5

Surveillance and Assessment

INTRODUCTION

Surveillance, one of the three core functions of public health, is defined as the ongoing, systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health action to reduce morbidity and mortality and to improve health (German et al., 2001; IOM, 1988). During the latter half of the 20th century, much of the focus of surveillance activities in the United States was on describing variations in the major causes of death and associated risk factors for fatal diseases. The results of these surveillance activities have been used to guide research investments and subsequent public health and health care interventions to address the major causes of mortality, including cardiovascular diseases and cancer; the associated chronic diseases, including obesity, hypertension, and hyperlipidemia; and behavioral risk factors, including poor diet, physical inactivity, and smoking.

Life expectancy has improved over the past century, primarily as a result of public health interventions, such as tobacco control efforts, that have reduced the risk of the leading chronic diseases, such as heart disease, stroke, and cancer (Remington and Brownson, 2011). More recent evidence suggests that the increases in life expectancy during the past 20 years have come from improvements in disease management rather than in disease prevention (McGovern et al., 1996). However, mortality data from 2000 to 2007 demonstrate wide variation in life expectancy across counties in the United States and an overall relative decline in life expectancy for most communities compared with other nations (Kulkarni et al., 2011).



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5 Surveillance and Assessment INTRODUCTION Surveillance, one of the three core functions of public health, is defined as the ongoing, systematic collection, analysis, interpretation, and dis- semination of data regarding a health-related event for use in public health action to reduce morbidity and mortality and to improve health (German et al., 2001; IOM, 1988). During the latter half of the 20th century, much of the focus of surveillance activities in the United States was on describ- ing variations in the major causes of death and associated risk factors for fatal diseases. The results of these surveillance activities have been used to guide research investments and subsequent public health and health care interventions to address the major causes of mortality, including cardiovas- cular diseases and cancer; the associated chronic diseases, including obesity, hypertension, and hyperlipidemia; and behavioral risk factors, including poor diet, physical inactivity, and smoking. Life expectancy has improved over the past century, primarily as a result of public health interventions, such as tobacco control efforts, that have reduced the risk of the leading chronic diseases, such as heart disease, stroke, and cancer (Remington and Brownson, 2011). More recent evidence suggests that the increases in life expectancy during the past 20 years have come from improvements in disease management rather than in disease prevention (McGovern et al., 1996). However, mortality data from 2000 to 2007 demonstrate wide variation in life expectancy across counties in the United States and an overall relative decline in life expectancy for most communities compared with other nations (Kulkarni et al., 2011). 187

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188 LIVING WELL WITH CHRONIC ILLNESS In addition to life expectancy, available evidence suggests that self- reported health status has not improved among retirees (Hung et al., 2011) or has declined in the general population (Jia and Lubetkin, 2009; Zack et al., 2004) and persons with certain chronic illnesses (Pan et al., 2006). However, these findings are not consistent (Salomon et al., 2009), as some data suggest that the prevalence of disability is decreasing (Manton, 2008), and in some surveys health status is improving (Salomon et al., 2009), over time. These disparate findings likely result from lack of standardized meth- ods of measurement of the complex components and determinants of health status and disability (NRC, 2009), and they suggest that the current sur- veillance systems are insufficient for tracking progress in efforts to monitor trends in quality of life in the United States overall or within communities. Despite uncertainty about trends in quality of life in the United States, the evidence is clear that more people are living with chronic illnesses as a result of increasing prevalence of some illnesses (e.g., obesity) and longer survival among patients diagnosed with chronic illness. Moreover, the rising costs of health care, along with evidence from research focused on patterns of health care utilization and costs, have focused attention on the societal burden of chronic diseases, particularly multiple chronic conditions (MCCs) (Tinetti and Studenski, 2011). Together, the aging of the popula- tion, the decline in relative life expectancy and possibly the quality of life, and unsustainable increases in health care costs combine to create a rap- idly growing burden of chronic illness that demands more comprehensive surveillance beyond mortality and risk factors to address these problems. The goal of living well with chronic illness and efforts to control the growing societal burden of chronic illness start with enhanced surveillance to provide data necessary to plan, implement, and evaluate effectiveness of interventions at the individual and population levels. This chapter has several objectives: 1. To describe a conceptual framework for chronic disease surveillance. 2. To describe how public health surveillance may be used to inform public policy decisions to improve the quality of life of patients living with chronic illnesses. 3. To examine current data sources and methods for surveillance of certain chronic diseases and identify gaps. 4. To describe potential for surveillance system integration. 5. To describe future data sources, methods, and research directions for surveillance to enhance living well with chronic illness.

