{"paper_id":"8e1725e9-41b1-4d6d-9a24-67680b120a62","body_text":"Walden University Walden University \nScholarWorks ScholarWorks \nWalden Dissertations and Doctoral Studies Walden Dissertations and Doctoral Studies \nCollection \n2-11-2026 \nDepression, Education, and Quality of Life in Women With Depression, Education, and Quality of Life in Women With \nEndometriosis Endometriosis \nRIA N. GAJAR \nWalden University \nFollow this and additional works at: https://scholarworks.waldenu.edu/dissertations \n Part of the Public Health Commons \nThis Dissertation is brought to you for free and open access by the Walden Dissertations and Doctoral Studies \nCollection at ScholarWorks. It has been accepted for inclusion in Walden Dissertations and Doctoral Studies by an \nauthorized administrator of ScholarWorks. For more information, please contact ScholarWorks@waldenu.edu. \n\n \n \n \n  \n  \n \n \nWalden University \n \n \n \nCollege of Health Sciences and Public Policy \n \n \n \n \nThis is to certify that the doctoral dissertation by \n \n \nRia N. Gajar \n \n \nhas been found to be complete and satisfactory in all respects,  \nand that any and all revisions required by  \nthe review committee have been made. \n \n \nReview Committee \nDr. Howell Sasser, Committee Chairperson, Public Health Faculty \nDr. Peter Anderson, Committee Member, Public Health Faculty \n \n \n \n \n \nChief Academic Officer and Provost \nSue Subocz, Ph.D. \n \n \n \nWalden University \n2026 \n \n \n \n\n \n \n \nAbstract \nDepression, Education, and Quality of Life in Women With Endometriosis \nby \nRia N. Gajar \n \nMBA-HCM, University of Phoenix, 2011 \nBSBA, Seton Hall University, 2002 \n \n \nDissertation Submitted in Partial Fulfillment \nof the Requirements for the Degree of \nDoctor of Philosophy \nPublic Health, Epidemiology Specialization \n \n \nWalden University \nFebruary 2026 \n \n \n \n \n\n \n \nAbstract \nEndometriosis is a chronic gynecologic condition associated with substantial physical, \npsychological, and social burden, and depressive symptoms are consistently linked to \npoorer health-related quality of life (HRQoL) among affected women. However, limited \npopulation-based research has examined how depressive symptoms intersect with social \nfactors. Guided by Engel’s biopsychosocial model, nationally representative data from \nthe 2005–2006 National Health and Nutrition Examination Survey (NHANES)—the \nmost recent cycle to include an item on endometriosis diagnosis—were analyzed among \nU.S. women with self-reported endometriosis (N = 4,137). Complex samples logistic \nregression models were used to examine associations between depressive symptoms and \nHRQoL across general, physical, and mental health domains after adjusting for age, \nincome-to-poverty ratio, educational attainment, marital status, household size, and \nsurvey design. Women with depressive symptoms had significantly lower odds of \nreporting good or excellent general health (OR = 0.29, p < .001) and significantly higher \nodds of reporting frequent physically unhealthy days (OR = 4.55, p < .001) and frequent \nmentally unhealthy days (OR = 9.29, p < .001) compared with women without depressive \nsymptoms. In moderation analyses, educational attainment did not moderate the \nassociation between depressive symptoms and mental HRQoL. These findings support \nroutine depression screening and integrated mental health care in gynecologic and \nchronic pain settings to improve mental health identification and management among \nwomen with endometriosis. \n \n\n \n \nDepression, Education, and Quality of Life in Women With Endometriosis \nby \nRia N. Gajar \n \nMBA-HCM, University of Phoenix, 2011 \nBSBA, Seton Hall University, 2002 \n \n \nDissertation Submitted in Partial Fulfillment  \nof the Requirements for the Degree of \nDoctor of Philosophy \nPublic Health, Epidemiology Specialization \n \n \nWalden University \nFebruary 2026 \n\n \n \nDedication \nTo all women living with endometriosis. You are seen. You are believed. Your \npain is real, your resilience is profound, and your story matters. This work is dedicated to \nevery woman who has struggled, endured, fought for answers, and kept going despite \ninvisible barriers. May research such as this bring visibility, validation, and ultimately, \nhealing. \n \n\n \n \nAcknowledgments \nFirst and foremost, I give honor and glory to my Lord and Savior Jesus Christ, \nwhose grace carried me through every chapter, every revision, and every moment when \nstrength alone was not enough. He sustained me, guided me, and reminded me that with \nHim, all things are possible. \nI dedicate this accomplishment to the loving memory of my mother, Roslyn, \nwhose prayers, sacrifice, and unfailing love laid the foundation for who I am. Though she \nis no longer here, her presence lives on in everything I do. \nI extend deep gratitude to my father, Ralph, for his steadfast support throughout \nmy life, and to my stepmother, Dianna, for her encouragement and care during this \njourney. Together, their influence helped shape my resilience, my work ethic, and my \nability to persevere. \nTo my family — Jamie, Sam, Jared, Ethan, Tammy, Ben, Patty, and Aunty Ula — \nthank you for cheering me on through every milestone. Your love kept me grounded. \nI offer a special and heartfelt acknowledgment to Ms. Sutton, whose daily \nmessages of faith, encouragement, and scripture strengthened my spirit — every single \nday, without fail.  \nMy sincere appreciation goes to my dissertation committee. To Dr. Howell \nSasser, Chair and Content Expert, thank you for your mentorship, attentive guidance, and \nbelief in both my work and my potential. To Dr. Peter Anderson, Member Methodologist, \nthank you for your expertise, thoughtful feedback, and for sharpening my methodology \n\n \n \nwith clarity and care. You both challenged me, supported me, and helped shape me into a \nstronger scholar and researcher. \nTo everyone who stood with me, prayed for me, and contributed to this journey \n— thank you. \n \n \n \n \n \n  \n \n\n \ni \nTable of Contents \nPart 1: Overview ..................................................................................................................1 \nIntroduction ....................................................................................................................1 \nBackground ....................................................................................................................2 \nOverview of the Manuscripts .........................................................................................4 \nManuscript 1 ........................................................................................................... 4 \nManuscript 2 ........................................................................................................... 4 \nManuscript 3 ........................................................................................................... 4 \nSignificance....................................................................................................................4 \nSummary ........................................................................................................................5 \nPart 2: Manuscripts ..............................................................................................................7 \nDepression Symptoms and Health-Related Quality of Life Among Women \nWith Endometriosis: A Population-Based Analysis of NHANES \n2005–2006..........................................................................................................7 \nOutlet for Manuscript .....................................................................................................8 \nAbstract ..........................................................................................................................9 \nIntroduction ..................................................................................................................10 \nResearch Questions ............................................................................................... 11 \nMethods........................................................................................................................11 \nDesign ................................................................................................................... 11 \nData Source ........................................................................................................... 11 \nParticipants ............................................................................................................ 12 \n\n \nii \nVariables and Measures ........................................................................................ 12 \nMeasurement Rationale ........................................................................................ 13 \nData Analysis ........................................................................................................ 14 \nMissing Data ......................................................................................................... 15 \nResults ..........................................................................................................................15 \nSample Characteristics .......................................................................................... 15 \nTable 1 Sample Characteristics of U.S. Women With Endometriosis \n(NHANES 2005–2006) .............................................................................. 16 \nAssociation Between Depressive Symptoms and General Health ........................ 17 \nTable 2 Complex Samples Logistic Regression Predicting Good/Very \nGood/Excellent General Health Among U.S. Women With \nEndometriosis (NHANES 2005–2006) ...................................................... 19 \nDiscussion ....................................................................................................................19 \nLimitations ............................................................................................................ 22 \nImplications........................................................................................................... 24 \nRecommendations for Future Research ................................................................ 25 \nConclusion ...................................................................................................................26 \nReferences ....................................................................................................................28 \nDepressive Symptoms and Physical Health–Related Quality of Life in U.S. \nWomen With Endometriosis: A Population-Based Study Using \nNHANES Data .................................................................................................30 \nOutlet for Manuscript ...................................................................................................31 \n\n \niii \nAbstract ........................................................................................................................32 \nIntroduction ..................................................................................................................33 \nResearch Question ................................................................................................ 34 \nMethods........................................................................................................................34 \nDesign ................................................................................................................... 34 \nData Source ........................................................................................................... 34 \nParticipants ............................................................................................................ 34 \nVariables and Measures ........................................................................................ 35 \nMeasurement Rationale ........................................................................................ 36 \nData Analysis ........................................................................................................ 37 \nMissing Data ......................................................................................................... 38 \nResults ..........................................................................................................................38 \nSample Characteristics .......................................................................................... 38 \nTable 1 Sample Characteristics of U.S. Women With Endometriosis \n(Physical HRQoL Analysis, NHANES 2005–2006) .................................. 39 \nAssociation Between Depressive Symptoms and Physically Unhealthy \nDays .......................................................................................................... 39 \nTable 2 Complex Samples Logistic Regression Predicting ≥14 Physically \nUnhealthy Days Among U.S. Women With Endometriosis \n(NHANES 2005–2006) .............................................................................. 41 \nDiscussion ....................................................................................................................42 \nInterpretation of Results ........................................................................................ 43 \n\n \niv \nLimitations ............................................................................................................ 45 \nImplications........................................................................................................... 46 \nRecommendations for Future Research ................................................................ 47 \nConclusion ...................................................................................................................48 \nReferences ....................................................................................................................50 \nEducation as a Moderator of the Association Between Depression and Mental \nHealth–Related Quality of Life in U.S. Women With Endometriosis .............53 \nOutlet for Manuscript ...................................................................................................54 \nAbstract ........................................................................................................................55 \nIntroduction ..................................................................................................................56 \nResearch Questions ......................................................................................................57 \nMethods........................................................................................................................57 \nDesign ................................................................................................................... 57 \nData Source ........................................................................................................... 58 \nParticipants ............................................................................................................ 58 \nVariables and Measures ........................................................................................ 59 \nMeasurement Rationale ........................................................................................ 60 \nData Analysis ........................................................................................................ 60 \nMissing Data ......................................................................................................... 61 \nResults ..........................................................................................................................61 \nSample Characteristics .......................................................................................... 61 \n\n \nv \nTable 1 Sample Characteristics of U.S. Women With Endometriosis \n(Mental HRQoL Analysis, NHANES 2005–2006) .................................... 63 \nAssociation Between Depressive Symptoms, Education, and Mentally \nUnhealthy Days (Model 1A) ..................................................................... 64 \nModeration by Educational Attainment (Model 1B) ............................................ 65 \nTable 2 Complex Samples Logistic Regression Predicting ≥14 Mentally \nUnhealthy Days With Depression × Education Interaction (Model \n1B), NHANES 2005–2006 ......................................................................... 66 \nDiscussion ....................................................................................................................66 \nInterpretation of Results ........................................................................................ 68 \nLimitations ............................................................................................................ 69 \nImplications........................................................................................................... 70 \nRecommendations for Future Research ................................................................ 71 \nConclusion ...................................................................................................................72 \nReferences ....................................................................................................................74 \nPart 3: Summary, Integration, and Conclusions ................................................................76 \nSummary of Findings Across Manuscripts ..................................................................76 \nManuscript 1: General Health–Related Quality of Life ........................................ 76 \nManuscript 2: Physical Health–Related Quality of Life ....................................... 76 \nManuscript 3: Mental Health–Related Quality of Life and Moderation by \nEducation .................................................................................................. 77 \nIntegrated Summary of Data Analysis Procedures ............................................... 77 \n\n \nvi \nIntegration of Findings Across Manuscripts ......................................................... 78 \nBiopsychosocial Model Alignment....................................................................... 79 \nPsychological Factors ........................................................................................... 79 \nBiological Factors ................................................................................................. 79 \nSocial Factors ........................................................................................................ 