0
Article |

Parental Tolerance of False-positive Newborn Screening Results FREE

Lisa A. Prosser, MS, PhD; Joseph A. Ladapo, PhD; Donna Rusinak, BA; Susan E. Waisbren, PhD
[+] Author Affiliations

Author Affiliations: Center for Child Health Care Studies, Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care (Dr Prosser and Ms Rusinak), Program for Health Decision Science, Harvard School of Public Health (Dr Prosser), and Department of Psychiatry, Children's Hospital Boston (Dr Waisbren), Boston, Massachusetts; and PhD Program in Health Policy, Harvard University, Cambridge, Massachusetts (Dr Ladapo).


Arch Pediatr Adolesc Med. 2008;162(9):870-876. doi:10.1001/archpediatrics.2008.1.
Text Size: A A A
Published online

Objective  To measure parental tolerance for a false-positive newborn screening result by assessing perceived quality of life for screening results and health states associated with expanded newborn screening programs for metabolic disorders.

Design  Perceived quality of life was measured using time trade-off and willingness-to-pay questions for a false-positive newborn screening result and other conditions associated with metabolic disorders (ie, short-term hospitalization, dietary treatments, and developmental delay).

Setting  Telephone or in-person interviews were conducted from October 1, 2004, through January 31, 2006, for 2 populations in Massachusetts and Pennsylvania.

Participants  Parents of children who had a false-positive newborn screening result (n = 91) and parents of children with normal screening results (n = 50).

Intervention  Telephone interviews.

Main Outcome Measures  Time trade-off and willingness-to-pay amounts.

Results  Median time trade-off and willingness-to-pay amounts for parents of children with false-positive screening results were both 0 compared with parents of children with normal screening results who had median values of 1 week (P = .07) and $100 (P < .001). For both populations, dietary treatments and developmental delay elicited higher time trade-off and willingness-to-pay amounts compared with ratings for experiencing a false-positive newborn screening result or short-term hospitalization because of an undiagnosed metabolic disorder.

Conclusions  Parents have a high tolerance for false-positive newborn screening results. Preferences for test outcomes and other health states associated with screening for metabolic disorders should be included in cost-effectiveness and cost-benefit analyses of expanded newborn screening programs.

Figures in this Article

Forty-seven states currently conduct expanded newborn screening programs for metabolic disorders.1 The introduction of tandem mass spectrometry has allowed for screening of additional disorders from a single disk of the newborn blood specimen with little additional testing cost.2 Although early diagnosis of these conditions leads to improved treatment and health outcomes, expanded screening also results in a significant increase in the number of false-positive screening results.3 Estimates of the number of false-positive screening results for each true-positive screening result range from 12 to 60 or more, depending on the specificity of individual tests included in the panel.3,4 A false-positive screening result is defined as an out-of-range result that, after further follow-up testing, is not shown to indicate a metabolic disorder.

Metabolic disorders covered in newborn screening panels occur infrequently, but the consequences of undetected conditions can be substantial. For example, MCADD (medium-chain acyl coenzyme A dehydrogenase deficiency) has an annual incidence of approximately 1 in 17 000 US births and, if untreated, can lead to developmental delay and death. Early identification and dietary treatment, which includes avoidance of fasting and avoidance of intake of medium- and long-chain fatty acids, usually prevent these outcomes.5

Despite projected benefits of newborn screening, potential negative consequences are also possible.6 Repeating the initial screening test following an out-of-range result may cause stress and anxiety for parents of newborns and may have a long-term effect on the parent-child relationship.7,8 Given the potential for continued expansion in the number of disorders included in newborn screening programs, cost-effectiveness analyses should include a full accounting of the psychological and financial costs associated with false-positive screening results.9

Psychological and financial costs may also be associated with the treatment of disorders identified through newborn screening. Dietary treatment, which differs by disorder, can range from avoidance of fasting to extremely restrictive low-protein diets. Long-term adherence to these regimens can be challenging for children and their families.10 This study examined losses in health-related quality of life associated with a false-positive newborn screening result, comparing parents of children who experienced a false-positive screening result with parents of children with normal screening results. In addition, parents were asked to value health states associated with an undiagnosed metabolic disorder (ie, hospitalization, developmental delay, and dietary treatments).

