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Using comparative e ffectiveness research to remedy health disparities Health disparities are an important and continuing problem of considerable research importance. Comparative effectiveness research (CER) is an excellent vehicle to evaluate interventions to remedy disparities. We classify CER for disparities at three levels of science: basic biology, care and systems, and social and cultural context. In basic biology, genomics will delineate treatments for specific individuals and populations. Care and systems interventions are most important research areas to improve process and quality measures. However, there is evidence that correction of healthcare processes disparities will not be sufficient to improve health and that social and cultural research may be key in this regard. The methodology of CER for disparities is the same as that of other research with randomized controlled trials the gold standard and database analysis, and other observational and quasi-experimental methods important and effective. In addition, mixed methods and multilevel modeling offer promise. Community involvement in research and patient preferences among high-quality choices need to be included in planning of CER.
Joel Kupersmith*1,2 & David LaBarca3 Georgetown University Medical Center, Washington, DC, USA 2 Kupersmith Associates, Washington, DC, USA 3 Department of Veterans Affairs, Washington, DC, USA *Author for correspondence: [email protected] 1
KEYWORDS: basic biology n care n comparative effectiveness research n culture n disparities n research methodology n social n systems
Despite considerable research interest, health disparities persist and are continuing issues for patients and the healthcare system. Comparative effectiveness research (CER) offers a most useful approach to address disparities, and this area of research should be high among its priorities [1]. The body of disparities CER is ready to be expanded in a variety of ways. Definitions/background
Health disparities [2] are defined by the Institute of Medicine [3] as differences in the quality of care (i.e., manifestation, treatment and outcomes of disease) received by different populations who have similar access to care (i.e., similar health insurance and health provider contact), and similar preferences and needs for treatment. Determinants of health disparity include disease biology and differential behavior of patients and providers [4]. Disparities can involve different groups – ethnic, racial, economic, sexual orientation, cultural, rural, inner city, disabled individuals and others. In addition, although the term ‘disparity’ (or ‘inequality’ or ‘inequity’) may apply to any comparisons between groups [2], it is a common understanding that it applies to a comparison between a disadvantaged group and others. CER has been defined as the generation and synthesis of evidence that compares the benefits and harms of alternative methods (i.e., interventions) to prevent, diagnose, treat and monitor a clinical condition, or to improve the delivery of care [3,5]. The goals of CER are to help patients, clinicians, purchasers and policy-makers make informed decisions that improve individual and population health. Three phases of disparities research have been suggested. Phase one is ‘detection’ – that is, defining health disparities, identifying populations and developing measures to study them. Phase two is understanding why disparities exist and identifying factors that explain gaps in health and healthcare between groups. Phase
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three involves evaluation and implementation of interventions to reduce or eliminate health and healthcare disparities [6], and these may be tailored to a specific group or be generic. In that it examines choices of interventions, CER is generally in the phase three category. As such, CER offers a scientific and valuable technique to determine which interventions are better for specific disadvantaged populations and who will benefit most from or are likely to be harmed by them [7]. CER can compare strategies between groups or strategies within a disadvantaged group to determine how to remedy disparities. However, the body of CER studies performed to date often does not include the appropriate comparison groups to examine CER. In addition, minorities are often proportionately under-represented in clinical trials, preventing the evaluation of any heterogeneities of treatment effects that may occur [5,8]. Thus, much of the CER study output has not examined disparities or issues in disadvantaged groups and special efforts need to be applied to undertake disparities CER. On the other hand, a number of factors are now favorable to the growth of disparities CER. Both the Patient Centered Outcomes Research Institute and the NIH have made the study of disparities a priority in funding [9,10]. Interest in ‘big data’ and the formation of large databases as well as the development of practice-based research networks [11] will provide considerable group-related data to facilitate disparities studies. There is also much more emphasis on community-based inputs in research and in many areas a community infrastructure with interest in healthcare research has evolved.
in African–American and Hispanic individuals over the age of 65 years [18]. The US Department of Veterans Affairs (VA) has been a major site of disparities research with a distinguishing advantage – the healthcare economic factors in disparities are largely absent (although economic factors outside the healthcare system may still have an important impact). Examples of VA studies: in diabetics, African– American, Hispanic and Native American individuals are more likely to have poorer glycemic control than white individuals (although not in all studies) [19], and rural residents poorer control than those in urban areas [20]; and Hispanic individuals appear to have a higher mortality risk from traumatic brain injury than African– American or white individuals (whose rates are similar) [21]. Many more examples could be given of disparities between different groups and the topic has been the subject of numerous reviews [4,19,22]. CER has an important role in testing and comparing interventions to improve health in the disadvantaged and reduce or eliminate disparities with a potentially major impact. As indicated in the report of the US Federal Coordinating Council on Comparative Effectiveness Research, “CER will be an important tool to inform decisions for those [priority] populations to reduce health disparities including medical and assistive devices, procedures and surgery, behavioral changes and delivery of systems” [5]. In this review, we classify CER by level or hierarchy of the science – basic biology, care and systems, and social/cultural context. We first consider care and healthcare systems, which have been the core of disparities research.
