ISSN: 2378-315X BBIJ

Biometrics & Biostatistics International Journal
Research Article
Volume 2 Issue 7 - 2015
Psychological Distress and the Leading Cancers among American Adults: An Evidence from the 2013 National Health Interview Survey
Abdullah Albalawi and Munni Begum*
Department of Mathematical Sciences, Ball State University, USA
Received: October 25, 2015 | Published: November 30, 2015
*Corresponding author: Munni Begum, Department of Mathematical Sciences, Ball State University, Muncie, IN 47306, USA, Tel: 1-765-285-8673; Email:
Citation: Albalawi A, Begum M (2015) Psychological Distress and the Leading Cancers among American Adults: An Evidence from the 2013 National Health Interview Survey. Biom Biostat Int J 2(7): 00053. DOI: 10.15406/bbij.2015.02.00053

Abstract

The objective of this study is to determine the prevalence of psychological distress (PD) among cancer patients and to investigate the association of PD with various socio-demographic factors. We consider the 2013 National Health Interview Survey, which is a large survey of the US non-institutionalized civilian population. PD is determined with a standardized questionnaire (K6). Cancer diagnoses are determined on the basis of self-report. For the purpose of this study, four different types of cancer are selected based on the leading number of deaths caused by them. We fit three commonly used ordinal regression models to the ordinal response PD for overall cancer patients. According to the goodness of fit criterion, AIC, we select the adjacent category model as best model for PD. All the predictors along with affliction by cancer are found to be significant determinants of PD. Additional analysis shows that the psychological distress does not differ across the four subtypes of cancer, breast, colon, lung and prostate cancer. However, psychological distress differs across the race of the respondents.

Keywords: Psychological distress; Breast; Colon; Lung and Prostate cancer; K6; Ordinal logistic models

Abbreviations

PD: Psychological Distress; WHO: World Health Organization; CHD: Chronic Heart Disease; BC: Breast Cancer; CC: Colon Cancer; LC: Lung Cancer; PC: Prostate Cancer; K6: Kessler 6-qusation Screening Scale; NHIS: National Health Interview Survey; NCHS: National Center for Health and Statistics; CDC: Centers for Disease control and prevention; AIC: Akai Information criterion; CO: Cumulative Odds Model; CR: Continuation Ratio Model; AC: Adjacent Categories Model; SAS: Statistical Analysis System

Introduction

Cancer is a significant health dilemma in the US and in many countries worldwide. It is one of the leading causes of death around the world. Every year, nearly 10.9 million individuals are diagnosed with cancer worldwide and around 6.7 million individuals die from it. The World Health Organization (WHO) anticipates that death from this disease will continue to rise, with an estimated 11.5 million deaths in 2030 [1]. Cancer specialists and medical scientists believe that psychological matters affect the progression of cancer, but the evidence remains unconvincing. Many studies focusing on mental health of cancer patients investigated the psychological impact and stigma of affliction due to cancer [2-5]. Cancer patients naturally experience many kinds of psychological problems due to the physical and mental afflictions caused by the long-term adverse health due to cancer. A number of studies demonstrate a clear link between the affliction from cancer and psychological distress (PD) measured by the symptoms of depression and anxiety [5-15]. PD is a significant problem for patients with cancer at every stage of their disease. Assessment and management of psychological distress are imperative in order to ease the burden on patients and to help them cope with their diagnosis and treatment [16]. A review of the psychological distress literature concludes that there are no simply identifiable characteristics of patients that can readily predict who has the potential need for psychosocial assessment and intervention [5,6]. To explore the impact of PD, many research investigations have been carried out in many countries examining various factors that manipulate the psychological distress among cancer patients. A systematic review on PD among cancer patients conducted by Yeh M et al. [8] found that the majority of cancer patients face significant psychological and emotional distress at sometime during the course of the illness.

There are various risk factors that have been linked to mental illness. Numerous investigations were conducted to determine the factors that play a significant role on PD among cancer patients [4-13,17]. For example, studies conducted in order to identify the risk factors of PD in females with breast cancer revealed that age, marital status, and education of the patient were the significant factors associated to PD [11,18,19]. Based on prior studies, the socio-demographic and clinical factors of health (i.e., BMI, insurance status, physical activity, smoking, drinking alcohol, etc.) may enhance distress. A common factor that is found to affect PD and cancer was race/ethnicity [1,3,20]. Other studies showed there is correlation between all covariates under consideration and the PD being the response variable. In particular, older women with breast cancer had a significantly higher level of distress Sunderland M et al. [18]. These factors can be used to identify individuals who may be at risk of experiencing PD. In this study, our objectives are to determine the prevalence of psychological distress among cancer patients and to investigate the association of psychological distress with various socio-demographic factors.

Although a number of studies investigate PD among cancer patients, the risk factors of PD among the leading sub-types of cancers are not well studied. In this study, we selected sub-types of cancer according to the estimates of the leading new cancer cases and death since 2013 in the US. The most frequently diagnosed cancers that occurred in both males and females in 2013 were breast cancer (BC), colon cancer (CC), lung cancer (LC), and prostate cancer (PC) [4]. In particular, this study explores and compares the prevalence of overall cancer and that off our leading cancer subtypes namely, BC, CC, LC, and PC and the association with psychological distress among the black and white American adults.

Data and Research Methods

Data are obtained from the 2013 National Health Interview Survey(NHIS), which is a large annual survey conducted on a random sample of individuals living in the United States [21,22]. This survey is across-sectional household survey of the US population conducted annually by the National Center for Health Statistics (NCHS), Center for Disease Control and Prevention (CDC). Face-to-face interviews were carried out at respondents’ houses. But, for those who were not home a follow-up was performed over the telephone. This survey employed a randomly selected, stratified, and multistage area design that produced nationally representative sample of households. Data from the NHIS are organized into a number of different types of files. We consider the sample adult module, which contains health-related information on randomly selected adults in the households. According to National Center for Health Statistics during the data collection period in 2013, there were 42, 294 eligible individuals, of which 34, 557 (81.7%) were interviewed.