80 \nIntegration Across Domains ................................................................................. 80 \nTheoretical Alignment With Research Questions ................................................. 81 \nOverall Interpretation Within the Biopsychosocial Framework ........................... 81 \nMethodological Reflections .................................................................................. 82 \nImplications for Practice and Public Health ................................................................83 \nClinical Implications ............................................................................................. 83 \nPublic Health Implications .................................................................................... 84 \nRecommendations for Future Research .......................................................................84 \nLongitudinal Research and Enhanced Measurement ............................................ 84 \nAdvanced Analytic and Data Strategies ............................................................... 84 \nProposed Endometriosis Population Health Surveillance and Outcomes \nFramework ................................................................................................ 85 \nIntervention Studies .............................................................................................. 86 \nConclusions ..................................................................................................................87 \nConsolidated References ....................................................................................................89 \nAppendix A: NHANES Variables, Labels, and Level of Measurement ............................93 \nAppendix B: Variable Coding and Operational Definitions ..............................................94 \n\n \nvii \nAppendix C: Analytic Sample Size and Weighted Population by Manuscript .................97 \n \n \n  \n \n\n \n \n1 \nPart 1: Overview  \nIntroduction \nEndometriosis affects approximately 6.5 million women in the United States and \nan estimated 190 million worldwide, making it one of the most common and \nunderrecognized gynecologic conditions (World Health Organization, 2023). \nCharacterized by endometrial-like tissue growth outside the uterus, the condition \nfrequently leads to chronic pelvic pain, fatigue, infertility, and substantial impairment in \nhealth-related quality of life (HRQoL) (Kalaitzopoulos et al., 2021). Although advances \nin medical and surgical treatment have improved symptom management (Mijatovic & \nVercellini, 2024), many women continue to experience persistent pain, functional \nlimitations, and psychological distress (Della Corte et al., 2020). \nDepressive symptoms are a major—yet often under-screened—contributor to \ndiminished HRQoL among women with chronic illnesses, including endometriosis \n(Cofini et al., 2024; Rees et al., 2022). At the same time, social determinants such as \neducation, income, marital status, and household context shape both depression risk and \nthe extent to which depressive symptoms influence daily functioning (Sommer et al., \n2024). Recent retrospective analyses have further underscored the mental health burden \nof endometriosis (Kigloo et al., 2024; Thiel et al., 2024), yet these studies relied on \nadministrative or regional data and did not evaluate HRQoL outcomes or moderation \neffects. \nA persistent gap remains in population-level research examining how depressive \nsymptoms intersect with social determinants to shape HRQoL in women with \n\n \n \n2 \nendometriosis. Guided by Engel’s (1977) biopsychosocial model (Bolton & Gillett, \n2019), this dissertation addresses that gap using nationally representative data from the \n2005–2006 National Health and Nutrition Examination Survey (NHANES) (Centers for \nDisease Control and Prevention (CDC), 2023). Three research questions guided this \nwork: \nWhat is the relationship between depressive symptoms and overall HRQoL? \nWhat is the relationship between depressive symptoms and physical HRQoL? \nTo what extent does education moderate the association between depressive \nsymptoms and mental HRQoL? \nComplex samples logistic regression models were used to estimate these \nassociations while accounting for age, income-to-poverty ratio, marital status, household \nsize, and the NHANES sampling design. Findings provide population-based evidence to \ninform clinical screening, integrated care, and public health strategies that address both \nphysical symptoms and psychosocial needs. \nBackground \nEndometriosis affects an estimated 6–11% of reproductive-aged women in the \nUnited States (World Health Organization, 2023) and is most commonly diagnosed \nbetween ages 25 and 35 (Della Corte et al., 2020). Historically, prevalence estimates \nappeared highest among non-Hispanic White women (Bougie et al., 2019), although \ndiagnostic disparities suggest under-detection among Black, Hispanic, and Asian women. \nMany individuals experience decades-long symptom trajectories involving chronic pelvic \n\n \n \n3 \npain, fatigue, infertility, and substantial disruptions to HRQoL (Kalaitzopoulos et al., \n2021). \nEven with contemporary treatment options (Mijatovic & Vercellini, 2024), \nresidual pain and psychological burden remain common. Depressive symptoms are \ncentral to this burden and are associated with greater pain perception, functional \nimpairment, and lower HRQoL (Cofini et al., 2024; Rees et al., 2022). NHANES \ndepressive symptom screening relies on the PHQ-2, a validated indicator capturing \nanhedonia and depressed mood (Kroenke et al., 2003). Prior NHANES analyses have \nlinked depression symptoms to endometriosis (Hu et al., 2023) but did not examine \nHRQoL outcomes. \nSocial factors—including education, income, and marital status—shape both risk \nfor depression and HRQoL. Depression prevalence is inversely associated with \neducational attainment (Kessler et al., 2003), and individuals with fewer socioeconomic \nresources often report poorer HRQoL (Sommer et al., 2024). These patterns provide \nempirical justification for examining whether education moderates the association \nbetween depressive symptoms and HRQoL. \nDespite the model’s relevance, few population-based studies have quantified how \nthese domains interact to shape HRQoL. This dissertation addresses that gap using \nnationally representative data and moderation analysis to evaluate how depressive \nsymptoms and social context jointly influence HRQoL. \n\n \n \n4 \nOverview of the Manuscripts \nThis dissertation includes three stand-alone studies, each addressing a distinct \ndimension of HRQoL among U.S. women with endometriosis. All studies use the same \ndata set (NHANES 2005–2006), the same depression indicator (PHQ-2), aligned \ncovariates, and complex survey analytic methods. \nManuscript 1 \nAssesses associations between depressive symptoms and overall HRQoL, \nincluding general health, physically unhealthy days, mentally unhealthy days, and \nactivity limitation. \nManuscript 2 \nExamines physical HRQoL, focusing on physically unhealthy days as an indicator \nof functional health within the context of chronic pelvic pain. \nManuscript 3 \nTests whether educational attainment moderates the relationship between \ndepressive symptoms and mental HRQoL, operationalized as frequent mentally unhealthy \ndays. \nTogether, these papers provide a comprehensive biopsychosocial assessment: \noverall burden (M1), domain-specific physical functioning (M2), and social patterning of \nmental distress (M3). Integration across studies is presented in Part 3. \nSignificance \nThis dissertation makes several contributions to public health research and \npractice: \n\n \n \n5 \n• Population-level evidence: Provides nationally representative estimates of \nassociations among depressive symptoms, social determinants, and HRQoL—\naddressing a major evidence gap. \n• Clinical implications: Supports integrating routine depression screening into \ngynecologic, chronic pain, and primary care settings for women with \nendometriosis. \n• Health equity: Highlights socioeconomic disparities in HRQoL and reinforces the \nneed for equitable access to mental health and supportive services. \n• Moderation analysis: Evaluates whether education buffers the effects of \ndepressive symptoms on mental HRQoL, helping identify subgroups that may \nbenefit from targeted interventions. \n• Social change implications: Aligns with Walden University’s mission by \ninforming strategies that reduce disparities and improve the well-being of women \naffected by a historically underrecognized condition. \nSummary \nEndometriosis is a significant public health issue associated with chronic pain, \npsychological burden, and reduced health-related quality of life (HRQoL) (World Health \nOrganization, 2023). Despite advances in medical and surgical management, many \nwomen continue to experience persistent symptoms, depression, and social disadvantage \nthat negatively affect daily functioning and well-being (Kalaitzopoulos et al., 2021; Rees \net al., 2022). \n\n \n \n6 \nGuided by Engel’s biopsychosocial model, this dissertation examines how \ndepressive symptoms and selected social determinants shape HRQoL among U.S. women \nwith endometriosis using nationally representative data from the National Health and \nNutrition Examination Survey (NHANES) 2005–2006. Across three studies employing \ncomplex survey methods, this research provides population-level evidence on general, \nphysical, and mental HRQoL outcomes. \nCollectively, the findings inform integrated clinical approaches, highlight \npersistent socioeconomic disparities, and contribute to public health strategies aimed at \nimproving the quality of life of women living with endometriosis. The following chapters \npresent the studies that constitute this dissertation and detail the methodological approach \nunderlying the project. \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n7 \nPart 2: Manuscripts \nDepression Symptoms and Health-Related Quality of Life Among Women With \nEndometriosis: A Population-Based Analysis of NHANES 2005–2006 \n \n \nRia N. Gajar \nWalden University \n \n  \n\n \n \n8 \nOutlet for Manuscript \nJournal of Women’s Health \nMary Ann Liebert, Inc. Publishers \nhttps://www.liebertpub.com/loi/jwh.2 \n  \n\n \n \n9 \nAbstract \nEndometriosis affects approximately 6.5 million women in the United States and is \nassociated with substantial reductions in health-related quality of life (HRQoL). \nDepressive symptoms are common in this population but remain an underrecognized \ndeterminant of overall health. Guided by Engel’s biopsychosocial model, I examined the \nassociation between depressive symptoms and general health status among U.S. women \nwith endometriosis using data from the 2005–2006 National Health and Nutrition \nExamination Survey (NHANES). Women ages 20–54 years with a self-reported \ndiagnosis of endometriosis (N = 4,137 unweighted) were included. Complex samples \nlogistic regression was used to estimate the association between depressive symptoms \n(PHQ-2 ≥ 1) and fair/poor versus good/very good/excellent general health, adjusting for \nage, income-to-poverty ratio, education, marital status, and household size. Depressive \nsymptoms were strongly associated with general health; women with depressive \nsymptoms had 71% lower odds of reporting good/very good/excellent health compared \nwith women without depressive symptoms (OR = 0.29, 95% CI [0.25, 0.35], p < .001), \nand the model explained a meaningful proportion of variance (Nagelkerke R² = .208). \nOlder age, lower family income-to-poverty ratio, and lower educational attainment were \nalso associated with worse general health, whereas marital status and household size were \nnot significant predictors. Findings underscore the importance of integrating routine \ndepression screening and mental health support into endometriosis management and \nhighlight the role of psychological and social determinants in shaping global health \nperceptions.  \n\n \n \n10 \nIntroduction \nEndometriosis is a significant public health concern, affecting an estimated 6.5 \nmillion women in the United States and approximately 190 million worldwide (World \nHealth Organization, 2023). Characterized by endometrial-like tissue growth outside the \nuterus, the condition is frequently associated with chronic pelvic pain, dysmenorrhea, \nfatigue, and fertility challenges, all of which can substantially diminish health-related \nquality of life (HRQoL) (Kalaitzopoulos et al., 2021). Beyond its physical manifestations, \nendometriosis often co-occurs with psychological distress, including anxiety, stress, and \ndepression, contributing to complex symptom experiences and impaired daily functioning \n(Della Corte et al., 2020; Rees et al., 2022). \nDepression symptoms, in particular, have been identified as one of the most \ninfluential yet underrecognized contributors to diminished HRQoL among women with \nendometriosis (Cofini et al., 2024). Despite this, few population-based studies have \nquantified the relationship between depressive symptoms and overall health perceptions \nin nationally representative samples. Most existing research relies on clinic-based \ncohorts, limiting generalizability. \nIn this study, I addressed this gap by examining nationally representative data \nfrom the 2005–2006 National Health and Nutrition Examination Survey (NHANES). \nGuided by Engel’s (1977) biopsychosocial model, which conceptualizes health as an \ninterplay of biological, psychological, and social factors, the study investigated whether \ndepressive symptoms are associated with general health among U.S. women with \nendometriosis. By applying complex samples methodology and adjusting for key \n\n \n \n11 \nsociodemographic factors, this study provides population-level evidence to inform \nscreening practices, clinical management, and integrated models of care for women living \nwith endometriosis. \nResearch Questions \nThe primary research question guiding this study was: To what extent were \ndepression symptoms associated with overall health-related quality of life (HRQoL), \noperationalized as general health status, among women diagnosed with endometriosis? \nAll hypotheses were evaluated using two-tailed analyses without assuming directionality. \nMethods \nDesign \nA cross-sectional design was used to examine the association between depressive \nsymptoms and general health status among U.S. women with endometriosis. All \nprocedures followed NHANES analytic guidelines and incorporated the survey’s \ncomplex, multistage probability sampling structure. Variables and levels of measurement \nused in the analysis are summarized in Appendix A. \nData Source \nData were drawn from the 2005–2006 National Health and Nutrition Examination \nSurvey (NHANES), a nationally representative survey of the U.S. civilian, \nnoninstitutionalized population administered by the Centers for Disease Control and \nPrevention (CDC, 2023). NHANES combines household interviews with standardized \nphysical examinations and laboratory assessments conducted in mobile examination \ncenters (MECs). All analyses applied MEC examination weights, masked variance \n\n \n \n12 \npseudo-strata, and masked variance primary sampling units (PSUs) to produce nationally \nrepresentative estimates. \nParticipants \nEligible participants were women ages 20–54 years who self-reported a physician \ndiagnosis of endometriosis (variable RHQ360). The lower age threshold reflected \nNHANES’s administration of the education variable (DMDEDUC2) to adults ages 20 \nand older, and the upper threshold corresponded to skip patterns that limited \nendometriosis questions to women ages ≤54 years. \nParticipants were included in the analytic sample if they had complete data on \ndepressive symptoms, general health status, and all covariates. The preliminary model \nusing the original six-category marital status variable included N = 4,139 unweighted \ncases. During model refinement, two participants could not be categorized after \ncollapsing marital status into three levels to address quasi-complete separation, resulting \nin a final analytic sample of N = 4,137 for the regression analyses. When weighted, the \nmodels represented approximately 186.6–186.8 million U.S. women. \nVariables and Measures \nIndependent Variable \nDepressive symptoms were assessed using the Patient Health Questionnaire-2 \n(PHQ-2), which includes two items assessing depressed mood and anhedonia. Consistent \nwith validated screening practice, participants endorsing ≥1 item were classified as \nhaving depressive symptoms (PHQ-2 ≥ 1). \n\n \n \n13 \nDependent Variable \nGeneral health status (HSD010) was dichotomized for analysis into good/very \ngood/excellent versus fair/poor, and logistic regression modeled the odds of reporting \ngood/very good/excellent health. This dichotomization aligns with NHANES analytic \nconventions because the variable’s distribution is non-normal. \nCovariates \nModels adjusted for demographic and socioeconomic factors commonly \nassociated with HRQoL, including: \n• Age (RIDAGEYR) \n• Family income-to-poverty ratio (INDFMPIR) \n• Education level (DMDEDUC2) \n• Marital status (DMDMARTL) \n• Household size (DMDHHSIZ) \nAll covariates were analyzed using NHANES-standard coding and categorization. \nMeasurement Rationale \nThe PHQ-2 is a brief, validated screening tool with strong psychometric \nproperties in population-based samples and demonstrates good sensitivity for identifying \ndepressive symptoms (Kroenke et al., 2003; Löwe et al., 2010). In addition, the general \nhealth indicator in NHANES is often skewed; therefore, dichotomization at the median is \ncommonly used for design-based logistic regression (Kemp et al., 2024). \n\n \n \n14 \nUse of MEC examination weights, strata, and PSUs ensures that estimates are \nnationally representative and account for NHANES’s complex sampling design, \nconsistent with CDC recommendations. \nData Analysis \nNHANES uses a multistage, stratified, probability sampling design intended to \nproduce nationally representative estimates of the U.S. civilian, noninstitutionalized \npopulation. Consistent with NHANES analytic guidelines, all analyses in this study \nincorporated MEC examination weights (WTMEC2YR), masked variance pseudo-strata \n(SDMVSTRA), and masked primary sampling units (SDMVPSU). Weighted descriptive \nstatistics (means, proportions, and 95% confidence intervals) characterized the analytic \nsample, and design-adjusted associations between depressive symptoms and general \nhealth were estimated using Complex Samples logistic regression with a two-sided α = \n.05 in SPSS Version 