausal women’s decision support systems. The UK Women’s Health Strategy is another example of such data on which the consultation was based on 15 . The King's Fund report showcase that their basic need revolves around being heard, seriously attended to, and being provided options that are both respectful and relevant to their situations 18 . Until such principles are seamlessly integrated into policies and clinical practices, the discrepancy between evidence and implementation will continue. These findings carry important implications for clinical management and policy frameworks in the UK and globally. Clinical guidelines should reflect the distinct needs of women undergoing surgical, medical and natural menopause, incorporating routine mental health screening and offering multidisciplinary management approaches that include pain, sleep, and emotional wellbeing interventions. The striking symptom burden in perimenopause also demands earlier recognition and intervention before the formal onset of menopause particularly given that anxiety and psychological symptoms were most pronounced at this stage. From a policy perspective, menopause care must be reframed through a life-course, person-centred approach that moves beyond a “one-size-fits-all” model. National strategies should integrate menopausal health into broader women's health policies and prioritise access to culturally competent care, especially for underserved groups. Importantly, the evidence presented here strengthens the rationale for initiatives such as the MARIE project, which aims to develop inclusive, data-driven tools that are reflective of the diverse menopausal experiences across populations and healthcare systems. Strengths and Limitations This study possesses several notable strengths, foremost among them its large, well-characterised UK cohort and the use of both baseline and follow-up assessments, which enable the exploration of symptom trajectories over time. The inclusion of diverse menopausal stages and statuses perimenopause, natural, surgical, and medically induced menopause allows for meaningful subgroup comparisons, enhancing the clinical relevance of the findings. The application of multiple validated instruments such as HADS, GCS, ISI, BAT, MRS, QPDS, and HrQoL strengthens internal validity by offering a multidimensional perspective on psychological, somatic, and quality of life outcomes. The use of statistical adjustments for menopausal stage in subgroup analyses further refines the interpretation of observed effects. Additionally, the study’s inclusion of geographical and socio-demographic data provides valuable context for understanding regional variations and potential disparities. The study offers a robust foundation for informing tailored interventions and policy responses to menopausal health. Limitations include predominantly white and highly educated, limiting the generalisability of findings to more ethnically and socioeconomically diverse populations despite greater attempts to engage these populations. Attrition between baseline and follow-up reduced the analytical sample. Participation from Wales and Northern Ireland restricted the power of comparative analyses. Finally, although symptom severity was explored, hormonal levels and clinical diagnoses were not corroborated with biological data to make definitive inferences regarding physiological mechanisms due to limited funding. Conclusion Differences in psychological, physiological, and quality of life outcomes across menopausal stages and statuses indicate greater awareness is warranted alongside of funding for research and NHS service delivery. The complex intersecting physical and mental health impact of menopause indicate a necessity for tailored evidence-based interventions and their testing, such as the MARiE tool. Healthcare services and workplace policies must evolve to better support the diverse and prolonged experiences of menopause, especially among medically vulnerable and working populations. Declarations Funding: NIHR Research Capability Fund Conflicts of interest: All authors report no conflict of interest. The views expressed are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Department of Health and Social Care or the Academic institutions. Availability of data and material: The PIs and the study sponsor may consider sharing anonymous data upon reasonable a request. Code availability: Not applicable Author contributions: GD developed the ELEMI program and the MARIE project. This was furthered by GD and PP. GD, KE, PP, JT, LS and HFK submitted and secured the ethics approval for the study. KM, VC, LS, KR, SH, KP, GD, PP, VT, RP and HFK collected data. JS, JQS and GD conducted the data analysis. GD wrote the first draft and was furthered by all other authors. VP and PP edited and formatted all versions of the manuscript. All authors critically appraised, reviewed and commented on all versions of the manuscript. All authors read and approved the final manuscript. Ethics approval: Health Research Authority and Health and Care Research Wales Approval (22/EE/0158) Consent to participate: Obtained Consent for publication: All authors consented to publish this manuscript Acknowledgements : MARIE Consortium: Aini Hanan binti Azmi, Alyani binti Mohamad Mohsin, Arinze Anthony Onwuegbuna, Artini binti Abidin, Ayyuba Rabiu, Chijioke Chimbo, Chinedu Onwuka Ndukwe, Choon-Moy Ho, Chinyere Ukamaka Onubogu, Diana Chin-Lau Suk, Divinefavour Echezona Malachy, Emmanuel Chukwubuikem Egwuatu, Eunice Yien-Mei Sim, Farhawa binti Zamri, Fatin Imtithal binti Adnan, Geok-Sim Lim, Halima Bashir Muhammad, Ifeoma Bessie Enweani-Nwokelo, Ikechukwu Innocent Mbachu, Jinn-Yinn Phang, John Yen-Sing Lee, Joseph Ifeanyichukwu Ikechebelu, Juhaida binti Jaafar, Karen Christelle, Kathryn Elliot, Kim-Yen Lee, Kingsley Chidiebere Nwaogu, Lee-Leong Wong, Lydia Ijeoma Eleje, Min-Huang Ngu, Noorhazliza binti Abdul Patah, Nor Fareshah binti Mohd Nasir, Kathleen Riach, Norhazura binti Hamdan, Nnanyelugo Chima Ezeora, Nnaedozie Paul Obiegbu, Nurfauzani binti Ibrahim, Nurul Amalina Jaafar, Odigonma Zinobia Ikpeze, Obinna Kenneth Nnabuchi, Pooja Lama, Puong-Rui Lau, Rakshya Parajuli, Rakesh Swarnakar, Raphael Ugochukwu Chikezie, Rosdina Abd Kahar, Safilah Binti Dahian, Sapana Amatya, Sing-Yew Ting, Siti Nurul Aiman, Sunday Onyemaechi Oriji, Susan Chen-Ling Lo, Sylvester Onuegbunam Nweze, Damayanthi Dasanayaka, Nimesha Wijayamuni, Prasanna Herath, Thamudi Sundarapperuma, Jeevan Dhanasiri, Vaitheswariy Rao, Xin-Sheng Wong, Xiu-Sing Wong, Yee-Theng Lau, Heitor Cavalini, Jean Pierre Gafaranga, Emmanuel Habimana, Chigozie Geoffrey Okafor, Assumpta Chiemeka Osunkwo, Gabriel Chidera Edeh, Esther Ogechi John, Kenechukwu Ezekwesili Obi, Oludolamu Oluyemesi Adedayo, Odili Aloysius Okoye, Chukwuemeka Chukwubuikem Okoro, Ugoy Sonia Ogbonna, Chinelo Onuegbuna Okoye, Babatunde Rufus Kumuyi, Onyebuchi Lynda Ngozi, Nnenna Josephine Egbonnaji, Oluwasegun Ajala Akanni, Perpetua Kelechi Enyinna, Yusuf Alfa, Theresa Nneoma Otis, Catherine Larko Narh Menka, Kwasi Eba Polley, Isaac Lartey Narh, Bernard B. Borteih, Andy Fairclough, Kingsley Emeka Ekwuazi, Michael Nnaa Otis, Jeremy Van Vlymen, Chidiebere Agbo, Francis Chibuike Anigwe, Kingsley Chukwuebuka Agu, Chiamaka Perpetua Chidozie, Chidimma Judith Anyaeche, Clementine Kanazayire, Jean Damascene Hanyurwimfura, Nwankwo Helen Chinwe, Stella Matutina Isingizwe, Jean Marie Vianney Kabutare, Dorcas Uwimpuhwe, Melanie Maombi, Ange Kantarama, Uchechukwu Kevin Nwanna, Benedict Erhite Amalimeh, Theodomir Sebazungu, Elius Tuyisenge, Yvonne Delphine Nsaba Uwera, Emmanuel Habimana, Nasiru Sani and Amarachi Pearl Nkemdirim References Tariq B, Phillips S, Biswakarma R, Talaulikar V, Harper JC (2023) Women’s knowledge and attitudes to the menopause: a comparison of women over 40 who were in the perimenopause, post menopause and those not in the peri or post menopause. BMC Womens Health 23(1):460 Crandall CJ, Mehta JM, Manson JE (2023) Management of menopausal symptoms: a review. 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The life-course approach: from theory to practice: case stories from two small countries in Europe (2021) https://www.who.int/europe/publications/i/item/9789289053266 (accessed 18-07-2025 Department of Health & Social Care (2022) E. Women's Health Strategy for England Royal College of Obstetricians and Gynaecologists Better for women. https://www.rcog.org.uk/better-for-women/ (accessed 18-07-2025 Office for Health Improvement (2014) and Disparities. Women's health: migrant health guide Charlotte Wickens DJ (2022) Has the Women’s Health Strategy listened to what women really need? The King's Fund Additional Declarations The authors declare no competing interests. Supplementary Files Suppliment1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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version.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7471671/v1/c81f31af328d7409b5594868.png"},{"id":90318349,"identity":"127afd34-21a7-4d0d-8e85-2a0f89d4ce56","added_by":"auto","created_at":"2025-09-01 10:33:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7471671/v1/36b3b4f52917b901d1e77440.png"},{"id":90321738,"identity":"c7dcccd2-2bbd-4591-86de-0086207421b2","added_by":"auto","created_at":"2025-09-01 10:57:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2569699,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7471671/v1/a871686f-a153-4b1a-ba3e-ecf61fcfff35.pdf"},{"id":90318351,"identity":"b2dc6f2b-2dad-474c-8b3c-31770d5e5b2c","added_by":"auto","created_at":"2025-09-01 10:33:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57620,"visible":true,"origin":"","legend":"","description":"","filename":"Suppliment1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7471671/v1/090fce8eb44e43f233d5b2b6.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAn Exploration of the Physical and Mental Health Impact among a Diverse Population in the United Kingdom Experiencing Perimenopause and Menopause (MARIE UK-WP2a)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Evidence before this study ","content":"\u003cp\u003eLimited understanding of the physical and mental health impact of the perimenopausal, menopausal and post-menopausal experience, and health outcomes have been reported in a UK based diverse population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdded value of this study\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MARIE project is the first multi-work package (WP) initiative led by the United Kingdom (UK) to explore the experiences and health outcomes of cis women, transgender and LGBTQ+ populations experiencing natural, medical or surgical menopause.\u0026nbsp;This study provides new knowledge and insight on symptom profiles in a diverse UK based population addressing critical evidence gaps and informing inclusive, culturally competent healthcare policies and practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImplications of all the available evidence\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study impacts policy reforms, research equity, equality and changes to clinical practice, healthcare professional training, NHS service design and public health messaging.\u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003ePerimenopause, menopause, and post-menopause collectively affect an estimated 13\u0026nbsp;million people in the United Kingdom (UK), representing nearly one-third of the female population and an increasing number of transgender and gender-diverse individuals.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Menopause is clinically defined as the permanent cessation of menstruation for 12 consecutive months.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The perimenopausal phase may begin years earlier and is marked by fluctuating hormone levels and a wide range of symptoms, while post-menopause encompasses the years following the final menstrual period. Common symptoms include vasomotor disturbances such as hot flushes and night sweats, sleep disruption, low mood, anxiety, brain fog, memory lapses, joint and muscle pain, vaginal dryness, and reduced libido.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e While some individuals experience mild or transient symptoms, others report debilitating impacts that can affect personal relationships, employment, and overall quality of life. The severity and duration of symptoms can vary widely due to biological, psychological, social, and cultural factors, yet this variability is often poorly captured in clinical assessments and service delivery.