Abstract
BACKGROUND:
Caring for children with hemophilia A (HA) impacts many aspects of parents’ lives. How this translates to caregivers’ utilization of health services is unknown, and its elicitation can inform future evaluations of interventions that address caregiver burden for HA.
OBJECTIVE:
To understand the impact of caring for children with HA on parents’ utilization of nonmental and mental health services by comparing 1-year costs and number of claims with parents of children without HA.
METHODS:
Retrospective matched cohort study using MarketScan commercial medical and pharmacy claims from 2011 to 2019. Children with HA were male sex, aged younger than 18 years, dependent policyholders, and had at least 1 HA-related medical claim from 2011 to 2018 and either an HA-related procedure or drug claim. Parents of children with HA (PCH) were primary or secondary policyholders, shared the same family ID as children with HA, and were continuously enrolled for 1-year post-index. PCH were matched (1:2) with parents of children without HA on age, sex, beneficiary type, child’s age, number of children, index month and year, health plan type, employment status, and region. Primary outcomes were nonmental and mental health care costs (2020 US dollars). Secondary outcomes were number of nonmental health outpatient claims and utilization of mental health outpatient or drug claim. Subgroup analyses excluding parents with HA were also conducted. Productivity loss was also explored. Outcomes were compared using 2-sided, paired t-tests, and McNemar test.
RESULTS:
1,068 PCH met inclusion criteria and were matched to 2,122 control parents. Mean 1-year cost for PCH was higher for nonmental health (mean difference $1,826 [95% CI = −1,000 to 4,652; P = 0.20]) and similar for mental health services (mean difference $14 [95% CI = −77 to 105; P = 0.76]). When parents with HA were excluded in the subgroup analyses, mental health cost was significantly higher for PCH (mean difference $676 [95% CI = 399 to 953; P < 0.001]). PCH had more nonmental health outpatient claims compared with parents of children without HA (mean difference 1.9 [95% CI = −1.1 to 4.9; P = 0.21]) and were 1.2 times (95% CI = 0.99 to 1.45; P = 0.07) more likely to have a mental health outpatient or drug claim.
CONCLUSIONS:
PCH had moderately higher health care costs and utilization compared with parents of children without HA; however, these results were not statistically significant. Future studies to better characterize HA disease severity and assess its impact on caregiver burden or to expand caregivers to spouses of adult patients with HA may be warranted. Limitations include inability to ascertain severity of HA in children and the use of claims data to capture complex effects on health care utilization.
Plain language summary
This study helps capture aspects of caregiver burden for parents caring for children with hemophilia A (HA). We used medical and pharmacy claims to compare the amount of mental and nonmental health care use between parents of children with and without HA. There was a trend toward higher health care costs for parents of children with HA. This was significant with mental health care use when parents with HA themselves were excluded, which strongly suggests a link between mental health burden and caregiving in HA.
Implications for managed care pharmacy
Caregiving for children with HA can be burdensome and may translate to higher utilization of health care resources at a family level that should be considered in payers’ value assessment frameworks. Differentiation of therapies by nontraditional value elements, such as caregiver burden, helps payers prioritize patient access to high-value therapies and inform policies that not only support pediatric patients with HA but also support parent caregivers, especially if newer therapies have the potential to decrease their health care costs.
Hemophilia A (HA) is a rare, genetic bleeding disorder caused by a deficiency in factor VIII (FVIII) and occurs predominantly in male individuals.1 Disease severity depends on FVIII levels and is associated with frequency of bleeds, which can occur at muscles and joints, and are sometimes fatal.1,2 Complications can be long term and include pain, arthropathy, and a negative impact on physical functioning and quality of life.2 Current prophylaxis and treatment options include infusions of FVIII or injections with a bispecific antibody designed to mimic the function of FVIII. Gene therapies are currently in development.3,4
Diagnosis typically occurs in the first 3 years of life, and thus parents are often the primary caregivers.5,6 A body of qualitative evidence informs our current understanding of the experience of parent caregivers. Caregivers have demonstrated substantial burden, reporting most frequently emotional stress, financial burden, and time required.7 Additional focus group interviews highlighted stress and difficulty as caregivers cope with their child’s disease.8 Studies in hemophilia caregivers have used various validated instruments, including the CarerQol, Hemophilia Quality of Life Questionnaire for Adults, and Parental Needs Scale for Rare Diseases, that have also captured the previously mentioned experiences, as well as depression, anxiety, and economic impact due to lost days from work.6,8,9-13 No studies have yet compared productivity loss (ie, work time lost) between parent caregivers of children with and without HA in the United States.6,11 However, how these emotional, mental, and financial effects translate to parents’ utilization of health care is unknown. We hypothesized that we would observe lower costs and utilization of nonmental health services for parents of children with HA, potentially driven by these parents seeking less health care, such as preventative services, because of financial burden and personal sacrifice of time. In addition, we hypothesized higher costs and utilization for mental health services for parents of children with HA, potentially driven by the negative impact on emotional and mental health for these parents.