SURVEY PROTOCOL

We conducted 30-minute surveys with 2 populations in Massachusetts and Pennsylvania: parents of children who received a false-positive newborn screening result and parents of children with normal screening results. Parents of children who received a false-positive screening result were enrolled by a private screening laboratory in Pennsylvania and by metabolic centers in Massachusetts when follow-up testing was performed. Parents of children with normal screening results were randomly selected from the Commonwealth of Massachusetts Department of Public Health Birth Registry of Vital Records and Statistics for live births from December 1, 2004, through September 30, 2005. Birth registry data, from which parents of children with normal screening results were selected, include only children born to married couples. We interviewed 1 parent from each family by telephone. Surveys were conducted from October 1, 2004, through January 31, 2006. Human studies approval was obtained for all aspects of the study from the institutional review boards at Children's Hospital Boston and Harvard Pilgrim Health Care.

Parents were presented with 5 descriptions: (1) the experience of one's child having a false-positive newborn screening result; (2) a hospitalization for an undiagnosed metabolic disorder; (3) a child with significant developmental delays; (4) a child with a metabolic disorder that required a protein-restricted diet, as in phenylketonuria and other disorders of amino acid metabolism; and (5) a child with a metabolic disorder that required frequent feeding and carnitine supplementation, as in many of the fatty acid disorders (Figure 1). Parents of children who had experienced a false-positive screening result were asked to recall the experience of the false-positive screening result. Parents of children with normal screening results were provided with a hypothetical description of the false-positive screening result.

Place holder to copy figure label and caption
Figure 1.

Health state descriptions.

Graphic Jump Location

This study used a preference-based approach to measure health-related quality of life. Preference-based approaches provide a summary value for a respondent's valuation of the quality of life of a particular health state, incorporating positive and negative aspects of a health state into a single number. The 2 most commonly used approaches for valuing preferences in health are utilities and willingness to pay. A utility value is typically scaled from 1.0 (representing perfect health) to 0.0 (representing a health state judged equivalent to being dead, although health states worse than death can be scaled at <0). The willingness-to-pay approach measures the value of health outcomes using dollars. Cost-effectiveness and cost-benefit analyses use such preference-based measures to establish a metric for assessing how people perceive or value specific health states.1113

Preferences (values) for health state descriptions were elicited using time trade-off and willingness-to-pay questions in response to hypothetical or experienced health scenarios. Time trade-off questions required the respondents to indicate the amount of time they would be willing to lose from their own life to prevent a specific health event in their child (Figure 1). Time trade-off amounts are preference-based measures and can be transformed into utilities.13 Theoretically, time trade-off amounts provided by respondents should be lower for less severe health states (respondents willing to give up less time) and higher for more severe health states. Willingness-to-pay questions required respondents to state the maximum amount of money they would be willing to pay to prevent a health event in their child. In our study, for both time trade-off and willingness-to-pay questions, respondents were asked whether they would be willing to pay a specified opening bid (such as 1 week or $20). Depending on the answer to the first bid, the respondents were asked to respond to either a higher or lower second bid, which was then followed by a final open-ended question about their maximum willingness to trade time from the end of their lives or willingness to pay in dollars from money that was available to them at the time of the interview. Respondents were randomized to 4 different groups of opening bids to minimize anchoring bias,14 in which the amount of the opening bid can influence the final value of time or dollars. For example, initial bids for the time trade-off questions in 1 version were 2 days, 1 week, 2 weeks, or 1 month. Parents were explicitly instructed to include changes in their own health-related quality of life due to a hypothetical scenario. Specifically, parents were asked to consider their own pain and suffering, inconvenience, and lost time for productive activities attributable to their child's illness or the need for a subsequent newborn screening test when responding to the questions (Figure 2).

