Campbell, John Y.

DerSarkissian, M

Duh, MS

Haider, BA

Kageleiry, A

Lefebvre, P

Samuelson, D

Skotko, BG

Harvard University - DASH 

Income Growth Trajectory ForParents Of Children With DownSyndrome In The United StatesThe Harvard community has made thisarticle openly available.  Please share  howthis access benefits you. Your story mattersCitation Samuelson, D, A Kageleiry, MS Duh, P Lefebvre, JY Campbell, MDerSarkissian, BA Haider, and BG Skotko. 2016. “Income GrowthTrajectory For Parents Of Children With Down Syndrome In TheUnited States.” Proceedings of ISPOR 21st Annual InternationalMeeting, Washington DC, May 21-25, 2016.Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:34331449Terms of Use This article was downloaded from Harvard University’s DASHrepository, and is made available under the terms and conditionsapplicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAPPresented at the ISPOR 21st Annual International Meeting, Washington, DC,  May 21–25, 2016Background• The new non-invasive prenatal screening (NIPS) test, available as of October 2011, allows couples to learn whether their fetus has Down syndrome (DS) as early as  9 weeks into gestation• Given the availability of this test, more couples are expected to receive a prenatal diagnosis of DS1–4 • Upon receiving a prenatal DS diagnosis, expectant parents must make decisions about their pregnancy options with limited time and information5• Some couples may be concerned about the financial impact of raising a child with DS• Costs include both direct costs, such as paying for care, and indirect costs, such  as the potential impact of having to care for the child, on career progression  and income• Previous research has examined the impact of having a child on parents’ earnings6–8• To our knowledge, no research has examined the differential impact on income growth trajectory of parents of children with DS as compared to parents of children without any chromosomal conditionsStudy ObjectiveTo determine whether the rate of income growth among parents of children with  DS differs from that among parents of children without chromosomal conditionsMethodsData• Data from the OptumHealth Reporting and Insights employer-based claims database were used to conduct this retrospective cohort study• The database contains administrative claims (medical and pharmacy claims)  and eligibility information for over 18 million individuals who are privately insured through their employers, including primary subscribers and their covered beneficiaries• The database covers the period from the first quarter of 1999 through the first quarter of 2015 and has been cited in many peer-reviewed publications9–11 Selection Criteria• Parents were selected to be included in the study if they were enrolled in their family insurance plan as a plan subscriber; had an identifiable child (<18 years  of age) on their insurance plan; had ≥2 consecutive years of income information; and had discernable demographic and enrollment characteristics to use in the matching algorithm• DS study group: parents of a child with at least one medical claim associated  with a diagnosis of DS (ICD-9-CM code: 758.0x)• Control group: parents of one or more children, all without any diagnoses  of chromosomal conditions (ICD-9-CM code: 758.xx)Observation Time• A panel of pairs of consecutive years of parents’ income information was created. Parents with more than two consecutive years of income data were included multiple times (Figure 1)• Parents in the control group with multiple children were included multiple times within the panel for each childFigure 1. Study SchemeDate of birth Age 1 Age 2 Age 3 Age 4 Age 5 Age 6 Years of available income data  Panel observation 1   Panel observation 2Panel observation 3Patient Matching• Parents within the DS study panel were matched to parents within the control  panel in a 1:1 ratio using a greedy matching algorithim12,13• Exact matching was used to match parent observations on gender, parental  