Attention to measuring and communicating the value of health technologies has increased in the United States, especially for those that are priced high and indicated for rare diseases such as HA. However, the need for a broader value framework continues to be debated.14,15 Quantifying caregiver impact and productivity loss is increasingly important to help inform these broader value frameworks and debate on their use.14
Accordingly, the aim of this analysis is to capture the health care burden of a pediatric HA diagnosis on parents of children with HA through a retrospective claims and productivity database analysis. More specifically, the following research question will be addressed: is a pediatric HA diagnosis associated with parents’ utilization of nonmental health and mental health–related services as well as productivity loss? This information can be used to aid decision makers under budgetary constraints to evaluate and compare new technologies for HA. The primary objectives were to compare mean 1-year health care costs for parents of children with and without HA for (1) nonmental health and (2) mental health services. The secondary objectives were to compare (1) mean number of nonmental health outpatient services and (2) utilization of mental health outpatient services or drugs. An exploratory objective was to compare work hours lost between parents of children with and without HA.
Methods
STUDY DESIGN AND DATA SOURCE
This was a retrospective, cross-sectional matched cohort study using medical and drug claims to compare nonmental and mental health care costs, as well as utilization and absenteeism, between parents who have children with and without HA.
The IBM MarketScan Commercial Claims and Encounters (CCAE) and Health Productivity Management (HPM) databases, consisting of data collected from employers, were used to capture health care costs, utilization, and work hours lost because of absenteeism. The CCAE database includes deidentified medical and drug claims for a nationally representative sample of individuals with employer-sponsored private health insurance in the United States.16 The HPM database includes data on workplace absence, short-term and long-term disability, and workers’ compensation for a subset of the population in the CCAE database.16 Data from the HPM database can be linked to medical and pharmacy claims for included employees.16 A Family Identifier field, which links family members enrolled together under a single health insurance policy, was added to the MarketScan CCAE database in 2011.17 Thus, the study period was from January 1, 2011, to December 31, 2019, when latest data were available. Both databases adhered to the Health Insurance Portability and Accountability Act of 1996, and institutional review board approval at the University of Washington was not required, as no risk to study participants was involved.16
SAMPLE
Children With HA. Patients with HA were identified with the following criteria: (1) younger than age 18 years, (2) male sex, (3) child or dependent status on the insurance policy, (4) no more than 1 inpatient or outpatient medical claim with an HA diagnosis code (International Classification of Diseases, Ninth Revision [ICD-9], or International Classification of Diseases, Tenth Revision [ICD-10], code: ICD-9 286.0 or 1CD-10 D66), and (5) either an HA-related medical procedure or drug claim (Supplementary Table 1 (216.1KB, pdf) , available in online article). These criteria were informed by previous MarketScan claims studies.18-21 Only male participants were included in the children with HA population because hemophilia is rare in female individuals, and the optimal approach to identify them is unknown.22 Female participants with an HA-related claim are likely carriers, have another coagulation disorder, or were miscoded.18,22 One of the child’s HA-related medical claims between 2011 and 2018 was randomly chosen as the parent’s index date to address calendar year and within year biases.
Parents of Children With HA. Parents of children with HA were identified who met the following criteria: (1) primary or secondary policyholder, (2) shared the same family ID as an HA child, (3) medical data available 1-year pre-index, and (3) continuous enrollment 1-year post-index. Primary and secondary beneficiaries under the same insurance policy as children with HA were assumed to be parents as well as primary caregivers.23 Both parents, if available, were included in analyses as individual parents to increase sample size and because both may be involved in caregiving. If a parent was identified more than once (ie, a duplicate) because he or she was linked to more than 1 HA child, then the parent was included only once to avoid inflating utilization and costs. Parents’ medical claims data from the 1-year period before the index date were used to calculate their Charlson Comorbidity Index (CCI), which is the most extensively studied comorbidity index that takes into account the number and seriousness of comorbid diseases (Supplementary Table 2 (216.1KB, pdf) ).24,25
Parents Without Children With HA. Control parents were identified who met the following criteria: (1) primary or secondary policyholder, (2) shared the same family ID as a child aged younger than 18 years and at least 1 non-HA medical claim, (3) medical data available 1-year pre-index, and (4) continuous enrollment 1-year post-index. Control parents were excluded if their child had an HA-related medical claim during the same year as the child’s non-HA medical claim and the 1-year post-index period. Index dates for control parents were randomly assigned in the year of the non-HA child’s medical claim, which was also randomly selected from 2011 to 2018.
