Editorial Leveraging Birth Defects Surveillance Data for Health Services Research In this editorial, we define health services research (HSR) and its relevance and importance to birth defects surveillance and research. We briefly discuss key HSR concepts, several types of HSR data sources, and the linkage of these data for birth defects research. We also examine some challenges in data linkages and conclude by identifying research gaps in HSR for children with birth defects and their families. Health services research is multidisciplinary and broad in scope, examining how financing systems, organization structures and processes, social factors, health technologies and personal behaviors affect access to care, cost and quality of care, health and well-being (AHRQ, 2012). The leveraging of birth defects surveillance data for HSR has been noted as a critical strategy to further the public health research priorities for birth defects, including congenital heart defects (Oster et al., 2013a), craniosynostosis (Rasmussen et al., 2008a), Down syndrome (Rasmussen et al., 2008b) and orofacial clefts (Yazdy et al., 2007).
Key Concepts and Challenges in Health Services Research Related To Birth Defects Surveillance And Research There are several key concepts in HSR related to birth defects research, including healthcare and hospital resource use and costs, access to care, and socioeconomic status (SES). Patterns of healthcare use include inpatient hospital stays, acute care (e.g., emergency department) encounters, outpatient, and physician visits. Researchers have examined the use and outcomes of surgical procedures and referral to specialty clinics or centers, including the timeliness of referrals and receipt of services, for children with birth defects (Cassell et al., 2009; Fixler et al., 2014, Pasquali et al., 2014). Many birth defects HSR studies also have estimated the costs of medical care (Waitzman et al., 1996, 2005; CDC, 2007; Ouyang et al., 2007, 2010; Russo and Elixhauser, 2007; Boulet et al., 2008, 2009a, 2009b; Cassell et al., 2008, 2011; Weiss et al., 2009; Radcliff et al., 2012; Derrington et al., 2013; Peterson et al., 2013a; Simeone et al., 2014; Dawson et al., in press).
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Published online 5 November 2014 in Wiley Online Library (wileyonlinelibrary. com). Doi: 10.1002/bdra.23330
C 2014 Wiley Periodicals, Inc. V
Estimates of the incremental costs of services for children with birth defects relative to unaffected children can be used to assess economic impact and, together with evaluations of preventive services and policies, can allow for estimates of the benefits from prevention. A full “costof-illness” (COI) analysis entails additional estimates, including special education, out-of-pocket costs to families, lost parental earnings, and lost productivity due to premature death or work disability. A pioneering COI analysis of major birth defects estimated most of these costs, excluding lost parental earnings (Waitzman et al., 1996). Subsequently, cost estimates for spina bifida from that study were updated (Waitzman et al., 2005; Grosse et al., 2008), and the lifetime costs of spina bifida were used in costbenefit and cost-effectiveness analyses of folic acid fortification and supplementation strategies (Grosse et al., 2005, 2008). Using birth defects registry and parental survey data from Arkansas, lost parental earnings were estimated (Tilford et al., 2009). Parents of children with spina bifida were sampled from the Arkansas Reproductive Health Monitoring System (ARHMS), a population-based, statewide birth defects registry, and parents of unaffected children were sampled from the Current Population Survey from the same time period (Tilford et al., 2009). Analyses of medical costs have also been used to assess clinical interventions for birth defects. For example, a study of hospitalization costs for infants with critical congenital heart defects (CCHD) using the Florida Birth Defects Registry (FBDR) examined factors associated with timely diagnosis of CCHD during infancy (Peterson et al., 2013a). The results of that analysis were then used as inputs in a cost-effectiveness analysis of newborn pulse oximetry screening for CCHD (Peterson et al., 2013b). Measuring access to care for children with birth defects can be done either directly or indirectly. Direct measures can include surveying parents to identify perceptions of access to care and barriers to care, which can be financial, psychosocial, cultural or linguistic, geographic, or organizational (Newacheck et al., 2000; Cassell et al., 2012, 2013, in press). Indirect measures of access, including geographic proximity or travel time to centers providing specialized care, can be easier to collect (Case et al., 2008; Fixler et al., 2012; Cassell et al., 2013; Delmelle et al., 2013). Challenges in measuring access to care include imperfect recall of parent reported travel time and distance; missing residential address at birth and over time (i.e., at different time points as child gets older); and
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missing addresses of hospitals or clinics where children received services over time. Measures of SES, racial/ethnic disparities, and payer status are also important aspects of HSR. SES can be assessed at both the area and individual levels, the former, through U.S. Census based metrics of family income (e.g., Hirsch et al., 2011), and the latter, through single variables, such as parental education, income and insurance or an index of several variables combined (e.g., Yang et al., 2008; Wehby et al., 2012; Knight et al., in press). Expected payer status is frequently included in analyses of birth defects HSR (e.g., Dawson et al., 2013; Peterson et al., 2013a; Kucik et al., 2014), although it is difficult to determine to what extent payer status is an indicator of SES and access to care; individuals with inadequate or no private insurance who have high medical needs (e.g., a major birth defect) often enroll in Medicaid to receive coverage, regardless of poverty and income level. Similarly, HSR studies often use hospital characteristics, such as the nursery care level or birth hospital level of care, as predictors of outcomes (e.g., Cassell et al., 2009; Dawson et al., 2013), but that can reflect selfselection if women with prenatal diagnoses deliver in hospitals with level III nurseries.