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189 SURVEILLANCE AND ASSESSMENT CONCEPTUAL FRAMEWORK FOR CHRONIC DISEASE SURVEILLANCE The ultimate goal of public health is to promote health and prevent disease occurrence or to limit progression from preclinical to symptomatic disease through primary and secondary prevention, respectively. Health promotion is the process of enabling people to gain increasing control over and improve their health. Primary prevention is usually addressed through interventions targeting lifestyle risk factors or environmental exposures among illness-free persons, including smoking, physical inactivity, and over- weight/obesity. Secondary prevention among asymptomatic persons with preclinical illness may include a range of interventions comprised of early detection, immunizations, and chemoprevention. Because of the public health emphasis on health promotion and disease prevention (especially primary and secondary prevention), chronic disease surveillance has traditionally focused on major risk factors for disease and the occurrence of chronic diseases. However, although primary and sec- ondary prevention may have relevance for persons with chronic illnesses to prevent the development of other comorbid illnesses, a more immediate concern for individuals is how to live well, which involves a balance be- tween their experience living with chronic illness(es) and associated costs (i.e., value). Moreover, from a societal perspective, interventions to improve the patient experience need to be cost-effective and contribute to improving the health of the population. Thus, there is a strong rationale for expanded surveillance of chronic diseases to measure not only the factors that in- crease the “upstream” risk of chronic diseases but also the relevant health “downstream” outcomes associated with living well with chronic illness (Porter, 2010). Table 2-1 provides an excellent framework for establishing a com- prehensive chronic disease surveillance system. Such a surveillance system should collect data along the entire chronic disease continuum—from up- stream risk factors to end of life care and for the purposes of promoting living well among persons with chronic illness. Such systems should collect information on symptoms, functional impairment, self-management bur- den, and burden to others. Integrating the multiple potential measures of health status and deter- minants of health, including risk factors and interventions and costs, will be necessary for the ideal surveillance system to assess the status of patients living well with chronic illness and the societal impacts. The need to inte- grate these multiple measures has been emphasized in a recent Institute of Medicine (IOM) report on a framework for surveillance of cardiovascular and chronic respiratory diseases (IOM, 2011) and in reviews by others

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190 LIVING WELL WITH CHRONIC ILLNESS (Fielding and Teutsch, 2011; Porter, 2010). Briefly, the conceptual frame- work for an ideal surveillance system to enhance living well with chronic illness incorporates the life course model that describes health status on a spectrum from illness-free to death and the ecological model of multiple determinants of health, including individual characteristics (i.e., biologi- cal makeup, health literacy and beliefs, health-related behaviors), family and community environments (i.e., social, economic, cultural, physical), and health-related interventions (i.e., public health, policy, clinical care). Measuring variations and disparities among subpopulations—for example, by age, race, gender, residence, and other factors—is a critical part of any public health surveillance system. Surveillance of chronic diseases may also be used to monitor progress in achieving the triple aim of health care improvement: that is, to improve the patient experience, to control costs, and to improve the health of the population (Institute for Healthcare Improvement, [a]). These three aims provide further dimensions for defining relevant metrics and data sources of an enhanced surveillance system to monitor the multiple determinants and outcomes of living well with chronic illness, including the individual, the health system, and the population/community levels (Table 5-1). Moreover, these metrics and data sources reflect the multi-pronged interventions neces- sary for optimizing management and outcomes for patients with chronic ill- ness, as described in the enhanced Chronic Care Model (Barr et al., 2003). Although there is abundant evidence that the health status of patients with chronic illnesses and the quality of health care and associated costs (i.e., value) is not optimal in the United States (IOM, 2001; Porter, 2010), limited data are available on what it means at the individual level to live well (Porter, 2010; Thacker et al., 2006). Ideally, living well is defined by patients’ values and goals regarding their physical, emotional, and social functioning. However, wide variation in patients’ perspectives presents a major challenge for conducting surveillance of living well with chronic illness; the definition of living well was discussed in detail in Chapter 1. Moreover, because of barriers to access and the low value of health care in the United States, the current policy focus is largely on enhancing access and increasing value by improving quality, reducing costs by decreasing use of ineffective and/or high-cost interventions, and improving the processes of care. However, the determinants of living well with chronic illness are more complex, and these efforts alone will not adequately support patients in these circumstances. Porter (2010) has described a health outcome hierarchy focused on health care delivery, which can be applied to provide a framework for de- signing a comprehensive measurement system to enhance living well with chronic illness. The principles described in this framework are as follows:

