4

Surveillance and Research

Surveillance is one of the cornerstones of public health, and it provides the basis for public health decision making (Lee et al., 2010). The World Health Organization defines surveillance as “the continuous, systematic collection, analysis and interpretation of health-related data,” and notes that surveillance is critical for determining and monitoring the incidence and distribution of a health issue (WHO, 2016). Sound decisions about public policy and the prioritization of resources depend on having relevant and timely surveillance data. Surveillance of vision impairment and eye health is limited, however, and the lack of adequate surveillance has a substantial impact on public health efforts to address vision problems. The inability to detect and monitor prevalence and trends in the impact of vision impairment and blindness across the United States makes it difficult to characterize the burden of poor eye health among population groups and geographic locations and to then recommend specific effective interventions. A systematic and ongoing collection of relevant data about risk factors, the determinants of visual health, care practices and related health outcomes is needed to determine the nature and extent of the public health burden of eye disease and vision loss; to identify risk factors and at-risk populations; to discover disparities in access, care, and outcomes; and to tailor interventions to the needs of the public (West and Lee, 2012). This report has emphasized the need to prevent, correct, and slow the progression of eye disease; surveillance measures that focus only on an endpoint of vision impairment and blindness without regard to clinical measures that allow for identification of staging of diseases from early to advanced will miss opportunities to halt progression. A surveillance system for visual

health may also serve as a valuable source of data for etiologic studies (CDC, 2011a).

Research, which builds on surveillance, includes epidemiologic, behavioral, and laboratory research (Lee et al., 2010). Surveillance data can be used to suggest hypotheses that can be tested in research studies. Typically, such studies about eye health focus on the natural history of eye disease and vision loss, the risk and protective factors for eye disease and vision loss, and comorbidities. From a population health perspective, the role of the social determinants of eye health is important to understand. The relevant social determinants include socioeconomic, cultural, and environmental conditions, as well as social and community networks (Dahlgren and Whitehead, 1991).

Vision impairment and blindness are appropriate targets for surveillance and research because they adversely affect a large portion of the population, affect populations unequally, can be reduced by treatment and preventive efforts, and will become an increasing burden as the population ages (Saaddine et al., 2003). Eye health can be both an ultimate, or an intermediate outcome. It can be an endpoint that results from a malfunction or compromised physiological function of the eye or visual system. In other cases, reduced vision is an intermediate outcome that can signal the presence of underlying or co-existing diseases and conditions. Understanding the risk and protective factors (including treatment), the progression of eye diseases, related outcomes, and disparities associated with eye health for specific populations can, therefore, be used to reduce vision impairment and blindness more efficiently and to improve the overall management and treatment of diseases and conditions that affect population health. The determination of surveillance and research priorities should be similar to those used by Healthy People or Leading Health Indicators.

Surveillance and research on eye health and vision impairment can be used for a number of purposes, including

• Estimating the magnitude of the problem of vision impairment and the preventable burden;
• Understanding the natural history of eye disease from a population health perspective;
• Understanding how specific interventions halt the progression of disease from early to advanced stages;
• Understanding the relationship between risk factors and eye health outcomes;
• Documenting the existence of social determinants and policies affecting visual health;
• Evaluating prevention and control strategies;
• Detecting changes in health practice;

• Assessing the quality of eye care;
• Assessing the impact of occupational health and safety devices;
• Assessing the unmet need for timely care for specific disorders/conditions;
• Planning public health actions and the use of resources; and
• Identifying research needs.

This chapter identifies current surveillance and research methods, including surveys, population-based studies, patient information systems, and meta-analyses, and describes the strengths and limitations of each method. The chapter explores the challenges in obtaining data about eye diseases and vision impairment, and identifies opportunities to improve surveillance and research.

SURVEILLANCE

There is no current surveillance method that accurately and comprehensively measures the total burden of visual impairment and eye disease. Existing data collection methods and tools that are used to measure other health topics often ignore eye health, despite the available options for incorporating eye health measures into the data systems and tools that are used for other purposes. In addition, there is no single surveillance system that allows for the monitoring of prevalence and trends in eye health indicators within and between population groups. Vision health is a critical part of overall health, and the nation’s health information system should be able to monitor its epidemiology and treatment patterns in order to improve public health practice.

Surveys

Numerous health-related surveys conducted by the federal government collect information about vision and eye health. (See a sampling of these surveys in Table 4-1.) Several state and regional surveys, such as the California Health Interview Survey, also include questions about eye exams and severe vision problems. These surveys use a variety of methods. For example, the Behavioral Risk Factor Surveillance System (BRFSS) employs a telephone-based survey (either by landline or cell phone), the National Ambulatory Medical Care Survey (NAMCS) uses information collected by clinicians during patient visits, and the National Health and Nutrition Examination Survey (NHANES) consists of both in-person interviews and physical examinations. Although the vision-related questions vary among surveys, most collect relatively little information about vision and eye health as part of the core instruments. The BRFSS, which collects state-level

