Close kin influence COVID-19 precautionary behaviors and vaccine acceptance of older individuals

Abstract

The family plays a central role in shaping health behaviors of its members through social control and support mechanisms. We investigate whether and to what extent close kin (i.e., partner and children) have mattered for older people in taking on precautionary behaviors (e.g., physical distancing) and vaccination during the COVID-19 pandemic in Europe. Drawing on data from the Survey of Health, Ageing and Retirement in Europe (SHARE), we combine its Corona Surveys (June-August 2020 and June-August 2021) with pre-COVID information (October 2019-March2020). We find that having close kin (especially a partner) is associated with a higher probability of both adopting precautionary behaviors and accepting a COVID-19 vaccine. Results are robust to controlling for other potential drivers of precautionary behaviors and vaccine acceptance, as well as to accounting for co-residence with kin. Our findings suggest that policy makers and practitioners may differently address kinless individuals when promoting public policy measures.

Figure 1. Having close kin (partner and children) and COVID-19 precautionary behaviors

Notes: The graph shows the effect of the explanatory variable (having kin) in the form of Average Marginal Effects (AMEs) with 95% confidence intervals from nine separate logistic regression models (one for each of the considered precautionary behaviors). Each AME compares the predicted probability of adopting a precautionary behavior for one of the three groups of older adults who have kin available (e.g., those who have both a partner and children) with the predicted probability of the outcome for the reference group (kinless, i.e. older adults who lack both partner and children). All control variables are included in the models. Full estimates are available in Table S.1 in the Supplementary Materials. Data are from SHARE Corona Survey 1 (June-August 2020).

Figure 2. Having close kin (partner and children) and COVID-19 vaccine acceptance

Notes: The graph shows results for the effect of the explanatory variable (having kin) in the form of Average Marginal Effects (AMEs) with 95% confidence intervals from a multinomial logistic regression model for the three-level categorical outcome vaccine acceptance. Each AME compares the predicted probability of a certain outcome category (e.g., being vaccinated or willing to get the vaccine) for one of the three groups of older adults who have kin available (e.g., those who have both a partner and children) with the predicted probability for the reference group (kinless, i.e. older adults who lack both partner and children). All control variables are included in the models. Full estimates are available in Table S.2 in the Supplementary Materials. Data are from SHARE Corona Survey 2 (June-August 2021).

Figure 3. Having close kin (partner and children) and COVID-19 precautionary behaviors by age group (65+ in blue; 50-64 in red)

Notes: The graph shows the effect of the explanatory variable (having kin) in the form of Average Marginal Effects (AMEs) with 95% confidence intervals from nine separate logistic regression models (one for each of the considered precautionary behaviors). Each model includes an interaction between the explanatory variable and a dummy variable for age that distinguishes two groups. Thus, separate AMEs by age are obtained (65+ in blue; 50-64 in red). Each AME compares the predicted probability of adopting a precautionary behavior for each one of the three groups of older adults who have kin available (e.g., those who have both a partner and children) with that for the reference group (kinless, i.e. older adults who lack both partner and children). Statistically significant differences (p<0.05) between the AMEs of the two considered age groups are indicated by an “x” in correspondence of the bigger AME. All control variables are included in the models. Full estimates are available in Table S.3 in the Supplementary Materials. Data are from SHARE Corona Survey 1 (June-August 2020).

Figure 4. Having close kin (partner and children) and COVID-19 vaccine acceptance by age group (65+ in blue; 50-64 in red)

Notes: The graph shows results for the effect of the explanatory variable (having kin) in the form of Average Marginal Effects (AMEs) with 95% confidence intervals from a multinomial logistic regression model for the three-level categorical outcome vaccine acceptance. Each model includes an interaction between the explanatory variable and a dummy variable for age that distinguishes two groups. Thus, separate AMEs by age are obtained (65+ in blue; 50-64 in red). Each AME compares the predicted probability of a certain outcome category (e.g., being vaccinated or willing to get the vaccine) for each one of the three groups of older adults who have kin available (e.g., those who have both a partner and children) with that for the reference group (kinless, i.e. older adults who lack both partner and children). Statistically significant differences (p<0.05) between the AMEs of the two considered age groups are indicated by an “x” in correspondence of the bigger AME. All control variables are included in the models. Full estimates are available in Table S.4 in the Supplementary Materials. Data are from SHARE Corona Survey 2 (June-August 2021).