Matching. Parents of children with HA and control parents were matched 1:2 (to maximize sample size) by the following variables: (1) parent age (± 5 years), (2) parent sex, (3) parent beneficiary type (ie, primary or secondary policy holder), (4) child’s age (± 3 years), (5) number of children within the family, (6) health insurance plan type (health maintenance organization vs preferred provider organization vs other), (7) employment status (full-time vs not full-time), (8) CCI, (9) region, (10) index month (± 3 months), and (11) index year. Once parents of children without HA were identified, the number of children dependents enrolled under the parents’ family ID was counted to identify the number of children within the family.
OUTCOMES
Primary Outcomes. The primary outcomes were mean 1-year cost for all (1) nonmental health services and (2) mental health services. Services included inpatient, outpatient, and drug claims. Mental health services were (1) medical claims coded “mental” in the major diagnostic category field, which indicates the body system– or disease-related groupings of clinical conditions based on diagnosis codes, or (2) drug claims coded as “antidepressants” or “anxiolytic/sedative/hypnotic” in the therapeutic class field, which indicates the therapeutic/pharmacologic category of the drug product.26 Nonmental health services were all claims not coded for mental health services. Costs were reported in 2020 US dollars using the medical care component of the Consumer Price Index for urban consumers.27
Secondary Outcomes. Secondary outcomes were intended to capture the volume of nonmental health services sought by parents and utilization of mental health services. Thus, secondary outcomes were (1) mean number of nonmental health medical outpatient claims, excluding emergency department (ED) visits and (2) utilization of mental health medical outpatient services, excluding ED visits or drug claims coded as mental health related during the 1-year post-index period.
Subgroup Analysis. In a subgroup analysis, parents with an HA-related claim were excluded. HA is an inherited disease not captured in the CCI, and some parents of children with HA likely have HA themselves.1 An HA diagnosis in adult populations is associated with higher costs and utilization and could skew study results.18,19
Exploratory Outcome. Workplace productivity loss, defined as hours lost due to absenteeism, was measured during the 1-year post-index period for primary beneficiaries in the population. Reported reasons for absence could include sickness, disability, leave, recreational, Family Medical Leave Act, and other.28
STATISTICAL ANALYSIS
Baseline characteristics were summarized using mean and SD or median and IQR for continuous variables and count and percentage for categorical variables. Differences in mental health and HA diagnoses between the 2 cohorts pre-index were assessed using a 2-sided paired t-test with a significance level of 5%. Means and SDs were reported for both primary outcomes, the secondary outcome for nonmental health outpatient services, and exploratory outcome for hours lost. These outcomes were also assessed using a 2-sided paired t-test with a significance level of 5%. McNemar test was used to report an odds ratio (OR) with a 95% CI and P value for the proportion of parents who used mental health services. Mean costs for the primary outcomes were also stratified by parent beneficiary type and plotted on a histogram to better visualize cost differences between the overall population and beneficiary types. Datasets were constructed in SAS version 9.4 (SAS Institute), and analyses were performed in RStudio version 1.4.1106 (RStudio, PBC).
SENSITIVITY ANALYSIS
To check the robustness of the results from the primary analysis, we adjusted for the matching variables in an unmatched population. These variables were included in regression models for 1,068 parents of children with HA and 4,272 randomly selected control parents (4 times the number of matched parents of children with HA to maximize the sample size). A 2-part model was chosen to fit the data for mean health care costs.
Results
BASELINE CHARACTERISTICS
Of 305.8 million enrollees identified between 2011 and 2018, a total of 1,580 children with HA were identified. These children shared the same family ID with 1,068 unique parents that met inclusion criteria (Supplementary Table 3 (216.1KB, pdf) ). Nearly all parents were matched to 2 controls, except 14 parents, for a total of 2,122 control parents. Parents had a median age of 41 years, were mostly female (59%), were primary policyholders (55%), had no comorbidities captured by the CCI (85%), and worked full-time (67%; Table 1). For unmatched baseline characteristics, parents of children with HA had more mental health diagnoses (15% vs 12%; P = 0.053) and HA diagnoses (4% vs 1%; P < 0.001) pre-index compared with controls (Table 1).
