Population Health Management for Older Adults

Physical functioning

Physical functioning relies primarily on objective determinants and outcomes as it relates to issues that result as a function of a combination of age, illness, and mobility. The objective measures of age and chronic illness are particularly relevant as physical decline occurs (Holmes, Powell-Griner, Lethbridge-Cejku, & Heyman, 2009; Hughes et al., 2001). For example, some physical limitations of older adults are characterized by the inability to walk a quarter of a mile, stand for 2 hrs, stoop, bend or kneel, and lift or carry something more than 10 pounds (equal to bags of groceries; Holmes et al., 2009). There are a number of characteristics associated with reduced physical functioning, which also includes activities of daily living (ADLs; for example, bathing, dressing, etc.) and risk of fall. These characteristics include age, minority status, being female, lower education, depressive symptoms, and illnesses such as diabetes, arthritis, and heart failure (Bruce, Seeman, Merrill, & Blazer, 1994; V. C. Chang & Do, 2015; Dunlay et al., 2015; Gregg et al., 2000; Holmes et al., 2009; Lin et al., 2003; Reginster, 2002). Outcomes of poor physical functioning include continued decline, falls, worse mental health, and increased mortality (Ambrose, Paul, & Hausdorff, 2013; Makizako et al., 2015).

Another aspect of physical functioning limitations is sensory impairment. For older adults, the decline of hearing and vision has a significant impact on their well-being (Schneider et al., 2011). Determinants of sensory impairment include age, gender, and race (Lin, Thorpe, Gordon-Salant, & Ferrucci, 2011). Outcomes of sensory impairment include increased risk of falls, depression, cognitive impairment, and loneliness (Lin, 2011; Schneider et al., 2011).

Psychological well-being

Psychological well-being is a multidimensional construct that personifies the human capacity to continue to grow, adapt to our environment, and evolve as we age. For older adults in particular, psychological well-being is characterized as the ability to deal with life situations, to have and maintain positive and close relationships, self-acceptance of both self and others, and autonomy (Ryff, 1995; Ryff & Keyes, 1995). Predictors of high psychological well-being for older adults include social support, physical activity, spirituality, and higher socioeconomic status (Black et al., 2015; Holland & Holahan, 2003; Levin & Chatters, 1998). Conversely, depression, stress, and anxiety are predictors of low psychological well-being (Allerhand, Gale, & Deary, 2014; Ormel et al., 1998; Ryff, 2014). Outcomes of high psychological well-being include lower rates of cognitive decline, higher levels of resilience, and greater life satisfaction (Bowling & Iliffe, 2011; Gerstorf, Lovden, Rocke, Smith, & Lindenberger, 2007; Ryff, 2014). Notably, high psychological well-being is a key factor in successful aging and reduced mortality rates for both physically healthy and unhealthy older adults (Chida & Steptoe, 2008; Rowe & Kahn, 1997; Ryff, 2014; Steptoe, Deaton, & Stone, 2015).

Social well-being

Social well-being has been defined as the relative ability to deal with social challenges and how well one functions in the social world (Keyes, 1998). There are five major components of social well-being: social integration, social contribution, social coherence, social actualization, and social acceptance (Keyes, 1998). Social well-being has a significant impact on the health of older adults, with higher levels of social well-being related to lower levels of cortisol and inflammatory factors that contribute to a number of diseases including cardiovascular disease (CVD; Ryff, Singer, & Love, 2004). As adults age, their social networks and social roles change; some of these changes enhance their social well-being and some detract from it. For example, social networks may decrease in size but satisfaction with social networks may increase (Carstensen, 1992; Carstensen, Fung, & Charles, 2003).

Social relationships are an important indicator of quality of life, with those who have negative social relationships reporting worse quality of life independent of disease status (Liao & Brunner, 2016; Rook, 1984). Close positive family ties have particular relevance for the well-being of older adults (Fuller-Iglesias, Webster, & Antonucci, 2015). Negative social factors that reduce the social well-being of older adults include loneliness and social isolation (Singh & Misra, 2009). Loneliness can result from feeling a loss of meaning in life, lack of independence, and loss of loved ones (Smith, 2012). Social support is also a key influence on the social well-being of older adults. Among older adults, high levels of social support are associated with lower stress and better mental and physical well-being (Fuller-Iglesias, 2015; Thoits, 2011).

