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Short-term costs of preeclampsia to the United States health care system

      Background

      Preeclampsia is a leading cause of maternal morbidity and mortality and adverse neonatal outcomes. Little is known about the extent of the health and cost burden of preeclampsia in the United States.

      Objective

      This study sought to quantify the annual epidemiological and health care cost burden of preeclampsia to both mothers and infants in the United States in 2012.

      Study Design

      We used epidemiological and econometric methods to assess the annual cost of preeclampsia in the United States using a combination of population-based and administrative data sets: the National Center for Health Statistics Vital Statistics on Births, the California Perinatal Quality Care Collaborative Databases, the US Health Care Cost and Utilization Project database, and a commercial claims data set.

      Results

      Preeclampsia increased the probability of an adverse event from 4.6% to 10.1% for mothers and from 7.8% to 15.4% for infants while lowering gestational age by 1.7 weeks (P < .001). Overall, the total cost burden of preeclampsia during the first 12 months after birth was $1.03 billion for mothers and $1.15 billion for infants. The cost burden per infant is dependent on gestational age, ranging from $150,000 at 26 weeks gestational age to $1311 at 36 weeks gestational age.

      Conclusion

      In 2012, the cost of preeclampsia within the first 12 months of delivery was $2.18 billion in the United States ($1.03 billion for mothers and $1.15 billion for infants), and was disproportionately borne by births of low gestational age.

      Key words

      Related editorial, page 235.
      Click Supplemental Materials under article title in Contents at ajog.org
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      The clinical presentation of preeclampsia is highly variable and may be characterized by sudden progression requiring delivery within hours or days, or it could remain stable and progress very slowly over weeks. Nevertheless, pregnancies complicated by preeclampsia are associated with substantial maternal and neonatal complications. These complications could happen acutely with complete resolution, or the mother and infant may be left with residual injury. In addition, neonates who survive are at increased risk for long-term deficits related to residual injury or as a result of fetal programming in utero. Moreover, some women who survive may be left with residual deficits as well as the effects of maternal vascular programming (Table 1).
      Table 1Burden of early-onset preeclampsia in the United States
      Major maternal morbidity and mortalityAcute: death and intensive care unit complications
       – Eclampsia, stroke
       – Pulmonary edema
       – Myocardial ischemia
       – Admission to intensive care unit
       – Renal injury-failure with or without dialysis
       – Abruption, disseminated intravascular coagulation
       – Liver dysfunction, hematoma
      Long-term: residual morbidity
       – Neurological deficit
       – Renal failure requiring dialysis
       – Cardiomyopathy
      Maternal programming caused by preeclampsia
       – Coronary artery disease
       – Chronic hypertension
       – Metabolic syndrome
       – Renal insufficiency
       – Stroke
       – Retinal dysfunction
       – Premature death
      Major perinatal morbidity and mortalityAcute: death and neonatal intensive care hospitalization complications
       – Respiratory distress syndrome and bronchopulmonary dysplasia
       – Intraventricular hemorrhage, periventricular leukomalacia
       – Necrotizing enterocolitis, late sepsis
       – Retinopathy of prematurity
       – Prolonged neonatal intensive care hospitalization
      Long term: residual morbidity
       – Cerebral palsy, neurological deficits, seizure disorder
       – Learning disabilities
       – Blindness and hearing deficits
       – Chronic lung disease
       – Chronic heart disease
      Fetal programming: fetal origin of adult disease
       – Metabolic syndrome
       – Stroke, chronic heart disease, and others
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      This study estimated the annual epidemiological and health care cost burden of preeclampsia to both mothers and infants in the United States in 2012.

      Materials and Methods

      Study design and data sources

      We conducted a retrospective cohort study using secondary analysis of multiple data sets to estimate the national clinical and economic burden of preeclampsia in the United States. We required data that were nationally representative, longitudinal, and linked mothers to their infants and included International Classification of Disease, ninth revision, codes to ascertain both outcomes and costs during the 6 months leading to the birth event and the 12 months after delivery.
      Because no single data source met all of these requirements, we combined 5 of the data sets we reviewed (Appendix 1). To obtain mother-infant linkage and GA by week, we used data sources obtained by California Perinatal Quality Care Collaborative: the California Office of Statewide Health Planning and Development (OSHPD) data set linked to vital statistics and birth certificate data.

      Office of Statewide Health Planning and Development. Number of patients by type of coverage: California primary care clinics, 2011–2115. Available at: https://www.oshpd.ca.gov/. Accessed Nov. 29, 2016.