TABLE 1.
Baseline Characteristics of Matched Parents
| Characteristic | Parents of children with HA (N = 1,068) | Control parents (N = 2,122) | P valuea |
|---|---|---|---|
| Parent age, median (SD) | 41 (7.8) | 41 (7.7) | |
| Parent age group, N (%) | |||
| 18-34 | 238 (22.3) | 471 (22.2) | |
| 35-44 | 467 (43.7) | 934 (44.0) | |
| 45-54 | 313 (29.3) | 626 (29.5) | |
| 55-65 | 50 (4.7) | 91 (4.3) | |
| Parent sex, N (%) | |||
| Male | 469 (43.9) | 934 (44.0) | |
| Female | 699 (65.4) | 1,188 (56.0) | |
| Child’s age, median (SD) | 10 (5.5) | 10 (5.5) | |
| Number of children, mean (SD) | 4 (2.3) | 4 (2.2) | |
| Parent beneficiary type, N (%) | |||
| Primary | 591 (55.3) | 1,179 (55.6) | |
| Secondary | 477 (44.7) | 943 (44.4) | |
| CCI, N (%) | |||
| 0 | 906 (84.8) | 1,807 (85.2) | |
| 1 | 119 (11.1) | 237 (11.2) | |
| 2 | 27 (2.5) | 52 (2.5) | |
| 3+ | 16 (1.5) | 26 (1.2) | |
| Parent mental health diagnosis, N (%) | 155 (14.5) | 255 (12.0) | 0.053 |
| Parent HA diagnosis, N (%) | 43 (4.0) | 1 (0.5) | < 0.001 |
| Health plan type, N (%) | |||
| PPO | 633 (59.3) | 1,258 (59.3) | |
| HMO | 150 (14.0) | 298 (14.0) | |
| Other | 285 (26.7) | 566 (26.7) | |
| Employment status of primary policyholder, N (%) | |||
| Active full-time | 713 (66.8) | 1,419 (66.9) | |
| Other/unknown | 355 (33.2) | 703 (33.1) | |
| Geographic region, N (%) | |||
| Northeast | 218 (20.4) | 434 (20.5) | |
| North Central | 247 (23.1) | 492 (23.2) | |
| South | 386 (36.1) | 765 (36.1) | |
| West | 207 (19.4) | 411 (19.4) | |
| Unknown | 10 (0.9) | 20 (0.9) | |
a P values for other variables were not shown, as these variables (excluding “Parent mental health diagnosis” and “Parent HA diagnosis”) were intentionally matched between parents of children with HA and control parents. Thus, these characteristics have artificially been made to be the same between the 2 arms. P values have been shown for variables that were not included in matching.
CCI = Charlson Comorbidity Index; HA = hemophilia A; HMO = health maintenance organization; PPO = preferred provider organization.
PRIMARY OUTCOMES: COSTS FOR MENTAL AND NONMENTAL HEALTH SERVICES
The mean cost for nonmental health services was higher for parents of children with HA compared with control parents ($8,154 vs $6,328), but the mean difference of $1,826 (95% CI = −1,000 to 4,652) was not statistically significant (Figure 1). The mean cost for mental health services was similar for parents of children with HA compared with control parents ($255 vs $241); (mean difference $14 [95% CI = −77 to 105; P = 0.76]; Figure 1). Costs for parents who were primary and secondary policyholders were also stratified by policyholder type (Supplementary Figures 1 and 2 (216.1KB, pdf) ).
FIGURE 1.
Mean 1-Year Costs for Nonmental and Mental Health Services
SECONDARY OUTCOMES: UTILIZATION OF MENTAL AND NONMENTAL HEALTH SERVICES
The mean number of nonmental health outpatient claims (excluding ED visits) was slightly higher for parents of children with HA compared with control parents (28.3 vs 26.4), but the mean difference of 1.9 (95% CI = −1.1 to 4.9) was not statistically significant (P = 0.21; Table 2). Parents of children with HA were 1.2 times more likely to have a mental health outpatient (excluding ED visits) or pharmacy claim as compared with control parents (95% CI = 0.99 to 1.45; P = 0.07; Table 2).
TABLE 2.
