Expanded Newborn Screening for Genetic and Metabolic Disorders: Modeling Costs and Outcomes
By Hubbard, Heddy Bishop
Executive Summary Newborn screening for genetic and metabolic disorders is a state-based public health program in the United States, for the elimination and/or reduction of associated mortality, morbidity, and disabilities.
As new technologies for newborn screening and new interventions for treatment are realized, it will be increasingly important for health leaders and policymakers to have data to inform their decisions regarding expanding newborn testing.
The entire costs of a screening program, including not only instrumentation but also labor and time costs; initial, repeat, and confirmatory testing; screening sensitivity and specificity; and short and long-term followup, should be considered in decisions regarding expansion of screening programs.
The decision model cited in this study can serve as a tool in exploring alternatives for critical decisions regarding the addition of new disorders to existing newborn screening panels.
The evaluation of genetic disorders in this study can be used as a prototype of an approach to evaluating screening for any newborn genetic/metabolic disorder.
NEWBORN SCREENING FOR genetic and metabolic disorders is a state- based public health program in the United States for the elimination and/or reduction of associated mortality, morbidity, and disabilities (March of Dimes, 2004). Every state has a screening program, but due to each state’s unique public health authority, there is no national universal panel of newborn disorders. Lack of uniformity leads to both incomplete evidence of effectiveness and increased variation in services (Wennberg, 2004a, 2004b).
In 2005, a federal panel, under the auspices of the Health Resources and Services Administration, utilized findings from an American College of Medical Genetics (ACMG) study to recommend a panel for screening that includes 29 genetic/metabolic disorders. Based on specific evaluation criteria, the disorders were ranked from 1 (highest priority) to 29, with Medium-chain acyl-CoA dehydro- genase deficiency (MCADD) being first in the ranking and Beta- ketoth-iolase (BKT) ranking number 29 (ACMG, 2005). Because of this recommendation, several state newborn screening programs are expanding to include some or all of these 29 disorders.
Screening with tandem mass spectrometry (MS/MS) allows for simultaneous detection of a wide range of disorders, with a demonstrated specificity of 100% in identifying certain disorders (Maier et al., 2005). Therefore, the addition of new disorders to screening panels may seem like a fairly inexpensive or even “free” investment. However, the entire costs of a screening program, including not only instrumentation but also labor and time costs; initial, repeat, and confirmatory testing; screening sensitivity and specificity; and short and long-term followup, should be considered in decisions regarding expansion of screening programs.
A cost-consequence analysis utilizing decision methods is used to examine two disorders: MCADD, a disorder previously recommended for screening, and BKT, a disorder newly recommended for newborn screening. The research design is framed by the Behavioral Model of Health Services Use, developed by Andersen (1995) and others (Phillips, Morrison, Andersen, & Aday, 1998).
Genetic Disorders
Medium-chain acyl-CoA dehy-drogenase deficiency (MCADD). MCADD is a recessively inherited metabolic disorder of beta-oxidation of fatty acids (Keens, 2002). It classically presents during infancy and early childhood with a severe illness characterized by encephalo- pathy and hypoglycemia (Wang, Fernhoff, Hannon, & Khoury, 1999). This is usually precipitated by fever, gastroenteritis, and fasting.
Of children with MCADD presenting clinically without the benefit of newborn screening, between one in five and one in four children will die or suffer severe disability (Grosse, Khoury, Greene, Crider, & Pollitt, 2006). Approximately 33% of survivors will have irreversible neuro logic damage (Iafolla, Thompson, & Roe, 1994; Pollitt & Leonard, 1998). Those who survive their first crisis are at high risk of attention deficit disorder, speech problems, hypoto- nia, seizures, chronic headaches, abdominal pain, failure to thrive, and cerebral palsy (Nyhan & Ozand, 1998). Because of variable and nonspecific clinical presentation, MCADD is often not recognized or diagnosed (Grosse et al., 2006). Almost all deaths have been reported in undiagnosed persons (Carpenter, Wiley, Sim, Heath, & Wilcken, 2001).