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191 SURVEILLANCE AND ASSESSMENT • Outcomes have multiple dimensions, which ideally include one dimension at each tier and level — Tier 1: health status achieved or retained o Survival o Degree of health/recovery (e.g., quality of life, functional status) — Tier 2: Process of recovery o Time to recovery and return to normal activities (e.g., time to achieve functional status) o Disutility of care process (e.g., acute complications) — Tier 3: Sustainability of health o Sustainability of health/recovery and nature of recurrences (e.g., frequency of exacerbations) o Long-term consequences of therapy (e.g., care-induced illnesses) • Outcomes must be relevant to patients and their specific illness(es) (i.e., valid) • Multiple determinants of outcomes (e.g., disease-related, psycho- logical, social, lifestyle) must be measured • Measurement instruments must be standardized to provide reli- ability and comparability • Measurement instruments must be sensitive to change • Measurements must be ongoing and sustained In addition to the measurement of health status or outcomes at the individual level, comprehensive surveillance must incorporate measures of characteristics, exposures, and processes that affect health outcomes com- prising the multiple determinants of living well and their interactions at the levels of the individual, the family, and the community; health care–related interventions; and public policy. Individual health-related behaviors, includ- ing lifestyle (e.g., smoking, physical activity, diet) and self-management (e.g., medication adherence, action plans), are all influenced by patient characteristics, such as education level, health literacy, beliefs, activation, and self-efficacy. In turn, these characteristics and other exposures are partly influenced by an individual’s larger cultural, socioeconomic, and physical environments, comprised of family, work, and community. Finally, measurement of access to and utilization of health care and public health resources/interventions (e.g., structural interventions; see Katz, 2009) and coordination of care are needed to complete the assessment of factors that may contribute to patients living well with chronic illness. Given the complexity of measuring the multiple determinants and di- mensions of living well (i.e., quality of life, functional status), there is no single-best measure of living well for patients with chronic illness (Thacker

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192 TABLE 5-1 Matrix for Surveillance for Living Well with Chronic Illness Patient-Reported, Health Care Health Factors and Individual-Level Administrative Data Population-Based Outcomes Examples Information and Illness Registries Surveys and Assessments Environmental, Social, and Personal Determinants of Health Physical and built Air/water quality, Self-report Geo-coded addresses County health rankings, EPA, environment walking paths, food census deserts Social and economic Education, income, Self-report (personal County health rankings, Dept factors employment, social health record), social of Education, Dept of Justice, support support, caregiver census burden Policy, law, and Workplace policies on Self-report on awareness, JCAHO data (e.g., State- and county-level regulation smoking, immunization; enforcement of policies hospital smoking bans, databases of public health taxes on tobacco, sugar- and laws (BRFSS) health worker flu laws and taxes sweetened beverages vaccination levels) Health care access, Insurance coverage, Self-report (e.g., ACOVE Claims data, hospital MEPS, HCAHPS coordination, quality, immunizations, cancer RAND) discharge data, RHIOs and costs screening

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Health literacy, beliefs, Health literacy, self- Health risk appraisal motivations efficacy, activation (HRA) Health behaviors Smoking, diet, physical HRA BRFSS, NHIS, NHANES activity, unsafe sex Health Outcomes Social health outcomes Relationships and Self-report (personal National surveys function health record) Mental health outcomes Affect, behavior, Self-report (personal BRFSS (limited), NHIS cognition, PHQ-9 health record) Physical health outcomes ADLs, symptoms, Self-report (personal BRFSS (limited), NHIS functioning health record) Illness-specific outcomes Diabetes, arthritis, Electronic medical record Vital stats, SEER, claims NHIS, NHANES, BRFSS, cancer, dementia (EMR) data (e.g., costs) disability from CPS Primary uses of data Improve quality and Improve quality, manage Assess trends, burden, health outcomes (living costs, find “hot spots” disparities (by person and well) place), research NOTE: ACOVE RAND = Assessing Care of Vulnerable Elders—A RAND Health Project; ADLs = activities of daily living; BRFSS = Behavioral Risk Factor Surveillance System; CPS = Current Population Survey; EPA = Environmental Protection Agency; HCAHPS = Hospital Consumer Assessment of Healthcare Providers and Systems; JCAHO = Joint Commission on Accreditation of Healthcare Organizations; MEPS = Medical Expenditure Panel Survey; NHANES = National Health and Nutrition Examination Survey; NHIS = National Health Interview Survey; PHQ-9 = Patient Health Questionnaire-9; RHIO = Regional Health Information Organization; SEER = Surveillance, Epidemiology, and End Results. 193

