{"paper_id":"9797bbc8-2b51-4c70-b130-7f77d8cb540e","body_text":"Patient Preferences for Early Diagnosis of Endometriosis and Associated \nDeterminants in the United States: A Discrete Choice Experiment\nCarmen Lyttle-Nguessan, Ph.D., Vakaramoko Diaby, Ph.D.\nResearch Associate, Florida A&M University, Tallahassee, Florida 32307, United States.\nUniversity of Florida, 1225 Center Dr., Gainesville, Florida 32610, United States.\n Journal of Public Health Issues and Practices\nNguessan, C.L., &  Diaby, V . (2022). J Pub Health Issue Pract, 6(1): 196\nhttps://doi.org/10.33790/jphip1100196\nArticle Details\nArticle Type: Research Article\nReceived date: 10th  January, 2022 \nAccepted date: 26th February, 2022 \nPublished date: 28th February, 2022\n*Corresponding Author: Carmen Lyttle-Nguessan, Ph.D., Research Associate, Florida A&M University, Tallahassee, Florida \n32307, United States. E-mail: carmen.lyttlenguessa@famu.edu\n**Co-Author: Vakaramoko Diaby, Ph.D., University of Florida, 1225 Center Dr., Gainesville, Florida 32610, United States. \nE-mail: v.diaby@cop.ufl.edu\nCitation: Nguessan, C. L., Diaby, V ., (2022). Patient Preferences for Early Diagnosis of Endometriosis and Associated Deter-\nminants in the United States: A Discrete Choice Experiment. J Pub Health Issue Pract 6(1): 196. doi: https://doi.org/10.33790/\njphip1100196\nCopyright: ©2022, This is an open-access article distributed under the terms of the Creative Commons Attribution License \n4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source \nare credited.\nAbstract\nBackground: Endometriosis is a chronic and incurable gynecological \ndisease that mainly affects women of reproductive age worldwide. \nIt imposes clinical and economic burdens on patients, families, and \nsociety. A better understanding of the determinants of preferences \ntowards early diagnosis of endometriosis may help develop \nprograms and interventions to reduce the risk of more severe illness. \nWe quantified patient preferences for early endometriosis diagnosis \nand explored whether preferences vary on the patient characteristics \nand pre-established social determinants of health.\nMethods: A discrete choice experiment (DCE) was designed to elicit \nwomen's preferences and willingness to pay for early diagnosis of \nendometriosis. Women ages 18 and older were eligible to participate \nin the study. The attributes (and levels) considered to describe \nhypothetical scenarios included diagnosis (immediate/postponed), \nthe chance of advanced endometriosis and more severe illness (low/\nhigh), time away from living, and professional activities (8 days, 15 \ndays, 22 days and 30 days), and possible out-ofpocket costs ($0, $15, \n$60 and $210). The effects of participants' characteristics and social \ndeterminants of health on the preference for early diagnosis were \nmodeled using a Tobit model.\nResults: A total of 66 women with (2) or at-risk (64) of endometriosis \ncompleted the experiment. The respondents' age and insurance \nstatuses significantly influenced their preference or choice for early \ndiagnosis. On average, respondents were willing to give up $61.55 \nout-of-pocket cost to have a low risk of advanced endometriosis \nand more severe disease. The Tobit model indicates only age and \ninsurance variables significantly affected early diagnosis preference. \nThe results suggest that older ages and not having insurance increase \nthe likelihood of respondents choosing early diagnosis than the \nyounger age group and having insurance.\nConclusion: This study indicates the importance of considering \nthe patient characteristics and social determinants of health when \ndesigning and implementing health programs and interventions for \nendometriosis.\nKeywords: Willingness to Pay, Tobit Model, Attributes, Decision-\nMaking, Optimal Design, Health Outcomes, Individuals' \nCharacteristics, Age and Insurance Status\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                          ISSN- 2581-7264\nBackground\n   Endometriosis is a disease that can affect all women of reproductive \nage, regardless of race, ethnicity, or socioeconomic status [1,2]. The \nliterature defines endometriosis as the presence of the tissues of the \nendometrial lining outside the uterus inflaming areas of the body such \nas the ovaries, pelvis, abdominal cavity, and even the thorax and skin \n[2-5]. The disease imposes both clinical and economic burdens and \nconcerns individuals and society. In addition to the clinical effects, \nendometriosis can profoundly impact women's quality of life. In \nfact, in a study that assessed the quality-adjusted life years, women \ndescribed their experience of endometriosis to be worse than death \n[6,7].\n   There is no cure for endometriosis, and the treatment depends on \nseveral factors, including the disease's severity. Delays in treatment \nmay exacerbate the burden of the disease and reduce the quality \nof treatment outcome [6,8,9,]. Delays in diagnosis, high hospital \nadmission rates, surgical procedures, and incidences of comorbid \nconditions make endometriosis a more costly public health problem \nthan other chronic conditions such as migraine and Crohn's disease [10].\n   Studies have shown that age and insurance access often influence \ndecisions to seek early diagnosis and treatment for endometriosis [11, \n12]. Younger adults and individuals without insurance are less likely \nto get a routine medical checkup and seek medical attention before a \ncritical health issue [13]. A deeper understanding of the determinants \nof early diagnosis and treatments for endometriosis may help develop \ntargeted programs and interventions to reduce the risk of more severe \nillness or reduce the impact of the disease outcomes [14,15]. In \nthe absence of such critical evidence, women of reproductive age \nmay continue to suffer clinical, humanistic, and economic burdens \nassociated with endometriosis.