\u003c/p\u003e\u003cp\u003eIn the UK, current menopause care is primarily provided through general practice, with treatment options largely centred around hormone replacement therapy (HRT).\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e While HRT has been shown to be highly effective in relieving many menopausal symptoms and in preventing osteoporosis, access remains uneven\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Patient experiences highlight a concerning landscape where many individuals feel dismissed or misunderstood by healthcare professionals, particularly when presenting with psychological or non-specific symptoms.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Ethnic minority populations frequently encounter culturally insensitive care and language barriers, while trans and non-binary individuals often report a lack of recognition of their menopausal needs altogether. Medical or surgical menopause resulting from interventions such as chemotherapy, hysterectomy, or gender-affirming treatments is further under-researched and poorly supported in mainstream practice.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e These shortcomings are compounded by socioeconomic inequities, where access to information and services constrained by cost and location.\u003c/p\u003e\u003cp\u003eDespite growing awareness and national policy interest, including the publication of the National Institute of Care and Excellence (NICE) guideline NG23 and the establishment of the UK Menopause Taskforce, several challenges remain.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e The evidence base informing current guidelines is limited in diversity, with most studies focused on white, middle-class, cis-women from high-income settings. Long-term outcomes data, especially regarding the safety and efficacy of newer HRT formulations or interventions in minoritised populations, are still lacking. Additionally, public health messaging and workplace policies often fail to account for intersectional identities or the full spectrum of menopausal experience, resulting in fragmented support.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn research, funding for menopause remains disproportionately low in comparison to other health conditions affecting similar populations.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Existing studies frequently exclude or underrepresent individuals undergoing early menopause, those with chronic conditions, and LGBTQ\u0026thinsp;+\u0026thinsp;individuals. The psychosocial and occupational impacts of menopause have received relatively little structured investigation, despite clear indications that these domains are crucial to wellbeing and productivity.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eRationale for the MARIE Project\u003c/h3\u003e\n\u003cp\u003eIn light of the clinical and research gaps, we developed the MARIE\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e project to meet the unmet needs to explore the experiences of perimenopause, menopause, and post-menopause across cis-women, transgender, and LGBTQ\u0026thinsp;+\u0026thinsp;populations. By incorporating natural, medical, and surgical menopause within a diverse participant sample, MARIE aims to generate inclusive, actionable insights to inform equitable healthcare policies, culturally competent practice, and patient-centred interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cp\u003eThis study aimed to evaluate the impact of mental and physical health by way of symptom burden, psychosocial functioning and quality of life across perimenopausal, menopausal, and postmenopausal individuals, including those with natural and surgical menopause.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003eThe study was approved by Health Research Authority and Health and Care Research Wales Approval (22/EE/0158).\u003c/p\u003e\n\u003ch3\u003eDesign\u003c/h3\u003e\n\u003cp\u003eA mixed-methods, explanatory sequential design was developed to gather quantitative and qualitative data using digital approaches. All participants were allocated an anonymous ID and identifiable material were not recorded.\u003c/p\u003e\n\u003ch3\u003eEligibility\u003c/h3\u003e\n\u003cp\u003eAll participants that experienced perimenopausal, menopausal or post-menopausal symptoms over the age of 18 years, living in England, Wales, Scotland and Northern Ireland and willing to provide informed consent were included. To ensure equity of access to healthcare, individuals with learning disabilities were excluded from the study, provided they were able to provide informed consent to participate.\u003c/p\u003e\n\u003ch3\u003eRecruitment\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited through a range of digital and community-based platforms, such as Facebook, X (formerly Twitter), LinkedIn, and NHS Trust websites. We also utilised NHS app push notifications via primary care and collaborated with gynaecology clinics in acute hospitals, NHS Trust\u0026rsquo;s internal communication streams as well as NHS psychological therapies services (IAPT). Additionally, places of worship such as Hindu and Buddhist temples and mosques supported recruitment by signposting individuals to the study. Study information with an online link and QR code was provided where the participant information sheet and consent forms were made available. From the quantitative sample (n\u0026thinsp;=\u0026thinsp;1043), a sub-group (n\u0026thinsp;=\u0026thinsp;50) of participants that provided consent were purposively selected to take part in the qualitative interviews.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003eAll participants provided informed consent using the XM platform before completing clinically validated questionnaires of Hospital Anxiety and Depression Scale (HADS), Greene Climacteric Scale (GCS), Health-related quality of life (HrQoL), Menopausal rating scale (MRS), Quebec Pain Disability Scale (QPDS), Numeric pain rating scale (NPRS) and the Insomnia Severity Index (ISI). These were completed at baseline (day 0) and day-30. The aims, outcomes and outcome measures are demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were able to withdraw at any time, and data were anonymised in accordance with GDPR regulations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003edemonstrates aims, outcomes, outcome measures and questionnaire dimensions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAims\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOutcome measure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnalytical rationale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTo determine the mental health impact due to Menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMental health impact\u003c/p\u003e\u003cp\u003eCognitive impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS\u003c/p\u003e\u003cp\u003eInsomnia Severity Index Scale\u003c/p\u003e\u003cp\u003eTopic guide for the qualitative interview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEquity issues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerator\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTo determine the challenges associated with menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMental health impact\u003c/p\u003e\u003cp\u003eCognitive impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe Menopausal rating scale (MRS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTo assess menopausal symptom scale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOutcome measure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTo determine the change in symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePsychological impact\u003c/p\u003e\u003cp\u003eCognitive impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreene Climacteric Scale (GCS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBurn out due to menopausal symptoms and/or employment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOutcome measure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTo determine the quality of life among menopausal women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuality of life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealth related quality of life (HRQoL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePsychological impact - Anxiety \u0026amp; Depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOutcome measure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTo determine lower back pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePain disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuebec Pain Disability Scale (QPDS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePsychological impact - Sleep Quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOutcome measure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTo determine the wellbeing challenges whilst working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorkforce performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBurnout Assessment Tool (BAT-12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTo understand and assess menopausal symptoms and their\u003c/p\u003e\u003cp\u003eseverity during the three phases, the Greene Climacteric scale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOutcome measure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTo determine vasomotor\u003c/p\u003e\u003cp\u003esymptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuality of life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe Menopausal rating scale (MRS) and Greene Climacteric Scale (GCS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTo understand and assess lower back pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOutcome measure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStatistical analysis plan\u003c/h3\u003e\n\u003cp\u003eDemographic characteristics were presented as frequencies for categorical variables and means with standard deviations (SD) for continuous variables. To compare the differences in numerical variables between the baseline and follow-up, a t-test was employed. Categorical variables were evaluated using Pearson's chi-square test or Fisher's exact test, as appropriate for cell frequency distributions. Statistical analyses were completed using R version 4.4.3. Two-tailed p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Participants who failed to respond to any item within a given scale were excluded from the corresponding analysis. For scales with partial responses, missing item-level responses in assessment scales were addressed through multiple imputation implemented via the 'mice' package in R. The procedure employed predictive mean matching with 5 iterations, generating five complete datasets to preserve the statistical validity of subsequent inferences while accounting for missing-at-random assumptions.\u003c/p\u003e\n\u003ch3\u003eThematic analysis\u003c/h3\u003e\n\u003cp\u003eA sub-set of participants were invited to complete a qualitative interview using a semi-structured topics guide. A contextual analysis was conducted to report the healthcare services experience.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eInter-stage Menopausal Comparisons\u003c/h2\u003e\u003cp\u003eDifferences across menopausal stages were analysed using either ANOVA or the Kruskal-Wallis test, depending on whether the variables conformed to a normal distribution. Normality assumptions were formally evaluated using the Shapiro-Wilk test. Post-hoc pairwise comparisons for significant results employed Scheff\u0026eacute;'s method (ANOVA) or Dunn's test with Holm correction (Kruskal-Wallis). For categorical variables, the chi-square test or Fisher's exact test was utilised for comparison.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eTwo subgroup analyses were performed using geographical location and menopausal status (supplement-1). The analysis was adjusted for differential distributions of menopausal stages among subgroups. For continuous outcomes, analysis of covariance (ANCOVA) was used to compare adjusted marginal means with 95% confidence intervals (CI), controlling for menopausal stage as a covariate. Statistically significant ANCOVA results were indicated between-subgroup differences after menopausal stage adjustment. Categorical outcomes were analysed using Cochran-Mantel-Haenszel (CMH) tests to assess subgroup-outcome associations while maintaining menopausal stage stratification.