key: cord-0736756-q48eqp4p authors: Das Gupta, Debasree; Kelekar, Uma; Rice, Dominique title: Associations between living alone, depression, and falls among community-dwelling older adults in the US date: 2020-12-02 journal: Prev Med Rep DOI: 10.1016/j.pmedr.2020.101273 sha: d884c313223ac0684a9322d9162ee69eacf7da96 doc_id: 736756 cord_uid: q48eqp4p Social isolation is closely linked to depression and falls in late life and are common among seniors. Although the literature has highlighted age-related variations in these three geriatric conditions, evidence on heterogeneities across older adult age categories is lacking. To address this gap, we present cross-sectional analyses using indicators of social isolation, depression, and falls of older adults constructed from the most recent Behavioral Risk Factor Surveillance System (BRFSS) data. An age-based understanding is critical to improve health interventions since health changes occur at a faster rate among seniors than in any other population subgroup. We included all adults 60 years and older (n = 113,233) in the 2018 BRFSS landline dataset and used the status of living alone, depressive disorder diagnosis, and fall incidences reported by these seniors to respectively create the social isolation, depression, and fall indicators. We conducted multivariable logistic regressions to compare findings on these indicators across the three age categories of 60–69, 70–79, and 80 and above after adjusting for a common set of covariates. Results indicate that the likelihood of seniors living alone and reporting depression is the highest among those 80 years and above. Conversely, the odds of depressed seniors reporting falls is the greatest among the 60–69 year olds. Accordingly, we highlight key implications for targeted health promotion and care delivery to seniors. A recent publication from the National Academy of Sciences (NAS) (2020) underscored the high prevalence of social isolation among older adults in the US. While adverse health outcomes associated with social isolation of the elderly have been documented for decades (NAS, 2020 (NAS, , 2018 , lockdowns and physical distancing (Das Gupta and Wong, 2020a; Fong et al., 2020) during COVID19 are shining an intense spotlight on this topic (Holmes et al., 2020) . Social isolation is closely linked to depression and falls in late life and are common among seniors (Cudjoe et al., 2020; Durbin et al., 2016; Deandrea et al., 2010; Fiske et al., 2009; Freedman and Nicolle, 2020; Hayashi et al., 2020; Health Resources and Services Administration [HRSA] , 2019; Petersen et al., 2020; Quach and Burr, 2020) . All three conditions are critical public health concerns (Fiske et al., 2009; Healthy People, 2020; Kelsey et al., 2012; NAS, 2020; Satcher and Druss, 2010) given the progressive aging of populations across the US (Das Gupta and Wong, 2020b). A robust literature connects social isolation to older adult health (NAS, 2020; Veazie et al., 2019) with analysts identifying the role of depression as a mechanism through which isolation may impact falls of seniors (Elliott et al., 2009; Faulkner et al., 2003; Iaboni and Flint, 2013; NAS, 2020; Painter et al., 2012) . The evidence linking social isolation and depression (Domènech-Abella et al., 2019; Santini et al., 2020; Teo et al., 2015) , and depression and falls (Biderman et al., 2002; Deandrea et al., 2010; Holloway et al., 2016; Iaboni and Flint, 2013; Kao et al., 2012; Painter et al., 2012; Turcu et al., 2004) of older adults is strong. Prior studies examining these linkages report higher levels of depression among socially isolated older adults (Domènech-Abella et al., 2019; Santini et al., 2020; Teo et al., 2015) and increased falls of depressed seniors (Biderman et al., 2002; Iaboni and Flint, 2013; Kao et al., 2012) . Additionally, the literature has highlighted inter-age heterogeneity in various geriatric conditions including social isolation (Hawkley and Kocherginsky, 2018; Liu and Waite, 2014; NAS, 2020; Wilkinson and Marmot, 2003) , depression (Fiske et al., 2009; Weinberger et al., 2018) , and falls (Ambrose et al., 2013; Chang et al., 2004; Gale et al., 2016; Karlsson et al., 2013; Tinetti et al., 1988) of older adults. In fact, it was almost two decades back that the National Research Council (NRC, 2001) had underscored diversity in elderly health and comorbid conditions across chronological age ranges. Yet, evidence on how the relation between social isolation and depression and depression and falls may vary across older adult age categories remains under-explored (Fiske et al., 2009 ; Academies and of Sciences, Engineering, and Medicine (NAS), 2020; Academies and of Sciences Engineering, and Medicine (NAS), 2018). To address the above-mentioned gap, we present cross-sectional analyses using indicators of social isolation, depression, and falls of older adults constructed from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) data. More specifically, we conduct multivariable (logistic regression) analysis to evaluate how the relation between these elderly conditions vary across older adult age categories. Such a nuanced understanding is critical as health changes occur at a much faster rate among seniors than in any other population subgroup (Fiske et al., 2009; NAS, 