Pandemic policies and sleeping problems in older adults

* Gina da Silva Voss, present address: Faculty of Medicine, University of Porto Title: Pandemic policies and sleeping problems in older adults. Authors: Paula Maria Fernandes Rodrigues, Communication and Society Research Centre, University of Minho (Orcid: 0000-0002-1314-5925) Paulo Gabriel Salgado Silva, Communication and Society Research Centre, University of Minho (Orcid: 0000-0003-4897-1327) Gina da Silva Voss*, Communication and Society Research Centre, University of Minho (Orcid: 0000-0002-6798-4070) Roberto Martinez-Pecino, Universidad de Sevilla, Facultad de Psicologia (Orcid: 0000- 0003-0765-1407) Alice Maria Delerue Alvim de Matos, Department of Sociology, University of Minho (Orcid: 0000-0002-2394-4077) Abstract: Sleep quality is fundamental to physical and mental health. Recent research shows that the COVID-19 pandemic has affected individuals’ sleep quality. This study aims to investigate whether the containment and health policies (Oxford Coronavirus Government Response Tracker indexes) adopted by European countries and Israel during the pandemic are related to sleep problems in people aged 50 and over. A cross-sectional study was conducted using a logistic regression analysis based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE), collected in 27 European countries and Israel, in 2021. The results show that containment and health policies affect older adults’ sleep, once we neutralize the influence of the sociodemographic, economic, and health characteristics of the individuals and close contact with COVID-19. In fact, the more containment and health policies, the fewer chances of sleep problems. A possible explanation for this is that these policies give people over 50 a sense of safety and security in relation to COVID-19, which may reduce sleep problems. Keywords: COVID-19; Sleep; Health Public Policy; Middle-aged adults; Aged adults 1. Introduction Good quality sleep is fundamental for physical and mental health and well-being (Redline et al., 2019). On the other hand, poor sleep predicts chronic diseases and mortality (Cappuccio et al., 2010; Foley et al., 2004). Sleep problems lead to elevated levels of fatigue, reduced quality of life, increased risk of cardiovascular disease and psychiatric disorders, impaired decision making, compromised immune response and an increased likelihood of accidents (Ancoli-Israel & Ayalon, 2006; Medic et al., 2017). Sleep problems affect a significant number of older people worldwide. In a study by Foley et al. (1995) based on a sample of over 9,000 American older adults, 50% reported at least one sleep disturbance. Another piece of research, conducted by Chen et al. (2015), concluded that 25% to 35% of 2,230 middle-aged and older adults habitually sleep less than 6 hours per night. Older adults are also affected by increased sleep latency, reduced sleep efficiency, frequent awakenings during sleep, and great difficulty falling asleep again (Vitiello et al., 2004). In a study by Stranges et al. (2012) carried out in eight countries in Asia and Africa, 16.6% of 43,935 adults aged 50 years and older reported sleep problems. In the European older population, sleeping problems are common - with the prevalence rate variated from 16.6% in Denmark and Italy to 31.2% in Poland (van de Straat & Bracke, 2015). Sleep problems tend to intensify with advancing age (Mander et al., 2017; van de Straat et al., 2018). As individuals age, they experience changes in both the timing and quality of sleep. Ageing is associated with increased difficulty in staying asleep throughout the night and remaining alert during the day (Feinsilver, 2003). Other factors besides age are associated with sleeping problems. Gender is one of them since women tend to be more affected by sleep problems than men (Rani et al., 2022; van den Berg et al., 2009). High levels of education are also associated with sleep quality, leading to fewer sleep problems (van de Straat & Bracke, 2015). Furthermore, socioeconomic conditions are also associated with sleep problems. Based on a sample of 23,766 individuals aged 50 or older from various European countries, van de Straat et al. (2020) found that socioeconomic conditions influence the sleep problems of both men and women. Health is also associated with sleep disorders. Physical health conditions often contribute to older adults’ poor sleep (Stone & Li, 2019). In particular, chronic diseases like cardiovascular diseases, arthritis, lung disease and others are negatively associated with sleep quality (Foley et al., 2004). In terms of mental health, sleep disorders have been identified as a consequence of depression and anxiety (Leblanc et al., 2015), or as both a cause and consequence of depression. Indeed, longitudinal studies have shown a bidirectional relationship between depression and poor sleep quality: depression significantly increases the risk of experiencing poor sleep quality, while poor sleep quality serves as a predictor of future depressive episodes (Bao et al., 2017). Loneliness, a common experience among older adults (Chawla et al., 2021), is also linked to poor sleep quality later in life (Shankar, 2020). Conversely, older adults with greater