TABLE 4-1 Select U.S. Department of Health and Human Services Nationwide Population and Organizational Surveys with Vision Components

data and samples about 400,000 people annually, asked one vision question in the 2014 core questionnaire: “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” An optional diabetes module that has questions about eye exams and whether diabetes has affected the respondent’s eyes, and an optional vision module that was used from 2005 to 2010 asked about difficulty seeing, the receipt of eye exams, and diagnosed eye diseases. The National Health Interview Survey (NHIS) collects nationally representative data on nearly 100,000 respondents, using questions similar to BRFSS. A vision supplement was administered in 2002 and 2008. NHANES, which collects data on a representative sample of about 5,000 participants, is the only national survey that includes an objective, clinical measure of vision impairment (visual acuity of 20/50 or worse in the better-seeing eye) (Vitale et al., 2006). In addition, the NHANES examination has at times included components designed to measure the presence of eye diseases and conditions. For example, the 2005–2006 examination utilized two ophthalmological components in addition to vision measurements. Frequency doubling technology was used to test for visual field loss from diseases such as glaucoma, while the retinal imaging exam tested for retinal conditions such as diabetic retinopathy and age-related macular degeneration (AMD) (CDC, 2015e). However, NHANES has not conducted eye examinations or asked eye-related questions since 2008 (CDC, 2015e). In addition to information about vision and eye disease, these surveys collect demographic information and data about comorbidities and health risk factors.

These surveillance systems and the data collection instruments they use are powerful tools for measuring the prevalence of vision impairment and

eye disease, along with the associated risk factors and outcomes. Because surveys use representative and often large samples, the results can be generalized to the noninstitutionalized population. Because the surveys are usually conducted annually, the resulting data can be used to document trends over time. However, there are limitations to these data: the metrics vary among surveys, most surveys rely on self-reported data, data on vision and eye disease are collected and reported relatively infrequently, and few surveys can provide information at a local level. Because of these limitations, a survey that produces vision-related data that is timely, representative, and standardized remains an unmet goal in advancing eye health.

The following methodological issues constitute the key challenges in constructing a national surveillance strategy. First, the metrics used to assess vision impairment and eye disease vary among surveys. There are no standardized survey questions to measure vision or eye health, and, as a result, estimates of vision impairment and prevalence of eye diseases vary significantly. Surveys may use objective clinical measures (NHANES) or self-reported assessments (BRFSS, NHIS). Surveys may ask a general question such as “Do you have serious difficulty seeing?” (BRFSS core questionnaire), or may use specific scenarios to assess distance and near vision acuity, such as whether the respondent has difficulty reading the print in a newspaper or recognizing a friend across the street (BRFSS vision module). Publications that use these data to define visual impairment may use any of these metrics or a combination of them. The eye conditions that are studied in these surveys are generally limited to AMD, diabetic retinopathy, cataracts, and glaucoma, even though there are other common and disabling conditions (Wittenborn and Rein, 2016).

Because the survey questions, definitions, and measurements differ across surveys, it is challenging to compare visual impairment and eye disease estimates across surveillance systems. As a result, simple questions such as “How many people are blind?” are difficult to answer, and the estimates vary widely (Wittenborn and Rein, 2016). In addition to the issue of the standardization of questions to measure eye-related conditions, NHANES, BRFSS, and NHIS pose demographic and comorbidity-related questions in ways that may prompt different answers from the same individual (Zambelli-Weiner et al., 2012).

Second, most surveys rely on self-reported answers to a small number of questions. While these types of less resource-intensive surveys yield a large, representative sample size, self-reported information can be unreliable, especially because vision impairment is difficult to assess via survey questions. For example, the BRFSS vision module provides five options for reporting the degree of difficulty in reading a newspaper or seeing a friend across the street: no difficulty, a little difficulty, moderate difficulty, extreme

difficulty, and unable to do. It may not be easy for respondents to choose an answer from a spectrum of responses, and evidence suggests that individuals are not good at assessing their own vision (Wittenborn and Rein, 2016). The data gleaned from these survey responses provide an estimate of the prevalence of visual impairment, but the estimate is likely not as precise as would be an estimate obtained from a surveillance tool that uses clinical examinations to measure visual impairment.

Third, the frequency of data collection and reporting makes some surveys less suitable for recognizing trends or evaluating changes due to interventions. For example, NHANES data are collected annually but datasets and estimates are released only every 2 years in staggered amounts to accommodate the volume of data (CDC, 2015g). NAMCS and the National Hospital Ambulatory Medical Care Survey (NHAMCS) disseminate reports 24 months after data have been collected. Vision-specific data are not routinely or frequently collected. For example, NHIS had a vision supplement only in 2002 and 2008, NHANES has not consistently collected vision data since 2008, and the BRFSS vision module was discontinued after 2010.

Patient Data

Electronic Health Records

An enormous potential for surveillance exists in electronic health records (EHRs) from clinical providers. These records provide the most detailed health care data available, including examination and testing outcomes, diagnoses, procedures conducted, and services utilized (Rein, 2015). Public health programs in particular have the opportunity to benefit greatly from this newly available electronic health information, which has the potential to become a crucial component of surveillance. Public health practitioners need to leverage this electronic revolution by being innovative in their use of new and existing electronic data sources. For health departments, the use of EHR systems may be the next frontier in chronic disease surveillance (Maylahn, 2013). However, while EHRs present a huge opportunity to understand local conditions, access to these databases is often limited and can be proprietary. Local health department staff may not be able to make use of the data or may not have staff who can spend time analyzing the data.

