7
Crosscutting Issues in Assessing the Quality of Cancer Care

“Quality-of-life research has taught us the central role of the patient as the most important person in the assessment process. Although proxies and health care professionals can provide substitute judgment, the patient’s own preferences or values are most highly regarded.”

What Outcomes Matter to Patients: A Physician-Researcher Point of View

Patricia A. Ganz, 2002

“Numerous scientific studies provide the evidence that certain U.S. populations experience significant disparities in risk, incidence, disease-stage diagnosis, care received, and disease outcomes for cancer.”

Making Cancer Health Disparities History

Trans-HHS Cancer Health Disparities Progress Review Group (U.S. DHHS, 2004)

As the Georgia Cancer Coalition (GCC) establishes its system for monitoring the quality of cancer care, the Institute of Medicine (IOM) Committee on Assessing Improvements in Cancer Care in Georgia recommends that it carefully develop the capacity to assess the experience of cancer patients and to measure disparities in the quality of cancer care. This chapter provides guidance on these two important crosscutting issues.

CAPTURING CANCER PATIENTS’ EXPERIENCES

Responsiveness to patient-centered needs, preferences, and outcomes is a fundamental attribute of high-quality care (IOM, 2001; AHRQ, 2003). The IOM committee believes that evaluating patients’ experiences will be as



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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia 7 Crosscutting Issues in Assessing the Quality of Cancer Care “Quality-of-life research has taught us the central role of the patient as the most important person in the assessment process. Although proxies and health care professionals can provide substitute judgment, the patient’s own preferences or values are most highly regarded.” What Outcomes Matter to Patients: A Physician-Researcher Point of View Patricia A. Ganz, 2002 “Numerous scientific studies provide the evidence that certain U.S. populations experience significant disparities in risk, incidence, disease-stage diagnosis, care received, and disease outcomes for cancer.” Making Cancer Health Disparities History Trans-HHS Cancer Health Disparities Progress Review Group (U.S. DHHS, 2004) As the Georgia Cancer Coalition (GCC) establishes its system for monitoring the quality of cancer care, the Institute of Medicine (IOM) Committee on Assessing Improvements in Cancer Care in Georgia recommends that it carefully develop the capacity to assess the experience of cancer patients and to measure disparities in the quality of cancer care. This chapter provides guidance on these two important crosscutting issues. CAPTURING CANCER PATIENTS’ EXPERIENCES Responsiveness to patient-centered needs, preferences, and outcomes is a fundamental attribute of high-quality care (IOM, 2001; AHRQ, 2003). The IOM committee believes that evaluating patients’ experiences will be as

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia critical to assessing the quality of cancer care as deploying any of the quality indicators recommended in this report. Georgia should implement a quality-of-cancer-care patient-centered survey research program as soon as it is technically feasible. No other source of information can substitute for patients’ self reports on their preferences, outcomes, satisfaction, health care experiences and overall well-being (Cleary and Edgman-Levitan, 1997; IOM, 1999a, 2000, 2004; Ganz, 2002; Lawrence and Clancy, 2003; AHRQ, 2003; Drain and Clark, 2004). Georgia’s effort in this area is likely to be groundbreaking. GCC will face numerous and complex survey design decisions and should obtain expert advice. The use of patient surveys to assess the quality of community-based cancer care is a developing field of research (Schulman and Seils, 2003; Drain and Clark, 2004; Ayanian et al., 2004). There is an extensive literature validating numerous patient surveys, multi-symptom assessment tools, and quality-of-life instruments for cancer patients in clinical trials (Schag et al., 1991; Ware and Sherbourne, 1992; Cella et al., 1993, 1995; Esper et al., 1997; Brady et al., 1997; Cleary and Edgman-Levitan, 1997; Safran et al., 1998; McLachlan et al., 1998; Cleary, 1999; Ward et al., 1999). Unfortunately, few if any of these instruments have been tested in clinical settings where most patients seek care (Berry et al., 2004). The discussion of patient surveys that follows offers guidance on two aspects of developing an approach to capture cancer patients’ experiences: (1) the design of surveys of cancer patients; and (2) potential topics for cancer patient surveys. Designing Surveys of Cancer Patients When designing a survey to capture patients’ experiences, GCC should carefully consider its sampling methods to ensure, depending on the focus of the survey, that the sample population is representative of Georgia (IOM, 2000). Special attention should be given to sample size so that the surveys have sufficient statistical power to detect racial, ethnic, socioeconomic, and other subgroup differences. Uninsured and low-income patients may be particularly hard to reach, but they must be included in the survey because they are the patients most likely to be undertreated. GCC can learn and draw from the many published surveys on symptoms, quality of life, and satisfaction with cancer care (Table 7-1). A number of instruments have been developed for use with cancer patients and survivors (although, as noted above, few have been tested outside of clinical trials). Some surveys have modules tailored to specific cancers. The Functional Assessment of Cancer Therapy, for example, has individual modules for collecting data on physical, social/family, emotional, and functional well-being for breast, colorectal, lung, and prostate cancers (Cella et al., 1993,