      The California OSHPD data provide administrative and billing information on nearly all hospitalizations in California and contains follow-up data on infant and maternal outcomes. Only 2% of births are not linked to vital statistics in the cases of home births without hospitalization, births in military hospitals, and freestanding birthing centers.
      Information on GA was obtained from birth certificates. The linked data constituted 2,021,013 linked maternal-infant births during 2008–2011. We used the National Center for Health Statistics (NCHS) Natality File
      • Hetzel A.M.
      History and organization of the vital statistics system.
      to estimate national birth counts.
      For cost outcomes, we used a commercial claims data set that provides detailed information on medical costs, health conditions, and demographic characteristics and allows maternal-infant linkages in the data. However, because claims data are not nationally representative, we incorporated the Healthcare Cost and Utilization Project

      World Health Organization. Report of the Commission on Macroeconomics and Health-Macroeconomics and Health: investing in health for economic development. Geneva (Switzerland); 2001.

      data set, which does not allow for maternal-infant linkages, but provides a nationally representative sample of hospital costs by health condition. These data sets are described in further detail in Appendix 2. The study was approved exempt by Western Institutional Review Board since no patients were directly involved and all data were de-identified.

      Number of mothers with preeclampsia

      Because the NCHS does not contain information on preeclampsia, we conservatively assumed that the national annual rate of preeclampsia was similar to that observed in California OSHPD: 12.4% among mothers delivering <28 weeks, 22.3% for 29–33 weeks, 13.0% for 34–36 weeks, and 2.5% for ≥37 weeks.

      Lee HCB, M.V.:Green, S.:Butwick, A.J.:Druzin, M.:Melsop, K.:Ton, T.G.N. The burden of preeclampsia on preterm birth (abstract). Presented at the American Academy of Pediatrics 2016 national conference and exhibition.

      These estimates were multiplied by the number of infants born to women in the United States in 2012 to obtain counts.

      Adverse outcomes

      We identified a total of 8 types of maternal and 10 types of infant adverse events, as listed in Appendix 3. Adverse outcomes are the main driver of cost differences between nonpreeclamptic and preeclamptic pregnancies and births. To contextualize our cost estimates, we estimated the number of adverse outcomes in the United States for preeclamptic mothers and their infants, the effect of preeclampsia on the probability of an adverse outcome, and the association between types of adverse outcomes and costs.

      National probabilities and counts of outcomes

      All outcomes were assessed during a 1 year time period after birth and considered an adverse maternal or infant outcome. To predict the number of adverse outcomes associated with preeclampsia, we first used the California OSHPD data to estimate logistic regression models for each adverse maternal and infant outcome. However, because women giving birth in California are systematically different from those in the rest of the United States, we derived sampling weights and estimated weighted logistic regression models using inverse probability weighting techniques (see Appendix 4 for details).
      The weighted models were used to predict the probability of each maternal and infant outcome for mothers with preeclampsia at the national level. The probabilities were then multiplied by the total number of births to preeclamptic mothers in the United States in 2012 to obtain absolute counts for each adverse outcome.

      Association of preeclampsia with adverse maternal and infant outcomes

      To estimate the association between preeclampsia and adverse outcomes, we ran generalized estimating equations with logit links using all the variables from the weighted logistic regressions described below except GA. In other words, we allowed the effect of preeclampsia on outcomes to also include its effect on GA.
      Generalized estimating equations were used to account for the correlation between infants born to the same mother. For mothers with preeclampsia and their infants, we used the models to predict the probability of an adverse outcome in both the status quo in which the mother has preeclampsia and a counterfactual scenario in which she does not.

      Association of preeclampsia with gestational age

      To assess the association between preeclampsia and GA, we used linear regression models with weekly GA as the continuous outcome and preeclampsia as the independent variable. We adjusted our models for maternal age, infant sex, nulliparity, prenatal care, multiple births, maternal body mass index, insurance status, cigarette smoking, alcohol consumption, and diabetes.

