Ellen McCreedy

Decision aids for advance care planning: An overview of the state of the science

Advance care planning is a way to inform care choices for a patient who cannot express a preference and a planning tool that helps patients begin to prioritize their treatment goals. The preferences of seriously ill patients for life-sustaining interventions depend on their care goals. Some prioritize living longer to achieve life goals, whereas others may not wish to be kept alive when meaningful recovery or a particular quality of life is no longer possible (13). Religious and spiritual values and beliefs also affect goals of care (4, 5). Advance care planning helps to honor patient preferences and goals if incapacitating illness or injury prevents adequate communication (6). Decision aids help patients consider health care options. Such aids for advance care planning support the 3 key components of the process: learning about anticipated conditions and options for care; considering these options; and communicating preferences for future care, either orally or in writing. The most important information a decision aid can provide to a decision maker depends on the patients current health status and the predictability of illness trajectories (Figure). A healthy person may benefit most from general decision aids focused on choice of health care proxies and goals of care for hypothetical catastrophic situations, such as after loss of function or cognition or terminal illness. For patients with a life-threatening illness, appropriate aids focus on decisions to accept, withhold, or terminate specific treatments. Advance care planning with decision aids takes place in various settings; it is often done outside clinical settings, particularly among healthy older adults. Nonclinical partners in shared decision making may include family members, caregivers, or attorneys or other professionals. Figure. Continuum of health states during which advance care planning may be considered. Opportunity exists for expansion and improvement of advance care planning. A 2003 Agency for Healthcare Research and Quality (AHRQ) literature summary (7) found that fewer than 50% of the severely or terminally ill patients who were studied had an advance directive (a common outcome of the advance care planning process) in their medical records (811). Furthermore, only 12% of patients with an advance directive had received input from their physician in its development (9), and physicians were accurate only about 65% of the time when predicting patient preferences; they tended to assume that patients would want less life-prolonging treatment than they actually desired, even after reviewing the patients advance directive (12). Decision aids may improve participation in advance care planning and the effectiveness of communication by facilitating clear documentation across platforms and providers and by offering insights into why patients make the decisions they do. This review, commissioned as a technical brief by the AHRQ Effective Health Care Program, provides an overview of advance care planning decision aids for adults. It describes available tools, identifies a framework for future research, and summarizes published studies that used a decision aid as an intervention. Methods Key Informants In November 2013, we conducted semistructured telephone interviews (Appendix Table 1) with 7 key informants, including practicing clinicians and attorneys involved in advance care planning, experts in medical law and medical ethics, consumer advocates, and decision aid researchers and developers. We identified these informants via frequently listed and cited authors of relevant literature, Internet searches for persons with potentially relevant viewpoints, and nominations by other key informants. They contributed information about decision aids, the context in which they are used, and important issues to consider. Appendix Table 1. Interview Probes for Key Informants Literature Search We searched MEDLINE (via Ovid), the Cochrane Library, PsycINFO, and CINAHL from January 1990 to May 2014 using a search strategy based on relevant Medical Subject Headings terms and text words (Appendix Table 2). We also conducted a gray literature search of federal and state government Web sites, the Ottawa Hospital Research Institutes Decision Aid Library Inventory, Web sites of professional organizations, and leads from key informants for decision aids available to the public and in use. Appendix Table 2. MEDLINE Search Strategy We screened abstracts and full-text articles to identify English-language studies of any sample size and design that assessed the effect of a decision aid on outcomes relevant to advance care planning. We excluded studies that involved children or advance planning for psychiatric care. We also excluded studies of decision aids for current (not future or hypothetical) end-of-life decisions; studies of forms for completing advance directives, living wills, or provider orders for life-sustaining