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia TABLE 7-1 Examples of Survey Instruments That Measure the Patient Experience and Quality of Life Survey Sponsor/Developer Focus Cancer-specific instruments Assessment of Patients’ Experience of Cancer Care Study (currently under development) National Cancer Institute; Northern California Cancer Center Experience and perceptions of cancer survivors, including services received, access, communication, symptoms, pain and other side effects. Cancer Rehabilitation Evaluation System A. Coscarelli, R. Heinrich, and P. Ganz Physical, psychosocial, medical interaction, marital, and sexual quality of life Functional Assessment of Cancer Therapy (FACT), includes: —FACT-G, general; FACT-B, breast; FACT-C, colorectal; FACT-L, lung cancer; FACT-P, prostate D. Cella Institute for Health Services Research and Policy Studies, Northwestern University Physical, social/family, emotional, and functional well-being National Quality of Life Study American Cancer Society Needs and concerns of cancer survivors Quality of Life Questionnaire (QLQ), includes: —QLQ-C30, core module; QLQ-BR23, breast cancer module European Organization for Research and Treatment of Cancer Quality of life of cancer patients in clinical trials including physical, psychosocial, medical interactions, pain, sexual and other side effects General health instruments Ambulatory Care Experiences Survey (ACES), includes: —PCP-ACES, primary care; SF-ACES, primary care short form The Health Institute, Tufts-New England Medical Center Patients’ experiences with their primary care physician, specialist physicians, and health plan Consumer Assessment of Health Plan Survey (CAHPS), includes: —A-CAHPS, ambulatory care; H-CAHPS, hospital care; G-CAHPS, group practice care Agency for Health Care Research and Quality; Center for Medicare and Medicaid Services (CMS) Interpersonal aspects of health care

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia Survey Sponsor/Developer Focus Doctors’ Office Quality (pilot instrument under development) CMS Quality of ambulatory care for chronically ill patients, including patient’s experience of care Medical Outcomes Study, Short Form (SF)-12 and SF-36 CMS; The Health Institute, Tufts-New England Medical Center Functional status, well-being, and self-perceived health Primary Care Assessment Survey The Health Institute, Tufts-New England Medical Center Quality of physician-patient interactions and structural features of care 1995). The National Cancer Institute and the Northern California Cancer Center are currently testing a cancer-specific survey, called the Assessment of Patients’ Experience of Cancer Care, which draws upon the Consumer Assessment of Health Plans (CAHPS), the Primary Care Assessment Survey, and other primary care surveys (Arora, 2004; NCCC, 2004). More generic instruments, such as the Ambulatory Care Experiences Survey and the Primary Care Assessment Survey, collect data on patients’ experiences with their physicians and health plan independent of diagnosis. CAHPS, although first developed to determine health plan members’ satisfaction with their managed care organization, is now being adapted to assess the interpersonal aspects of health care in a variety of clinical settings (AHRQ, 2004). Target Population The target population is the group of people about whom the researcher wishes to draw conclusions; it should be clearly defined and standardized across surveys to allow comparisons. The committee recommends that GCC select population-based samples of persons with the most common types of cancer (i.e., breast, colorectal, lung, and prostate) and, within these groups, two subgroups: (1) recently diagnosed cancer patients (i.e., those diagnosed within the previous 6 to 18 months), because they are most likely to remember the details of their clinical care experiences; and (2) cancer survivors 5 to 6 years after diagnosis in order to capture the experiences of survivors. Survivors are a rapidly growing population whose needs have significant public health implications.