Place holder to copy figure label and caption
Figure 2.

Sample time trade-off and willingness-to-pay questions.

Graphic Jump Location

Data on sociodemographic characteristics, respondent's health status, whether the respondent's child had experienced any of the described conditions, and family composition were also collected. Parents whose children experienced a false-positive screening result were also asked to state the reason for the follow-up test.8 All parents completed the Parental Stress Index (detailed results are reported elsewhere).7,8

STATISTICAL ANALYSIS

Summary statistics for time trade-off amounts, utilities, and willingness to pay were calculated separately for the 2 respondent groups. For the time trade-off analyses, we calculated discounted time trade-off values using the estimated remaining life expectancy of each respondent and a discount rate of 3% per year. By dividing the discounted time trade-off amount by the discounted life expectancy for each respondent, we calculated the disutility for each health state. The term disutility refers to the decrease in quality of life from perfect health associated with a health state. For example, if a certain health state has a utility of 0.95, the disutility for this health state is 0.05. Utilities were calculated by subtracting disutility amounts from 1. Differences in time trade-off or willingness-to-pay amounts between parents of children with false-positive screening results and parents of children with normal screening results were evaluated using the Kolmogorov-Smirnov test.15

Observations were excluded if the interviewer classified the interview as invalid (ie, the respondent was unable to understand the questions and responses were mostly missing) or if orderings of the health states were illogical (ie, the amount traded for avoiding a temporary hospitalization was greater than or equal to the amount traded for avoiding lifelong developmental delay).16,17 A secondary analysis, which included illogical responses, was conducted to evaluate the sensitivity of results to this exclusion rule.18,19

The effects of respondent characteristics on time trade-off and willingness-to-pay amounts were evaluated using estimated random effects from the generalized linear mixed models version of Poisson regression (Stata statistical software, version 9; Stata Corp, College Station, Texas). Dependent variables included in the models were respondent age, number of children in the family, whether another child in the family had a chronic medical condition, race/ethnicity, respondent type, educational level, social class, marital status, self-reported health, and employment status. We also evaluated the effect of opening bids. Regression analyses also evaluated whether a parent's understanding of the correct reason for a follow-up test or scores on the Parental Stress Index were associated with time trade-off or willingness-to-pay values to avoid a false-positive screening result while controlling for other respondent characteristics.

PARTICIPANTS

The final sample included 91 parents of children with a false-positive screening result and 50 parents of children with a normal screening result. This sample did not include 11 refusals, 5 invalid interviews, and 13 respondents with illogical response patterns excluded from the total sample. Some respondents were excluded from the time trade-off analyses but included in the willingness-to-pay analyses or vice versa, depending on whether exclusion criteria were met.

The median age of the screened child was 8.5 months for the false-positive group and 7.4 months for the normal screening group. Respondents in the false-positive group were more likely to be nonwhite, to be unmarried, and to a have lower socioeconomic status (Table 1).

Table Graphic Jump LocationTable 1. Respondent Characteristicsa
TIME TRADE-OFF AND WILLINGNESS TO PAY

The median time trade-off amount to avoid a false-positive screening result was 0 for parents of children with false-positive screening results, whereas parents of children with normal screening results were willing to trade more time, with a median time trade-off amount of 1 week (P = .07) (Table 2). Of parents of children with false-positive screening results, 57% were not willing to trade any time to avoid a false-positive screening result compared with 33% of parents of children with normal screening results. The median disutility was 0 for an experienced false-positive screening result and 0.0011 for the hypothetical false-positive screening result (on a scale where 0 disutility would represent no change from perfect health). The median willingness to pay to avoid a false-positive screening result was also significantly lower for parents whose child experienced a false-positive screening result ($0) than for parents of children with normal screening results ($100) (P < .001) (Table 2).