Disparities exist
Care & systems
An extensive body of research has documented that disparities exist in many different circumstances and for many different reasons. They are present across the disease spectrum, although variable. A few examples: there are disparities in the use of reperfusion treatment and coronary angiography, but not in aspirin and b-blocker use after myocardial infarction [12]; African– American women are 34% more likely to die from breast cancer even though they are 10% less likely to be diagnosed than white women [5,13]; there are gaps between white and African–American individuals in use and outcomes of important surgical procedures [14–16] and in dental care [17]; and comorbidities are increased
An obvious and sizable target of disparities studies is to improve the quality process measures used to evaluate care in hospital systems (e.g., HEDIS [23]). It is well established that care and systems shortcomings contribute to disparities. For example, African–American individuals are likely to undergo surgery at hospitals with lower quality ratings [24]; there are disparities in treatment for depression [25], hypertension and other illnesses; and there was perceived discrimination by African–American patients in doctor– patient communication [19]. It is most important for CER to establish remedies for these process deficiencies. Targets might include comparisons of metrics related to screening for hypertension,
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hyperlipidemia, breast and colon cancer, visits, other healthcare functions and so on. However, an important limitation has also been uncovered – process improvements may not have an equivalent result in health outcomes. A recent study from the VA showed that improvement in healthcare system and clinical processes might not in fact comparably narrow disparities outcomes [13]. The study examined quality of care process measures – that is, eye examinations in diabetes, assessment of hemoglobin A1C (HgbA1C), low density lipoprotein (LDL) and blood pressure and screening for breast and colon cancer over the years 2000–2009 and compared them to outcomes (i.e., control of HgbA1C, LDL and blood pressure) in African–American and white individuals. Process measures improved for both African–American and white individuals during the 9 years of the study and, except for mammography, disparities were less than 2%. However, the measured gains in outcomes were much more modest and disparities for HgbA1C (6%), LDL (8–9%) and blood pressure (6%) remained much greater. Thus there was a disconnect between the application of commonly used quality improvement processes and the outcomes that should result. While CER studies of care and symptoms process are most important, they may not be sufficient to remedy disparities. Among possible explanations for this dichotomy are nonadherence to medication and genetic differences in disease and response to similar medical regimens. Thus, CER studies to improve health outcomes in the disadvantaged and reduce disparities must take into account group specificity, whether incorporating social/ cultural context (for better communication and thus better participation in care) on one hand and genetics on the other. Social/cultural context
CER involving social/cultural context both within and outside of the healthcare system are crucial and well-established vehicles to reduce disparities [26,27]. The need for such studies is signaled not only by the inability of the usual medical process studies to correct disparities, but also by adverse provider or systems interactions with disadvantaged groups such as discrimination toward, or false beliefs about, certain groups. For example, as noted above, the choice of hospitals with lower quality ratings by African–Americans was found to be based in part on perceived discrimination at
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certain institutions, evidence that communication improvements are necessary [24]. Culturally and socially sensitive interventions and their application are thus an important topic for CER both within the health system to improve utilization by disadvantaged groups and beyond into the community and the arena of public policy. Types of study might include: improving cultural competence via provider and health system worker training [28]; culturally tailored or generic educational interventions including those to improve health literacy; community health interventions (via community health clinics and other community organizations such as churches) [29], coordinated case management, community based participatory research [30] and economic approaches. CER and other studies of this nature have targeted specific groups, generally with the assumption that the usual forms of communication in the healthcare system do not reach certain disadvantaged groups. They try to improve conditions in disadvantaged groups and thus may or may not make comparisons that examine disparities directly. For example (from the VA), home messaging reduced disparities in glycemic control between African–American and white individuals [31]; peer mentoring (but not financial incentives) improved glucose control among African–American individuals with diabetes [32]; a meta-analysis of studies utilizing community education, individual counseling, mass media and community health worker interventions found improvement among African–American although not Hispanic individuals [33]. Social science studies differ from those of medical interventions in a number of ways and if they are to succeed as CER, need to be respectful of the nuances and complexities. Studies involving both social and medical science require crossdisciplinary teams of researchers and community involvement [22]. The interventions may consist of multiple components and details where important variations in wording can influence results. There can be heterogeneity between interventions used for the same purpose. It may not always be clear whether the intervention chosen is in fact the best and apparently useful instruments may not be effective. For example, a well-conceived series of studies of educational materials for African–American individuals concerning knee replacement in the VA had equivocal results [34]. For these reasons, pilot studies [35] and formative studies may be useful as are regional analyses of social disparities [36]. Social context studies also
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make a point about CER in general – that collaboration is important and nowhere is it more important than in disparities research. These would include not only a variety of scientific disciplines (e.g., medicine, social science, biostatistics and so on), but also, crucially, community partners. In turn, the teams established for the research can also be teams for promoting translation via researcher/community relationships, intermediaries in the community and interactions with community social and charitable organizations. Such a team approach is thus crucial to success not only of the research, but also to its ultimate use.