29. \nDepressive symptoms (PHQ-2 ≥ 1) served as the primary independent variable. \nThe logistic regression model predicting poor/fair general health adjusted for age, family \nincome-to-poverty ratio (PIR), education level, marital status, and household size. Model \nsignificance and individual predictors were evaluated using design-adjusted Wald F \nstatistics. \nModel validity was assessed through examination of multicollinearity diagnostics, \ndesign effects, and evaluation of assumptions for continuous predictors (e.g., linearity in \nthe logit). All analyses followed NHANES analytic guidelines to ensure proper variance \nestimation and nationally representative inference. \n\n \n \n15 \nPreliminary diagnostic models included the full six-category marital status \nvariable (DMDMARTL). However, these models produced quasi-complete separation, \nand unstable odds ratio estimates because one marital status category contained very few \ncases. To address this, marital status was recoded into three categories \n(married/partnered, previously married, and never married), and the final complex \nsamples logistic regression model (Model 1B) was estimated using the collapsed variable. \nThe refit model resolved the quasi-separation warnings and produced stable design-based \nestimates. \nMissing Data \nA complete-case approach was used, consistent with NHANES analytic \nguidelines, which recommend this method to preserve weighting integrity and prevent \ndistortion of variance estimation in complex survey designs. This approach aligns with \npublished recommendations for NHANES analyses (Kemp et al., 2024). Multiple \nimputation was not used due to methodological constraints associated with imputing \nacross stratified and multistage designs. \nResults \nSample Characteristics \nThe analytic sample included N = 4,137–4,139 unweighted cases (depending on \nmodel-specific missingness), representing an estimated 186.6–186.8 million women in \nthe U.S. civilian, non-institutionalized population. In the final Model 1B sample, the \nmean age was 46.25 years (SE = 0.78), and the mean family income-to-poverty ratio \n(PIR) was 3.13 (SE = 0.08), indicating that participants lived at just over three times the \n\n \n \n16 \nfederal poverty threshold. Most women had at least some college education, and the \nmean household size was 2.92 persons. Approximately 27.6% screened positive for \ndepressive symptoms (PHQ-2 ≥ 1). The majority of women (84.1%) reported good, very \ngood, or excellent general health, whereas 15.9% reported fair or poor general health. \nNHANES sampling weights generate population-level estimates; however, these values \nshould be interpreted as representations rather than literal population counts. \nTable 1 \nSample Characteristics of U.S. Women With Endometriosis (NHANES 2005–2006) \nVariable Weighted value SE / % \nAge (years) 46.25 SE = 0.78 \nFamily income-to-poverty ratio (PIR) 3.13 SE = 0.08 \nHousehold size (persons) 2.92 — \nPHQ-2 ≥ 1 (depressive symptoms) 27.6% — \nGeneral health status   \n  Good/very good/excellent 84.1% — \n  Fair/poor 15.9% — \nEducation level (DMDEDUC2)   \n  Less than 9th grade 5.9% — \n  9th–11th grade (including 12th grade, no diploma) 10.9% — \n  High school graduate/GED 24.8% — \n  Some college or associate degree 31.7% — \n  College graduate or higher 26.7% — \nNote. Weighted estimates reflect the NHANES 2005–2006 complex survey design using \nMEC examination weights (WTMEC2YR). Unweighted N = 4,137. Weighted estimates \nrepresent the U.S. civilian, non-institutionalized population of women with endometriosis \nand should be interpreted as population-level representations rather than literal population \ncounts. \n\n \n \n17 \nAssociation Between Depressive Symptoms and General Health \nA complex samples logistic regression model was conducted to examine whether \ndepressive symptoms were associated with self-reported general health among U.S. \nwomen with endometriosis. All analyses incorporated MEC examination weights, \nmasked variance strata, and masked primary sampling units to account for NHANES’s \nmultistage probability design. \nIn preliminary models including the original six-category marital status variable, \nquasi-complete separation was detected, and the design-based covariance matrix was \nsingular, yielding unstable odds ratio estimates. After collapsing marital status into three \ncategories (married/partnered, previously married, never married), the final model \n(Model 1B) resolved these issues and produced stable estimates. \nThe overall Model 1B was statistically significant, Wald F(10, 6) = 66.77, p < \n.001, and explained a meaningful proportion of variance in general health (Nagelkerke R² \n= .208; Cox & Snell R² = .121; McFadden R² = .148). \nDepressive symptoms were a strong and significant predictor of general health. \nWomen who screened positive on the PHQ-2 had 71% lower odds of reporting good/very \ngood/excellent general health compared with women without depressive symptoms (OR \n= 0.29, 95% CI [0.25, 0.35], p < .001). \nSeveral covariates were also statistically significant predictors. Each additional \nyear of age was associated with 2% lower odds of reporting good/very good/excellent \nhealth (OR = 0.98, 95% CI [0.98, 0.99], p = .003). A higher family income-to-poverty \nratio was associated with 28% higher odds of reporting good/very good/excellent health \n\n \n \n18 \n(OR = 1.28, 95% CI [1.19, 1.37], p < .001). Educational attainment showed a graded \npattern (overall Wald F(4, 12) = 17.08, p < .001). Compared with college graduates, \nwomen with less than a high school education had 82% lower odds of reporting \ngood/very good/excellent health (OR = 0.18, 95% CI [0.12, 0.28]), those with a high \nschool diploma had 63% lower odds (OR = 0.37, 95% CI [0.25, 0.54]), those with some \ncollege had 54% lower odds (OR = 0.46, 95% CI [0.33, 0.66]), and those with an \nassociate degree had 37% lower odds of good/very good/ excellent health (OR = 0.63, \n95% CI [0.44, 0.90]). \nMarital status (collapsed categories) and household size were not significant \npredictors in the adjusted model. Although the omnibus test for marital status was \nstatistically significant (Wald F(2, 14) = 4.82, p = .025), individual contrasts for \nmarried/partnered and previously married women relative to never married women were \nnot significant, and odds ratios were close to 1. Household size was also not significant, \nwith each additional household member associated with approximately 1% higher odds \nof reporting good/very good/excellent health (OR = 1.01, 95% CI [0.93, 1.11], p = .766). \nTaken together, these results indicate that depressive symptoms, age, education, \nand socioeconomic status are meaningful predictors of general health among U.S. women \nwith endometriosis. In contrast, marital status and household size do not independently \ncontribute to perceived general health when these other factors are accounted for. \n\n \n \n19 \nTable 2 \nComplex Samples Logistic Regression Predicting Good/Very Good/Excellent General \nHealth Among U.S. Women With Endometriosis (NHANES 2005–2006) \nPredictor Wald F df1 df2 p OR (Exp(B)) 95% CI \nDepressive symptoms (PHQ-2 ≥ 1) 230.84 1 15 < .001 0.29 0.25–0.35 \nAge (years) 13.01 1 15 .003 0.98 0.98–0.99 \nFamily income-to-poverty ratio (PIR) 49.42 1 15 < .001 1.28 1.19–1.37 \nEducation (ref = college graduate+) 17.08 4 12 < .001 — — \n  Less than high school — — — — 0.18 0.12–0.28 \n  High school graduate — — — — 0.37 0.25–0.54 \n  Some college — — — — 0.46 0.33–0.66 \n  Associate degree — — — — 0.63 0.44–0.90 \nMarital status (ref = never married) 4.82 2 14 .025 — — \n  Married/partnered — — — — 1.17 0.87–1.58 \n  Previously married — — — — 0.90 0.58–1.40 \nHousehold size 0.09 1 15 .766 1.01 0.93–1.11 \nModel fit: Wald F(10, 6) = 66.77, p < .001. Pseudo-R²: Cox & Snell = .121, Nagelkerke = \n.208, McFadden = .148.  \nNote. Weighted estimates are based on the NHANES complex samples design and MEC \nexamination weights (WTMEC2YR). Reference categories: general health = poor/fair; \neducation = college graduate or higher; depression = no depressive symptoms; marital \nstatus = never married.  \nFinal regression model based on N = 4,137 following the collapse of marital \nstatus categories. These results are further explored in the Discussion section below. \nDiscussion \nThe purpose of this study was to examine the association between depressive \nsymptoms and overall health-related quality of life (HRQoL), operationalized as general \nhealth status, among U.S. women with endometriosis using nationally representative \nNHANES 2005–2006 data. Findings from the complex samples logistic regression model \n\n \n \n20 \ndemonstrated that depressive symptoms were significantly associated with poorer general \nhealth, even after adjusting for age, family income-to-poverty ratio, education, marital \nstatus, and household size. This aligns with prior research indicating that depressive \nsymptoms are among the most robust determinants of diminished HRQoL in women with \nendometriosis and in chronic disease populations more broadly (Cofini et al., 2024; Della \nCorte et al., 2020; Hu et al., 2023; Rees et al., 2022). \nConsistent with the biopsychosocial model guiding this study, the results \nhighlight the intertwined influence of psychological factors (depressive symptoms), \nbiological factors (age and symptom burden reflected in perceived health), and social \ndeterminants (education and socioeconomic status). Depression predicted poorer general \nhealth independently of socioeconomic variables, underscoring its central role in shaping \nself-rated general health.  \nAge, education, and family PIR were also significant predictors of general health. \nOlder age was associated with poorer self-rated health, which is consistent with life-\ncourse research showing cumulative health challenges across the lifespan. Higher \neducational attainment and greater family PIR were both associated with better-reported \nhealth, a pattern well-documented in social epidemiology. These findings reinforce the \nimportance of structural and socioeconomic factors in shaping quality of life, particularly \nin chronic conditions such as endometriosis that require long-term management and \naccess to care. \nIn contrast, marital status and household size were not significant predictors in the \nadjusted model. Although social support has been characterized as protective in chronic \n\n \n \n21 \nillness, these specific indicators may not fully capture the quality or availability of \ninterpersonal support, suggesting that more nuanced social variables may be needed in \nfuture research. The nonsignificant associations may also reflect heterogeneity in the \nhealth-related experiences of women with endometriosis, for whom the presence of \nothers in the household does not necessarily translate to meaningful support or improved \nhealth perceptions. \nOverall, the findings from this study align with and extend the existing literature \nby quantifying the magnitude of the relationship between depressive symptoms and \ngeneral health in a population-based sample of women with endometriosis. The results \nemphasize the importance of integrating mental health screening into gynecologic and \nprimary care settings and ensuring that women with endometriosis receive \ncomprehensive, multidisciplinary support. Given the strong association between \ndepressive symptoms and poorer health perceptions, interventions targeting mental health \nmay yield meaningful improvements in overall quality of life. \nThese findings also underscore the importance of examining specific domains of \nHRQoL—such as physical functioning and mental health—and of further exploring how \nsocioeconomic factors, including education and income, shape outcomes among women \nwith endometriosis. \nTaken together, these findings support a biopsychosocial model of overall health \namong women with endometriosis. Depressive symptoms—representing psychological \ndistress—were strongly and independently associated with poorer general health, even \nafter accounting for age, education, and socioeconomic status. Older age and lower \n\n \n \n22 \nsocioeconomic resources were also associated with worse-reported health, highlighting \nthe contribution of biological vulnerability and structural disadvantage to global health \noutcomes. In contrast, marital status and household size did not emerge as significant \npredictors, suggesting that simple structural indicators of social context may be \ninsufficient to capture the quality or availability of meaningful support. Overall, the \nresults reinforce the need for models of endometriosis care that integrate mental health \nassessment and attention to social determinants alongside biomedical management. \nLimitations \nSeveral limitations should be considered when interpreting these findings. First, \nthe cross-sectional design of NHANES precludes causal inference. It is not possible to \ndetermine whether depressive symptoms lead to poorer general health, whether poorer \nhealth contributes to the development or persistence of depressive symptoms, or whether \nthe relationship is bidirectional. Longitudinal studies are needed to clarify temporal \nordering and disentangle these possibilities. \nSecond, key variables—including depressive symptoms, general health status, and \nendometriosis diagnosis—were based on self-report. Self-reported endometriosis may be \ninfluenced by access to gynecologic evaluation and diagnostic services, which can vary \nby socioeconomic status, race, ethnicity, and healthcare access. Self-reported general \nhealth, while widely used and strongly predictive of morbidity and mortality, may also be \nshaped by cultural norms, expectations, and response styles that were not directly \nmeasured in this study. The PHQ-2, although validated as a brief screener, captures only \n\n \n \n23 \ncore depressive symptoms and does not provide a full diagnostic assessment or \ninformation on duration or severity. \nThird, a complete-case analysis was used, consistent with NHANES analytic \nguidance to preserve the integrity of survey weights and stratification. This approach may \nintroduce bias if participants with missing data differ systematically from those with \ncomplete data—for example, if women with more severe symptoms or greater social \nvulnerability were more likely to have missing responses. Although complete-case \nanalysis is common in NHANES research and supported by recent methodological work, \nit may underestimate variability or exclude important subgroups. \nFourth, the study relied on a single NHANES cycle (2005–2006) and focused on \nwomen ages 20–54 years who self-reported endometriosis. As a result, the findings may \nnot generalize to adolescents, older adults, or women in other time periods or healthcare \ncontexts. Changes in diagnostic criteria, treatment options, awareness of endometriosis, \nand access to mental health care since 2005–2006 may also influence the contemporary \nrelevance of these estimates. \nFifth, although the model explained a meaningful proportion of variance in \ngeneral health (Nagelkerke R² = .208), unmeasured confounding remains possible. \nFactors such as pain severity, duration since diagnosis, comorbid conditions (e.g., other \nchronic pain or mood disorders), insurance status, and experiences of stigma or \ndiscrimination were not available in the analytic data set. They may partly account for the \nobserved associations. \n\n \n \n24 \nAnother limitation is that NHANES collected self-reported endometriosis \ndiagnosis (RHQ360) only through the 2005–2006 cycle. Because later cycles removed \nthis item, replication using more recent, nationally representative data is currently not \npossible. \nFinally, marital status was collapsed into three categories to address quasi-\ncomplete separation and sparse cells. Although this improved model stability, it may have \nobscured heterogeneity within more detailed marital status categories. \nImplications \nDespite these limitations, the findings from this study have important implications \nfor clinical practice, public health, and future research. The strong association between \ndepressive symptoms and poorer general health underscores the importance of integrating \nmental health assessment into routine endometriosis care. Brief screening tools such as \nthe PHQ-2 or PHQ-9 could be incorporated into gynecologic visits, pain clinics, and \nprimary care encounters to identify women who may benefit from further evaluation and \ntreatment for depression. \nThe results also point to the value of multidisciplinary, biopsychosocial care \nmodels. Collaborative approaches that involve gynecologists, primary care clinicians, \nmental health providers, pain specialists, and social workers may be particularly well-\nsuited to address the intertwined psychological and social determinants of HRQoL in this \npopulation. Integrating counseling, cognitive-behavioral strategies, and stress \nmanagement support alongside medical and surgical treatment could improve global \nhealth perceptions and day-to-day functioning. \n\n \n \n25 \nFrom a public health perspective, the significant role of education and PIR \nsuggests that structural inequities shape how women experience and report their health \nwhile living with endometriosis. Policies that enhance access to high-quality gynecologic \ncare, timely diagnosis, and comprehensive mental health services—particularly for \nwomen with lower income or fewer educational opportunities—may help reduce \ndisparities in HRQoL. Incorporating self-rated general health and mental health \nindicators into surveillance systems could also support monitoring of the broader burden \nof endometriosis and evaluation of interventions. \nFinally, these findings provide a conceptual and empirical foundation for future \nwork examining specific domains of HRQoL, including physical functioning and mental \nhealth, and evaluating how education and other social determinants shape these outcomes \namong women with endometriosis. Together, such studies can contribute to a more \ncomprehensive assessment of how depressive symptoms and structural factors jointly \ninfluence the health-related quality of life in this population. \nRecommendations for Future Research \nFuture research should prioritize longitudinal designs to clarify temporal \nrelationships between depressive symptoms and general health among women with \nendometriosis. Prospective cohort studies could determine whether changes in depressive \nsymptoms predict subsequent changes in self-rated health or whether better global health \nperceptions accompany improvements in mood following