treatment that did not include an educational component, help clarify values, or prompt action; and studies that focused on implementation science questions. The reviewers read the full text of selected articles and used a standardized data extraction form to collect reported information about study populations, decision aids, and outcomes. One reviewer abstracted data by using standardized abstraction tables, and a second reviewer provided a quality check. We used the criteria developed by the International Patient Decision Aids Standards (IPDAS) Collaboration to provide a structure for describing and comparing decision aids. These criteria have been used formally to judge quality and effectiveness in existing systematic literature reviews (13, 14). Because we followed technical brief methods, we did not synthesize outcomes, rate risk of bias, or grade the strength of evidence of the literature. Role of the Funding Source The Minnesota Evidence-based Practice Center (EPC) prepared this technical brief with funding from AHRQ. The EPC collaborated with AHRQ to develop the research protocol. Staff at AHRQ helped formulate questions and reviewed the draft report but were not involved in the study selection, data extraction, or drafting of the manuscript for publication. The full report is available at www.effectivehealthcare.ahrq.gov. Results Existing Advance Care Planning Decision Aids and Context for Use In shared clinical decision making, patients and clinicians use evidence-based knowledge, weigh options against treatment goals, and consensually arrive at a clinically prudent decision concordant with patient preferences (15, 16). Although advance care planning lies within the bounds of clinical decision making, it differs from many well-studied decision processes for medical procedures (such as surgical or nonsurgical options for cancer) because patients can make decisions with no health care provider involvement by using readily available, do-it-yourself decision aids. These aids tend to target persons with only general risks for life-threatening conditions, for whom advance care planning may involve considering a wide range of possible future scenarios, eliciting preferred goals of care, or choosing a health care proxy. Although not exhaustive, Table 1 summarizes advance care planning decision aids that target a general, predominantly healthy, older adult audience. These aids, identified through the gray literature search and by key informants, are relatively easy to find online by using common search engines. The most popular issues they address include designation of a health care proxy, clarification of values and desire for comfort care at the end of life, information on living wills or other advance directives, conversation prompts for talking to loved ones or physicians about wishes, and general preferences for various life-sustaining treatments. These aids vary in the degree to which they include the 3 components of our working definition of advance care planning decision aids, which is based on the IPDAS criteria (13, 14): an education component, a structured approach to thinking about the choices a patient faces, and a way for those choices to be communicated. Table 1. Examples of General Advance Care Planning Decision Aids Publicly Available on the Internet General decision aids for advance care planning are often used in conjunction with tools to document the decisions. Health care preferences can be documented in an advance directive and stored at a Web site, such as MyDirectives (www.MyDirectives.com). One or more proxies and their powers can be documented in a durable power of attorney for health care or as part of a more comprehensive advance directive. Health care providers can record advance care planning results (from oral discussions or an advance directive) in health care records; a specific order (such as a do-not-resuscitate order); or a template most commonly called a Physician Orders for Life-Sustaining Treatment form (found at www.polst.org), which has the advantage of serving as standing orders. Most patients gain clarity about what information can best support specific advance care planning treatment decisions as they move from hypothetical to actual clinical decisions and their familiarity with health states increases or when the health state for which a decision is needed becomes more certain. For patients with predictable progressive disease (such as amyotrophic lateral sclerosis), chronic critical illness, or frailty, a structured approach to decisions in advance care planning often requires information on prognosis. Table 2, which is not exhaustive, summarizes decision aids for advance care planning that target patients with a life-limiting illness, for which the decision trajectory is often more clearly defined. These tools are distinct from the general population tools in Table 1 because they are more likely to focus on a single advance care planning topic. They also are more likely to be designed by decision-making