Table Graphic Jump LocationTable 2. Time Trade-off and Willingness-to-Pay Values for Avoiding a False-positive Screening Result

For both populations, dietary treatments and developmental delay elicited higher time trade-off and willingness-to-pay amounts compared with ratings for experiencing a false-positive newborn screening result or short-term hospitalization due to an undiagnosed metabolic disorder. Mean time trade-off amounts to avoid a short-term hospitalization were 8.8 months for parents of children with false-positive screening results and 2.6 months for parents of children with normal screening results (Table 3). For developmental delay, the mean discounted time trade-off amount was somewhat higher for parents of children with normal screening results (60.3 months) compared with parents of children with false-positive screening results (46.8 months); however, time trade-off results were not significantly different across the 2 groups of parents. Mean disutilities ranged from 0.028 for short-term hospitalization to 0.202 for developmental delay (Table 3). Mean disutilities were much higher than medians because of the skewness of the responses. Willingness-to-pay results showed similar patterns across health states and respondent groups and were also not significantly different between the 2 respondent groups (Table 3).

Table Graphic Jump LocationTable 3. Time Trade-off Amounts, Disutilities, and Willingness-to-Pay Amounts for Health State Valuations by Respondent Type, Given as Means (95% Confidence Intervals)a

Including respondents with illogical response patterns yielded similar results (additional information available from the authors). The only significant difference was a higher time trade-off amount for avoiding a short-term hospitalization when illogical responses were included for parents of children with a normal screening result. Time trade-off and disutility amounts were not consistently higher or lower when illogical responses were included. Willingness-to-pay values were lower when illogical responses were included but not significantly different from the primary analysis.

In the multivariate regression analysis, respondent sociodemographic characteristics did not have a significant effect on time trade-off or willingness-to-pay responses except that families with more children in the family responded with lower willingness-to-pay and time-trade off amounts (P = .049). Scores on the parental stress index were not correlated with time trade-off or willingness-to-pay responses. Knowing the correct reason for the follow-up test was also not associated with time trade-off or willingness-to-pay amounts to avoid a false-positive screening result.

As newborn screening programs increase the number of metabolic disorders included in the panel, the incidence of false-positive screening results will increase.3 Our results indicate that parents have a high tolerance for false-positive newborn screening results. More than half of the parents in the false-positive screening result group were not willing to pay anything to avoid false-positive screening results. Willingness-to-pay amounts for parents who had experienced a false-positive screening result were significantly lower than those for parents of children with normal screening results, suggesting that the false-positive screening result experience was not as bad as imagined by those who had not experienced it. Scores on the Parental Stress Index were not associated with time trade-off or willingness-to-pay responses, suggesting that, given the consequences of a metabolic disorder, parents are willing to tolerate the stress.

Some parents, however, were willing to trade or pay relatively high amounts to avoid the false-positive screening result experience. These were the parents who appeared to have less knowledge of newborn screening. Knowledge of the correct reason for the follow-up blood test was not associated with time trade-off or willingness-to-pay amounts for parents whose children had experienced a false-positive screening result, but our sample size was small. Additional research should focus on whether education for parents to improve understanding of newborn screening could mitigate loss in quality of life associated with false-positive screening results.

These findings are consistent with other studies7,8 that have shown some increase in parental stress associated with a false-positive newborn screening result. From an economic perspective, the loss in health-related quality of life associated with a false-positive newborn screening result appears small for most respondents and may not have a substantial effect on the results of economic evaluations of newborn screening for metabolic disorders. One study20 of the cost-effectiveness of screening for MCADD assumed a much higher disutility for a false-positive screening result (0.03) and still yielded favorable cost-effectiveness results for the screening program.