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194 LIVING WELL WITH CHRONIC ILLNESS et al., 2006), and illness-specific measures may not detect the entire pa- tient experience (Monninkhof et al., 2004; Yeh et al., 2004). Therefore, an aggregate index of living well will consist of multiple measures from the individual, health care system, community, and policies to characterize the population (Table 5-1). However, a further challenge for surveillance of living well with chronic illness is that the majority of patients may have MCCs, which further supports the need for generic measures of health outcomes in contrast to illness-specific measures. In summary, the best way to meet the goal of living well with chronic illness is to prevent chronic illness in the first place and, if that fails, to manage the illness to improve quality of life and prevent the development of additional chronic illness. Doing so requires a comprehensive surveil- lance system that includes incentives for individuals and organizations to participate in surveillance activities. The characteristics of a surveillance system to enhance living well with chronic illness are complex and integrate a number of measures of the multiple determinants and multiple dimensions of outcome most relevant to patients. Individual patient-level measures are discussed in the section below on Current Data Sources and Surveillance Methods. USE OF SURVEILLANCE TO INFORM PUBLIC POLICY DECISIONS Public health surveillance systems may be used to inform public policy decisions to improve the prevention and control of chronic illnesses at the individual or population level. In this section, we review how surveillance (i.e., data collection and reporting) at various levels (e.g., individual, com- munity, health system, state, national) may be used to promote living well with chronic illness. In broad terms, these systems may be used to • promote dissemination of evidence-based programs and policies, especially when a gap exists between research and practice; • target interventions to areas or populations of greatest need (e.g., where health disparities are greatest); and • evaluate the effectiveness of new or emerging interventions. When the evidence is strong for interventions that could effectively address a gap, the surveillance effort should be focused on closing this gap by promoting the implementation of an evidence-based intervention. For example, surveillance systems have been used to demonstrate continued exposure to cigarette smoke in the workplace, the lack of advice given by physicians to quit smoking, or the slow uptake of breast, cervical, or colorectal cancer screening. Because of the complexity of the determinants of living well, effective dissemination of interventions will most often re-

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195 SURVEILLANCE AND ASSESSMENT quire system-level changes at the local, state, or national level (i.e., policy, rules, regulation, culture), which are discussed in greater detail in Chapters 3 and 4. Public health surveillance systems can also be used to identify dispari- ties in all aspects of chronic disease prevention and control. Monitoring and reducing health disparities has been a central focus of the U.S. Department of Health and Human Services (HHS) Healthy People efforts over the past 30 years. In Healthy People 2000, the focus was to reduce health dispari- ties among Americans. Healthy People 2010 emphasized eliminating, not just reducing, health disparities. In Healthy People 2020, that goal was expanded even further to achieve health equity, to eliminate disparities, and to improve the health of all groups. Surveillance efforts in chronic disease should build on the Healthy People 2020 effort by carefully monitoring health disparities, defined as “a particular type of health difference that is closely linked with social, eco- nomic, and/or environmental disadvantage. Health disparities adversely af- fect groups of people who have systematically experienced greater obstacles to health based on their racial or ethnic group; religion; socioeconomic status; gender; age; mental health; cognitive, sensory, or physical disability; sexual orientation or gender identity; geographic location; or other charac- teristics historically linked to discrimination or exclusion.” Finally, when evidence of effective interventions is not strong, surveil- lance systems can provide information about the effect of programs or poli- cies in actual populations and guide future improvement efforts. Ideally, the evidence base for effective chronic disease prevention programs and policies would be developed through an explicit public health research agenda. However, evidence often evolves during the implementation of programs and policies in actual practice, using data collected in well-designed surveil- lance systems or population-based surveys. As more programs and policies are directed toward helping people live well with chronic illness, compre- hensive surveillance systems will help evaluate their impact on populations throughout the United States. CURRENT DATA SOURCES AND SURVEILLANCE METHODS As described in previous sections, surveillance of living well with chronic illness is a complex phenomenon requiring multiple methods and data sources to adequately characterize and track. Overall, there are three levels of data, including patient, health system, and population. A detailed review of current population-based and health system data sources was re- cently conducted by IOM on the surveillance of cardiovascular and chronic respiratory diseases (IOM, 2011). In this section we provide an overview of available data sources (Table 5-2) and methods specific to the surveil-