Types of Data Sources for Health Services Research in Birth Defects: Administrative, Clinical, And Survey Data ADMINISTRATIVE DATA
Administrative data are of two main types: health insurance claims and facility-based data. Claims data have several advantages, including inpatient and outpatient care data, ability to track individual patients over time, and multiple encounters for specific public and private payers. Medicare and Medicaid data can be obtained with approval from the Centers for Medicare and Medicaid Services (CMS) or from state health departments. Researchers may also be able to obtain Medicaid data from their own state. Proprietary claims databases, such R Commercial Claims and Encounters, have as MarketScanV been used to estimate medical costs associated with spina bifida (Ouyang et al., 2007, 2010), Down syndrome (Boulet et al., 2008), congenital heart defects (Boulet et al., 2009a), orofacial clefts (Boulet et al., 2009b), and fetal alcohol syndrome (Amendah et al., 2011). Conventional U.S. health insurance claims databases, with the exception of Medicare data for the population aged 65 and over, are not population-based, which poses a limitation. In particular, private insurance databases can under-represent individuals with birth defects, such as spina bifida or Down syndrome or other disabling conditions, such as hemophilia or sickle cell disease, who are disproportionately covered by public payers (Amendah et al., 2010; Guh et al., 2012; Radcliff et al., 2012). Another limitation is attrition as a result of individuals switching
health plans or payers. To address the issue of attrition, it is standard practice to use continuous enrollment (e.g., 111 months per year) as an inclusion criterion. Also, when children with private insurance have services reimbursed by public payers, those payments are not included in the private claims database, thereby underestimating total costs. This underestimation provides the rationale to use both public and private payer data and link to claims and birth defects surveillance data to estimate the total costs of birth defects. Several states have established all-payer claims databases (APCD) that include both public and private claims data linked at the individual level (Peters et al., 2014). APCD are population-based and avoid the problems of attrition in payer-specific claims data and understatement of costs for individuals with both private and public insurance coverage. To date, they have been used by states to assess overall healthcare use and costs. In the future, when APCD data become more widely available, birth defects HSR will undoubtedly take advantage of these data. Facility-based administrative data include nonclinical records for patients, regardless of expected payer. Most states have databases of hospital records, for both inpatient and outpatient clinics. Some of these databases include encrypted individual identifiers that allow selected users to track multiple encounters for the same individual, but for most, the unit of observation is the encounter, not an individual patient. The Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project offers access to nationwide databases of samples of hospital records that are useful for analyses of aggregate healthcare use associated with birth defects (Robbins et al., 2006; CDC, 2007; Russo and Elixhauser, 2007; Simeone et al., 2014). In addition, data from the Pediatric Health Information System (PHIS), which contains administrative data from 43 participating children’s hospitals, has been analyzed for congenital heart defects (Oster et al., 2013b; Pasquali et al., 2013). Stand-alone administrative databases have limitations for birth defects HSR. For example, it is difficult to accurately classify birth defects (Frohnert et al., 2005; Pasquali et al., 2013). In addition, services are limited to the focus of the organizations collecting the data. By linking birth defects surveillance data and vital records to administrative data, many of the limitations of administrative databases (e.g., accuracy of coding) can be overcome or at least attenuated. CLINICAL DATA
Clinical data entail information found in medical records, such as results of diagnostic procedures (e.g., echocardiography), surgical information, prenatal diagnoses, verbatim diagnoses, and laboratory results. Clinical information on birth defects is used in case-control studies like the National Birth Defects Prevention Study and Slone
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Epidemiology Center Birth Defects Study (Yoon et al., 2001; Rasmussen et al., 2003; Margulis et al., 2012). Another example of clinical data is the Society for Thoracic Surgeons (STS) database. The STS is the largest pediatric heart surgery database that collects information on preoperative, operative, and 30 day outcomes postsurgery on all children having heart surgery at more than 100 participating hospitals in the United States (Pasquali et al., 2013). Clinical information on birth defects is critical for accurate diagnoses and procedures and can aid in examining HSR questions. SURVEY DATA