MCADD is prevalent in Caucasians and is particularly common in populations of northern European origin (Carpenter et al., 2001). On the basis of U.S. studies of screening program data, Grosse and colleagues (2006) report that the pooled estimate of incidence of MCADD for the United States is 1 in 17,000 births, with an approximate 95% confidence interval from 1 in 15,000 to 1 in 20,000. For purposes of this study, the incidence of MCADD is 1:17,000.
Diagnostic evaluation for MCADD includes urine organic acids, plasma acylcarnitine analysis, and urine acylglycine analysis. The diagnosis is confirmed through mutation analysis of the MCADD gene (ACMG, 2006; Insinga, Laessig, & Hoffman, 2002).
Researchers, clinicians, and policymakers need to understand the costs and outcomes of MCADD screening for several reasons: it is fairly common, it is frequently screened by MS/MS technology, it was recommended as one of nine disorders for screening prior to the latest federal recommendation to expand the number of disorders, and it is the disorder given the highest priority score in the evaluation system for screening criteria developed by ACMG (2005). In addition, early diagnosis may be potentially effective in reducing morbidity and mortality (Iafolla et al., 1994; Wilson, Champion, Collins, Clayton, & Leonard, 1999).
Beta-ketothiolase deficiency (BKT). The second genetic disorder included in this study is an organic acid disorder called Mitochon- drial acetoacetyl-CoA thiolase (T2), commonly known as Beta-ketoth- iolase deficiency. A rare disorder, it has a prevalence of less than 1:100,000 (Watson, 2004) and has been reported in only 50 to 60 individuals worldwide (Fukao, 2004; Monastiri, Amri, Limam, Kaabachi, & Guediche, 1999). For purposes of this study, the prevalence is 1:100,000.
Mutations in the ACAT1 gene are the cause of BKT deficiency. The enzyme produced by this gene plays a major role in breaking down proteins and fats in the diet. When the enzyme is reduced or eliminated, the body is unable to process ketones properly. Hence, ketone levels rise and the blood becomes acidic, which impairs tissue function, especially in the central nervous system (National Library of Medicine, 2005).
The average age of onset of this disorder is from 6 to 24 months. The disorder is characterized by normal development, followed by a gradual loss of mental and motor skills. The symptoms include vomiting, dehydration, dyspnea, malaise, and occasionally convulsions. These ketoacidotic attacks can sometimes lead to coma. Attacks are often triggered by infection, fasting, and stress (National Library of Medicine, 2005). Usually, no symptoms are manifested between intermittent ketoacidotic episodes (Fukao, 2004).
After initial and/or second-tier screening for BKT, a confirmatory diagnosis is made through urinary urine organic acid analysis and plasma acylcarnitines (Fukao, 2004).
BKT was not included in earlier (pre-2005) recommendations for screening. It is screened by MS/MS, it is very rare, and it is ranked as the last disorder on the panel of 29 recommended disorders for a uniform national screening panel.
Methods
Study design. States vary in their newborn screening policies and practices. For purposes of this study, data on screening practices were gathered from Maryland’s 2005 newborn screening program. The program provides testing and followup for approximately 70,000 babies, and MS/MS is utilized to screen over 30 disorders (Association of State and Territorial Health Officials, 2005; National Newborn Screening and Genetics Resource Center [NNSGRC], 2005). Information on the screening process was obtained from personal communication with Dr. Susan Panny, Maryland Department of Health and Mental Hygiene.
The Maryland screening program involves an initial screening test, and 95% of newborns receive a repeat screen. If an initial and/ or repeat screen is positive, the newborn is referred to a specialty center for confirmatory testing.