      Estimating costs

      Predicting health care costs of preeclampsia

      We estimated the association between preeclampsia and costs for mothers and infants in 2 stages, as displayed in Figure 1. In the first stage, we developed a population-representative data set that contained maternal covariates, health outcomes, and costs. In the second stage, we used this data set to estimate the national burden of preeclampsia. Here we describe the steps in more detail.
      Figure thumbnail gr1
      Figure 1Methodology to estimate national cost burden of preeclampsia in 2012
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      In the first stage, we developed a population-representative data set that contained maternal covariates, health outcomes, and costs by augmenting the existing OSHPD data with nationally corrected predicted costs in 3 steps. First, we estimated the cost for mothers and infants using regression models in claims data (step 1, Figure 1). Because preeclampsia often requires additional prenatal care, we estimated maternal costs from 6 months before to 12 months after birth. The infant model captured costs for 12 months following birth. Second, we used the claims-based cost models to impute costs for each mother or infant in the California OSHPD data as if they had been covered under private insurance (step 2, Figure 1). Finally, we multiplied imputed costs by correction factors from national Healthcare Cost and Utilization Project data to obtain nationally representative costs (step 3, Figure 1). Appendix 5 details the economic models and correction factors.
      In the second stage, we used linear regressions applied to the data set created in the first stage to estimate the relationship between preeclampsia and nationally corrected imputed costs for mothers and between GA and nationally corrected imputed costs for infants (step 4, Figure 1). In these regressions, we used the same adjustments and sample weights as those used to estimate adverse outcomes, as described in the previous text.
      In the regression of nationally corrected imputed costs for infants, we allowed costs by GA to differ by preeclampsia status but found no evidence that preeclampsia influenced costs for infants after conditioning on GA. We therefore calculated the average costs for mothers by GA and preeclampsia status and calculated the average costs for infants only by GA (step 5, Figure 1). Average predicted costs for mothers and infants were multiplied by birth counts to determine health care costs for preeclamptic mothers and their infants (step 6, Figure 1).
      To estimate the marginal impact of preeclampsia in step 6, we calculated maternal costs caused by preeclampsia as the net difference between pregnancies with and without preeclampsia at each GA, after adjusting for maternal covariates.
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      Net costs of preeclampsia for infants were estimated by comparing observed total costs of infants from preeclamptic births to total costs in a counterfactual scenario in which we assumed the average cost of an infant, had it not been born from preeclamptic pregnancy, was equal to the average cost of an infant born 2 weeks later. We did not include costs caused by the loss of life using estimates of the value per quality-adjusted life-year (QALY).

      Effect of outcomes on costs

      To provide further insight into how adverse outcomes relate to costs, we estimated linear regression models of costs as a function of adverse outcomes, separately for mothers and infants using claims data. Although claims data are not representative, we adjusted for a number of maternal covariates, so the regression coefficients should provide reasonable estimates of the effects of the adverse outcomes on cost.
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      However, because costs tend to be higher for commercially insured patients, we report both unadjusted mean differences in costs between those with and without preeclampsia and mean differences in costs adjusted by our estimated cost correction factor.