Additional Outcomes
| Secondary outcome: Number of nonmental health outpatient claims | ||||
|---|---|---|---|---|
| Parents of children with HA (N = 1,068) | Control parents (N = 2,122) | P value | ||
| Nonmental health outpatient claim, mean ± SD | 28.3 ± 44.1 | 26.4 t 44.3 | ||
| Mean difference (95% CI) | 1.9 (−1.1 to 4.9) | 0.21 | ||
| Secondary outcome: Use of mental health services | ||||
| Control parents | ||||
| Use | No use | Total | ||
| Parents of children with HA | Use | 75 | 226 | 301 |
| No use | 189 | 578 | 767 | |
| Total | 264 | 804 | 1,068 | |
| Exploratory outcome: Lost work hours in primary beneficiaries | ||||
| Parents of children with HA (N = 1,068) | Control parents (N = 1,068) | P value | ||
| Number of hours absent, mean ± SD | 10.7 ± 54.1 | 16.9 ± 65.8 | ||
| Mean difference (95% CI) | −6.4 (−13 to 0.4) | 0.06 | ||
HA = hemophilia A.
SUBGROUP ANALYSIS OF PARENTS WITHOUT HISTORY OF HEMOPHILIA
In a subgroup analysis, parents with HA were excluded (43 parents of children with HA and 1 control parent). Given the low attrition (44 of 3,190), baseline characteristics for the subgroup analysis were not assessed, as they were unlikely to be meaningful. Differences in 1-year mean cost for nonmental health services ($1,402 [95% CI = −1,430 to 4,236; P = 0.33]) and number of nonmental health outpatient claims (1.9 [95% CI = −1.6 to 3.9; P = 0.40]) were not significant between parents of children with HA and controls. However, parents of children with HA had a significantly higher 1-year mean cost for mental health services, with a mean difference of $676 (95% CI = 399 to 953; P < 0.001). Parents of children with HA were also 1.2 times more likely to have a mental health outpatient or pharmacy claim (95% CI = 1.01 to 1.51; P = 0.04).
EXPLORATORY OUTCOME: PRODUCTIVITY
The mean number of hours lost was lower for parents of children with HA who were primary beneficiaries, as compared with control parents who were primary beneficiaries (10.7 vs 16.9), but the mean difference of −6.4 (95% CI = −13 to 0.4) was not statistically significant (Table 2).
SENSITIVITY ANALYSIS
Parents of children with HA were significantly more likely to incur mental health costs (OR 1.20 [95% CI = 1.03 to 1.40; P = 0.02]; Supplementary Table 4 (216.1KB, pdf) ). Among parents who did have mental health costs, the cost difference between parents of children with HA and control parents was not significant. Likelihood of incurring nonmental health costs (OR 1.12 [95% CI = 0.82 to 1.56; P = 0.48]) and the cost difference in those who did incur costs were not significantly different between parents of children with HA and control parents (Supplementary Table 4 (216.1KB, pdf) ).
Discussion
In this retrospective, cross-sectional study using claims data, we compared 1-year mean health care costs and utilization of nonmental and mental health services between parents of children with and without HA. As previously mentioned, we hypothesized lower costs and utilization of nonmental health services and higher costs and utilization for mental health services for parents of children with HA. However, moderately, but nonsignificantly, higher health care costs and utilization of both service types for parents of children with HA were observed. Moreover, when parents with HA themselves were excluded, significant differences were detected in mental health–related outcomes. The likelihood of incurring mental health costs was significantly higher in parents of children with HA in the sensitivity analysis as well. This suggests mental health utilization in parents may be attributable to a pediatric HA diagnosis in the family. However, this warrants further confirmatory analysis. In addition, parents of children with HA lost fewer work hours because of absenteeism than control parents, but this difference was not statistically significant. In addition, work hours lost may be due to recreational reasons (eg, vacation time) or reasons separate from caregiving in MarketScan.
The lack of significant differences in the primary analysis may have been due to insufficient sample size and emphasizes the challenges of evaluating the effects of rare diseases on families using claims data. The primary outcomes for 1,068 parent pairs had a power of 6% and 43% for mental and nonmental mean health care costs, respectively. However, when parents with a history of HA were excluded in the subgroup analysis, mental health cost and likelihood of having a mental health claim were slightly higher and statistically significant for parents of children with HA, as compared with parents with children without HA. A hemophilia diagnosis is challenging for families; parents need to accept the child’s illness, as well as learn how to manage the disease and adjust family life.29-31 The result of the subgroup analysis suggests that parents with HA themselves may have different ways of coping and managing a child with HA compared with parents who do not have HA. Determining causality was not an objective of this study, however, and may be an area for future research. Consideration of other study designs (eg, different outcomes, using a larger dataset, or prospective data collection) in this space may be warranted. Assessing impact on caregivers for patients with severe HA or expanding caregivers to spouses of adult patients with HA may yield more meaningful results.