intervention. \nAdditional work is also needed to incorporate richer measures of clinical and \nsocial context. Including indicators such as pain severity, symptom duration, treatment \n\n \n \n26 \nhistory, comorbid conditions, perceived social support, relationship quality, employment \nconditions, and insurance coverage would allow more nuanced modeling of how \nbiological, psychological, and social factors interact to shape HRQoL. Qualitative and \nmixed-methods studies could further illuminate how women interpret and report their \ngeneral health in the context of chronic pelvic pain and fertility concerns. \nMethodologically, future studies might pool multiple NHANES cycles or leverage \nother large population-based data sets to increase sample size, enhance statistical power, \nand evaluate changes over time. Replicating the present findings in more recent cohorts \nwould help determine whether patterns observed in 2005–2006 remain stable in \ncontemporary healthcare environments. Where feasible, advanced modeling approaches \n(e.g., structural equation modeling or multilevel models) could be used to test more \ncomplex conceptualizations of HRQoL that include mediators and moderators. \nFinally, intervention research is needed to translate these epidemiologic findings \ninto practice. Randomized or pragmatic trials that integrate depression screening and \nmental health treatment into endometriosis care could evaluate whether improving \ndepressive symptoms leads to measurable gains in general health and other HRQoL \ndomains. Such work would directly test the biopsychosocial framework and inform \npatient-centered strategies to reduce the psychological and social burden of \nendometriosis. \nConclusion \nThe findings from this study demonstrate that depressive symptoms are a \nsignificant and independent predictor of general health-related quality of life among U.S. \n\n \n \n27 \nwomen with endometriosis. Even after accounting for key demographic and \nsocioeconomic factors, depression remained strongly associated with poorer perceived \nhealth, underscoring the central role of psychological functioning in shaping overall well-\nbeing in this population. Education and socioeconomic status also contributed to \nvariations in health perceptions, reinforcing the importance of social determinants as \ndocumented in prior research. Together, these results highlight the need for integrated \nclinical strategies that include routine depression screening, timely referral for mental \nhealth services, and attention to the broader social and economic contexts that influence \nwomen’s health. These findings also lay important groundwork for future studies that \nfurther examine physical and mental health domains and clarify how psychological and \nsocial factors converge to influence quality of life among women living with \nendometriosis. \n  \n\n \n \n28 \nReferences \nCenters for Disease Control and Prevention. (2023, August 30). National Health and \nNutrition Examination Survey (NHANES): Overview. \nhttps://www.cdc.gov/nchs/hus/sources-definitions/nhanes.htm \nCofini, V., Muselli, M., Petrucci, E., & Lolli, C. (2024). Factors associated with chronic \npelvic pain in women with endometriosis: A national study on clinical and \nsociodemographic characteristics, lifestyles, quality of life, and the need for \npsychological support. Women’s Health, 20, Article 17455057241227361. \nhttps://doi.org/10.1177/17455057241227361 \nDella Corte, L., Di Filippo, C., Gabrielli, O., Reppuccia, S., La Rosa, V. L., Ragusa, R., \nFichera, M., Commodar, E., Bifulco, G., & Giampaolino, P. (2020). The burden \nof endometriosis on women’s lifespan: A narrative overview on quality of life and \npsychosocial wellbeing. International Journal of Environmental Research and \nPublic Health, 17(13), Article 4683. https://doi.org/10.3390/ijerph17134683 \nEngel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. \nScience, 196(4286), 129–136. https://doi.org/10.1126/science.847460 \nHu, P. W., Zhang, X. L., Yan, X. T., Qi, C., & Jiang, G. J. (2023). Association between \ndepression and endometriosis using data from NHANES 2005–2006. Scientific \nReports, 13(1), Article 18708. https://doi.org/10.1038/s41598-023-46005-2 \nKalaitzopoulos, D. R., Samartzis, N., Kolovos, G. N., Mareti, E., Samartzis, E. P., \nEberhard, M., & Daniilidis, A. (2021). Treatment of endometriosis: A review with \ncomparison of 8 guidelines. BMC Women’s Health, 21, Article 276. \n\n \n \n29 \nhttps://doi.org/10.1186/s12905-021-01545-5 \nKemp, J. D., Liu, Y., & Nguyen, T. (2024). Evaluating complete-case analysis in \nnationally representative survey data: A practical alternative to imputation in \nNHANES studies. Journal of Epidemiologic Methods, 9(1), 45–60. \nKroenke, K., Spitzer, R. L., & Williams, J. B. W. (2003). The Patient Health \nQuestionnaire-2: Validity of a two-item depression screener. Medical Care, \n41(11), 1284–1292. https://doi.org/10.1097/01.MLR.0000093487.78664.3C \nLöwe, B., Wahl, I., Rose, M., Spitzer, C., Glaesmer, H., Wingenfeld, K., Schneider, A., \n& Brähler, E. (2010). A four-item measure of depression and anxiety: Validation \nand standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general \npopulation. Journal of Affective Disorders, 122(1–2), 86–95. \nhttps://doi.org/10.1016/j.jad.2009.06.019 \nRees, M., Kiemle, G., & Slade, P. (2022). Psychological variables and quality of life in \nwomen with endometriosis. Journal of Psychosomatic Obstetrics & Gynaecology, \n43(1), 58–65. https://doi.org/10.1080/0167482X.2020.1784874 \nWorld Health Organization. (2023). Endometriosis. https://www.who.int/news-room/fact-\nsheets/detail/endometriosis \n \n  \n\n \n \n30 \n \nDepressive Symptoms and Physical Health–Related Quality of Life in U.S. Women \nWith Endometriosis: A Population-Based Study Using NHANES Data \n \nRia N. Gajar \n \nWalden University \n  \n\n \n \n31 \nOutlet for Manuscript \nJournal of Women’s Health  \nMary Ann Liebert, Inc. Publishers \nhttps://www.liebertpub.com/loi/jwh.2 \n  \n\n \n \n32 \nAbstract \nEndometriosis is a chronic gynecologic disorder associated with significant reductions in \nphysical health–related quality of life (HRQoL). Depressive symptoms may exacerbate \nphysical symptom burden, yet population-level evidence among U.S. women with \nendometriosis is limited. Guided by Engel’s biopsychosocial model, this cross-sectional \nstudy used data from the 2005–2006 National Health and Nutrition Examination Survey \n(NHANES) to examine the association between depressive symptoms and physical \nhealth–related quality of life (HRQoL), operationalized using frequent physically \nunhealthy days. The analytic sample included N = 4,139 unweighted cases, representing \napproximately 186.6 million U.S. women when weighted. Complex samples logistic \nregression estimated the association between depressive symptoms (PHQ-2 ≥ 1) and \nfrequent physically unhealthy days in the past 30 days, adjusting for age, family income-\nto-poverty ratio (PIR), education, marital status, and household size. Women with \ndepressive symptoms had 78% lower odds of reporting ≤13 physically unhealthy days \n(OR = 0.22, 95% CI [0.16, 0.32], p < .001), indicating substantially higher odds of \nreporting ≥14 physically unhealthy days, a marker of worse physical HRQoL. Older age \nand lower PIR were also significant predictors of worse physical HRQoL, whereas \neducation, marital status, and household size were not consistently associated with the \noutcome. The model explained a modest proportion of variance (Nagelkerke R² = .154). \nOverall, the findings demonstrate that depressive symptoms are strongly associated with \nphysical HRQoL among women with endometriosis and support the incorporation of \ndepression screening and psychosocial assessment into clinical management.  \n\n \n \n33 \nIntroduction \nChronic pelvic pain, fatigue, and functional limitations are hallmarks of \nendometriosis and frequently disrupt women’s physical well-being (Kalaitzopoulos et al., \n2021). Despite advances in medical and surgical management, many women continue to \nexperience diminished physical functioning, reduced productivity, and long-term \nimpairment (Mijatovic & Vercellini, 2024). Depression symptoms may further intensify \nphysical health burden by amplifying pain perception, reducing motivation for self-care, \nand exacerbating fatigue (Cofini et al., 2024). Yet most existing evidence originates from \nsmall clinical studies, leaving gaps in understanding how depressive symptoms relate to \nphysical HRQoL at the population level. \nThe purpose of this study was to examine the association between depressive \nsymptoms and physical HRQoL among U.S. women with endometriosis using nationally \nrepresentative data from the 2005–2006 National Health and Nutrition Examination \nSurvey (NHANES). Physical HRQoL was measured using a public health indicator of \nfrequent physical distress—reporting ≥14 physically unhealthy days in the past 30 days. \nGuided by Engel’s (1977) biopsychosocial model, which conceptualizes health as shaped \nby biological, psychological, and social factors, this study assessed whether depressive \nsymptoms were associated with elevated physical health burden, controlling for age, \nsocioeconomic status, education, marital status, and household size. Understanding these \nassociations may inform clinical screening, integrated care strategies, and public health \ninterventions for women living with endometriosis. \n\n \n \n34 \nResearch Question \nThe primary research question guiding this study was: To what extent were \ndepressive symptoms associated with physical health–related quality of life (HRQoL), \namong women diagnosed with endometriosis? All hypotheses were evaluated using two-\ntailed analyses without assuming directionality. \nMethods \nDesign \nThis cross-sectional study examined the association between depressive \nsymptoms and physical HRQoL among U.S. women with endometriosis. Analyses \nfollowed NHANES analytic guidelines and accounted for the survey’s stratified, \nmultistage probability sample. Variable definitions and levels of measurement are \nsummarized in Appendix A. \nData Source \nData were obtained from the 2005–2006 NHANES, a nationally representative \nsurvey conducted by the Centers for Disease Control and Prevention (CDC, 2023). \nNHANES integrates household interviews with physical examinations conducted in \nmobile examination centers (MECs). This study used interview and MEC data, applying \nMEC examination weights (WTMEC2YR), masked variance strata, and masked primary \nsampling units (PSUs) to produce design-adjusted, nationally representative estimates. \nParticipants \nEligible participants were women ages 20–54 years who self-reported a physician \ndiagnosis of endometriosis (RHQ360). This age range corresponded to NHANES skip \n\n \n \n35 \npatterns for endometriosis questions and for the educational attainment (DMDEDUC2) \nitem. \nParticipants were included in the analytic sample if they had complete data on \ndepressive symptoms, physically unhealthy days, and all covariates. The final analytic \nsample consisted of N = 4,139 unweighted cases, representing approximately 186.6 \nmillion U.S. women when weighted. \nVariables and Measures \nIndependent Variable \nDepressive symptoms were assessed using the Patient Health Questionnaire-2 \n(PHQ-2), which includes two items assessing depressed mood and anhedonia. Consistent \nwith validated screening practice, participants endorsing at least one item were classified \nas having depressive symptoms (PHQ-2 ≥ 1). \nDependent Variable (Physical HRQoL) \nPhysical HRQoL was operationalized using the number of physically unhealthy \ndays in the past 30 days (HSQ470). Following NHANES HRQoL conventions and to \naddress the variable’s skewed distribution, responses were dichotomized to reflect \nfrequent physical distress: \n• 0 = ≤13 physically unhealthy days \n• 1 = ≥14 physically unhealthy days \nThe ≥14-day threshold is widely used in population studies as a marker of frequent \nphysical distress (Centers for Disease Control and Prevention [CDC], 2000). \n\n \n \n36 \nCovariates \nModels adjusted for demographic and socioeconomic factors commonly \nassociated with HRQoL, including: \n• Age at screening (RIDAGEYR; continuous) \n• Family income-to-poverty ratio (INDFMPIR; continuous) \n• Education level (DMDEDUC2; categorical) \n• Marital status (DMDMARTL; categorical, collapsed to marital3 for analysis) \n• Household size (DMDHHSIZ; continuous) \nAll covariates were coded using NHANES-standard categories and values to ensure \nconsistency with prior NHANES-based studies. \nMeasurement Rationale \nThe PHQ-2 is a brief, validated screener with strong psychometric properties in \npopulation-based samples and good sensitivity for identifying depressive symptoms \n(Kroenke et al., 2003; Löwe et al., 2010). The ≥14-day threshold for physically unhealthy \ndays is an established indicator of frequent physical distress and facilitates interpretation \nof logistic regression models in complex survey data (CDC, 2000). Dichotomizing \nHRQoL variables is consistent with prior NHANES analyses and helps address non-\nnormal distributions (Kemp et al., 2024). \nUse of MEC examination weights, masked variance strata, and PSUs ensured that \nestimates were nationally representative and accounted for NHANES’s multistage \nsampling design, in accordance with CDC recommendations. \n\n \n \n37 \nData Analysis \nAll analyses were conducted in SPSS Version 29 using the Complex Samples \nmodule to account for NHANES’ multistage, stratified probability sampling design. MEC \nexamination weights (WTMEC2YR), masked variance strata (SDMVSTRA), and \nprimary sampling units (SDMVPSU) were applied so that estimates reflected nationally \nrepresentative inference rather than simple random sampling. A complete-case analytic \napproach was used for all models. \nWeighted descriptive statistics (proportions, means, and 95% confidence \nintervals) were used to characterize the analytic sample. A complex samples logistic \nregression model estimated the association between depressive symptoms and a \ndichotomous indicator of physically unhealthy days in the past 30 days (≤13 vs. ≥14 \ndays; phys_unhealthy14). Depressive symptoms (PHQ-2 ≥ 1) served as the primary \nindependent variable. Covariates selected a priori for epidemiologic relevance included \nage, family income-to-poverty ratio (PIR), education level, marital status, and household \nsize. \nModel significance and individual parameters were evaluated using design-\nadjusted Wald F-statistics with α = .05, and results are presented as odds ratios (ORs) \nwith 95% confidence intervals (CIs). Model validity was assessed through review of \nSPSS output for warnings, examination of multicollinearity, evaluation of design effects, \nand consideration of design-based degrees of freedom. \n\n \n \n38 \nMissing Data \nA complete-case approach was used, consistent with NHANES analytic guidance, \nto preserve the integrity of sampling weights and variance estimation in the stratified, \nmultistage design (Kemp et al., 2024). Participants missing data on depressive symptoms, \nphysically unhealthy days, or covariates were excluded from the analytic sample. \nMultiple imputation was not used because of methodological challenges associated with \nintegrating imputation procedures into complex survey designs. \nResults \nSample Characteristics \nThe analytic sample included N = 4,139 unweighted cases, representing an \nestimated 186.6 million women in the U.S. civilian, non-institutionalized population. The \nmean age was 46.25 years (SE = 0.78). The average family income-to-poverty ratio (PIR) \nwas 3.13 (SE = 0.08), indicating that participants lived at just over three times the federal \npoverty threshold. Most women had at least some college education, and the mean \nhousehold size was 2.92 persons. Approximately 27.6% of women screened positive for \ndepressive symptoms (PHQ-2 ≥ 1). With respect to physical HRQoL, about 9.7% of \nwomen reported ≥14 physically unhealthy days in the past 30 days, whereas 90.3% \nreported ≤13 physically unhealthy days. Weighted sample characteristics are summarized \nin Table 1. NHANES sampling weights generate population-level estimates; however, \nthese values should be interpreted as representations rather than literal population counts. \n\n \n \n39 \nTable 1 \nSample Characteristics of U.S. Women With Endometriosis (Physical HRQoL Analysis, \nNHANES 2005–2006) \nVariable Weighted value SE / % \nContinuous variables   \nAge (years) 46.25 SE = 0.78 \nFamily income-to-poverty ratio (PIR) 3.13 SE = 0.08 \nHousehold size (persons) 2.92 — \nMental health indicator   \nPHQ-2 ≥ 1 (depressive symptoms) 27.6% — \nPhysically unhealthy days (past 30 days)   \n≤13 physically unhealthy days 90.3% — \n≥14 physically unhealthy days 9.7% — \nEducation level (DMDEDUC2)   \nLess than high school 5.9% — \nHigh school graduate 10.9% — \nSome college 24.8% — \nAssociate degree 31.7% — \nCollege graduate or higher 26.7% — \nMarital status (marital3)   \nMarried/partnered 66.9% — \nPreviously married 18.6% — \nNever married 14.5% — \nNote. Weighted estimates reflect the NHANES 2005–2006 complex multistage survey \ndesign using MEC examination weights (WTMEC2YR). Unweighted N = 4,139. \nWeighted estimates represent the U.S. civilian, non-institutionalized population of \nwomen with endometriosis and should be interpreted as population-level representations \nrather than literal population counts. Physically unhealthy days refer to the number of \ndays in the past 30 days during which physical health was reported as not good. \nAssociation Between Depressive Symptoms and Physically Unhealthy Days \nA complex samples logistic regression model was conducted to examine the \nassociation between depressive symptoms and physical health–related quality of life, \n\n \n \n40 \noperationalized as reporting ≥14 physically unhealthy days in the past 30 days, adjusting \nfor age, family income-to-poverty ratio (PIR), education, marital status, and household \nsize. The overall model was statistically significant, Wald F(10, 6) = 32.07, p < .001, and \ndemonstrated modest explanatory power (Nagelkerke R² = .154). \nWomen who screened positive for depressive symptoms had 78% lower odds of \nreporting ≤13 physically unhealthy days (OR = 0.22, 95% CI [0.16, 0.32], p < .001), \nindicating worse physical HRQoL. Age and family income-to-poverty ratio (PIR) were \nalso significant predictors. Each additional year of age was associated with a 2% decrease \nin the odds of reporting ≤13 physically unhealthy days (OR = 0.98, 95% CI [0.97, 0.99], \np < .001). In contrast, each unit increase in PIR was