Medical Decision Making

Objective. Assigning utilities to hypothetical health states requires that the health states be described in adequate detail, but there is no agreement on exactly how health states should be described. We assess utilities from the general public for health states common in scleroderma (SSc) by describing the health states in writing alone v. with photographs of patients with SSc. Methods. Subjects rated several SSc health states on a 0 to 100 rating scale (RS) and completed computer-assisted time tradeoff (TTO; range, 0.0—1.0) and standard gamble (SG; range, 0.0—1.0) assessments. Half of the subjects were assigned to be shown photographs of patients with SSc health states in addition to written health state descriptions, whereas the other half were given only the written descriptions. Results. Of the 213 participants, 133 (62%) were female, 138 (65%) were Caucasian, and 62 (29%) were African American. Median RS, TTO, and SG scores for 5 SSc health states ranged from 20 to 70, 0.28 to 0.94, and 0.50 to 0.90, respectively. In bivariate analyses, showing pictures was associated with lower RS scores for 2 of 5 health states and lower SG values for all 5 health states (P < 0:05 for comparison of pictures v. no pictures), but with no difference in TTO values. Multivariable analyses revealed negative associations between pictures and SG valuations for the 3 most severe SSc health states (R² range, 0.04—0.08). Conclusion. Adding pictures of people with SSc to written health state descriptions can affect valuations of SSc health states, although the effect differs by valuation measurement method and by health state severity.

Background . People overestimate the impact of health conditions on happiness, putatively because they focus excessively on resulting negative consequences while disregarding the impact of other unchanged aspects of life on happiness. However, typically, inferences about accuracy have been based on a confound of the viewpoint of judgments (Self/Other) with whether the respondent has the condition (Have/Not-have)—an important issue because people often judge themselves as different to others. This study measured Haves' and Not-haves' judged impact on happiness—for self and other—of several chronic health conditions, and whether ``defocusing'' respondents improved judgment. Method . 80 Haves and 80 Not-haves predicted the impact of health conditions on their own and others' happiness using a questionnaire, after some participated in a defocusing exercise. Haves also indicated their preferences for their health condition over other conditions. Results . Although Haves made more accurate forecasts than Not-haves, both overestimated the impact of health conditions on others' happiness— yet defocusing respondents prior to prediction had no effect. Haves were aware that Not-haves misjudge Haves' happiness, but underestimated the bias in Not-haves' judgments. Whereas Haves judged they were more happy than other Haves, Not-haves predicted they would be less happy than others if living with a health condition. Finally, Haves' preferences for health conditions exhibited an endowment effect. Conclusion . The existence of an impact bias is not attributable to the confounding of self/other and Have/Not-have in other studies. People who have a condition forecasted others' happiness more accurately, suggesting that experience of one condition helps in comprehending life with another.

Background. Caregiver productivity costs are an important component of the overall cost of care for individuals with birth defects and developmental disabilities, yet few studies provide estimates for use in economic evaluations. Objective. This study estimates labor market productivity costs for caregivers of children and adolescents with spina bifida. Methods. Case families were recruited from a state birth defects registry in Arkansas. Primary caregivers of children with spina bifida (N = 98) reported their employment status in the past year and demographic characteristics. Controls were abstracted from the Current Population Survey covering the state of Arkansas for the same time period (N = 416). Estimates from regression analyses of labor market outcomes were used to calculate differences in hours worked per week and lifetime costs. Results. Caregivers of children with spina bifida worked an annual average of 7.5 to 11.3 hours less per week depending on the disability severity. Differences in work hours by caregivers of children with spina bifida translated into lifetime costs of $133,755 in 2002 dollars using a 3% discount rate and an age- and sex-adjusted earnings profile. Including caregivers' labor market productivity costs in prevention effectiveness estimates raises the net cost savings per averted case of spina bifida by 48% over the medical care costs alone. Conclusions. Information on labor market productivity costs for caregivers can be used to better inform economic evaluations of prevention and treatment strategies for spina bifida. Cost-effectiveness calculations that omit caregiver productivity costs substantially overstate the net costs of the intervention and underestimate societal value.