Decision Aids for Advance Care Planning

Advance care planning is a way to inform care choices for a patient who cannot express a preference and a planning tool that helps patients begin to prioritize their treatment goals. The preferences of seriously ill patients for life-sustaining interventions depend on their care goals. Some prioritize living longer to achieve life goals, whereas others may not wish to be kept alive when meaningful recovery or a particular quality of life is no longer possible (13). Religious and spiritual values and beliefs also affect goals of care (4, 5). Advance care planning helps to honor patient preferences and goals if incapacitating illness or injury prevents adequate communication (6). Decision aids help patients consider health care options. Such aids for advance care planning support the 3 key components of the process: learning about anticipated conditions and options for care; considering these options; and communicating preferences for future care, either orally or in writing. The most important information a decision aid can provide to a decision maker depends on the patients current health status and the predictability of illness trajectories (Figure). A healthy person may benefit most from general decision aids focused on choice of health care proxies and goals of care for hypothetical catastrophic situations, such as after loss of function or cognition or terminal illness. For patients with a life-threatening illness, appropriate aids focus on decisions to accept, withhold, or terminate specific treatments. Advance care planning with decision aids takes place in various settings; it is often done outside clinical settings, particularly among healthy older adults. Nonclinical partners in shared decision making may include family members, caregivers, or attorneys or other professionals. Figure. Continuum of health states during which advance care planning may be considered. Opportunity exists for expansion and improvement of advance care planning. A 2003 Agency for Healthcare Research and Quality (AHRQ) literature summary (7) found that fewer than 50% of the severely or terminally ill patients who were studied had an advance directive (a common outcome of the advance care planning process) in their medical records (811). Furthermore, only 12% of patients with an advance directive had received input from their physician in its development (9), and physicians were accurate only about 65% of the time when predicting patient preferences; they tended to assume that patients would want less life-prolonging treatment than they actually desired, even after reviewing the patients advance directive (12). Decision aids may improve participation in advance care planning and the effectiveness of communication by facilitating clear documentation across platforms and providers and by offering insights into why patients make the decisions they do. This review, commissioned as a technical brief by the AHRQ Effective Health Care Program, provides an overview of advance care planning decision aids for adults. It describes available tools, identifies a framework for future research, and summarizes published studies that used a decision aid as an intervention. Methods Key Informants In November 2013, we conducted semistructured telephone interviews (Appendix Table 1) with 7 key informants, including practicing clinicians and attorneys involved in advance care planning, experts in medical law and medical ethics, consumer advocates, and decision aid researchers and developers. We identified these informants via frequently listed and cited authors of relevant literature, Internet searches for persons with potentially relevant viewpoints, and nominations by other key informants. They contributed information about decision aids, the context in which they are used, and important issues to consider. Appendix Table 1. Interview Probes for Key Informants Literature Search We searched MEDLINE (via Ovid), the Cochrane Library, PsycINFO, and CINAHL from January 1990 to May 2014 using a search strategy based on relevant Medical Subject Headings terms and text words (Appendix Table 2). We also conducted a gray literature search of federal and state government Web sites, the Ottawa Hospital Research Institutes Decision Aid Library Inventory, Web sites of professional organizations, and leads from key informants for decision aids available to the public and in use. Appendix Table 2. MEDLINE Search Strategy We screened abstracts and full-text articles to identify English-language studies of any sample size and design that assessed the effect of a decision aid on outcomes relevant to advance care planning. We excluded studies that involved children or advance planning for psychiatric care. We also excluded studies of decision aids for current (not future or hypothetical) end-of-life decisions; studies of forms for completing advance directives, living wills, or provider orders for life-sustaining treatment that did not include an educational component, help clarify values, or prompt action; and studies that focused on implementation science questions. The reviewers read the full text of selected articles and used a standardized data extraction form to collect reported information about study populations, decision aids, and outcomes. One reviewer abstracted data by using standardized abstraction tables, and a second reviewer provided a quality check. We used the criteria developed by the International Patient Decision Aids Standards (IPDAS) Collaboration to provide a structure for describing and comparing decision aids. These criteria have been used formally to judge quality and effectiveness in existing systematic literature reviews (13, 14). Because we followed technical brief methods, we did not synthesize outcomes, rate risk of bias, or grade the strength of evidence of the literature. Role of the Funding Source The Minnesota Evidence-based Practice Center (EPC) prepared this technical brief with funding from AHRQ. The EPC collaborated with AHRQ to develop the research protocol. Staff at AHRQ helped formulate questions and reviewed the draft report but were not involved in the study selection, data extraction, or drafting of the manuscript for publication. The full report is available at www.effectivehealthcare.ahrq.gov. Results Existing Advance Care Planning Decision Aids and Context for Use In shared clinical decision making, patients and clinicians use evidence-based knowledge, weigh options against treatment goals, and consensually arrive at a clinically prudent decision concordant with patient preferences (15, 16). Although advance care planning lies within the bounds of clinical decision making, it differs from many well-studied decision processes for medical procedures (such as surgical or nonsurgical options for cancer) because patients can make decisions with no health care provider involvement by using readily available, do-it-yourself decision aids. These aids tend to target persons with only general risks for life-threatening conditions, for whom advance care planning may involve considering a wide range of possible future scenarios, eliciting preferred goals of care, or choosing a health care proxy. Although not exhaustive, Table 1 summarizes advance care planning decision aids that target a general, predominantly healthy, older adult audience. These aids, identified through the gray literature search and by key informants, are relatively easy to find online by using common search engines. The most popular issues they address include designation of a health care proxy, clarification of values and desire for comfort care at the end of life, information on living wills or other advance directives, conversation prompts for talking to loved ones or physicians about wishes, and general preferences for various life-sustaining treatments. These aids vary in the degree to which they include the 3 components of our working definition of advance care planning decision aids, which is based on the IPDAS criteria (13, 14): an education component, a structured approach to thinking about the choices a patient faces, and a way for those choices to be communicated. Table 1. Examples of General Advance Care Planning Decision Aids Publicly Available on the Internet General decision aids for advance care planning are often used in conjunction with tools to document the decisions. Health care preferences can be documented in an advance directive and stored at a Web site, such as MyDirectives (www.MyDirectives.com). One or more proxies and their powers can be documented in a durable power of attorney for health care or as part of a more comprehensive advance directive. Health care providers can record advance care planning results (from oral discussions or an advance directive) in health care records; a specific order (such as a do-not-resuscitate order); or a template most commonly called a Physician Orders for Life-Sustaining Treatment form (found at www.polst.org), which has the advantage of serving as standing orders. Most patients gain clarity about what information can best support specific advance care planning treatment decisions as they move from hypothetical to actual clinical decisions and their familiarity with health states increases or when the health state for which a decision is needed becomes more certain. For patients with predictable progressive disease (such as amyotrophic lateral sclerosis), chronic critical illness, or frailty, a structured approach to decisions in advance care planning often requires information on prognosis. Table 2, which is not exhaustive, summarizes decision aids for advance care planning that target patients with a life-limiting illness, for which the decision trajectory is often more clearly defined. These tools are distinct from the general population tools in Table 1 because they are more likely to focus on a single advance care planning topic. They also are more likely to be designed by decision-making