Decision analysis, a quantitative method for determining the optimal strategy under conditions of uncertainty, was used. Decision analysis allows for integrating information from the published literature and other sources. Three steps were implemented as part of this cost-focused decision analysis: (a) the construction of a decision tree utilizing decision analysis software (TreeAge Pro; TreeAge Software, Inc., Williamstown, MA) that identified decision alternatives and their costs and outcomes; (b) the assignment of probabilities to outcomes and costs of each of the two disorders under study; and (c) the use of sensitivity analysis, a process that tests the stability of the baseline conclusions over a range of plausible probabilities. The variables in the decision model include the prevalence of disease, the cost of screening (instrumentation, labor, operational expenses, and confirmatory test-ing), and the sensitivity/specificity of the screening technology. The outputs of the model include the cost of the overall screening program, the true positives, the false positives, the true negatives, and the false negatives for each disorder.
True positives are newborns who are correctly screened as having the disease; false positives are those who do not have the disease but who have a positive screening test; true negatives are those who are correctly identified as not having disease; and false negatives are those who have the disease but are incorrectly identified as not having the disease.
Decision model assumptions. The decision model reflects that all newborns in Maryland are screened, that the majority of newborns receive repeat screening, that those who test positive receive confirmatory testing, and that the confirmatory test is the gold standard (it has 100% sensitivity and specificity). Those who truly have the disease will test positive and those without the disease will test negative. In addition, the decision model demonstrates that infants who have a positive confirmatory test receive the correct diagnosis, that they receive appropriate treatment, and that this treatment has a benefit. The assumption is that a false- negative test has the same outcomes as not screening an infant with the disease. Heterozygotes for the conditions are unlikely to develop clinical signs/symptoms and thus are considered not to have the disease (early detection will not improve their long-term outcomes). It is further assumed that newborns with false-positive results receive no further evaluation after the confirmatory tests are negative (they do may not incur further costs in followup, evaluation, etc.).
Decision model parameters
Costs. Costs of newborn screening were obtained from the literature (ACMG, 2005; Insinga et al., 2002; Schoen, Baker, Colby, & To, 2002; Venditti et al., 2003) and from expert opinion (personal communication with William Slimak, Pediatrix Medical Group; Mimi Blitzer, University of Maryland; and Teresa Kruisselbrink, Mayo Clinic). All cost figures are inflated to 2005 dollars utilizing the consumer price index from the U.S. Bureau of Labor Statistics (2006).
Initial screening. Utilizing MS/MS for newborn screening programs has an incremental cost of approximately $10 per specimen (ACMG/ American Society of Human Genetics [ASHG] Test and Technology Transfer Committee Working Group, 2000). Pediatrix, a private lab that screens approximately one-third of Maryland’s newborns, reports an approximate cost of $10.25 per specimen, or $20.50 for both the initial and repeat screens (personal communication with William Slimak, Pediatrix Medical Group). For purposes of this study, screening is set at $10.25 for an initial test and $20.50 for both an initial and repeat test.
Confirmatory testing. Reported costs of confirmatory testing vary. Also, the overall cost includes screening costs as well as clinical costs.
Insinga and colleagues (2002) reported costs for confirmatory testing for MCADD, inflated to 2005 dollars, as $1,631. Venditti and colleagues (2003) reported that the cost for confirmatory testing for MCADD, including time costs for parents, was $2,120. When the cost of parent involvement is subtracted from the confirmatory costs the cost is approximately $2,020 for 2003 or $2,176 when inflated to 2005 dollars.
Schoen and colleagues (2002) reported the 2002 costs of false- positive results by calculating the costs of an immediate response by a nurse coordinator ($100), a visit to an urgent care center ($110), laboratory tests ($600), and consultation with a geneticist ($200). For 2005, the total dollars would be inflated to approximately $1,140.
Costs for confirmatory testing were also obtained from personal communication with Teresa Kruissel-brink, Biochemical Genetics, Mayo Clinic: urine organic acids ($192.60), urine acylglycines ($309.90), and plasma acylcarnitines ($194.60).
From the various sources cited and for purposes of this study, confirmatory testing for MCADD, including laboratory tests and clinical evaluation, is approximately $1,560. The cost of confirmatory laboratory testing and clinical evaluation for BKT is estimated at $1,060.