Surveys or focus groups of parents of children with birth defects can provide qualitative information on barriers to care, financial impacts on families, and perceptions of quality of care. For example, mothers of children with isolated orofacial clefts identified by birth defects surveillance programs in Arkansas, Iowa, and New York were surveyed about team care, quality of care, and outcomes (Austin et al., 2010). Focus groups conducted in Utah and Idaho among parents of children with orofacial clefts assessed parental satisfaction with quality of care (Stone et al., 2010). Parental surveys of children with orofacial clefts assessing quality of life and satisfaction of care also have been conducted using data from the North Carolina Birth Defects Monitoring Program (NCBDMP) (Cassell et al., 2013, in press). Surveys can also elicit information on health-related quality of life (HRQoL) of children and their caregivers. Using the ARHMS, surveys were conducted to assess HRQoL for children and caregivers for spina bifida (Tilford et al., 2005) and craniofacial malformations (Payakachat et al., 2011). Using birth defects surveillance data from Arkansas, Iowa, and New York, a joint study on children with orofacial clefts and their caregivers assessed children’s behavioral health (Wehby et al., 2012), HRQoL (Wehby et al., 2014a), and mental health among mothers (Dabit et al., 2014).
Linking Birth Defects Surveillance Data to Other Data Sources Birth defects surveillance data have important strengths. They provide uniformity of coding for birth defects and include clinical verification of the birth defects in states or portions of states with active case finding. Accurate coding of defects is crucial to allow for examination of the impact of combinations of defects. Because birth defects surveillance data are routinely linked to vital records, information on survival and on demographic characteristics, such as maternal race/ethnicity and parental education, are available. In contrast, birth defects surveillance data generally lack measures of health services; however, linkages with other datasets are possible. In addition to hospital discharge databases, examples of databases that can be
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linked to birth defects surveillance data include Medicaid/ Children’s Health Insurance Program (CHIP) claims, Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), maternity care coordination, prenatal care information, newborn screening, early intervention, special education, U.S. Census, and National Death Index (NDI). For example, in Michigan, birth defects surveillance data were linked to vital records, NDI, hospital discharges from that state and neighboring states, and U.S. Census block group data to examine survival of children with hypoplastic left heart syndrome (Hirsch et al., 2011). Several states (e.g., Massachusetts and North Carolina) link birth defects surveillance data to early intervention data. These data can be used to examine the number of children with specific birth defect types who receive referrals to these services, the timeliness of such services, and factors associated with receipt of early intervention services. Linking program data to these other data sources could potentially identify optimal healthcare approaches, educational needs, and help ensure timely referral to appropriate services and appropriate care transitions. Several state programs have published HSR analyses of linked hospital discharge and birth defects surveillance databases. A pioneering study of this type linked birth defects surveillance data from the California Birth Defects Monitoring Program (CBDMP) for 1988 to 1989 to hospital discharges as well as Medicaid claims data and school records to assess costs attributable to birth defects (Waitzman et al., 1996). Since 1998, the Massachusetts Pregnancy to Early Life Longitudinal (PELL) Data System has routinely linked birth/delivery hospital records to birth certificates and linked those records longitudinally to birth defects data, death certificates, and postbirth hospitalizations as well as other databases, including WIC, early intervention, and hearing screening. Analyses of PELL data on hospitalization and costs have been published for Down syndrome (Derrington et al., 2013) and craniofacial malformations (Weiss et al., 2009). The FBDR linked information for children with selected major birth defects to longitudinal hospital discharge data for 1998 to 2008. Analyses of hospitalization use and costs have been reported for spina bifida (Radcliff et al., 2012), CCHD (Peterson et al., 2013a; Dawson et al., 2013), and Down syndrome (Dawson et al., in press), and the methodology for such data linkage was recently published (Salemi et al., 2013). Accessing claims data that can be linked to birth defects data is a bigger challenge. At least two states have linked birth defects surveillance data with Medicaid claims data, California (Waitzman et al., 1996) and North Carolina (Cassell et al., 2008, 2009, 2011). A few birth defects surveillance programs already have plans to link their data to a state APCD, and, in the future, more states may do so. Because APCDs are population-based, they will for the first time allow comprehensive, population-based analyses of costs of birth defects in the United States.