\n   In this study, we quantified patient preferences for early diagnosis of \nendometriosis and explored whether preferences vary on the patient \ncharacteristics and pre-established social determinants of health.\n   No other DCE, to our knowledge, has addressed the role of age \nand insurance status in influencing preferences on early diagnosis of \nendometriosis. This study highlights the importance of accounting for \nindividual preferences in improving decision-making for diagnosing \nand treating endometriosis.\n\nPage 2 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                                ISSN- 2581-7264\nMaterials and Methods\n   DCEs have become a common technique in health economics \nresearch providing information on relevant characteristics (attributes) \nof services and programs [16]. Typically, a DCE elicits preferences \nthat estimate individuals' value on a particular good or service [17, \n18]. In this, the researcher asked respondents to choose between two or\nmore alternatives among several scenarios in which they described \nseveral attributes regarding the good or service at different levels. \nOne step in DCE is to gather demographics and socioeconomic \ninformation to help explain the individuals' preferences or choices. \nWe collected several demographic information, including age and \ninsurance.\nTarget population and subgroups\n   Women 18 years and older at risk for or diagnosed with \nendometriosis were eligible to participate in the study. Non-English-\nspeaking individuals cognitively impaired needing a caregiver to \ncomplete a survey were excluded. We targeted women with potential \nrisk for endometriosis and rich information on endometriosis. \nThe literature defines women at risk for endometriosis as women \ncurrently menstruating, using replacement estrogen, and with \nopportunity for diagnosis [19]. This definition suggests a broad age \ngroup and characteristics and provided guidance in determining \neligible participants for our research. This study identified suitable \nwomen aged 18-64 years. Still, it did not exclude older women if they \nhave a history of endometriosis since they may add richness to \nthe research. While endometriosis predominantly affects younger \nwomen, the most severe cases are found in older and postmenopausal \nwomen. Moreover, older women have a greater risk for endometriosis-\nassociated ovarian cancer [20,21].\n   We excluded non-English speaking individuals from taking the \nsurvey since it was written only in English and otherwise invalid. In \naddition, a non-English language survey instrument would be costly, \ntime-consuming, and difficult to validate. Because of the potential\ncognitive demands of the DCE instrument, we excluded individuals \nwith cognitive impairment and who needed caregiver assistance in \ncompleting the survey from the survey.\nSetting and location\n   We conducted the study in Tallahassee, Florida. We identified the \neligible participants from various group settings, mainly among \nstudents, the general community setting, and a local community \nhealth center.\nSample size\n   Sample size calculations are complicated for DCE, and the literature \nhas several recommendations for what it should be [22-25]. For this \nstudy, we based the sample size primarily on convenience.\nData collection\n   A pre-tested DCE survey instrument was hand-delivered to \nindividuals at health clinics and other group settings to collect \nindividuals' preferences effectively. The questionnaire gathered \nchoice and demographic data such as age, race, education, insurance \nstatus, employment status, knowledge, endometriosis status \n(diagnosed or not), and annual household income. The second \nsection of the questionnaire comprised the choice task with a series \nof 16 paired scenarios, each requiring respondents to select one of \ntwo options. The questionnaire included only the English language.\nDiscrete choice experiment\n   A discrete choice experiment instrument was created and \nadministered to all participants (Table 1). The final attribute selection \nincluded diagnosis (immediate, postponed); chance of advanced \nendometriosis and more severe illness (low, high); time away from \nwork, education, daily living activities (8 days, 15days, 22days, \n30days), and cost to you not covered by insurance ($0, $15, $60, \n$210). We used literature search and expert opinion to define the \nattributes and levels and pre-test the survey with selected women \nwilling to participate (n=10).\nAttribute Levels Description\nDiagnosis (Diag) Immediate Diagnosis of the disease is done now and not put off/\nback. This choice may allow for early treatment.\nPostponed Diagnosis of the disease is delayed or put off/back. \nThis decision may lead to delayed treatment\nChance of advanced \nendometriosis and more \nsevere illness (RISK)\nLow 10% or less chance of advanced endometriosis\n  and more severe illness\nHigh Greater than 10% and up to 100% chance of advanced \nendometriosis and more severe illness\nTime away from work, \neducation, daily living \nactivities (TIME)\n8days Average time for minimal cases of the disease\n15days Average time for mild cases of the disease\n22days Average time for moderate cases of the disease\n30days Average time for severe cases of the disease\nCost to you (Not covered by \ninsurance)[COST]\n$0 Co-payment for preventive/wellness care\n$15 Co-payment for primary care visit for an illness\n$60 Co-payment for a specialist visit without surgery. This \ncost also includes imaging services\n$210 Average co-payment for surgery (ambulatory and \noutpatient hospital). Based on the average co- payment \nfor three selected health insurance plans\nfor ambulatory and outpatient surgery.