There is also an emphasis in the literature on the importance of incorporating more subjective data into the model of successful aging as the objective measures (disease and functioning) severely limit the number of individuals who are considered to age successfully (Cho, Martin, & Poon, 2012, 2015; Pruchno, Wilson-Genderson, & Cartwright, 2010). For example, Pruchno et al. (2010) proposed a two-factor model of successful aging by incorporating objective and subjective data. Objective data included physical functioning, pain (frequency, intensity, and interference), and number of chronic conditions. Subjective questions asked participants how successfully or how well they thought they had aged and life ratings. This two-factor model found that age and gender had strong associations with objective aging but not with the subjective factor. Additional research by Cho et al. (2012) found that only about 15% of octogenarians fit the Rowe and Kahn criteria of successful aging. However, the subjective aspect of successful aging identified 62% as successful agers. These studies highlight successful aging as a fluid and modifiable construct on the continuum of health. On the health continuum, some individuals may be coping with disease and are working to manage multiple chronic conditions and feel they have also aged successfully. As a result of this subjective aspect, they may incorporate better health behaviors that improve their objective health status.

Connecting the subjective and objective aspects of health for older adults within the context of population health and successful aging is a natural fit. The theory of population health acknowledges this subjectivity within the context of objective metrics of health and provides a structure to measure and interpret the holistic health of older adults.

Lifestyle behavior interventions

The purpose of a lifestyle behavior intervention is to modify poor lifestyle behaviors to improve health, reduce risk of disease, or slow progression of disease (Glanz, Rimer, & Viswanath, 2008; Montaño & Kasprzyk, 2008). On the continuum of health, the majority of older adults already have chronic conditions and therefore lifestyle modification is of great significance. The three most relevant modifiable lifestyle behaviors are physical inactivity (or sedentary lifestyle), healthy diet, and tobacco usage. These lifestyle behaviors have strong associations with a number of diseases (Mokdad et al., 2003; Warburton, Nicol, & Bredin, 2006). Therefore, lifestyle behavior modifications can prevent older adults from moving further along the continuum of health. Successful interventions require regular and frequent contact with participants and need to include multiple modes of delivery (e.g., in-person, online, etc.; Mouton & Cloes, 2015; Svetkey et al., 2008; Zbikowski, Magnusson, Pockey, Tindle, & Weaver, 2012). In addition, addressing lifestyle behaviors requires multiple levels of interventions (Sallis, Owen, & Fisher, 2008). For example, interventions to improve healthy eating are only successful if there is sustainable access to healthy food.

Utilizing a theoretical approach when designing a lifestyle intervention is of great importance and a lack of integrating theory may partly explain limited successful outcomes (Glanz, Rimer, & Viswanath, 2008; Painter, Borba, Hynes, Mays, & Glanz, 2008). Theories such as the health belief model or the transtheoretical model of health behavior change are the most frequently used (Painter et al., 2008; Prochaska & Velicer, 1997; Rosenstock, 1974). These theories incorporate the individual’s level of willingness to change, assessment of risk, and self-efficacy (Prochaska & Velicer, 1997; Rosenstock, 1974). Self-efficacy, a belief in the ability to achieve success, is of particular importance in health behavior change for older adults (Grembowski et al., 1993; Schwarzer & Renner, 2000). Older adults with high self-efficacy have lower health risks than those with low self-efficacy (Grembowski et al., 1993). Self-efficacy has also been found to be a significant predictor of physical activity, smoking cessation, and healthy diet (Bauman et al., 2012; Grembowski et al., 1993; McAuley et al., 2011).

Physical activity declines as adults age with those more than the age of 60 reporting the lowest levels (Troiano et al., 2008). Interventions designed specifically for older adults to increase physical activity can be successful by improving physical functioning, cognition, disease management, and psychological well-being (Black et al., 2015; Colcombe & Kramer, 2003; Cress, Buchner, Questad, Esselman, & Schwartz, 1999). Healthy food consumption and physical activity are frequently linked together in research because of their relationship to each other and their shared relationship with obesity (Rosenheck, 2008; Villareal, Apovian, Kushner, & Klein, 2005; Wareham, van Sluijs, & Ekelund, 2005). The rising rate of obesity is the number one health concern for Americans (Flegal, Carroll, Ogden, & Curtin, 2010); a number of interventions have aimed to improve older adults’ eating habits to reduce their weight and improve physical functioning. These studies find that combining both diet and physical activity yields the most successful results (Rejeski, Mihalko, Ambrosius, Bearon, & McClelland, 2011; Villareal et al., 2011; Witham & Avenell, 2010).