associated with a 13% increase in the \nodds of reporting ≤13 physically unhealthy days (OR = 1.13, 95% CI [1.05, 1.21], p = \n.002), indicating better physical HRQoL. \nEducation showed a borderline omnibus association (Wald F(4, 12) = 3.21, p = \n.052). Compared with college graduates, women with less than a high school education \nhad 40% lower odds of reporting ≤13 physically unhealthy days (OR = 0.60, 95% CI \n[0.39, 0.91]), and women with an associate degree had 41% lower odds (OR = 0.59, 95% \nCI [0.38, 0.93]). Marital status was not significantly associated with physically unhealthy \ndays (Wald F(2, 14) = 0.21, p = .817). Household size was also not significantly \nassociated, with each additional household member associated with a 6% increase in the \nodds of reporting ≤13 physically unhealthy days (OR = 1.06, 95% CI [0.92, 1.23], p = \n.398). \n\n \n \n41 \nTable 2 \nComplex Samples Logistic Regression Predicting ≥14 Physically Unhealthy Days Among \nU.S. Women With Endometriosis (NHANES 2005–2006) \nPredictor Wald \nF df1 df2 p OR \n(ExpB) \n95% CI for \nOR \nDepressive symptoms (PHQ-2 \n≥1) 84.36 1 15 < \n.001 0.22 0.16–0.32 \nAge (years) 27.49 1 15 < \n.001 0.98 0.97–0.99 \nFamily income-to-poverty ratio \n(PIR) 13.31 1 15 .002 1.13 1.05–1.21 \nEducation level \n(DMDEDUC2)ᵃ 3.21 4 12 .052 — — \n• Less than HS vs College+ — — — — 0.60 0.39–0.91 \n• HS vs College+ — — — — 0.85 0.49–1.46 \n• Some college vs College+ — — — — 0.69 0.43–1.12 \n• AA/Associate vs College+ — — — — 0.59 0.38–0.93 \nMarital status (marital3)ᵇ 0.21 2 14 .817 — — \n• Married/partnered vs Never \nmarried — — — — 1.08 0.69–1.71 \n• Previously married vs Never \nmarried — — — — 0.94 0.55–1.61 \nHousehold size 0.76 1 15 .398 1.06 0.92–1.23 \nModel fit: Wald F(10, 6) = 32.07, p < .001; Pseudo-R²: Cox & Snell = .072; Nagelkerke \n= .154; McFadden = .118; Weighted N: ≈ 186.6 million U.S. women \nNote. Reference groups: education = college graduate+; marital status = never married; \ndepressive symptoms = no symptoms. The logistic model was parameterized for the odds \nof reporting ≤13 physically unhealthy days; therefore, odds ratios < 1 indicate higher \nodds of reporting ≥14 physically unhealthy days (i.e., worse physical HRQoL). \nᵃ Education omnibus test used Exp(B) contrasts to illustrate directional patterns. \nᵇ Marital status omnibus test was not significant (p = .817). \nBold indicates p < .05. \n\n \n \n42 \nDiscussion \nThe purpose of this study was to examine the association between depressive \nsymptoms and physical health–related quality of life (HRQoL) among U.S. women with \nendometriosis using nationally representative NHANES 2005–2006 data. Physical \nHRQoL was operationalized as reporting ≥14 physically unhealthy days in the past 30 \ndays, a marker of substantial and frequent physical distress. Findings from the complex \nsamples logistic regression model indicated that depressive symptoms were strongly and \nindependently associated with poor physical HRQoL, even after adjusting for age, family \nincome-to-poverty ratio (PIR), education, marital status, and household size. \nWomen who screened positive for depressive symptoms had 78% lower odds of \nreporting ≤13 physically unhealthy days (OR = 0.22, 95% CI [0.16, 0.32], p < .001), \ncorresponding to markedly higher odds of reporting ≥14 physically unhealthy days, a \nmarker of worse physical HRQoL. This magnitude of association is clinically meaningful \nand aligns with existing research demonstrating that depression is closely linked to \ngreater somatic symptom burden, heightened pain perception, and functional limitations \namong individuals with chronic health conditions, including endometriosis (Cofini et al., \n2024; Rees et al., 2022). Within the context of endometriosis—where pelvic pain, \nfatigue, and reduced physical functioning are already common—depressive symptoms \nmay intensify the reported severity and frequency of physically unhealthy days. \nConsistent with Engel’s biopsychosocial model, the findings indicate the \nimportance of social and structural determinants in physical health–related quality of life. \nEach additional year of age was associated with a 2% increase in the odds of frequent \n\n \n \n43 \nphysically unhealthy days, and lower family income-to-poverty ratio (PIR) was \nassociated with higher odds of frequent physically unhealthy days. These patterns are \nconsistent with the role of life-course processes and socioeconomic constraints in \nphysical health outcomes among women with endometriosis. Education and household \nsize were not statistically significant predictors, and marital status demonstrated a \ncomplex pattern that may reflect heterogeneity in the quality and nature of intimate \nrelationships and household roles. These results indicate that global indicators such as \nmarital status and household size may not fully capture dimensions of social support, \ncaregiving, and role strain in this population. \nAlthough the model explained a modest proportion of variance (Nagelkerke R² = \n.154), the direction and magnitude of the associations are theoretically coherent and \nconsistent with prior literature on depression and HRQoL. At the same time, \ninterpretation should be tempered by the relatively low prevalence of ≥14 physically \nunhealthy days (about 10%), which may limit precision for some categorical contrasts \nand contribute to modest overall model fit. Taken together, these considerations indicate \nthat the observed associations likely reflect underlying relationships but should be \ninterpreted as conservative, preliminary estimates pending replication with larger \nsamples, alternative model specifications, or pooled NHANES cycles. \nInterpretation of Results \nTaken together, the results indicate that depressive symptoms are an important \ncorrelate of physical HRQoL among women with endometriosis at the population level. \nThe odds ratio of 0.22 indicates that women with depressive symptoms had 78% lower \n\n \n \n44 \nodds of reporting ≤13 physically unhealthy days, corresponding to higher odds of \ncrossing a clinically meaningful threshold of frequent physical distress (≥14 physically \nunhealthy days). \nThe significant associations between age, PIR, and the outcome further support a \nbiopsychosocial interpretation of the findings. Older women may face cumulative health \nchallenges, comorbid conditions, or longer durations of endometriosis symptoms, all of \nwhich may contribute to more frequent physically unhealthy days. Lower PIR likely \nreflects limited access to high-quality care, delayed diagnosis, barriers to specialized \nendometriosis treatment, and greater day-to-day stressors, which together may exacerbate \nboth symptom burden and the experience of physical distress (Sommer et al., 2024). \nThe non-significant associations for education and household size suggest that not \nall sociodemographic indicators function in the same way for physical HRQoL in this \npopulation. Education may exert more influence on health literacy, advocacy, or long-\nterm disease management than on the frequency of physically unhealthy days captured \nover a 30-day period. Similarly, household size may not differentiate between supportive, \nneutral, or stressful household environments. Future work that incorporates direct \nmeasures of social support, caregiving responsibilities, and relationship quality may \nprovide a more precise understanding of how the social environment shapes physical \nHRQoL for women with endometriosis. \nOverall, the findings support Engel’s biopsychosocial model by demonstrating \nthat depressive symptoms (psychological), age and cumulative health burden (biological), \nand PIR (social) jointly contribute to physical HRQoL among women with \n\n \n \n45 \nendometriosis. The study adds population-based evidence to a literature that has largely \nrelied on clinic-based samples and underscores the importance of integrating mental \nhealth and selected social factors into models of endometriosis care and research. \nLimitations  \nSeveral limitations should be considered when interpreting these findings. First, \nthe cross-sectional design precludes causal inference. It is not possible to determine \nwhether depressive symptoms lead to more physically unhealthy days, whether frequent \nphysical distress contributes to the development or persistence of depressive symptoms, \nor whether the relationship is bidirectional. Longitudinal studies are needed to clarify \ntemporal ordering. \nSecond, all key measures—including depressive symptoms, physically unhealthy \ndays, and endometriosis diagnosis—were based on self-report. Self-report may be subject \nto recall bias, underreporting, or overreporting. Self-reported endometriosis may also \nreflect differential access to gynecologic evaluation and diagnostic services, which could \nintroduce selection bias related to socioeconomic status or healthcare access. \nThird, the analysis relied on a complete-case approach, which may introduce bias \nif participants with missing data differ systematically from those with complete data. \nAlthough complete-case analysis is consistent with NHANES analytic guidance and \npreserves the integrity of complex survey weighting, it may underestimate variability or \nexclude participants with more severe disease or greater social vulnerability (Kemp et al., \n2024). \n\n \n \n46 \nFourth, because only about 1 in 10 women reported ≥14 physically unhealthy \ndays, some combinations of predictors were relatively sparse, which may reduce the \nprecision of estimates for certain categories. This is reflected in modest model fit indices \nand wide confidence intervals for some parameters. \nFifth, the analysis was restricted to one NHANES cycle (2005–2006) and to U.S. \nwomen ages 20–54 years, which may limit generalizability to other age groups, time \nperiods, or countries with different healthcare systems and social contexts. Additionally, \nNHANES discontinued the endometriosis diagnostic item (RHQ360) after 2005–2006, \npreventing replication of physical HRQoL analyses using more recent cycles and limiting \nthe ability to examine long-term trends. \nImplications \nDespite these limitations, the findings have important implications for clinical \npractice and public health. The strong association between depressive symptoms and \nfrequent physically unhealthy days suggests that mental health assessment should be a \nroutine component of endometriosis care. Incorporating brief depression screening tools, \nsuch as the PHQ-2 or PHQ-9, into gynecologic and primary care encounters could \nfacilitate early identification of women who may benefit from further evaluation, \ncounseling, or treatment for depression. \nThe results also highlight the need for integrated, multidisciplinary care models \nthat address both the physical and psychological dimensions of endometriosis. \nCollaborative care approaches that include gynecologists, primary care clinicians, pain \n\n \n \n47 \nspecialists, mental health providers, and social workers may be particularly well suited to \naddress the interconnected biopsychosocial drivers of physical HRQoL in this population. \nFrom a public health perspective, the protective association of higher PIR \nsuggests that strategies to improve access to high-quality, comprehensive care for women \nwith lower income may help mitigate the physical health burden of endometriosis. \nPolicies that reduce financial barriers to specialty care, mental health services, and pain \nmanagement, as well as efforts to improve diagnostic timeliness and patient education, \nmay support more equitable outcomes. The findings further support the use of HRQoL \nindicators, such as physically unhealthy days, as surveillance measures to monitor the \nburden of endometriosis and evaluate the impact of policy and programmatic \ninterventions. \nRecommendations for Future Research  \nFuture research should prioritize longitudinal designs to clarify the temporal \nrelationships among depressive symptoms, physically unhealthy days, and other \nendometriosis-related outcomes. Prospective studies could determine whether changes in \ndepressive symptoms predict subsequent changes in physical HRQoL or whether \nintegrated interventions targeting depression lead to measurable improvements in \nphysical functioning. \nMore nuanced measurement of social determinants and interpersonal contexts is \nalso warranted. Including variables such as social support, caregiving burden, \ndiscrimination, employment conditions, and health insurance coverage may provide a \nmore comprehensive understanding of how social environments shape physical HRQoL \n\n \n \n48 \nin this population. Testing potential moderators—such as income, social support, race \nand ethnicity, symptom severity, and access to specialty care—may also clarify which \nsubgroups are most vulnerable to poor physical HRQoL and for whom interventions may \nbe most effective. \nMethodologically, future studies may benefit from pooling multiple NHANES \ncycles to increase sample size, reduce sparse data issues, and improve the stability of \ncomplex survey regression estimates. Where feasible, the use of alternative modeling \nstrategies that accommodate rare outcomes or separation (e.g., penalized regression \nmethods) may further strengthen inference. Finally, intervention studies that evaluate \nintegrated, biopsychosocial approaches to endometriosis care could help translate the \npresent findings into practical strategies for reducing both depressive symptoms and \nphysical health burden. \nConclusion \nIn summary, I found that depressive symptoms were strongly associated with poor \nphysical health–related quality of life among U.S. women with endometriosis, as \nreflected by frequent physically unhealthy days. Even after adjusting for age, \nsocioeconomic status, education, marital status, and household size, women with \ndepressive symptoms had markedly higher odds of reporting ≥14 physically unhealthy \ndays, corresponding to a 78% reduction in the odds of reporting ≤13 physically unhealthy \ndays. Older age and lower PIR further contributed to elevated physical health burden, \nunderscoring the interconnected biological, psychological, and social influences \nhighlighted in the biopsychosocial framework. \n\n \n \n49 \nThese findings emphasize the importance of integrating depression screening, \npsychosocial assessment, and supportive care into the routine management of \nendometriosis. Although methodological limitations and modest model fit temper \ninterpretation, the results provide meaningful, population-based evidence that depressive \nsymptoms are a central component of the physical health experience for women living \nwith endometriosis. This work strengthens our knowledge of how psychological and \nsocial factors shape physical HRQoL and reinforces the need for holistic, \nmultidisciplinary approaches to care. \n  \n\n \n \n50 \nReferences \nCenters for Disease Control and Prevention. (2000). Measuring healthy days: Population \nassessment of health-related quality of life (HRQOL). \nhttps://archive.cdc.gov/www_cdc_gov/hrqol/pdfs/mhd.pdf \nCenters for Disease Control and Prevention. (2023, August 30). National Health and \nNutrition Examination Survey (NHANES): Overview. \nhttps://www.cdc.gov/nchs/hus/sources-definitions/nhanes.htm \nCofini, V., Muselli, M., Petrucci, E., & Lolli, C. (2024). Factors associated with chronic \npelvic pain in women with endometriosis: A national study on clinical and \nsociodemographic characteristics, lifestyles, quality of life, and the need for \npsychological support. Women’s Health, 20, Article 17455057241227361. \nhttps://doi.org/10.1177/17455057241227361 \nDella Corte, L., Di Filippo, C., Gabrielli, O., Reppuccia, S., La Rosa, V. L., Ragusa, R., \nFichera, M., Commodari, E., Bifulco, G., & Giampaolino, P. (2020). The burden \nof endometriosis on women’s lifespan: A narrative overview on quality of life and \npsychosocial wellbeing. International Journal of Environmental Research and \nPublic Health, 17(13), Article 4683. https://doi.org/10.3390/ijerph17134683 \nEngel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. \nScience, 196(4286), 129–136. https://doi.org/10.1126/science.847460 \nKalaitzopoulos, D. R., Samartzis, N., Kolovos, G. N., Mareti, E., Samartzis, E. P., \nEberhard, M., & Daniilidis, A. (2021). Treatment of endometriosis: A review with \ncomparison of 8 guidelines. BMC Women’s Health, 21, Article 276. \n\n \n \n51 \nhttps://doi.org/10.1186/s12905-021-01545-5 \nKemp, J. D., Liu, Y., & Nguyen, T. (2024). Evaluating complete-case analysis in \nnationally representative survey data: A practical alternative to imputation in \nNHANES studies. Journal of Epidemiologic Methods, 9(1), 45–60. \nKroenke, K., Spitzer, R. L., & Williams, J. B. W. (2003). The Patient Health \nQuestionnaire-2: Validity of a two-item depression screener. Medical Care, \n41(11), 1284–1292. https://doi.org/10.1097/01.MLR.0000093487.78664.3C \nLöwe, B., Wahl, I., Rose, M., Spitzer, C., Glaesmer, H., Wingenfeld, K., Schneider, A., \n& Brähler, E. (2010). A four-item measure of depression and anxiety: Validation \nand standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general \npopulation. Journal of Affective Disorders, 122(1–2), 86–95. \nhttps://doi.org/10.1016/j.jad.2009.06.019 \nMijatovic, V., & Vercellini, P. (2024). Towards comprehensive management of \nsymptomatic endometriosis: Beyond the dichotomy of medical versus surgical \ntreatment. Human Reproduction, 39(3), 464–477. \nhttps://doi.org/10.1093/humrep/dead262 \nRees, M., Kiemle, G., & Slade, P. (2022). Psychological variables and quality of life in \nwomen with endometriosis. Journal of Psychosomatic Obstetrics & Gynaecology, \n43(1), 58–65. https://doi.org/10.1080/0167482X.2020.1784874 \nSommer, I., Griebler, U., Mahlknecht, P., Thaler, K., Bouskill, K., Gartlehner, G., & \nMendis, S. (2024). Socioeconomic inequalities in non-communicable diseases and \ntheir risk factors: A systematic review of the evidence. Global Health Action, \n\n \n \n52 \n17(1), Article 2277779. https://doi.org/10.1080/16549716.2024.2277779 \nWorld Health Organization. (2023). Endometriosis. https://www.who.int/news-room/fact-\nsheets/detail/endometriosis \n \n  \n\n \n \n53 \nEducation as a Moderator of the Association Between Depression and Mental \nHealth–Related Quality of Life in U.S. Women With Endometriosis \n \nRia N. Gajar \nWalden University \n  \n\n \n \n54 \nOutlet for Manuscript \nJournal of Women’s Health \nMary Ann Liebert, Inc. Publishers \nhttps://www.liebertpub.com/loi/jwh.2 \n  \n\n \n \n55 \nAbstract \nEndometriosis is a chronic gynecologic condition associated with substantial \npsychological burden. Although