We carried out a secondary data analysis of the National Survey of Satisfaction of Users in Health 2016 (ENSUSALUD from the Spanish acronym) (http://portal.susalud.gob.pe/blog/encuestas-de-satisfaccion-a-nivel-nacional-ensusalud-2016/). This cross-sectional survey was performed by the National Institute of Statistics (INEI, from the Spanish acronym) and the National Health Authority (SUSALUD from the Spanish acronym).
Participants were selected from a complex, nationally representative, and regionally stratified probability sample. The ENSUSALUD-2016 dataset has primary and secondary unit samples of health care centers and health professionals, respectively. Physicians and nurses working (a minimum of 12 months at the time of the survey) in 185 health care centers in all regions of Peru completed the questionnaire for primary care professionals (ENSUSALUD questionnaire 2). In our study, only physician data were included. We excluded physicians older than 65 years, who did not report income levels, or had missing data on their place of work. Initially, there were 2,216 participants, but 116 cases were excluded based on the inclusion and exclusion criteria (Figure 1). Thus, 2,100 participants were included in our analysis.
Three scales were used to measure different elements of job satisfaction. These were General Professional Activity, Health Services Management, and Working Conditions of the Health Center. These scales presented adequate psychometric properties and were validated in an earlier study that used data from ENSUSALUD-2016 48. Job satisfaction was measured on a 5-point Likert scale ranging from 1 to 5 (5 = very satisfied; 4 = satisfied; 3 = neither satisfied nor dissatisfied; 2 = dissatisfied; 1 = very dissatisfied).
1) Satisfaction scale on General Professional Activity: This scale evaluated the level of satisfaction according to the general aspects of professional work, including the relationship between the patient and health professional, professional achievements, job availability, and occupational hazard perception. The instrument had six items with one dimension (comparative fit Index [CFI] = 0.946; root mean square error of approximation [RMSEA] = 0.071; standardized root mean square residual [SRMR) = 0.035). Furthermore, the test had a high level of reliability (α = 0.70; ω = 0.70). The invariance of measurement was achieved for civil status, having a chronic disease, and people who had a work-related disease 48.
2) Health Services Management Satisfaction Scale: This scale measured the level of satisfaction on management in the health care center. The scale considered the following components: satisfaction with managing human and economic resources, availability and use of medication, and alignment of tasks according to skills. The instrument consisted of eight items with one dimension (CFI = 0.972; RMSEA = 0.081; SRMR = 0.028). The test had a high level of reliability (α = 0.90; ω = 0.90), and the invariance of measurement was identified for sex, age, civil status, medical specialty, working in more than one institution, work-related illness, self-reporting of having a chronic illness, and the scheduling of health personnel shifts 48
3) Satisfaction Scale on the Working Conditions of the Health Center: The instrument focused on measuring the level of satisfaction regarding working conditions. The indicators evaluated satisfaction regarding the promotion of optimal conditions, administrative regulation of the health center, workload, working schedule, income, improvement potential, infrastructure and equipment, employee–boss relationship, and health center cleaning services. The instrument consisted of 11 items with two dimensions (CFI = 0.914; RMSEA = 0.080; SRMR = 0.055). The first dimension was about individual working conditions (α = 0.81; ω = 0.81; 8 items), and the second dimension was about structural working conditions (α = 0.81; ω = 0.82; 3 items). The invariance of measurement was achieved with regards to sex, age group, marital status, medical specialty, working in more than one institution, working time, and work-related chronic illness 48
The Maslach Burnout Inventory: Human Service Survey (MBI-HSS) was used 49. This questionnaire had Likert-type response scales with 22 items divided into three dimensions (CFI = 0.974; RMSEA (IC90%) = 0.052(0.048–0.055); SRMR = 0.059). However, in our exploratory factorial analysis, we found that seven items presented factorial loads lower than 0.40; hence we removed them from a second analysis, and we obtained a better fit in the model with a total of 15 items (CFI = 0.972; RMSEA (IC90%) = 0.049(0.044–0.054); SRMR = 0.047). The scale had seven answer options (0 = never; 1 = a few times a year or less; 2 = once a month or less; 3 = a few times a month; 4 = once a week; 5 = several times a week; 6 = every day). The first dimension, emotional exhaustion, had five items (α = 0.72; ω = 0.79), the second dimension, which was depersonalization, had five items (α = 0.68; ω = 0.83), and the last dimension, which was personal accomplishment, also had five items (α = 0.67; ω = 0.81) 35.