Sensitivity and specificity. Sensitivity ranges were obtained from the literature. Sensitivity for MS/MS in screening for MCADD ranges from approximately 95% to 100% (Andersen et al., 2001; Chace, Hillman, Van Hove, & Naylor, 1997; Insinga et al., 2002; Maier et al., 2005; McCandless, 2004; Pandor, Eastham, Beverley, Chilcott, & Paisley, 2004; Pollitt et al., 1997; Seddon, Gray, Pollitt, Iitia, & Green, 1997; Tarini, Christakis, & Welch, 2006). While Schoen and colleagues (2002), and Venditti and colleagues (2003) reported a sensitivity of 100%, for purposes of this study, the sensitivity of MS/MS for screening MCADD is set at 98%.
Specificity is calculated by one minus the number of presumed false positives divided by the num ber of newborns screened in Maryland. For the study, the initial screen specificity is 99.96% and the repeat screen specificity is 99.90%.
The diagnostic sensitivity of screening for organic acidurias (BKT) was 100% (Schulze et al., 2003). For purposes of this study, a more conservative figure of 98% is used for the sensitivity of MS/ MS for screening BKT. The specificity for BKT is calculated as 99.82% for the initial screen and 99.89% for the repeat screen. The sensitivity and specificity of confirmatory tests of both disorders were assumed to be 100% (gold standard).
Table 1 represents the decision model parameters, including incidence of disease, diagnostic accuracy, and cost of screening.
Results
Decision tree. The model representing either MCADD or BKT is represented graphically as a decision tree in Figure 1. The outcomes of newborn screening were determined only to the point of diagnosis because the impact of early detection on the natural history of the disorders has not been delineated well enough to determine long- term outcomes. Results of the base case analysis for MCADD and BKT are shown in Table 2.
The base case analysis for MCADD demonstrates that with a prevalence of 1:17,000, and sensitivity, specificity, and cost for initial, repeat, and confirmatory testing as outlined in Table 1, the expected number of cases of MCADD per 100,000 newborns is 5.6, with approximately 140 false-positive results. The cost of screening per newborn is $22.77 and the cost of the entire screening program is $2,277,000.
The base case analysis for BKT demonstrates that with a prevalence of 1:100,000 and sensitivity and specificity of initial, repeat, and confirmatory testing, and cost of confirmatory testing as outlined in Table 1, the expected number of cases of BKT per 100,000 newborns is less than one with a false-positive rate of approximately 290. The additional cost of screening per newborn is $3.08, and the additional cost to the screening program is $308,000. Results of the sensitivity analysis are shown in Tables 3 and 4.
The results of these sensitivity analyses demonstrate the follow- ing:
True positives. As the prevalence of disease increases, so does the number of true positives identified by newborn screening, with little change in false-positive results.
False positives. The results of the screening program are most sensitive to the specificity of the screening test. When the specificity decreases, the number of false-positive results increases dramatically, as does the cost of the screening program due to increased confirmatory testing.
True negatives. Infants who have negative test results receive no benefit from screening. These infants, who are negative for disease, incur the cost of initial and/or repeat screening.
False negatives. As demonstrated in the tables, false-negative results are rare events and do not dramatically affect the cost of the entire screening program. However, undiagnosed disease may adversely affect the health of the infant who becomes symptomatic and increases health care costs in the treatment of a seriously ill child.
Applying the base care figures to the number of newborns screened in Maryland per year, the probable cases of MCADD per 70,000 newborns is approximately four. The probable number of false- positive results is 98, and the cost of the newborn screening program is $1,593,000 per year. For BKT, the probable number of newborns with disease in Maryland is less than one, the number of false positives is approximately 204, and the additional cost to the screening program is $215,600.
Discussion
The outcomes of newborn screening vary depending on the prevalence of disease and the precision of the screening technology. Cost is affected by changes in these variables.