\nTable 1 Description of attributes and levels\n\nPage 3 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                               ISSN- 2581-7264\nExperimental design\n   We designed the DCE using the optimal design approach from \nthe Street, Burgess, and Louviere [26] table format. This approach \nsystematically coded the levels starting with 0 and counting \naccording to attribute levels. For instance, \"0, 1, 2, and 3\" was coded \nfor a 4-level attribute, and 0 and 1 for a 2-level attribute. The design \npermits one to choose a format that matches the number of levels and\nattributes in one's research plan. We used the Table 9 version optimal\ndesign for two 2-level and 4-level attributes (Table 2A&B). Table \n2B comprises the level names of the different attributes of the study. \nThe optimal design approach we used automatically checked for \northogonality, level balance, and minimal levels overlapping [26]. \nWe asked respondents to choose their preferred treatment effect \nscenario from the options labeled A and B (Figure 1).\nSet#                            Option 1                    Option 2\nA1 A2 A3 A4 A1 A2 A3 A4\n1 0 0 0 0 1 1 1 1\n2 0 1 0 2 1 0 1 3\n3 1 0 2 0 0 1 3 1\n4 1 1 2 2 0 0 3 3\n5 1 1 0 3 0 0 1 0\n6 1 0 0 1 0 1 1 2\n7 0 1 2 3 1 0 3 0\n8 0 0 2 1 1 1 3 2\n9 1 1 3 0 0 0 0 1\n10 1 0 3 2 0 1 0 3\n11 0 1 1 0 1 0 2 1\n12 0 0 1 2 1 1 2 3\n13 0 0 3 3 1 1 0 0\n14 0 1 3 1 1 0 0 2\n15 1 0 1 3 0 1 2 0\n16 1 1 1 1 0 0 2 2\nTable 2A Optimal design codes for two 2-level attributes and two 4-level attributes\nSource: Street, Burgess, and Louviere, 2005, Table 9, p. 465\nSet#                                 Option 1                                Option 2\nA1 A2 A3 A4 A1 A2 A3 A4\n1 Post Low 8days $0 Immed High 15days $15\n2 Post High 8days $60 Immed Low 15days $210\n3 Immed Low 22days $0 Post High 30days $15\n4 Immed High 22days $60 Post Low 30days $210\n5 Immed High 8days $210 Post Low 15days $0\n6 Immed Low 8days $15 Post High 15days $60\n7 Post High 22days $210 Immed Low 30days $0\n8 Post Low 22days $15 Immed High 30days $60\n9 Immed High 30days $0 Post Low 8days $15\n10 Immed Low 30days $60 Post High 8days $210\n11 Post High 15days $0 Immed Low 22days $15\n12 Post Low 15days $60 Immed High 22days $210\n13 Post Low 30days $210 Immed High 8days $0\n14 Post High 30days $15 Immed Low 8days $60\n15 Immed Low 15days $210 Post High 22days $0\n16 Immed High 15days $15 Post Low 22days $60\nBased on: Street, Burgess and Louviere, 2005, Table 9, p. 465\nNote: Post=Postponed; Immed=Immediate are the levels for the diagnosis attribute\nTable 2B Optimal design for two 2-level attributes and two 4-level attributes\n\nPage 4 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                               ISSN- 2581-7264\nData collection\n   Women visiting at a local community health clinic, in the general \npublic, and at students, gatherings were hand delivered the pre-tested \nsurvey instrument to complete independently. The cover letter of the \nquestionnaire also served as informed consent. The letter informed \nparticipants that their participation was completely voluntary and \nof their right to withdraw at any time without penalty. Completed \nquestionnaires were delivered directly to the research team/researcher \nfor data inputting.\nData Analysis\n   The DCE experimental design and data analysis are directly linked. \nThe response choice (Option A or Option B) is the dependent variable \nin the statistical model in which we estimated utility from observed \nchoices. The data were organized and summarized with descriptive \nstatistics (e.g., frequency, percentage and, standard deviation).\n   A mixed logit model with random effects was used to assess the \nimpact of attribute levels on participants' preferences for early \ndiagnosis of endometriosis. The variable cost was assumed log-\nnormally distributed while the remaining variables were normally \ndistributed. The model estimates the value or utility each respondent \nattaches to the different levels of the attributes and how the levels of \nthe attributes impact individuals' choices. Using Hiligsmann et al. \n[27] as a guide, we specified the model in Equation 1.     \nVij = β\n0 + (β1 + n1i ) COSTj + (β2 + n2і ) TIME_15dj + (β3\n          + n3i ) TIME_22dj + (β41 + n4i ) TIME_30dj + (β5\n          + n5і ) IMMED1DIAGj + (β6 + n6і)HIGH1RISKj + εij     (1)\n   Where  Vij indicates the utility that an individual i assigns to an \nintervention j. Vij is modeled as the sum of two parts: a systematic \npart based on the attributes in the DCE and an error or stochastic part \n(random component), E\nij. The random component is a function of \nthe unobserved attributes and variation in an individual's preference \n[27,25]. β\n0 is the constant reflecting the preferences for selected \noption or intervention relative to no option/intervention, (β 1 to β 6) \nthe mean attribute utility weights in the population, and n1ito n6i error \nterms for individual-specific unexplained variation in the utility \nweights.