levels of social participation have better sleep outcomes (Chen et al., 2016). The pandemic was particularly challenging for older adults because of the restrictions governments imposed to protect them from COVID-19 infection, especially the requirement for social isolation. This may have affected their quality of sleep. Indeed, Koroleva et al. (2021) found an association between the social isolation of individuals aged 50 and over - due to the pandemic - and changes in their psyco-emotional state characterized by sleep problems, nervousness, and frustration. Additionally, Khalaila & Shiovitz-Ezra (2023) in a study involving 45,636 European and Israeli individuals, aged 60 or older, concluded that social distancing during the pandemic was associated with more symptoms of depression, and consequently, increased sleep problems. In studies with samples that included younger individuals, drawing on a broader sample of 22,330 individuals aged 18 to 99, Morin et al. (2022) found that symptoms of depression, anxiety and insomnia increased during the pandemic. In another study, 57.1% of 2,291 respondents over 18 years of age revealed poor sleep quality, 32,1% showed high levels of anxiety, 41,8% experienced psychological stress, and 7.6% showed relevant post-traumatic stress symptomatology related to the COVID-19 pandemic (Casagrande et al.,2020). In view of these results, the following questions can be posed: did the containment and health policies imposed by governments during the COVID-19 pandemic have an impact on the sleep of older adults? Did the policies bring them a sense of relief or, on the contrary, did they lead to increased stress? A few studies have evaluated the association between one of the dimensions of containment policies, more precisely, confinement, and sleep (Merikanto et al., 2022; Morin et al., 2021; Partinen et al., 2021), but to the best of our knowledge, no study to date has analysed the association between global containment and health policies and sleep problems. Hence, this study aims to investigate whether the containment and health policies (Oxford Coronavirus Government Response Tracker indexes) adopted by European countries and Israel during the pandemic are related to sleep problems in people aged 50 and over. 2. Materials and Methods 2.1.Study Population This research uses data from the Survey of Health, Ageing and Retirement in Europe (SHARE), a multidisciplinary and cross-national biennial panel survey that provides data on the health, socio-economic status, and social and family networks of more than 140,000 individuals aged 50 years and older, covering 27 European countries and Israel. More precisely, it uses data from the wave 9 Corona Survey 2 (release 0), but also from the wave 8 (release 1.0.0), wave 7 (release 7.1.1) and wave 6 (release 7.1.0) databases that were used to access information on education and sleep problems before the pandemic. For more details of the methodology, please see Bergmann et al. (2019); Börsch-Supan (2022a; 2022b; 2022c; 2022d); Börsch-Supan et al. (2013); Malter & Börsch-Supan (2017) and Scherpenzeel et al. (2020). The SHARE target population consists of representative samples of individuals aged 50 years and over who have their regular domicile in a SHARE country (Europe and Israel). Data is collected in face-to-face interviews using the computer-assisted personal interviewing method (CAPI), except for the Corona Surveys where the data was collected using the computer-assisted telephone interviewing method (CATI). Proxy interviews are allowed if the respondent is unable to participate in the survey, for example, because of hearing loss, speech impairment, Alzheimer’s disease, or difficulty maintaining concentration throughout the interview. Some questionnaire modules are referred to as non-proxy sections because they cannot be completed by others on behalf of the individual. For this study, the sample was limited to respondents aged 50 plus at the time of the interview from 27 European countries, plus Israel (N=47155). The sample distribution among the countries is as follows: Austria (2219), Germany (1967), Sweden (955), Netherlands (709), Spain (1677), Italy (3206), France (1814), Denmark (1554), Greece (3242), Switzerland (1682), Belgium (3397), Israel (1184), Czech Republic (2063), Poland (2698), Luxembourg (826), Hungary (826), Portugal (997), Slovenia (2841), Estonia (3944), Croatia (1821), Lithuania (1211), Bulgaria (644), Cyprus (608), Finland (1282), Latvia (893), Malta (617), Romania (1394) and Slovakia (884). Additional information on the characteristics of the country samples is available on the OSF open access platform (see Appendix 01, OSF: https://osf.io/gk4yw/). 2.2.Measures 2.2.1. Outcome variable The outcome variable is sleep problems, provided by the question: ‘Have you had trouble sleeping recently?’. Respondents who answered that they had ‘Trouble with sleep or recent change in pattern’ were classified as ‘having sleep problems’ and those who answered ‘No trouble sleeping’ were classified as ‘not having sleep problems’. 