CHAPTER 2: STUDY DESIGN AND ANALYTIC METHODS
2.22. Potential limitations:
1. Using SF-12 MCS to identify participants with depression in the prior 12 months exploits a high correlation between SF-12 MCS measured at a single point in time and receiving a clinical diagnosis of depression within the 12 months preceding measurement of SF-12 MCS. As such, SF-12 MCS is not diagnostic (hence the term “probable depression”).
2. Self-reports of clinical diagnosis of depression or treatment of depression may be affected by recall bias and social desirability bias from stigma. Several studies have provided some insight into the magnitude of these biases and are presented in Table 2.4. Findings in the negative agreement column suggests that recall and social desirability biases may affect about 1 in 10 indicated participants on average. This limitation may introduce measurement error that is
26
expected to be uncorrelated with any variable in the model - resulting in larger standard errors (or type 2 error) rather than biased estimates.
3. No PCaP/HCaP-NC participant had end-stage prostate cancer at enrollment. Hence, study findings may not extend to this group of patients.
4. All NC ProCESS participants had received treatment for prostate cancer. Hence, study findings may not be generalizable to prostate cancer survivors on active surveillance.
27
Figure 2.1: Kinser et al.’s conceptual framework of individual stress vulnerability, depression, and health outcomes.
SES= socioeconomic status; ECD= early childhood development; depression= major depressive disorder; MDEs= major depressive episode; QoL= quality of life.
28
29
Figure 2.3: Aim 3’s conceptual model.All plausible moderators (except personality traits) and all plausible confounders (except mood and generalized anxiety disorders) are available in the NC ProCESS dataset.
30
Figure 2.4: Schematic showing how study PCaP/HCaP-NC participation changed over time
31
Table 2.1: Suggested threshold SF-12 mental composite scores for identifying individuals with presumed depression in population studies.
Study Look-back
period
Threshold Sens Spec AUC LR+ LR– Vilagut et al. (2013)[11] 30-day depression 45.6 86% 88% 0.87 7.17 0.16 Vilagut et al. (2013)[11] 12-month depression 48.9 74% 83% 0.78 4.35 0.31 Gill et al. (2006)[57] 4-week depression 45.0 87% 83% 0.92 5.12 0.16 Santos et al. (2011)[19] Current depression 43.0 73% 90% 0.88 7.30 0.3
Sens, Sensitivity; Spec, specificity; AUC, Area under the curve; LR+, positive likelihood ratio; LR-, negative likelihood ratio. For comparison, sensitivities and specificities of a selection of depression screening tools are as follows: PHQ 9 (Sens = 88%; Spec = 88%);[58] PHQ 2 (Sens = 83%; Spec = 90);[59] BDI (Sens = 85%; Spec = 88%);[60] HADS (Sens = 85%; Spec = 81%).[60]
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Table 2.2: Other variables for key hypotheses in Aims 1 and 2
Variable Definition Type Specification/range
Sociodemographic variables
Age a Age of participant at enrollment Integers Age at enrollment in single years
Race b Race Binary Black or white
Income a Income category Categorical ≤$20000, $20001 – $40000,
$40001 – $70000 and >$70000
Education b Highest level of education Categorical Less than high school, high school and more than high school
Residence a Residence Binary Rural or urban
Marital status a Marital status Binary Currently married vs. previously/never married. Employment status Employment status Binary Currently employed vs. not
currently employed
Depression and cancer history variables
History of
depression prior to enrollment in PCaP b
A history of depression prior to enrollment in PCaP (based on self- reported baseline data).
Binary 1 = Yes, 0 = no.
History of depression during PCaP/HCaP-NC a, c
If SF-12 MCS was ≤ 48.9 between enrollment and the penultimate survey wave.
Binary 1 = Yes, 0 = no
Years a Number of Years between prostate
cancer diagnosis and survey. Continuous –
Cancer stage b Cancer stage at diagnosis Categorical Local, regional and metastatic
Health status variables
Comorbidities a Carlson comorbidity index Binary 0 – 1 vs. ≥ 2 Visits a Number of visits to providers in the
prior 12 months (lagged) Binary ≤3 vs. > 3 Insurance a Health insurance coverage Binary Insured or uninsured Social/emotional
support a Participates in a prostate cancer support group or has emotional support from friends, family or other individuals, communities or organizations.
Binary Yes or no
Treatment
decisional regret a If the participant regrets his chosen cancer treatment modality Binary Yes or no
Lifestyle
Smoking status a Current use of any tobacco-
containing product Binary Yes or no
Drinking status a Current consumption of alcohol
33
Adherence toexercise
recommendations a
In Metabolic Equivalent Tasks per
hour (MET/hr) [61] Binary 0: < 600 per week 1: ≥ 600 per week a – time varying variables. b – time invariant variables. c – This variable is NOT a lagged version of depression (SF-12): it can only change from 0 to 1.
34
Table 2.3: Showing MAX-PC and SF-12 total and subscale scores to be evaluated, and the hypothesized correlations.