It is possible that our study design, in which participants were interviewed 6 months after the resolution of the false-positive screening result, may not have fully captured the stress and anxiety experienced during the waiting period. In addition, the description of a false-positive screening result in this study did not include any possible long-term effects, such as increased worry for the health of a child who experienced a false-positive screening result, as was found in the larger follow-up study.7

Substantial quality-of-life losses, as measured by time trade-off and willingness-to-pay amounts, were associated with developmental delay, a known outcome of many metabolic disorders. We also measured considerable quality-of-life losses for dietary treatments required to treat metabolic disorders. To our knowledge, the loss in quality of life for dietary treatments has not previously been evaluated and suggests that the difficulty of currently available treatment regimens should also be considered in cost-effectiveness or cost-benefit analyses of newborn screening programs.

Sample sizes were small for both respondent groups. The samples were also geographically limited, potentially limiting the generalizability of our results. The sample of parents of children with normal screening results was drawn from the Massachusetts Birth Registry, which is restricted to children of married parents. The respondent groups differed in sociodemographic characteristics, in part because of the characteristics of the birth registry used for identifying parents of children with normal screening results; however, sociodemographic characteristics were not significant in the regression analysis. Another potential limitation is that this study did not measure potential economic losses for any long-term effects associated with a false-positive screening result but focused on the short period of waiting after the subsequent test until the normal result was communicated to the parent. Methodologic issues also exist for valuing children's health using existing methods for time trade-off and willingness-to-pay questions, including the potential biases of using parent proxies, which was required for this study because of the age of the children whose health was being considered.21,22

Results of the secondary analysis demonstrated that these results were largely robust to the exclusion of illogical responses. Lower willingness-to-pay values in the secondary analysis were likely due to the inclusion of some respondents who were not willing to pay any amount for any health state, a response pattern that could be interpreted as a protest response.23 If these were protest responses, then they should be excluded from the primary analysis because these respondents may have a nonzero value for avoiding the health states under consideration but are objecting to some feature of the survey with a zero willingness-to-pay response.14 It can be difficult to distinguish true protest responses from those that truly place zero value on the health states in question,14 but our results were not sensitive to the inclusion of these response patterns. Exclusion rates for the parents of children with false-positive or normal screening results were consistent with similar studies that report exclusion rates of 5% to 10%.2428

Improved technology is likely to make the expansion of newborn screening programs increasingly more attractive. As new conditions are considered for inclusion in screening panels, quality-of-life effects for all related outcomes should be considered. In particular, preferences for test outcomes associated with screening for metabolic disorders should be included in cost-effectiveness and cost-benefit analyses of expanded newborn screening programs. Our results suggest that parents of newborns may tolerate false-positive screening results, and further research should explore whether education could improve this tolerance.

Correspondence: Lisa A. Prosser, MS, PhD, Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, 133 Brookline Ave, Sixth Floor, Boston, MA 02215 (lisa_prosser@hphc.org).

Accepted for Publication: February 6, 2008.

Author Contributions:Study concept and design: Prosser and Waisbren. Acquisition of data: Prosser, Rusinak, and Waisbren. Analysis and interpretation of data: Prosser, Ladapo, Rusinak, and Waisbren. Drafting of the manuscript: Prosser, Ladapo, Rusinak, and Waisbren. Critical revision of the manuscript for important intellectual content: Prosser, Ladapo, Rusinak, and Waisbren. Statistical analysis: Prosser and Ladapo. Obtained funding: Prosser and Waisbren. Administrative, technical, or material support: Rusinak. Study supervision: Prosser, Rusinak, and Waisbren.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grant 2R01HG002085-04 from the National Human Genome Research Institute.

Role of the Sponsor: The funding body had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Additional Contributions: We thank the many parents who participated in the interviews. Elizabeth Gurian, MS, Nancy Chace, BA, and Sue Lewis, BA, conducted interviews for this study; Pediatrix Screening collaborated in the recruitment of parents of children who experienced a false-positive test result; Fran Rohr, MS, RD, assisted in understanding the nature and difficulties of the treatments described in the survey; and Tracy Lieu, MD, MPH, provided guidance in the development of the study.