2.2.2. Interest variables The containment and health indexes from the Oxford Coronavirus Government Response Tracker (OxCGRT) (Hale et al., 2020) are our interest variables. The containment index is related to measures to control the pandemic (school and workplace closures, cancelled public events, restrictions on gatherings, public transport closures, stay-at-home requirements, restrictions on internal movement and international travel controls). The health system policies index is a construct that uses six policy indicators (public information campaigns, testing policy, the extent of contact tracing, requirements to wear face coverings, guidelines for vaccine delivery for different groups, and procedures for protecting older adults). These two indexes result from disaggregating the original Oxford ‘containment and health’ index. In effect, we distinguished health-related indicators from containment-related ones to verify whether they have different effects on the sleep quality of older people. For both indexes, we calculated the mean score for each country from 31 July 2020 to 31 July 2021. The indices take values on a scale of 0 to 100, with higher values reflecting more severe containment or health policies. 2.2.3. Control variables We included as control variables: age (50+ years old), gender (male/female), education (low, medium, and high), financial distress, self-reported physical health, number of chronic diseases, anxiety, depression, loneliness, closeness of contact with COVID-19 and sleep problems before the pandemic. Education is measured using the International Standard Classification of Education - ISCED-97. It is divided into three categories: primary schooling or less (low education attainment), secondary education (medium education attainment) and post-secondary education (high education attainment). This information is derived from waves 6, 7 and 8 because it was not collected in wave 9 Corona Survey 2. The economic situation of the respondents is analyzed through their self-perception of financial stress. We aggregated the individuals who have "great difficulty" or "some difficulty" making ends meet and those who make ends meet "fairly easily" or "easily". Physical health is a self-reported condition that ranges from 1 (poor) to 5 (excellent). The number of chronic diseases varies between 0 and 7. Anxiety was assessed by the question: “In the last month, have you felt nervous, anxious, or on edge?” with two answer options (‘yes’ and ‘no’); and depression by the question: "In the last month, have you felt sad or depressed?". The respondent was considered ‘depressed’ if they answered ‘yes’ and ‘not depressed’ if they answered ‘no’. A respondent was considered a lonely person if they answered “often” to the question "How much of the time do you feel lonely?”. Those who declared that they felt lonely "some of the time" or "hardly ever or never" were not considered to be lonely. The variable Closeness with COVID-19 is based on four questions: "Since your last interview, did you or anyone close to you experience symptoms that you would attribute to the COVID illness, e.g., cough, fever, difficulty breathing, or loss of sense of taste or smell?"; "Since your last interview, have you or anyone close to you been tested for the coronavirus and the result was positive, meaning that the person had the COVID disease?"; "Since your last interview, have you or anyone close to you been hospitalized due to an infection from the coronavirus?”; and “Since your last interview, has anyone close to you died due to an infection from the coronavirus?”. All the questions are dichotomous (yes or no), and the respondents who answered ‘yes’ to at least one of the four questions were considered to have had close contact with COVID-19. Finally, we derived information from waves 6, 7, and 8 concerning sleep problems before the pandemic, based on the same question as the one available in the wave 9 Corona 2 questionnaire. 2.3. Analysis To characterize our study population, univariate descriptive statistics were generated using calibrated individual weights, as the SHARE survey does not have a uniform sample design. Afterwards, in order to evaluate the association between the indexes (containment and health) and sleep problems, a logistic regression was performed. The need for multilevel models was tested by calculating the Intraclass Correlation Coefficient (ICC) of a null model to determine the extent to which the variance in sleep problems was explained by country differences (see Appendix 02, OSF: https://osf.io/gk4yw/). The ICC of the null model was 4.2%, lower than the recommended cut-point of 5%, so there was no need to use multilevel modeling (LeBreton & Senter, 2007). The logistic regression analysis was carried out in stages. Model 1 is a null model, i.e. not adjusted for any independent variable. This is the reference model for the prevalence of sleep problems for all countries. In Model 2 (A, B and C), the variables of interest were tested, one at a time and both simultaneously, without considering the control variables. Finally, Model 3 (A, B and C) provides information on the association between the containment and health policies adopted during the pandemic (separately and then simultaneously), and sleep problems, controlling for the main variables associated with the latter, according to the literature. Other regression models