As noted on the population health continuum, health behavior interventions can be for those at risk of developing a condition or disease or to manage a condition or disease and reduce the risk of other conditions and diseases. One diabetes prevention study randomized non-diabetics to one of three groups: lifestyle modification (diet and exercise), medication, or control group (Knowler et al., 2002). Those randomized to the lifestyle intervention group were the least likely to develop diabetes over a 3-year period, highlighting the importance of modifiable health behaviors.

Fall risk interventions

As the number of severity of chronic conditions or diseases increases across the continuum of health, so does the risk of falls. Falls are the leading cause of fatal and non-fatal injuries and have a significant impact on quality of life (Houry, Florence, Baldwin, Stevens, & McClure, 2016). Although falls are considered a preventable health issue, it is estimated that about 40% of older adults fall every year, increasing health care utilization and costs (Ambrose et al., 2013; Houry et al., 2016). Predictors of falls and recurrent falls include gender (female), visual impairment, cognitive impairment, medications, poor physical functioning, poor balance, reduced gait, the need of a walking assistant (cane, walker), fear of falling, low body weight, and history of falls (Ambrose et al., 2013; Mirelman et al., 2012; Rubenstein, 2006; Shumway-Cook, Baldwin, Polissar, & Gruber, 1997).

The U.S. Preventive Service Task Force (Moyer, 2012) recommends exercise, physical therapy, and vitamin D to reduce the risk of falls. Exercise interventions, in particular, have demonstrated high success in reducing the risk of falls (J. T. Chang et al., 2004; Rubenstein, 2006). Some novel approaches of exercise, such as Tai Chi and videogames (Nintendo Wii), have been tested. These interventions are considered low cost and have demonstrated success in reducing fall risk and improving physical functioning with the added benefit of participant enjoyment (Bieryla & Dold, 2013; Jorgensen et al., 2012; Li et al., 2005). These interventions also demonstrate that across the continuum of health, there are a number of opportunities to reduce risk and improve well-being.

Psychosocial interventions

Several interventions aim to improve psychological or social well-being; however, these studies demonstrate mixed results (Hogan, Linden, & Najarian, 2002; Netz, Wu, Becker, & Tenenbaum, 2005). Physical activity and mindfulness training interventions have significant effects on psychological well-being (Carmody & Baer, 2008; Chiesa & Serretti, 2009; Netz et al., 2005; Penedo & Dahn, 2005). However, many mindfulness interventions are related to chronic pain or high levels of stress (Chiesa & Serretti, 2009; Morone & Greco, 2007). Interventions to improve social well-being for older adults have also shown some benefits (Dickens, Richards, Greaves, & Campbell, 2011). One meta-analysis reviewing social well-being interventions for older adults found that the most successful were those that used a theoretical approach, group support, and had active participation (Dickens et al., 2011). In addition, the use of technology interventions have also been successful in improving the psychological and social well-being of older adults (Jones, Ashurst, Atkey, & Duffy, 2015; Shapira, Barak, & Gal, 2007). One study found that older adults who completed an Internet-use training program reported greater contact with friends and family, less loneliness, and better mental well-being (Jones et al., 2015). As these programs demonstrate, the overlap of clinical and psychosocial determinants and outcomes demonstrates the need for a more comprehensive and integrative model addressing health and health promotion across the continuum of health.

Disease management interventions

Disease management (DM) programs were originally developed for high-cost patients dealing with a specific chronic disease such as diabetes, asthma, or congestive heart failure (CHF). Their purpose was to implement evidence-based practice to reduce cost by reducing emergency room visits and hospitalizations, improving quality of life, and improving clinical outcomes (Shelton, 2002). However, a number of reviews and meta-analyses have found that DM programs generally do not reduce cost and have limited health outcomes (de Bruin, Heijink, Lemmens, Struijs, & Baan, 2011; McCall & Cromwell, 2011; Pimouguet, Le Goff, Thiébaut, Dartigues, & Helmer, 2011; Savard, Thompson, & Clark, 2011). However, literature supporting the benefits of DM programs indicates that they can improve quality of life and reduce cost especially for two of the most expensive and common conditions: CVD and diabetes (Chodosh et al., 2005; Griffo et al., 2013; The Look AHEAD Research Group, 2010; Tricco et al., 2012; Yu et al., 2004).