We consider Kessler6 (K6) [23-26] to measure psychological distress (PD) among cancer patients. This measure K6 contains a six-item instrument, for each part the respondents were asked how frequently they experienced symptoms of psychological distress (sad, nervous, restless, hopeless, everything was an effort, and worthless) during the past 30 days. Each question has a 5-point scale with ranges from 0= ‘none of the time’ to5=‘all of the time’ in the NHIS. Since K6 scores lie between 0 and 4 on the Likert scale, the total of the responses scores is ranged between 0 and 24. We collapsed these scores into three categories as: 0-7 indicating a low level of PD, 8-12 indicating a moderate level of PD, and 13-24 indicating high PD. The predictor variables are selected as those found to be relevant according to the literature review.

Statistical models for ordinal response PD

We consider the three ordinal regression models: cumulative odds (CO), continuation ratio (CR), and adjacent categories (AC) models for psychological distress (PD) [25-27,30]. The best-fit model among these three is used to report the statistically significant determinants of PD among cancer patients. The cumulative odds (CO) or the proportional odds (PO) model is appropriate when the response categories are based on one or more latent continuous response variable. A CO model with  categories is divided to  logit equations. We use the category ordering by forming logit s of cumulative probabilities,

P( Yj x )= π 1 ( x )++ π j ( x ),j=1,,J. MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcLbsaqaaaaa aaaaWdbiaadcfajuaGdaqadaGcpaqaaKqba+qadaWccaGcpaqaaKqz GeWdbiaadMfacqGHKjYOcaWGQbaak8aabaqcLbsapeGaamiEaaaaaO GaayjkaiaawMcaaKqzGeGaeyypa0JaeqiWdaxcfa4damaaBaaajeai baqcLbmapeGaaGymaaWcpaqabaqcfa4dbmaabmaak8aabaqcLbsape GaamiEaaGccaGLOaGaayzkaaqcLbsacqGHRaWkcqGHMacVcqGHRaWk cqaHapaCjuaGpaWaaSbaaKqaGeaajugWa8qacaWGQbaal8aabeaaju aGpeWaaeWaaOWdaeaajugib8qacaWG4baakiaawIcacaGLPaaajugi biaacYcacaWGQbGaeyypa0JaaGymaiaacYcacqGHMacVcaGGSaGaam Osaiaab6caaaa@5F97@

The cumulative logit s are defined as:

logit[ P( Yj x ) ]=log P( Yj x ) 1P( Yj x ) =log π 1 ( x )++ π j ( x ) π j+1 ( x )++ π J1 ( x ) ,j=1,,J1 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGSbGaam4BaiaadEgacaWGPbGaamiDamaadmaapaqaa8qacaWG qbWaaeWaa8aabaWdbmaaliaapaqaa8qacaWGzbGaeyizImQaamOAaa WdaeaapeGaamiEaaaaaiaawIcacaGLPaaaaiaawUfacaGLDbaacqGH 9aqpciGGSbGaai4BaiaacEgadaWcaaWdaeaapeGaamiuamaabmaapa qaa8qadaWccaWdaeaapeGaamywaiabgsMiJkaadQgaa8aabaWdbiaa dIhaaaaacaGLOaGaayzkaaaapaqaa8qacaaIXaGaeyOeI0Iaamiuam aabmaapaqaa8qadaWccaWdaeaapeGaamywaiabgsMiJkaadQgaa8aa baWdbiaadIhaaaaacaGLOaGaayzkaaaaaiabg2da9iGacYgacaGGVb Gaai4zamaalaaapaqaa8qacqaHapaCpaWaaSbaaKqzadqaa8qacaaI Xaaak8aabeaapeWaaeWaa8aabaWdbiaadIhaaiaawIcacaGLPaaacq GHRaWkcqGHMacVcqGHRaWkcqaHapaCpaWaaSbaaKqzadqaa8qacaWG Qbaak8aabeaapeWaaeWaa8aabaWdbiaadIhaaiaawIcacaGLPaaaa8 aabaWdbiabec8aW9aadaWgaaqcLbmabaWdbiaadQgacqGHRaWkcaaI Xaaak8aabeaapeWaaeWaa8aabaWdbiaadIhaaiaawIcacaGLPaaacq GHRaWkcqGHMacVcqGHRaWkcqaHapaCpaWaaSbaaKqzadqaa8qacaWG kbGaeyOeI0IaaGymaaGcpaqabaWdbmaabmaapaqaa8qacaWG4baaca GLOaGaayzkaaaaaiaacYcacaWGQbGaeyypa0JaaGymaiaacYcacqGH MacVcaGGSaGaamOsaiabgkHiTiaaigdaaaa@8AAD@ (1)

The CO or PO Form of cumulative logit model is:

logit[ P( Yj x ) ]= α j + β T x. MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacaWGSbGaam4BaiaadEgacaWGPbGaamiDamaadmaapaqaa8qa caWGqbWaaeWaa8aabaWdbmaaliaapaqaa8qacaWGzbGaeyizImQaam OAaaWdaeaapeGaamiEaaaaaiaawIcacaGLPaaaaiaawUfacaGLDbaa cqGH9aqpcqaHXoqypaWaaSbaaKqbGeaapeGaamOAaaqcfa4daeqaa8 qacqGHRaWkcqaHYoGypaWaaWbaaeqajuaibaWdbiaadsfaaaqcfaOa amiEaiaac6caaaa@4F71@ (2)