The American Academy of Ophthalmology launched an ophthalmology electronic clinical registry, IRIS^TM (Intelligent Research in Sight), in 2014. The IRIS registry collects uniform data, both clinical and patient-reported information, that can be analyzed to inform clinical decision making. IRIS captures data from 80 percent of U.S. ophthalmology practices

(Parke, 2015). As of January 2016, the IRIS registry represented data from 14 million unique patients (Parke et al., 2016).

Similarly, the American Optometric Association’s Measures and Outcomes Registry for Eyecare (MORE), a clinical data registry launched in 2015, integrated EHR data from multiple systems to facilitate reporting for Centers for Medicare & Medicaid Services’ (CMS’s) Physician Quality Reporting System, and created an evidence base for future clinical decisions and research directions (AOA, 2015).

CMS has approved both IRIS and MORE as Qualified Clinical Data Registries, making substantial data available not only for quality reporting and CMS initiatives such as the EHR incentive program or the Value-based Payment Modifier program, but also for research utility through tracking outcomes and comparing clinical practice data (CMS, 2016a). However, Rein has cautioned that differences may exist between the clinical practices represented in the database and other populations of interest (i.e., populations without access to treatment) (Rein, 2015). In addition to IRIS and MORE, there are other commercial and not-for-profit sources of EHR data available, such as Epic Systems, though few of the data are related to vision or eye health (Rein, 2015).

Administrative Claims

Administrative claims from private insurers, Medicare, or Medicaid are a source of surveillance data about vision and eye-related health status and care utilization. Claims data rely on billing codes that indicate whether a service or procedure was performed or whether a patient received a diagnosis. These data are limited by the lack of detail inherent in billing codes and the possibility of human error in coding (Rein, 2015). Diagnostic codes may be too broad or too narrow, leading to under- or over-reporting of eye conditions (Elliott et al., 2012). However, administrative claims have many advantages versus other data sources. Data may be available over time for the same patient, provided that he or she remains enrolled in the same insurance plan (Rein, 2015). Large care providers, such as CMS or the U.S. Department of Veterans Affairs, have data that are representative of the subpopulations they serve (Elliott, 2012). Data from private insurers are not necessarily representative of the general population, but there are a few sources of data that include many patients. These include the MarketScan^TM research databases, which are commercial products offering fully integrated patient-level data from more than 170 million patients, and claims from Vision Service Plan (VSP) vision and eye care insurance, which include acuity readings and cover 77 million patients (Truven Health Analytics, 2016; VSP, 2016).

Medicare data in particular have utility for surveillance. Medicare covers nearly all of the over-65 population. Medicare claims provide longitudinal data, because people generally stay enrolled in Medicare from age 65 on. The large amount of data available in Medicare claims allows for examination of lower-incidence conditions (Elliott et al., 2012). Medicare data have been used for a number of studies, including an analysis of 9 years of data to estimate how many beneficiaries with diabetes and chronic eye diseases receive annual eye exams (Lee et al., 2003).

Patient information from EHRs or administrative claims as a source of surveillance data has an advantage over data from surveys or research studies because it is real-world data collected in real time: it captures diagnostic information, how care is actually being provided, and the outcomes of that care (Elliott et al., 2012). However, these data are only collected when patients present themselves for care and therefore do not include information about the people who cannot or will not use clinical care. In addition, no one source of patient data covers the entirety of eye care. Services may be provided by optometrists, ophthalmologists, or general practitioners, and vision care is often paid for out of pocket or with separate vision insurance (Wittenborn and Rein, 2016). This division among multiple health and payment systems means that in order to get the full picture of eye care, patient data will have to be gathered from multiple sources.

Challenges

Although there is considerable quantitative and qualitative information about vision impairment and eye health available from myriad sources—surveys, EHRs, and administrative claims—each of these sources has its limitations. There is no existing surveillance system that systematically collects data to track prevalence, the incidence of new cases, and disparities in vision health in order to identify the causes of these disparities, to determine the stage of eye disease progression, and to develop and “monitor public health initiatives, programs, and policies aimed at reducing the burden of visual impairment and eye disease and eliminating existing disparities” (CDC, 2011a, p. 8). The lack of a comprehensive system is a major impediment to identifying and addressing the challenges and opportunities for public health action.

Vision and eye health surveillance is constrained by a number of overarching challenges. There are problems with data quality and collection, including a lack of standardized definitions to allow comparisons. Some populations—including groups that may be most at risk—are not captured by current surveillance methods. Technical and organizational challenges impede the integration of data sources and surveillance systems. Finally,

there is a lack of sustained support for surveillance activities at the federal level, and very limited support at state and local levels.

Scope and Quality of Data

Vision surveillance suffers from the limited scope of data that are available at the national, state, and local levels. National and state surveys and patient data systems collect some information about the presence of eye disease and vision impairment, but they fail to measure many other factors linked to visual impairment or eye disease. None of the surveillance methods discussed above measures the total burden of vision impairment and blindness, which includes the extent of visual acuity loss; the presence and stage of specific eye disease, comorbidities, and health-risk behaviors; social or physical limitations imposed by low vision; quality of life (QOL) measures; and the existence of barriers to accessing health care and rehabilitation services. Most surveillance focuses primarily on prevalence and incidence, and instruments do not usually measure factors such as access to care, barriers to the use of care, health literacy, or access to assistance, all of which may greatly affect the health and QOL of a person with visual impairment or eye disease (Wittenborn and Rein, 2016).