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eCohort, data were collected from 1043 participants at baseline and 602 participants during follow-up (Fig.\u0026nbsp;1). Among these, 198 participants at baseline and 64 participants at follow-up did not respond to any assessment scales. Consequently, the final samples for analysis included 845 individuals at baseline and 538 individuals at follow-up.\u003c/p\u003e\u003cp\u003eThe cohort comprised 845 baseline and 538 follow-up participants, with a median age of 52 years (IQR 48\u0026ndash;55). Significant inter-stage differences in anxiety severity were observed (baseline: p\u0026thinsp;=\u0026thinsp;0.012; follow-up: p\u0026thinsp;=\u0026thinsp;0.017), with perimenopause demonstrating elevated anxiety level versus post-menopause (baseline: p\u0026thinsp;=\u0026thinsp;0.013; follow-up: p\u0026thinsp;=\u0026thinsp;0.013). Additionally, both HADS Depressed (baseline: p\u0026thinsp;=\u0026thinsp;0.001; follow-up: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and GCS Depressed (baseline: p\u0026thinsp;=\u0026thinsp;0.004; follow-up: p\u0026thinsp;=\u0026thinsp;0.003) demonstrated significantly higher depressive severity in surgical menopause compared to natural menopause across baseline and follow-up assessments.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eParticipant characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the demographic characteristics of the UK cohort, comprising 845 baseline participants and 538 follow-up completers. The UK-based menopausal women demonstrated a median age of 52 years (IQR: 48\u0026ndash;55), with median menarche onset at 13 years (IQR: 12\u0026ndash;14) and median menopause commencement at 47 years (IQR: 43\u0026ndash;50). Majority of the sample at both time points were White (94.0% at baseline; 96.8% at follow-up), with minimal representation from Asian, Black, and Mixed/Other ethnic groups. Asian participants comprised only 1.4% at baseline and dropped to 0.7% at follow-up; similarly, Black participants constituted 2.3% at baseline and also fell to 0.7% at follow-up. The representation of individuals from Mixed or Other ethnic backgrounds remained low but slightly more stable (2.3\u0026ndash;1.7%).\u003c/p\u003e\u003cp\u003eStratification by England (n\u0026thinsp;=\u0026thinsp;553, 65.7%), Scotland (n\u0026thinsp;=\u0026thinsp;247, 29.3%), Wales (n\u0026thinsp;=\u0026thinsp;16, 1.9%), Northern Ireland (n\u0026thinsp;=\u0026thinsp;8, 1.0%) and other (n\u0026thinsp;=\u0026thinsp;18, 2.1%), with follow-up retention indicated proportional representation across regions (England: n\u0026thinsp;=\u0026thinsp;351, 65.4%; Scotland: n\u0026thinsp;=\u0026thinsp;157, 29.2%; Wales: n\u0026thinsp;=\u0026thinsp;10, 1.9%; Northern Ireland: n\u0026thinsp;=\u0026thinsp;7, 1.3%, Other: n\u0026thinsp;=\u0026thinsp;12, 2.2%).\u003c/p\u003e\u003cp\u003eMenopausal stage classification showed baseline distribution of 328 Perimenopause (39.0%), 299 menopause (35.5%), and 190 Post-menopause (22.6%), with follow-up cohorts demonstrating comparable phase distribution: 188 Perimenopausal (35.2%), 191 Menopausal (35.8%), and 149 post-menopausal (27.9%) participants. The menopause status at baseline showed 12 medical (1.5%), 711 natural (86.1%), and 103 surgical (12.4%) cases, whereas follow-up contained 8 medical (1.5%), 446 natural (83.0%), and 83 surgical (15.5%) menopausal statuses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eindicates the population characteristics of the sample. Bold values indicate statistical significance (\u0026sect; indicates t test, \u0026Dagger; indicates Chi-squared test, \u0026dagger; indicates Fisher\u0026rsquo;s exact test)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;845)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFollow-up\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;538)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.12(\u0026plusmn;\u0026thinsp;5.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.30(\u0026plusmn;\u0026thinsp;5.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.573\u0026sect;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52(48\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52(48\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of First Period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.81(\u0026plusmn;\u0026thinsp;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.88(\u0026plusmn;\u0026thinsp;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.472\u0026sect;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(12\u0026ndash;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(12\u0026ndash;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at Menopause Onset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.13(\u0026plusmn;\u0026thinsp;5.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.21(\u0026plusmn;\u0026thinsp;5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.810\u0026sect;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47(43\u0026ndash;50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47(43\u0026ndash;50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12(1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19(2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed/Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19(2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e787(94.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e520(96.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.080\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWoman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrefer not to say\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCohabiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120(14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66(12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72(8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51(9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e518(61.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e518(61.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93(11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64(12.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.751\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently Reside\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEngland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e553(65.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e351(65.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern Ireland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18(2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScotland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247(29.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157(29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.982\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational Qualification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA-level, higher grade or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165(19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118(22.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCSE, O\u0026rsquo;Level, standard grade or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124(14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61(11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal educational qualifications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6(0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostgraduate (e.g., MA or PhD) or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e239(28.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157(29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUndergraduate (e.g., BA or BSc) or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e306(36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200(37.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.245\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCared for a family member\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed casually\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18(2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed full-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500(60.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321(60.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed part-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e227(27.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147(27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome maker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17(3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26(3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.996\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenopausal Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e299(35.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191(35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerimenopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e328(39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188(35.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190(22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149(27.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25(3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenopausal status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12(1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNatural menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e711(86.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e446(83.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103(12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83(15.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.290\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaking Prostap or Zoladex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e820(99.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e536(99.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.496\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremature Ovarian Failure Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23(2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15(2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e802(97.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e522(97.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.999\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHad A Hysterectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92(11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73(13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e735(88.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e464(86.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.200\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAge at different menopausal stages\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the median age across menopausal stages were quantified as follows: perimenopausal women demonstrated 49 years (IQR 46\u0026ndash;51), menopausal stage 53 years (IQR 50\u0026ndash;55), and postmenopausal stage 56 years (IQR 52\u0026ndash;59). And there were statistically significant differences in age among different menopausal stages (Kruskal-Wallis test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003e​Age at different menopausal stages.\u003c/b\u003e \u003cb\u003eBold values indicate statistical significance (Kruskal-Wallis test).