Aim . To estimate the cost-effectiveness of universal childhood rotavirus vaccination in Belgium, taking into account the impact of caregiver burden and the burden of sick children for whom no medical care is sought (``no medical care''). Methods . A cohort of newborns is modeled in relation to costs and health outcomes for rotavirus disease, distinguishing episodes leading to consultations, hospitalizations, and deaths from no medical care episodes. Fully funded universal vaccination is compared with no vaccination as well as with the current situation in Belgium, whereby the 2-dose Rotarix or the 3-dose RotaTeq vaccine can be bought at market prices, which are partially reimbursed. Results . Compared with no vaccination, fully funded universal rotavirus vaccination would cost 51,030 per quality-adjusted life year (QALY) gained with Rotarix and 65,767 with RotaTeq (for society, 7572 and 30,227 per QALY, respectively). However, there is considerable uncertainty due to some analytical choices: the proportion of simulations with an acceptable incremental cost-effectiveness ratio (given a willingness to pay 50,000 for an additional QALY), increases from 2%/0.6% (Rotarix/RotaTeq) to 86%/59% when considering no medical care, and including 2 caregivers to estimate QALY loss instead of zero. Uncertainty is greater still under the societal than under the health care payer perspective. Conclusion . For the Belgian health care payer, at current vaccine prices, universal childhood rotavirus vaccination is unlikely to be judged cost-effective versus no vaccination but would be a more efficient and equitable choice than continuing with current practice.

Background . More than one quarter of Korean American children are uninsured, and many are eligible for children's health insurance programs. The objective of this study is to evaluate the effectiveness and cost-effectiveness of different school-based health insurance strategies to provide coverage to uninsured Korean American children. Method . The authors used a quasi-experimental nonequivalent control group design, conducted from July to December 2005 in Los Angeles, California. The subjects were Korean American children in 3 groups/schools (n = 1181). Parents received a variation of outreach methods (i.e., information sheets, school site presentations, automated telephone messages, personal telephone calls) and application assistance (i.e., telephone helpline, on-site assistance). The authors used bivariate and multivariable analysis to assess effectiveness. Cost-effectiveness was performed using a 3-stage model and Monte Carlo simulation. Results . Of the uninsured in the intensive intervention group, 41% applied for insurance compared with 13% of the control group (P = 0:002). This success was due to personal telephone calls. Of the uninsured in the intensive intervention group, 23% enrolled in insurance compared with 10% of the control group (P = 0:048). The most common reason reported by parents for nonenrollment despite assistance was failure to mail in the application. Despite the small increase in insurance enrollment from the intensive intervention strategy compared with the control group, it is cost-effective. Conclusions . Personal telephone calls are effective in increasing outreach for children's health insurance application. However, more research is needed to investigate why parents fail to mail in the application. Ultimately, insuring more children will result in cost-effective improvements in quality-adjusted life years.

Background. Decisional conflict is defined as personal uncertainty about which course of action to take when choice among competing options involves risk, regret, or challenge to personal life values. It is influenced by inadequate knowledge, unclear values, inadequate support, and the perception that an ineffective decision has been made. Until recently, it has been studied at the individual level, which ignores the interpersonal system between patients and physicians. Objective. To explore the effect of feeling uninformed, unclear values, inadequate support, and the perception that an ineffective decision has been made on one own's outcome (actor effect) and on the other person's outcome (partner effect). Methods. After a clinical encounter, modifiable deficits and personal uncertainty were measured in physicians and patients using the Decisional Conflict Scale. Structural equation modeling was used to measure the parameters of the Actor-Partner Interdependence Model. Results. A total of 112 dyads of physicians and patients were included in the analysis. For both patients and physicians, 2 actor effects, unclear values (P < 0:0001) and the perception that an ineffective decision has been made (P < 0:0001), were found to be positively correlated with personal uncertainty. One partner effect, feeling uninformed (P=0:03), was found to be negatively correlated with personal uncertainty. Conclusions. Personal uncertainty of patients and physicians is influenced not only by their respective deficits but also by the deficits of the other member of the dyad. Our results indicate that the more unclear the expression of their own values and the more they perceive that an ineffective choice had been made, the more both physicians and patients experience personal uncertainty. They also indicate that the less uninformed they feel, the more both physicians and patients experience personal uncertainty.