Kaye  CICommittee on Genetics,Accurso  F  et al.  Introduction to the newborn screening fact sheets. Pediatrics 2006;118 (3) 1304- 1312
PubMed
Insinga  RPLaessig  RHHoffman  GL Newborn screening with tandem mass spectrometry: examining its cost-effectiveness in the Wisconsin Newborn Screening Panel. J Pediatr 2002;141 (4) 524- 531
PubMed
Tarini  BAChristakis  DAWelch  HG State newborn screening in the tandem mass spectrometry era: more tests, more false-positive results. Pediatrics 2006;118 (2) 448- 456
PubMed
Zytkovicz  THFitzgerald  EFMarsden  D  et al.  Tandem mass spectrometric analysis for amino, organic, and fatty acid disorders in newborn dried blood spots: a two-year summary from the New England Newborn Screening Program. Clin Chem 2001;47 (11) 1945- 1955
PubMed
Grosse  SDKhoury  MJGreene  CLCrider  KS The epidemiology of medium chain acyl-CoA dehydrogenase deficiency: an update. Genet Med 2006;8 (4) 205- 212
PubMed
Botkin  JRClayton  EWFost  NC  et al.  Newborn screening technology: proceed with caution. Pediatrics 2006;117 (5) 1793- 1799
PubMed
Gurian  EAKinnamon  DDHenry  JJWaisbren  SE Expanded newborn screening for biochemical disorders: the effect of a false-positive result. Pediatrics 2006;117 (6) 1915- 1921
PubMed
Waisbren  SEAlbers  SAmato  S  et al.  Effect of expanded newborn screening for biochemical genetic disorders on child outcomes and parental stress. JAMA 2003;290 (19) 2564- 2572
PubMed
Grosse  SDBoyle  CAKenneson  AKhoury  MJWilford  BS From public health emergency to public health service: the implications of evolving criteria for newborn screening panels. Pediatrics 2006;117 (3) 923- 929
PubMed
Bilginsoy  CWaitzman  NLeonard  COErnst  SL Living with phenylketonuria: perspectives of patients and their families. J Inherit Metab Dis 2005;28 (5) 639- 649
PubMed
Bennett  KJTorrance  GW Measuring health state preferences and utilities: rating scale, time trade-off, and standard gamble techniques. Spilker  BQuality of Life and Pharmacoeconomics in Clinical Trials. 2nd ed. Philadelphia, PA Lippincott-Raven1996;253- 265
Russell  LBGold  MRSiegel  JEDaniels  NWeinstein  MC The role of cost-effectiveness analysis in health and medicine. JAMA 1996;276 (14) 1172- 1177
PubMed
Neumann  PJGoldie  SJWeinstein  MC Preference-based measures in economic evaluation in health care. Annu Rev Public Health 2000;21587- 611
PubMed
Bateman  IJCarson  RTDay  B  et al.  Economic Valuation With Stated Preference Techniques: A Manual.  Northampton, MA Edward Elgar2002;
Sheskin  DJ Handbook of Parametric and Nonparametric Statistical Procedures. 2nd ed. New York, NY Chapman & Hall2000;
Craig  BMRamachandran  S Relative risk of a shuffled deck: a generalizable logical consistency criterion for sample selection in health state valuation studies. Health Econ 2006;15 (8) 835- 848
PubMed
Lenert  LASturley  ARupnow  M Toward improved methods for measurement of utility: automated repair of errors in elicitations. Med Decis Making 2003;23 (1) 67- 75
PubMed
Giesler  RBAshton  CMBrody  B  et al.  Assessing the performance of utility techniques in the absence of a gold standard. Med Care 1999;37 (6) 580- 588
PubMed
Devlin  NJHansen  PKind  PWilliams  A Logical inconsistencies in survey respondents' health state valuations: a methodological challenge for estimating social tariffs. Health Econ 2003;12 (7) 529- 544
PubMed
Venditti  LNVenditti  CPBerry  GT  et al.  Newborn screening by tandem mass spectrometry for medium-chain acyl-CoA dehydrogenase deficiency: a cost-effectiveness analysis. Pediatrics 2003;112 (5) 1005- 1015
PubMed
Prosser  LAWittenberg  E Valuing children's health: response issues with using parents as proxies.  Paper presented at: Annual Meeting of the Society for Medical Decision Making October 15, 2006 Boston, MA
Prosser  LAHammitt  JKKeren  R Measuring health preferences for use in cost-utility and cost-benefit analyses of interventions in children: theoretical and methodological considerations. Pharmacoeconomics 2007;25 (9) 713- 726
PubMed
Mitchell  RCCarson  RT Using Surveys to Value Public Goods: The Contingent Valuation Method.  Washington, DC Resources for the Future1989;
Prosser  LAKuntz  KMBar-Or  AWeinstein  MC Patient and community preferences for treatments and health states in multiple sclerosis. Mult Scler 2003;9 (3) 311- 319
PubMed
Prosser  LARay  GTO'Brien  M  et al.  Preferences and willingness to pay for health states prevented by pneumococcal conjugate vaccine. Pediatrics 2004;113 (2) 283- 290
PubMed
Prosser  LABridges  CBUyeki  TM  et al.  Values for preventing influenza-related morbidity and vaccine adverse events in children. Health Qual Life Outcomes March2005;318
PubMed
Brown  GCSharma  SBrown  MMKistler  J Utility values and age-related macular degeneration. Arch Ophthalmol 2000;118 (1) 47- 51
PubMed
Salkeld  GCameron  IDCumming  RG  et al.  Quality of life related to fear of falling and hip fracture in older women: a time trade off study. BMJ 2000;320 (7231) 341- 346
PubMed