age at child’s birth, child’s age at the beginning of the two year income observation period, and parental work industry• A propensity score was calculated for each parent observation using logistic regression to estimate the probability of belonging to the DS study panel;  the propensity score model included terms for the parents’ census region  and health insurance plan type• Parent observations in the DS study panel and control panel were matched  to within a quarter of a standard deviation of the propensity scoreBaseline Characteristics, Outcomes, and Statistical Analysis• Baseline characteristics• Parent characteristics: age, sex, region, insurance plan type, work industry• Child’s age at time of study• Study outcomes• Mean annual income growth between consecutive years with at least 6 months  of populated income information• Annual incomes adjusted to 2015 USD using the Consumer Price Index• Analyses• Mean log annual income growth was compared between the two panels using Wilcoxon signed-rank testsFigure 2: Sample SelectionDS Study Panel (Pre-Match)All beneficiaries in OptumHealth 1999Q1–2015Q1 N=19,144,931Parents of a child with ≥1  claim for DS and identifiable characteristicsN=9,641Parents have ≥2  consecutive years of income data while child is <18 years of age N=4,065Number of 2-year consecutive  data segments in panelN=17,063Control Panel (Pre-Match)All beneficiaries in OptumHealth 1999Q1–2015Q1N=19,144,931Parents of a child with no claims for chromosomal conditions and identifiable characteristicsN=5,109,454Parents have ≥2  consecutive years of income data while child is <18 years of ageN=1,522,487Number of 2-year consecutive  data segments in panel N=5,475,875ResultsBaseline Characteristics of Study Participants: Pre- vs. Post-Matching• After matching, parents of children with DS were similar to their matched controls on baseline characteristics (N=17,062 per matched panel) (Table 1)Table 1. Baseline Characteristics of Parents in DS Study and Control PanelsPre-Match Post-Match DS study panelN=17,063Control panelN=5,475,875P-Value1DS study panelN=17,062Control panelN=17,062P-Value1Descriptive characteristics  Female, N (%) 8,371 (49.1) 2,698,059 (49.3) 0.580  8,371 (49.1) 8,371 (49.1) 1.000Age at child's birth (years), mean (SD) 34.3 (6.3) 29.3 (6.4) <.001 * 34.3 (6.3) 34.3 (6.3) 1.000Region, N (%)  Midwest 4,615 (27.0) 1,273,517 (23.3) <.001 * 4,614 (27.0) 4,614 (27.0) 1.000Northeast 2,619 (15.3) 880,675 (16.1) 0.009 * 2,619 (15.3) 2,615 (15.3) 0.952South 6,403 (37.5) 2,032,009 (37.1) 0.260  6,403 (37.5) 6,399 (37.5) 0.964West 3,249 (19.0) 1,182,957 (21.6) <.001 * 3,249 (19.0) 3,257 (19.1) 0.912Insurance Plan Type, N (%)  HMO 1,812 (10.6) 782,635 (14.3) <.001 * 1,812 (10.6) 1,808 (10.6) 0.944Indemnity 1,082 (6.3) 329,050 (6.0) 0.068  1,082 (6.3) 1,070 (6.3) 0.789POS 3,683 (21.6) 1,049,814 (19.2) <.001 * 3,683 (21.6) 3,679 (21.6) 0.958PPO 9,751 (57.1) 2,946,801 (53.8) <.001 * 9,750 (57.1) 9,779 (57.3) 0.751Other 735 (4.3) 367,575 (6.7) <.001 * 735 (4.3) 726 (4.3) 0.810Work industry, N (%)  Financial Services 930 (5.5) 410,549 (7.5) <.001 * 929 (5.4) 929 (5.4) 1.000Healthcare 1,241 (7.3) 470,972 (8.6) <.001 * 1,241 (7.3) 1,241 (7.3) 1.000Manufacturing/Energy 2,807 (16.5) 816,354 (14.9) <.001 * 2,807 (16.5) 2,807 (16.5) 1.000Retail/Consumer Goods 918 (5.4) 427,007 (7.8) <.001 * 918 (5.4) 918 (5.4) 1.000Shipping/Transportation 5,234 (30.7) 1,503,797 (27.5) <.001 * 5,234 (30.7) 5,234 (30.7) 1.000Technology 4,698 (27.5) 1,503,807 (27.5) 0.836  4,698 (27.5) 4,698 (27.5) 1.000Other 1,235 (7.2) 343,389 (6.3) <.001 * 1,235 (7.2) 1,235 (7.2) 1.000Child's age at time of study, N (%)  < 3 years 2,184 (12.8) 492,475 (9.0) <.001 * 2,184 (12.8) 2,184 (12.8) 1.0003–5 years 3,324 (19.5) 820,454 (15.0) <.001 * 3,323 (19.5) 3,323 (19.5) 1.0006–9 years 4,292 (25.2) 1,209,769 (22.1) <.001 * 4,292 (25.2) 4,292 (25.2) 1.00010–15 years 5,676 (33.3) 2,125,185 (38.8) <.001 * 5,676 (33.3) 5,676 (33.3) 1.000> 15 years 0 (0.0) 0 (0.0) -- 0 (0.0) 0 (0.0) --Notes* P-value <0.05.