tested in this study are available on the OSF's open-access platform (see Appendix 03, OSF: https://osf.io/gk4yw/). Odds ratios (OR), 95 percent confidence intervals (CIs), and significance (where p-values of < 0.05 were considered statistically significant) were calculated. The multicollinearity of the interest and control variables was previously tested with the variance inflation factor (VIF). All VIF values are close to 1, indicating that there is no multicollinearity (see Appendix 04, OSF: https://osf.io/gk4yw/). The number of observations in the database is large and representative of the population. This increases the model's ability to generalize to new data, as it allows the model to capture the true variability and underlying relationships between the variables. The absence of multicollinearity associated with the large number of observations reduces the likelihood of the model overfitting. Statistical analyses were conducted using R software, version 4.0.2, and IBM SPSS Statistics 27. The results of this study support a number of recommendations for public policies. When public entities must implement measures to protect health, providing the population, especially older adults, with reliable information and countering fake news is essential. Promoting computer and communication technology courses for older adults is crucial in order to enable them to acquire digital literacy to avoid social isolation or loneliness when confinement is necessary. Public entities should also use virtual platforms for several activities, such as religious meetings, psychological support, cognitive training, etc. The availability of telephone helplines and using digital media to activate family and proximity networks can significantly reduce loneliness among confined people, especially older adults. It can also be a way of keeping this population more informed about restriction policies in a pandemic or crisis. Ethical standards The SHARE study is subject to continuous ethical review. SHARE-ERIC activities related to human research are guided by international research ethics principles, such as the Respect Code of Practice for Socio-Economic Research and the Declaration of Helsinki. In waves 1 to 4, the SHARE study was reviewed and approved by the Ethics Committee of the University of Mannheim. Wave 4 of SHARE and the following waves were reviewed and approved by the Ethics Council of the Max Planck Society and the Ethics Councils of the participating countries. Acknowledgments The authors acknowledge the important contribution of the funding partners (see the financial support section). Financial support The SHARE data collection has been funded by the European Commission through the 5th framework programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life). Further support by the European Commission through the 6th framework programme (projects SHARE-I3, RII-CT-2006-062193, as an Integrated Infrastructure Initiative, COMPARE, CIT5-CT-2005-028857, as a project in Priority 7, Citizens and Governance in a Knowledge Based Society, and SHARE-LIFE (CIT4-CT- 2006- 028812)), through the 7th framework programme (SHARE-PREP (No 211909), SHARELEAP (No 227822), M4 (No 261982), and DASISH (No 283646); through Horizon 2020 (SHAREDEV3 (No 676536), SERISS (No 654221), SSHOC (No 823782), SHARECOHESION (No 870628), SHARE-COVID19 (No 101015924), RItrain (No 654156) and ERIC Forum (No 823798)) and by DG Employment, Social Affairs & Inclusion through VS 2015/0195, VS 2016/0135, VS 2018/0285, VS 2019/0332, VS 2020/0313, SHARE-EUCOV: GA No 101052589 and EUCOVII: GA No 101102412 is gratefully acknowledged. In Portugal, this study was financially supported by Calouste Gulbenkian Foundation and by the Portuguese Foundation for Science and Technology through national funds (PIDDAC) and co-financed by the European Regional Development Fund (ERDF), through the Operational Programme PORNorte and PORLisboa, AAC No 01/SAICT/2016, Application No 022209-DATALAB. The funders had no role in the study design, data collection, analysis, preparation of the manuscript, or decision to publish. Data Availability Statement: Data are available in a public, open access repository: https://osf.io/gk4yw/ References Ancoli-Israel, S., & Ayalon, L. (2006). Diagnosis and treatment of sleep disorders in older adults. Am J Geriatr Psychiatry, 14(2), 95-103. https://doi.org/10.1097/01.JGP.0000196627.12010.d1 Bao, Y. P., Han, Y., Ma, J., Wang, R. J., Shi, L., Wang, T. Y., He, J., Yue, J. L., Shi, J., Tang, X. D., & Lu, L. (2017). 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Table 1 - Characteristics of the studied population Study population (N=47155) Female (%) 54.15 Age, mean (SD) 66.49 (10.08) Education (%) Low 33.18 Medium 39.14 High 27.68 Financial distress (yes) (%) 28.81 Self-reported physical health, mean (SD) 2.82 (0.98) Number of chronic diseases, mean (SD) 1.24 (1.11) Anxious (yes) (%) 32.24 Loneliness (yes) (%) 7.59 Depressed (yes) (%) 30.54 Close contact with COVID-19 (yes) (%) 45.65 Sleep problems before the pandemic (yes) (%) 30.87 Containment index, mean (SD) 59.01 (7.59) Health index, mean (SD) 70.81 (7.50) Source: SHARE Corona 2 Survey, release 0. Weighted data. N = 47155. Notes: SD (standard deviation).