be specific interactions of genetic mutations with the environment, such as interactions of asthma, which is more common in certain minority groups, with ozone and other pollutants [41]. While it is clear that genetics may elucidate racial differences relevant to healthcare, there are pitfalls. Much of the medical genetic variation is within groups rather than between groups and there is the danger that individual differences may be attributed to an entire racial or ethnic group, causing inappropriate decision-making and care [42]. In addition, while genetic variations may be of interest and appear of note, their clinical significance needs to be established.
Basic biology
As noted above, the definition of disparities includes ‘disease biology’ as a determinant and genetic differences are of course an important component. There are underlying differences in the basic biology of diseases, which may have similar phenotypes and manifestations. For example, hypertension in African–Americans differs from that in Caucasians in a variety of ways. Pharmacogenetic-related mutations [37] may also be important determinants for differences in responsiveness to drugs or drug metabolism and potentially contribute to disparities when commonly used treatments are given to groups who have a high proportion of such genes. BiDil® (isosorbide dinitrate and hydralazine hcl), an afterload-reducing medication used in congestive heart failure, may be an example of racial differences in response based on a singlerace study of African–Americans with congestive heart failure. While there is some controversy regarding the conclusion, genetic and therefore racial differences in drug response do appear to exist [38]. As genomic information from large databases such as the VA’s Million Veteran Program unfolds [39], it is important that the resulting data inform CER in different groups in relation to disparities. An example of study in this area would be a comparison of drug treatment in individuals with and without a certain gene, as has been found in the case of Coumadin (warfarin) and Plavix® (Bristol-Myers Squibb, NY, USA; clopidogrel) [40]. As genetic differences in conditions such as hypertension (common in African–American individuals), diabetes (more common in African–American, Native American and Pacific Islander individuals) and atherosclerosis become better known, it will be possible to design studies with both group and individual significance in this area. There may
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CER methodologies
A variety of methodologies can be used in disparities CER depending on the situation and those chosen should be tailored to circumstance. Whether one should compare different groups of individuals or target a single group also depends on circumstances and feasibility. Studies comparing groups are desirable, but of course a cultural intervention aimed at African–American, Hispanic or Native American individuals needs to be targeted to that individual group (although one might design complex interventional studies). In addition, studies comparing groups may require large numbers and cost, making them difficult to carry out. The basic methodologies of CER are for the most part the same as used in other research approaches [43]. Randomized controlled trials and practical clinical trials are the gold standard but because of cost and feasibility, they can only be directed at a limited number of questions. Alternate and quasi-experimental designs are important methodologic tools. These include natural experiments using instrumental variables (where changes due to factors unrelated to treatment replace randomization), propensity scores and others. Adaptive designs are potentially useful as techniques to reduce the numbers of subjects necessary and to create more efficient studies. Site randomization of medical centers [44] is another approach, as is the more difficult randomization of providers or within-hospital sections (in the private sector disparities are mainly between institutions [45], while in the VA they are mainly intra-institutional [13]). Apart from CER studies specifically aimed at disparities, it is also important for investigators to include sufficient numbers of minority groups as part of design in as many CER studies
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as possible. This would enable examination of heterogeneities of treatment effects via subgroup analysis of minority groups within these studies with the proviso that strict methodological standards be used, especially regarding prior study planning and statistical power [8]. ‘Big data’ observational studies using large clinically rich databases will be major research approaches to CER disparities. Examination of these databases using refined statistical approaches to compare groups will be an important and efficient technique for the future to acquire large amounts of data regarding disparities [46]. Combined approaches, such as multilevel modeling and mixed methods are potentially useful in the study of disparity-related CER. Multilevel modeling takes into account the fact that variables have an implicit hierarchy and incorporates all levels. The levels used in classifying disparities CER above (e.g., care/systems and social/cultural) represent such hierarchies. Multilevel studies can be combined, in parallel or sequential. Statistical analysis is complex and must be applied with rigor [47]. Mixed methods research merges quantitative and qualitative approaches – a frequent example is the combination of focus group and survey studies. Here, qualitative research can help inform quantitative instruments and generate hypotheses. Studies can be performed sequentially or convergent and there are a variety of techniques for merging data [48,49]. Community-based participatory research, defined by the Agency for Healthcare Research and Quality as “an approach that incorporates formalized structures to ensure community participation” [50], has special value for disparities research. As indicated earlier, building trust and partnerships and understanding the cultural factors that apply in both the community and healthcare setting are important in CER design. Disparities research of all kinds depends on a strong relationship of the research establishment and the community [49,51]. To assure the best use of any data derived, CER trial methodologies need to consider translation of the research into healthcare use at the beginning of the study. In this regard, the VA has piloted approaches, for example, CER via point of care randomization and health services research/health system collaborative approaches [52,53] that foster translation. Site and section randomization also enlist the system in a manner that fosters translation and use.