\n   We coded the categorical variables (TIME, DIAG, and RISK) to \nreflect the levels of the primary attributes. For example, the variable \nlabeled HIGH1RISKj refers to the attribute with the level of risk \nconsidered high. Out-of-pocket cost is the COST attribute treated \nas with the level of risk considered high. Out-of-pocket cost is the\nCOST attribute treated as calculating the WTP values [25]. We used \nDummy codes to describe the categorical variables (Table 3). At any \ngiven moment, one level will take a value of 1, and 0 for all others \n[25]. The coefficient signs reflect whether the attribute has a positive \nor negative effect on the intervention utility.\nAAttribute\nIndividual choice of interventions for endo \ntreatment\nOption A Option B\nDiagnosis Immediate Postponed (Delayed)\nChance of more serious/\nsevere illness\nLow High\nTime away from work, \neducation, and daily \nliving activities\n8 days 15 days\nCost to you (Not \ncovered by insurance)\n$15 $60\nWhich of the two \noptions would you \nprefer? (Check one)\n          \n      Option (A)       Option (B)\nFigure 1. Example of our study choice set\nAttributes Regression Label Level Modeling\nCost to you (out-of-pocket) COST $0 Continuous*\n$15\n$60\n$210\nTime away from work, education, \nand daily living activities\nTIME TIME_8d Dummy variable\nTIME_15d\nTIME_22d\nTIME_30d\nDiagnosis DIAG IMMED1DIAG; \nPOST2DIAG\nDummy variable\n \nChance of advanced endometriosis \nand more serious illness\nRISK LOW1RISK; \nHIGH2RISK\nDummy variable\nTable 3. Attributes, regression coding, levels, and modeling\n*Out-of-pocket cost is treated as a continuous variable in the regression model in the regression analysis, and it \nhas given one column only (Unlike the categorical dummy attributes (WHO 2012, p.52)\n\n\nPage 5 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                              ISSN- 2581-7264\n   We use the WTP to quantify an individual's tradeoff and utility \nby calculating the marginal substitution rate. WTP estimates for the \ncategorical attributes were calculated as the ratio of the coefficients \nof the attributes (numerator) and cost attribute (denominator). A WTP \nvalue represents how much one is willing to pay (or give up) for a \nunit change in the attribute and is calculated by taking the ratio of the \nmean parameter for the attribute level to the mean parameter related \nto the cost (or other continuous quantitative variables). For example, \nwhat individuals are willing to pay, on average, to reduce the risk \n(chance) of advanced endometriosis and more serious (or for early \n[immediate] diagnosis). Likewise, they were willing to pay to spend \nless time away from work, education, and daily living activities. We \nuse the mixed logit model (MXL) for the WTP estimates.\n   We used a Tobit model to investigate the effects of respondents' \ncharacteristics on their choice for immediate diagnosis. The Tobit \nmodel estimates a linear relationship between variables when either \nleft – or right- censoring the dependent variable [28]. The dependent \nvariable was the proportion of selected profiles that contained the \nattribute \"immediate diagnosis\" for each respondent. Because the \nTobit model requires 15 observations per variable (participant-level \ncharacteristics) included in the model, a forward stepwise regression \nwas conducted to identify statistically significant variables in \npredicting our dependent variable at a 5% significance level. We \ncoded the respondents' background information (Section 3 of the \nsurvey), assigning a single identifier name for each question. For \ninstance, questions 17 to 26 were named 'knowledge', 'information', \n'diagnosed', 'stage', 'insurance', 'race', 'age', 'education', 'employment', \nand 'income' respectively. We used these as the independent variables \nin the analysis.\n   We tested the goodness of fit of the Tobit models using a log-\nlikelihood ratio (LR) and Wald Chi-square tests. The data were \nanalyzed using SAS version 9.4 and Stata version 12.0.\nAssumptions\n   This study has several assumptions relating to the participants \nand the variables used. We assumed that the participants have the \ncognitive ability to make a rational choice independent of a caregiver \nand have some knowledge about endometriosis. This ability helped \nthem make the tradeoffs in the decision-making process. We also \nassumed that the payment vehicle (cost attribute) represented the \ntypical out-of-pocket healthcare cost for an individual seeking \nhealthcare. We also took that the out-of-pocket cost means the actual \nco-payment insurance for preventive/wellness care, diagnostic and \nsurgical procedures, and specialist visits. These assumptions imply    \nthat an individual has sought medical assistance (medical visit) at one \ntime or the other.\n   We based the cost attribute on co-payments from selected HMO \n(Health Maintenance Organization) insurance providers such as Blue \nCross/Blue Shield (Blue), Capital Health Plan (CHP), and Humana. \nWe based cost attribute calculations on potential healthcare visits or \ncare such as surgery (ambulatory and outpatient hospital), specialist, \nimaging, preventive care/ wellness, and primary care. These \ndetermined the four payment (cost attribute) levels. We averaged \nambulatory and inpatient hospital copayment amounts for the three \ninsurance plans to combine as one payment level ($210). Likewise, \nthe specialist and imaging costs were combined to form another \ngroup ($60). Overall, the cost attribute included only network or \nreferred provider cost based on a single visit for the specific care and \nthe average of health plans co-payments per visit. Preventive care, \nalso known as wellness care, costs is $0 for all health insurance plans. \nCo-payment for primary care visit for an illness is $15.