Police Brutality and Black Health: Setting the Agenda for Public Health Scholars

We investigated links between police brutality and poor health outcomes among Blacks and identified five intersecting pathways: (1) fatal injuries that increase population-specific mortality rates; (2) adverse physiological responses that increase morbidity; (3) racist public reactions that cause stress; (4) arrests, incarcerations, and legal, medical, and funeral bills that cause financial strain; and (5) integrated oppressive structures that cause systematic disempowerment. Public health scholars should champion efforts to implement surveillance of police brutality and press funders to support research to understand the experiences of people faced with police brutality. We must ask whether our own research, teaching, and service are intentionally antiracist and challenge the institutions we work in to ask the same. To reduce racial health inequities, public health scholars must rigorously explore the relationship between police brutality and health, and advocate policies that address racist oppression.

Does Cognitive Training Prevent Cognitive Decline?: A Systematic Review

Fear of losing ones cognitive ability to Alzheimer disease and related dementias (ADRD) and ultimately declining to a state considered by many to be worse than death (1) is driving a growing brain-training industry. Cognitive training programs, marketed to otherwise healthy adults and persons with a recent diagnosis of mild cognitive impairment (MCI), make bold claims for reversing brain aging. Such claims include the ability to boost cognitive reserve in midlife (with cognitive reserve referring to both the mismatch between clinical symptoms of dementia and pathologic brain lesion load at death and the repeatedly demonstrated association between educational achievement and dementia risk). However, few studies have evaluated the effect of cognitive training programs on cognitive decline or the onset of dementia, which is the outcome of interest for most people who buy these programs. This review systematically evaluates the existing literature on the effectiveness of cognitive training in preventing cognitive decline and ADRD. It is part of a larger systematic review commissioned by the National Institute on Aging to address a range of potential interventions to slow cognitive decline and prevent or delay dementia. Methods We developed and followed a standard protocol that was posted on the Agency for Healthcare Research and Quality (AHRQ) Web site (www.effectivehealthcare.ahrq.gov). Full details of the methods, including literature searches, findings, and evidence tables, are available in the final report (2). Data Sources and Searches We searched Ovid MEDLINE, PsycINFO, EMBASE, and the Cochrane Central Register of Controlled Trials for relevant literature published between January 2009 and July 2017 (see Part A of the Supplement, for searches) and hand-searched reference lists of selected articles. We identified studies published before 2009 by reviewing studies included and excluded from the 2010 AHRQ review on preventing Alzheimer disease and cognitive decline (3). Supplement. Cognitive Training Supplement Study Selection Two investigators independently reviewed titles and abstracts of search results and screened the full text of potentially eligible references. We included randomized trials of cognitive training interventions enrolling adults with either normal cognition or MCI if the studies followed participants for at least 6 months, provided cognitive performance or incident dementia outcomes, and were published in English. We excluded studies that enrolled only persons diagnosed with dementia. The final health outcome of interest was incident ADRD. Intermediate health outcomes of interest included performance on cognitive testing, biomarker protein levels, brain matter volume, and brain cell activity level. No restrictions were placed on sample size or comparator type. Data Extraction and Quality Assessment One reviewer extracted study, population, intervention, comparator, and setting characteristics as well as the funding source from all eligible studies. Risk of bias was assessed independently from full texts by 2 investigators using an instrument based on AHRQ guidance (4). Risk of bias was individually reviewed overall and for each outcome and time point, and was summarized as low, medium, or high on the basis of a summary of bias risk across risk-of-bias domains and confidence that results were credible given the studys limitations. Outcomes and adverse events were extracted from studies with low to medium risk of bias. A second reviewer checked the quality of all data. Data Synthesis and Analysis Only studies with low or medium risk of bias were summarized, because we judged findings from studies with high risk of bias to lack validity, have little meaning, or be easily misinterpreted. Because studies used a highly varied set of outcome measures, neuropsychological tests were categorized by the following specific cognitive domains to facilitate analysis: executive function, attention, and processing speed; memory; language; and visuospatial abilities (Supplement Table A1). The domains of executive function, attention, and processing speed were grouped together because cognitive tests frequently measure all 3 of these related domains. Because studies analyzed and reported cognitive test results in many different ways, making it difficult or impossible to determine effect size or to assess whether between-group