Humanistic and economic burden associated with depression in the United States: A cross-sectional survey analysis | BMC Psychiatry

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Study design and data source

This study was conducted using data from the US National Health and Wellbeing Survey (NHWS 2017; NOT= 75004). The NHWS is a self-administered Internet-based survey of a sample of adults (aged ≥ 18 years) that provides “real-world” patient information on 165 treatment conditions. Potential survey respondents are recruited through a web-based general purpose consumer panel. The panel recruits its members through opt-in emails, co-registration with panel partners, email newsletter campaigns, banner placements and affiliate networks. All respondents who explicitly agreed to be a panel member registered through a unique email address and completed an extensive demographic registration profile. A quota sampling procedure (using data from the Current Population Survey of the US Census) was used to ensure that the final NHWS sample was representative of the 2017 US adult population in that regarding age, gender and race/ethnicity. Informed consent was obtained from all respondents and all parties ensured the protection of patients’ personal data. The study protocol and questionnaire were reviewed by the Pearl Institutional Review Board and granted exempt status.

Study sample

Respondents aged 18 to 64 “with a diagnosis of depression” (not= 8853) or ‘without diagnosis of depression’ (not= 30,478) were included in the analysis. Respondents with a diagnosis of depression: those who reported having been diagnosed with depression by their doctor and reported having suffered from depression in the past 12 months) [2]. These respondents were then stratified by depression severity, as determined by Patient Health Questionnaire-9 (PHQ-9) scores: none/minimal (score = 0–4; not= 1876), mild (score = 5–9; not= 2801), moderate (score = 10–14; not= 1938), moderately severe (score = 15–19; not= 1376), or severe (score = 20–27; not= 862). Respondents without a diagnosis of depression: those who did not have a self-reported medical diagnosis of depression, reported not having suffered from depression in the past 12 months, and had PHQ-9 scores ≤ 4 [17] (Fig. 1). Respondents with a diagnosis of bipolar disorder and those who said they had not suffered from depression in the past 12 months but had been diagnosed were excluded from the study.

Fig. 1

US NHWS eligible sample for participants ages 18-64

aPatients with a diagnosis of depression were stratified according to the PHQ-9 score at the time of the survey. MDQ, Mood Disorder Questionnaire; NHWS, National Health and Wellbeing Survey; PHQ-9, the Patient Health Questionnaire 9; United States, United States

Measures

Demographics and health characteristics

Demographic variables such as age, gender, employment status, race/ethnicity, marital status, education, household income, insurance status, body mass index (BMI), smoking status, alcohol consumption, exercise behavior and Charlson’s comorbidity index (CCI) were collected. . The ICC represents a weighted sum of multiple comorbid conditions predictive of mortality with higher scores indicating a greater comorbid burden for the patient [18]. Disease-specific diagnoses, including depression, anxiety, and sleep disturbances, were also analyzed.

Symptoms of depression, anxiety and sleep disturbances

Symptoms of depression rated that prompted respondents to seek medical attention included self-reported depressed mood and other emotional problems, changes in eating and sleeping habits, mental changes (eg, forgetfulness, difficulty thinking , difficulty concentrating) and social and physical problems. Sleep problems including self-reported difficulty falling asleep, difficulty staying awake, daytime sleepiness, leg cramps/problems, night sweats/hot flashes, and poor sleep quality were assessed. Anxiety was assessed based on self-reported diagnoses of anxiety disorders and self-reported experiences of anxiety. Additionally, anxiety was measured by the Generalized Anxiety Disorder-7 (GAD-7) scale (Supplementary Table 1) [19].