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CER limitations & pitfalls
CER in disparities has many possible pitfalls, a number of which have been noted above. Generally, limitations and pitfalls are similar to those of other forms of research. Disparities studies may involve complex interventions potentially in multiple groups, necessitating large numbers. For CER studies. choice of appropriate comparators to evaluate disparities may be challenging. The fact that disease is often more severe in the disadvantaged needs to be taken into account. There is a necessary lack of blinding for such tools as educational interventions. Results of studies may be site specific, especially research involving systems approaches. Most disparities. CER studies are not of long duration and it may not be clear how long the particular intervention continues to be effective and whether the health outcomes persist. Mixed methods research and multilevel models have a number of pitfalls in study design and execution [47–49] necessitating caution in their use. Informed consent documents can be a barrier to research enrollment in minorities and disadvantaged groups. Lack of trust, misinformation about the consent process and difficulties in understanding the language can discourage enrollment. Among suggestions for improvement are plain language summaries, allowing material to be brought back to family members (who may be the deciders) and most especially one-on-one interactions of researchers with potential research subjects [54]. Patient preference is another important factor in high quality care and part of the definition of disparities noted above. It also relates to the issue of equalization versus equitization. Equitization presumes high-quality care (but not patient preference due to misinformation), which, however, is not necessarily ‘the same’ among different patients and groups in the light of patient preferences. Research studies often do not incorporate these preferences into design and they may not be well understood [55]. Involving patients in research planning and protocols that include patient surveys are some of the ways to incorporate their preferences in CER. Conclusion
Overall disparities in care and outcomes persist in healthcare and it is imperative that they be addressed and overcome. It is also imperative that CER, which offers an important scientific approach to disparities research, be used to accomplish these ends.
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Future perspective
Disclaimer
The following are hopeful future possibilities: ■■ Due to oversight of process and performance measures, disparities in care and systems will be reduced;
The views expressed here are those of the authors and do not necessarily reflect those of the Department of Veterans Affairs.
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Social and cultural research will increase and its methodology will improve;
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Genetic information and associated treatment strategies will increase;
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Use of large databases for disparities CER will increase.
Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.
Executive summary Definition/background ■■ Comparative effectiveness research (CER) is a most useful approach to address disparities. ■■ The three phases of disparities are detection, understanding why disparities exist, and evaluation and implementation of interventions. ■■ CER is a valuable technique to determine which interventions are better to address disparities. ■■ A number of factors are now favorable to disparities CER, such as funding priorities in the Patient Centered Outcomes Research Institute and NIH, and the formation of large databases. Disparities exist ■■ The classification used in this paper relies on levels of science: basic biology, care and systems (considered first) and social/ cultural context. Care & systems ■■ This is an obvious and most important target for improvement via CER disparities research. ■■ However, correction of care and systems process disparities may not remedy health disparities. Social/cultural context ■■ There is a need for CER studies on this level, targeting specific groups. ■■ Studies in this area are nuanced and complex. Basic biology ■■ Genomics offers great promise in the future to delineate therapeutic and other specificity. CER methodologies ■■ These include randomized controlled trials and practical clinical trials as the gold standard, database analysis, quasi-experimental designs, multilevel modeling and mixed methods. ■■ Community involvement is important. Limitations & pitfalls ■■ These are similar to those in other clinical research. ■■ They include choice of appropriate comparators, the necessity for large numbers, site specificity of system studies and the relatively short duration of studies. ■■ Informed consent documents can be a barrier to research enrollment in minorities and needs to be addressed. ■■ Patient preference among high-quality care choices should be considered.