differences in scores or subscores were clinically meaningful (Supplement Table A2), we analyzed and reported cognitive test results by direction of effect and statistical significance. When we identified at least 2 studies or 1 large study (>500 participants) for a treatment comparison, 2 reviewers graded strength of evidence for each outcome on the basis of study limitations, directness, consistency, and precision; otherwise, strength of evidence was graded as insufficient. Assessments were confirmed by consensus. Role of the Funding Source The National Institute on Aging of the National Institutes of Health requested this report from the AHRQ Evidence-based Practice Center Program. The funding agencies provided comments on draft reports but had no role in data collection, analysis, interpretation, or manuscript development. Results We identified 35 publications of 34 unique randomized controlled trials of cognitive training interventions, 11 of which had medium or low risk of bias (516). Only 1 trial was industry funded (8), whereas in 3 cases, trial funding was not reported (9, 15, 16). (See the Supplement Figure and Supplement Tables C1 to C4 for the literature flow diagram, evidence tables, and risk-of-bias assessments.) The Table summarizes the overall strength-of-evidence findings. For cognitively normal adults, moderate-strength evidence suggests that cognitive training in a particular domain improves performance in that domain compared with inactive or attention control populations. These results are driven largely by the results from the ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly) trial. Low-strength evidence suggests that for persons with MCI, cognitive training in a particular domain does not improve performance in that domain compared with controls. The MCI trials have more limitations and are less precise than the studies conducted with cognitively normal participants. Evidence is insufficient for incident MCI or ADRD outcomes. Table. Summary of Conclusions and Strength of Evidence for Cognitive Training in Adults With Normal Cognition or MCI* Studies in Cognitively Normal Populations Six trials with low to medium risk of bias tested training interventions in cognitively normal older adults (510). Sample sizes for the selected studies ranged from 40 to 2832 participants. Interventions lasted from 2 weeks to 6 months; follow-up ranged from 6 months to 2 years. Three of the 6 trials used only computer-based interventions (68), 2 used a combination of computer and noncomputer (paper-and-pencil) interventions (5, 9), and 1 used group-based competition to increase divergent thinking (10). Three of the computer-based interventions were designed to increase performance on a specific cognitive domain (such as processing speed) (5, 6, 9), 1 used computers for cognitive stimulation more generally (7), and 1 used a computer program designed to train several cognitive domains (8). Comparators included both inactive (5, 7, 8, 10) and attention controls (6, 9). No studies reported adverse effects. The largest trial of cognitive training, ACTIVE, randomly assigned 2832 older adults (mean age, 74 years) without clinically significant cognitive impairment to 1 of 3 training groups or a no-contact control group (5). In each training group, a different cognitive domain was targeted: memory, reasoning, or processing speed. Participants in the intervention groups received 10 trainings of 60 to 70 minutes over 6 weeks. Cognitive testing outcomes included measuring changes in domain-specific test performance. Patient-centered cognitive outcomes included measuring changes in everyday problem solving (such as the ability to identify information on medication bottles), everyday speed (such as the time required to find food items on a grocery shelf), driving, and degree of dependency in completing activities of daily living and instrumental activities of daily living. Incident MCI or ADRD was not a prespecified outcome. Although 5- and 10-year outcomes from the ACTIVE trial have been published (17, 18), only the results from the 2-year study had a medium risk of bias (5). At 2 years, ACTIVE participants showed improvement in the cognitive domains in which they were trained (for example, those who received memory training improved on memory-related tasks compared with control participants), but no statistically significant differences were found among groups with regard to other cognitive outcomes (for example, persons who received training in memory did not do better than control participants on reasoning tasks). Intervention and control groups did not differ in other patient-centered cognitive outcomes at 2-year follow-up. Modeled on the visual process and speed training group of the ACTIVE trial, IHAMS (the Iowa Healthy and Active Minds Study) (n= 681) (6) randomly assigned adults by age group (50 to 64 years vs. 65 years) to visual processing speed training at the study center, visual processing speed training on the participants home computer, or computerized crossword puzzles (attention control group). Two-hour training sessions were held once a week for 5 weeks. Participants assigned to the intervention at the training center also received a booster training at 11 months. One year after training, both intervention groups showed statistically significant improvement in the primary outcome of the Useful Field of View test compared with the attention control group. The IHAMS participants also were administered 8 secondary cognitive tests on which they had not been tr