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By linking several different data sources, including parental survey data, to birth defects registries, educational achievement and academic outcomes can be examined. At least four states have published on academic outcomes for children with birth defects, using birth defects surveillance data. The aforementioned California study linked CBDMP surveillance data and school records to identify the frequency of special education placements for children with major birth defects (Waitzman et al., 1996). The NCBDMP surveyed parents of children with orofacial clefts and parents of children without a major birth defect and asked about quality of life and school outcomes (Knight et al., in press). Based on the results from the study, the NCBDMP subsequently linked birth defects registry data to parental survey data and educational records for children with isolated orofacial clefts and children without a structural birth defect. In the near future, the NCBDMP will have educational records for children with congenital heart defects as well. Recently, the Iowa Registry for Congenital and Inherited Disorders conducted record linkages to school records (Wehby et al., 2014b). Metropolitan Atlanta Congenital Defects Program (MACDP) birth defects surveillance data were linked to special education records to better understand use of special education services among children with orofacial clefts (Yazdy et al., 2008) and gastrointestinal anomalies (Hamrick et al. 2010).
Challenges in Data Linkages While there are many advantages to linking birth defects surveillance data to other databases, conducting such linkages can be challenging because of the need for the same identifying variables in multiple databases. Social security number, date of birth, mother and/or infant’s name, birth certificate number, and Medicaid identification number are variables commonly used to link hospital discharge and/or Medicaid data to birth defects surveillance data (Cassell et al., 2009, 2011; Salemi et al., 2011, 2012, 2013). Missing data can be more common among Hispanics and other minority racial/ethnic groups as was found in some recent HSR studies using FBDR data linked to longitudinal hospital discharge data (Radcliff et al., 2012; Peterson et al., 2013a; Dawson et al., 2013, in press; Kucik et al., 2014). The degree of complexity of the data linkages will depend on the matching variables available in the datasets. Most data linkages require collaboration between agencies and stakeholders, who may have different goals, and institutional review board approvals and data use agreements from multiple agencies may be needed. Other challenges include limited resources to hire staff to conduct linkages, support survey design and distribution to parents, and conduct and report analytical findings. To leverage data from linked databases to generate additional analyses and publications, states could consider partnering with universities and other agencies that have analysts looking for high quality data for analytic projects. The March of Dimes
awarded funds to the University of North Carolina at Charlotte, University of South Florida, and Florida Department of Health to develop longitudinal data linkages and analyze HSR questions for selected birth defects. Junior researchers at participating institutions, including CDC’s National Center on Birth Defects and Developmental Disabilities, took the lead on analyses and worked closely with senior researchers. Additional collaborations of this type, including novel longitudinal linkages with databases beyond hospital discharges, are needed to make advances in birth defects HSR.
Future Health Services Research Needs in Birth Defects Longitudinal studies are needed to better understand the financial burden of birth defects, including estimates of special education costs, mental health costs, caregiver costs, out-of-pocket costs, and the frequency of work disability and loss of productivity. Future research could assess factors associated with access to care, timeliness of services, and cost to determine populations in need of services and to help better understand survival, disparities, outcomes, and quality of life of children with birth defects or other congenital disorders, such as those detected through public health newborn screening programs. Other gaps include impact of comorbidities on longer-term outcomes and health service use and costs, how to track people receiving care in different places, transitions to adolescence and adulthood, quality of life, and barriers to care. Birth defects surveillance data provide many opportunities to link with other data sources. This editorial has shared selected state experiences of using linked data for HSR analyses. Our hope is that this discussion will spark interest in conducting such linkages and leveraging birth defects surveillance data for HSR to ultimately improve the lives of children with birth defects and their families.
Cynthia H. Cassell,1 Scott D. Grosse,1 and Russell S. Kirby2 1 National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia 2 Birth Defects Surveillance Program, Department of Community and Family Health, College of Public Health, University of South Florida, Tampa, Florida
Acknowledgments We thank our colleagues for their insightful discussions, helpful comments, and suggestions about this editorial content: Cheryl Broussard, Suzanne Gilboa, Cara Mai, Leslie O’Leary, Tiffany Riehle-Colarusso, and Norman Waitzman.