Infants with true-positive results for MCADD and BKT appear healthy in early infancy, but may develop serious illness during the first years of life. The first acute symptoms of MCADD are fatal in approximately 30% to 50% of patients. If treatment and/or the avoidance of fasting were begun before symptoms appeared, these deaths might be prevented (ACMG/ASHG Test and Technology Transfer Committee Working Group, 2000). For this reason, early diagnosis of children who are true positives and will become symptomatic is crucial. However, many patients with MCADD do not have symptoms of the disease and therefore would not be diagnosed without newborn screening. Schoen and colleagues (2002) believe that, due to the infrequency of symptoms and sequelae and the low cost of treatment, the economic impact of MCADD is smaller than the impact of less- common inborn errors of metabolism. The estimates of death and morbidity for MCADD range widely (Grosse et al., 2006; Iafolla et al., 1994; Pollitt et al., 1997; Pollitt & Leonard, 1998; Schoen et al., 2002). The identification and treatment of newborns with mutations for newborn disorders such as MCADD is controversial because it is not known if these infants would ever have medical problems (Andresen et al., 1997; Zschocke et al., 2001). In addition, it is not known if some newborns would have serious episodes and others not because of modifying factors. Newborn screening may help answer some of these questions (Kaye et al., 2006).
Varying the specificity of the screening technology results in the largest variation of false-positive findings. When the specificity is less precise, the number of false positives is substantially higher and the cost of the screening program increases due to additional confirmatory testing. One study suggests that there may be 12 false positive-results for every true case of certain newborn disorders (Zytkovicz et al., 2001). Another study puts the ratio at more than 50:1 for specific disorders (Kwon & Farrell, 2000).
Screening may benefit those who truly have disease but may actually harm those who receive false-positive results. False- positive test results can have a long-term detrimental effect on the parents (Schoen et al., 2002; Waisbren et al., 2003). Waisbren et al. (2003) found that even 6 months later, mothers of children with false-positive results reported worry about their child’s health. These mothers also reported that their child needed extra parental care. Parental worry and concern can change the relationship of parents with their newborns, and a parental attitude of overprotection can lead to more frequent hospitalization of these infants for dissimilar illnesses (Waisbren et al., 2003).
In one study, parental stress and family dysfunction were higher for false positives than for a control group, as measured by the standardized Parenting Stress Index, the Parent-Child Dysfunctional Scale, and a Difficult Child Scale. Even when a false result is in error, it can still increase the expectation of disease (Gurian, Kinnamon, Henry, & Waisbren, 2006; Waisbren et al., 2003).
Infants with true negative test results are considered unaffected and thus are treated like normal infants (Venditti et al., 2003). These children receive no benefit of screening because they are without disease. However, they do incur the cost of screening, both initial and repeat screening.
False-negative test results are of concern but, as shown in the tables, they are rare events. While they do not critically influence the cost of the entire screening program, they may compromise the health of the infant who has a false-negative diagnosis. Venditti and colleagues (2003) estimated that 10% of unscreened infants who have a condition (who are assumed to be similar to infants who have a false-negative result) and survive until diagnosis will experience severe neurologic damage and significant disability, such as cerebral palsy. The investigators suggest that early screening for MCADD reduces morbidity and mortality at an incremental cost below the range for established health care interventions. To decrease false results, attention must be paid to improving laboratory results so that they are accurate.
False test results will never be totally eliminated. Parental education through effective communication is an important part of the care for families who receive false test results (Kwon & Farrell, 2000). Unfortunately, education for parents regarding false- positive results is lacking, thereby compounding the misinformation, worry, and anxiety (Fant, Clark, & Kemper, 2005).
Maryland reported three cases of MCADD for 2004-2005. This chance inconsistency with the decision tree’s predictive value of 4.1 per 70,000 (per year) may be due to the rarity of disease, the limited number of years of screening examined, or a disparity between assumed prevalence and actual prevalence. MCADD is prevalent in Caucasians, particularly in populations of northern European origin. It is less common in the Hispanic population, although some cases have been detected in a pilot MS/MS phase of the California newborn screening program. Only a few affected African-Americans and Native Americans have been detected (Matern & Rinaldo, 2000). Only a few Asians with MCADD have been identified through newborn screening (Tajima et al., 2004). Maryland’s population of screened newborns is approximately 31% African American, 6% Hispanic, 4% Asian/Pacific islanders, and < 1% American Indian. Therefore, approximately 40% of newborns screened in Maryland are not Caucasian (NNSGRC, 2005). This could account for the fact that the prevalence of MCADD in Maryland was lower than predicted.