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TABLE 5-2 Selected Chronic Disease Data Sources and Surveillance Systems 196 Data System Example Strengths Limitations Notifiable diseasesa,b,c State-based lead poisoning • ata are available at the local level. D • equires participation by community- R reporting systems • sually coupled with a public health U based clinicians. response (e.g., lead paint removal). • linician-based systems have low C • etailed information can be collected D reporting rates. to aid in designing control programs. • ctive reporting systems are time- A • aboratory-based systems are L consuming and expensive. inexpensive and effective. Vital statisticsa,b,c Death certificates • ata are widely available at the local, D • ause of death information may C state, and national levels. be inaccurate (e.g., lack of autopsy • opulation-based. P information). • an monitor trends in age-adjusted C • o information about risk factors. N disease rates. • an target areas with increased C mortality rates. Sentinel surveillanceb,c Sentinel Event Notification • ow-cost system to monitor selected L • equires motivated reporting providers. R System for Occupational diseases. • ay not be representative. M Risks (SENSOR) • sually coupled with a public health U response (e.g., asbestos removal following report of mesothelioma). • rovides information on risk factors P and disease severity. Disease Cancer registries • ata are increasingly available D • ystems are expensive. S registriesb,c throughout the United States. • ata are affected by patient out- D • ncludes accurate tissue-based I migration from one geographic unit to diagnoses. another. • rovides stage-of-diagnosis data P • isk factor information is seldom R available. available.

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Health surveysb,c Behavioral risk factor • onitors trends in risk factor M • nformation is based on self-reports. I surveillance telephone prevalence. • ay be too expensive to conduct at the M surveys • an be used for program design and C local level. evaluation. • ay not be representative due to M nonresponse (e.g., telephone surveys). Administrative data Hospital discharge systems • eflects regional differences on R • ften lacks personal identifiers. O collection systemsb,c disease hospitalization rates. • ates may be affected by changing R • an capture cost information. C patterns of diagnosis based on • ata are readily available. D reimbursement mechanisms. • ne of few sources of morbidity O • ifficult to separate initial from D data. recurrent hospitalizations. U.S. censusa,b,c Poverty rates by county • equired to calculate rates. R • ollected infrequently (every 10 years). C • mportant predictors of health status. I • ay undercount certain populations M • vailable to all communities and A (e.g., the poor, homeless persons). readily available online. aData are available from most local public health agencies. bData are available from most state departments of health. cData are available from many U.S. federal health agencies (e.g., Centers for Disease Control and Prevention, National Cancer Institute, Health Care Financing Administration). SOURCE: Remington, P.L., M.V. Wegner, and A.M. Rohan. 2010. Chronic disease surveillance. In Chronic Disease Epidemiology and Control, 3rd ed., edited by P.L. Remington, R.C. Brownson, and M.V. Wegner. Pp. 98–99. Washington, DC: America Public Health Association. 197