To the best of our knowledge, no other analyses have assessed health care and productivity impacts of a pediatric HA diagnosis on parents. As such, there is a paucity of evidence on cost differences between caregivers and their controls. However, in one study for spouse caregivers of patients with Alzheimer disease (AD), AD spouses had a significantly greater cost difference between pre- and post-AD diagnosis than control spouses for AD/mental health drugs but not for total costs.32 Additional analyses of family/caregiver impacts have been conducted in cancer, pediatric Crohn disease (CD), and pediatric intensive care unit (ICU) hospitalizations. Lower health care use and more health diagnoses were observed in spouses of patients with cancer.33 Higher productivity loss was observed for parents of pediatric patients with CD.23 Fewer mental health diagnoses and use of mental health care were observed for parents whose children were hospitalized in the ICU.34 However, comparison between results and those of other analyses is difficult, as different health care utilization mechanisms may be involved for spouse caregivers, across different patient age groups and conditions, and across acute vs chronic disease management. For example, caregiving for a spouse with cancer may be more expensive, and caregiving for a child hospitalized in the ICU is less of a long-term commitment. Results are not generalizable across disease states and patient populations; thus, future research to expand the literature for caregiver burden in HA is warranted.
Limitations of claims data encourages consideration of other measures that can help quantify the health burden experienced by parents who provide care in severe pediatric diseases. Further validation of caregiver impact measurement tools, especially on caregivers’ health and in the hemophilia space (eg, Hemophilia Caregiver Impact), needs to be prioritized.35 These health-related quality-of-life measurements can be mapped to preference-based utility measures for inclusion in economic evaluations.
LIMITATIONS
Several limitations must be considered when interpreting the results of this study. First, the severity of HA for diagnosed children whose parents were included in the analysis was unable to be ascertained. Inclusion criteria of at least 1 HA-related claim and either an HA-related drug or procedure for children with HA captured patients interacting more with the health care system and may require more care. Future research is needed to better characterize HA disease severity in claims data, as well as assess impact on parents caring for very sick children with HA (eg, frequent bleeds or infusions). Second, employment information regarding parents who were secondary policyholders was unknown. Thus, secondary beneficiaries were unable to be matched on employment status and rather only primary beneficiaries were matched on employment status. Third, the study design was cross-sectional and identified prevalent and not incident HA cases. Parents’ involvement at a specific point along a patient’s disease course was unable to be ascertained. Consequently, this analysis did not control for time since HA diagnosis, and thus we were unable to explicitly understand how duration of caregiving impacted caregivers’ health care expenditures. However, the age of the child was controlled for in matching. This can approximate time since diagnosis because HA is a genetic condition and most children with HA are diagnosed within the first couple years of life.5 Fourth, unobserved confounders, such as family income, race and ethnicity, and use of professional caregiving, were not adjusted for because these data were not available in MarketScan. These factors may impact parents’ willingness to seek out health services. However, employment status, which is available information and potentially indicative of a family’s socioeconomic status, was included as a variable in matching. Lastly, both parents from the same family were included for 70% of parents of children with HA and 1% of parents of children without HA. Both parents were included to increase overall sample size and because both may be involved in caregiving. However, costs may be correlated between parents from the same family. Thus, we stratified the primary outcomes by parent policyholder type.
Conclusions
Parents with children with HA were found to have moderately, but nonsignificantly, higher mean costs and utilization of both nonmental and mental health services. Future research exploring other approaches to measure health care use and productivity in parent caregivers of children with HA is warranted.