      Results

      National probabilities and counts of outcomes

      Table 2 presents characteristics of mothers who gave birth in California in 2008–2011 by preeclampsia status. Table 3 provides differences in demographic and risk factor characteristics used to calculate the probability of giving birth in California vs elsewhere in the United States. In general, mothers giving birth in California had lower educational attainment, were less likely to be non-Hispanic black, and were more likely to be of Asian and Pacific Islander background. Diabetes and chronic hypertension were less common among mothers in California. These differences were weighted in national models.
      Table 2Characteristics of mothers delivering infants in California, 2008–2011
      A total of 33,322 linked records were missing on preeclampsia status
      Characteristic
      Percentages are calculated among nonmissing values of each characteristic.
      Without preeclampsia (n = 1,918,498)With preeclampsia (n = 69,193)
      n%n%
      Maternal age, y
       <20168,6118.8808911.7
       20–24408,61021.314,73621.3
       25–29516,50826.916,29023.5
       30–34482,99025.215,33822.2
       35–39271,33614.110,58315.3
       40–4466,0943.436715.3
       45–4940310.24310.6
       50 or older2950.0510.1
      Infant male sex982,60451.236,01452.0
      Parity, live born
       1752,04239.240,93059.2
       2–51,129,49258.926,79438.8
       6–934,1371.813271.9
       10 or more15530.1860.1
      Prenatal visits
       082300.45170.8
       1–9316,33816.914,19121.2
       10–191,476,51879.148,03571.8
       20–2957,5883.134295.1
       30–3971710.45620.8
       40–4914200.11420.2
       50 or more3460.0380.1
      Multiples (yes)27,1461.440365.8
      Body mass index
       Underweight72,5684.115572.5
       Normal weight887,49749.922,90536.2
       Overweight458,58925.817,40027.5
       Obese I220,99712.411,22417.7
       Obese II88,7735.057829.1
       Obese III50,7632.944307.0
      Maternal race
       Non-Hispanic white524,50427.817,26425.5
       Non-Hispanic black108,2185.762419.2
       Non-Hispanic Asian187,93310.037425.5
       Non-Hispanic Pacific Islander63,4173.432314.8
       Non-Hispanic American Indian/Alaska Native84760.44090.6
       Non-Hispanic other15450.1630.1
       Hispanic US born454,51024.118,93428.0
       Hispanic foreign born539,76528.617,84126.3
      Insurance
       Medi-Cal917,59047.933,80249.0
       Private896,19846.831,79346.0
       Uninsured44,0252.315382.2
       Other57,5453.019092.8
      Smoking during pregnancy43,4202.315052.2
      Alcohol consumption21250.11280.2
      Renal disease3490.02600.4
      Prior diabetes14,7710.827944.0
      Gestational diabetes138,0577.2860812.4
      Cardiovascular disease7450.03090.4
      Chronic lung disease6210.02660.4
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      a A total of 33,322 linked records were missing on preeclampsia status
      b Percentages are calculated among nonmissing values of each characteristic.
      Table 3Characteristics of mothers delivering infants in the United States, by geography
      National Center for Health Statistics, 2004.
      CharacteristicsNational births (excluding California) (n = 3,573,333)California births (n = 545,758)
      Maternal age, y, mean (SD)27.4(6.2)28.1(6.3)
      Maternal education, n, %
       Less than high school725,81920.5148,68028.1
       High school1,642,41746.5250,88847.3
       College or higher1,165,14333.0130,53424.6
      Infant male sex, n, %1,829,33951.2278,85851.1
      Maternal race, n, %
       Non-Hispanic white2,564,27272.3402,65274.9
       Non-Hispanic black556,06715.731,0825.8
       Non-Hispanic Asian/Pacific Islander154,4674.466,88612.4
       Non-Hispanic American Indian/Alaska Native/other39,1801.127520.5
       Hispanic US born91,6992.611,2582.1
       Hispanic foreign born142,6614.023,2824.3
      Diabetes, n, %133,1333.713,3602.5
      Chronic hypertension, n, %37,7381.116580.3
      Gestational age, wks, mean (SD)38.6(2.6)38.8(2.4)
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      a National Center for Health Statistics, 2004.
      Table 4 reports national predicted counts of adverse maternal events within 12 months of delivery in 2012 among women with preeclampsia, according to GA. The total number of adverse maternal events was predicted to be 21,460, with hemorrhage (6.1%) and thrombocytopenia (4.0%) constituting the most common events among mothers. Across all GA, we estimated 72 maternal deaths among women with preeclampsia during the 12 months after delivery.
      Table 4Number of adverse maternal outcomes in United States within 12 months of delivery among mothers with preeclampsia by gestational age, using California Office of Statewide Health Planning and Development combined with natality data from the National Center for Health Statistics
      GA, wksMothers with preeclampsiaARF, %CVA or TIA, %MI, %Seizures, %Thrombo-cytopenia, %Hemorrhage, %DIC, %Death, %Total events
      <283605122 (3.4)7 (0.2)7 (0.2)122 (3.4)232 (6.4)352 (9.8)95 (2.6)4 (0.1)941
      28–3323,624700 (3.0)102 (0.4)17 (0.1)700 (3.0)1231 (5.2)1374 (5.8)417 (1.8)41 (0.2)4581
      34–3641,856576 (1.4)91 (0.2)11 (0.03)576 (1.4)1790 (4.3)2475 (5.9)474 (1.1)14 (0.03)6006
      37 or more87,595389 (0.5)131 (0.1)14 (0.03)398 (0.5)3052 (3.5)5288 (6.0)637 (0.7)13 (0.01)9931
      Total156,6811796 (1.1)331 (0.2)49 (0.03)1795 (1.1)6306 (4.0)9489 (6.1)1622 (1.0)72 (0.1)21,460
      ARF, acute renal failure; CVA/TIA, cerebrovascular and transient ischemic accidents; DIC, disseminated intravascular coagulation; MI, myocardial infarction.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Table 5 describes adverse infant outcomes born to mothers with preeclampsia within the 12 months after birth by GA. The predicted number of adverse infant outcomes was estimated to be 38,561, with the most prevalent adverse outcomes being neonatal respiratory distress syndrome (10.9%) and sepsis (7.4%).
      Table 5Adverse outcomes in the United States within 12 months of delivery among infants born to mothers with preeclampsia in 2012, by gestational age
      GA, wksBorn to mothers with preeclampsiaFetal distress (%)RDS (%)ROP (%)NEC (%)IVH (%)PVL (%)BPD (%)Sepsis (%)Seizures (%)Death (%)Total
      <28360583 (2.3)2256 (62.6)965 (26.8)304 (8.4)693 (19.2)79 (2.2)1030 (28.6)1287 (35.7)42 (1.2)1413 (39.2)8151
      28–3323,624635 (2.7)9753 (41.3)920 (3.9)582 (2.5)1398 (5.9)127 (0.5)981 (4.2)4783 (20.3)213 (0.9)642 (2.7)20,035
      34–3641,856444 (1.1)2225 (5.2)18 (0.04)75 (0.2)118 (0.3)10 (0.02)79 (0.2)1949 (4.7)171 (0.4)179 (0.4)5268
      37 or more87,5952439 (2.8)376 (0.4)14 (0.02)15 (0.02)27 (0.03)4 (0.0)99 (0.1)1954 (2.2)4 (0.0)173 (0.2)5106
      Total156,6813601 (2.6)14,611 (10.9)1918 (1.4)976 (0.7)2236 (1.7)220 (0.2)2189 (1.6)9974 (7.4)430 (0.3)2407 (1.8)38,561
      BPD, bronchopulmonary dysplasia; IVH, intraventricular hemorrhage; NEC, necrotizing enterocolitis; PVL, cystic periventricular leukomalacia; RDS, respiratory distress syndrome; ROP, retinopathy of prematurity.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      In multivariable models estimating the association between preeclampsia and GA, we found that mothers with preeclampsia in California had lower GA by 1.6 weeks (P < .001). Nationally, we estimated that preeclampsia was associated with a lower GA by 1.7 weeks (P < .001).