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multicentered US case-control study. Am J Epidemiol 167: 145–154. Yazdy MM, Honein MA, Rasmussen SA, et al. 2007. Priorities for future public health research in orofacial clefts. Cleft Palate Craniofac J 44:351–357. Yazdy, MM, Autry AR, Honein MA, Frias JL. 2008. Use of special education services by children with orofacial clefts. Birth Defects Res A Clin Mol Teratol 82:147–154. Yoon PW, Rasmussen SA, Lynberg MC, et al. 2001. The National Birth Defects Prevention Study. Public Health Rep 116:32–40.
Key Concepts and Challenges in Health Services Research Related To Birth Defects Surveillance And Research There are several key concepts in HSR related to birth defects research, including healthcare and hospital resource use and costs, access to care, and socioeconomic status (SES). Patterns of healthcare use include inpatient hospital stays, acute care (e.g., emergency department) encounters, outpatient, and physician visits. Researchers have examined the use and outcomes of surgical procedures and referral to specialty clinics or centers, including the timeliness of referrals and receipt of services, for children with birth defects (Cassell et al., 2009; Fixler et al., 2014, Pasquali et al., 2014). Many birth defects HSR studies also have estimated the costs of medical care (Waitzman et al., 1996, 2005; CDC, 2007; Ouyang et al., 2007, 2010; Russo and Elixhauser, 2007; Boulet et al., 2008, 2009a, 2009b; Cassell et al., 2008, 2011; Weiss et al., 2009; Radcliff et al., 2012; Derrington et al., 2013; Peterson et al., 2013a; Simeone et al., 2014; Dawson et al., in press).
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Published online 5 November 2014 in Wiley Online Library (wileyonlinelibrary. com). Doi: 10.1002/bdra.23330
C 2014 Wiley Periodicals, Inc. V
Estimates of the incremental costs of services for children with birth defects relative to unaffected children can be used to assess economic impact and, together with evaluations of preventive services and policies, can allow for estimates of the benefits from prevention. A full “costof-illness” (COI) analysis entails additional estimates, including special education, out-of-pocket costs to families, lost parental earnings, and lost productivity due to premature death or work disability. A pioneering COI analysis of major birth defects estimated most of these costs, excluding lost parental earnings (Waitzman et al., 1996). Subsequently, cost estimates for spina bifida from that study were updated (Waitzman et al., 2005; Grosse et al., 2008), and the lifetime costs of spina bifida were used in costbenefit and cost-effectiveness analyses of folic acid fortification and supplementation strategies (Grosse et al., 2005, 2008). Using birth defects registry and parental survey data from Arkansas, lost parental earnings were estimated (Tilford et al., 2009). Parents of children with spina bifida were sampled from the Arkansas Reproductive Health Monitoring System (ARHMS), a population-based, statewide birth defects registry, and parents of unaffected children were sampled from the Current Population Survey from the same time period (Tilford et al., 2009). Analyses of medical costs have also been used to assess clinical interventions for birth defects. For example, a study of hospitalization costs for infants with critical congenital heart defects (CCHD) using the Florida Birth Defects Registry (FBDR) examined factors associated with timely diagnosis of CCHD during infancy (Peterson et al., 2013a). The results of that analysis were then used as inputs in a cost-effectiveness analysis of newborn pulse oximetry screening for CCHD (Peterson et al., 2013b). Measuring access to care for children with birth defects can be done either directly or indirectly. Direct measures can include surveying parents to identify perceptions of access to care and barriers to care, which can be financial, psychosocial, cultural or linguistic, geographic, or organizational (Newacheck et al., 2000; Cassell et al., 2012, 2013, in press). Indirect measures of access, including geographic proximity or travel time to centers providing specialized care, can be easier to collect (Case et al., 2008; Fixler et al., 2012; Cassell et al., 2013; Delmelle et al., 2013). Challenges in measuring access to care include imperfect recall of parent reported travel time and distance; missing residential address at birth and over time (i.e., at different time points as child gets older); and
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missing addresses of hospitals or clinics where children received services over time. Measures of SES, racial/ethnic disparities, and payer status are also important aspects of HSR. SES can be assessed at both the area and individual levels, the former, through U.S. Census based metrics of family income (e.g., Hirsch et al., 2011), and the latter, through single variables, such as parental education, income and insurance or an index of several variables combined (e.g., Yang et al., 2008; Wehby et al., 2012; Knight et al., in press). Expected payer status is frequently included in analyses of birth defects HSR (e.g., Dawson et al., 2013; Peterson et al., 2013a; Kucik et al., 2014), although it is difficult to determine to what extent payer status is an indicator of SES and access to care; individuals with inadequate or no private insurance who have high medical needs (e.g., a major birth defect) often enroll in Medicaid to receive coverage, regardless of poverty and income level. Similarly, HSR studies often use hospital characteristics, such as the nursery care level or birth hospital level of care, as predictors of outcomes (e.g., Cassell et al., 2009; Dawson et al., 2013), but that can reflect selfselection if women with prenatal diagnoses deliver in hospitals with level III nurseries.