Each cumulative logit has its intercept α j MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeqySde2aaS baaSqaaiaadQgaaeqaaaaa@38B1@ . The { α j } MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaai4Eaa baaaaaaaaapeGaaeySd8aadaWgaaqcfasaa8qacaqGQbaajuaGpaqa baGaaiyFaaaa@3BD3@ are increasing in j MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacaWGQbaaaa@3793@ because P( Yj x ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacaqGqbWaaeWaa8aabaWdbmaaliaapaqaa8qacaqGzbGaeyiz ImQaaeOAaaWdaeaapeGaaeiEaaaaaiaawIcacaGLPaaaaaa@3DE9@ increases in j MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacaWGQbaaaa@3793@ for fixed x MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacaWG4baaaa@37A2@ .For illustration, in our study a three-category outcome will have two binary log it equations based on the following comparisons: 1 vs. 2&3, 1&2 vs. 3. The CO is used to predict the odds of a person being at or below any particular level of Psychological Distress (PD). The following CO model is fitted to our data using the equation form (2):

  1. Low vs. (Moderate & High):
  2. logit[ P( Y1 x ) ]=log( π 1 π 2 + π 3 )= α 1 + β 1 x 1 + β 2 x 2 + β 3 x 3 + β 4 x 4 + β 5 x 5 + β 6 x 6 + β 7 x 7 + β 8 x 8 + β 9 x 9 + β 10 x 10 + β 11 x 11 + β 12 x 12 + β 13 x 13 + β 14 x 14 + β 15 x 15 + β 16 x 16 + β 17 x 17 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaqabeaaqaaaaa aaaaWdbiaadYgacaWGVbGaam4zaiaadMgacaWG0bWaamWaa8aabaWd biaadcfadaqadaWdaeaapeWaaSGaa8aabaWdbiaadMfacqGHKjYOca aIXaaapaqaa8qacaWG4baaaaGaayjkaiaawMcaaaGaay5waiaaw2fa aiabg2da9iGacYgapaGaai4BaiaacEgapeWaaeWaa8aabaWdbmaala aapaqaa8qacqaHapaCpaWaaSbaaeaapeGaaGymaaWdaeqaaaqaa8qa cqaHapaCpaWaaSbaaKqzadqaa8qacaaIYaaak8aabeaapeGaey4kaS IaeqiWda3damaaBaaajugWaeaapeGaaG4maaGcpaqabaaaaaWdbiaa wIcacaGLPaaacqGH9aqpcqaHXoqypaWaaSbaaKqzadqaa8qacaaIXa aak8aabeaapeGaey4kaSIaeqOSdi2damaaBaaajugWaeaapeGaaGym aaGcpaqabaWdbiaadIhapaWaaSbaaKqzadqaa8qacaaIXaaak8aabe aapeGaey4kaSIaeqOSdi2damaaBaaajugWaeaapeGaaGOmaaGcpaqa baWdbiaadIhapaWaaSbaaKqzadqaa8qacaaIYaaak8aabeaapeGaey 4kaSIaeqOSdi2damaaBaaajugWaeaapeGaaG4maaGcpaqabaWdbiaa dIhapaWaaSbaaKqzadqaa8qacaaIZaaak8aabeaapeGaey4kaSIaeq OSdi2damaaBaaajugWaeaapeGaaGinaaGcpaqabaWdbiaadIhapaWa aSbaaKqzadqaa8qacaaI0aaak8aabeaapeGaey4kaSIaeqOSdi2dam aaBaaajugWaeaapeGaaGynaaGcpaqabaWdbiaadIhapaWaaSbaaKqz adqaa8qacaaI1aaak8aabeaapeGaey4kaSIaeqOSdi2damaaBaaaju gWaeaapeGaaGOnaaGcpaqabaWdbiaadIhapaWaaSbaaKqzadqaa8qa caaI2aaak8aabeaapeGaey4kaSIaeqOSdi2damaaBaaajugWaeaape GaaG4naaGcpaqabaWdbiaadIhapaWaaSbaaKqzadqaa8qacaaI3aaa k8aabeaapeGaey4kaScabaGaeqOSdi2damaaBaaajugWaeaapeGaaG ioaaGcpaqabaWdbiaadIhapaWaaSbaaKqzadqaa8qacaaI4aaak8aa beaapeGaey4kaSIaeqOSdi2damaaBaaajugWaeaapeGaaGyoaaGcpa qabaWdbiaadIhapaWaaSbaaKqzadqaa8qacaaI5aaak8aabeaacqGH RaWkpeGaeqOSdi2damaaBaaajugWaeaapeGaaGymaiaaicdaaOWdae qaa8qacaWG4bWdamaaBaaajugWaeaapeGaaGymaiaaicdaaOWdaeqa a8qacqGHRaWkcqaHYoGypaWaaSbaaKqzadqaa8qacaaIXaGaaGymaa GcpaqabaWdbiaadIhapaWaaSbaaKqzadqaa8qacaaIXaGaaGymaaGc paqabaWdbiabgUcaRiabek7aI9aadaWgaaqcLbmabaWdbiaaigdaca aIYaaak8aabeaapeGaamiEa8aadaWgaaqcLbmabaWdbiaaigdacaaI Yaaak8aabeaapeGaey4kaSIaeqOSdi2damaaBaaajugWaeaapeGaaG ymaiaaiodaaOWdaeqaa8qacaWG4bWdamaaBaaajugWaeaapeGaaGym aiaaiodaaOWdaeqaa8qacqGHRaWkcqaHYoGypaWaaSbaaKqzadqaa8 qacaaIXaGaaGinaaGcpaqabaWdbiaadIhapaWaaSbaaKqzadqaa8qa caaIXaGaaGinaaGcpaqabaWdbiabgUcaRiabek7aI9aadaWgaaqcLb mabaWdbiaaigdacaaI1aaak8aabeaapeGaamiEa8aadaWgaaqcLbma baWdbiaaigdacaaI1aaak8aabeaapeGaey4kaSIaeqOSdi2damaaBa aajugWaeaapeGaaGymaiaaiAdaaOWdaeqaa8qacaWG4bWdamaaBaaa jugWaeaapeGaaGymaiaaiAdaaOWdaeqaa8qacqGHRaWkcqaHYoGypa WaaSbaaKqzadqaa8qacaaIXaGaaG4naaGcpaqabaWdbiaadIhapaWa aSbaaKqzadqaa8qacaaIXaGaaG4naaGcpaqabaaaaaa@F110@ (3)