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218 LIVING WELL WITH CHRONIC ILLNESS example, sales of over-the-counter medications or emergency room visits. Movement toward electronic medical records may make such surveillance feasible with medical records, but innovative applications of more popula- tion-based data have also been explored. Data mining of information col- lected by search engines or in either general or disease-specific online social networks may be more sensitive to such detection methods. However, one limitation of this approach is that such methods are more sensitive to short- term changes (e.g., influenza epidemics; see Signorini et al., 2011). In addi- tion, people with chronic illness are less likely to have Internet access than others, raising the possibility of selection bias in who provides information. However, people with chronic illnesses who go online are more likely to seek out others with similar health concerns than are those without chronic illness (Fox, 2011), indicating that this may be a fruitful area for surveil- lance, as more people with chronic illness obtain access to the Internet. Research on Measurement of Chronic Disease Although the need for surveillance is a well-established function of pub- lic health, surveillance of chronic illness to enhance living well is complex and presents a number of challenges that will require further investigation at the individual, health organization, and population levels. Moreover, the effectiveness of potential future methods and data sources for surveillance to drive improvement will need to be determined. Individual Level The measurement of patient-reported outcomes continues to be an ac- tive area of investigation (as previously described for PROMIS), and much of the research focus has been on reliability, validity, and responsiveness to change. For example, there is limited evidence on sources of variation of HRQoL, including gender (Cherepanov et al., 2010), season (Jia and Lu- betkin, 2009), and MCCs (Chen et al., 2011). Further research is needed to determine other sources of variation (e.g., health literacy, socioeconomic factors). Moreover, the reliability of surveillance of HRQoL remains con- troversial, in need of further research (Avendano et al., 2009; Salomon et al., 2009). For specific illnesses, further qualitative research to obtain the patient’s perspective (e.g., illness intrusiveness, regrets about treatment decisions) may be needed to strengthen the validity of measures of patient- reported outcomes for measuring living well. In addition to patient-reported outcomes, little is known about the feasibility and potential usefulness of objective, longitudinal measures of functional and cognitive capacity for surveillance. Finally, in the context of health care reform and concern about costs, the burden and costs versus benefits of surveillance of patient-

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219 SURVEILLANCE AND ASSESSMENT reported outcomes and other individual-level measures will need to be determined. Health Organization Level A major component of the transformation process currently under way in the U.S. health care system is comparative effectiveness research (Conway and Clancy, 2009; Dougherty and Conway, 2008); although this research agenda is focused on health care interventions, chronic disease surveillance is the first step in the intervention process. Ongoing health ser- vices research is needed to assess the effectiveness of surveillance methods using the EMR (Kmetik et al., 2011) and of methods for public reporting of health care organization performance (Mukamel et al., 2010). Chassin and colleagues (2010) recently reviewed the current status of surveillance measurement of hospital performance and outline four criteria for account- ability measures, which provide a framework for future research focused on surveillance of health care organizations: (1) there is a strong evidence base showing that the care process leads to improved outcomes; (2) the measure accurately captures whether the evidence-based care process has, in fact, been provided; (3) the measure addresses a process that has few interven- ing care processes that must occur before the improved outcome is real- ized; and (4) implementing the measure has little or no chance of inducing unintended adverse consequences. The rising costs of health care and wide geographic variation in health care costs has increasingly focused attention on determining the value of health care. However, there are a number of challenges in estimating the cost of disease (Rosen and Cutler, 2009), which will require ongoing research. The rapid growth in new technology and research evidence, combined with gaps between evidence and practice, along with unsustainable growth in health care costs has focused nationwide attention on innovative, col- laborative efforts to “learn about the best uses of new technologies at the same rate that it produces new technologies”—termed the rapid learning health system (IOM, 2007, p. 210). The foundation for the learning health care system is large EMR research databases (IOM, 2007), and current examples of where these research databases are being used for health care improvement include the Veterans Administration and Kaiser Permanente. Population Level The general surveillance-related research needs at the population level are similar to the needs described for the individual and health organiza- tion level, including the reliability, validity, and responsiveness to change of surveillance measures. At the population level, these measurement charac-

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220 LIVING WELL WITH CHRONIC ILLNESS teristics will apply to the social and environmental determinants of living well, including policies and regulations. Summary Because of the complex determinants of living well, including factors at the patient, health care system, and population levels, surveillance to drive and monitor the effectiveness of public health action to reduce dis- ability and improve function and quality of life requires multidimensional longitudinal measurement. In addition to fundamental research on mea- surement reliability, validity, and responsiveness to change, overarching questions remain on what measurements are minimally needed for effective surveillance and how frequently data collection should be performed. At the patient level, recent population-based studies suggest that relatively simple individual-level measures of life satisfaction (Strine et al., 2008) and well-being (Gerstorf et al., 2010) are associated with a number of health outcomes; when measured longitudinally, a rapid slope of decline is predictive of mortality 3 to 5 years before death (Gerstorf et al., 2010). Further research is needed, but these results suggest that surveillance using a composite of relatively simple measures of life satisfaction and well-being, combined with measures at the health care system (e.g., access) and popula- tion (e.g., policies) levels, may be useful for monitoring the effectiveness of health care and public health interventions to promote living well among patients with chronic illness. CONCLUSION The committee’s statement of task asks “How can public health sur- veillance be used to inform public policy decisions to minimize adverse life impacts?” This question can be answered first by considering the types of public policy decisions that can affect the quality of life among persons liv- ing with chronic illness. Surveillance is the first step in the change process to drive interventions to address gaps for patients with chronic illnesses to live well and to improve the nation’s health and economic well-being by reducing disability and improving quality of life and functioning. This shift in focus from only extending life to living well has the potential to facili- tate decision making at the individual, health care system, and population levels that optimizes outcomes not only for patients and families but also for society. For example, advanced care planning using evidence from surveil- lance on prognosis and options enables patients and families to make more informed decisions that improve satisfaction and quality of life and reduce suffering (Curtis, 2011). Although this evidence may be obtained