The lack of standardized measures of vision impairment, eye health, and related health indicators also impairs the collection and analysis of surveillance data. No gold standard method of measuring vision impairment or the presence of eye disease has been developed or evaluated (Crews, 2015). Furthermore, current evaluation methods fall short of providing a comprehensive understanding of the impact of vision impairment and blindness. For example, the most commonly used instrument to assess vision-related QOL in the eye health literature is the National Eye Institute Visual Function Questionnaire (VFQ-25), although many other measurement approaches and instruments are in use (Heintz et al., 2012; Hirneiss, 2014; Mangione et al., 2001). Psychometric flaws have been noted concerning the VFQ-25 in its original structure, and other versions have been developed (Kowalski et al., 2012; Pesudovs et al., 2010). While generic QOL instruments capture some aspects of vision-related QOL, scales that only include items related to visual acuity or central vision are unlikely to capture the QOL impact of visual field deficits.

Consequently, surveys do not use the same metrics, and the ones that they use are not necessarily correlated with the clinical definitions coded in EHR and claims data. Standardized measures are difficult to implement in part because vision loss is not an all-or-nothing concept. The loss of sight occurs across a continuum, with varying degrees of impairment and functionality. Vision may be assessed with any number of measures, including acuity, contrast sensitivity, visual field, or night vision (Wittenborn and

Rein, 2016). There is no consensus on how vision impairment should be defined, so it is measured in different ways in different surveillance instruments (Wittenborn and Rein, 2016). Likewise, eye conditions can vary from minor to fatal and can be short-lived or chronic; this complexity makes it difficult to identify which conditions to include in surveillance and how. Because visual impairment and eye disease are often undiagnosed, a surveillance instrument that relies on self-reported diagnosis to identify people with eye disease may underestimate prevalence (Wittenborn and Rein, 2016). One population-based study analyzing the agreement between self-reported data and medical record for prevalence of eye disease and eye care utilization suggested national estimates tended to underestimate rates of eye disease and overestimate eye care utilization (MacLennan et al., 2013).

Limited Local Data

Although national surveillance collects information about vision impairment and eye health, there are limited data available at the local level, where interventions and policy decisions are often developed and implemented. The BRFSS provides state estimates, but the data are not broken down further by county or municipality, while other national surveys such as NHIS and NHANES provide only national or regional estimates (IOM, 2011). Increasingly, states and local communities are investing resources for data collection at the county level or in local jurisdictions. This trend has accelerated with the new requirements in the Patient Protection and Affordable Care Act of 2010 that tax-exempt hospitals must conduct an assessment of the health needs of their catchment areas and begin to address them (Rosenbaum and Margulies, 2011). This requirement builds on current best practices of hospitals and hospital systems, which include the strategic investment of resources and the building of community partnerships, including local groups and leaders in public health, with the goal of improving overall community health. Every 3 years, hospitals must conduct community health needs assessments (CHNAs). These coincide with assessments done by local health departments and others in the community. The goals of these assessments are to “identify existing health care resources and prioritize community health needs” (Stoto, 2013, p. 4). Each hospital also must develop an implementation strategy to meet the needs identified by its CHNA and a set of performance measures to track progress (Stoto, 2013). These actions must be reported to the Internal Revenue Service using Schedule H of Form 990 as an obligation for tax exemption. At the same time, the Public Health Accreditation Board established prerequisites of national accreditation for local health departments, including having completed a community health assessment, community health improvement plan, and

an agency-wide strategic plan within the past 5 years (CDC, 2016d; PHAB, 2016; Riley et al., 2012). Data about the risk factors for eye disease and vision loss could be collected as part of these assessments, but eye-related issues must compete against the constellation of other public health problems present in communities.

One opportunity to improve the availability of local data involves combining data from community surveys with data from state and local surveillance systems to characterize patterns of risk, health outcomes, and relevant determinants. An example is the public health surveillance of cancer, in which data about cancer risk factors can be supplemented with information from cancer registries. Using geographic information system methods, data from different sources can be linked to help guide local cancer screening and treatment programs targeting high-risk or underserved communities (Birkhead and Maylahn, 2010). For vision surveillance, survey estimates of vision indicators can be augmented with clinical information about eye disease to produce a more complete picture of the eye health in a local community.

Availability of Data

Because public health surveillance is motivated by the public’s concerns about a health issue, its impact on various groups, and what can be done to address it, surveillance data need to be made available, analyzed, and disseminated in a timely manner. However, as discussed above, some surveys collect and disseminate data infrequently. Furthermore, data on eye and vision health are often inaccessible to those who are not subject-matter experts, and it may be difficult for a policy maker or member of the public to easily find accurate estimates of vision impairment or eye disease (Wittenborn and Rein, 2016).

Populations Not Captured

The surveillance of visual impairment and eye disease, whether conducted by surveys or patient information databases, inevitably fails to include or represent some groups of people. For example, surveillance that relies on patient data, as found in EHRs or administrative claims, excludes people who are outside of the traditional clinical health system and who may be uninsured, underserved, or part of at-risk populations. People who cannot speak English may be underrepresented in surveys. While most national surveys that are conducted via interviews accommodate non-English speakers through the use of translators or bilingual interviewers, many of these services are restricted to the Spanish language or are only available in areas containing large numbers of non-English speaking respondents (Islam et al.,

2010). Low-resource communities—including rural, homeless, and undocumented populations—may be excluded from surveys and studies because they are difficult to contact. For example, the BRFSS, because it collects data by telephone, necessarily samples only those people who have access to a telephone. There are myriad ways in which surveillance methods fail to capture some populations; new or improved surveillance instruments should be designed to mitigate this as much as possible.