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eEach Menopausal stage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerimenopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMenopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePost-menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eMean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003cp\u003eMedian(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;328\u003c/p\u003e\u003cp\u003e48.72(\u0026plusmn;\u0026thinsp;3.95)\u003c/p\u003e\u003cp\u003e49(46\u0026ndash;51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;299\u003c/p\u003e\u003cp\u003e51.89(\u0026plusmn;\u0026thinsp;5.64)\u003c/p\u003e\u003cp\u003e53(50\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;190\u003c/p\u003e\u003cp\u003e54.78(\u0026plusmn;\u0026thinsp;6.50)\u003c/p\u003e\u003cp\u003e55(52\u0026ndash;59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollow-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eMean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003cp\u003eMedian(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;188\u003c/p\u003e\u003cp\u003e48.58 (\u0026plusmn;\u0026thinsp;3.79)\u003c/p\u003e\u003cp\u003e49(46\u0026ndash;51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;191\u003c/p\u003e\u003cp\u003e51.64(\u0026plusmn;\u0026thinsp;5.42)\u003c/p\u003e\u003cp\u003e53(50\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;149\u003c/p\u003e\u003cp\u003e54.59(\u0026plusmn;\u0026thinsp;6.11)\u003c/p\u003e\u003cp\u003e56(52\u0026ndash;59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMultiscale Assessment Outcomes\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, HADS indicate that half of the menopausal women exhibited abnormal anxiety levels (baseline: n\u0026thinsp;=\u0026thinsp;471, 55.7%; follow-up: n\u0026thinsp;=\u0026thinsp;272, 50.6%), with over 20% categorized as borderline abnormal anxiety (baseline: n\u0026thinsp;=\u0026thinsp;200, 23.7%; follow-up: n\u0026thinsp;=\u0026thinsp;128, 23.8%). For depressive symptoms, 30.3% (n\u0026thinsp;=\u0026thinsp;256) and 28.4% (n\u0026thinsp;=\u0026thinsp;153) of participants met criteria for abnormal depression at baseline and follow-up, respectively, while borderline abnormal depression was observed in 27.6% (n\u0026thinsp;=\u0026thinsp;233) and 26.8% (n\u0026thinsp;=\u0026thinsp;144) of cases across the two time points.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePost-hoc pairwise comparisons for age at different menopausal stages. \u003cem\u003eBold values indicate statistically significant pairwise differences (Dunn test with Holm correction)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eP-value of Pairwise comparisons\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePerimenopause\u003c/p\u003e\u003cp\u003evs. Menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePerimenopause vs.\u003c/p\u003e\u003cp\u003ePost-menopause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMenopause vs.\u003c/p\u003e\u003cp\u003ePost-menopause\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003cp\u003eFollow-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe psychological domain of GCS yielded baseline scores of 16.54 (\u0026plusmn;\u0026thinsp;5.84) and follow-up scores of 16.06 (\u0026plusmn;\u0026thinsp;5.85), while the physiological domain showed 6.68 (\u0026plusmn;\u0026thinsp;4.11) at baseline and 6.74 (\u0026plusmn;\u0026thinsp;4.08) at follow-up. Vasomotor domain scores decreased marginally from 2.47 (\u0026plusmn;\u0026thinsp;1.81) at baseline to 2.26 (\u0026plusmn;\u0026thinsp;1.67) at follow-up. Within the GCS Psychological subscales, clinically significant anxiety was observed in 348 participants (41.5%) at baseline, and 192 individuals (36.1%) at follow-up. Similarly, the Depressed subscales identified 280 cases (33.4%) meeting clinical thresholds for depression at baseline, with a follow-up prevalence of 166 participants (31.2%).\u003c/p\u003e\u003cp\u003eISI scores indicate a comparable distribution of insomnia severity between the two groups, with no significant difference in mean scores (13.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.70 vs 13.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.623). Similarly, the proportions of participants across insomnia severity categories (no, sub-threshold, moderate, severe) were not statistically different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.527). NPRS showed a slightly higher mean pain score in one group (3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29) compared to the other (3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07), although the difference in pain severity categories reached statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), suggesting differences in how participants reported pain intensity. BAT scores across all four domains showed no significant differences between groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.2 for all subscales). MRS and HrQoL scores were also similar between the groups (MRS: 20.22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.69 vs 20.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.603; HrQoL: 10.38\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37 vs 10.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.259). QPDS) scores and severity levels showed no significant differences (mean: 16.64\u0026thinsp;\u0026plusmn;\u0026thinsp;17.33 vs 16.63\u0026thinsp;\u0026plusmn;\u0026thinsp;17.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.992; categorical distribution \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.667). Overall, there were no substantial differences between groups across most domains, except for pain severity, which showed a statistically significant variation (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eParticipants\u0026apos; scale scores where\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eBold values indicate statistical significance (\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026sect;\u003cem\u003e\u0026nbsp;indicates t test, \u0026Dagger; indicates Chi-squared test, \u0026dagger; indicates Fisher\u0026rsquo;s exact test)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHADS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHADS\u003c/p\u003e\n \u003cp\u003eHADS(Anxious)\u003c/p\u003e\n \u003cp\u003eHADS(Depressed)\u003c/p\u003e\n \u003cp\u003eHADS Anxious levels\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eBorderline abnormal\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eHADS Depressed levels\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eBorderline abnormal\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.41(\u0026plusmn;8.03)\u003c/p\u003e\n \u003cp\u003e11.10(\u0026plusmn;4.55)\u003c/p\u003e\n \u003cp\u003e8.31(\u0026plusmn;4.35)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e174(20.6%)\u003c/p\u003e\n \u003cp\u003e200(23.7%)\u003c/p\u003e\n \u003cp\u003e471(55.7%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e356(42.1%)\u003c/p\u003e\n \u003cp\u003e233(27.6%)\u003c/p\u003e\n \u003cp\u003e256(30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.85(\u0026plusmn;7.93)\u003c/p\u003e\n \u003cp\u003e10.64(\u0026plusmn;4.51)\u003c/p\u003e\n \u003cp\u003e8.21(\u0026plusmn;4.31)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e138(25.7%)\u003c/p\u003e\n \u003cp\u003e128(23.8%)\u003c/p\u003e\n \u003cp\u003e272(50.6%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e241(44.8%)\u003c/p\u003e\n \u003cp\u003e144(26.8%)\u003c/p\u003e\n \u003cp\u003e153(28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.205\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.068\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.674\u0026sect;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.068\u0026Dagger;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.606\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003cp\u003eGCS(Anxious)\u003c/p\u003e\n \u003cp\u003eGCS(Depressed)\u003c/p\u003e\n \u003cp\u003eGCS(Psychological)\u003c/p\u003e\n \u003cp\u003eGCS(Physiological)\u003c/p\u003e\n \u003cp\u003eGCS(Vasomotor)\u003c/p\u003e\n \u003cp\u003eGCS Anxious levels\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eClinically Anxious\u003c/p\u003e\n \u003cp\u003eGCS Depressed levels\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eClinically Depressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.62(\u0026plusmn;10.07)\u003c/p\u003e\n \u003cp\u003e8.73(\u0026plusmn;3.12)\u003c/p\u003e\n \u003cp\u003e7.81(\u0026plusmn;3.31)\u003c/p\u003e\n \u003cp\u003e16.54(\u0026plusmn;5.84)\u003c/p\u003e\n \u003cp\u003e6.68(\u0026plusmn;4.11)\u003c/p\u003e\n \u003cp\u003e2.47(\u0026plusmn;1.81)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e491(58.5%)\u003c/p\u003e\n \u003cp\u003e348(41.5%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e559(66.6%)\u003c/p\u003e\n \u003cp\u003e280(33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.01 (\u0026plusmn;9.77)\u003c/p\u003e\n \u003cp\u003e8.56(\u0026plusmn;3.10)\u003c/p\u003e\n \u003cp\u003e7.51(\u0026plusmn;3.35)\u003c/p\u003e\n \u003cp\u003e16.06(\u0026plusmn;5.85)\u003c/p\u003e\n \u003cp\u003e6.74(\u0026plusmn;4.08)\u003c/p\u003e\n \u003cp\u003e2.26(\u0026plusmn;1.67)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e340(63.9%)\u003c/p\u003e\n \u003cp\u003e192(36.1%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e366(68.8%)\u003c/p\u003e\n \u003cp\u003e166(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.263\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.324\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.101\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.144\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.800\u0026sect;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u0026sect;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.053\u0026Dagger;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.437\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eISI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eISI\u003c/p\u003e\n \u003cp\u003eISI levels\u003c/p\u003e\n \u003cp\u003eNo insomnia\u003c/p\u003e\n \u003cp\u003eSub-threshold insomnia\u003c/p\u003e\n \u003cp\u003eModerate insomnia\u003c/p\u003e\n \u003cp\u003eSevere insomnia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.78(\u0026plusmn;6.70)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e154(19.1%)\u003c/p\u003e\n \u003cp\u003e293(36.3%)\u003c/p\u003e\n \u003cp\u003e234(29.0%)\u003c/p\u003e\n \u003cp\u003e127(15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.60(\u0026plusmn;6.65)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e108(20.4%)\u003c/p\u003e\n \u003cp\u003e183(34.5%)\u003c/p\u003e\n \u003cp\u003e167(31.5%)\u003c/p\u003e\n \u003cp\u003e72(13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.623\u0026sect;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.527\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNPRS\u003c/p\u003e\n \u003cp\u003eNPRS levels\u003c/p\u003e\n \u003cp\u003eNo pain\u003c/p\u003e\n \u003cp\u003eMild pain\u003c/p\u003e\n \u003cp\u003eModerate pain\u003c/p\u003e\n \u003cp\u003eSevere pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.87(\u0026plusmn;2.29)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46(6.2%)\u003c/p\u003e\n \u003cp\u003e308(41.3%)\u003c/p\u003e\n \u003cp\u003e293(39.3%)\u003c/p\u003e\n \u003cp\u003e98(13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.79(\u0026plusmn;2.07)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16(3.3%)\u003c/p\u003e\n \u003cp\u003e235(48.2%)\u003c/p\u003e\n \u003cp\u003e174(35.7%)\u003c/p\u003e\n \u003cp\u003e63(12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.549\u0026sect;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.028\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBAT\u003c/p\u003e\n \u003cp\u003eBAT(Exhaustion)\u003c/p\u003e\n \u003cp\u003eBAT(Mental distance)\u003c/p\u003e\n \u003cp\u003eBAT(Cognitive impairment)\u003c/p\u003e\n \u003cp\u003eBAT(Emotional impairment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.25 (\u0026plusmn;0.79)\u003c/p\u003e\n \u003cp\u003e3.53 (\u0026plusmn;0.93)\u003c/p\u003e\n \u003cp\u003e2.83 (\u0026plusmn;1.04)\u003c/p\u003e\n \u003cp\u003e3.39 (\u0026plusmn;0.90)\u003c/p\u003e\n \u003cp\u003e3.00 (\u0026plusmn;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.22 (\u0026plusmn;0.79)\u003c/p\u003e\n \u003cp\u003e3.51 (\u0026plusmn;0.89)\u003c/p\u003e\n \u003cp\u003e2.80 (\u0026plusmn;1.06)\u003c/p\u003e\n \u003cp\u003e3.37 (\u0026plusmn;0.90)\u003c/p\u003e\n \u003cp\u003e2.94 (\u0026plusmn;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.424\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.600\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.651\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.609\u0026sect;\u003c/p\u003e\n \u003cp\u003e0.227\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.22 (\u0026plusmn;7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.00 (\u0026plusmn;7.