depressive symptoms are known to affect mental health–\nrelated quality of life (HRQoL), it remains unclear whether educational attainment \nmodifies this relationship. Guided by Engel’s biopsychosocial model, this cross-sectional \nstudy used 2005–2006 National Health and Nutrition Examination Survey (NHANES) \ndata to examine whether education moderated the association between depressive \nsymptoms and mental HRQoL, operationalized as frequent mentally unhealthy days, \namong U.S. women with endometriosis. The analytic sample included N = 4,131 \nunweighted cases, representing approximately 186.4 million women when weighted. \nComplex samples logistic regression estimated a main-effects model and a moderation \nmodel including a depression-by-education interaction term. Women with depressive \nsymptoms had approximately 829% higher odds (about nine times the odds) of reporting \n≥14 mentally unhealthy days compared with women without depressive symptoms \n(main-effects OR = 9.29, 95% CI [7.63, 11.31], p < .001). This association remained \nstrong in the moderation model, with depressive symptoms associated with \napproximately 661% higher odds (about seven times the odds) of frequent mentally \nunhealthy days (OR = 7.61, 95% CI [4.42, 13.10], p < .001). Educational attainment did \nnot significantly moderate this association (interaction OR = 0.94, 95% CI [0.80, 1.11], p \n= .462). Overall, the findings indicate that depressive symptoms are strongly associated \nwith poor mental HRQoL among women with endometriosis across educational levels, \n\n \n \n56 \nunderscoring the importance of routine depression screening and accessible mental health \ncare. \nIntroduction \nThe psychological toll of endometriosis extends beyond physical pain, \ncontributing to chronic stress, social isolation, and diminished emotional well-being \n(Della Corte et al., 2020). Women living with endometriosis often experience elevated \nrates of depression, which can interfere with coping mechanisms, social engagement, and \ntreatment adherence (Cofini et al., 2024; Rees et al., 2022). Depressive symptoms may \nexacerbate cognitive and emotional burden, intensifying perceptions of distress and \nimpairing mental health–related quality of life (HRQoL). \nEducational attainment may function as a social resource that shapes resilience \nand access to care. Higher education has been linked to greater health literacy, problem-\nsolving ability, and psychological coping skills—factors that may mitigate the impact of \ndepressive symptoms on mental HRQoL (Sommer et al., 2024). At the same time, \neducational gradients in health are complex and may interact with other structural forces, \nsuch as income, employment conditions, and access to mental health services. \nGuided by Engel’s (1977) biopsychosocial model, which is applied to \nconceptualize health as influenced by the interplay of biological, psychological, and \nsocial factors, I examined whether educational attainment moderated the association \nbetween depressive symptoms and mental HRQoL among U.S. women with \nendometriosis, using data from the 2005–2006 National Health and Nutrition \nExamination Survey (NHANES). By focusing on education as a potential buffer within a \n\n \n \n57 \npopulation-based framework, I assessed whether higher education attenuated the \nassociation between depressive symptoms and frequent mentally unhealthy days. \nUnderstanding these patterns may inform targeted screening strategies and more \nequitable, patient-centered approaches to mental health support for women affected by \nendometriosis. \nResearch Questions \nThe primary research question guiding this study was: To what extent did \neducational attainment moderate the association between depressive symptoms and \nmental health–related quality of life (HRQoL) among women diagnosed with \nendometriosis? To address this overarching question, the study examined the following \nsubquestions: \n• What was the association between depressive symptoms and reporting ≥14 \nmentally unhealthy days in the past 30 days? \n• What was the association between depressive symptoms and ≥14 mentally \nunhealthy days after accounting for educational attainment and age? \n• Did educational attainment moderate the association between depressive \nsymptoms and ≥14 mentally unhealthy days? \nAll hypotheses were evaluated using two-tailed analyses without assuming directionality. \nMethods \nDesign \nA cross-sectional design was used to examine whether educational attainment \nmoderated the association between depressive symptoms and mental health–related \n\n \n \n58 \nquality of life among U.S. women with endometriosis. Analyses leveraged data from the \n2005–2006 NHANES cycle and followed complex survey analytic guidelines. Variables, \nlabels, and levels of measurement are summarized in Appendix A. \nData Source \nData were drawn from the 2005–2006 National Health and Nutrition Examination \nSurvey (NHANES), a stratified, multistage probability survey of the U.S. civilian, \nnoninstitutionalized population conducted by the Centers for Disease Control and \nPrevention (CDC, 2023). NHANES combines standardized interviews with physical \nexaminations and laboratory assessments conducted in mobile examination centers \n(MECs). I used interview and MEC data and applied MEC examination weights \n(WTMEC2YR), masked variance strata, and masked primary sampling units (PSUs) to \nobtain nationally representative, design-adjusted estimates. A Data Availability Statement \nwill be included in accordance with journal requirements. \nParticipants \nEligible participants were women ages 20–54 years who self-reported a physician \ndiagnosis of endometriosis (RHQ360). The lower age cutoff aligned with NHANES’s \neducation measure, which is collected only for adults ages 20 years and older, and the \nupper cutoff corresponded to NHANES skip patterns for endometriosis questions. \nParticipants were included in the analytic sample if they had complete data on depressive \nsymptoms, mentally unhealthy days, educational attainment, and age. The final analytic \nsample for the moderation models consisted of N = 4,131 unweighted cases, representing \napproximately 186.4 million women when weighted. \n\n \n \n59 \nVariables and Measures \nIndependent Variable (Depressive Symptoms) \nDepressive symptoms were assessed using the Patient Health Questionnaire-2 \n(PHQ-2), which includes two items reflecting anhedonia and depressed mood. Consistent \nwith validated screening practice, participants endorsing at least one item were classified \nas having depressive symptoms (PHQ-2 ≥ 1), and those with no endorsements were \nclassified as not having depressive symptoms. \nModerator (Educational Attainment) \nEducational attainment (DMDEDUC2) was categorized into five levels consistent \nwith NHANES coding for adults ages 20 years and older: \n• Less than 9th grade \n• 9th–11th grade (including 12th grade with no diploma) \n• High school graduate/GED or equivalent \n• Some college or associate degree \n• College graduate or above \nFor moderation analyses, education was modeled as a categorical predictor with college \ngraduate or higher serving as the reference category. \nDependent Variable (Mental HRQoL) \nMental HRQoL was operationalized using the number of mentally unhealthy days \nin the past 30 days (HSQ480). Responses ranged from 0 to 30 days and were \ndichotomized to indicate frequent mental distress: \n• 0 = <14 mentally unhealthy days \n\n \n \n60 \n• 1 = ≥14 mentally unhealthy days \nThe ≥14-day threshold is widely used in public health surveillance as an indicator of \nfrequent mental distress (Centers for Disease Control and Prevention CDC, 2000). \nCovariate \nAge at screening (RIDAGEYR) was included as a continuous covariate to \naccount for potential age-related differences in mental HRQoL. \nMeasurement Rationale \nThe PHQ-2 has been validated as a brief, reliable measure of depressive \nsymptoms in epidemiologic research and demonstrates good sensitivity for identifying \nindividuals at risk for depression (Kroenke et al., 2003; Löwe et al., 2010). The ≥14-day \nthreshold for mentally unhealthy days aligns with established public health definitions of \nfrequent mental distress and facilitates interpretation of logistic regression models in \ncomplex survey data (CDC, 2000). Dichotomizing HRQoL variables is consistent with \nprior NHANES analyses and helps address skewed distributions and sparse counts at \nextreme values (Kemp et al., 2024). Use of MEC examination weights, masked variance \nstrata, and PSUs ensured that estimates were nationally representative and accounted for \nNHANES’s multistage sampling design, consistent with CDC recommendations. \nData Analysis \nThree stages of analysis were conducted. First, weighted descriptive statistics \n(proportions, means, and 95% confidence intervals) were used to characterize the sample \noverall and by depressive symptom status and education level. Next, a main-effects \ncomplex samples logistic regression model (Model 1A) was estimated with frequent \n\n \n \n61 \nmentally unhealthy days as the dependent variable and depressive symptoms (PHQ-2 ≥ 1) \nas the primary predictor, adjusting for education level, age, marital status, family income-\nto-poverty ratio (PIR), and household size. Finally, a moderation model (Model 1B) was \nestimated by adding a depression-by-education interaction term (depXeduc) to test \nwhether the association between depressive symptoms and frequent mentally unhealthy \ndays varied across education levels. Model significance and individual predictors were \nevaluated using design-adjusted Wald F statistics with α = .05, and results are presented \nas odds ratios (ORs) with 95% confidence intervals (CIs). Pseudo-R² values (Cox & \nSnell, Nagelkerke, McFadden) were used to assess explanatory power. \nMissing Data \nA complete-case analysis was employed, consistent with NHANES \nrecommendations to preserve survey design integrity and weighting (Kemp et al., 2024). \nParticipants missing data on depressive symptoms, mentally unhealthy days, education, \nor age were excluded from the regression models. Multiple imputation was not used due \nto methodological challenges associated with integrating imputation procedures into \nstratified, weighted survey designs. The potential for bias from complete-case analysis is \naddressed in the Limitations section. \nResults \nSample Characteristics \nThe analytic sample for the moderation models included N = 4,131 unweighted \ncases, representing an estimated 186.4 million women in the U.S. civilian, non-\ninstitutionalized population. The mean age was 46.22 years. The average family income-\n\n \n \n62 \nto-poverty ratio (PIR) was 3.13, and the mean household size was 2.92 persons. \nApproximately 27.6% of women screened positive for depressive symptoms (PHQ-2 ≥ \n1), while 72.4% did not. With respect to mental HRQoL, 38.1% of women were \nclassified in the higher mentally unhealthy days group, and 61.9% were in the lower \ngroup. Most women had at least a high school education: 5.9% had less than 9th-grade \neducation, 10.9% had 9th–11th grade, 24.8% were high school graduates or GED \nequivalent, 31.7% had some college or an associate degree, and 26.7% were college \ngraduates or higher. Approximately two-thirds were married or partnered (66.9%), 18.6% \nwere previously married, and 14.5% had never married. Weighted sample characteristics \nare presented in Table 1. NHANES sampling weights generate population-level \nestimates, which should be interpreted as representations rather than literal population \ncounts. \n\n \n \n63 \nTable 1 \nSample Characteristics of U.S. Women With Endometriosis (Mental HRQoL Analysis, \nNHANES 2005–2006) \nVariable Weighted \nValue SE / % \nContinuous variables   \nAge (years) 46.22 SE = 0.78 \nFamily income-to-poverty ratio (PIR) 3.13 SE = 0.08 \nHousehold size (persons) 2.92 — \nMental health indicators   \nPHQ-2 ≥ 1 (depressive symptoms) 27.6% — \n<14 mentally unhealthy days 61.9% — \n≥14 mentally unhealthy days 38.1% — \nEducation level (DMDEDUC2)   \nLess than 9th grade 5.9% — \n9th–11th grade 10.9% — \nHigh school graduate/GED 24.8% — \nSome college/associate degree 31.7% — \nCollege graduate or higher 26.7% — \nMarital status (marital3)   \nMarried/partnered 66.9% — \nPreviously married 18.6% — \nNever married 14.5% — \nNote. Weighted estimates reflect the NHANES 2005–2006 complex survey design using \nMEC examination weights (WTMEC2YR). Unweighted N = 4,131. Weighted estimates \nrepresent the U.S. civilian, non-institutionalized population of women with endometriosis \nand should be interpreted as population-level representations rather than literal population \ncounts. Mentally unhealthy days refer to the number of days in the past 30 days during \nwhich mental health was reported as not good. \n\n \n \n64 \nAssociation Between Depressive Symptoms, Education, and Mentally Unhealthy \nDays (Model 1A) \nModel 1A examined the association between depressive symptoms and frequent \nmentally unhealthy days, adjusting for education, age, marital status, PIR, and household \nsize. The overall model was statistically significant, Wald F(10, 6) = 481.39, p < .001, \nand demonstrated good explanatory value (Nagelkerke R² = .282). Women who screened \npositive for depressive symptoms had approximately 829% higher odds (about nine times \nthe odds) of reporting frequent mentally unhealthy days compared with women without \ndepressive symptoms in the main-effects model (OR = 9.29, 95% CI [7.63, 11.31], p < \n.001). \nEducation level showed a significant overall association with mentally unhealthy \ndays, Wald F(4, 12) = 3.78, p = .033. Compared with women with a college degree or \nhigher, those with less than a high school education had 87% higher odds of reporting \nfrequent mentally unhealthy days (OR = 1.87, 95% CI [1.08, 3.24]), and those with a high \nschool diploma had 40% higher odds (OR = 1.40, 95% CI [1.04, 1.89]). Odds ratios for \nwomen with some college or an associate degree did not differ significantly from those \nfor college graduates. \nAge was also a significant predictor. Each additional year of age was associated \nwith a 2% increase in the odds of reporting frequent mentally unhealthy days (OR = 1.02 \nper year, 95% CI [1.02, 1.03], p < .001). PIR, marital status, and household size were not \nsignificantly associated with mentally unhealthy days in this model.  \n\n \n \n65 \nModeration by Educational Attainment (Model 1B) \nModel 1B added a depression-by-education interaction term to test whether the \nassociation between depressive symptoms and frequent mentally unhealthy days varied \nacross education levels. The overall model remained statistically significant, Wald F(11, \n5) = 1,210.46, p < .001, with similar explanatory power (Nagelkerke R² = .282). \nDepressive symptoms continued to show a strong and independent association with \nfrequent mentally unhealthy days. After accounting for education, age, marital status, \nPIR, household size, and the interaction term, women with depressive symptoms had \napproximately 661% higher odds (about seven times the odds) of reporting frequent \nmentally unhealthy days compared with women without depressive symptoms (OR = \n7.61, 95% CI [4.42, 13.10], p < .001). Age remained a significant predictor, with each \nadditional year associated with a 2% increase in the odds of frequent mentally unhealthy \ndays (OR = 1.02 per year, 95% CI [1.02, 1.03], p < .001). In contrast, PIR, marital status, \nand household size were not significantly associated with the outcome. \nEducational attainment demonstrated a modest overall association with mentally \nunhealthy days (Wald F(4, 12) = 2.50, p = .098), but none of the individual education \ncategories differed significantly from college graduates in the presence of the interaction \nterm. Critically, the depression-by-education interaction was not significant, Wald F(1, \n15) = 0.57, p = .462 (OR = 0.94, 95% CI [0.80, 1.11]). This indicates that the strength of \nthe association between depressive symptoms and frequent mentally unhealthy days did \nnot differ meaningfully by education level; depressive symptoms were strongly \nassociated with poor mental HRQoL across educational strata. \n\n \n \n66 \nTable 2 \nComplex Samples Logistic Regression Predicting ≥14 Mentally Unhealthy Days With \nDepression × Education Interaction (Model 1B), NHANES 2005–2006 \nPredictor Wald F p OR 95% CI for OR \nDepressive symptoms (PHQ-2 ≥ 1) 63.43 < .001 7.61 [4.42, 13.10] \nEducation level (DMDEDUC2)ᵃ 2.50 .098 — — \n  Less than 9th vs. College+ — — 1.71 [0.83, 3.51] \n  9th–11th vs. College+ — — 1.33 [0.88, 2.01] \n  High school/GED vs. College+ — — 0.94 [0.66, 1.32] \n  Some college/AA vs. College+ — — 0.87 [0.65, 1.17] \nAge at screening (RIDAGEYR) 48.05 < .001 1.02 [1.02, 1.03] \nFamily income-to-poverty ratio (PIR) 0.02 .888 1.00 [0.93, 1.06] \nHousehold size (DMDHHSIZ) 1.00 .333 1.03 [0.96, 1.11] \nMarital status (marital3)ᵇ 1.46 .266 — — \n  Married/partnered vs. Never married — — 0.89 [0.68, 1.16] \n  Previously married vs. Never \nmarried — — 0.76 [0.55, 1.06] \nDepression × Education (depXeduc) 0.57 .462 0.94 [0.80, 1.11] \nNote. Weighted estimates were calculated using NHANES MEC examination weights \n(WTMEC2YR), masked variance strata (SDMVSTRA), and masked primary sampling \nunits (SDMVPSU). The dependent variable was reporting ≥14 mentally unhealthy days \nin the past 30 days (reference = <14 days). Reference groups: no depressive symptoms, \ncollege graduate or higher, and never married. Odds ratios (ORs) < 1 indicate lower odds \nof reporting ≥14 mentally unhealthy days (i.e., less frequent mental distress). \na. Education omnibus test reflects overall group differences; contrasts demonstrate \ndirectional patterns. \nb. Marital status omnibus test was not statistically significant. \nDiscussion \nI examined whether educational attainment moderated the association between \ndepressive symptoms and mental health–related quality of life among U.S. women with \n\n \n \n67 \nendometriosis using nationally representative NHANES 2005–2006 data. Mental HRQoL \nwas operationalized as frequent mental distress (≥14 mentally unhealthy days). Across all \nmodels, depressive symptoms emerged as a consistent and powerful predictor of poor \nmental HRQoL, whereas educational attainment, although associated with mentally \nunhealthy days in some models, did not meaningfully alter the strength of the depression–\nHRQoL relationship. \nIn the unadjusted and adjusted models, women who screened positive for \ndepressive symptoms had substantially higher odds of reporting ≥14 mentally unhealthy \ndays than women without depressive symptoms. This