Health-Related Quality of Life (HRQoL) and Health Services

Abbreviated Survey Instrument Version 2 (SF-36v2)

HRQOL was assessed using the SF-36v2 [20], which is a general-purpose generic health status instrument consisting of 36 questions. The instrument is designed to report eight health domains (Physical Functioning, Role-Physical, Body Pain, General Health, Vitality, Social Functioning, Role-Emotional, and Mental Health) and two summary scores (Physical Component Summary [PCS] and summary of the mental component [MCS]). Each domain and the PCS and MCS scores are normalized to a mean of 50 and a standard deviation of 10 for the US population. Higher scores indicate better health [20]. Parameters related to SF-36v2 were studied based on the health status of the last 4 weeks. In addition, the health utility index was calculated using the Short-Form 6 Dimensions (SF-6D). The SF-6D is a single index measure based on health preferences using values ​​from the general population and provides scores on a theoretical scale of 0 to 1 with higher scores indicating better health. [21].

EuroQol health questionnaire in 5 dimensions

The EuroQol 5-Dimensional Health Questionnaire (EQ-5D-5L) [21] consists of a descriptive system (EQ-5D) and a visual analog scale (EQ VAS). The descriptive system is composed of five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The VAS EQ (score: 0 to 100) indicates the self-rated state of health of the respondent, the evaluation criteria being “Best imaginable state of health” (score = 100) and “Worst imaginable state of health” ( score = 0). Lower overall scores on the EQ-5D-5L health services indicate higher disability. The most recent version with 5-point rating scales for each dimension was used in this study [22]. EQ-5D-5L utility scores were calculated by mapping the five-level descriptive system (EQ-5D-5L) to the three-level value set (EQ-5D-3L) using the l mapping approach (“crosswalk”) developed by van Hout et al. [23]. Health states were mapped using a country-specific set of values.

Work Productivity and Activity Impairment (WPAI)

Loss of work productivity was measured using the WPAI questionnaire [24], a validated six-item instrument that consists of four measures: absenteeism (the percentage of work time missed due to health in the past seven days), presenteeism impairment (the percentage of impairment suffered past seven days due to health), overall loss of productivity at work (overall impairment believes it to be a combination of absenteeism and presenteeism), and decreased activity (the percentage decrease in daily activities due to their health over the past seven days). Only respondents who reported being employed full-time or part-time provided data on absenteeism, presenteeism and overall work disability; all respondents provided data on activity disorders.

Health Resource Utilization (HRU)

Healthcare utilization was defined by the number of healthcare provider (HCP) visits (e.g. GP, internist, cardiologist, gynecologist, etc.), number of emergency department (ER) visits and the number of times hospitalized over the past six years. month. All outcome measures and scales [19,20,21, 24, 25] used in this study are detailed in Supplementary Table 1.

statistical analyzes

Chi-square and analysis of variance (ANOVA) tests were used to determine significant differences for categorical and continuous variables, respectively. These results were used to characterize the differences between respondents with and without a diagnosis of depression as well as between no/minimal, mild, moderate, moderately severe and severe depression and informed the selection of covariates for the multivariate models.

Generalized linear models (GLMs) were used to control for demographic variables, health characteristics and comorbidity variables to compare HRQoL, WPAI and HRU between respondents with and without a diagnosis of depression and according to the severity of depression. symptoms in respondents with a diagnosis of depression. Only variables statistically significant in the bivariate analysis and of clinical importance were included in the regression models. GLMs with a negative binomial distribution were used for skewed data (eg, WPAI and HRU).

Covariates included in the multivariate models were: age (continuous), gender (male vs. female), ethnicity (black, Hispanic, other vs. white [reference]), marital status (single, refused to answer vs. married/living with a partner [reference]), education (below university, unwilling to answer in relation to college education [reference]), income (

In bivariate analyses, comparisons were made for: (a) ‘diagnosed with depression’ groups versus ‘without diagnosis of depression’ groups, and (b) across severity groups using a global omnibus test. In multivariate analyses, comparisons were made for: (a) the “with depression diagnosis” group versus the “without depression diagnosis” group (reference), and (b) the mild, moderate, moderately severe, or severe compared to the “none/minimal” severity group (reference). A p-value of less than 0.05 was considered statistically significant.

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