Objective . To assess the effect on risk-adjustment of inpatient mortality rates of progressively enhancing administrative claims data with clinical data that are increasingly expensive to obtain. Data Sources . Claims and abstracted clinical data on patients hospitalized for 5 medical conditions and 3 surgical procedures at 188 Pennsylvania hospitals from July 2000 through June 2003. Methods . Risk-adjustment models for inpatient mortality were derived using claims data with secondary diagnoses limited to conditions unlikely to be hospital-acquired complications. Models were enhanced with one or more of 1) secondary diagnoses inferred from clinical data to have been present-on-admission (POA), 2) secondary diagnoses not coded on claims but documented in medical records as POA, 3) numerical laboratory results from the first hospital day, and 4) all available clinical data from the first hospital day. Alternative models were compared using c-statistics, the magnitude of errors in prediction for individual cases, and the percentage of hospitals with aggregate errors in prediction exceeding specified thresholds. Results . More complete coding of a few under-reported secondary diagnoses and adding numerical laboratory results to claims data substantially improved predictions of inpatient mortality. Little improvement resulted from increasing the maximum number of available secondary diagnoses or adding additional clinical data. Conclusions . Increasing the completeness and consistency of reporting a few secondary diagnosis codes for findings POA and merging claims data with numerical laboratory values improved risk adjustment of inpatient mortality rates. Expensive abstraction of additional clinical information from medical records resulted in little further improvement.

Background . The effects of variation in disease progression between individuals on the effectiveness of screening have been assessed extensively in the literature. For several diseases, progression may also vary within individuals over time. The authors study the effects of intraindividual variation and the combined effects of inter- and intraindividual variation in disease progression on the effectiveness of screening. Methods . The authors investigated the risk reduction of aneurysmal subarachnoid hemorrhage (SAH) achieved by screening for intracranial aneurysms in a simulation study as a function of the inter- and intraindividual variation in the risk of aneurysm rupture. They also extended a previously constructed Markov model for the cost-effectiveness analysis of screening for new aneurysms in patients with clipped aneurysms after SAH. A time-varying risk of aneurysm rupture was introduced, and the influence of this variation on cost-effectiveness was assessed. Results . The risk reduction provided by screening decreased with increasing intraindividual variation in disease progression. The expected number of prevented instances of SAH was overestimated by 58% in this simulation study when high degrees of inter- and intraindividual variation were present. Interindividual variation alone resulted in up to 33% overestimation and intraindividual variation in up to 43% overestimation. In the extended Markov model, screening benefits were overestimated by 24% when a high degree of intraindividual variation was present but ignored. Conclusions . If intraindividual variation in disease progression is ignored in decision models, subsequent cost-effectiveness analyses of screening strategies will overestimate the benefits provided by screening. This bias is comparable to, but partially independent of, the bias caused by ignoring interindividual heterogeneity.

Purpose . To describe a method for quantitatively dealing with drug dose in comparative effectiveness reviews. Second-generation antidepressants are used as an example to illustrate this method and to determine whether dose influences conclusions on comparative effectiveness. Methods . Studies previously identified in a systematic review of second-generation antidepressants were included if data on drug dose were available. The usual dosing range for each drug was defined and then used to create 2- and 3-level dose categories. Placebo-controlled data were used to calculate overall effect sizes for the drug class and effect sizes stratified by drug dose. Meta-regression tested the impact of dose on effect size. Weighted mean differences and risk ratios were calculated for comparative studies, stratifying by whether compared doses were equivalent. Results . The dose classification method was able to identify dose-response trends in the context of meta-analysis. Compared to low-dose studies, medium- and high-dose studies had a 1- to 2-point greater differential in mean Hamilton Depression Rating Scale (HAM-D) change (P < 0:001). Dose was not a statistically significant predictor of categorical HAM-D response. Among comparative trials with nonequivalent doses, trends favored higher dose categories but generally were not statistically significant. Conclusions . A structured method for quantitatively dealing with drug dose in comparative effectiveness reviews is described, with application to the second-generation antidepressants. Dose-dependent reductions in HAM-D scores were identified, although differences did not translate into better response rates for higher doses. Dose equivalency was not a significant factor among comparative studies in second-generation antidepressants.