  3. (Low & Moderate) vs. High:
  4. logit[ P( Y2 x ) ]=log( π 1 + π 2 π 3 )= α 2 + β 1 x 1 + β 2 x 2 + β 3 x 3 + β 4 x 4 + β 5 x 5 + β 6 x 6 + β 7 x 7 + β 8 x 8 + β 9 x 9 + β 10 x 10 + β 11 x 11 + β 12 x 12 + β 13 x 13 + β 14 x 14 + β 15 x 15 + β 16 x 16 + β 17 x 17 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaqabeaaqaaaaa aaaaWdbiaadYgacaWGVbGaam4zaiaadMgacaWG0bWaamWaa8aabaWd biaadcfadaqadaWdaeaapeWaaSGaa8aabaWdbiaadMfacqGHKjYOca aIYaaapaqaa8qacaWG4baaaaGaayjkaiaawMcaaaGaay5waiaaw2fa aiabg2da9iGacYgapaGaai4BaiaacEgapeWaaeWaa8aabaWdbmaala aapaqaa8qacqaHapaCpaWaaSbaaKqzadqaa8qacaaIXaaak8aabeaa peGaey4kaSIaeqiWda3damaaBaaajugWaeaapeGaaGOmaaGcpaqaba aabaWdbiabec8aW9aadaWgaaqcLbmabaWdbiaaiodaaOWdaeqaaaaa a8qacaGLOaGaayzkaaGaeyypa0JaeqySde2damaaBaaajugWaeaape GaaGOmaaGcpaqabaWdbiabgUcaRiabek7aI9aadaWgaaqcLbmabaWd biaaigdaaOWdaeqaa8qacaWG4bWdamaaBaaajugWaeaapeGaaGymaa GcpaqabaWdbiabgUcaRiabek7aI9aadaWgaaqcLbmabaWdbiaaikda aOWdaeqaa8qacaWG4bWdamaaBaaajugWaeaapeGaaGOmaaGcpaqaba WdbiabgUcaRiabek7aI9aadaWgaaqcLbmabaWdbiaaiodaaOWdaeqa a8qacaWG4bWdamaaBaaajugWaeaapeGaaG4maaGcpaqabaWdbiabgU caRiabek7aI9aadaWgaaqcLbmabaWdbiaaisdaaOWdaeqaa8qacaWG 4bWdamaaBaaajugWaeaapeGaaGinaaGcpaqabaWdbiabgUcaRiabek 7aI9aadaWgaaqcLbmabaWdbiaaiwdaaOWdaeqaa8qacaWG4bWdamaa BaaajugWaeaapeGaaGynaaGcpaqabaWdbiabgUcaRiabek7aI9aada WgaaqcLbmabaWdbiaaiAdaaOWdaeqaa8qacaWG4bWdamaaBaaajugW aeaapeGaaGOnaaGcpaqabaWdbiabgUcaRaqaaiabek7aI9aadaWgaa qcLbmabaWdbiaaiEdaaOWdaeqaa8qacaWG4bWdamaaBaaajugWaeaa peGaaG4naaGcpaqabaWdbiabgUcaRiabek7aI9aadaWgaaqcLbmaba WdbiaaiIdaaOWdaeqaa8qacaWG4bWdamaaBaaajugWaeaapeGaaGio aaGcpaqabaWdbiabgUcaRiabek7aI9aadaWgaaqcLbmabaWdbiaaiM daaOWdaeqaa8qacaWG4bWdamaaBaaajugWaeaapeGaaGyoaaGcpaqa baWdbiabgUcaRiabek7aI9aadaWgaaqcLbmabaWdbiaaigdacaaIWa aak8aabeaapeGaamiEa8aadaWgaaqcLbmabaWdbiaaigdacaaIWaaa k8aabeaapeGaey4kaSIaeqOSdi2damaaBaaajugWaeaapeGaaGymai aaigdaaOWdaeqaa8qacaWG4bWdamaaBaaajugWaeaapeGaaGymaiaa igdaaOWdaeqaa8qacqGHRaWkcqaHYoGypaWaaSbaaKqzadqaa8qaca aIXaGaaGOmaaGcpaqabaWdbiaadIhapaWaaSbaaKqzadqaa8qacaaI XaGaaGOmaaGcpaqabaWdbiabgUcaRiabek7aI9aadaWgaaqcLbmaba WdbiaaigdacaaIZaaak8aabeaapeGaamiEa8aadaWgaaqcLbmabaWd biaaigdacaaIZaaak8aabeaapeGaey4kaSIaeqOSdi2damaaBaaaju gWaeaapeGaaGymaiaaisdaaOWdaeqaa8qacaWG4bWdamaaBaaajugW aeaapeGaaGymaiaaisdaaOWdaeqaa8qacqGHRaWkcqaHYoGypaWaaS baaKqzadqaa8qacaaIXaGaaGynaaGcpaqabaWdbiaadIhapaWaaSba aKqzadqaa8qacaaIXaGaaGynaaGcpaqabaWdbiabgUcaRiabek7aI9 aadaWgaaqcLbmabaWdbiaaigdacaaI2aaak8aabeaapeGaamiEa8aa daWgaaqcLbmabaWdbiaaigdacaaI2aaak8aabeaapeGaey4kaSIaeq OSdi2damaaBaaajugWaeaapeGaaGymaiaaiEdaaOWdaeqaa8qacaWG 4bWdamaaBaaajugWaeaapeGaaGymaiaaiEdaaOWdaeqaaaaaaa@F249@ (4)