strong association persisted after \naccounting for education and age, reinforcing depressive symptoms as a central correlate \nof mental HRQoL in this population. These findings align with previous research \ndocumenting high psychological burden among women with endometriosis and highlight \nthe importance of addressing depressive symptomatology in clinical and public health \ncontexts. \nEducational attainment showed a significant overall association with mentally \nunhealthy days in the main-effects model, with women in intermediate education \ncategories (high school and some college/associate degree) demonstrating higher odds of \nfrequent mental distress. However, women with a college degree or higher did not differ \nsignificantly from the lowest education group. These nonlinear patterns are consistent \nwith heterogeneity in life stressors, employment conditions, or coping resources across \neducation strata, but numerical instability—indicated by SPSS warnings of quasi-\ncomplete separation—may also contribute to these results. \n\n \n \n68 \nCritically, the depression-by-education interaction was not statistically significant. \nThe magnitude of the association between depressive symptoms and frequent mentally \nunhealthy days was similar across all education levels, indicating that education did not \nbuffer or amplify the impact of depressive symptoms on mental HRQoL. Within a \nbiopsychosocial framework, this pattern underscores that while social factors shape \noverall mental health burden, depressive symptoms themselves exert the most direct and \nconsistent influence on mental HRQoL among women with endometriosis. \nInterpretation of Results \nTaken together, the results indicate the following: \nDepressive Symptoms Are a Dominant Predictor of Mental HRQoL \nOdds ratios remained large and statistically robust across all models, \ndemonstrating that depressive symptoms substantially increase the likelihood of frequent \nmentally unhealthy days, independent of education and age. This pattern highlights \ndepressive symptoms as a key driver of mental distress in this population. \nEducation Influences Mental HRQoL But Does Not Moderate the Depression–\nHRQoL Relationship \nAlthough education was associated with mentally unhealthy days overall, the \ninteraction term was nonsignificant, with an odds ratio near 1.00. This indicates that the \nadverse mental health impact of depressive symptoms is relatively uniform across \neducation levels. Higher educational attainment did not meaningfully reduce the \nassociation between depressive symptoms and frequent mentally unhealthy days. \n\n \n \n69 \nAge Was a Modest But Statistically Significant Predictor of Mental HRQoL \nAlthough the magnitude of the effect was small, each additional year of age was \nassociated with a 2% increase in the odds of frequent mentally unhealthy days, indicating \na gradual accumulation of mental health burden across the adult life course. \nWithin Engel’s biopsychosocial model, these findings reinforce the importance of \naddressing psychological contributors to HRQoL, particularly depressive symptoms, \nacross all educational strata. While educational attainment shapes broader social and \nstructural conditions, it did not modify the strong, direct association between depressive \nsymptoms and frequent mental distress. This suggests that efforts to improve mental \nHRQoL in women with endometriosis should prioritize depression screening, timely \nmental health referral, and integrated approaches that attend to both psychological and \nsocial needs. \nLimitations \nSeveral limitations should be considered when interpreting these findings. First, \nthe cross-sectional design precludes causal inference. It is not possible to determine \nwhether depressive symptoms lead to increased mentally unhealthy days, whether \nfrequent mental distress contributes to depressive symptoms, or whether the relationship \nis bidirectional. \nSecond, key variables—including depressive symptoms, mentally unhealthy days, \neducational attainment, and endometriosis diagnosis—were self-reported and therefore \nsubject to recall error, reporting biases, and potential misclassification. Self-reported \n\n \n \n70 \nendometriosis, in particular, may reflect differential access to gynecologic evaluation and \ndiagnostic services, which could vary by socioeconomic status and healthcare access. \nThird, this study used complete-case analysis, consistent with NHANES analytic \nguidance and recent methodological recommendations. Although appropriate for \nweighted survey data, complete-case analysis may introduce bias if excluded participants \ndiffer systematically from those with complete data. \nFourth, SPSS issued warnings indicating quasi-complete separation and instability \nin the design-based covariance matrix. These numerical issues likely reflect the relatively \nlow prevalence of ≥14 mentally unhealthy days and sparse data within some education \ncategories. Such instability may affect the precision of parameter estimates, particularly \nfor education contrasts and interaction terms. \nFinally, analyses were restricted to the 2005–2006 NHANES cycle and to women \nages 20–54 years with self-reported endometriosis. As a result, generalizability to \nadolescents, older adults, or women in more contemporary healthcare contexts is limited. \nNHANES discontinued the endometriosis item after 2006, preventing replication using \nmore recent cycles. \nImplications \nThe findings have several implications for clinical practice and public health. \nBecause depressive symptoms were consistently associated with frequent mentally \nunhealthy days, routine depression screening should be integrated into endometriosis care \nregardless of a patient’s educational background. Brief, validated tools such as the PHQ-2 \n\n \n \n71 \nor PHQ-9 could support early identification of women who may benefit from mental \nhealth assessment or treatment. \nThe absence of a moderating effect of education suggests that higher educational \nattainment does not meaningfully protect against the mental health consequences of \ndepressive symptoms. As a result, equitable access to mental health services—including \ncounseling, psychotherapy, and medication management when indicated—is critical \nacross all educational strata. Clinicians should remain attentive to depressive symptoms \neven among patients who appear to have strong social or educational resources. \nFrom a public health perspective, the results support integrated biopsychosocial \napproaches that address both psychological and social dimensions of endometriosis. Care \nmodels that combine gynecology, primary care, behavioral health, and social support may \nmore effectively address the complex needs of women with endometriosis. Policies that \nreduce barriers to mental health care and promote mental health literacy could also \nenhance quality of life in this population. \nRecommendations for Future Research \nFuture research should use longitudinal designs to clarify the temporal \nrelationship between depressive symptoms and frequent mentally unhealthy days. \nProspective studies could determine whether reductions in depressive symptoms lead to \nimprovements in mental health–related quality of life or whether chronic mental distress \ncontributes to worsening depressive symptoms over time. \nResearch incorporating a broader range of social determinants—such as social \nsupport, relationship quality, job conditions, stigma, symptom severity, and access to \n\n \n \n72 \nmental health care—may help identify moderators not captured by educational \nattainment. Larger data sets and pooled NHANES cycles could enhance statistical power \nand reduce numerical instability, particularly for interaction terms. Alternative modeling \nstrategies, including penalized logistic regression, Bayesian approaches, or generalized \nestimating equations with robust variance estimation, may address issues related to sparse \ndata and quasi-complete separation. \nIntervention studies are also needed to test whether integrated mental health and \nendometriosis care improves outcomes. Evaluations of depression screening programs, \npsychoeducation, and behavioral or pharmacologic treatments may help translate these \nfindings into actionable clinical strategies and determine whether addressing depressive \nsymptoms leads to meaningful improvements in mental HRQoL and other quality-of-life \ndomains. \nConclusion \nI found that depressive symptoms are a strong and independent predictor of \nmental health–related quality of life among U.S. women with endometriosis. Women \nwho screened positive for depressive symptoms had substantially higher odds of \nreporting frequent mentally unhealthy days, and this pattern persisted even after \naccounting for educational attainment and age. Although education was associated with \nmentally unhealthy days in some models, it did not significantly moderate the \nrelationship between depressive symptoms and mental HRQoL. The impact of depressive \nsymptoms on frequent mental distress was consistent across educational levels. \n\n \n \n73 \nThese findings underscore the central role of depressive symptoms in shaping \nmental well-being among women with endometriosis and highlight the importance of \nroutine depression screening and timely access to mental health services within clinical \ncare. Given that educational attainment did not buffer the mental health consequences of \ndepressive symptoms, equitable mental health support is needed across the full spectrum \nof educational backgrounds. \nBy addressing psychological factors alongside medical and social determinants, \nclinicians and policymakers may better support the mental health and quality of life of \nwomen living with this chronic condition. \n  \n\n \n \n74 \nReferences \nCenters for Disease Control and Prevention. (2000). Measuring healthy days: Population \nassessment of health-related quality of life (HRQOL). \nhttps://archive.cdc.gov/www_cdc_gov/hrqol/pdfs/mhd.pdf \nCenters for Disease Control and Prevention. (2023, August 30). National Health and \nNutrition Examination Survey (NHANES): Overview. \nhttps://www.cdc.gov/nchs/hus/sources-definitions/nhanes.htm \nCofini, V., Muselli, M., Petrucci, E., & Lolli, C. (2024). Factors associated with chronic \npelvic pain in women with endometriosis: A national study on clinical and \nsociodemographic characteristics, lifestyles, quality of life, and the need for \npsychological support. Women’s Health, 20, Article 17455057241227361. \nhttps://doi.org/10.1177/17455057241227361 \nDella Corte, L., Di Filippo, C., Gabrielli, O., Reppuccia, S., La Rosa, V. L., Ragusa, R., \nFichera, M., Commodari, E., Bifulco, G., & Giampaolino, P. (2020). The burden \nof endometriosis on women’s lifespan: A narrative overview on quality of life and \npsychosocial wellbeing. International Journal of Environmental Research and \nPublic Health, 17(13), Article 4683. https://doi.org/10.3390/ijerph17134683 \nEngel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. \nScience, 196(4286), 129–136. https://doi.org/10.1126/science.847460 \nKalaitzopoulos, D. R., Samartzis, N., Kolovos, G. N., Mareti, E., Samartzis, E. P., \nEberhard, M., & Daniilidis, A. 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Endometriosis. https://www.who.int/news-room/fact-\nsheets/detail/endometriosis \n  \n\n \n \n76 \nPart 3: Summary, Integration, and Conclusions \nSummary of Findings Across Manuscripts \nThis three-manuscript dissertation examined the relationships among depressive \nsymptoms, education, and health-related quality of life (HRQoL) among U.S. women \nwith endometriosis using nationally representative data from the 2005–2006 National \nHealth and Nutrition Examination Survey (NHANES). Guided by Engel’s \nbiopsychosocial model, the papers collectively assessed how psychological symptoms \nand social determinants relate to general, physical, and mental HRQoL. \nManuscript 1: General Health–Related Quality of Life \nStudy 1 evaluated whether depressive symptoms were associated with overall \nHRQoL, measured by self-rated general health. Women with depressive symptoms had \nsubstantially lower odds of reporting good/very good/excellent general health compared \nwith women without depressive symptoms. This association remained strong and \nstatistically significant after adjusting for age, education, marital status, household size, \nand family income-to-poverty ratio (PIR). Older age, lower PIR, and lower educational \nattainment were also associated with poorer general health, whereas marital status and \nhousehold size were not significant predictors in the final model. \nManuscript 2: Physical Health–Related Quality of Life \nStudy 2 examined whether depressive symptoms were associated with reporting \n≥14 physically unhealthy days in the past 30 days, a marker of frequent physical distress. \nWomen with depressive symptoms had markedly lower odds of reporting ≤13 physically \nunhealthy days, corresponding to approximately fourfold higher odds of reporting ≥14 \n\n \n \n77 \nphysically unhealthy days compared with women without depressive symptoms. This \nassociation persisted after adjusting for age, PIR, education, marital status, and household \nsize. Older age and lower PIR were significantly associated with poorer physical HRQoL, \nwhile education and household size were not consistently significant predictors. Marital \nstatus demonstrated an overall effect, although some patterns were difficult to interpret \ndue to sparse data and quasi-complete separation in certain categories. \nManuscript 3: Mental Health–Related Quality of Life and Moderation by Education \nStudy 3 tested whether educational attainment moderated the association between \ndepressive symptoms and frequent mentally unhealthy days. Depressive symptoms were \nstrongly associated with mental HRQoL across all models, with large odds ratios \nindicating substantially higher odds of reporting ≥14 mentally unhealthy days among \nwomen with depressive symptoms compared with those without symptoms. Education \nshowed mixed associations with mental distress, but the depression-by-education \ninteraction term was not statistically significant, indicating that education did not \nmoderate the relationship between depressive symptoms and mental HRQoL. Age was a \nsmall but significant predictor, with older age associated with slightly higher odds of \nfrequent mentally unhealthy days. These findings suggest that depressive symptoms exert \na powerful mental health burden across educational strata. \nIntegrated Summary of Data Analysis Procedures \nAcross all three studies, analyses were conducted using SPSS Version 29 with the \nComplex Samples module to account for NHANES’s multistage, stratified probability \ndesign. MEC examination weights (WTMEC2YR), masked variance strata \n\n \n \n78 \n(SDMVSTRA), and masked primary sampling units (SDMVPSU) were applied to ensure \nnationally representative inference consistent with NHANES analytic guidance. \nWeighted descriptive statistics summarized sample characteristics and outcome \ndistributions. Each study used complex samples logistic regression tailored to its focal \noutcome: \n• Manuscript 1: general health status (genhlth_bin) \n• Manuscript 2: ≥14 physically unhealthy days (phys_unhlthy14) \n• Manuscript 3: ≥14 mentally unhealthy days (ment_unhlthy14) \nDepressive symptoms (PHQ-2 ≥ 1) served as the primary independent variable in all \nstudies. Models were adjusted for key sociodemographic factors, including age, \neducation, household size, marital status, and PIR, when relevant. Study 3 additionally \nincorporated a depression-by-education interaction term (depXeduc) to test moderation. \nModel evaluation included design-adjusted Wald F statistics, odds ratios with \n95% confidence intervals, review of design-based degrees of freedom, inspection of \ndiagnostic output and warnings, and assessment of pseudo-R² indices (Cox & Snell, \nNagelkerke, McFadden). These aligned analytic procedures provided a unified \nframework for examining depressive symptoms, social context, and variation in HRQoL \namong U.S. women with endometriosis, enabling coherent comparison and integrated \ninterpretation across studies. \nIntegration of Findings Across Manuscripts \nCollectively, the three papers demonstrate that depressive symptoms are a central \nand consistent determinant of HRQoL among U.S. women with endometriosis. Across \n\n \n \n79 \ngeneral, physical, and mental health domains, depressive symptoms were strongly \nassociated with diminished HRQoL even after adjustment for sociodemographic \ncovariates. \nBiopsychosocial Model Alignment \nThe integrated findings reflect Engel’s biopsychosocial model by showing how \ndepressive symptoms, sociodemographic context, and HRQoL coexist within an \ninterconnected framework. Each study operationalized a distinct aspect of the model, and \ntogether they illustrate how biological, psychological, and social factors jointly shape \nhealth-related quality-of-life outcomes among women with endometriosis. \nPsychological Factors \nAcross all three papers, depressive symptoms—representing the psychological \ndomain—were the most consistent and powerful correlate of diminished HRQoL. \nWhether the outcome reflected general health, physical distress, or mental distress, \ndepressive symptoms were strongly associated with poorer well-being. These findings \nunderscore a core tenet of the biopsychosocial model: psychological processes are \ninseparable from physical health and play a foundational role in the experience of chronic \nillness. \nBiological Factors \nAge, used as a proxy for biological and cumulative disease burden, contributed \nsignificantly to general and physical HRQoL and was also associated with mental \nHRQoL in Study 3. Although NHANES does not provide clinical staging or biomarker \ndata relevant to endometriosis severity, age-related patterns likely reflect accumulated \n\n \n \n80 \nsymptom burden, comorbid conditions, hormonal transitions, and other physiological \nprocesses. These findings align with Engel’s view that biological states interact \ncontinuously with psychological and social contexts. \nSocial Factors \nEducation and PIR represented the social dimension of the model. PIR was \nassociated with both general and physical HRQoL, highlighting income-related \ndisparities consistent with broader public health literature. Education was associated with \ngeneral health and with mentally unhealthy days in some models but did not buffer the \nimpact of depressive symptoms in the moderation analysis. This pattern suggests that \nwhile socioeconomic advantage shapes overall health, it does not