      Association of preeclampsia with maternal and infant outcomes

      Figure 2 examines the predicted probability of maternal and infant adverse outcomes for pregnancies with and without preeclampsia. Overall, preeclampsia increased the probability that a mother would have at least 1 adverse outcome from 4.6% to 10.1% and the probability that an infant would have at least 1 outcome from 7.9% to 14.2%. For mothers, preeclampsia had the largest influence on the likelihood of hemorrhage (3.1% to 6.0%) and thrombocytopenia (0.9% to 3.7%). For infants, preeclampsia had the largest effect on the probability of respiratory distress syndrome (1.9% to 6.6%) and sepsis (3.0% to 5.4%), although effects on predicted probabilities were large for a number of other adverse outcomes.
      Figure thumbnail gr2
      Figure 2Predicted probability of adverse maternal and infant outcomes for pregnancies
      These probabilities are determined in pregnancies with and without preeclampsia.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.

      Health care costs of preeclampsia in the United States

      Table 6 presents national estimates of mean costs of preeclamptic pregnancies and related births in the United States in 2012 by GA, separated into maternal and infant cost episodes. Lower GA births were associated with higher costs (Table 6 and Figure 3). For example, the combined mean maternal and infant cost per pregnancy ranged from $311,700 for GA <28 weeks to $23,035 for GA ≥37 weeks. The share of total health care costs accounted for by the infant was 26% for GA ≥37 weeks vs 91% for GA <28 weeks. Total health care costs for preeclamptic pregnancies, including the normal costs associated with birth, were estimated to be $6.4 billion, summed across mothers and infants for all GAs.
      Table 6Estimated unit and total health care cost for preeclampsia patients in the United States, by gestational age at birth (2012) using California Office of Statewide Health Planning and Development and commercial claims data
      Costs<28 wks (3604)28-33 wks (23,624)34-36 wks (41,856)37 wks or longer (87,596)All (156,680)
      Maternal cost per birth$29,131$24,063$19,692$17,021$19,075
      Infant cost per birth$282,570$59,803$11,112$6013$21,847
      Combined cost per birth$311,701$83,866$30,804$23,035$40,922
      Total health care cost$1.2 billion$2.0 billion$1.3 billion$2.0 billion$6.4 billion
      Total cost because of infant cost, %91%71%36%26%
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Figure thumbnail gr3
      Figure 3Mean decrease in costs for infants born 2 weeks later
      The costs are by gestational age.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Patients with preeclampsia had higher rates of underlying comorbidities that may be associated with higher infant and maternal costs independent of the effects of preeclampsia. As such, it is important to understand what proportion of higher costs among preeclamptic pregnancies is attributable to preeclampsia vs other confounding factors. In our maternal cost model, we estimated that the existence of preeclampsia increased costs by $6583 per birth; summing across our estimated 156,680 births to preeclamptic mothers, this implies $1.03 billion in increased total maternal health care costs.
      The average cost of preeclampsia per infant was calculated by GA based on the costs associated with a 2 week reduction in GA (as shown in Figure 3), multiplied by the infant prevalence by GA, and then summed across all GAs. We found that a reduction in GA by 2 weeks was estimated to increase costs for all infants by $1.15 billion (Appendix 6). Therefore, preeclampsia was estimated to cost the US health care system an additional $2.18 billion above the usual maternal and infant costs associated with birth.