Types of Data Sources for Health Services Research in Birth Defects: Administrative, Clinical, And Survey Data ADMINISTRATIVE DATA
Administrative data are of two main types: health insurance claims and facility-based data. Claims data have several advantages, including inpatient and outpatient care data, ability to track individual patients over time, and multiple encounters for specific public and private payers. Medicare and Medicaid data can be obtained with approval from the Centers for Medicare and Medicaid Services (CMS) or from state health departments. Researchers may also be able to obtain Medicaid data from their own state. Proprietary claims databases, such R Commercial Claims and Encounters, have as MarketScanV been used to estimate medical costs associated with spina bifida (Ouyang et al., 2007, 2010), Down syndrome (Boulet et al., 2008), congenital heart defects (Boulet et al., 2009a), orofacial clefts (Boulet et al., 2009b), and fetal alcohol syndrome (Amendah et al., 2011). Conventional U.S. health insurance claims databases, with the exception of Medicare data for the population aged 65 and over, are not population-based, which poses a limitation. In particular, private insurance databases can under-represent individuals with birth defects, such as spina bifida or Down syndrome or other disabling conditions, such as hemophilia or sickle cell disease, who are disproportionately covered by public payers (Amendah et al., 2010; Guh et al., 2012; Radcliff et al., 2012). Another limitation is attrition as a result of individuals switching
health plans or payers. To address the issue of attrition, it is standard practice to use continuous enrollment (e.g., 111 months per year) as an inclusion criterion. Also, when children with private insurance have services reimbursed by public payers, those payments are not included in the private claims database, thereby underestimating total costs. This underestimation provides the rationale to use both public and private payer data and link to claims and birth defects surveillance data to estimate the total costs of birth defects. Several states have established all-payer claims databases (APCD) that include both public and private claims data linked at the individual level (Peters et al., 2014). APCD are population-based and avoid the problems of attrition in payer-specific claims data and understatement of costs for individuals with both private and public insurance coverage. To date, they have been used by states to assess overall healthcare use and costs. In the future, when APCD data become more widely available, birth defects HSR will undoubtedly take advantage of these data. Facility-based administrative data include nonclinical records for patients, regardless of expected payer. Most states have databases of hospital records, for both inpatient and outpatient clinics. Some of these databases include encrypted individual identifiers that allow selected users to track multiple encounters for the same individual, but for most, the unit of observation is the encounter, not an individual patient. The Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project offers access to nationwide databases of samples of hospital records that are useful for analyses of aggregate healthcare use associated with birth defects (Robbins et al., 2006; CDC, 2007; Russo and Elixhauser, 2007; Simeone et al., 2014). In addition, data from the Pediatric Health Information System (PHIS), which contains administrative data from 43 participating children’s hospitals, has been analyzed for congenital heart defects (Oster et al., 2013b; Pasquali et al., 2013). Stand-alone administrative databases have limitations for birth defects HSR. For example, it is difficult to accurately classify birth defects (Frohnert et al., 2005; Pasquali et al., 2013). In addition, services are limited to the focus of the organizations collecting the data. By linking birth defects surveillance data and vital records to administrative data, many of the limitations of administrative databases (e.g., accuracy of coding) can be overcome or at least attenuated. CLINICAL DATA
Clinical data entail information found in medical records, such as results of diagnostic procedures (e.g., echocardiography), surgical information, prenatal diagnoses, verbatim diagnoses, and laboratory results. Clinical information on birth defects is used in case-control studies like the National Birth Defects Prevention Study and Slone
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Epidemiology Center Birth Defects Study (Yoon et al., 2001; Rasmussen et al., 2003; Margulis et al., 2012). Another example of clinical data is the Society for Thoracic Surgeons (STS) database. The STS is the largest pediatric heart surgery database that collects information on preoperative, operative, and 30 day outcomes postsurgery on all children having heart surgery at more than 100 participating hospitals in the United States (Pasquali et al., 2013). Clinical information on birth defects is critical for accurate diagnoses and procedures and can aid in examining HSR questions. SURVEY DATA