Where π 1 , π 2 , π 3 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacaqGapWdamaaBaaajqwba+Faa8qacaaIXaaajuaGpaqabaWd biaacYcacaqGapWdamaaBaaajuaibaWdbiaaikdaaKqba+aabeaape Gaaiilaiaabc8apaWaaSbaaKqbGeaapeGaaG4maaqcfa4daeqaaaaa @430E@ are the probability of being in low, moderate, and high levels, respectively. β 1 ,, β 17 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacaqGYoWdamaaBaaajuaibaWdbiaaigdaaKqba+aabeaapeGa aiilaiabgAci8kaacYcacaqGYoWdamaaBaaajuaibaWdbiaaigdaca aI3aaajuaGpaqabaaaaa@4060@ are the regression coefficients and x 1 ,, x 17 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacaqG4bWdamaaBaaajuaibaWdbiaaigdaa8aabeaajuaGpeGa aiilaiabgAci8kaacYcacaqG4bWdamaaBaaajuaibaWdbiaaigdaca aI3aaajuaGpaqabaaaaa@3FE6@ are the covariate factors.

In a continuation ratio (CR) model, the ratio of probabilities as follows,

π 1 π 2 , π 1 + π 2 π 3 ,, π 1 ++ π J1 π J MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qadaWcaaWdaeaapeGaeqiWda3damaaBaaajuaibaWdbiaaigda a8aabeaaaKqbagaapeGaeqiWda3damaaBaaajuaibaWdbiaaikdaa8 aabeaaaaqcfa4dbiaacYcadaWcaaWdaeaapeGaeqiWda3damaaBaaa juaibaWdbiaaigdaaKqba+aabeaapeGaey4kaSIaeqiWda3damaaBa aajuaibaWdbiaaikdaaKqba+aabeaaaeaapeGaeqiWda3damaaBaaa juaibaWdbiaaiodaaKqba+aabeaaaaWdbiaacYcacqGHMacVcaGGSa WaaSaaa8aabaWdbiabec8aW9aadaWgaaqcfasaa8qacaaIXaaapaqa baqcfa4dbiabgUcaRiabgAci8kabgUcaRiabec8aW9aadaWgaaqcfa saa8qacaWGkbGaeyOeI0IaaGymaaqcfa4daeqaaaqaa8qacqaHapaC paWaaSbaaKqbGeaapeGaamOsaaqcfa4daeqaaaaaaaa@5D5F@

OR

π 1 π 2 ++ π J , π 2 π 3 ++ π J ,, π J1 π J MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qadaWcaaWdaeaapeGaeqiWda3damaaBaaajuaibaWdbiaaigda aKqba+aabeaaaeaapeGaeqiWda3damaaBaaajuaibaWdbiaaikdaaK qba+aabeaapeGaey4kaSIaeyOjGWRaey4kaSIaeqiWda3damaaBaaa juaibaWdbiaadQeaaKqba+aabeaaaaWdbiaacYcadaWcaaWdaeaape GaeqiWda3damaaBaaajuaibaWdbiaaikdaaKqba+aabeaaaeaapeGa eqiWda3damaaBaaajuaibaWdbiaaiodaaKqba+aabeaapeGaey4kaS IaeyOjGWRaey4kaSIaeqiWda3damaaBaaajuaibaWdbiaadQeaaKqb a+aabeaaaaWdbiaacYcacqGHMacVcaGGSaWaaSaaa8aabaWdbiabec 8aW9aadaWgaaqcfasaa8qacaWGkbGaeyOeI0IaaGymaaqcfa4daeqa aaqaa8qacqaHapaCpaWaaSbaaKqbGeaapeGaamOsaaqcfa4daeqaaa aaaaa@5FF8@

are modeled through “continuation–ratio logit s”. Thus, the model can be written as:

log( π j π j+1 ++ π J )= x j T β j MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qaciGGSbGaai4BaiaacEgadaqadaWdaeaapeWaaSaaa8aabaWdbiab ec8aW9aadaWgaaqcLbmabaWdbiaadQgaaOWdaeqaaaqaa8qacqaHap aCpaWaaSbaaKqzadqaa8qacaWGQbGaey4kaSIaaGymaaGcpaqabaWd biabgUcaRiabgAci8kabgUcaRiabec8aW9aadaWgaaqcLbmabaWdbi aadQeaaOWdaeqaaaaaa8qacaGLOaGaayzkaaGaeyypa0JaamiEa8aa daqhaaqcLbmabaWdbiaadQgaa8aabaWdbiaadsfaaaGccqaHYoGypa WaaSbaaKqzadqaa8qacaWGQbaak8aabeaaaaa@55D0@ (5)

The continuation ratio model provides the log odds of the response being in the category j. For our study, J=3, we can estimate the odds of the respondents PD as “Low” vs. “Moderate” and the odds of these levels are in “Low” and “Moderate” versus “High” using:

log( π 1 π 2 ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qaciGGSbGaai4BaiaacEgadaqadaWdaeaapeWaaSaaa8aabaWdbiab ec8aW9aadaWgaaqcLbmabaWdbiaaigdaaOWdaeqaaaqaa8qacqaHap aCpaWaaSbaaKqzadqaa8qacaaIYaaak8aabeaaaaaapeGaayjkaiaa wMcaaaaa@42DD@ and log( π 1 + π 2 π 3 ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qaciGGSbGaai4BaiaacEgadaqadaWdaeaapeWaaSaaa8aabaWdbiab ec8aW9aadaWgaaqcLbmabaWdbiaaigdaa8aabeaak8qacqGHRaWkcq aHapaCpaWaaSbaaKqzadqaa8qacaaIYaaak8aabeaaaeaapeGaeqiW da3damaaBaaajugWaeaapeGaaG4maaGcpaqabaaaaaWdbiaawIcaca GLPaaaaaa@47D0@

The CR may be easier than CO in terms of interpretation if we are interested in finding the probability for individual categories π j MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qacqaHapaCpaWaaSbaaKqbGeaapeGaamOAaaqcfa4daeqaaaaa @3A5C@ .