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.603\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHrQoL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHrQoL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.38 (\u0026plusmn;6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.75 (\u0026plusmn;5.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.259\u0026sect;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQPDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQPDS\u003c/p\u003e\n \u003cp\u003eQBDS levels\u003c/p\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003cp\u003eVery Severe\u003c/p\u003e\n \u003cp\u003eExtreme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.64(\u0026plusmn;17.33)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e377(45.9%)\u003c/p\u003e\n \u003cp\u003e293(35.7%)\u003c/p\u003e\n \u003cp\u003e110(13.4%)\u003c/p\u003e\n \u003cp\u003e36(4.4%)\u003c/p\u003e\n \u003cp\u003e5(0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.63(\u0026plusmn;17.84)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e253(47.8%)\u003c/p\u003e\n \u003cp\u003e177(33.5%)\u003c/p\u003e\n \u003cp\u003e74(14.0%)\u003c/p\u003e\n \u003cp\u003e19(4.4%)\u003c/p\u003e\n \u003cp\u003e6(1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.992\u0026sect;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.667\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eScale Outcomes across Menopausal Stages\u003c/h2\u003e\n \u003cp\u003eHADS revealed significant differences in anxiety levels across menopausal stages at both baseline and follow-up (Chi-squared test: baseline p\u0026thinsp;=\u0026thinsp;0.010; follow-up p\u0026thinsp;=\u0026thinsp;0.025). Post-hoc analyses confirmed that perimenopausal women exhibited significantly higher anxiety than postmenopausal women (baseline p\u0026thinsp;=\u0026thinsp;0.010; follow-up p\u0026thinsp;=\u0026thinsp;0.025). Significant differences in total GCS scores were observed across baseline p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and follow-up p\u0026thinsp;=\u0026thinsp;0.007. Post-hoc comparisons indicated menopausal women scored higher than post-menopausal women at both timepoints (p\u0026thinsp;\u0026lt;\u0026thinsp;0.010; follow-up p\u0026thinsp;=\u0026thinsp;0.047). Although the scores of Anxious (baseline: p\u0026thinsp;=\u0026thinsp;0.010, follow-up: p\u0026thinsp;=\u0026thinsp;0.027) and Depressed (baseline: p\u0026thinsp;=\u0026thinsp;0.002, follow-up: p\u0026thinsp;=\u0026thinsp;0.002) on the indicated significant differences among different menopausal stages, depression at follow-up was statistically significant (p\u0026thinsp;=\u0026thinsp;0.011). The differences in GCS Physiological domain among different menopausal stages were significant (baseline: p\u0026thinsp;=\u0026thinsp;0.003, follow-up: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Post-hoc pairwise comparisons showed a higher p-value during perimenopause than post-menopause score (baseline: p\u0026thinsp;=\u0026thinsp;0.029, follow-up: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). BAT demonstrated significant burnout variations across menopausal stages at baseline (p\u0026thinsp;=\u0026thinsp;0.017) and follow-up (p\u0026thinsp;=\u0026thinsp;0.018). Post-hoc analyses showed severe burnout among perimenopausal women in comparison to post-menopausal women (baseline p\u0026thinsp;=\u0026thinsp;0.031; follow-up p\u0026thinsp;=\u0026thinsp;0.020). Further details are provided in Tables \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eScale scores across different menopausal stages (baseline data)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBold values indicate statistical significance (* indicates Kruskal-Wallis test, ^ indicates Anova test, \u0026Dagger; indicates Chi-squared test, \u0026dagger; indicates Fisher\u0026rsquo;s exact test)\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eEach Menopausal stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerimenopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMenopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHADS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHADS\u003c/p\u003e\n \u003cp\u003eHADS(Anxious)\u003c/p\u003e\n \u003cp\u003eHADS(Depressed)\u003c/p\u003e\n \u003cp\u003eHADS Anxious levels\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eBorderline abnormal\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eHADS Depressed levels\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eBorderline abnormal\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.71 (\u0026plusmn;7.84)\u003c/p\u003e\n \u003cp\u003e11.38 (\u0026plusmn;4.44)\u003c/p\u003e\n \u003cp\u003e8.33(\u0026plusmn;4.26)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58 (17.7%)\u003c/p\u003e\n \u003cp\u003e70 (21.3%)\u003c/p\u003e\n \u003cp\u003e200 (61.0%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e136 (41.5%)\u003c/p\u003e\n \u003cp\u003e98 (29.9%)\u003c/p\u003e\n \u003cp\u003e94 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.00 (\u0026plusmn;7.99)\u003c/p\u003e\n \u003cp\u003e11.40 (\u0026plusmn;4.57)\u003c/p\u003e\n \u003cp\u003e8.65(\u0026plusmn;4.30)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55 (18.4%)\u003c/p\u003e\n \u003cp\u003e78 (26.1%)\u003c/p\u003e\n \u003cp\u003e166 (55.5%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e119 (39.8%)\u003c/p\u003e\n \u003cp\u003e77 (25.8%)\u003c/p\u003e\n \u003cp\u003e103 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.14 (\u0026plusmn;8.26)\u003c/p\u003e\n \u003cp\u003e10.31 (\u0026plusmn;4.53)\u003c/p\u003e\n \u003cp\u003e7.84 (\u0026plusmn;4.61)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54 (28.4%)\u003c/p\u003e\n \u003cp\u003e46 (24.2%)\u003c/p\u003e\n \u003cp\u003e90 (47.4%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90 (47.4%)\u003c/p\u003e\n \u003cp\u003e47 (24.7%)\u003c/p\u003e\n \u003cp\u003e53 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032^\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.012*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.098*\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u0026Dagger;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.242\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003cp\u003eGCS(Anxious)\u003c/p\u003e\n \u003cp\u003eGCS(Depressed)\u003c/p\u003e\n \u003cp\u003eGCS(Psychological)\u003c/p\u003e\n \u003cp\u003eGCS(Physiological)\u003c/p\u003e\n \u003cp\u003eGCS(Vasomotor)\u003c/p\u003e\n \u003cp\u003eGCS Anxious levels\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eClinically Anxious\u003c/p\u003e\n \u003cp\u003eGCS Depressed levels\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eClinically Depressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.65 (\u0026plusmn;9.65)\u003c/p\u003e\n \u003cp\u003e8.86 (\u0026plusmn;3.03)\u003c/p\u003e\n \u003cp\u003e7.98 (\u0026plusmn;3.12)\u003c/p\u003e\n \u003cp\u003e16.85 (\u0026plusmn;5.55)\u003c/p\u003e\n \u003cp\u003e6.75 (\u0026plusmn;4.13)\u003c/p\u003e\n \u003cp\u003e2.23 (\u0026plusmn;1.64)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e189 (58.0%)\u003c/p\u003e\n \u003cp\u003e137 (42.0%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e220 (67.5%)\u003c/p\u003e\n \u003cp\u003e106 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.18 (\u0026plusmn;9.84)\u003c/p\u003e\n \u003cp\u003e9.02 (\u0026plusmn;3.03)\u003c/p\u003e\n \u003cp\u003e8.12 (\u0026plusmn;3.18)\u003c/p\u003e\n \u003cp\u003e17.14 (\u0026plusmn;5.60)\u003c/p\u003e\n \u003cp\u003e7.13 (\u0026plusmn;4.12)\u003c/p\u003e\n \u003cp\u003e2.86 (\u0026plusmn;1.97)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e169 (56.9%)\u003c/p\u003e\n \u003cp\u003e128 (43.1%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e186 (62.6%)\u003c/p\u003e\n \u003cp\u003e111 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.46 (\u0026plusmn;10.20)\u003c/p\u003e\n \u003cp\u003e8.22 (\u0026plusmn;3.28)\u003c/p\u003e\n \u003cp\u003e7.03 (\u0026plusmn;3.51)\u003c/p\u003e\n \u003cp\u003e15.25 (\u0026plusmn;6.25)\u003c/p\u003e\n \u003cp\u003e5.81 (\u0026plusmn;3.85)\u003c/p\u003e\n \u003cp\u003e2.32 (\u0026plusmn;1.69)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e114 (60.6%)\u003c/p\u003e\n \u003cp\u003e74 (39.4%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e136 (72.3%)\u003c/p\u003e\n \u003cp\u003e52 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001^\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.039*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.002*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.005*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.003*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.715\u0026Dagger;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.082\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eISI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eISI\u003c/p\u003e\n \u003cp\u003eISI levels\u003c/p\u003e\n \u003cp\u003eNo insomnia\u003c/p\u003e\n \u003cp\u003eSub-threshold insomnia\u003c/p\u003e\n \u003cp\u003eModerate insomnia\u003c/p\u003e\n \u003cp\u003eSevere insomnia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.34 (\u0026plusmn;6.64)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64 (20.6%)\u003c/p\u003e\n \u003cp\u003e114 (36.8%)\u003c/p\u003e\n \u003cp\u003e89 (28.7%)\u003c/p\u003e\n \u003cp\u003e43 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.71 (\u0026plusmn;6.44)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (15.6%)\u003c/p\u003e\n \u003cp\u003e99 (34.3%)\u003c/p\u003e\n \u003cp\u003e95 (32.9%)\u003c/p\u003e\n \u003cp\u003e50 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.28 (\u0026plusmn;7.10)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39 (21.2%)\u003c/p\u003e\n \u003cp\u003e71 (38.6%)\u003c/p\u003e\n \u003cp\u003e44 (23.9%)\u003c/p\u003e\n \u003cp\u003e30 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.267\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNPRS\u003c/p\u003e\n \u003cp\u003eNPRS levels\u003c/p\u003e\n \u003cp\u003eNo pain\u003c/p\u003e\n \u003cp\u003eMild pain\u003c/p\u003e\n \u003cp\u003eModerate pain\u003c/p\u003e\n \u003cp\u003eSevere pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.81 (\u0026plusmn;2.30)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20 (6.8%)\u003c/p\u003e\n \u003cp\u003e115 (39.1%)\u003c/p\u003e\n \u003cp\u003e123 (41.8%)\u003c/p\u003e\n \u003cp\u003e36 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.89 (\u0026plusmn;2.14)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (4.2%)\u003c/p\u003e\n \u003cp\u003e118 (45.4%)\u003c/p\u003e\n \u003cp\u003e100 (38.5%)\u003c/p\u003e\n \u003cp\u003e31 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.93 (\u0026plusmn;2.39)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (5.9%)\u003c/p\u003e\n \u003cp\u003e69 (40.6%)\u003c/p\u003e\n \u003cp\u003e65 (38.2%)\u003c/p\u003e\n \u003cp\u003e26 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.908*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.599\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBAT\u003c/p\u003e\n \u003cp\u003eBAT(Exhaustion)\u003c/p\u003e\n \u003cp\u003eBAT(Mental distance)\u003c/p\u003e\n \u003cp\u003eBAT(Cognitive impairment)\u003c/p\u003e\n \u003cp\u003eBAT(Emotional impairment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.30 (\u0026plusmn;0.78)\u003c/p\u003e\n \u003cp\u003e3.58 (\u0026plusmn;0.90)\u003c/p\u003e\n \u003cp\u003e2.91 (\u0026plusmn;1.01)\u003c/p\u003e\n \u003cp\u003e3.43 (\u0026plusmn;0.88)\u003c/p\u003e\n \u003cp\u003e3.04 (\u0026plusmn;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.32 (\u0026plusmn;0.76)\u003c/p\u003e\n \u003cp\u003e3.65 (\u0026plusmn;0.89)\u003c/p\u003e\n \u003cp\u003e2.85 (\u0026plusmn;1.03)\u003c/p\u003e\n \u003cp\u003e3.44 (\u0026plusmn;0.91)\u003c/p\u003e\n \u003cp\u003e3.06 (\u0026plusmn;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.11 (\u0026plusmn;0.86)\u003c/p\u003e\n \u003cp\u003e3.34 (\u0026plusmn;1.02)\u003c/p\u003e\n \u003cp\u003e2.69 (\u0026plusmn;1.11)\u003c/p\u003e\n \u003cp\u003e3.33 (\u0026plusmn;0.93)\u003c/p\u003e\n \u003cp\u003e2.85 (\u0026plusmn;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.006*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.069*\u003c/p\u003e\n \u003cp\u003e0.364*\u003c/p\u003e\n \u003cp\u003e0.068*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.75 (\u0026plusmn;7.