Lack of Integration Between Clinical and Public Health Data

Data from surveys and other population-based surveillance conducted by federal, state, and local public health agencies can contribute relevant information about eye health, especially for the country as a whole. Likewise, there is an abundance of data collected during patient health care visits that can be accessed from EHRs or administrative claims. Integrating these surveillance systems would allow for a more comprehensive picture of visual health. However, major constraints prevent this information from being aggregated or shared to create richer and more useful data. For example, there are technical constraints, including software incompatibility and lack of interoperability of datasets. Commercial and legal constraints affect data sharing and availability. Perhaps most importantly, coordination and collaboration among surveillance actors are limited. As a result, the nation lacks standard metrics for measuring vision impairment and eye health, there are limited sources for baseline data that can be used in surveillance and research, and significant hurdles stand in the way of integration or sharing between surveillance systems. Data that could be used to improve public health practice and clinical care are instead fragmented and siloed (CDC, 2011a).

Modest Funding Support

Vision and eye health surveillance efforts have received only modest attention and support from federal agencies. Several federal programs focus on eye health, including the National Eye Institute (NEI) and the National Eye Health Education Program (NEHEP) at the National Institutes of Health (NIH), and the Vision Health Initiative (VHI), which is housed in the Division of Diabetes Translation at the Centers for Disease Control and Prevention (CDC). These programs tend to emphasize research and education rather than surveillance activities. More recently, the NIH has begun to focus more heavily on genomics and personalized medicine than on population-level health. While these NIH research priorities are valuable in generating new knowledge, they may not offer the same potential impact as population-wide interventions and more traditional public

health approaches to disease, which include the monitoring and tracking of diseases to identify populations at risk and how interventions affect subgroups.

Over the past several years, funding for eye-related activities at the NIH and the CDC has remained fairly constant or has dropped slightly. The NEI funding,¹ representing about 2 percent of all NIH funding, has remained around $700 million since 2010 (NIH, 2015). At the CDC, since 2010 the annual budget for the VHI has declined in both total amount and as a percentage of the overall CDC budget. The proportion of the VHI budget allocated to glaucoma-specific activities has remained relatively constant—at 86 percent—since 2011 (CDC, 2011b, 2012, 2013, 2014, 2016b). Funding and staffing for community surveillance activities is limited as well. Funding for local health departments has been cut on the federal, state, and local levels in recent years, and as a result, thousands of public health jobs have been eliminated (NACCHO, 2015).

However, integrating vision and eye components into the Multi-Ethnic Study of Atherosclerosis (MESA) was a way to “conduct surveillance by leveraging a relatively small investment of funds to build on an extensive existing study infrastructure” (Cotch, 2016, p. E1). MESA was originally designed as a multicenter community-based study on cardiovascular disease, risk factors, and outcomes in four different ethnic and racial groups (MESA, 2016a). Retinal photography and a refraction assessment were added to the study, and the inclusion of these components introduced access to the data collected from the rest of the study on comorbid conditions, genetic profiles, behaviors, and multiple health outcomes, with the potential of providing a broader, comprehensive picture of health (Cotch, 2016; MESA, 2016b). Research from the initiative included a study that published prevalence rates of AMD by age, gender, and four racial and ethnic groups using objective, clinical measures as the means to collect data (Klein et al., 2006).

RESEARCH

Surveillance is used to collect essential data on the prevalence of vision impairment and eye disease, the presence of risk factors, and details about the populations affected. Research builds on this data but is distinct in that it may include additional data on the causes and risk factors associated with vision impairment and eye disease, such as socioeconomic status, the presence of comorbidities, and access to eye care, and it may be used to

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¹ Funding reported is the actual amount from the Congressional Appropriations, prior to obligations for HIV research and transfer costs.

identify correlations or causation between these factors and disease. Surveillance can lead to the identification of research gaps and helps justify the resources for research (Lee et al., 2012). As discussed earlier in this report, there is an urgent need for more research on vision health, including basic, clinical, applied, and translational research. Particularly important for the health of the population will be research that elucidates the effectiveness of population health interventions in preventing and reducing eye disease by addressing the underlying social determinants of health and primary and secondary prevention. Research can be performed by collecting original data, or through an analysis of existing data.

Types of Research

Analysis of Surveillance Data

Surveillance data, such as data from surveys, can be used to answer research questions by combining data from studies that compare vision and eye disease to other factors such as socioeconomic status, race and ethnicity, or geographic area. For example, Chou and colleagues (2012) used several years of BRFSS data in order to assess the prevalence of annual eye care among diabetic adults who were visually impaired, analyzed by state, race and ethnicity, education, and annual income. Participants were defined as “visually impaired” if they indicated that they had moderate or severe difficulty reading print or recognizing a friend across the street. Zhang and colleagues (2012) combined data from NHANES and NHIS in order to explore disparities in vision health by race and ethnicity, education, and economic status. Kirtland and colleagues (2015) analyzed American Community Survey data to describe the geographic pattern of severe vision loss and to assess its association with poverty level. These types of analyses allow researchers to stretch the utility of survey data by combining and comparing data across years and surveys. However, the ability to conduct meta-analyses is constrained because of the limitations of surveys discussed above: metrics that vary from survey to survey, reliance on self-reported data, and infrequent data collection and reporting.