Finally the adjacent categories (AC) model considers the ratios of probabilities for successive categories:

π 1 π 2 , π 2 π 3 ,, π J1 π J MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaieaaaaaa aaa8qadaWcaaWdaeaapeGaeqiWda3damaaBaaajuaibaWdbiaaigda a8aabeaaaKqbagaapeGaeqiWda3damaaBaaajuaibaWdbiaaikdaaK qba+aabeaaaaWdbiaacYcadaWcaaWdaeaapeGaeqiWda3damaaBaaa juaibaWdbiaaikdaaKqba+aabeaaaeaapeGaeqiWda3damaaBaaaju aibaWdbiaaiodaaKqba+aabeaaaaWdbiaacYcacqGHMacVcaGGSaWa aSaaa8aabaWdbiabec8aW9aadaWgaaqcfasaa8qacaWGkbGaeyOeI0 IaaGymaaqcfa4daeqaaaqaa8qacqaHapaCpaWaaSbaaKqbGeaapeGa amOsaaqcfa4daeqaaaaaaaa@5205@

The AC model can be written as:

log( π j π j+1 )= x j T β j MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qaciGGSbGaai4BaiaacEgadaqadaWdaeaapeWaaSaaa8aabaWdbiab ec8aW9aadaWgaaqcLbmabaWdbiaadQgaa8aabeaaaOqaa8qacqaHap aCpaWaaSbaaKqzadqaa8qacaWGQbGaey4kaSIaaGymaaGcpaqabaaa aaWdbiaawIcacaGLPaaacqGH9aqpcaWG4bWdamaaDaaajugWaeaape GaamOAaaWdaeaapeGaamivaaaakiabek7aI9aadaWgaaqcLbmabaWd biaadQgaaOWdaeqaaaaa@4E5B@ (6)

Which is equivalent to ,

log( P( y i =j ) P( y i =j+1 ) )=log( π j π j+1 )= β 0j + β 1 x 1 ++ β p1 x p1 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qaciGGSbGaai4BaiaacEgadaqadaWdaeaapeWaaSGaa8aabaWdbiaa dcfadaqadaWdaeaapeGaamyEa8aadaWgaaqcLbmabaWdbiaadMgaaO Wdaeqaa8qacqGH9aqpcaWGQbaacaGLOaGaayzkaaaapaqaa8qacaWG qbWaaeWaa8aabaWdbiaadMhapaWaaSbaaKqzadqaa8qacaWGPbaak8 aabeaapeGaeyypa0JaamOAaiabgUcaRiaaigdaaiaawIcacaGLPaaa aaaacaGLOaGaayzkaaGaeyypa0JaciiBaiaac+gacaGGNbWaaeWaa8 aabaWdbmaalaaapaqaa8qacqaHapaCpaWaaSbaaKqzadqaa8qacaWG Qbaak8aabeaaaeaapeGaeqiWda3damaaBaaajugWaeaapeGaamOAai abgUcaRiaaigdaaOWdaeqaaaaaa8qacaGLOaGaayzkaaGaeyypa0Ja eqOSdi2damaaBaaajugWaeaapeGaaGimaiaadQgaaOWdaeqaa8qacq GHRaWkcqaHYoGypaWaaSbaaKqzadqaa8qacaaIXaaak8aabeaapeGa amiEa8aadaWgaaqcLbmabaWdbiaaigdaaOWdaeqaa8qacqGHRaWkcq GHMacVcqGHRaWkcqaHYoGypaWaaSbaaKqzadqaa8qacaWGWbGaeyOe I0IaaGymaaGcpaqabaWdbiaadIhapaWaaSbaaKqzadqaa8qacaWGWb GaeyOeI0IaaGymaaGcpaqabaaaaa@7889@

This model assumes that the effect of each independent variable to be the same for all adjacent pairs of categories. All computations are conducted using SAS version 9.3 (SAS Institute, Cary, NC, [31]) and the R computing environment (Version3.11, TheRProject). SAS isused to manage the data and create analysis variables. In R, we used the VIGAM package to fit all of the three models.

Results

The summary statistics and the prevalence of psychological distress cross classified by all the predictor variables are presented in Table 1. About 90% of the respondents fall in the low distress category, followed by 7%and3%in the moderate and high categories, respectively. About 36% of respondents is from the South region while 16%, 21% and 27% is from the Northeast, the Midwest, and the West respectively. Most of the participants (76%) are white American. There are 12086 (68%) respondents in the age range of 31-64 years old. More than 65% of the sample has a high school education or above. About 38% of the respondents are smokers while 71% of them are alcoholic. Approximately, 20% of the respondents do not have insurance. More than half of them have an annual income in the range of $0-$34,999. About 4% of the respondents with cancer have low distress level. The number of cancer patients in the high distress level is 44. More singles are found to be in high distress level (2%) compared to married people (0.73%). About 73% of the respondents with insurance have low distress level while 17% of people without insurance have low distress level. In terms of the income, people who have low income tend to have high distress compared to high-income people. Pre valence of four sub-types of cancer cases by gender are shown in Table 2.