Figure 5. Having close kin (partner and children) and COVID-19 precautionary behaviors by gender (women in blue; men in red)

Notes: The graph shows the effect of the explanatory variable (having kin) in the form of Average Marginal Effects (AMEs) with 95% confidence intervals from nine separate logistic regression models (one for each of the considered precautionary behaviors). Each model includes an interaction between the explanatory variable and a dummy variable for gender. Thus, separate AMEs by gender are obtained (women in blue; men in red). Each AME compares the predicted probability of adopting a precautionary behavior for each one of the three groups of older adults who have kin available (e.g., those who have both a partner and children) with that for the reference group (kinless, i.e. older adults who lack both partner and children). Statistically significant differences (p<0.05) between the AMEs of the two genders are indicated by an “x” in correspondence of the bigger AME. All control variables are included in the models. Full estimates are available in Table S.5 in the Supplementary Materials. Data are from SHARE Corona Survey 1 (June-August 2020).

Figure 6. Having close kin (partner and children) and COVID-19 vaccine acceptance by gender (women in blue; men in red)

Notes: The graph shows results for the effect of the explanatory variable (having kin) in the form of Average Marginal Effects (AMEs) with 95% confidence intervals from a multinomial logistic regression model for the three-level categorical outcome vaccine acceptance. Each model includes an interaction between the explanatory variable and a dummy variable for gender. Thus, separate AMEs by gender are obtained (women in blue; men in red). Each AME compares the predicted probability of a certain outcome category (e.g., being vaccinated or willing to get the vaccine) for each one of the three groups of older adults who have kin available (e.g., those who have both a partner and children) with that for the reference group (kinless, i.e. older adults who lack both partner and children). Statistically significant differences (p<0.05) between the AMEs of the two genders are indicated by an “x” in correspondence of the bigger AME. All control variables are included in the models. Full estimates are available in Table S.6 in the Supplementary Materials. Data are from SHARE Corona Survey 2 (June-August 2021).

Figure 7. Having close kin (partner and children) and COVID-19 precautionary behaviors by country groups (South-East Europe in blue; North-West Europe in red)

Notes: The graph shows the effect of the explanatory variable (having kin) in the form of Average Marginal Effects (AMEs) with 95% confidence intervals from nine separate logistic regression models (one for each of the considered precautionary behaviors). Each model includes an interaction between the explanatory variable and a dummy variable for country that distinguishes two groups (South-East Europe in blue; North-West Europe in red). Each AME compares the predicted probability of adopting a precautionary behavior for each one of the three groups of older adults who have kin available (e.g., those who have both a partner and children) with that for the reference group (kinless, i.e. older adults who lack both partner and children). Statistically significant differences (p<0.05) between the AMEs of the two considered country groups are indicated by an “x” in correspondence of the bigger AME. All control variables are included in the models. Full estimates are available in Table S.7 in the Supplementary Materials. Data are from SHARE Corona Survey 1 (June-August 2020).

Figure 8. Having close kin (partner and children) and COVID-19 vaccine acceptance by country groups (South-East Europe in blue; North-West Europe in red)

Notes: The graph shows results for the effect of the explanatory variable (having kin) in the form of Average Marginal Effects (AMEs) with 95% confidence intervals from a multinomial logistic regression model for the three-level categorical outcome vaccine acceptance. Each model includes an interaction between the explanatory variable and a dummy variable for country that distinguishes two groups (South-East Europe in blue; North-West Europe in red). Each AME compares the predicted probability of a certain outcome category (e.g., being vaccinated or willing to get the vaccine) for each one of the three groups of older adults who have kin available (e.g., those who have both a partner and children) with that for the reference group (kinless, i.e. older adults who lack both partner and children). Statistically significant differences (p<0.05) between the AMEs of the two considered country groups are indicated by an “x” in correspondence of the bigger AME. All control variables are included in the models. Full estimates are available in Table S.8 in the Supplementary Materials. Data are from SHARE Corona Survey 2 (June-August 2021).