Population-Based Community Studies

Population-based community studies are the “backbone of eye disease epidemiological knowledge” (Wittenborn and Rein, 2016). This type of research collects detailed information about a specific population. The population can be defined in a variety of ways—for example, by geographic area (e.g., Framingham Heart Study or Baltimore Eye Survey), ethnic group (e.g., Chinese American Eye Study), or occupation (e.g., Nurses’ Health

Study)—and the population may be studied either in its entirety or by a representative sample. Population-based community studies can be either ongoing and long-term, or one-time assessments of a population.

Ongoing population-based community studies can provide valuable insight into the life course of a disease and the relationship of risk factors to disease outcomes. These prospective cohort studies collect a wide variety of data over a period of time from the same population. One of the longest-running eye studies is the Beaver Dam Eye Study, which began in 1988 with baseline examinations of nearly all of the 43- to 84-year-old residents of Beaver Dam, Wisconsin (about 5,000 initial participants) (Beaver Dam Eye Study, 2014). Researchers conducted follow-up examinations every 5 years on all cohort members (more than 1,900 in each follow-up period), resulting in more than 300 publications of the study findings. The study observed the natural history of several eye diseases, tracked the decline in vision as participants aged, and measured the relationship between eye conditions and long-term exposures such as blood pressure levels (Beaver Dam Eye Study, 2014). The Rotterdam Study in the Netherlands, another population-based community cohort study, has been collecting information on thousands of residents of Rotterdam since 1990 and is focused on cardiovascular, neurological, endocrine, and ophthalmological diseases (Hofman et al., 2007).

In addition to these long-term community studies, researchers also conduct one-time assessments of specific populations. For example, the Chinese American Eye Study sought to understand the prevalence of visual impairment and eye disease in Chinese Americans living in Monterey Park, California. Each of the 4,570 participants completed a questionnaire about health- and eye-related behaviors, risk factors, and their QOL, and underwent a clinical examination (Varma et al., 2013).

The main limitation to population-based community studies is the potential for differences between the study population and the general population and thus a lack of generalizability (IOM, 2011). Because the population studied is usually a specific group of people, rather than a representative sample of the general population, data about prevalence or risk factors may not be applicable to people outside the group. Moreover, respondents may differ from nonrespondents even in the population studied. However, these types of studies also have significant benefits. The data are usually based on examinations rather than on self-reporting, so prevalence and incidence numbers may be more accurate (Wittenborn and Rein, 2016). Using a prospective cohort allows researchers to draw correlations between disease outcomes and behavioral or environmental factors, while minimizing selection and recall bias (Kukull and Ganguli, 2012). In addition, in those cases when the participation of the cohort is ongoing, researchers can return to the cohort repeatedly for clinical examinations,

biomarker measurements, or new areas of investigation. This study method can be optimal for collecting information about a minority group. For example, because the Chinese American Eye Study selected its participants from the U.S. city with the highest percentage of Chinese Americans, it was less time- and resource-intensive than drawing a representative sample of Chinese Americans from across the United States. While the results may not be generalizable to the entire U.S. population, the information may be used to estimate prevalence and health issues in the U.S. Chinese American population using census data (Rein, 2015).

Meta-Analysis of Population-Based Studies

The meta-analysis of population-based studies can provide insight into links between vision and eye health and other factors. The findings of individual population-based studies may not be applicable to the general population; however, if the results can be replicated across multiple studies, the validity and generalizability are strengthened (Kukull and Ganguli, 2012). In order to replicate findings, the studies must use the same standardized measurements and outcomes so that the data can be compared. The Beaver Dam Eye Study developed an imaging system and a standardized scale for the severity of certain eye conditions that were subsequently used in several other population-based community studies. As a result, it has been possible to compare data among these multiple studies and draw more robust conclusions from the findings. For example, the Beaver Dam Eye Study found an association between smoking and the development of cataract and AMD. Two other ongoing studies using the same system and scale—the Blue Mountains Eye Study in Australia and the Rotterdam Study in the Netherlands—found the same correlation, despite differences in the populations (The Board of Regents of the University of Wisconsin System, 2014). Similarly, a collaborative study between the Multi-Ethnic Pediatric Eye Disease Study and the Baltimore Pediatric Eye Disease Study used the same protocol and measurements to collect information on vision impairment from more than 10,000 children. The population studied lived in Los Angeles or Baltimore and included large percentages of minority children (44 percent African American and 32 percent Hispanic) (NEI/NIH, 2011). By using the same protocol on a large, diverse study population, researchers increased the generalizability of the results.