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221 SURVEILLANCE AND ASSESSMENT from clinical trials, longitudinal population-based surveillance of patients with chronic illnesses provides the strongest external validity. Moreover, this enhanced decision making is associated with lower health care costs (Morrison et al., 2011). For the second question—What consequences of chronic diseases are most important to the nation’s health and economic well-being?—the re- sponse is more complex and cannot be limited only to consequences, be- cause of the multiple determinants of chronic disease. Therefore, measures used for surveillance of living well must be multidimensional and include measures of determinants of health at the individual, health system, and population levels as well as health-related outcomes (e.g., patient-reported outcomes, functioning) that are relevant to patients living well. More- over, these individual-level consequences should be associated with lower societal costs. Finally, it must be feasible to collect these data elements longitudinally. If providers and communities are going to be rewarded for prevent- ing and controlling chronic disease, better data systems are needed. These data systems should draw on individual-level data (e.g., from the electronic health record) and include three types of information: • Risk of chronic illness (e.g., through a health risk appraisal) • Presence of chronic illnesses • Measures of quality of life and functioning Risk factor information is needed in order to reward chronic disease prevention efforts. Providers who invest in clinical or community-based prevention programs are unlikely to see the outcomes in lower rates of chronic diseases given the low incidence and lag between interventions and outcomes. However, these interventions can produce short-term changes in behaviors and other risk factors. Information about chronic diseases should be collected on all persons. Some of this information can be obtained from electronic health records. However, signs and symptoms of chronic illnesses are often lacking in health care systems and should be collected as part of a health assessment. Finally, information on disability and quality of life should be collected at the individual level. A variety of methods exist, but standard definitions are needed (similar to the risk factor definitions developed for the BRFSS) to permit comparison between communities and over time (NRC, 2009). The technology exists today to collect comprehensive health informa- tion on everyone living in a community. However, there are a number of barriers to be overcome, such as budget constraints, organizational inertia for prioritizing/replacing existing methods, lack of sharing agreements, lack of incentives, lack of standard definitions, and limitations of workforce

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222 LIVING WELL WITH CHRONIC ILLNESS capacity. Decision-making processes are needed to optimize surveillance ac- tivities despite limited resources. Research and demonstration projects will be needed to address the many barriers to enhancing surveillance systems to promote “living well.” Grants could be provided to first demonstrate the feasibility of collecting this information in large health systems, and eventually in entire communities. Ultimately, priorities for surveillance need to be driven by a number of factors, including the burden of illness (e.g., frequency, disability, costs) for individuals and society, and availability of cost-effective interventions. RECOMMENDATIONS 16–17 The committee provides two recommendations to address the question how can public health surveillance be used to inform public policy decisions to minimize adverse life impacts. Recommendation 16 The committee recommends that the secretary of HHS encourage and support pilot tests by health care systems to collect patient-level infor- mation, share deidentified data across systems, and make them avail- able at the local, state, and national levels in order to monitor and improve chronic illness outcomes. These data should include patient self-reported outcomes of health-related quality of life and functional status in persons with chronic illness. Recommendation 17 The committee recommends that the secretary of HHS establish and support a standing national work group to oversee and coordinate multidimensional chronic diseases surveillance activity, including ob- taining patient-level data on health-related quality of life and functional status from electronic medical records and data on the implementa- tion and dissemination of effective chronic disease interventions at the health care system and the community level, including longitudinal health outcomes. REFERENCES Abrams, T.E., M. Vaughan-Sarrazin, V.S. Fan, and P.J. Kaboli. 2011. Geographic isolation and the risk for chronic obstructive pulmonary disease–related mortality. Annals of Internal Medicine 155(2):80–86.

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