Surveys or focus groups of parents of children with birth defects can provide qualitative information on barriers to care, financial impacts on families, and perceptions of quality of care. For example, mothers of children with isolated orofacial clefts identified by birth defects surveillance programs in Arkansas, Iowa, and New York were surveyed about team care, quality of care, and outcomes (Austin et al., 2010). Focus groups conducted in Utah and Idaho among parents of children with orofacial clefts assessed parental satisfaction with quality of care (Stone et al., 2010). Parental surveys of children with orofacial clefts assessing quality of life and satisfaction of care also have been conducted using data from the North Carolina Birth Defects Monitoring Program (NCBDMP) (Cassell et al., 2013, in press). Surveys can also elicit information on health-related quality of life (HRQoL) of children and their caregivers. Using the ARHMS, surveys were conducted to assess HRQoL for children and caregivers for spina bifida (Tilford et al., 2005) and craniofacial malformations (Payakachat et al., 2011). Using birth defects surveillance data from Arkansas, Iowa, and New York, a joint study on children with orofacial clefts and their caregivers assessed children’s behavioral health (Wehby et al., 2012), HRQoL (Wehby et al., 2014a), and mental health among mothers (Dabit et al., 2014).
Linking Birth Defects Surveillance Data to Other Data Sources Birth defects surveillance data have important strengths. They provide uniformity of coding for birth defects and include clinical verification of the birth defects in states or portions of states with active case finding. Accurate coding of defects is crucial to allow for examination of the impact of combinations of defects. Because birth defects surveillance data are routinely linked to vital records, information on survival and on demographic characteristics, such as maternal race/ethnicity and parental education, are available. In contrast, birth defects surveillance data generally lack measures of health services; however, linkages with other datasets are possible. In addition to hospital discharge databases, examples of databases that can be
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linked to birth defects surveillance data include Medicaid/ Children’s Health Insurance Program (CHIP) claims, Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), maternity care coordination, prenatal care information, newborn screening, early intervention, special education, U.S. Census, and National Death Index (NDI). For example, in Michigan, birth defects surveillance data were linked to vital records, NDI, hospital discharges from that state and neighboring states, and U.S. Census block group data to examine survival of children with hypoplastic left heart syndrome (Hirsch et al., 2011). Several states (e.g., Massachusetts and North Carolina) link birth defects surveillance data to early intervention data. These data can be used to examine the number of children with specific birth defect types who receive referrals to these services, the timeliness of such services, and factors associated with receipt of early intervention services. Linking program data to these other data sources could potentially identify optimal healthcare approaches, educational needs, and help ensure timely referral to appropriate services and appropriate care transitions. Several state programs have published HSR analyses of linked hospital discharge and birth defects surveillance databases. A pioneering study of this type linked birth defects surveillance data from the California Birth Defects Monitoring Program (CBDMP) for 1988 to 1989 to hospital discharges as well as Medicaid claims data and school records to assess costs attributable to birth defects (Waitzman et al., 1996). Since 1998, the Massachusetts Pregnancy to Early Life Longitudinal (PELL) Data System has routinely linked birth/delivery hospital records to birth certificates and linked those records longitudinally to birth defects data, death certificates, and postbirth hospitalizations as well as other databases, including WIC, early intervention, and hearing screening. Analyses of PELL data on hospitalization and costs have been published for Down syndrome (Derrington et al., 2013) and craniofacial malformations (Weiss et al., 2009). The FBDR linked information for children with selected major birth defects to longitudinal hospital discharge data for 1998 to 2008. Analyses of hospitalization use and costs have been reported for spina bifida (Radcliff et al., 2012), CCHD (Peterson et al., 2013a; Dawson et al., 2013), and Down syndrome (Dawson et al., in press), and the methodology for such data linkage was recently published (Salemi et al., 2013). Accessing claims data that can be linked to birth defects data is a bigger challenge. At least two states have linked birth defects surveillance data with Medicaid claims data, California (Waitzman et al., 1996) and North Carolina (Cassell et al., 2008, 2009, 2011). A few birth defects surveillance programs already have plans to link their data to a state APCD, and, in the future, more states may do so. Because APCDs are population-based, they will for the first time allow comprehensive, population-based analyses of costs of birth defects in the United States.