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.44 (\u0026plusmn;7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.54 (\u0026plusmn;7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHrQoL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHrQoL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.17 (\u0026plusmn;6.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.69 (\u0026plusmn;6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.65 (\u0026plusmn;6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.181*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQPDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQPDS\u003c/p\u003e\n \u003cp\u003eQBDS levels\u003c/p\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003cp\u003eVery Severe\u003c/p\u003e\n \u003cp\u003eExtreme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.93 (\u0026plusmn;15.64)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e149 (47%)\u003c/p\u003e\n \u003cp\u003e124 (39.1%)\u003c/p\u003e\n \u003cp\u003e33 (10.4%)\u003c/p\u003e\n \u003cp\u003e10 (3.2%)\u003c/p\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.28 (\u0026plusmn;18.33)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e129 (43.9%)\u003c/p\u003e\n \u003cp\u003e97 (33%)\u003c/p\u003e\n \u003cp\u003e50 (17%)\u003c/p\u003e\n \u003cp\u003e16 (5.4%)\u003c/p\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.04 (\u0026plusmn;18.61)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e87 (47.3%)\u003c/p\u003e\n \u003cp\u003e63 (34.2%)\u003c/p\u003e\n \u003cp\u003e22 (12%)\u003c/p\u003e\n \u003cp\u003e10 (5.4%)\u003c/p\u003e\n \u003cp\u003e2 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.142*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.179\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePost-hoc pairwise comparisons for Scale scores across different menopausal stages\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScales\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eP-value of Pairwise comparisons\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerimenopause\u003c/p\u003e\n \u003cp\u003evs. Menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerimenopause vs.\u003c/p\u003e\n \u003cp\u003ePost-menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMenopause vs.\u003c/p\u003e\n \u003cp\u003ePost-menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHADS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHADS\u003c/p\u003e\n \u003cp\u003eHADS(Anxious)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003cp\u003eGCS(Anxious)\u003c/p\u003e\n \u003cp\u003eGCS(Depressed)\u003c/p\u003e\n \u003cp\u003eGCS(Psychological)\u003c/p\u003e\n \u003cp\u003eGCS(Physiological)\u003c/p\u003e\n \u003cp\u003eGCS(Vasomotor)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAT\u003c/p\u003e\n \u003cp\u003eBAT(Exhaustion)\u003c/p\u003e\n \u003cp\u003eBAT(Emotional impairment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003eFollow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBold values indicate statistically significant differences (Scheff\u0026eacute;\u0026apos;s method for ANOVA; Dunn\u0026apos;s test with Holm correction for Kruskal-Wallis).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eEmployed women experiencing insomnia may face compounded health burdens, particularly when sleep disturbances co-occur with pain and psychological distress. The study highlights moderate to severe levels of back pain and burnout across menopausal stages, which, in employed individuals, may reduce productivity, affect mental health, and contribute to work-related stress. Psychological symptoms such as anxiety and depression more prevalent in perimenopausal and surgically menopausal women can further disrupt sleep quality, creating a cyclical relationship between mental distress, pain perception, and fatigue. For those with coexisting conditions like endometriosis, which itself is associated with chronic pelvic and back pain, this burden is amplified. Women undergoing medical menopause due to conditions such as endometriosis may experience abrupt hormonal shifts, leading to more intense vasomotor symptoms, mood instability, and pain exacerbation. These intersecting challenges can severely impact functioning in the workplace and daily life, underscoring the need for comprehensive, multidisciplinary management. Interventions must address pain relief, sleep support, and psychological care, especially in occupational settings, to improve overall quality of life and prevent long-term disability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eSymptom severity\u003c/h2\u003e\n \u003cp\u003eSymptom severity varied markedly across menopausal stages and types (Fig.\u0026nbsp;2). Perimenopausal participants reported the highest levels of anxiety and cognitive disruption, including difficulty concentrating and forgetfulness, as measured by HADS and the Burnout Assessment Tool. Women undergoing surgical menopause consistently demonstrated the most severe depressive symptoms, sleep disturbance, vasomotor complaints, and pain, particularly back pain, across both timepoints. The Greene Climacteric Scale revealed greater psychological symptom burden in perimenopausal individuals, while the MRS and ISI highlighted heightened somatic and sleep-related difficulties in the surgical menopause group. In contrast, postmenopausal participants reported significantly lower symptom severity across most domains. Despite this, quality of life scores remained moderately impaired in all groups, with a greater burden observed in those experiencing complex or medically induced menopausal transitions. Notably, employed women with pre-existing conditions such as endometriosis experienced amplified symptoms across psychological, cognitive, and somatic domains, particularly when undergoing medical\u003c/p\u003e\n \u003cp\u003emenopause. Recurrent \u0026amp; Refractory Endometriosis with or without subfertility have enormous impact on variability of menopausal symptoms. These patterns underscore the heterogeneous nature of menopausal symptomatology and the need for stratified support.\u003c/p\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSymptom Profile by Menopausal Stage\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eNatural Menopause\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSymptom Domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerimenopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMenopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost\u003cstrong\u003e-\u003c/strong\u003emenopause\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep Disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVasomotor Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychological Distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuality of Life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSurgical Menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep Disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVasomotor Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychological Distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuality of Life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMedical Menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep Disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVasomotor Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychological Distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuality of Life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eContextual summary from qualitative interviews\u003c/h2\u003e\n \u003cp\u003eWidespread dissatisfaction with NHS GP services among women navigating menopause, with 76% (n\u0026thinsp;=\u0026thinsp;38) expressing discontent and many turning to private care due to barriers in access and quality of care. Participants strongly advocated for GP-level menopause training and the integration of menopause clinics into primary care, highlighting structural deficits in current provision. Limited access to specialist gynaecologists, especially for those experiencing complex or surgical menopause, points to systemic bottlenecks and unmet informational needs. Delays in diagnosis and treatment were frequently attributed to inadequate GP knowledge and fragmented commissioning of specialist services. Furthermore, the underutilisation of non-hormonal treatments, despite their relevance for certain individuals, reflects a lack of inclusive care pathways. Policy-level insights demonstrated overwhelming support for multidisciplinary teams (84%) and educational resources (88%), signalling a collective call for systemic reform. These findings underscore the urgency for a nationally coordinated, patient-centred women\u0026rsquo;s health strategy embedded within NHS structures to address long-standing gaps in menopause care.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eWorkplace impact\u003c/h2\u003e\n \u003cp\u003eCritical gaps in workplace support for menopausal individuals were identified with 64% (n\u0026thinsp;=\u0026thinsp;32) reporting a negative impact on their ability to work and only 36% (n\u0026thinsp;=\u0026thinsp;18) receiving any form of support. Limited access to formal policies with only 22% reporting a menopause policy. Approximately 28% confirmed flexible working was on offer. Alarmingly, some participants had left employment entirely due to menopause-related difficulties, raising concerns around workforce retention and economic inequality. Poor coping was strongly associated with absence of support structures, with 30% (n\u0026thinsp;=\u0026thinsp;15) reporting inadequate coping, particularly where no policies or flexibility were in place. Conversely, access to supportive measures was linked to better workplace outcomes, reinforcing the importance of adaptation. These findings point to an urgent need for menopause-inclusive occupational health frameworks, mandatory workplace policies, and gender-sensitive human-reso strategies to promote wellbeing and equity for menopausal staff and ensure sustained participation in the labour market.\u003c/p\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eis the contextual overview from qualitative interviews conducted among 50 participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ePrimary care\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAcute care\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePolicy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot happy with the GP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHappy with the GP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing a private GP due to issues with NHS GP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarriers with GP Practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWould want the GPs to be trained in menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGP practice should have menopause clinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnder the care of a NHS gynaecologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnder the care of a private gynaecologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHad a radical hysterectomy and would like more information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational material should be provided\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA multidisciplinary team dedicated to menopause will be good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eI would like to see a womens health policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI would like to see a menopause friendly policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv\u003e\u003cstrong\u003eTable 10 indicates the impact in the work-place based on qualitative interviews (n=50)\u003c/strong\u003e\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eImpact in the work place\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegatively impacted work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupport provided by the work place\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMenopause policy available at work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlexible working available at work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoping at work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot coping well at work\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eParticipant Not working\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eParticipant Not working ; left due to menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eParticipant Not working\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eParticipant Not working; left due to menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eParticipant Not working; left due to menopause\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis UK-based cohort study provides a rich overview of the lived experience of menopause through a multidimensional lens, encompassing psychological, physiological, and quality of life indicators. Significant differences emerged across menopausal stages and statuses, with perimenopausal participants experiencing higher anxiety than their postmenopausal counterparts, and women with surgical menopause demonstrating greater severity of depressive symptoms, pain, vasomotor disturbances, and insomnia than those undergoing natural menopause. These outcomes were consistently observed across the HADS, GCS, ISI, NPRS and BAT. Additionally, regional comparisons revealed that women in Scotland reported higher pain levels compared to their counterparts in England, although most other symptom scores did not differ significantly by geography after adjustment for menopausal stage. Greater cognitive impairment among perimenopausal women compared to post-menopausal counterparts suggests the need for early recognition and supportive interventions during this transitional stages. Overall, the study reflects substantial variability in menopausal symptoms and outcomes, highlighting the interplay between biological, medical, and contextual factors. This has been utilised to co-create the MARiE intervention toolkit.