Vision Problems in the U.S. (VPUS), a project of Prevent Blindness America, is a meta-analysis of 12 population-based studies, including the 3 discussed above. VPUS uses the data from these studies, along with census data, to develop age-, race-, and sex-specific prevalence estimates on both the national and state levels (VPUS, 2012). The VPUS online database allows users to customize a research inquiry; for example, a user could

compare glaucoma rates in black females between three different states, or look at vision impairment by age in California. VPUS has estimates for four eye conditions (AMD, cataract, diabetic retinopathy, and glaucoma) and four categories of vision (vision impairment, blindness, hyperopia, and myopia). While VPUS provides valuable, easy-to-understand information about vision and eye disease, there are several limitations to the data. The studies on which VPUS relies may not be representative of the current U.S. population—all of the study populations were from small geographic areas and were not probabilistically sampled, 5 of the 12 studies are international, and some of the data are up to 30 years old (Wittenborn and Rein, 2016). Furthermore, the scope of the VPUS data is limited, with data on only four eye disorders and no information on rates of under-diagnosis of eye conditions (Wittenborn and Rein, 2016).

Population Health Administrative Databases

Databases that link information from different sources at an individual level can be a useful tool for population-based research. Population health databases are used to track the health of a population of interest based on agreed-upon metrics. Although these databases rarely include measures specific to eye health, they may contain measures that are risk factors for poor eye health. For example, America’s Health Rankings includes indicators on diabetes, high blood pressure, and smoking, all of which may affect eye health. These types of databases, which also include Healthy People 2020, Community Health Status Indicators, and data collected by the Trust for America’s Health, can be linked to eye-specific databases in order to answer research questions about relationships between eye health and environmental, behavioral, or other risk factors.

Challenges

Although there are many potential avenues for conducting eye-related population health research, there are several challenges. First, there is no existing research database that includes comprehensive information about visual impairment and eye health, as well as comorbidities, QOL, and other issues that are affected by and affect eye health. The major population health databases do not include information about eye health, and eye-specific databases usually do not include general health information. As discussed above, VPUS is a valuable source of research data, but it is limited by the scope of the data. While it is possible to triangulate data between databases, a truly comprehensive database would allow for more in-depth, accurate research.

Second, there is limited coordination or integration among the research efforts of federal government agencies and private players. The federal government supports basic, clinical, applied, and translational research on vision and eye health, but this support is spread across multiple agencies. Eye-related research is supported by agencies including the Health Resources and Services Administration (HRSA), CMS, and the U.S. Department of Veterans Affairs. The NIH’s support is spread across several institutes and programs including the NEI, NEHEP, the National Institute of Diabetes and Digestive and Kidney Diseases, and the National Institute of Minority Health and Health Disparities. VHI at the CDC has made an effort to create a coordinated public health framework for blindness and vision impairment and has promoted the inclusion of eye-related measures in the BRFSS and the NHANES (CDC, 2016a). However, there is no entity responsible for coordinating eye-related research, and federal budget support for eye and vision research has been fairly constant or has fluctuated. In the private sector, several organizations support population health eye research, including Research to Prevent Blindness, the Association for Research in Vision and Ophthalmology (ARVO), and the National Alliance for Eye and Vision Research. These organizations provide funding to researchers to perform basic clinical, applied, and translational research about eye disease and vision impairment. However, there is no coordinating body or national agenda to help integrate and prioritize these activities, and thus each agency or organization operates independently. On the local level, public health departments suffer from low funding, a plethora of competing demands, and a lack of coordination and leadership.

OPPORTUNITIES

Surveillance System

The United States needs a well-designed surveillance system that continuously collects, analyzes, and reports on population data in a standardized, timely, and continuous manner that accurately represents the population of interest (Lee et al., 2012; West and Lee, 2012; Zambelli-Weiner et al., 2012). Building this system from the local jurisdiction up would help ensure that the work of local, state, and federal public health agencies is coordinated and focused, and that the insights derived from the system can be widely and effectively disseminated. Standardization of the definitions, data elements, and collection methods would allow for comparisons among datasets that could validate the surveillance measures, strengthen evidence, and expand findings. More timely dissemination of surveillance data would enable decision makers to deploy resources in an expedient manner to better address the needs of at-risk populations. Representative sampling is essential to the

methodological rigor and accuracy of surveillance findings, and it allows public health professionals to design appropriate and targeted interventions.

Steps have been taken to begin the process of creating a Visual Health Surveillance System. The CDC awarded a grant to investigators at NORC at the University of Chicago to develop a system that will produce new estimates for important eye health risk factors and outcomes including the use of eye care, as well as standardized visual health indicators and other resources for researchers (Rein, 2015). There are other opportunities to move forward in developing a comprehensive surveillance system; (1) Current surveillance methods, particularly national surveys, can be expanded and enhanced in order to better measure the total burden of eye disease and visual impairment and their impact in communities. National surveys should include eye-related questions in the core questionnaire each year, and eye examinations should be reincorporated into routine surveillance; (2) Clinical and public health data can be combined in a more integrated approach, taking advantage of recent developments in methods of measurement and technology; and (3) Collaboration and coordination can be increased among the surveillance community so that data and other resources are used more efficiently.

In addition to expanding and enhancing current surveillance instruments, surveillance should include other variables that would likely be helpful in identifying and characterizing important risk and protective factors for poor eye health. For example, along with prevalence and incidence data, a surveillance instrument could collect information on the determinants of eye health, including social and environmental determinants, and the impacts of eye disease and vision impairment. Given the stagnation in NIH and CDC funding for eye-related activities, it is difficult to imagine that new surveillance activities specific only to eye health and vision impairment would be sufficiently comprehensive. However, population health databases that track these types of variables are available and could be used to supplement vision-specific surveillance activities.

