EJIHPE | Free Full-Text | Associations of Mental Health Issues with Health Literacy and Vaccination Readiness against COVID-19 in Long-Term Care Facilities—A Cross-Sectional Analysis
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2.2. Participants
A purposive sample of up to 1000 people in need of care were recruited at three study sites in Bavaria (Munich, Erlangen and Würzburg). In addition, about 200 HCWs were recruited. People in need of care were identified via their general practitioner (GP), the long-term care facility they live in, via outpatient care services or informal caregivers, or via self-referral. Irrespective of how prospective people in need of care were identified, they were either enrolled by their GP or a study physician.
The GP recruitment was carried out within 240 GCP-qualified practices of the Bavarian Research Practice Network (BayFoNet) [23]. Additionally, eligible GPs with a past or current focus on managing patients with COVID-19 were identified. The participating GPs received compensation for their work within the study (participant inclusion and information, baseline examination, conducted surveys). For the recruitment of study participants (people in need of care and HCWs) from inpatient and outpatient care facilities, we used a list of about 700 eligible facilities in Bavaria with documented COVID-19 outbreaks (reporting system of the Bavarian State Office for Health and Food Safety).
2.2.1. Eligibility Criteria for Patients
People in need of care or support were eligible if they receive financial support through public care insurance according to an officially assessed care level (“Pflegegrad”) or a score of ≥5 on the 7-point Clinical Frailty Scale (CFS) [24]. Exclusion criteria were an estimated life expectancy of
2.2.2. Eligibility Criteria for Participating HCWs
HCWs were eligible for recruitment if they are at least 18 years old and if they were employed in an outpatient or inpatient long-term care facility.
2.5. Bias
As in most research in outpatient care, the external validity of our findings is vulnerable to participation bias. For example, it is conceivable that non-responding institutions were particularly burdened by the pandemic. Therefore, we provided a mobile study team (including study nurse and study physician), that no additional resources were required to conduct the study. Furthermore, our vaccine-specific interim evaluation was only a small proportion of the topics surveyed. Therefore, it was not apparent to potential study participants that the survey would ask for health literacy or vaccination readiness. Consequently, it has not to be assumed that only study participants in favor of vaccinations were represented in the survey.
2.8. Statistical Methods
In this analysis, metric and normally distributed data were presented with mean and standard deviation, while metric and non-normally distributed data were presented with median and Q1–Q3. For categorical data, frequency and percentage were presented.
Univariate and multivariate ordinal logistic regression models were calculated. The assumptions of ordinal regression with regard to multicollinearity and proportional odds were examined using suitable statistical methods (correlation coefficient, full likelihood ratio test).
To evaluate people in need of care, demographic and psychosocial characteristics were considered as independent variables in the ordinal regression models (outcome health literacy (HLS-EU-Q16): age, sex, marital status (not married/widowed/married), ethnic origin (Caucasian/other), education (non-academic degree/academic degree), type of care (inpatient/outpatient), Barthel Index (Score 0–30/35–80/85–95/100), legal representative (yes/no), symptoms of depression (PHQ9 score metric or ≥10), and symptoms of anxiety disorder (GAD7 score metric or ≥10)).
Demographic and psychosocial characteristics were considered as independent variables in the ordinal regression models to evaluate HCWs as well (outcome vaccination readiness (5C): age, sex, marital status (not married/widowed/married), ethnic origin (Caucasian/other), education (non-academic degree/academic degree), type of care (inpatient/outpatient), symptoms of burnout (MBI emotional exhaustion, MBI depersonalization, MBI personal accomplishment), function in the facility (nursing staff/elderly care staff), employment relationship (full-time employment/part-time employment), and care for COVID-19-infected patients (yes/no)).
All independent variables that were significant in the univariate models (p-value < 0.05) were included in the multivariate model. Dependent variables were health literacy (HLS-EU-Q16) for people in need of care and items indicating vaccination readiness (Confidence, Complacency, Constraints, Calculation, and Collective Responsibility (5C)) among HCWs.
The effect of independent variables on the dependent outcome variables (health literacy (HLS-EU-Q16) or vaccination readiness (5C)) was expressed as odds ratios (ORs) with 95% confidence intervals (CIs). The significance level was set at α = 0.05. Missing data are indicated on the item level.
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