\nResults\n   We distributed 72 questionnaires to individuals, received 67 \nin return, representing a response rate of 93%. We excluded \none questionnaire because the individual, per Hiligsmann and \nothers' [27] recommendation, did not complete at least five of the \nchoice sets in the DCE task. We included the remaining 66 (92%) \nquestionnaires for data analysis. Respondents' socio-demographics \nand health characteristics are in Table 3. There was no restriction on \nparticipation based on individuals' race and ethnicity, but individuals \nwere mainly black (about 72%). The other 28% comprises whites, \nAsians, Hispanics, and mixed races. Individuals were primarily in \n19-29 and 30-49 age groups, and none of the respondents fell in \nthe extreme upper (70+) or lower (18 or less) age groups. Of those \nresponding, two (3.13%) were diagnosed with endometriosis but did \nnot know the stage of their disease. The percent of those insured to \nsome extent was 86.\n   We did a pre-test with 10 participants as face validity to test the entire \nsurvey instrument's clarity, ease (or difficulty), and comprehension. \nWe gave the participants follow-up questions to determine their \nunderstanding of the DCE choice task and the length and ease of \nthe instrument. Almost all participants indicated the choice task was \nstraightforward, generally not tricky, and understandable. We \nalso asked an endometriosis expert to evaluate the instrument's \nnoteworthiness based on the contents. We revised and updated the \nsurvey instrument based on any comments or discourses.\nFrequency Percent (%)\nAge (years)\n19 -29 34 52.31\n30-49 23 35.38\n50-69 8 12.32\nMissing 1 N/A\nEducational Level (“Education”)\nGrade school or less 2 3.08\nSome high school 1 1.54\nHigh school graduate 2 3.08\nSome college 14 21.54\nCollege graduate 24 36.92\nGraduate or \nprofessional degree\n22 33.85\nMissing 1 N/A\nTable 3. to be cont...\n\nPage 6 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                               ISSN- 2581-7264\nEmployment Status (“Employment”)\nUnemployed 6 9.23\nEmployed part-time 16 24.62\nEmployed Full-time 24 36.92\nEmployed \nseasonally\n0 0\nRetired 0 0\nStudent 18 27.69\nHomemaker 1 1.54\nMissing 1 N/A\nAnnual Gross household income (\"Income\")\nLess than $10,000 16 25\n$10,000 to $24,999 19 29.69\n$25,000 to $49,999 14 21.88\n$50,000 to $74,999 9 14.04\n$75,000 to $99,999 2 3.13\n$100,000 to \n$124,999\n2 3.13\n$175,000 to \n$199,999\n2 3.13\nMissing 2 N/A\nRace/Ethnicity (“Race”)\nBlack/African \nAmerican\n46 71.88\nWhite/Caucasian 8 12.5\nAsian 2 3.13\nHispanic 1 1.56\nOther (mixed races, \nArab)\n7 10.94\nMissing 2 N/A\nInsurance coverage (“Insurance”)\nYes 56 86.15\nNo 7 10.77\nNot sure 2 3.08\nMissing 1 N/A\nPrior knowledge about endometriosis (\"Knowledge\")\nYes 51 78.46\nNo 14 21.54\nWhere prior knowledge came from(\"Information\")\nSchool 20 30.77\nWork 4 6.15\nHealthcare \npractitioner\n11 15.92\nOther 17 26.15\nNo information 13 20\nMissing 1 N/A\nTable 3. to be cont...\n\nPage 7 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                               ISSN- 2581-7264\nDiagnosed with endometriosis (\"Diagnosed\")\nYes 2 3.13\nNo 62 96.88\nMissing 2 N/A\nStage at diagnosis (\"Stage\")\nNot sure 2 3.08\nNot applicable 63 96.92\nMissing 1 N/A\nTable 3 Summary of respondents' characteristics\nthat would lower their risk of endometriosis, and 3) to spend less \ntime away from work, education and other daily living activities. For \ninstance, respondents were willing (on average) to give up $61.55 \nout-of-pocket cost to have a low risk of advanced endometriosis and \nmore severe disease.\nWillingness to pay analysis\n   The WTP values for attributes levels are in Table 4. WTP results \nwere not statistically significant, but the values are noteworthy \neconomically. Though the results are not statistically significant, the \nvalues indicate that respondents would be willing to give up money \n1) to put off their diagnosis for a later time, 2) to have an intervention\nAttributes and levels Willingness to pay (Conf. Interval)\nDiagnosis (Reference level: postponed) -1.038 (-4.34, 2.86)\nChance of advanced endometriosis and\nmore serious illness (reference level: \nLow)\n-61.55 (-276.48, 153.38)\nTime away from work, education, daily living activities (Reference level: 8days)\nTIME_15days -15.27 (-68.69, 38.14)\nTIME_22days -8.19 (-36.51, 20.13)\nTIME_30days -16.58 (-74.42, 41.26)\nCI: Confidence interval. Note: Data presented as mean (95% confidence interval \noverall), negative WTP means that individuals are willing to sacrifice out-of-pocket \ncost to receive the attributes/levels\nTable 4. Results of the willingness to pay analysis\nTobit model: Stepwise model selection results\n   The results of the stepwise Tobit model are in Table 4. The final \nmodel was statistically significant (p < 0.01) compared to an empty\nmodel. Of the variables introduced into the model, only age and \ninsurance significantly affected early diagnosis preference.\nEarly/\nImmediate \ndiagnosis\nEstimated \ncoefficient\nStd. Error t-value P value [95% \nConf. \nInterval]\nConstant 0.41599 0.02755 15.1 0.000*** 0.3609, \n0.4711\nAge 0.01995 0.00879 2.27 0.027** 0.0025, \n0.0375\nInsurance 0.02855 0.01351 2.11 0.039** 0.0015,\n0.0556\nStd. Error: Standard Error; Number of observations=62; 10 left-censored observations \nat early/immediate diagnosis <= 0.4375; 52 uncensored observations at early/\nimmediate diagnosis\nLog likelihood = 72.555689; Pseudo R2 =-0.0716\n**p < 0.05; ***p < 0 .01\nTable 4 Stepwise Tobit model results\n\nPage 8 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                               ISSN- 2581-7264\nbeing observable only between the values 0.4375 (lower bound) and\n0.6875 (upper bound). The visual inspection of the data supports \nusing a Tobit model to analyze the data. The results of the Tobit \nmodel are in Table 5. The overall model was statistically significant \n(p < 0.05).