Opportunities to integrate and harmonize clinical and public health data into surveillance systems should be actively explored. As discussed above, a necessary first step toward integration will be the standardization of surveillance methods and tools used to collect surveillance data. The data elements are often not consistent across measurement approaches and may not align with the diagnostic criteria used by clinicians. Without standardization, it may not be possible to completely integrate these sources. However, efforts could be made to harmonize the data by identifying similar data elements in each instrument and linking these elements across instruments in order to compare and analyze the data (Wittenborn and Rein, 2016). Existing, non-standardized data can also be combined and analyzed through statistical techniques such as small-area estimation

(Wittenborn and Rein, 2016). This technique combines data from multiple sources in order to produce robust estimates that could not be produced by any one data source (Wittenborn and Rein, 2016). However, this technique generally requires state and local data, which are currently lacking. These methods of integrating data across settings and populations would give researchers access to rich, real-world data that could be used to improve our understanding and approach to visual impairment and eye health.

To ensure that surveillance activities are aligned across agencies, methods, and geographic areas and to avoid duplicate efforts, more collaboration and coordination will be needed between and within the eye and vision health community, and among population health surveillance experts. Key players include the NIH, Research to Prevent Blindness, ARVO, HRSA, the Agency for Healthcare Research and Quality, the Association of State and Territorial Health Officials (especially chronic disease directors and epidemiologists), and the National Association of County and City Health Officials, and the CDC. These players could work together to identify key elements of a surveillance system, including which conditions to include, consistent definitions that are correlated with clinical diagnosis criteria, standardized measurements, and methods for integrating and analyzing the data (Wittenborn and Rein, 2016). Bringing together a wide range of stakeholders at every step of the development process will help to ensure buy-in and acceptance for the resulting surveillance system (Wittenborn and Rein, 2016).

National Research Agenda

A research consortium dedicated to eye and vision health will be essential to promote and coordinate the above areas for potential improvement to eye-related surveillance and research. A research consortium can develop a national research agenda, which can serve to align and coordinate efforts, and to ensure that the most important areas of inquiry in instrument development and research design are prioritized. The CDC took steps toward this end through the creation of a panel of 14 experts “to identify action steps and priorities to strengthen national and state surveillance systems to help assess and monitor disparities in eye health, vision loss, and access to eye care over time and respond to national, state, and local needs” (CDC, 2015h). While this panel was a step in the right direction, an ideal research consortium would have a larger presence and focus. Likewise, the Vision Research Consortium at the University of California, Irvine, is a laudable effort; however, its focus only on basic, clinical, and applied research and its limited geographic range makes it insufficiently inclusive to serve as a national research consortium (University of California, 2016). There are several examples of other research consortia that could act as models for a

national vision and eye health consortium. For example, the Pediatric Eye Disease Investigator Group (PEDIG) is a collaborative network, funded by the NEI, that coordinates multi-center research focusing on pediatric eye disorders. PEDIG’s work is coordinated by a network chair, an executive committee, a steering committee, and a data and safety monitoring committee (PEDIG, 2016). PEDIG has conducted multiple randomized controlled trials on such disorders as amblyopia, strabismus, myopia, and nasolacrimal duct obstruction, and is an example of ophthalmology and optometry working together. Another model consortium is the Resuscitation Outcomes Consortium (ROC). ROC is a public–private collaboration among the National Heart, Lung, and Blood Institute; the U.S. Army Medical Research and Materiel Command; the Canadian Institutes of Health Research; Defence Research and Development Canada; the American Heart Association; and the Heart and Stroke Foundation of Canada. ROC supports research on cardiopulmonary arrest and severe traumatic injury by providing the infrastructure necessary to conduct multiple clinical trials in order to quickly translate scientific advances into clinical outcomes (ROC, 2016). Although financial support for ongoing ROC activities remains tenuous, it provides an example of how the public and private sectors can coordinate research and direct funding to conduct research more efficiently on

areas that promise the most change. Other examples of research consortia include the Vision Health Research Council in Canada, the Chronic Lymphocytic Leukemia Research Consortium, and the Public Health Research Consortium in the United Kingdom (CRC, 2016; PHRC, 2016; Vision Health Research Council, 2002).

The development of a national research consortium and a national research agenda would help promote vision and eye health by coordinating and prioritizing research to address causes, consequences, and unmet needs of those affected. The consortium, which should include private and public stakeholders, could develop an agenda to drive epidemiologic, clinical, and applied research, as well as health services and population health research. To support inquiry into other research gaps identified throughout this report, this consortium would need to address key surveillance and research limitations, such as those listed in Box 4-1.

CONCLUSION

A systematic and ongoing collection of relevant data about risk factors, determinants of visual health, care practices, and related health outcomes associated with eye health and visual impairment is currently lacking. Currently information is drawn from an array of surveys, EHRs, and administrative claims data, but it is difficult to triangulate information from these sources because of inconsistencies in definitions, measures, and other problems inherent in data collection activities that are discordant or developed independently. To fully understand the nature and extent of the public health burden of eye disease and to better inform practitioners and policy makers about what is needed to improve eye health, a coherent surveillance system is needed.

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