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By linking several different data sources, including parental survey data, to birth defects registries, educational achievement and academic outcomes can be examined. At least four states have published on academic outcomes for children with birth defects, using birth defects surveillance data. The aforementioned California study linked CBDMP surveillance data and school records to identify the frequency of special education placements for children with major birth defects (Waitzman et al., 1996). The NCBDMP surveyed parents of children with orofacial clefts and parents of children without a major birth defect and asked about quality of life and school outcomes (Knight et al., in press). Based on the results from the study, the NCBDMP subsequently linked birth defects registry data to parental survey data and educational records for children with isolated orofacial clefts and children without a structural birth defect. In the near future, the NCBDMP will have educational records for children with congenital heart defects as well. Recently, the Iowa Registry for Congenital and Inherited Disorders conducted record linkages to school records (Wehby et al., 2014b). Metropolitan Atlanta Congenital Defects Program (MACDP) birth defects surveillance data were linked to special education records to better understand use of special education services among children with orofacial clefts (Yazdy et al., 2008) and gastrointestinal anomalies (Hamrick et al. 2010).
Challenges in Data Linkages While there are many advantages to linking birth defects surveillance data to other databases, conducting such linkages can be challenging because of the need for the same identifying variables in multiple databases. Social security number, date of birth, mother and/or infant’s name, birth certificate number, and Medicaid identification number are variables commonly used to link hospital discharge and/or Medicaid data to birth defects surveillance data (Cassell et al., 2009, 2011; Salemi et al., 2011, 2012, 2013). Missing data can be more common among Hispanics and other minority racial/ethnic groups as was found in some recent HSR studies using FBDR data linked to longitudinal hospital discharge data (Radcliff et al., 2012; Peterson et al., 2013a; Dawson et al., 2013, in press; Kucik et al., 2014). The degree of complexity of the data linkages will depend on the matching variables available in the datasets. Most data linkages require collaboration between agencies and stakeholders, who may have different goals, and institutional review board approvals and data use agreements from multiple agencies may be needed. Other challenges include limited resources to hire staff to conduct linkages, support survey design and distribution to parents, and conduct and report analytical findings. To leverage data from linked databases to generate additional analyses and publications, states could consider partnering with universities and other agencies that have analysts looking for high quality data for analytic projects. The March of Dimes
awarded funds to the University of North Carolina at Charlotte, University of South Florida, and Florida Department of Health to develop longitudinal data linkages and analyze HSR questions for selected birth defects. Junior researchers at participating institutions, including CDC’s National Center on Birth Defects and Developmental Disabilities, took the lead on analyses and worked closely with senior researchers. Additional collaborations of this type, including novel longitudinal linkages with databases beyond hospital discharges, are needed to make advances in birth defects HSR.
Future Health Services Research Needs in Birth Defects Longitudinal studies are needed to better understand the financial burden of birth defects, including estimates of special education costs, mental health costs, caregiver costs, out-of-pocket costs, and the frequency of work disability and loss of productivity. Future research could assess factors associated with access to care, timeliness of services, and cost to determine populations in need of services and to help better understand survival, disparities, outcomes, and quality of life of children with birth defects or other congenital disorders, such as those detected through public health newborn screening programs. Other gaps include impact of comorbidities on longer-term outcomes and health service use and costs, how to track people receiving care in different places, transitions to adolescence and adulthood, quality of life, and barriers to care. Birth defects surveillance data provide many opportunities to link with other data sources. This editorial has shared selected state experiences of using linked data for HSR analyses. Our hope is that this discussion will spark interest in conducting such linkages and leveraging birth defects surveillance data for HSR to ultimately improve the lives of children with birth defects and their families.
Cynthia H. Cassell,1 Scott D. Grosse,1 and Russell S. Kirby2 1 National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia 2 Birth Defects Surveillance Program, Department of Community and Family Health, College of Public Health, University of South Florida, Tampa, Florida
Acknowledgments We thank our colleagues for their insightful discussions, helpful comments, and suggestions about this editorial content: Cheryl Broussard, Suzanne Gilboa, Cara Mai, Leslie O’Leary, Tiffany Riehle-Colarusso, and Norman Waitzman.
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