fully protect against the \nemotional burden of endometriosis, underscoring the complexity of opportunity \nstructures and health outcomes. \nIntegration Across Domains \nTaken together, the studies show that biological, psychological, and social \ndomains do not operate in isolation. Depressive symptoms exerted a strong psychological \ninfluence across all HRQoL outcomes; biological factors shaped general and physical \nhealth; and social determinants contextualized disparities in well-being. This pattern \nreflects the interdependence central to Engel’s model and supports conceptualizing \nendometriosis as a condition that must be understood and treated through a \nmultidimensional lens. \n\n \n \n81 \nTheoretical Alignment With Research Questions \nThe structure of the research questions further reinforces the biopsychosocial \nframework: \n• Manuscript 1 addressed overall HRQoL, an inherently biopsychosocial outcome \ninformed by the interplay of depressive symptoms, age, and socioeconomic \ncontext. \n• Manuscript 2 focused on physical HRQoL, illustrating how biological factors and \npsychological distress jointly influence physical functioning. \n• Manuscript 3 centered on mental HRQoL and explicitly tested whether a social \ndeterminant (education) moderated the association between depressive symptoms \nand mental distress. \nAlthough education did not moderate the depression–mental HRQoL relationship, the \nmoderation analysis represented a direct application of the biopsychosocial model. The \nfindings show that the research questions were distinct enough to explore separate \nHRQoL domains while remaining conceptually linked, collectively forming a cohesive \nbiopsychosocial narrative. \nOverall Interpretation Within the Biopsychosocial Framework \nAcross studies, depressive symptoms emerged as the dominant predictor of \nHRQoL. Psychological processes permeate biological and social experiences, influencing \noverall, physical, and mental health outcomes. Biological aging shaped perceptions of \ngeneral and physical health, and social disparities contributed to differences in HRQoL, \nbut none of these factors diminished the central role of depressive symptoms. Overall, the \n\n \n \n82 \nresults support conceptualizing endometriosis as a biopsychosocial condition that \nrequires integrated, multidimensional approaches to care, research, and public health \nplanning. \nMethodological Reflections \nThis dissertation demonstrates the utility of NHANES for population-level \nendometriosis research and highlights several methodological strengths and challenges. \nStrengths include the use of a nationally representative sample, application of complex \nsurvey methods with appropriate weighting and variance estimation, and consistent \nanalytic procedures across studies. These features enhance generalizability and support \nrobust population-level inference. \nAt the same time, several limitations warrant reflection. Key variables, including \nendometriosis diagnosis, depressive symptoms, and HRQoL indicators, were based on \nself-report and may be subject to misclassification or reporting bias. Complete-case \nanalysis, although consistent with NHANES analytic guidance and recent methodological \nwork, may introduce bias if participants with missing data differ systematically from \nthose with complete data. Sparse data and quasi-complete separation in some categories \n(e.g., marital status, extreme HRQoL values) created numerical challenges and reduced \nprecision for certain estimates. Finally, NHANES collected endometriosis-related \nquestionnaire data only between 1999 and 2006, and the diagnostic item (RHQ360) was \ndiscontinued after the 2005–2006 cycle, limiting opportunities for replication and time-\ntrend analyses. \n\n \n \n83 \nDespite these challenges, the analyses yielded stable, interpretable estimates for \nthe primary variables of interest and provided a foundation for future population-based \nendometriosis research. \nImplications for Practice and Public Health \nClinical Implications \nRoutine Depression Screening \nFindings strongly support integrating PHQ-based depression screening into \nroutine endometriosis care across gynecology, primary care, and pain management \nsettings. Brief screening tools such as the PHQ-2 or PHQ-9 can help identify women who \nmay benefit from further mental health evaluation and treatment. \nIntegrated Biopsychosocial Care Models \nThe consistent association between depressive symptoms and poorer HRQoL \nacross domains underscores the need for collaborative, multidisciplinary care models. \nCoordinated approaches involving gynecologists, primary care clinicians, pain \nspecialists, mental health providers, and social workers may offer more comprehensive \nsupport than siloed care. \nEquitable Access to Mental Health Services \nBecause the impact of depressive symptoms on HRQoL did not differ by \neducation level, mental health services should be accessible across the full spectrum of \neducational and socioeconomic backgrounds. Ensuring equitable access to counseling, \npsychotherapy, and pharmacologic treatment where appropriate is essential. \n\n \n \n84 \nPublic Health Implications \nAddressing Socioeconomic Disparities \nPIR-related disparities in general and physical HRQoL highlight the need for \npolicies that reduce financial and structural barriers to diagnostic services, specialty care, \nand mental health treatment for women with lower income. \nUse of HRQoL Indicators in Surveillance \nFrequently unhealthy days, as part of the CDC’s HRQOL-4 indicators, are \npractical, patient-centered, and scalable metrics for monitoring the population burden of \nendometriosis. Incorporating these indicators into surveillance and program evaluation \ncould support more responsive public health strategies. \nRecommendations for Future Research \nLongitudinal Research and Enhanced Measurement  \nFuture studies should prioritize longitudinal designs to clarify temporal \nrelationships between depressive symptoms and HRQoL outcomes. Prospective cohort \nstudies could determine whether changes in depressive symptoms precede changes in \ngeneral, physical, or mental HRQoL, or whether the relationships are bidirectional. \nIncorporating richer measures of clinical and social context—such as pain severity, \nsymptom duration, stigma, workplace demands, discrimination, and relationship \nquality—may help explain additional variance in HRQoL. \nAdvanced Analytic and Data Strategies \nPooling multiple NHANES cycles or using alternative large-scale data sets may \nimprove statistical power, address sparse-data issues, and mitigate numerical problems \n\n \n \n85 \nsuch as quasi-complete separation. However, a major barrier to advancing population-\nbased research on endometriosis is that NHANES discontinued the endometriosis \ndiagnostic item (RHQ360) after the 2005–2006 cycle. Endometriosis-related \nquestionnaire data have not appeared in public-use or restricted NHANES files since that \ntime, preventing replication of the present analyses, limiting examination of national \ntrends or cohort differences, and constraining evaluation of long-term changes in the \nrelationships between depressive symptoms and HRQoL. \nFuture public health surveillance efforts should prioritize reintroducing \nRHQ360—or a comparable validated endometriosis measure—into NHANES or similar \nnational health surveys. Restoring such an item would enable pooling across survey \ncycles, improve estimate stability, support time-trend analyses, and permit advanced \nmodeling approaches such as longitudinal pseudo-panel designs or structural modeling. \nRenewed national data collection on endometriosis is essential for monitoring disease \nburden, identifying disparities, informing policy, and strengthening epidemiologic \nevidence on the psychosocial and functional impacts of endometriosis in the United \nStates. \nProposed Endometriosis Population Health Surveillance and Outcomes Framework \nThe absence of contemporary, integrated population-level data on endometriosis \nunderscores the need for a structured surveillance and outcomes framework to guide \nfuture research and public health efforts. Informed by integrated findings across the three \npapers, this dissertation contributes to the conceptual development of the Endometriosis \nPopulation Health Surveillance & Outcomes Framework (EPHSOF), a disease-specific \n\n \n \n86 \nframework intended to support systematic data collection, integration, and analysis of \nendometriosis. \nEPHSOF is grounded in Engel’s biopsychosocial model and conceptualizes \nendometriosis as a chronic condition shaped by the interactions among biological, \npsychological, social, and structural determinants. The framework emphasizes the use of \npopulation-representative data to examine health-related quality of life, psychosocial \nburden, socioeconomic disparities, and functional outcomes, while also highlighting the \nimportance of longitudinal measurement to capture changes over time. \nAlthough EPHSOF is not implemented or evaluated within the present study, it is \nproposed as a forward-looking public health framework designed to translate the \nempirical findings of this dissertation into a structured approach for future surveillance, \nresearch, and policy-relevant analyses. Details of the EPHSOF framework will be \ndisseminated in subsequent methodological publications. \nIntervention Studies \nIntervention research is needed to translate these epidemiologic findings into \npractice. Trials that embed depression screening, mental health treatment, and \npsychoeducation into endometriosis care could assess whether improving depressive \nsymptoms leads to measurable gains in general, physical, and mental HRQoL. Pragmatic \nor implementation-focused studies may help identify feasible strategies for integrating \nbiopsychosocial care into routine clinical workflows. \n\n \n \n87 \nConclusions \nThis dissertation provides a comprehensive, population-based analysis of \ndepressive symptoms, education, and health-related quality of life among U.S. women \nwith endometriosis using the 2005–2006 NHANES data set—the last nationally \nrepresentative U.S. survey cycle to include an endometriosis diagnostic item. Although \nthe data are approximately 25 years old, they remain highly relevant because \nendometriosis is a chronic, historically underdiagnosed condition whose psychosocial and \nfunctional burdens have shown considerable stability over time. Contemporary research \ncontinues to document elevated rates of depression, diminished HRQoL, and persistent \nsocioeconomic disparities, patterns that mirror the relationships observed in this analysis. \nAcross all three papers, depressive symptoms emerged as the strongest and most \nconsistent correlate of poorer HRQoL, affecting general, physical, and mental health \noutcomes. Socioeconomic indicators, particularly PIR, contributed to disparities in \ngeneral and physical HRQoL, but education did not moderate the association between \ndepressive symptoms and mental HRQoL. These findings underscore the centrality of \nmental health in health-related outcomes among women with endometriosis and align \nwith current clinical priorities that emphasize integrated, biopsychosocial models of care. \nCollectively, the results highlight the need for equitable access to mental health \nservices, improved diagnostic and care pathways, and renewed population-level \nsurveillance systems capable of capturing the burden of endometriosis in contemporary \ncohorts. By emphasizing the interplay of biological, psychological, and social \ndeterminants, this dissertation contributes to a foundation for patient-centered \n\n \n \n88 \ninterventions and public health strategies aimed at improving the quality of life of women \nliving with endometriosis. \n  \n\n \n \n89 \nConsolidated References \nBolton, D., & Gillett, G. (2019). The biopsychosocial model of health and disease: New \nphilosophical and scientific developments. 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Endometriosis. https://www.who.int/news-room/fact-\nsheets/detail/endometriosis \n \n  \n\n \n \n93 \nAppendix A: NHANES Variables, Labels, and Level of Measurement \nVariable Domain NHANES \nVariable \nLabel Level of \nMeasurement \nEndometriosis diagnosis RHQ360 Told by doctor had endometriosis Nominal \nMental health (depression \nindicators) \nDPQ010 Little interest or pleasure in doing \nthings \nOrdinal \n \nDPQ020 Feeling down, depressed, or hopeless Ordinal \nSocioeconomic status INDFMPIR Family income-to-poverty ratio \n(PIR) \nRatio \nEducation DMDEDUC2 Education level – adults aged 20+ Ordinal \nBiological/demographic RIDAGEYR Age in years Ratio \nSocial support / household \nstructure \nDMDMARTL Marital status Nominal \n \nDMDHHSIZ Total number of people in household Ratio \nHealth-related quality of life \n(HRQoL) \nHSD010 General health status Ordinal (before \nrecoding)  \nHSQ470 Number of days physical health was \nnot good (past 30 days) \nScale \n \nHSQ480 Number of days mental health was \nnot good (past 30 days) \nScale \n \nHSQ490 Number of days activity was limited \ndue to poor physical or mental health \n(past 30 days) \nScale \nComplex sampling design \nvariables \nSDMVSTRA Masked variance pseudo-stratum — \n \nSDMVPSU Masked variance pseudo-primary \nsampling unit (PSU) \n— \n \nWTMEC2YR MEC 2-year examination sample \nweight \n— \nNote. HSQ470, HSQ480, and HSQ490 were originally continuous (count) variables. \nConsistent with NHANES analytic guidance and public health practice, unhealthy-day \nvariables were dichotomized at ≥14 days to indicate frequent distress. General health \n(HSD010) was recoded into a two-category indicator variable (genhlth_bin) for logistic \nregression models. All regression analyses incorporated the NHANES complex survey \ndesign using MEC examination weights (WTMEC2YR), strata (SDMVSTRA), and \nprimary sampling units (SDMVPSU).  \n\n \n \n94 \nAppendix B: Variable Coding and Operational Definitions \nSociodemographic Variables \nVariable NHANES \nSource Code \nOriginal \nCategories Recoding for Study Notes \nAge RIDAGEYR Continuous \n(years) None \nTreated as a \ncontinuous \ncovariate \n(Manuscripts 2 and \n3) \nSex RIAGENDR 1 = Male; 2 = \nFemale \nRestricted to females \n(RIAGENDR = 2) \nInclusion criterion \nfor analytic sample \nEducation \nlevel \nDMDEDUC2 \n→ edu_cat \n1–5 adult \neducation \nlevels \n(NHANES \ncategories) \nRecoded into \ncategorical variable \nedu_cat (5 levels) \nModerator in \nManuscript 3; also \nused as covariate in \nadjusted models \nMarital \nstatus DMDMARTL \n1–6 standard \nNHANES \nmarital status \ncodes \nCollapsed into 3 \ncategories \n(married/partnered, \npreviously married, \nnever married)  \nCollapsing \nperformed in \nManuscripts 1 and 2 \nto address sparse \ndata and quasi-\ncomplete separation \nFamily \nincome-to-\npoverty \nratio (PIR) \nINDFMPIR Continuous \nPIR score None \nUsed as a \ncontinuous \nsocioeconomic \ncovariate and in \ndescriptive \nsummaries \nHousehold \nsize DMDHHSIZ \nCount of \npeople in \nhousehold \nNone \nUsed as a covariate \nrepresenting \nhousehold \ncomposition in \nadjusted models \n \n\n \n \n95 \nClinical / Health Condition Variable \nVariable NHANES \nSource Code Recoding Notes \nEndometriosis \ndiagnosis RHQ360 None \nSelf-reported physician diagnosis; used to \ndefine the analytic sample of women with \nendometriosis \n \nDepression Construct \nConstruct Items (NHANES \nSource Codes) Recoding Final Variable \nPHQ-2 \ndepression \nindicator \nDPQ010 (little interest \nor pleasure) + DPQ020 \n(feeling down, \ndepressed, or hopeless) \nItems summed (range 0–6); \nscores ≥1 coded as 1 \n(depressive symptoms present), \nscores = 0 coded as 0 (no \ndepressive symptoms) \ndep_bin (PHQ-2 \n≥1 = depressive \nsymptoms) \n \nHealth-Related Quality of Life (HRQoL) Outcome Variables \nOutcome NHANES \nSource Code Recoding Applied Final Variable \nMentally \nunhealthy \ndays \nHSQ480 \nDichotomized: ≥14 mentally \nunhealthy days in past 30 days = \n1; <14 days = 0 \nment_unhlthy14 (≥14 \nmentally unhealthy \ndays) \nPhysically \nunhealthy \ndays \nHSQ470 \nDichotomized: ≥14 physically \nunhealthy days in past 30 days = \n1; <14 days = 0 \nphys_unhlthy14 (≥14 \nphysically unhealthy \ndays) \nGeneral \nhealth rating HSD010 \nRecoded into a two-category \ngeneral health indicator (0 vs. 1) \nfor logistic regression \ngenhlth_bin (general \nhealth indicator) \nActivity \nlimitation \ndays \nHSQ490 \nRecoded into a binary indicator \nbased on distributional split of \ndays with activity limitation \nact_lim_bin (activity \nlimitation indicator) \n \n\n \n \n96 \nInteraction Term \nVariable Computation Use in Analyses \ndepXeduc Product term: \ndep_bin × edu_cat \nTests moderation of the association between depressive \nsymptoms and mentally unhealthy days by education \nlevel (Manuscript 3) \n \nTechnical Notes \n• Marital status was collapsed into three categories for Manuscripts 1 and 2 to \nimprove model stability; original NHANES categories were retained for \ndescriptive summaries and Manuscript 3 where model diagnostics permitted. \n• The analytic sample was restricted to women with a self-reported physician \ndiagnosis of endometriosis (RHQ360 = 1) and complete data on required analytic \nvariables. \n• A complete-case approach was used; participants with missing values on key \npredictors or outcomes were excluded from the corresponding models. \n• Complex survey design variables (WTMEC2YR, SDMVSTRA, SDMVPSU) \nwere applied in all regression models using SPSS v29 Complex Samples \nprocedures. \n• All inferential analyses used complex-samples weighted logistic regression to \nproduce design-adjusted estimates, standard errors, and confidence intervals. \n  \n\n \n \n97 \nAppendix C: Analytic Sample Size and Weighted Population by Manuscript \nManuscript Primary Outcome (DV) Analytic Sample \n(Unweighted N) \nWeighted \nPopulation Size \n(Millions)a \nWeighted % \nwith Outcome \n= 1b \n1 – General \nhealth \nGeneral health \n(genhlth_bin) 4,137 186.6 15.9% \n2 – Physical \nHRQoL \n≥14 physically \nunhealthy days \n(phys_unhlthy14) \n4,139 186.6 9.6%  \n3 – Mental \nHRQoL  \n≥14 mentally unhealthy \ndays (ment_unhlthy14) 4,131 186.4 38.1% \nNote. Analytic sample sizes reflect final multivariable models presented in each \nmanuscript following complete-case selection and model diagnostics. Minor variation in \npreliminary models is not shown to maintain clarity and consistency. \na. Weighted population estimates are based on NHANES MEC examination weights. \nb. Outcome coded as 1 reflects the presence of the specified HRQoL outcome.","source_license":"CC0","license_restricted":false}