Hearing loss: Why does It matter for nursing homes?

Over the past decade, hearing loss has emerged as a key issue for aging and health. We describe why hearing loss may be especially disabling in nursing home settings and provide an estimate of prevalence using the Minimum Data Set (MDS v.3.0). We outline steps to mitigate hearing loss. Many solutions are inexpensive and low-tech, but require significant awareness and institutional commitment.

The Meaning and Predictive Value of Self-rated Mental Health among Persons with a Mental Health Problem:

Self-rated health is a valid measure of health that predicts quality of life, morbidity, and mortality. Its predictive value reflects a conceptualization of health that goes beyond a traditional medical model. However, less is known about self-rated mental health (SRMH). Using data from the Medical Expenditure Panel Survey (N = 2,547), we examine how rating your mental health as good—despite meeting criteria for a mental health problem—predicts outcomes. We found that 62% of people with a mental health problem rated their mental health positively. Persons who rated their mental health as good (compared to poor) had 30% lower odds of having a mental health problem at follow-up. Even without treatment, persons with a mental health problem did better if they perceived their mental health positively. SRMH might comprise information beyond the experience of symptoms. Understanding the unobserved information individuals incorporate into SRMH will help us improve screening and treatment interventions.

What we learned through asking about evidence: A model for interdisciplinary student engagement

ABSTRACT Traditional university learning modalities of lectures and examinations do not prepare students fully for the evolving and complex world of gerontology and geriatrics. Students involved in more active, self-directed learning can develop a wider breadth of knowledge and perform better on practical examinations. This article describes the Evidence in Aging (EIA) study as a model of active learning with the aim of preparing students to be effective interdisciplinary researchers, educators, and leaders in aging. We focus particularly on the experiences and reflections of graduate students who collaborated with faculty mentors on study design, data collection, and analysis. Students acquired new methodological skills, gained exposure to diverse disciplines, built interdisciplinary understanding, and cultivated professional development. The EIA study is a model for innovative student engagement and collaboration, interactive learning, and critical scholarly development. Lessons learned can be applied to a range of collaborative research projects in gerontology and geriatrics education.

Decomposition of moral hazard

This study seeks to simulate the portion of moral hazard that is due to the income transfer contained in the coinsurance price reduction. Healthcare spending of uninsured individuals from the MEPS with a priority health condition is compared with the predicted counterfactual spending of those same individuals if they were insured with either (1) a conventional policy that paid off with a coinsurance rate or (2) a contingent claims policy that paid off by a lump sum payment upon becoming ill. The lump sum payment is set to be equal to the insurers predicted spending under the coinsurance policy. The proportion of moral hazard that is efficient is calculated as the proportion of total moral hazard that is generated by this lump sum payment. We find that the efficient proportion of moral hazard varies from disease to disease, but is the highest for those with diabetes and cancer.