REFERENCES
- 1.National Hemophilia Foundation. Hemophilia A. Accessed July 30, 2020. https://www.hemophilia.org/bleeding-disorders-a-z/types/hemophilia-a
- 2.Hoots K, Shapiro A. Clinical manifestations and diagnosis of hemophilia. In: Post TW, ed. UpToDate. Waltham, MA: UpToDate, Inc; 2019. Accessed July 30, 2020. https://www.uptodate.com/contents/clinical-manifestations-and-diagnosis-of-hemophilia [Google Scholar]
- 3.National Hemophilia Foundation. Products licensed in the US. Accessed July 30, 2020. https://www.hemophilia.org/healthcare-professionals/guidelines-on-care/products-licensed-in-the-us#1
- 4.National Hemophilia Foundation. MASAC Document 263 – MASAC recommendations concerning products licensed for the treatment of hemophilia and other bleeding disorders. Accessed July 30, 2020. https://www.hemophilia.org/healthcare-professionals/guidelines-on-care/masac-documents/masac-document-263-masac-recommen-dations-concerning-products-licensed-for-the-treatment-of-hemophilia-and-other-bleeding-disorders
- 5.Soucie JM, Evatt B, Jackson D. Occurrence of hemophilia in the United States. The Hemophilia Surveillance System Project Investigators. Am J Hematol. 1998;59(4):288-94. doi: [DOI] [PubMed] [Google Scholar]
- 6.DeKoven M, Karkare S, Kelley LA, et al. Understanding the experience of caring for children with haemophilia: cross-sectional study of caregivers in the United States. Haemophilia. 2014;20(4):541-49. doi: 10.1111/hae.12379 [DOI] [PubMed] [Google Scholar]
- 7.DeKoven M, Karkare S, Lee C, et al. Impact of haemophilia with inhibitors on caregiver burden in the United States. Haemophilia. 2014;20(6):822-30. doi: 10.1111/hae.12501 [DOI] [PubMed] [Google Scholar]
- 8.Beeton K, Neal D, Watson T, Lee CA. Parents of children with haemophilia–a transforming experience. Haemophilia. 2007;13(5):570-79. doi: 10.1111/j.1365-2516.2007.01494.x [DOI] [PubMed] [Google Scholar]
- 9.Dekoven M, Wisiewski T, Petrilla A, et al. Health-related quality of life in haemophilia patients with inhibitors and their caregivers. Haemophilia. 2013;19(2):287-93. doi: 10.1111/hae.12019 [DOI] [PubMed] [Google Scholar]
- 10.Khair K, Pelentsov L. Assessing the supportive care needs of parents with a child with a bleeding disorder using the Parental Needs Scale for Rare Diseases (PNS-RD): a single-centre pilot study. Haemophilia. 2019;25(5):831-37. doi: 10.1111/hae.13826 [DOI] [PubMed] [Google Scholar]
- 11.von Mackensen S, Westesson LM, Kavakli K, et al. The impact of psychosocial determinants on caregivers’ burden of children with haemophilia (results of the BBC study). Haemophilia. 2019;25(3): 424-32. doi: 10.1111/hae.13684 [DOI] [PubMed] [Google Scholar]
- 12.Schwartz CE, Powell VE, Su J, Zhang J, Eldar-Lissai A. The impact of extended half-life versus conventional factor product on hemophilia caregiver burden. Qual Life Res. 2018;27(5):1335-45. doi: 10.1007/s11136-018-1792-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Saviolo-Negrin N, Cristante F, Zanon E, Canclini M, Stocco D, Girolami A. Psychological aspects and coping of parents with a haemophilic child: a quantitative approach. Haemophilia. 1999;5(1):63-68. doi: 10.1046/j.1365-2516.1999.00213.x [DOI] [PubMed] [Google Scholar]
- 14.Shafrin J, Dennen S, Pednekar P, et al. For which diseases do broader value elements matter most? An evaluation across 20 ICER evidence reports. J Manag Car Spec Pharm. 2021;27(5):650-59. doi: 10.18553/jmcp.2021.20471 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Drummond MF, Neumann PJ, Sullivan SD, et al. Analytic considerations in applying a general economic evaluation reference case to gene therapy. Value Health. 2019;22(6):661-68. doi: 10.1016/j.jval.2019.03.012 [DOI] [PubMed] [Google Scholar]
- 16.IBM Watson Health. IBM MarketScan research databases for life sciences researchers. 2018. Accessed May 30, 2021. https://www.ibm.com/downloads/cas/0NKLE57Y
- 17.Truven Health Analytics. Commercial claims and encounters: Medicare supplemental, data year 2016 edition. Truven Health Analytics, Inc. Accessed May 30, 2021. https://theclearcenter.org/wp-content/uploads/2020/01/IBM-MarketScan-User-Guide.pdf