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Using comparative e ffectiveness research to remedy health disparities Health disparities are an important and continuing problem of considerable research importance. Comparative effectiveness research (CER) is an excellent vehicle to evaluate interventions to remedy disparities. We classify CER for disparities at three levels of science: basic biology, care and systems, and social and cultural context. In basic biology, genomics will delineate treatments for specific individuals and populations. Care and systems interventions are most important research areas to improve process and quality measures. However, there is evidence that correction of healthcare processes disparities will not be sufficient to improve health and that social and cultural research may be key in this regard. The methodology of CER for disparities is the same as that of other research with randomized controlled trials the gold standard and database analysis, and other observational and quasi-experimental methods important and effective. In addition, mixed methods and multilevel modeling offer promise. Community involvement in research and patient preferences among high-quality choices need to be included in planning of CER.
Joel Kupersmith*1,2 & David LaBarca3 Georgetown University Medical Center, Washington, DC, USA 2 Kupersmith Associates, Washington, DC, USA 3 Department of Veterans Affairs, Washington, DC, USA *Author for correspondence: [email protected] 1
KEYWORDS: basic biology n care n comparative effectiveness research n culture n disparities n research methodology n social n systems
Despite considerable research interest, health disparities persist and are continuing issues for patients and the healthcare system. Comparative effectiveness research (CER) offers a most useful approach to address disparities, and this area of research should be high among its priorities [1]. The body of disparities CER is ready to be expanded in a variety of ways. Definitions/background
Health disparities [2] are defined by the Institute of Medicine [3] as differences in the quality of care (i.e., manifestation, treatment and outcomes of disease) received by different populations who have similar access to care (i.e., similar health insurance and health provider contact), and similar preferences and needs for treatment. Determinants of health disparity include disease biology and differential behavior of patients and providers [4]. Disparities can involve different groups – ethnic, racial, economic, sexual orientation, cultural, rural, inner city, disabled individuals and others. In addition, although the term ‘disparity’ (or ‘inequality’ or ‘inequity’) may apply to any comparisons between groups [2], it is a common understanding that it applies to a comparison between a disadvantaged group and others. CER has been defined as the generation and synthesis of evidence that compares the benefits and harms of alternative methods (i.e., interventions) to prevent, diagnose, treat and monitor a clinical condition, or to improve the delivery of care [3,5]. The goals of CER are to help patients, clinicians, purchasers and policy-makers make informed decisions that improve individual and population health. Three phases of disparities research have been suggested. Phase one is ‘detection’ – that is, defining health disparities, identifying populations and developing measures to study them. Phase two is understanding why disparities exist and identifying factors that explain gaps in health and healthcare between groups. Phase
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three involves evaluation and implementation of interventions to reduce or eliminate health and healthcare disparities [6], and these may be tailored to a specific group or be generic. In that it examines choices of interventions, CER is generally in the phase three category. As such, CER offers a scientific and valuable technique to determine which interventions are better for specific disadvantaged populations and who will benefit most from or are likely to be harmed by them [7]. CER can compare strategies between groups or strategies within a disadvantaged group to determine how to remedy disparities. However, the body of CER studies performed to date often does not include the appropriate comparison groups to examine CER. In addition, minorities are often proportionately under-represented in clinical trials, preventing the evaluation of any heterogeneities of treatment effects that may occur [5,8]. Thus, much of the CER study output has not examined disparities or issues in disadvantaged groups and special efforts need to be applied to undertake disparities CER. On the other hand, a number of factors are now favorable to the growth of disparities CER. Both the Patient Centered Outcomes Research Institute and the NIH have made the study of disparities a priority in funding [9,10]. Interest in ‘big data’ and the formation of large databases as well as the development of practice-based research networks [11] will provide considerable group-related data to facilitate disparities studies. There is also much more emphasis on community-based inputs in research and in many areas a community infrastructure with interest in healthcare research has evolved.
in African–American and Hispanic individuals over the age of 65 years [18]. The US Department of Veterans Affairs (VA) has been a major site of disparities research with a distinguishing advantage – the healthcare economic factors in disparities are largely absent (although economic factors outside the healthcare system may still have an important impact). Examples of VA studies: in diabetics, African– American, Hispanic and Native American individuals are more likely to have poorer glycemic control than white individuals (although not in all studies) [19], and rural residents poorer control than those in urban areas [20]; and Hispanic individuals appear to have a higher mortality risk from traumatic brain injury than African– American or white individuals (whose rates are similar) [21]. Many more examples could be given of disparities between different groups and the topic has been the subject of numerous reviews [4,19,22]. CER has an important role in testing and comparing interventions to improve health in the disadvantaged and reduce or eliminate disparities with a potentially major impact. As indicated in the report of the US Federal Coordinating Council on Comparative Effectiveness Research, “CER will be an important tool to inform decisions for those [priority] populations to reduce health disparities including medical and assistive devices, procedures and surgery, behavioral changes and delivery of systems” [5]. In this review, we classify CER by level or hierarchy of the science – basic biology, care and systems, and social/cultural context. We first consider care and healthcare systems, which have been the core of disparities research.