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia Sampling Frame A sampling frame is a list or other organized record of a population from which a survey sample is drawn. The committee recommends that Georgia sample patients from its central, population-based cancer registries.1 This will require careful consideration of patient confidentiality issues in light of the Health Insurance Portability and Accountability Act (HIPAA) of 1996. Under HIPAA regulations, central cancer registries are considered public health authorities with the legal authority “to collect or receive such information for the purposes of preventing or controlling disease, injury, vital events such as birth or death, and the conduct of public health surveillance, public health investigations, and public health interventions.”2 Nevertheless, the committee urges GCC to explore the legal implications of using Georgia’s central registries for quality-of-care research. It is beyond the scope of this study to evaluate this issue further. Georgia’s registries currently find at least 97 percent of all incident cancer cases in the state and, for each case, collect patient demographic information including residence, standardized racial and ethnic data, cancer site, tumor stage and extent of disease, initial course of treatment, and other data elements (Bayakly, 2003). Substantial delays in data collection are characteristic of registry operations nationwide—up to 2 years may elapse from the time cancer cases are diagnosed until all required patient data are entered into a registry’s database (IOM, 2000). Georgia must invest in expanding registry capacity to identify and follow up cancer cases soon after diagnosis. For special studies of cancers with relatively short survival times, such as lung cancer, GCC could use an expedited process—rapid case ascertainment—to accelerate reporting of new cancer diagnoses (NCI, 2003). Another method is to have the registry’s data collection effort interface with the electronic medical records that many hospitals and practices are beginning to adopt. Oversampling By oversampling specific subpopulations of interest, GCC will ensure sufficient statistical power to measure differences in outcomes (NRC, 2004). This will be particularly critical to analyses of disparities (see discussion below). For example, a study of prostate cancer outcomes may be especially concerned with the experiences of African-American men living in rural 1   See Chapter 2, Concepts, Methods, and Data Sources, for a description of the Georgia registries. 2   45 CFR 164.512.

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia versus metropolitan areas. If so, a disproportionately larger subsample of rural African-American men may be needed. GCC should use patient surveys only to collect data that are best collected from patients themselves. The costs of obtaining survey data for small or geographically concentrated racial and ethnic groups will make it infeasible to collect such data on a regular basis (NRC, 2004). On the other hand, GCC could conduct periodic targeted studies on specific groups in specific areas. Doing this would be a feasible way of collecting meaningful data on important subgroups over time. In addition, the Centers for Disease Control and Prevention-sponsored Behavioral Risk Factor Surveillance System provides an affordable option for oversampling selected subpopulations in its survey on risk behaviors (CDC, 2004). GCC should take advantage of this option to obtain additional, more representative data from the cancer-related components of that survey. Potential Topics for Cancer Patient Surveys The IOM committee recommends that GCC seek direct patient input on the quality of cancer care. By analyzing and reporting the findings from well-designed patient surveys and quality-of-life instruments, GCC can inform providers, policy makers, and consumers about how well cancer patients are being served across the continuum of care. There are critical insights into the needs and preferences of those affected by cancer—beyond considerations of treatment efficacy—that can only be gained by asking cancer patients and survivors (Schulman and Seils, 2003). Furthermore, patients have been shown to be the best source of information on their functional status, treatment-related symptoms, satisfaction and interpersonal issues, and access to needed services (Ayanian et al., 2004). Important domains for patient survey research (Table 7-2) include the following: Functional status. Functional status refers to the ability of patients to do what they need and want to do, and encompasses a wide variety of patient-focused outcomes including physical functioning (e.g., walking and climbing stairs), emotional well-being (e.g., role-limiting anxiety or fear of recurrence), and social functioning (e.g., isolation, ability to work) (Ganz, 2002). Symptoms. Symptom experiences—what the patient feels—are basic to quality of life (Cella et al., 2002). Cancer patients’ symptoms may be due to physiologic changes related to their cancer or to the treatment for cancer. Pain, nausea, fatigue, depression, and anxiety are commonplace among cancer patients regardless of the cancer site (IOM, 2004). Treatments for some cancers, including prostate and breast cancer, cause signifi-