Figures

Place holder to copy figure label and caption
Figure 1.

Health state descriptions.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Sample time trade-off and willingness-to-pay questions.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Respondent Characteristicsa
Table Graphic Jump LocationTable 2. Time Trade-off and Willingness-to-Pay Values for Avoiding a False-positive Screening Result
Table Graphic Jump LocationTable 3. Time Trade-off Amounts, Disutilities, and Willingness-to-Pay Amounts for Health State Valuations by Respondent Type, Given as Means (95% Confidence Intervals)a

References

Kaye  CICommittee on Genetics,Accurso  F  et al.  Introduction to the newborn screening fact sheets. Pediatrics 2006;118 (3) 1304- 1312
PubMed
Insinga  RPLaessig  RHHoffman  GL Newborn screening with tandem mass spectrometry: examining its cost-effectiveness in the Wisconsin Newborn Screening Panel. J Pediatr 2002;141 (4) 524- 531
PubMed
Tarini  BAChristakis  DAWelch  HG State newborn screening in the tandem mass spectrometry era: more tests, more false-positive results. Pediatrics 2006;118 (2) 448- 456
PubMed
Zytkovicz  THFitzgerald  EFMarsden  D  et al.  Tandem mass spectrometric analysis for amino, organic, and fatty acid disorders in newborn dried blood spots: a two-year summary from the New England Newborn Screening Program. Clin Chem 2001;47 (11) 1945- 1955
PubMed
Grosse  SDKhoury  MJGreene  CLCrider  KS The epidemiology of medium chain acyl-CoA dehydrogenase deficiency: an update. Genet Med 2006;8 (4) 205- 212
PubMed
Botkin  JRClayton  EWFost  NC  et al.  Newborn screening technology: proceed with caution. Pediatrics 2006;117 (5) 1793- 1799
PubMed
Gurian  EAKinnamon  DDHenry  JJWaisbren  SE Expanded newborn screening for biochemical disorders: the effect of a false-positive result. Pediatrics 2006;117 (6) 1915- 1921
PubMed
Waisbren  SEAlbers  SAmato  S  et al.  Effect of expanded newborn screening for biochemical genetic disorders on child outcomes and parental stress. JAMA 2003;290 (19) 2564- 2572
PubMed
Grosse  SDBoyle  CAKenneson  AKhoury  MJWilford  BS From public health emergency to public health service: the implications of evolving criteria for newborn screening panels. Pediatrics 2006;117 (3) 923- 929
PubMed
Bilginsoy  CWaitzman  NLeonard  COErnst  SL Living with phenylketonuria: perspectives of patients and their families. J Inherit Metab Dis 2005;28 (5) 639- 649
PubMed
Bennett  KJTorrance  GW Measuring health state preferences and utilities: rating scale, time trade-off, and standard gamble techniques. Spilker  BQuality of Life and Pharmacoeconomics in Clinical Trials. 2nd ed. Philadelphia, PA Lippincott-Raven1996;253- 265
Russell  LBGold  MRSiegel  JEDaniels  NWeinstein  MC The role of cost-effectiveness analysis in health and medicine. JAMA 1996;276 (14) 1172- 1177
PubMed