Cardiac rehabilitation programs target individuals who have experienced or are at risk of a cardiac event (heart attack, angioplasty, stent surgery). The program provides a supervised exercise program, education, and counseling to improve health and prevent further cardiac events (Fletcher et al., 2013). Research has demonstrated the benefits of cardiac rehabilitation to improve clinical outcomes, increase physical activity, improve quality of life, and reduce mortality (Lavie, Berra, & Arena, 2013; Lavie & Milani, 2011; B.-J. Martin et al., 2012; Oldridge, 2012; Shepherd & While, 2012). Cardiac rehabilitation is most effective for patients who adhere to the program (B.-J. Martin et al., 2012), yet interventions to increase adherence have mixed results (Antypas & Wangberg, 2014; Karmali et al., 2014). Factors that improve adherence include self-monitoring, action planning, and individual counseling (Karmali et al., 2014), as well as strong social networks, social support, and self-efficacy (Tkatch et al., 2011; Woodgate, Brawley, & Shields, 2007).

Type 2 diabetes is a serious growing health concern. In 2010, the Centers for Disease Control and Prevention (CDC, 2011) reported that 27% of adults 65 and older have diabetes, which is a significant cause of other serious health conditions including kidney failure, heart disease, and stroke. One of the strongest risk factors for diabetes is obesity; therefore, improving one’s health behaviors and health lifestyle are the most effective ways to reduce risk for and manage diabetes (Knowler et al., 2002). A number of studies have demonstrated that diabetes self-management education (DSME) can be effective in preventing and managing the disease (Haas et al., 2013; Stellefson, Dipnarine, & Stopka, 2013). However, older adults are underrepresented in clinical trials related to diabetes management and treatment, and there are different guidelines for treating diabetes and its comorbidities for older adults (Kirkman et al., 2012). Factors that contribute to the success of DSME programs include high levels of social support and self-efficacy and the use of theory-based or technology-based programs (Byrne et al., 2012; Ceriello et al., 2012; Cotter, Durant, Agne, & Cherrington, 2014; Rosland et al., 2015).

The lack of consistent results for DM programs highlights the need for a more comprehensive approach rather than targeting just one disease or health behavior (Krause et al., 2006; Shelton, 2002). The overlap of both the determinants and outcomes of multiple chronic conditions indicates that health behavior, risk, and DM interventions and programs are not independent and should be addressed with a more integrated approach. Programs attempting to do this are small in nature and the feasibility of implementation on a larger scale is unknown (Krause et al., 2006). Furthermore, some research demonstrates that an increase in conditions may actually decrease the likelihood of completing a DM program such as cardiac rehabilitation (Forhan, Zagorski, Marzonlini, Oh, & Alter, 2013). Therefore, a number of challenges need to be addressed when considering comprehensive PHM programs.

Case management (CM)/care coordination interventions

Chronic DM becomes more complex as individuals move along the continuum of health to the very ill category. Multiple chronic conditions require a multidimensional approach to addressing the health needs of older adults, especially with concurrent medical episodes (Boyd et al., 2005). In CM programs, case managers help organize the health care needs of high-cost individuals by coordinating a multidisciplinary team to provide high-quality care, and thus help improve quality of life and reduce costs prior to and post hospitalization (Hickam et al., 2013). Individuals are identified for these programs through Hierarchal Condition Category (HCC) risk scores, which are derived from medical claims (McCall & Cromwell, 2011). CM programs are endorsed by the Centers for Medicare and Medicaid Services (CMS) and covered by Medicare although their limited impact on reducing cost has been documented (McCall & Cromwell, 2011; Schore, Brown, & Cheh, 1998).

CM program success is frequently determined by reduced hospitalizations (Askren-Gonzalez & Frater, 2012). Key factors, identified through CMS data, that contribute to successful programs include frequent in-person contact, assistance in communication between patients and their providers, education, medication management, and extensive transition care after hospitalizations (Brown, Peikes, Peterson, Schore, & Razafindrakoto, 2012). Additional variables that contribute to the success of CM programs include the length of time engaged in the program and focusing on individuals with the highest risk (Hawkins et al., 2015; Peikes, Peterson, Brown, Graff, & Lynch, 2012). There is also a significant need to include a mental health component in these programs (Bao, Casalino, & Pincus, 2013; Zulman et al., 2014).

Although CM programs can be beneficial, they primarily target those at the end of the health continuum (consequently a relatively small percentage of the population). Importantly, there is little focus on quality of life and lifestyle modifications that could restore quality of life post hospitalization and reduce repeat episodes, ultimately reducing cost. Individuals should not have to wait until they are at the end of this continuum to receive comprehensive quality and effective care. Therefore, when developing a comprehensive PHM program for older adults, all of these components could have the potential to promote successful aging independent of an individual’s objective health status.