\nA. Tobit model: Effects of respondents' characteristics on \npreferences\n   Figure 2 shows the distribution of our dependent variable \"proportion \nof selected profiles that contained the attribute immediate diagnosis\" \n(prop_early). The data is left-censored, with our dependent variable \nFigure 2. Distribution of the dependent variable prop_early\nNote: prop_early: proportion of selected profiles that contained immediate diagnosis, per \nindividual\nEarly/Immedi \nate diagnosis\nEstimated \ncoefficient\nStd. Error t-value P value Conf. Interval\nConstant 0.4842 0.0087 55.87 0.000*** 0.4669, 0.5075\nAge (Reference: 19-29 yrs)\n30-49 yrs 0.0253 0.0131 1.94 0.057* -0.0008, \n0.0515\n50-69 yrs 0.0371 0.0191 1.94 0.057* -0.0012, \n0.0753\nInsurance (Reference: Having insurance)\nNo insurance 0.0399 0.0196 2.04 0.046** 0.00076, \n0.0791\nNot sure 0.03439 0.0344 1.00 0.322b -0.0344 , \n0.1032\nYrs: Years; Std. Error: Standard Error; Conf. Interval: Confidence interval; Number of observations=65 \n10 left-censored observations at early/immediate diagnosis <= 0.4375\n55 uncensored observations at early/immediate diagnosis Log likelihood = 77.968594; Pseudo R2 \n=-0.0761\n*p < 0.1; **p < 0.05; ***p < 0 .01; bNot significant\nTable 5. Tobit model: Effects of respondents' and insurance status on preference of early diagnosis\n   As mentioned before, we modeled only age and insurance to explain \nthe effect of preference on early diagnosis. The estimated coefficients \nof the variables had positive signs, which suggest positive effects \n(increase) on the dependent variable prop_early. The results indicate\nthat older age and not having insurance increased the likelihood \nof respondents choosing immediate or early diagnosis compared \nto the younger age group and having insurance, respectively. For \nexample, if the dependent variable prop_early were not censored, the\n\nPage 9 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                               ISSN- 2581-7264\nB. Tobit model: Marginal effects of respondents' characteristics \non preferences\n   The results for the Tobit marginal effect analysis are in Table 6. \nThe marginal effects calculated the exact change on the truncated \nexpected value of our dependent variable prop_early. For example, \nbeing aged 19-29 would cause the truncated expected value of prop_\nearly to increase by 0.854 points.\nestimated coefficient for age category 30-49 years (0.0253) would \nmean that prop_early is 0.025 points higher for respondents in 30-\n49 to respondents in the age group 19-29. Likewise, the estimated \ncoefficient for no insurance (0.0399) would mean that prop_early is \n0.04 points higher for respondents in the category of no insurance \nthan respondents with insurance. Since the data are censored, then it\nis latent censored variable y that is linearly related with the \nindependent variables age and insurance. As a result, a marginal \neffect analysis of the effect of the variable age and insurance is \nneeded to draw more accurate conclusions.\nDelta-method\nMargin Std. Error z-value P value Conf. Interval\nAge\n19-29 yrs 0.854 0.048 17.83 0.000 0.760, 0.948\n30-49 yrs 0.942 0.030 31.09 0.000 0.883, 1.001\n50-69 yrs 0.965 0.030 31.76 0.000 0.905, 1.0241\nInsurance\nHave \ninsurance\n0.886 0.036 24.91 0.000 0.817, 0.956\nNo insurance 0.977 0.022 44.11 0.000 0.933, 1.020\nNot sure 0.970 0.045 21.72 0.000 0.883, 1.058\nTable 6. Tobit model: Marginal effects of respondents' age and insurance status on the preference of \nearly diagnosis\nConf. Interval: Confidence interval; Std. error: Standard error; Number of observations=65; \nCensored (observable) expected value (y*) is (0.4375, 0.6875\nDiscussion\nStudy findings\n   This study, to our knowledge, is the first to use DCE to examine \nthe role of age and insurance status in influencing preferences on \nearly diagnosis of endometriosis. Our mixed logit model results \nsuggest that respondents prefer to put off diagnosis later. Given that \nour participants are generally younger individuals (ages 19-29) may \nshed some light regarding why women do not get diagnosed early. \nFurthermore, our results indicate that individuals without insurance \nare more likely to prefer immediate/early diagnosis of endometriosis, \nwhich is not generally what we would expect. However, studies \nsuggest that being insured does not guarantee an early diagnosis of \na condition, and non-Hispanic blacks (compared to non-Hispanic \nwhites) are less likely (even insured) to get an early diagnosis [29]. \nOur respondents were mainly non- Hispanic blacks, which might \nexplain the differences.\n   A Tobit model was estimated to explain the impact of the \nrespondents' characteristics on the choice for early diagnosis. \nThis evaluation attempted to answer what factors prompt women \ntoward earlier diagnosis and why they do not get diagnosed early.\nThe significant explanatory variables were age and insurance. The \nresults suggest that older individuals and those without insurance \nprefer early diagnosis. Studies showed that younger individuals delay \nseeking medical attention than their older counterparts.