Neumann  PJGoldie  SJWeinstein  MC Preference-based measures in economic evaluation in health care. Annu Rev Public Health 2000;21587- 611
PubMed
Bateman  IJCarson  RTDay  B  et al.  Economic Valuation With Stated Preference Techniques: A Manual.  Northampton, MA Edward Elgar2002;
Sheskin  DJ Handbook of Parametric and Nonparametric Statistical Procedures. 2nd ed. New York, NY Chapman & Hall2000;
Craig  BMRamachandran  S Relative risk of a shuffled deck: a generalizable logical consistency criterion for sample selection in health state valuation studies. Health Econ 2006;15 (8) 835- 848
PubMed
Lenert  LASturley  ARupnow  M Toward improved methods for measurement of utility: automated repair of errors in elicitations. Med Decis Making 2003;23 (1) 67- 75
PubMed
Giesler  RBAshton  CMBrody  B  et al.  Assessing the performance of utility techniques in the absence of a gold standard. Med Care 1999;37 (6) 580- 588
PubMed
Devlin  NJHansen  PKind  PWilliams  A Logical inconsistencies in survey respondents' health state valuations: a methodological challenge for estimating social tariffs. Health Econ 2003;12 (7) 529- 544
PubMed
Venditti  LNVenditti  CPBerry  GT  et al.  Newborn screening by tandem mass spectrometry for medium-chain acyl-CoA dehydrogenase deficiency: a cost-effectiveness analysis. Pediatrics 2003;112 (5) 1005- 1015
PubMed
Prosser  LAWittenberg  E Valuing children's health: response issues with using parents as proxies.  Paper presented at: Annual Meeting of the Society for Medical Decision Making October 15, 2006 Boston, MA
Prosser  LAHammitt  JKKeren  R Measuring health preferences for use in cost-utility and cost-benefit analyses of interventions in children: theoretical and methodological considerations. Pharmacoeconomics 2007;25 (9) 713- 726
PubMed
Mitchell  RCCarson  RT Using Surveys to Value Public Goods: The Contingent Valuation Method.  Washington, DC Resources for the Future1989;
Prosser  LAKuntz  KMBar-Or  AWeinstein  MC Patient and community preferences for treatments and health states in multiple sclerosis. Mult Scler 2003;9 (3) 311- 319
PubMed
Prosser  LARay  GTO'Brien  M  et al.  Preferences and willingness to pay for health states prevented by pneumococcal conjugate vaccine. Pediatrics 2004;113 (2) 283- 290
PubMed
Prosser  LABridges  CBUyeki  TM  et al.  Values for preventing influenza-related morbidity and vaccine adverse events in children. Health Qual Life Outcomes March2005;318
PubMed
Brown  GCSharma  SBrown  MMKistler  J Utility values and age-related macular degeneration. Arch Ophthalmol 2000;118 (1) 47- 51
PubMed
Salkeld  GCameron  IDCumming  RG  et al.  Quality of life related to fear of falling and hip fracture in older women: a time trade off study. BMJ 2000;320 (7231) 341- 346
PubMed

Correspondence

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).
Submit a Comment

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Topics
PubMed Articles