Public health officials must grapple with the costs and outcomes of newborn screening and must deal with financial and resource tradeoffs when expanding their programs. This study is not an endorsement of screening or not screening. Rather, this is a model to consider when deciding on the allocation of resources and tradeoffs in public health programs.
Limitations
The assumptions of the decision model are reasonably based on what is known; however, much of the information on newborn screening is based mainly on expert opinion. Therefore, a sensitivity analysis was performed to examine the impact of changes on these assumptions. Other limitations are that data on the sensitivity and specificity of testing for BKT are limited and that the model stops at the point of diagnosis. Long-term costs and outcomes are not considered. The costs vary across settings/states and have not been generalized. In addition, in a cost-consequence analysis, there is no explicit decision criterion; rather, decision makers must weigh the costs and consequences qualitatively when they are deciding whether to expand a screening program.
Conclusions
As advanced technologies for screening and new interventions for treatment are realized, it will be increasingly important for health leaders and policymakers to have data to inform their decisions regarding expanding newborn testing. For sound decisions to be made about expanding newborn screening programs, the costs of the entire screening program (the costs of getting people into screening, doing the screening, notification, followup of results, diagnosis, and treatment of resultant diseases) and outcomes (true and false results) need to be taken into account, rather than a sole focus on the cost of initial screening.
With the availability of multiplex technology for screening, some may consider the addition of new disorders as a cost-free investment; however, this study demonstrates that there is a cost. State directors need to decide if they can spend their limited resources on additional screening or elsewhere in their health care programs. It is important that they have data to inform their decisions.
Given that newborn screening is for rare disorders, it is especially important to maintain accurate specificity of initial screening tests. Screening truly benefits those who have disease. Those who receive false-positive results may experience some harms.
Decision analysis quantifies costs, consequences, and tradeoffs at both the individual and state levels. It highlights the most important variables to include for the best data. The decision model cited in this study can serve as a tool in exploring alternatives for critical decisions regarding the addition of new disorders to existing newborn screening panels. The evaluation of genetic disorders in this study can be used as a prototype of an approach to evaluating screening for any newborn genetic/metabolic disorder. Additional data sources are necessary for future studies. These impending studies should continue to address the cost effectiveness and cost benefit of screening for genetic disorders in newborn populations.
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HEDDY BISHOP HUBBARD, PhD, MPH, RN, FAAN, is Director of Guidelines, American Urological Association, Linthicum, MD.
ACKNOWLEDGMENT: The author expresses her appreciation to William Lawrence, MD, Agency for Healthcare Research and Quality (AHRQ), and Patricia Hinton Walker, PhD, RN, FAAN, Uniformed Services University of the Health Sciences (USUHS), for their support and guidance in the execution of this study and development of this manuscript; to Susan Panney, MD, Maryland Department of Health and Mental Health, William Slimak, Pediatrix Medical Group, Teresa Kruisselbrink, Biochemical Genetics at the Mayo Clinic, and Mimi Blitzer, PhD, University of Maryland, for fiscal and/or operational information; and to Margaret Rutherford, AHRQ, for editorial review and comment.
NOTES: This article was developed while the author was Senior Advisor for Nursing, AHRQ, and a Doctoral Student at the Graduate School of Nursing, USUHS, Bethesda, MD.
The author was an employee of the U.S. Federal Government when this work was conducted and prepared for publication. Therefore, it is not subject to the Copyright Act, and copyright cannot be transferred.
DISCLAIMER: The views expressed in this article are those of the author, and no official endorsement by the U.S. Agency for Healthcare Research and Quality, the U.S. Department of Health and Human Services, or the Uniformed Services University of the Health Sciences is intended or should be inferred.
Copyright Anthony J. Jannetti, Inc. Nov/Dec 2007
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