\u003c/p\u003e\u003cp\u003eClinically significant interpretation of these findings indicates a marked disparity in symptom severity between women experiencing surgical and natural menopause. Surgical menopause was associated with significantly higher depression and burnout scores, more severe vasomotor and physiological symptoms, and a reduced health-related quality of life. These differences remained statistically significant after controlling for menopausal stage, indicating that they are not simply a function of timing but are intrinsically linked to the abrupt hormonal changes following oophorectomy or hysterectomy. In contrast, the more gradual hormonal transition in natural menopause may allow for greater physiological adaptation, which could explain the comparatively lower symptom burden. The results also suggest that perimenopause, though often overlooked, is a period of significant vulnerability, especially with respect to anxiety and psychological distress. These insights emphasise the need for stratified care and early intervention strategies that account for both the type and timing of menopause.\u003c/p\u003e\u003cp\u003e The study demonstrates a bidirectional relationship between mental and physical health, particularly the cognitive and affective consequences of abrupt hormonal shifts in surgical menopause although this is insufficiently explored in current guidelines. While the British Menopause Society (BMS)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e promotes individualised care, operational guidance remains primarily focused on vasomotor and genitourinary symptoms, often underemphasising cognitive, psychological, and regional contextual differences. Notably, the study\u0026rsquo;s findings of elevated pain levels in Scottish women and the consistent cognitive burden in perimenopause point to geographic and temporal factors that NICE\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and BMS documents do not currently reflect. This critical comparison reveals that although current UK guidelines provide a foundational framework for menopause management, they do not adequately capture the stratified, intersectional, and psychosocial complexities highlighted by the MARIE WP2a UK-cohort. The findings argue for urgent guideline refinement, integrating tailored mental health support, early interventions for perimenopausal women, and explicit attention to surgical menopause as a distinct clinical trajectory requiring targeted care pathways.\u003c/p\u003e\u003cp\u003eThe most striking finding was the consistently poorer outcomes among women who experienced surgical menopause, which had a cascading effect across multiple dimensions of wellbeing. This group reported higher levels of depression, insomnia, pain, and vasomotor symptoms, alongside reduced quality of life and increased burnout collectively reflecting a more intense and sustained symptom experience. For individuals undergoing surgical menopause, these findings carry significant personal and clinical relevance. They may face an elevated risk of psychological distress and somatic burden without adequate support. For many, the procedure is medically necessary, yet the lack of structured, long-term support services post-surgery can compound health challenges. This evidence underscores the importance of proactive education, shared decision-making, and the provision of tailored support pathways, including mental health services and hormone replacement therapy, where clinically appropriate. Women who undergo surgical menopause must not be treated as a homogenous group; rather, they require targeted support to mitigate these elevated risks.\u003c/p\u003e\u003cp\u003eGiven the unique situations and the real-world impact that emerges from this cohort study, it's clear that the considerations of shared-decision making and personalisation of care processes should be non-negotiable. The NHS in England endorses and promotes shared-decision making as one of the core pillars of clinical care, especially for long-term conditions like menopause where essential phrases such as \u0026lsquo;hormone therapy\u0026rsquo; and \u0026lsquo;non-hormonal strategies\u0026rsquo; require more than just clinical approval-they must reflect the individual's values, preferences, and lived experiences\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. As with several critical aspects of women's health, the World Health Organization (WHO) underscores the importance of shared decision making as a fundamental quality care principle at its viscid centre which is equally impacted by social, biological, and psychological dimensions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition, the UK Women\u0026rsquo;s Health Strategy for England outlines concerning gaps in the healthcare systems with respect to women's lifespan issues, specifically mentioning menopause where women often feel \"dismissed\" or \u0026ldquo;unsupported\u0026rdquo; by the system \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Our findings echo this with an emphasis on a need to improve psychological relief during perimenopause. The Royal College of Obstetricians and Gynaecologists (RCOG) advocates for an improved integrated, multi-disciplinary approach with more physical, mental, and sexual health components and calls for a shift from symptom-based care to a more personalised, holistic care model\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This includes screening for cognitive and emotional impact, especially in those with surgical menopause, and the provision of menopause education at primary and specialist care levels.\u003c/p\u003e\u003cp\u003eMigrant women are of particular concern regarding health inequalities because they are likely hidden in data and excluded from the provision of services\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The UK Migrant Health Guide recognises menopause as an emerging health issue, noting that culturally stigmatised disorders, coupled with the need for primary care, result in poor access to treatment and care which, ultimately, leads to the poor health outcomes due to over-stretched primary care services\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Culturally sensitive care is needed not only for ethical reasons but also for effective clinical practice. Policy makers need to design stratified health care systems for migrant minorities, women with less socioeconomic status and ethnic underclasses, taking into account cultural beliefs and the interpretation of symptoms, and health seeking behaviours and pathways.\u003c/p\u003e\u003cp\u003eIn this context, studies such as the MARIE project are positioned perfectly to close this gap by incorporating stratified, multicultural, multi-perspectival data into menopausal women\u0026rsquo;s decision support systems. The UK Women\u0026rsquo;s Health Strategy is another example of such data on which the consultation was based on\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The King's Fund report showcase that their basic need revolves around being heard, seriously attended to, and being provided options that are both respectful and relevant to their situations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Until such principles are seamlessly integrated into policies and clinical practices, the discrepancy between evidence and implementation will continue.\u003c/p\u003e\u003cp\u003eThese findings carry important implications for clinical management and policy frameworks in the UK and globally. Clinical guidelines should reflect the distinct needs of women undergoing surgical, medical and natural menopause, incorporating routine mental health screening and offering multidisciplinary management approaches that include pain, sleep, and emotional wellbeing interventions. The striking symptom burden in perimenopause also demands earlier recognition and intervention before the formal onset of menopause particularly given that anxiety and psychological symptoms were most pronounced at this stage. From a policy perspective, menopause care must be reframed through a life-course, person-centred approach that moves beyond a \u0026ldquo;one-size-fits-all\u0026rdquo; model. National strategies should integrate menopausal health into broader women's health policies and prioritise access to culturally competent care, especially for underserved groups. Importantly, the evidence presented here strengthens the rationale for initiatives such as the MARIE project, which aims to develop inclusive, data-driven tools that are reflective of the diverse menopausal experiences across populations and healthcare systems.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThis study possesses several notable strengths, foremost among them its large, well-characterised UK cohort and the use of both baseline and follow-up assessments, which enable the exploration of symptom trajectories over time. The inclusion of diverse menopausal stages and statuses perimenopause, natural, surgical, and medically induced menopause allows for meaningful subgroup comparisons, enhancing the clinical relevance of the findings. The application of multiple validated instruments such as HADS, GCS, ISI, BAT, MRS, QPDS, and HrQoL strengthens internal validity by offering a multidimensional perspective on psychological, somatic, and quality of life outcomes. The use of statistical adjustments for menopausal stage in subgroup analyses further refines the interpretation of observed effects. Additionally, the study\u0026rsquo;s inclusion of geographical and socio-demographic data provides valuable context for understanding regional variations and potential disparities. The study offers a robust foundation for informing tailored interventions and policy responses to menopausal health.\u003c/p\u003e\u003cp\u003eLimitations include predominantly white and highly educated, limiting the generalisability of findings to more ethnically and socioeconomically diverse populations despite greater attempts to engage these populations. Attrition between baseline and follow-up reduced the analytical sample. Participation from Wales and Northern Ireland restricted the power of comparative analyses. Finally, although symptom severity was explored, hormonal levels and clinical diagnoses were not corroborated with biological data to make definitive inferences regarding physiological mechanisms due to limited funding.