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia TABLE 7-2 Potential Domains and Topics for Cancer Patient Surveys Domain Potential Topics Functional status Physical, emotional, spiritual, and social functioning Treatment-related symptoms —All cancers Pain, nausea, depression, anxiety —Breast Lymphedema —Colorectal Diarrhea —Lung Shortness of breath —Prostate Incontinence, impotence Satisfaction Interpersonal care including patient preferences, patient-provider relationship, treatment decisions, feeling informed especially with respect to treatment decision making, coordination of care, expectations Access Out-of-pocket costs, barriers to needed services, timeliness cant morbidity. The longitudinal Prostate Cancer Outcomes Study, for instance, found that after radical prostatectomy, radiation treatment, or hormone therapy, substantial proportions of patients with localized prostate cancer reported having problems related to impotence, incontinence, or bowel function (Potosky et al., 2000). Satisfaction and interpersonal issues. Patients’ health care experiences have been linked to clinically important, intermediate outcomes such as adherence to treatment regimens and following instructions after a hospital stay—underscoring the significance of monitoring satisfaction and interpersonal experiences, such as patient preferences, patient-provider communication, adequate information for treatment decision making, knowledge of diagnostic and treatment expectations, and coordination of care (DiMatteo et al., 1993; Weinfurt, 2003; Schulman and Seils, 2003; Wickizer et al., 2004; DiMatteo, 2004). Access to care. Patient surveys that ask patients to report on the timeliness, financial burdens, and other barriers to cancer-related services will provide GCC important direction in how to approach quality improvement. There is a well-established literature showing that access to health care is integral to survival and quality of life (Ayanian et al., 1993; IOM, 1999b, 2002, 2003b; Roetzheim et al., 2000b; Bradley et al., 2003; McDavid et al., 2003; Gornick et al., 2004).

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia EVALUATING DISPARITIES IN CANCER CARE Gross disparities exist in the behaviors and environmental conditions that lead to cancer, as well as in the incidence, diagnosis, treatment, and outcomes of cancer (IOM, 1999b, 2003b; Landis et al., 2004; Jemal et al., 2004). The IOM committee believes that the quality of Georgia’s cancer care cannot improve meaningfully without addressing the state’s unequal cancer burden. What Causes Disparities in Health and Health Care? The reasons for disparities in health and health care are not well understood. Race, ethnicity, and socioeconomic status are correlated with one another and each has been shown to independently contribute to an individual’s health (NRC, 2004). It is clear, for example, that socioeconomic factors, early cancer detection, and cancer survival are closely linked (Gorey et al., 2000; Ponce et al., 2003). Health insurance coverage and family income, in particular, are critical determinants of cancer outcomes largely because of barriers to access and delays in diagnosis (Ayanian et al., 1993; Roetzheim et al., 2000a; Gonzalez et al., 2001; McDavid et al., 2003). An extensive literature has made clear that patient’s age is often associated with the type of cancer care received (Hodgson et al., 2001; Harlan et al., 2002; Ayanian et al., 2003; Lyman et al., 2003; Richardson, 2004). Numerous studies have also shown that cancer survival is worse for Medicaid enrollees compared with other insured persons (Ayanian et al., 1993; Bradley et al., 2003). Health insurance and poverty, however, do not fully explain cancer disparities. There is also a profound and unequal burden of cancer associated with race and ethnicity (IOM, 1999b). The disproportionate burden of cancer among African Americans, for example, is well documented (IOM, 1999b, 2003b; U.S. DHHS, 2004; Ward et al., 2004). African Americans, compared with all other racial or ethnic groups in the United States, have the highest mortality rate from all cancer sites combined and from breast, colorectal, lung, and prostate cancers individually (Table 7-3) (ACS, 2004). Compared with cancer death rates for white men and women, the cancer death rate is 43 percent higher for African-American men and 19 percent higher for African-American women. Data Infrastructure Needed to Reduce Cancer Disparities The IOM committee urges Georgia to improve its cancer information systems so that high-quality racial, ethnic, and socioeconomic data are readily available. The importance of building a data infrastructure to under-