MAX – PC
Total score SF-12 subscales Prostate cancer
anxiety Fear of recurrence PSA anxiety
Mental health Hypothesis: r < 0 Hypothesis: r < 0 Hypothesis: r » 0 Hypothesis: r < 0 General health Hypothesis: r < 0 Hypothesis: r < 0 Hypothesis: r » 0 Hypothesis: r < 0 Role emotional Hypothesis: r < 0 Hypothesis: r < 0 Hypothesis: r » 0 Hypothesis: r < 0 Vitality Hypothesis: r < 0 Hypothesis: r < 0 Hypothesis: r » 0 Hypothesis: r < 0 Role physical Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 Social
functioning Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 Bodily pain Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 Physical
functioning Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 SF-12 PCS Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 Hypothesis: r » 0 SF-12 MCS r = -0.36** [29] r = -0.38** [29] r = 0.04 [29] r = -0.39** [29] PSA, prostate specific antigen; r, Pearson’s rho; r < 0 implies a negative and statistically significant correlation; r » 0 implies no correlation. PCS, physical composite score; MCS, mental composite score.
35
Table 2.4: Evidence on the validity of self-report in identifying a history of provider- diagnosed depression.
Study Positive agreement Negative
agreement
Smith et al. (2008)[62] 51% 96%
Sanchez-Villegas et al. (2008)[63] 79% (57 – 100) 88% (71 – 100)
36
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CHAPTER 3: SOCIODEMOGRAPHIC FACTORS ASSOCIATED WITH THE OCCURRENCE AND DIAGNOSIS OF DEPRESSION IN PROSTATE CANCER
SURVIVORS INTRODUCTION
Common depressive disorders (including major and persistent depressive disorders – hereafter referred to as depression) affect over 1 in 5 cancer survivors.[1-3] These disorders preclude patient activation, prolong hospital stays, and have adverse effects on the cost of care and quality of life of cancer survivors.[4-7] Appropriate treatment of depression improves one’s chances of remission and/or
recovery,[8, 9] and may avert adverse health outcomes (including relapse/recurrence,[10] low health related quality of life [HRQOL],[11] and suicide/self-harm).[7, 12] Hence, primary and secondary prevention of depression are cancer care priorities.[13-15] These priorities are particularly important in prostate cancer survivors because men are usually reluctant to report depressive symptoms or seek mental health care.[16-18] Yet, little is known about sociodemographic factors associated with the occurrence of depression, or their pattern of occurrence in prostate cancer survivors.[19] These pieces of information are expected to inform clinical and community-level interventions that target depression. They could also inform/support sociodemographic characteristics listed as risk factors for depression in American Cancer Society’s prostate cancer survivorship guidelines.[20]
Many depressed cancer survivors remain undiagnosed for reasons such as poor clinician- recognition,[21, 22] low depression screening rates (15% in primary care settings),[13-15] and patients’ reluctance or inability to seek help.[23, 24] The rate of depression diagnosis is about 47% in the general population, [21, 22, 25] 12% in adults with diabetes, [26] and 20% in low income women with breast and gynecological cancers.[27] However, little is known about sociodemographic factors associated with, or the rate of clinician diagnoses of depression in prostate cancer survivors. This information will provide some insight into mental health disparities, facilitate mapping of a depression treatment pathway,[28] and
41
inform programs that increase depression diagnosis rates in prostate cancer survivors. The overarching goal of this study is to describe sociodemographic factors associated with the occurrence and diagnosis of depression in prostate cancer survivors.
METHODS
Conceptual frameworks
Assessing factors associated with depression
This analysis was guided by Kinser and colleagues conceptual framework for individual stress vulnerability, depression and health outcomes (see Figure 3.1).[29] The authors state that certain
sociodemographic factors, perceived social support, lifestyle choices, acute/chronic burdens (e.g. regret or remorse after a healthcare decision) and interpersonal situations manifest as stress vulnerabilities that are linked to the occurrence of depression. Hence, I posited that depression is a function of
sociodemographic, clinical, health status and lifestyle factors. My hypothesis was that sociodemographic factors like increasing age, being African American, having less education, living in a rural area, being unmarried, being unemployed and having a low income are positively associated with depression in prostate cancer survivors.[20, 30]
Assessing factors associated with depression diagnosis
This analysis was guided by Klinkman’s Competing demands in psychosocial care model (see Figure 3.2).[31] The author states that patients’ and clinicians’ attributes affect who gets diagnosed with depression. Patients’ attributes include sociodemographic characteristics, clinical factors and health status factors. Relevant patient attributes may be 1) sociodemographic characteristics (e.g. race,[32] age,[33] income,[34] educational attainment,[35] employment status,[34] rural or urban residence,[35] and marital status),[33] 2) clinical characteristics (e.g. a history of depression,[36] severity of depression, [30] and cancer stage at diagnosis),[35] or 3) other individual-level characteristics like health insurance coverage [37, 38] Charlson comorbidity index,[39] and number of annual visits to primary care clinics).[40] Clinicians’ attributes include knowledge/expertise, beliefs/attitudes, type of patient encounter, practice policies, and alternative/competing demands. However, given that currently available evidence suggests
42
that clinicians’ attributes do not predict who gets diagnosed with depression,[41] I hypothesized that the patient attributes of increasing age, being African American, having less education, living in a rural area, being unmarried, being unemployed and having a low income are negatively associated with diagnoses of depression. These hypotheses were motivated by findings in other patient populations. [30]
Data
I used panel data from the North Carolina-Louisiana Prostate Cancer Project (PCaP). [42, 43] PCaP was a multidisciplinary study of social, individual, and tumor-level causes of racial differences in prostate cancer aggressiveness.[43] Eligible participants were diagnosed with prostate cancer on or after July 1, 2004 and were identified using state tumor registries. There were 1,031 North Carolinian
participants (African Americans and Caucasians only) enrolled between September 2004 and December