Disparities exist
Care & systems
An extensive body of research has documented that disparities exist in many different circumstances and for many different reasons. They are present across the disease spectrum, although variable. A few examples: there are disparities in the use of reperfusion treatment and coronary angiography, but not in aspirin and b-blocker use after myocardial infarction [12]; African– American women are 34% more likely to die from breast cancer even though they are 10% less likely to be diagnosed than white women [5,13]; there are gaps between white and African–American individuals in use and outcomes of important surgical procedures [14–16] and in dental care [17]; and comorbidities are increased
An obvious and sizable target of disparities studies is to improve the quality process measures used to evaluate care in hospital systems (e.g., HEDIS [23]). It is well established that care and systems shortcomings contribute to disparities. For example, African–American individuals are likely to undergo surgery at hospitals with lower quality ratings [24]; there are disparities in treatment for depression [25], hypertension and other illnesses; and there was perceived discrimination by African–American patients in doctor– patient communication [19]. It is most important for CER to establish remedies for these process deficiencies. Targets might include comparisons of metrics related to screening for hypertension,
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hyperlipidemia, breast and colon cancer, visits, other healthcare functions and so on. However, an important limitation has also been uncovered – process improvements may not have an equivalent result in health outcomes. A recent study from the VA showed that improvement in healthcare system and clinical processes might not in fact comparably narrow disparities outcomes [13]. The study examined quality of care process measures – that is, eye examinations in diabetes, assessment of hemoglobin A1C (HgbA1C), low density lipoprotein (LDL) and blood pressure and screening for breast and colon cancer over the years 2000–2009 and compared them to outcomes (i.e., control of HgbA1C, LDL and blood pressure) in African–American and white individuals. Process measures improved for both African–American and white individuals during the 9 years of the study and, except for mammography, disparities were less than 2%. However, the measured gains in outcomes were much more modest and disparities for HgbA1C (6%), LDL (8–9%) and blood pressure (6%) remained much greater. Thus there was a disconnect between the application of commonly used quality improvement processes and the outcomes that should result. While CER studies of care and symptoms process are most important, they may not be sufficient to remedy disparities. Among possible explanations for this dichotomy are nonadherence to medication and genetic differences in disease and response to similar medical regimens. Thus, CER studies to improve health outcomes in the disadvantaged and reduce disparities must take into account group specificity, whether incorporating social/ cultural context (for better communication and thus better participation in care) on one hand and genetics on the other. Social/cultural context
CER involving social/cultural context both within and outside of the healthcare system are crucial and well-established vehicles to reduce disparities [26,27]. The need for such studies is signaled not only by the inability of the usual medical process studies to correct disparities, but also by adverse provider or systems interactions with disadvantaged groups such as discrimination toward, or false beliefs about, certain groups. For example, as noted above, the choice of hospitals with lower quality ratings by African–Americans was found to be based in part on perceived discrimination at
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certain institutions, evidence that communication improvements are necessary [24]. Culturally and socially sensitive interventions and their application are thus an important topic for CER both within the health system to improve utilization by disadvantaged groups and beyond into the community and the arena of public policy. Types of study might include: improving cultural competence via provider and health system worker training [28]; culturally tailored or generic educational interventions including those to improve health literacy; community health interventions (via community health clinics and other community organizations such as churches) [29], coordinated case management, community based participatory research [30] and economic approaches. CER and other studies of this nature have targeted specific groups, generally with the assumption that the usual forms of communication in the healthcare system do not reach certain disadvantaged groups. They try to improve conditions in disadvantaged groups and thus may or may not make comparisons that examine disparities directly. For example (from the VA), home messaging reduced disparities in glycemic control between African–American and white individuals [31]; peer mentoring (but not financial incentives) improved glucose control among African–American individuals with diabetes [32]; a meta-analysis of studies utilizing community education, individual counseling, mass media and community health worker interventions found improvement among African–American although not Hispanic individuals [33]. Social science studies differ from those of medical interventions in a number of ways and if they are to succeed as CER, need to be respectful of the nuances and complexities. Studies involving both social and medical science require crossdisciplinary teams of researchers and community involvement [22]. The interventions may consist of multiple components and details where important variations in wording can influence results. There can be heterogeneity between interventions used for the same purpose. It may not always be clear whether the intervention chosen is in fact the best and apparently useful instruments may not be effective. For example, a well-conceived series of studies of educational materials for African–American individuals concerning knee replacement in the VA had equivocal results [34]. For these reasons, pilot studies [35] and formative studies may be useful as are regional analyses of social disparities [36]. Social context studies also
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make a point about CER in general – that collaboration is important and nowhere is it more important than in disparities research. These would include not only a variety of scientific disciplines (e.g., medicine, social science, biostatistics and so on), but also, crucially, community partners. In turn, the teams established for the research can also be teams for promoting translation via researcher/community relationships, intermediaries in the community and interactions with community social and charitable organizations. Such a team approach is thus crucial to success not only of the research, but also to its ultimate use.