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia TABLE 7-3 Incidence and Mortality Rates for Four Leading Cancers in Georgia, by Gender, Race, and Ethnicity, 2001a Cancer Incidence, by Race/Ethnicity (per 100,000) All Races White African American Hispanic-Latinob All sites Male 570.9 548.0 687.8 337.0 Female 385.9 399.5 351.4 344.3 Breast, female In situ 27.5 28.9 24.4 NA Invasive 124.6 131.6 105.4 101.3 Colorectal Male 61.0 57.8 76.4 39.4 Female 41.7 39.2 49.9 45.8 Lung and bronchus Male 108.1 105.7 123.0 55.0 Female 51.8 56.2 37.5 37.8 Prostate 173.8 152.4 270.4 103.9 Cancer Mortality, by Race/Ethnicity (per 100,000) All Races White African American Hispanic-Latinob All sites Male 263.4 246.9 343.9 NA Female 164.1 161.1 175.6 NA Breastc 24.9 23.8 29.1 NA Colorectal Male 23.2 21.0 33.0 NA Female 17.7 15.2 27.1 NA Lung and bronchus Male 91.7 90.6 101.2 NA Female 40.8 44.3 29.7 NA Prostate 33.5 25.2 71.5 NA aAge-adjusted to the 2000 U.S. standard population. bAll races. cInvasive female breast cancer only. NOTE: NA = Not available. SOURCE: U.S. Cancer Statistics Working Group, 2004.

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia take the challenge of reducing cancer disparities is underscored by Georgia’s rapid growth and increasing diversity. Georgia was the fastest growing southern state in the 1990s and, mirroring trends elsewhere in the United States, the state is becoming ever more racially and ethnically diverse (Box 7-1). These demographic trends are inextricably linked with health insurance and poverty. In Georgia and throughout the nation, insurance and poverty status vary considerably by race and ethnicity (Table 7-4 and Table 7-5). Two aspects of building a state data infrastructure for understanding and addressing health disparities require particular attention and action: (1) standardizing race and ethnicity data; and (2) creating the capacity to BOX 7-1 Rapidly Changing Demographics in Georgia Georgia’s population is rapidly growing and becoming increasingly diverse. During the 1990s, the state population grew by more than 26 percent to total 8.2 million in 2000. Part of this growth was due to an astonishing migration of African Americans to Georgia that led to an almost 35 percent increase in the state’s African-American population. African Americans now make up an estimated 28.7 percent of Georgia’s residents while white persons represent 65.1 percent. Georgia’s Asian population doubled during the 1990s. Although only 2.1 percent of Georgians are Asian, most Asians live in metropolitan Atlanta. In 2000, more than 7 percent of Gwinnett County, just outside Atlanta, was Asian. The state’s ethnic makeup is also changing, mirroring trends across the United States. The Hispanic population is the fasting growing minority group in the state. From 1990 to 2000, there was an almost 300 percent rise in the number of Hispanic residents in Georgia, an increase from 108,922 to 435,227 persons. In 2000, Hispanic persons were just 5 percent of Georgia residents but their presence varies dramatically by county. In two northern counties (i.e., Hall and Whitfield), for example, one in five residents is Hispanic. Race and Ethnicity of Georgia’s Population in 2000 White 65.1% Black 28.7% Asian 2.1% Othera 4.2% Hispanic or Latino origin (any race) 5.3% aOther includes American Indians and Alaskan Natives, Native Hawaiians and other Pacific Islanders, and respondents who belong to two or more racial categories. SOURCE: Office of Planning and Budget, 2004; U.S. Census Bureau, 2004.