\nLimitations\n   While the Tobit model seemed to be the best fit for our data, \ngiven a censored distribution of our dependent variable, there were \nlimitations to its use. For example, the Tobit model generally requires \nat least 15 observations per variable included, while our data only \ncontained 66 observations with several patient-level variables. \nTherefore, we had to remove some variables before modeling the \ndata, impacting the results' accuracy. The mean WTP results were not \nstatistically significant, but the values are noteworthy economically. \nThe negative signs of the values reveal that individuals are willing\nto give up the out-of-pocket cost to avoid difficult or uncomfortable \nsituations. At the same time, ordinarily, they would prefer low out-\nof-pocket costs. This concept is consistent with common sense logic \nand other DCE studies [30].\nGeneralizability and Current knowledge\n   This study could benefit health professionals and decision-makers, \nespecially given the long delays diagnosed with endometriosis. \nThis study provides opportunities to explore programs and products \nto improve treatment outcomes of endometriosis by considering \nindividuals' preferences. For instance, the results indicate that \nyounger individuals are more likely to postpone diagnosis, suggesting \nthe need for programs targeting youths and younger adults, perhaps \nhighlighting the long-term benefit of early diagnosis. In addition, \nthe wide variation in the individuals' preferences highlights the \nimportance of incorporating individuals' preferences and informed \nand shared decision-making processes in improving endometriosis \ntreatment and outcomes.\nConclusion\n   No other DCE addressed the role of age and insurance status \nin influencing preferences on early diagnosis of endometriosis. \nThis study highlights the importance of accounting for individual \npreferences and demography in improving decision-making for \ndiagnosing and treating endometriosis. The respondents' age \nand insurance status significantly influence their choice for early \ndiagnosis. The respondents' preferences' results provide opportunities \nto examine current practices. Since this was exploratory research, \nwe recommend further investigation about the influence of age and \ninsurance status on the decision for early diagnosis of endometriosis. \nFuture studies may investigate more diverse demographics and \nspatial impacts.\nAdditional/Supplemental file\nAdditional file: data on the selection of attributes used in DCE?\nFunding\n   This research received no funding\n\nPage 10 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                               ISSN- 2581-7264\n10. Schindler, A. E. (2011). Dienogest in long-term treatment of \nendometriosis. International Journal of Women's Health, 3, \n175-184. http://dx.doi.org/10.2147/IJWH.S5633.\n11. Kirzinger, W. K., Cohen, R. A., &. Gindi, R.M. (2012). Health \ncare access and utilization among young adults aged 19–25: \nEarly release of estimates from the National Health Interview \nSurvey, January–September 2011. Division of Health Interview \nStatistics, National Center for Health Statistics, May 2012. \nAvailable from: http://www.cdc.gov/nchs/nhis/releases.htm. \n12. Ponce, N., Glenn, B., Shimkhada, R., Scheitler, A.J.,& Ko, M. \n(2017). Barriers to Breast Cancer Care in California: A report to \nthe California Breast Cancer Research Program. UCLA Center \nFor Health Policy Research, 10960 Wilshire Blvd. Suite 1550, \nLos Angeles, CA 90024.\n13. Taber, J. M., Leyva, B., & Persoskie, A. (2015). Why do People \nAvoid Medical Care? A Qualitative Study Using National Data. \nJournal of General Internal Medicine, 30 (3), 290-297. doi: \n10.1007/s11606-014-3089-1\n14. van Dijk, L. J., Nelen, W. L., D’Hooghe, T. M., Dunselman, \nG. A., Hermens, R. P., Bergh, C…., Kremer, J. A. (2011). The \nEuropean Society of Human Reproduction and Embryology \nguideline for the diagnosis and treatment of endometriosis: \nan electronic guideline implementability appraisal. \nBioMed Central Implementation Science, 6(7). http://www.\nimplementationscience.com/content/6/1/7 \n15. Shah,D.K., Moravek, M.B., Vahratian, A., Dalton, V .K., & \nLebovic, D.L. (2010). Public Perceptions of Endometriosis: \nPerspectives from both genders. Acta Obstetricia et \nGynecologica, 89, 646-650. \n16. de Bekker-Grob, E. W., Ryan, M., & Gerard, K. (2010). Discrete \nchoice experiments in health economics: A review of the \nliterature. Health Economics. Published online in Wiley Online \nLibrary (wileyonlinelibrary.com). DOI: 10.1002/hec.1697 \n17. Viney, R., Lancsar, E., & Louviere, J. Discrete choice \nexperiments to measure consumer preferences for health and \nhealthcare. Expert Review of Pharmacoeconomics & Outcomes \nResearch, 2(4), August 2002. DOI: 10.1586/14737167.2.4.319. \n18. Rubin, G., Bate, A., George, A., Shackley, P., & Hall, N.( 2006). \nPreferences for access to the GP: A discrete choice experiment. \nThe British Journal of General Practice 56(531): 743–748. \n19. American College of Obstetricians and Gynecologists [ACOG]. \n(2010) Management of Endometriosis. Washington DC: \nAmerican College of Obstetricians and Gynecologists (ACOG); \n2010 July 14 p. (ACOG practice bulletin; no. 114). [129 \nreferences]. \n20. Wei, J., William, J., & Bulun, S. (2011). Endometriosis and \novarian cancer: A review of clinical, pathologic, and molecular \naspects. International Journal of Gynecological Pathology, \n30(6), 553-568. doi:10.1097/PGP.0b013e31821f4b85. \n21. Pavone, M. E., & Lyttle, B.M. (2015). Endometriosis and \novarian cancer: links, risks, and challenges faced. International \nJournal of Women's Health, 5(7), 663-672.