be specific interactions of genetic mutations with the environment, such as interactions of asthma, which is more common in certain minority groups, with ozone and other pollutants [41]. While it is clear that genetics may elucidate racial differences relevant to healthcare, there are pitfalls. Much of the medical genetic variation is within groups rather than between groups and there is the danger that individual differences may be attributed to an entire racial or ethnic group, causing inappropriate decision-making and care [42]. In addition, while genetic variations may be of interest and appear of note, their clinical significance needs to be established.
Basic biology
As noted above, the definition of disparities includes ‘disease biology’ as a determinant and genetic differences are of course an important component. There are underlying differences in the basic biology of diseases, which may have similar phenotypes and manifestations. For example, hypertension in African–Americans differs from that in Caucasians in a variety of ways. Pharmacogenetic-related mutations [37] may also be important determinants for differences in responsiveness to drugs or drug metabolism and potentially contribute to disparities when commonly used treatments are given to groups who have a high proportion of such genes. BiDil® (isosorbide dinitrate and hydralazine hcl), an afterload-reducing medication used in congestive heart failure, may be an example of racial differences in response based on a singlerace study of African–Americans with congestive heart failure. While there is some controversy regarding the conclusion, genetic and therefore racial differences in drug response do appear to exist [38]. As genomic information from large databases such as the VA’s Million Veteran Program unfolds [39], it is important that the resulting data inform CER in different groups in relation to disparities. An example of study in this area would be a comparison of drug treatment in individuals with and without a certain gene, as has been found in the case of Coumadin (warfarin) and Plavix® (Bristol-Myers Squibb, NY, USA; clopidogrel) [40]. As genetic differences in conditions such as hypertension (common in African–American individuals), diabetes (more common in African–American, Native American and Pacific Islander individuals) and atherosclerosis become better known, it will be possible to design studies with both group and individual significance in this area. There may
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CER methodologies
A variety of methodologies can be used in disparities CER depending on the situation and those chosen should be tailored to circumstance. Whether one should compare different groups of individuals or target a single group also depends on circumstances and feasibility. Studies comparing groups are desirable, but of course a cultural intervention aimed at African–American, Hispanic or Native American individuals needs to be targeted to that individual group (although one might design complex interventional studies). In addition, studies comparing groups may require large numbers and cost, making them difficult to carry out. The basic methodologies of CER are for the most part the same as used in other research approaches [43]. Randomized controlled trials and practical clinical trials are the gold standard but because of cost and feasibility, they can only be directed at a limited number of questions. Alternate and quasi-experimental designs are important methodologic tools. These include natural experiments using instrumental variables (where changes due to factors unrelated to treatment replace randomization), propensity scores and others. Adaptive designs are potentially useful as techniques to reduce the numbers of subjects necessary and to create more efficient studies. Site randomization of medical centers [44] is another approach, as is the more difficult randomization of providers or within-hospital sections (in the private sector disparities are mainly between institutions [45], while in the VA they are mainly intra-institutional [13]). Apart from CER studies specifically aimed at disparities, it is also important for investigators to include sufficient numbers of minority groups as part of design in as many CER studies
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as possible. This would enable examination of heterogeneities of treatment effects via subgroup analysis of minority groups within these studies with the proviso that strict methodological standards be used, especially regarding prior study planning and statistical power [8]. ‘Big data’ observational studies using large clinically rich databases will be major research approaches to CER disparities. Examination of these databases using refined statistical approaches to compare groups will be an important and efficient technique for the future to acquire large amounts of data regarding disparities [46]. Combined approaches, such as multilevel modeling and mixed methods are potentially useful in the study of disparity-related CER. Multilevel modeling takes into account the fact that variables have an implicit hierarchy and incorporates all levels. The levels used in classifying disparities CER above (e.g., care/systems and social/cultural) represent such hierarchies. Multilevel studies can be combined, in parallel or sequential. Statistical analysis is complex and must be applied with rigor [47]. Mixed methods research merges quantitative and qualitative approaches – a frequent example is the combination of focus group and survey studies. Here, qualitative research can help inform quantitative instruments and generate hypotheses. Studies can be performed sequentially or convergent and there are a variety of techniques for merging data [48,49]. Community-based participatory research, defined by the Agency for Healthcare Research and Quality as “an approach that incorporates formalized structures to ensure community participation” [50], has special value for disparities research. As indicated earlier, building trust and partnerships and understanding the cultural factors that apply in both the community and healthcare setting are important in CER design. Disparities research of all kinds depends on a strong relationship of the research establishment and the community [49,51]. To assure the best use of any data derived, CER trial methodologies need to consider translation of the research into healthcare use at the beginning of the study. In this regard, the VA has piloted approaches, for example, CER via point of care randomization and health services research/health system collaborative approaches [52,53] that foster translation. Site and section randomization also enlist the system in a manner that fosters translation and use.