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia TABLE 7-4 Percentage of Nonelderly Persons in Georgia Who Are Uninsured, by Race and Ethnicity, 2002-2003   Percentage Uninsured Nonelderly Georgia residents 18 White, non-Hispanic 15 Black, non-Hispanic 20 Hispanic 43 Other NA NOTE: Nonelderly includes persons under age 65. Hispanic persons may be of any race. NA = Not available. SOURCE: Urban Institute and Kaiser Commission on Medicaid and the Uninsured, based on pooled March 2002-2003 Current Population Surveys. Kaiser Family Foundation, 2004. TABLE 7-5 Percentage of Persons in Georgia Who Are Living in Poverty, by Race and Ethnicity, 2002-2003   Percentage in Poverty All Georgia residents 13 White, non-Hispanic 11 Black, non-Hispanic 26 Hispanic 30 Other 9 NOTE: Persons in poverty defined as those with family incomes less than 100 percent of the federal poverty level. The federal poverty level for a family of three was $15,260 in 2003. Hispanic persons may be of any race. NA = Not available. SOURCE: Urban Institute and Kaiser Commission on Medicaid and the Uninsured, based on pooled March 2002-2003 Current Population Surveys. Kaiser Family Foundation, 2004. analyze socioeconomic factors through standardized geographical data. These are discussed below. Standardizing Race and Ethnicity Data If Georgia is to mitigate racial and ethnic disparities in the quality of cancer care, it must produce high-quality, standardized, race and ethnicity data. Without this capability, GCC will be unable to either monitor the disparity problem or develop adequate solutions. The IOM committee recommends that GCC use standardized categories of race and ethnicity in its cancer registries, medical records, claims, patient- and population-based surveys, and other cancer-related data col-

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia lection. GCC should adopt the federal Office of Management and Budget (OMB) minimum standards for categorizing race and ethnicity and apply the standards in all patient data collection (Box 7-2). The OMB standards are flexible enough to serve state as well as federal information needs (NRC, 2004). With Georgia’s growing Hispanic population, some attention to Hispanic cancer data is also recommended. There is some research suggesting that registries and vital records under-ascertain or misclassify cancer incidence among Hispanics (Swallen et al., 1997; Coronado et al., 2002). Bias in the cancer registry data collection methods is thought to contribute to the problem. The North American Association of Central Cancer Registries BOX 7-2 Standardizing Racial and Ethnic Categories for Public Policy Uses Federal and some state data collection systems use standard categories of race and ethnicity to comply with the requirements of the federal Office of Management and Budget (OMB). Since 1977, OMB has required these minimum standards to promote consistency in defining race and ethnicity for civil rights legislative use, monitoring equal treatment, and other public policy uses. OMB currently mandates the use of five racial categories and two ethnic categories. Subjects are simultaneously tabulated by race and ethnicity. In the U.S. Census, respondents may also select more than one race, allowing for very many combinations. OMB-Mandated Racial and Ethnic Categories Racial Categories Ethnic Categories (1) Black or African American (2) White (3) Asian (4) American Indian and Alaska Native (5) Native Hawaiian and other Pacific Islander (1) Hispanic or Latino (2) Non-Hispanic or Latino OMB standards are required in all federal census and survey data, federal administrative records, federally sponsored research, as well as in data collected by states for federal purposes. States collect much of the data that the federal government uses to study health and health care services, including the Vital Statistics Cooperative Program for vital statistics, the Healthcare Cost and Utilization Program for hospital discharge data, the Surveillance, Epidemiology, and End Results Program for cancer, the Behavioral Risk Factor Surveillance System, and the Medicaid program. Many privately sponsored surveys also use the OMB classifications. SOURCE: NRC, 2004; OMB, 1977.