The Patient Health Questionnaire (PHQ-2) was used to evaluate depressive symptoms in the last two weeks. This questionnaire had only two items, measured on a 4-point Likert scale (0 = not at all; 1 = several days; 2 = more than half of the time; 3 = nearly every day), and the scores ranged from 0 to 6. Using three or more points as a threshold, the PHQ-2 defined depressive symptoms with good sensitivity (82%) and specificity (90%) 46.
Data was collected on age, sex (male and female), whether the physicians had a specialty (no, in progress, and yes), whether they worked at another institution teaching, seeing patients, or performing administrative tasks (yes or no), whether they had a chronic illness (yes or no), whether they had a work-related illness (yes or no), whether they had been victims of physical, psychological, or sexual violence in the workplace (yes or no), the type of organization in which they worked (Ministry of Health, EsSalud, armed forces and national police, or private clinics), and time spent working. Additionally, self-reported monthly income was evaluated and categorized according to the minimum wage (less than 4 times, 4–10 times, and more than 10 times the minimum wage). The minimum wage was 750 Peruvian soles (PEN), equivalent to about US$ 222.5 as on November 2020.
Descriptive analysis of variables was performed, and a separate analysis was carried out for physicians with depressive symptoms (PHQ-2 scores ≥ 3 points) and for physicians without depressive symptoms (PHQ-2 scores ≤ 2 points). The analysis was adjusted by the weighting factor of the complex sampling.
Relationship between variables
A Pearson correlation was performed using a sample size weighting between the following variables: depressive symptoms (PHQ-2), burnout (MBI-HSS), and job satisfaction (three scales). These three variables were evaluated because they correspond to the theoretical framework proposed by Gray, Senabe, Naicker, Kgalamono, Yassi and Spiegel 47, and Rothenberger 37. Pearson’s correlation coefficient was considered as effect size, considering weak (r = 0.10), moderate (r = 0.30), and high (r = 0.50) values 50.
The chi-square tests were used to compare the relationship between sociodemographic characteristics and the variables of interest. Adjusted prevalence ratios (PR) were calculated using generalized linear models with robust variance estimations, assuming a Poisson distribution with log link functions 51. Potential confounders included in the adjusted model were sex, age, living with a partner, having a medical specialty, working in more than one institution, monthly income, work-related illness, self-reported chronic illness, working for the Ministry of Health, EsSalud, the armed forces and national police, or a private clinic, years of work in the institution, and experiences of physical, psychological, or sexual violence.
Structural regression model
For this analysis, we used the maximum likelihood estimation with robust standard errors 52 and Pearson matrices 53. We adjusted the analysis by the weighting factor of the complex sampling. Two models were evaluated based on the hypothesis that job satisfaction influences burnout syndrome, which influences depressive symptoms 37,47,54. The first model (model 1) evaluated the relationship between job satisfaction, burnout, and depressive symptoms. The second model (model 2) was based on model 1 but considered correlated errors between the dimensions. Because the variable had four dimensions, it was very likely that these dimensions were strongly associated, and correlated errors were found between them 51. The reason for using structural regression models over bivariate models was that they allowed for a single overall analysis of the different relationships among all the variables.
Evidence has been found that job satisfaction and burnout affect the mental health of health professionals, and a model was proposed to explain the dynamics between these three variables 55, with labor dissatisfaction as the main predictor 22. However, this study presents a model that explains how depressive symptoms occur in Peruvian doctors due to work dissatisfaction. This model explains a problem that persists in the health systems of several countries. Therefore, understanding this problem may help in the making of more efficient and effective interventional decisions 22,26.
The models were evaluated based on different goodness-of-fit indices. The comparative fit index (CFI) and Tucker–Lewis Index (TLI) were used; values were considered optimal when they were higher than 0.95. The Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA) with a confidence interval of 90%, both with values adequate if <0.08 51,56.
We performed the analysis according to the complex sampling in R Studio®, specifically with the packages “lavaan” , “lavaan.survey” , “semTools” , and “semPlot” 57.
The survey was anonymous, and no information in the database could be used to identify the participants. Hence, conducting this analysis did not represent an ethical risk for participants since there was no access to confidential data. The study did not require the approval of an ethics committee because it came from a secondary database that is open access. Therefore, no primary data collection was performed and the study did not involve an ethical risk for participants.