[1] P-values before matching were calculated using Wilcoxon rank-sum tests for continuous variables and chi-square tests for categorical variables.  P-values after matching were calculated using McNemar tests and Wilcoxon signed-rank tests.Income Growth• Parents of children with DS had lower mean annual income growth rate than their matched controls (4.0% vs. 4.1%, p=0.027). Though statistically significant, the magnitude of difference was small (Table 2)• No statistically significant differences existed in mean annual income growth rate when analyzed separately for mothers (4.0% vs. 4.2%, p=0.193) and fathers (3.9% vs. 4.0%, p=0.070) (Table 2)Table 2: Mean Annual Growth Rate in Incomes Amongst parents of Children with DS and Matched Controls DS study panel Control panel Difference P-Value1Overall N=17,062 N=17,062Mean log difference in annual income, mean ± SD 0.039 ± 0.082 0.040 ± 0.101 -0.0010.027*Average change in annual income (%)2 4.0% 4.1% -0.10%Mothers N=8,371 N=8,371Mean log difference in annual income, mean ± SD 0.040 ± 0.084 0.041 ± 0.107 -0.0010.193Average change in annual income (%)2 4.0% 4.2% -0.13%Fathers N=8,691 N=8,691Mean log difference in annual income, mean ± SD 0.039 ± 0.080 0.039 ± 0.095 -0.0010.070Average change in annual income (%)2 3.9% 4.0% -0.07%Notes* P-value <0.05[1] P-values were calculated using Wilcoxon signed-rank tests.[2] Percentages calculated from log differences using the following equation: average change=emean log difference – 1.Discussion• Parents of children with DS have lower mean annual income growth compared to those of children without chromosomal conditions. This difference was not statistically significant among mothers and fathers when analyzed separately• Using the 2014 US median income of $50,383 and $39,621 among yearly full-time working men and women, respectively, this difference in income growth translates into $35 annual reductions for fathers and $52 annual reductions for mothers • Previous studies have shown that having a child affects mothers’ salaries differently than fathers’. Our study did not test for an interaction between having a child with DS and parents’ gender, but further research is warranted• Further research is merited to determine whether the impact of having a child with DS on parents’ income differs with the age of the childLimitations• As claims data were used for the study, any missing information or administrative error may have resulted in confounding, selection bias, and/or measurement error• Administrative claims databases are used for the reimbursement of healthcare services and thus do not contain detailed information on other clinical disease characteristics that may affect parents’ income trajectory. Failure to adjust for these characteristics in the analysis may bias the results• The current study focuses on income data for plan subscribers, and the findings may not be generalizable to the income growth trajectory of spouses or secondary earners• Parents of children with DS who may not have received a DS diagnosis code on an insurance claim and thus are not included in the DS study group likely represent parents of relatively healthier children with DS, as doctors did not consider DS a co-occurring condition. These patients’ less severe health issues may have a smaller impact on their parents’ income growth trajectory.  Though it is unlikely, inclusion of these parents in the study as controls would have minimized the differences in mean annual income between the two groups• The current study is based on a commercial employer database, and families covered by Medicaid or Medicare are not represented in the database used• Propensity scores were not calculated separately for mothers and fathers, and thus residual confounding may remain in the gender-specific cohortsConclusions• Parents of children with DS experience lower mean annual income growth than parents of children without chromosomal conditions• Based on the 2014 US median