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDifferences in psychological, physiological, and quality of life outcomes across menopausal stages and statuses indicate greater awareness is warranted alongside of funding for research and NHS service delivery. The complex intersecting physical and mental health impact of menopause indicate a necessity for tailored evidence-based interventions and their testing, such as the MARiE tool. Healthcare services and workplace policies must evolve to better support the diverse and prolonged experiences of menopause, especially among medically vulnerable and working populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNIHR Research Capability Fund\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eAll authors report no conflict of interest. The views expressed are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Department of Health and Social Care or the Academic institutions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe PIs and the study sponsor may consider sharing anonymous data upon reasonable a request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eGD developed the ELEMI program and the MARIE project. This was furthered by GD and PP. GD, KE, PP, JT, LS and HFK submitted and secured the ethics approval for the study. KM, VC, LS, KR, SH, KP, GD, PP, VT, RP and HFK collected data. JS, JQS and GD conducted the data analysis. GD wrote the first draft and was furthered by all other authors. VP and PP edited and formatted all versions of the manuscript. All authors critically appraised, reviewed and commented on all versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eHealth Research Authority and Health and Care Research Wales Approval (22/EE/0158)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eObtained\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors consented to publish this manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: \u003cstrong\u003eMARIE Consortium:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAini Hanan binti Azmi, Alyani binti Mohamad Mohsin, Arinze Anthony Onwuegbuna, Artini binti Abidin, Ayyuba Rabiu, Chijioke Chimbo, Chinedu Onwuka Ndukwe, Choon-Moy Ho, Chinyere Ukamaka Onubogu, Diana Chin-Lau Suk, Divinefavour Echezona Malachy, Emmanuel Chukwubuikem Egwuatu, Eunice Yien-Mei Sim, Farhawa binti Zamri, Fatin Imtithal binti Adnan, Geok-Sim Lim, Halima Bashir Muhammad, Ifeoma Bessie Enweani-Nwokelo, Ikechukwu Innocent Mbachu, Jinn-Yinn Phang, John Yen-Sing Lee, Joseph Ifeanyichukwu Ikechebelu, Juhaida binti Jaafar, Karen Christelle, Kathryn Elliot, Kim-Yen Lee, Kingsley Chidiebere Nwaogu, Lee-Leong Wong, Lydia Ijeoma Eleje, Min-Huang Ngu, Noorhazliza binti Abdul Patah, Nor Fareshah binti Mohd Nasir, Kathleen Riach, Norhazura binti Hamdan, Nnanyelugo Chima Ezeora, Nnaedozie Paul Obiegbu, Nurfauzani binti Ibrahim, Nurul Amalina Jaafar, Odigonma Zinobia Ikpeze, Obinna Kenneth Nnabuchi, Pooja Lama, Puong-Rui Lau, Rakshya Parajuli, Rakesh Swarnakar, Raphael Ugochukwu Chikezie, Rosdina Abd Kahar, Safilah Binti Dahian, Sapana Amatya, Sing-Yew Ting, Siti Nurul Aiman, Sunday Onyemaechi Oriji, Susan Chen-Ling Lo, Sylvester Onuegbunam Nweze, Damayanthi Dasanayaka, Nimesha Wijayamuni, Prasanna Herath, Thamudi Sundarapperuma, Jeevan Dhanasiri, Vaitheswariy Rao, Xin-Sheng Wong, Xiu-Sing Wong, Yee-Theng Lau, Heitor Cavalini, Jean Pierre Gafaranga, Emmanuel Habimana, Chigozie Geoffrey Okafor, Assumpta Chiemeka Osunkwo, Gabriel Chidera Edeh, Esther Ogechi John, Kenechukwu Ezekwesili Obi, Oludolamu Oluyemesi Adedayo, Odili Aloysius Okoye, Chukwuemeka Chukwubuikem Okoro, Ugoy Sonia Ogbonna, Chinelo Onuegbuna Okoye, Babatunde Rufus Kumuyi, Onyebuchi Lynda Ngozi, Nnenna Josephine Egbonnaji, Oluwasegun Ajala Akanni, Perpetua Kelechi Enyinna, Yusuf Alfa, Theresa Nneoma Otis, Catherine Larko Narh Menka, Kwasi Eba Polley, Isaac Lartey Narh, Bernard B. Borteih, Andy Fairclough, Kingsley Emeka Ekwuazi, Michael Nnaa Otis, Jeremy Van Vlymen, Chidiebere Agbo, Francis Chibuike Anigwe, Kingsley Chukwuebuka Agu, Chiamaka Perpetua Chidozie, Chidimma Judith Anyaeche, Clementine Kanazayire, Jean Damascene Hanyurwimfura, Nwankwo Helen Chinwe, Stella Matutina Isingizwe, Jean Marie Vianney Kabutare, Dorcas Uwimpuhwe, Melanie Maombi, Ange Kantarama, Uchechukwu Kevin Nwanna, Benedict Erhite Amalimeh, Theodomir Sebazungu, Elius Tuyisenge, Yvonne Delphine Nsaba Uwera, Emmanuel Habimana, Nasiru Sani and Amarachi Pearl Nkemdirim\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTariq B, Phillips S, Biswakarma R, Talaulikar V, Harper JC (2023) Women\u0026rsquo;s knowledge and attitudes to the menopause: a comparison of women over 40 who were in the perimenopause, post menopause and those not in the peri or post menopause. BMC Womens Health 23(1):460\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrandall CJ, Mehta JM, Manson JE (2023) Management of menopausal symptoms: a review. JAMA 329(5):405\u0026ndash;420\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelanerolle G, Phiri P, Elneil S et al (2025) Menopause: a global health and wellbeing issue that needs urgent attention. Lancet Global Health 13(2):e196\u0026ndash;e8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlsugeir D, Wei L, Adesuyan M, Cook S, Panay N, Brauer R (2022) Hormone replacement therapy prescribing in menopausal women in the UK: a descriptive study. BJGP open ; 6(4)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKingsberg SA, Larkin LC, Liu JH (2020) Clinical effects of early or surgical menopause. Obstet Gynecol 135(4):853\u0026ndash;868\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCurrie H, Abernethy K, Hamoda H (2021) Vision for menopause care in the UK. Post Reproductive Health 27(1):10\u0026ndash;18\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiach K, Jack G (2021) Women\u0026rsquo;s health in/and work: Menopause as an intersectional experience. Int J Environ Res Public Health 18(20):10793\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCort\u0026eacute;s YI, Marginean V (2022) Key factors in menopause health disparities and inequities: beyond race and ethnicity. Curr Opin Endocr metabolic Res 26:100389\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerdonk P, Bendien E, Appelman Y (2022) Menopause and work: A narrative literature review about menopause, work and health. Work 72(2):483\u0026ndash;496\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelanerolle G, Ramakrishnan R, Hapangama D et al (2021) A systematic review and meta-analysis of the Endometriosis and Mental-Health Sequelae; The ELEMI Project. Women's Health 17:17455065211019717\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBritish Menopause Society. BMS Guidelines (2025) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://thebms.org.uk/publications/bms-guidelines/\u003c/span\u003e\u003cspan address=\"https://thebms.org.uk/publications/bms-guidelines/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 16-07-2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Institute for Health and Care Excellence (2025) Menopause: identification and management. 07 November 2024 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nice.org.uk/guidance/ng23\u003c/span\u003e\u003cspan address=\"https://www.nice.org.uk/guidance/ng23\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 16-07-\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNHS England Personalised care, shared decision making. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.england.nhs.uk/personalisedcare/shared-decision-making/\u003c/span\u003e\u003cspan address=\"https://www.england.nhs.uk/personalisedcare/shared-decision-making/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 18-07-2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. The life-course approach: from theory to practice: case stories from two small countries in Europe (2021) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/europe/publications/i/item/9789289053266\u003c/span\u003e\u003cspan address=\"https://www.who.int/europe/publications/i/item/9789289053266\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 18-07-2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDepartment of Health \u0026amp; Social Care (2022) E. Women's Health Strategy for England\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoyal College of Obstetricians and Gynaecologists Better for women. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcog.org.uk/better-for-women/\u003c/span\u003e\u003cspan address=\"https://www.rcog.org.uk/better-for-women/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 18-07-2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOffice for Health Improvement (2014) and Disparities. Women's health: migrant health guide\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharlotte Wickens DJ (2022) Has the Women\u0026rsquo;s Health Strategy listened to what women really need? The King's Fund\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Hamphire and Isle of Wight healthcare NHS foundation trust","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7471671/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7471671/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMenopause is associated with diverse physical and mental symptoms, yet variation across menopausal stages and modes of onset remains poorly characterised in United Kingdom (UK) based populations. This study aimed to evaluate symptom burden and quality of life across perimenopausal, menopausal, and postmenopausal individuals, including those with natural and surgical menopause.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA mixed-methods, prospective cohort study was conducted to explore perimenopausal, menopausal and post-menopausal experience in cis women, transgender and LGBTQ\u0026thinsp;+\u0026thinsp;populations in England, Wales, Scotland and Northern Ireland using the digital XM Qualtrics platform with psychometric and clinical scales including the Hospital Anxiety and Depression Scale (HADS), Greene Climacteric Scale (GCS), Insomnia Severity Index (ISI), Burnout Assessment Tool (BAT), Numeric Pain Rating Scale (NPRS), Menopause Rating Scale (MRS), and health-related quality of life (HrQoL) assessments. Quantitative data were gathered following informed consent at two time points, and qualitative interviews were conducted in a selected sub-cohort.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 845 baseline and 538 follow-up participants (median age 52), anxiety severity was significantly higher during perimenopause compared to post-menopause (baseline: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013; follow-up: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), while depressive symptoms were markedly greater in those with surgical menopause, as shown by both HADS (baseline: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; follow-up: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and GCS scores (baseline: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; follow-up: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). People experiencing surgical menopause displayed increased insomnia, back pain, vasomotor symptoms, and reduced quality of life. Cognitive symptoms, including forgetfulness and difficulty concentrating, were most pronounced among the perimenopausal cohort. Insomnia and burnout were moderately prevalent among employed people, especially when coexisting with conditions such as endometriosis. Regional differences were minimal, though participants from Scotland reported higher pain scores.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSymptom severity and variability is dependent on menopausal stage and mode of onset. The study findings highlight the need for targeted, stage-specific clinical pathways and workplace adaptations, particularly for those with complex medical histories such as endometriosis. A stratified, inclusive approach to menopause care is urgently required across policy and practice settings.\u003c/p\u003e","manuscriptTitle":"An Exploration of the Physical and Mental Health Impact among a Diverse Population in the United Kingdom Experiencing Perimenopause and Menopause (MARIE UK-WP2a)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:33:03","doi":"10.21203/rs.3.rs-7471671/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"591c2c45-3013-4dbf-a809-753bb1e64814","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53788265,"name":"Women's studies"}],"tags":[],"updatedAt":"2025-09-01T10:33:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:33:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7471671","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7471671","identity":"rs-7471671","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}