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia (NAACCR) has developed a computerized algorithm to address the problem (NAACCR Expert Panel on Hispanic Identification, 2003; Howe, 2004). Registries in California have had some experience with this issue (Stewart et al., 1999). Creating the Capacity to Analyze Socioeconomic Factors Socioeconomic status is associated with high-risk behaviors such as tobacco use, poor nutrition, physical inactivity, and obesity as well as barriers to appropriate cancer screening, early detection, treatment, and palliative care (IOM, 2003a). As a consequence, socioeconomic factors are also correlated with cancer and other health outcomes (IOM, 1999b; Freeman, 2003; NRC, 2004; Ward et al., 2004). These interrelationships imply that racial and ethnic disparities should be viewed in the context of social and economic conditions (NRC, 2004). GCC must therefore have the ability to analyze how cancer care quality varies not only by race and ethnicity but also by gender, age, income, geographic location, health insurance status, and other socioeconomic factors. If racial and ethnic groups can be disaggregated into more socially and culturally homogeneous subgroups, researchers will be better equipped to assess disparities and identify effective interventions (Braveman, 2003; U.S. DHHS, 2004). Historically, state-based collection of health-related data has been uneven and not standardized (NRC, 2004). Furthermore, since most health-related data systems draw from health records, little information on socioeconomic status has been collected. In social science research, socioeconomic status is commonly ascertained by developing indices combining measures of education, occupation, and income, but the routine collection of such information by cancer registries has not been possible because it usually cannot be found in medical records. Geocoding is the assignment of a code to a geographical location by matching an individual address to a census tract or other geographic unit, such as a county, public health district, or region. It can be an inexpensive and reliable way to capture socioeconomic variables for monitoring the cancer burden if the cancer registry maintains reliable records of patients’ addresses (Braveman, 2003; U.S. DHHS, 2004). Georgia should consider using currently available software to geocode its cancer registry records as each new cancer case is entered into the state’s surveillance database. Geocoded registry-based cancer cases could then be linked with geographic-specific data such as area-based socioeconomic variables, environmental data, and health care resources. With this step, Georgia’s cancer control professionals and researchers would have unprecedented capacity to assess the impact of social and contextual level variables on cancer incidence, diagnosis, treatment, and outcomes (Krieger et al., 2003; Singh et al., 2003).

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Assessing the Quality of Cancer Care: An Approach to Measurement in Georgia Thus, GCC’s quality monitoring could discern disparities between, for example, rural and urban African Americans. Otherwise, grouping the two populations in one racial group could well obscure poor outcomes in rural African-American populations. Implementing geocoding of cancer data would also put Georgia in step with the U.S. Healthy People 2010 call for increased use of geocoding in all major national, state, and local health data systems (U.S. DHHS, 2000). SUMMARY In this chapter, the IOM committee has addressed two related cross-cutting issues in assessing the quality of cancer care—first, the use of cancer patient surveys, and second, the conduct of health disparities research. Evaluating cancer patients’ experiences will be as critical to assessing the quality of cancer care as deploying the 53 quality-of-cancer-care measures recommended in this report. Moreover, cancer outcomes will not improve for Georgians unless disparities in the quality of cancer care are addressed. The IOM committee recommends that Georgia expand and enhance its cancer information systems to include a patient survey research program that focuses on functional status, symptoms, satisfaction, and access to care and build the data infrastructure needed to develop high-quality racial, ethnic, and socioeconomic data that can be used to address health disparities. Building the capacity to survey patients and measure disparities will be costly and should be carefully planned. Patient surveys should be used only to collect data that are best collected from patients themselves. Periodic, targeted studies on specific groups in specific areas would be a feasible way of collecting meaningful data on important subgroups over time. Socioeconomic data will be essential to better understanding racial and ethnic disparities. Geocoding is an inexpensive and reliable way to capture socioeconomic variables for monitoring the cancer burden. Georgia should consider using currently available software to geocode its cancer registry records as each new cancer case is entered into the state’s surveillance database. REFERENCES ACS (American Cancer Society). 2004. Cancer Facts & Figures 2004. Atlanta, GA: ACS. AHRQ (Agency for Healthcare Research and Quality). 2003. National Healthcare Quality Report. Rockville, MD: U.S. DHHS. ——. 2004. The Development of Ambulatory CAHPS (A-CAHPS). [Online] Available: http://www.cahps-sun.org/References/Newsdocs/CAHPSConnectionIssue1.htm#acahps [accessed May 17, 2004]. Arora NJ (aroran@mail.nih.gov). 2004. RE: APECC. E-mail to Jill Eden (jeden@nas.edu). November 1, 2004.

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