The assessment of the benefits and risks associated with a medicine's use requires careful consideration of a wealth of information of varying format and quality, ranging from efficacy and safety data derived from randomized clinical trials to statistical results from health outcomes studies to spontaneously reported adverse events. Contrary to the expectations of patients, physicians, and regulators, the literature offers little guidance as to how to strike an appropriate balance between benefit and risk. Although a qualitative listing of a medicine's benefits and risks is useful, much could be gained from a systematic and transparent process to evaluate a medicine's pre- and postmarketing performance. The authors propose a representational model based on multicriteria decision analysis that can incorporate both evaluative judgments from different perspectives (e.g., physician, patient) and quantitative data to inform tradeoffs between multiple benefit and multiple risk elements in a logically consistent and transparent manner. The model is designed to highlight the relative merits and deficits of treatment alternatives in well-defined and specific contexts. It is intended to serve as a common platform to facilitate focused benefit-risk tradeoff discussions between scientists, physicians, regulatory authorities, and pharmaceutical companies, and to assist in the communication of clear and consistent messages regarding those tradeoffs.

The utility of diagnostic tests derives from the ability to translate the population concepts of sensitivity and specificity into information that will be useful for the individual patient: the predictive value of the result. As the array of available diagnostic testing broadens, there is a temptation to de-emphasize history and physical findings and defer to the objective rigor of technology. However, diagnostic test interpretation is not always straightforward. One significant barrier to routine use of probability-based test interpretation is the uncertainty inherent in pretest probability estimation, the critical first step of Bayesian reasoning. The context in which this uncertainty presents the greatest challenge is when test results oppose clinical judgment. It is this situation when decision support would be most helpful. The authors propose a simple graphical approach that incorporates uncertainty in pretest probability and has specific application to the interpretation of unexpected results. This method quantitatively demonstrates how uncertainty in disease probability may be amplified when test results are unexpected (opposing clinical judgment), even for tests with high sensitivity and specificity. The authors provide a simple nomogram for determining whether an unexpected test result suggests that one should ``switch diagnostic sides.'' This graphical framework overcomes the limitation of pretest probability uncertainty in Bayesian analysis and guides decision making when it is most challenging: interpretation of unexpected test results.

Background . Osteoporosis is a common disorder of the skeleton that increases bone fragility and the risk of fracture. Bisphosphonates have become the reference treatment for postmenopausal osteoporosis because of their proven efficacy in reducing fracture rates. The effectiveness of bisphosphonates is, however, limited by poor treatment adherence and persistence with treatment. A model has been designed to simulate the impact of improving persistence rates on treatment effectiveness. Methods . The Markov model followed a cohort of patients over 10 years to estimate the total number of incident osteoporotic fractures by age for the overall population of women with diagnosed postmenopausal osteoporosis in France (mean age, 71.1 years ±9.6; range, 50-96 years). The impact of clinical efficacy, persistence, and residual treatment effects data on predicted fracture risk was also estimated in the model. Results . Predicted numbers of incident fractures appeared consistent with published data. Compared with no treatment, the relative risk of fracture over 10 years was 0.831 for weekly bisphosphonate treatment with an assumed persistence rate of 51% after 1 year (absolute risk reduction = 11.4%). This relative risk decreased to 0.731 (absolute risk reduction=18.1%) if hypothetical full-treatment persistence was achieved. In terms of public health, improving persistence with bisphosphonate treatment by only 20% could have the same impact as a 20.2% increase in clinical efficacy. The benefit associated with improved persistence declines as full persistence is approached. Conclusion . Improving persistence can increase treatment effectiveness. Giving greater priority to persistence interventions might have a greater impact on the health of osteoporotic women than advances in treatment efficacy.

Background . A matching heuristic (MH) model of decision making has been evaluated previously in a series of studies on medical decision making. The authors' purpose is to evaluate an extended MH model that considers the prevalence of cue values. Methods . Data from 2 previous studies were reanalyzed, one on judgments regarding drug treatment of hyperlipidemia and the other on diagnosing heart failure. The original MH model and the extended MH model were compared with logistic regression (LR) in terms of fit to actual judgments, number of cues, and the extent to which the cues were consistent with clinical guidelines. Results . There was a slightly better fit with LR compared with MH. The extended MH model gave a significantly better fit than the original MH model in the drug treatment task. In the diagnostic task, the number of cues was significantly lower in the MH models compared to LR, whereas in the therapeutic task, LR could be less or more frugal than the matching heuristic models depending on the significance level chosen for inclusion of cues. For the original MH model, but not for the extended MH model or LR, the most important cues in the drug treatment task were often used in a direction contrary to treatment guidelines. Conclusions . The extended MH model represents an improvement in that prevalence of cue values is adequately taken into account, which in turn may result in better fit and in better agreement with medical guidelines in the evaluation of cues.