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CER limitations & pitfalls
CER in disparities has many possible pitfalls, a number of which have been noted above. Generally, limitations and pitfalls are similar to those of other forms of research. Disparities studies may involve complex interventions potentially in multiple groups, necessitating large numbers. For CER studies. choice of appropriate comparators to evaluate disparities may be challenging. The fact that disease is often more severe in the disadvantaged needs to be taken into account. There is a necessary lack of blinding for such tools as educational interventions. Results of studies may be site specific, especially research involving systems approaches. Most disparities. CER studies are not of long duration and it may not be clear how long the particular intervention continues to be effective and whether the health outcomes persist. Mixed methods research and multilevel models have a number of pitfalls in study design and execution [47–49] necessitating caution in their use. Informed consent documents can be a barrier to research enrollment in minorities and disadvantaged groups. Lack of trust, misinformation about the consent process and difficulties in understanding the language can discourage enrollment. Among suggestions for improvement are plain language summaries, allowing material to be brought back to family members (who may be the deciders) and most especially one-on-one interactions of researchers with potential research subjects [54]. Patient preference is another important factor in high quality care and part of the definition of disparities noted above. It also relates to the issue of equalization versus equitization. Equitization presumes high-quality care (but not patient preference due to misinformation), which, however, is not necessarily ‘the same’ among different patients and groups in the light of patient preferences. Research studies often do not incorporate these preferences into design and they may not be well understood [55]. Involving patients in research planning and protocols that include patient surveys are some of the ways to incorporate their preferences in CER. Conclusion
Overall disparities in care and outcomes persist in healthcare and it is imperative that they be addressed and overcome. It is also imperative that CER, which offers an important scientific approach to disparities research, be used to accomplish these ends.
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Future perspective
Disclaimer
The following are hopeful future possibilities: ■■ Due to oversight of process and performance measures, disparities in care and systems will be reduced;
The views expressed here are those of the authors and do not necessarily reflect those of the Department of Veterans Affairs.
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Social and cultural research will increase and its methodology will improve;
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Genetic information and associated treatment strategies will increase;
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Use of large databases for disparities CER will increase.
Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.
Executive summary Definition/background ■■ Comparative effectiveness research (CER) is a most useful approach to address disparities. ■■ The three phases of disparities are detection, understanding why disparities exist, and evaluation and implementation of interventions. ■■ CER is a valuable technique to determine which interventions are better to address disparities. ■■ A number of factors are now favorable to disparities CER, such as funding priorities in the Patient Centered Outcomes Research Institute and NIH, and the formation of large databases. Disparities exist ■■ The classification used in this paper relies on levels of science: basic biology, care and systems (considered first) and social/ cultural context. Care & systems ■■ This is an obvious and most important target for improvement via CER disparities research. ■■ However, correction of care and systems process disparities may not remedy health disparities. Social/cultural context ■■ There is a need for CER studies on this level, targeting specific groups. ■■ Studies in this area are nuanced and complex. Basic biology ■■ Genomics offers great promise in the future to delineate therapeutic and other specificity. CER methodologies ■■ These include randomized controlled trials and practical clinical trials as the gold standard, database analysis, quasi-experimental designs, multilevel modeling and mixed methods. ■■ Community involvement is important. Limitations & pitfalls ■■ These are similar to those in other clinical research. ■■ They include choice of appropriate comparators, the necessity for large numbers, site specificity of system studies and the relatively short duration of studies. ■■ Informed consent documents can be a barrier to research enrollment in minorities and needs to be addressed. ■■ Patient preference among high-quality care choices should be considered.
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