The Variables

Category

The Psychological Distress Level

Total
N=17765
100%

Low
n=16018
90.17%

Moderate
n= 1288
7.25%

High
n= 459
2.58%

Region

Northeast

2556 (14.39%)

205(1.15%)

77 (0.43%)

2838 (15.98%)

 

Midwest

3341(18.81%)

265(1.49%)

87 ) 0.49%)

3693 (20.79%)

 

South

5830(32.82%)

449(2.53%)

171(0.96%)

6450(36.31%)

 

West

4291(24.15%)

369(2.08%)

124(0.70%)

4784(26.93%)

Sex

Male

7933(44.66%)

526(2.96%)

175(0.99%)

8634(48.60%)

 

Female

8085 (45.51%)

762(4.29 % )

284(1.60%)

9131 (51.40%)

Race

White

12212(68.74%)

976(5.49%)

339(1.91%)

13527(76.14%)

Black/African

2284(12.86%)

199(1.12%)

65(0.37%)

2548(14.34%)

Asian

1022(5.75%)

62(0.35%)

23(0.13%)

1107(6.23%)

 

Others

500(2.81%)

51(0.29%)

32(0.18%)

583(3.28%)

Age

(18-30)

4114(23.16%)

363(2.04%)

103(0.58%)

4580(25.78%)

(31-64)

10870(61.19%)

876(4.93%)

340(1.91%)

12086(68.03 % )

 

65+

1034(5.82%)

49(0.28%)

16(0.09%)

1099(6.19%)

Marital Status

Married

7344(41.34%)

439(2.47%)

129(0.73%)

7912(44.54%)

 

Unmarried

8674(48.83%)

849(4.78%)

330(1.86%)

9853(55.46%)

Cancer

Yes

792(4.46%)

87(0.49%)

44(0.25%)

923 (5.20%)

 

No

15226(85.71%)

1201(6.76%)

415(2.34%)

16842(94.80%)

Smoke

Yes

5840(32.87%)

593(3.34%)

251(1.41%)

6684(37.62%)

 

No

10178(57.29%)

695(3.91%)

208(1.17%)

11081(62.38%)

Activity

Yes

8899(50.09%)

618(3.48%)

183(1.03%)

9700(54.60%)

 

No

7119(40.07%)

670(3.77%)

276(1.55%)

8065(45.40%)

BMI

Underweight

215(1.21%)

18(0.10%)

7(0.04%)

240(1.35%)

Normal

5429(30.56%)

403(2.27%)

131(0.74%)

5963(33.57%)

Overweight

5590(31.47%)

426(2.40%)

126(0.71%)

6142(34.57%)

 

Obese

4784(26.93%)

441(2.48%)

195(1.10%)

5420(30.51%)

Insurance

Covered

13036(73.38%)

921(5.18%)

282(1.59%)

14239(80.15%)

 

Uncovered

2982(16.79%)

367(2.07%)

177(1.00%)

3526(19.85%)

Education

High School or less

5009(28.20%)

513(2.89%)

201(1.13%)

5723(32.22%)

 

> High School

11009(61.97%)

775(4.36%)

258(1.45%)

12042(67.78%)

Income

($0-$34,999)

8285(46.64%)

894(5.03%)

371(2.09%)

9550(53.76%)

($35,000-$74,999)

5375(30.26%)

320(1.80%)

69(0.39%)

5764(32.45%)

 

$75,000+

2358(13.27%)

74(0.42%)

19(0.11%)

2451(13.80%)

Table 1: Psychological distress across predictor variables (cancer in general), N=17765.

Body Mass Index (BMI) is a simple index of weight-for-height that is commonly used to classify underweight, overweight and obesity in adults. It is defined as the weight in kilograms divided by the square of the height in meters (kg/m2).

Types of Cancer

Categories

Gender

N=17765

Male

Female

Breast cancer

Yes

4

144

148

No

357

413

769

Colon cancer

Yes

13

11

24

No

347

547

894

Lung cancer

Yes

6

5

11

No

353

554

907

prostate cancer

Yes

82

0

82

No

278

0

278

Other types

Yes

256

402

658

No

105

160

265

Table 2: Sub-type of cancer cases by gender.

Figure 1 shows the distribution of prevalence of cancer across the levels of psychological distress. In the high distress level proportion prevalence of cancer is higher. Figure 2 shows that the distribution of PD levels across gender. Females are prone to be susceptible to belong higher distress level. Figure 3 shows the distribution of PD levels across the sub-types of cancer. Breast and ‘other’ types have more prevalence of moderate and high distress levels.

Figure 1: The distribution of PD among Cancer patients.
Figure 2: The distribution of respondents’ gender and PD.
Figure 3: The Distribution of Cancer Types and PD.

Association between psychological distress and each predictor variable is determined using a chi-squared test. Results of the bi-variant analysis are presented in Table 3. It shows most of the predictors, except region and alcohol consumption, are significantly associated with the response variable PD. All the significant predictors are then included in the CO, CR and AC model. All three models are implemented to estimate the regression parameters (β) and p-values based on World statistics. The statistical computing software R issued to fit these three models. Based on the goodness of fit criterion as Information Criteria (AIC) (Table 4) we selected AC model as the best-fit model for PD and the included predictor variables.

 

d.f

Chi-square

p-value

Region

6

3.4235

0.7541

SEX

2

56.7093

<.0001

Race

6

29.4704

<.0001

Age (Group in years)

4

26.5571

<.0001

Marital Status

2

118.3031

<.0001

Cancer

2

26.3698

<.0001

Smoking status

2

105.0631

<.0001

Physical Activity

2

68.8511

<.0001

Alcohol consumption

2

0.1908

0.9090

BMI

6

43.7622

<.0001

Insurance

2

176.8340

<.0001

Education

2

68.8976

<.0001

Income

4

306.9797

<.0001

Table 3: The association between PD of the respondents and predictors for (the cancer sample).

 

Ordinal Logistic Regression Model

Cumulative Odds (CO)

Adjacent category log it model (AC)

Continuation ratio model (CR)

AIC

12800.63

12765.06

12806.01

Table 4: The adequacy tool for model selection.