- 18.Guh S, Grosse SD, McAlister S, Kessler CM, Soucie JM. Healthcare expenditures for males with haemophilia and employer-sponsored insurance in the United States, 2008. Haemophilia. 2012;18(2):268-75. doi: 10.1111/j.1365-2516.2011.02692.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Valentino LA, Pipe SW, Tarantino MD, Ye X, Xiong Y, Luo MP. Healthcare resource utilization among haemophilia A patients in the United States. Haemophilia. 2012;18(3): 332-38. doi: 10.1111/j.1365-2516.2011.02677.x [DOI] [PubMed] [Google Scholar]
- 20.Guh S, Grosse SD, McAlister S, Kessler CM, Souice JM. Health care expenditures for Medicaid-covered males with haemophilia in the United States, 2008. Haemophilia. 2012;18(2):276-83. doi: 10.1111/j.1365-2516.2011.02713.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Thornburg CD, Adamski K, Cook K, et al. Health care costs and resource utilization among commercially insured adult patients with hemophilia A managed with FVIII prophylaxis in the United States. J Manag Care Spec Pharm. Published online December 27, 2021. doi: 10.18553/jmcp.2021.21368 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Farej R, Batt K, Afonja O, et al. Characterizing female patients with haemophilia A: administrative claims analysis and medical chart review. Haemophilia. 2020;26(3):520-28. doi: 10.1111/hae.13981 [DOI] [PubMed] [Google Scholar]
- 23.Kahn SA, Lin CW, Ozbay B, Wang A, Chao J, Skup M. Indirect costs and family burden of pediatric Crohn’s disease in the United States. Inflamm Bowel Dis. 2017;23(12):2089-96. doi: 10.1097/MIB.0000000000001268 [DOI] [PubMed] [Google Scholar]
- 24.Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8 [DOI] [PubMed] [Google Scholar]
- 25.de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity: a critical review of available methods. J Clin Epidemiol. 2003;56(3): 221-29. doi: 10.1016/s0895-4356(02)00585-1 [DOI] [PubMed] [Google Scholar]
- 26.Truven Health Analytics. 2016. Truven Health MarketScan commercial claims and encounters Medicare supplemental and coordination of benefits data dictionary. Truven Health Analytics, Inc. Accessed May 30, 2021. https://theclearcenter.org/wp-content/uploads/2020/01/IBM-MarketScan-Data-Dictionary.pdf [Google Scholar]
- 27.Federal Reserve Economic Data. Consumer Price Index for all urban consumers: medical care in US city average. Federal Reserve Bank of St. Louis. Accessed May 30, 2021. https://fred.stlouisfed.org/series/CPIMEDSL [Google Scholar]
- 28.Truven Health Analytics. 2015. Truven Health MarketScan database dictionary health & productivity management data dictionary. Truven Health, RED BOOK and MarketScan Database. Truven Health Analytics, Inc; 2015. [Google Scholar]
- 29.Saxena K. Barriers and perceived limitations to early treatment of hemophilia. J Blood Med. 2013;4: 49-56. doi: 10.2147/JBM.S43734 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Westesson LM, Sparud-Lundin C, Baghaei F, et al. Burden on parents of children with haemophilia: the impact of sociodemographic and child’s medical condition. J Clin Nurs. 2019;28(21-22): 4077-86. doi: 10.1111/jocn.15003 [DOI] [PubMed] [Google Scholar]
- 31.Westesson LM, Wallengren C, Baghaei F, Sparud-Lundin C. Reaching independence through forced learning: learning processes and illness management in parents of children affected by hemophilia. Qual Health Res. 2018;28(14):2142-54. doi: 10.1177/1049732318789631 [DOI] [PubMed] [Google Scholar]
- 32.Brixner D, McAdam-Marx C, Van Den Bos J, Peirman D. Cost one year post diagnosis for Alzheimer patients and their spouses vs. matched controls. J Am Geriatr Soc. 2010;58. [Google Scholar]
- 33.Hess LM, Wong-Jacobson SH, Peterson PM. Health and resource burden of a cancer diagnosis on the caregiver: an analysis of administrative claims data. BMC Health Serv Res. 2021;21(1):894. doi: 10.1186/s12913-021-06938-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Logan GE, Sahrmann JM, Gu H, Hartman ME. Parental mental health care after their child’s pediatric intensive care hospitalization. Pediatr Crit Care Med. 2020;21(11):941-48. doi: 10.1097/PCC.0000000000002559 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Schwartz CE, Powell VE, Eldar-Lissai A. Measuring hemophilia caregiver burden: validation of the Hemophilia Caregiver Impact measure. Qual Life Res. 2017;26(9):2551-62. doi: 10.1007/s11136-017-1572-y [DOI] [PMC free article] [PubMed] [Google Scholar]