\n22. Bridges, J. F .P., Hauber, A. B., Marshall, D., Lloyd, A., Prosser, \nL. A., Regier, D. A.,…, Mauskopf, J. (2011). Conjoint analysis \napplications in health—a Checklist: A report of the ISPOR Good \nResearch Practices for Conjoint Analysis Task Force. Value In \nHealth, 14(2011), 403-413. doi:10.1016/j.jval.2010.11.013. \n23. de Bekker-Grob , E. W. , Donkers, B., Jonker, M.F., & Stolk, \nE.A. (2015). Sample size requirements for discrete choice \nexperiments in healthcare: A practical guide. Patient, 8, 373-\n384. DOI 10.1007/s40271-015-0118-z. \nConflicts of interest: The authors declare that there is no \nconflict of interest\nList of abbreviations\nACOG American College of Obstetricians and Gynecologists\nASRM American Society of Reproductive Medicine\nCHP Capital Health Plan\nDiag Diagnosis\nDCE Discrete choice experiment\nHMO Health Maintenance Organization\nImmed Immediate\nPost Postponed\nProp_early   Proportion of preferences on immediate diagnosis\nSAS Statistical Analysis Systems\nStata Data analysis and statistics\nWERF World Endometriosis Research Foundation\nWHO World Health Organization\nWTP Willingness to pay\nAcknowledgments: I want to express special thanks to co-chair \nDr. Ellen Campbell and committee members Drs. C. Perry Brown, \nMichael Thomas, and Hong Xiao for supporting research during my \ndissertation. This article is a subpart of my dissertation.\nReferences\n1. Simoens, S., Hummelshoj, L., & D’Hooghe, T. (2007). \nEndometriosis: Cost estimates and methodological perspective. \nHuman Reproduction Update, 13(4), 395–404.\n2. Agarwal, A., & Subramanian, A. (2010). Endometriosis - \nmorphology, clinical presentations and molecular pathology. \nJournal of Laboratory Physicians, 2(1), 1-9. DOI: 10.4103/0974- \n2727.66699. \n3. Abbott, J., Hawe, J., Hunter, D., Holmes, M., Paul Finn, P., \n& Garry, R. (2004). Laparoscopic excision of endometriosis: \nA randomized, placebo-controlled trial.  Fertility and Sterility, \n82(4), October 2004. doi:10.1016/j.fertnstert.2004.03.046. \n4. Hediger, M. L., Hartnett, H. J., & Buck Louis, G. M. (2005). \nAssociation of endometriosis with body size and figure. \nFertility and Sterility, 84(5), November 2005. doi:10.1016/j.\nfertnstert.2005.05.029. \n5. Nezhat, C., Nezhat, F., & Nezhat, Ceana. (2012). Endometriosis: \nancient disease, ancient treatments. Fertility and Sterility, \n98(65). http://dx.doi.org/10.1016/j.fertnstert.2012.08.001. \n6. Gao, X., Outley, J., Botteman, M., Spalding, J., Simon, J. A., \n& Pashos, C. L. (2006) Economic burden of endometriosis. \nFertility and Sterility, 86, 1561-1572. \n7. Simoens, S., Dunselman, G., Dirksen, C., Hummelshoj, \nL., Bokor, A., Brandes, I., … Brodszky, V . (2012). The \nburden of endometriosis: Costs and quality of life of women \nwith endometriosis and treated in referral centres. Human \nReproduction, 27, 1292-2012.\n8. American Society for Reproductive Medicine [ASRM]. \n(2012). Endometriosis and infertility: A committee opinion. \nFertility and Sterility, 98(3). http://dx.doi.org/10.1016/j.\nfertnstert.2012.05.031 Practice Committee, American \nSociety for Reproductive Medicine, 1209 Montgomery Hwy., \nBirmingham, AL 35216. \n9. What are the treatments for endometriosis? (n.d). In National \nInstitute of Child Health and Human Development (NICHD) \n/National Institute of Health. Retrieved at: https://www.nichd.\nnih.gov/health/topics/endometri/conditioninfo/treatment#top. \n\nPage 11 of 11\n J Pub Health Issue Pract                                                                                                                                          JPHIP, an open access journal\nV olume 6. 2022. 196                                                                                                                                              ISSN- 2581-7264\n28. Tobit analysis. (n.d.) In Institute for Digital Research and \nEducation, UCLA. Retrieve April 11, 2017 from http://stats.\nidre.ucla.edu/stata/dae/tobit-analysis/.\n29. Hoffman, H. J., LaVerda, N. L., Levine, P. H., Young, H. A., \nAlexander, L. M., Patierno, S. R., & DC-PNRP Research \nGroup. (2011). Having health insurance does not eliminate race/\nethnicity-Associated delays in breast cancer diagnosis in the \nDistrict of Columbia. Cancer, 117(16), 3824-3832. doi:10.1002/\ncncr.25970. \n30. Wanders, J. O.P., Veldwijk, J., de Wits, G. A., Hart, H. E., van \nGils, P. F., & Lambooij, M. (2014). The effect of out-of-pocket \ncosts and financial rewards in a discrete choice experiment: an \napplication to lifestyle programs. BMC Public Health, 14:870. \nhttp://www.biomedcentral.com/1471-2458/14/870 \n24. Louviere, J. J., Islam T., Wasi, N., Street, D. & Burgess, L. ( \n2008). Designing discrete choice experiments: do optimal \ndesigns come at a price? Journal of Consumer Research, 35 \n360-375. \n25. World Health Organization. (2012). How to Conduct a Discrete \nChoice Experiment for Health Workforce Recruitment and \nRetention in Remote and Rural Areas: A User Guide with Case \nStudies: WHO Library Cataloguing-in-Publication Data. ISBN \n978 92 4 150480 5 (NLM classification: WA 390, www.who.int. \n26. Street, D.J., Burgess, L., & Louviere, J.J. (2005). Quick and \neasy choice sets: Constructing optimal and nearly optimal \nstated choice experiments. International Journal of Research in \nMarketing 22(4): 459–470. \n27. Hiligsmann, M., Dellaert, B.G., Dirksen, C.D., Weijden, T., \nGoemaere, S., Reginster, J., …Watson, V . (2014). Patients' \npreferences for osteoporosis drug treatment: A discrete choice \nexperiment. Arthritis Research and Therapy, 16, R36; http://\narthritisresearch.com/content/16/1/R36.","source_license":"CC0","license_restricted":false}