Table 5 presents the results of AC model. All the predictors are found significant 5% level of significant. Male are 1.23 times more likely to have low or moderated stress level than female. In other words, females tend to be in high distress level. White Americans are more likely to have low or moderate distress level compare to other groups. Inexact, they are 28% more relaxed than other group where as Black/African American and Asians 42% and 26% more likely to have low or moderate distress level, respectively. Respondents of aged 18-30 years are 36% less likely to have low or moderate distress level compare to the respondents of aged 65+years. People who are at the age of 31-64 years are 46% less likely to have low or moderated stress level compare to the aged 65+. It is to be noted that older people are more relaxed than the younger people. If a person has a cancer, then he/she is more likely to be in high distress level. In fact, a person with cancer is 35% less likely to have low or moderate distress level. Married people are 42% more likely to have low or moderated I stress level than singles. Smokers are less likely to have lower distress level than nonsmokers. If a person does some physical activity at least once a week, then he/she is 1.17 times higher chance to have low or moderated I stress level. Obese people are more likely to have high distress level. People with insurance are more likely to have low or moderate distress than people without insurance. In other words, covered respondents tend to be 28% times higher to be in low or moderated I stress level. Persons having at least high school diploma are more likely to be in low or moderated I stress level. Low-income group is less likely to be in lower distress level.

Predictors

Adjacent Category Log It Model

Estimate
(SE)

OR
(95% CI)

P-value

Intercept1()

2.87 (0.17)

 

 

Intercept2()

1.64 (0.185)

 

 

Sex

 

 

 

Male

0.21 (0.04)

1.23
(1.14,1.34)

<.0001

Female

 

1

 

Race

 

 

 

White

0.24 (0.08)

1.28
(1.07,1.52)

0.0004

Black

0.35 (0.09)

1.42
(1.17,1.72)

<.0001

Asian

0.23 (0.12)

1.26
(0.99,1.60)

0.005

Other

 

1

 

Age (in years)

 

 

 

(18-30)

-0.44 (0.11)

0.64
(0.51,0.8)

<.0001

(31-64)

-0.60 (0.10)

0.54
(0.44,0.67)

<.0001

65+

 

1

 

Marital Status

 

 

 

Married

0.35 (0.04)

1.42
(1.30,1.54)

<.0001

Unmarried

 

1

 

Cancer

 

 

 

Yes

-0.42 (0.07)

0.65
(0.56,0.75)

<.0001

No

 

1

 

Smoke

 

 

 

Yes

-0.32 (0.03)

0.72
(0.66,0.78)

<.0001

No

 

1

 

Activity

 

 

 

Yes

0.16 (0.04a)

1.17
(1.08,1.27)

<.0001

No

 

1

 

Table 5a: The results of the ordinal logistic regression model (AC).

Predictors

Adjacent Category Log It Model

Estimate
(SE)

OR
(95% CI)

P-value

BMI

 

 

 

Underweight

0.23 (0.16)

1.26
(0.92,1.7)

0.01

Normal

0.21 (0.04)

1.23
(1.12,1.3)

<.0001

Overweight

0.17 (0.04)

1.18
(1.08,1.3)

<.0001

Obese

 

1

 

Insurance

 

 

 

Covered

0.24 (0.04)

1.28
(1.17,1.39)

<.0001

Uncovered

 

1

 

Education

 

 

 

High school or less

 

1

 

>high school

0.06 (0.04)

1.06
(0.98,1.15)

0.01

Income

 

 

 

($0-$34,999)

-0.84 (0.09)

0.42
(0.35,0.51)

<.0001

($35,000-$74,999)

-0.32 (0.09)

0.72
(0.59,0.87)

<.0001

$75,000+

 

1

 

 

SE=Standard Error of the estimate; OR=odds ratio; CI=Confidence interval

Table 5b: The results of the ordinal logistic regression model (AC) (Can’t).

The risk factors for psychological distress obtained in Table 5 are based on the entire population. We found that respondents with cancer are more likely to be in the high distress level. Figure 3 shows the distribution of psychological distress levels over all the types of cancer. In order to test is there is any heterogeneity in PD across different sub-types of cancer, that if the distress levels are same for all types of cancer, we perform a chi-squared homogeneity test. Out of 923 people who have at least one type of cancer, 148 have breast cancer, 24 have colon cancer, 11 have lung cancer, 82 have prostate cancer and rest of them have other types of cancer or they won’t disclose it. Based on the test (p-value = 0.4323), we fail to reject the hypothesis of homogeneity of the distress level among the different types of cancer. So we can conclude that there is no difference in distress level among different types of cancer.

We also conducted a heterogeneity test for the distress level across race. Most of the respondents in the study are white American (76%) followed by African American (14%). To test if the distress level is same for all people who have different race, we employ a contingency analysis and use chi-square statistic to investigate the homogeneity of the distress level. We reject (p-value <0.0001) the hypothesis of homogeneity of the distress level overall people who have different race at 5% significance level. So we conclude that there is difference in distress level for different race, which is supported by the findings in the literature [3,4].

Discussion

This study is conducted to determine the prevalence of psychological distress among cancer patients and to investigate the association of psychological distress with various socio-demo graphic factors. Clearly, in terms of mental health, this study has found that cancer patients suffer from higher level of psychological distress than the general population. That is, individuals with cancer diagnostics are more likely to experience higher psychological distress than those without cancer.

In general, the results suggest that psychological distress (PD) is a burden among participants with the cancer, however particular types of cancer is not associated with the level of PD. The results from the ordinal logistic regression models suggest that individuals with self-reported cancer are more likely to experience PD when compared with those without cancer. We found a number of socio-demographic factors that contribute PD other than cancer. These include age, sex, race, marital status, physical activity, BMI, insurance, education level and income. Thus our findings confirm those by the previous findings in the literature. Sub group analysis of PD among the sub types of cancer (breast, colon, lung, and prostate) does not demonstrate any significant determinants of PD.

Psychological distress among cancer patients adds extra burden on them. An important finding is that differential psychological distress level exists a cross different race of overall cancer patients. However, among the different sub types of cancer (breast, colon, lung, and prostate) PD is not found to be different across race.

The psychological distress of cancer patients is not a straight forward subject to study. Numerous factors must be taken into account. However, the main advantage of this study is the availability of all representative population based sample. The NHIS has been conducted every year since1957. However, there are some limitations of this study. One limitation is that the self-reported nature of the data. Another limitation is that a bulk of missing information. There is not enough data to assess the association between the risk factors and the PD across different subtypes of the cancer.

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