2018; NRC, 2001) . Consequently, the younger older adults (60-69 years for example) are a group with health and social care needs that are distinct and different from the oldest 80 years and above (Arfken et al., 1994; NRC, 2001; Santoni et al., 2015) . We conducted analysis using the 2018 Behavioral Risk Factor Surveillance System (BRFSS) data (CDC, 2020) which is a leading source of health data in the US (Silva, 2014) . Conducted annually since the 1984 by the US Centers for Disease Control and Prevention (CDC) in collaboration with state health departments, the BRFSS is a nationally representative telephone-based cross-sectional survey of communitydwelling adults (≥18 years) in the US. It includes questions on such topics as chronic conditions, health risk behaviors, and access to and use of health services in addition to socio-demographic information (CDC, 2020) . Trained interviewers administer these questions from a standardized questionnaire with informed consent obtained, and confidentiality and voluntary participation highlighted at the outset. A probability-based multistage cluster sampling methodology is used to generate a representative sample in each of the 50 states, the District of Columbia, Guam, and Puerto Rico. In 2018, 437,436 adults participated in the survey and the median response rate across these states and regions was between 53.3% (landlines) and 43.4% (cellphones). Additional technical details on the complex survey design of the BRFSS are available at CDC (2020). The institutional review board (IRB) at CDC approved the BRFSS for research (CDC, 2018) . Consequently, our secondary analysis using this de-identified publicly available data did not need additional IRB approvals or participants' informed consent. One in two older adults experience incident depression at/after age 60 (Brodaty et al., 2001; Fiske et al., 2009) . We consider adults 60 years and older in the BRFSS dataset. In 2018, a majority of these older respondents (57%, n = 113,233) answered the BRFSS questions via a landline and constituted as the sample for this study. Outcome and explanatory variables: The BRFSS survey questions are specifically designed to facilitate public health research and surveillance for the purpose of health promotion and practice (Rolle-Lake and Robbins, 2020; Remington et al., 1988) . This dataset has been employed in prior studies to interpret depressive disorder (Miyakado-Steger and Seidel, 2019; Mazurek et al., 2020; Strine et al., 2008) , falls (Barbour et al., 2014; Bergen et al., 2016; Crews et al., 2016; Grundstrom et al., 2012) , and the status of living alone (Escobar-Viera et al., 2014; Kawachi et al., 1999) in the US. The explanatory and outcome variables for our study were similarly constructed using specific relevant questions in the 2018 BRFSS data. In 2018, the BRFSS included a question on lifetime diagnosis of depression that asked respondents: 'Has a doctor, nurse, or other health professional ever told you have a depressive disorder (including depression, major depression, dysthymia, or minor depression)?' It also asked respondents 45 years and older: 'In the past 12 months, how many times have you fallen?' (CDC, 2020) . For each of these questions, we coded the responses into two categories to obtain the depression (yes = 1 vs. no = 0) and falls (yes = 1 for falls ≥ 1 vs. no = 0) indicator variables. The BRFSS also asked landline respondents: 'Excluding adults living away from home, such as students away at college, how many members of your household, including yourself, are 18 years of age or older?' (CDC, 2020) . We coded the responses to this question also into two categories to obtain the living-alone (adults: 2 or more = 0 vs. adults: 1 = 1) indicator. In the literature, the status of "living alone" is highlighted as an objective quantitative marker (Holt-Lunstad et al., 2015) that increases the risk of social isolation (Ortiz, 2011) and lowers the potential of social support (Ennis et al., 2014) of seniors. This single-item indicator has been frequently used in studies on the topic of social isolation (Havinghurst, 1978; Hunt, 1978; Holt-Lunstad et al., 2015; NAS, 2018; Pohl et al., 2018; Shaw et al., 2017; Wenger et al., 1996) and in others examining older adult falls (Elliott et al., 2009; Flabeau et al., 2013; Kharicha et al., 2007) . In synergy, we operationalize seniors' status of living alone as a binary objective indicator distinct from the subjective perception of social isolation (Holt-Lunstad et al., 2015) . While selfreported status of "living alone" is the explanatory variable of interest in the depression (outcome) model, in the fall (outcome) model, depressive disorder diagnosis reported by seniors is the main predictor. Control variables: Prior analysts have reported common risk factors for older adult depression and falls (Biderman et al., 2002; Iaboni and Flint, 2013 ) that include socio-demographics (for example, age, sex, race, ethnicity) and socio-economics (for example, income, education, employment), and health conditions and health behaviors (NAS, 2020). Accordingly, in the multivariable analysis we considered age-categories, sex (male, female), race/ethnicity (White Non Hispanic (NH), Black NH, other NH, American Indian, and Hispanic), education (