income, this difference translates into $35 reductions in annual income growth  for fathers and $52 reductions for mothers• The potential impact on income growth is a part of the financial consideration for parents receiving a prenatal DS diagnosis; further research is needed to consider potential costs associated with caring for children with DS• The current study evaluated only the impact of having a child with DS on the plan subscriber’s income growth. The findings cannot be generalized to secondary earners, among whom the impact may be differentReferences1. Skotko BG. 2005. Prenatally diagnosed down syndrome: Mothers who continued their pregnancies evaluate their health care providers. Am J Obstet Gynecol 192: 670–677.2. Bianchi DW, Platt LD, Goldberg JD, Abuhamad AZ, Sehnert AJ, Rava RP, MatErnal BLood IS Source to Accurately diagnose fetal aneuploidy (MELISSA) DS case group. 2012. Genome-wide fetal aneuploidy detection by maternal plasma DNA sequencing. Obstet Gynecol 119: 890–901.3. Nicolaides KH, Syngelaki A, Ashoor G, Birdir C, Touzet G. 2012. Noninvasive prenatal testing for fetal trisomies in a routinely screened first-trimester population.  Am J Obstet Gynecol 207: 374.e1–374.e6.4. Norton ME, Brar H, Weiss J, Karimi A, Laurent LC, Caughey AB, Rodriguez MH, Williams J,3rd, Mitchell ME, Adair CD, Lee H, Jacobsson B, Tomlinson MW, Oepkes D, Hollemon D, Sparks AB, Oliphant A, Song K. 2012. Non-invasive chromosomal evaluation (NICE) study: Results of a multicenter prospective cohort study for detection of fetal trisomy 21 and trisomy 18. Am J Obstet Gynecol 207: 137.e1–137.e8.5. Zimmermann B, Hill M, Gemelos G, Demko Z, Banjevic M, Baner J, Ryan A, Sigurjonsson S, Chopra N, Dodd M, Levy B, Rabinowitz M. 2012. Noninvasive prenatal aneuploidy testing of chromosomes 13, 18, 21, X, and Y, using targeted sequencing of polymorphic loci. Prenat Diagn 32: 1233–1241.6. Budig M, England P. 2001. The wage penalty for motherhood. American Sociological Review. 66: 204–225.7. Waldfogel J. 1997. The effect of children on women’s wages. American Sociological Review. 62: 209–217.8. Glauber R. 2007. Marriage and the Motherhood Wage Penalty Among African Americans, Hispanics, and Whites. Journal of Marriage and the Family. 69: 951–961.9. Rice JB, Kirson NY, Shei A, Cummings AK, Bodnar K, Birnbaum HG, Ben-Joseph R. 2014. Estimating the costs of opioid abuse and dependence from an employer perspective: A retrospective analysis using administrative claims data. Appl Health Econ Health Policy 12: 435–446.10. Loftus EV,Jr, Skup M, Ozbay AB, Wu E, Guerin A, Chao J, Mulani P. 2014. The impact of moderate-to-severe Crohn’s disease on employees’ salary growth. Inflamm Bowel Dis 20: 1734–1738.11. Rice JB, Desai U, Cummings AK, Birnbaum HG, Skornicki M, Parsons NB. 2014. Burden of diabetic foot ulcers for medicare and private insurers. Diabetes Care 37: 651–658.12. Miettinen OS. 1969. Individual matching with multiple controls in the case of all-or-none responses. Biometrics 25: 339–355.13. Bergstralh EJ, Kosanke JL. 1995. Computerized matching of controls. Technical Report Series No. 56, Department of Health Science Research, Mayo Clinic, Rochester.PHS45Income Growth Trajectory for Parents of Children  with Down Syndrome in the United StatesDavid Samuelson, BA1; Andrew Kageleiry, BS1; Mei Sheng Duh, ScD, MPH1; Patrick Lefebvre, MA1;  John Campbell, PhD2; Maral DerSarkissian, PhD1; Batool Haider, MD, MS, ScD1; Brian G. Skotko, MD, MPP3,41 Analysis Group, Inc., Boston, MA, USA; 2 Department of Economics, Harvard University, Cambridge, MA, USA;  3 Division of Medical Genetics, Department of Pediatrics, Massachusetts General Hospital, Boston, MA, USA; 4Harvard Medical School, Boston, MA, USA

Income Growth Trajectory For Parents Of Children With Down Syndrome In The United States

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Income Growth Trajectory For Parents Of Children With Down Syndrome In The United States

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