      Relationship between preeclampsia outcomes on costs

      The results of multivariable linear regressions of costs as a function of maternal and infant adverse outcomes showed that most adverse outcomes were associated with high costs, especially for infants (see Table 2 in Appendix 6 for detailed results). For instance, intraventricular hemorrhage, bronchopulmonary dysplasia, periventricular leukomalacia, and infant death were all predicted to increase costs by more than $100,000 per patient.

      Comment

      Principal findings

      We found that preeclampsia is associated with a greater cost of birth to mothers at any given GA and that preeclampsia is associated with a lower GA at birth by approximately 1.7 weeks. The total cost burden of preeclampsia to the US health system was $1.03 billion for mothers and $1.15 billion for infants in 2012. Costs were considerably larger for infants born at lower GA: the cost burden of preeclampsia per birth ranged from $150,000 for infants born at 26 weeks of GA to $1311 at 36 weeks of GA.

      Strengths and limitations

      The epidemiological component of this study has several limitations. First, adverse outcomes are based on administratively coded data, and underascertainment is commonly reported in perinatal research using administrative data.
      • Ford J.B.
      • Roberts C.L.
      • Algert C.S.
      • Bowen J.R.
      • Bajuk B.
      • Henderson-Smart D.J.
      Using hospital discharge data for determining neonatal morbidity and mortality: a validation study.
      For instance, severe and more expensive conditions are more likely to be coded than milder forms of disease as demonstrated by a validation study of preeclampsia identified by International Classification of Disease, ninth revision codes.
      • Geller S.E.
      • Ahmed S.
      • Brown M.L.
      • Cox S.M.
      • Rosenberg D.
      • Kilpatrick S.J.
      International classification of diseases–9th revision coding for preeclampsia: how accurate is it?.
      As such, preeclampsia without severe features may be underreported and more likely misclassified as no preeclampsia than preeclampsia with severe features. Similarly, more severe and expensive conditions and procedures are more likely to be coded because of billing and reimbursement patterns.
      • Ferver K.
      • Burton B.
      • Jesilow P.
      The use of claims data in healthcare research.
      Second, birth certificate data, the source of NCHS data, do not document maternal preeclampsia. We assumed the national rate of preeclampsia was similar to that observed in California but expect this assumption to be conservative, given that prevalence of risk factors such as hypertension and diabetes is lower in California than nationally.

      Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS). Prevalence of Hypertension, 2011, US adults ages 20 and older. Available at: http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_bloodpressure.htm. Accessed Jan. 31, 2016.

      Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS). Diabetes data and statistics. Available at: http://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html. Accessed Jan. 31, 2016.

      Third, because no population-based data exist to describe both maternal and infant adverse outcomes within 12 months of delivery, we relied on models incorporating national sampling weights to predict national outcomes. The last NCHS data set to release geographical information was from 2004, after which state-specific information was withheld for privacy purposes. To the extent that geographic patterns have changed since 2004, our sampling weights may not precisely reflect current geographic distributions.
      Finally, the epidemiological data do not capture births at military hospitals. This omission may lead to bias if the relationship between preeclampsia and health outcomes differs between military hospitals and the hospitals in our epidemiological data.
      Our cost estimates are subject to a number of limitations as well. First, the California OSHPD did not contain cost information, so cost was imputed using insurance claims data. Second, we did not incorporate the direct effect of preeclampsia on infant costs, independent of GA. Third, our cost correction factors were estimated using birth event costs only, which may not be representative of subsequent costs following birth.
      We took a number of measures to assess the potential impact of these limitations. First, we compared our epidemiological predictions to nationally reported statistics. Our estimates are similar to 2010 estimates from the Centers for Disease Control and Prevention, which found that 64 deaths occurred among mothers with preeclampsia or eclampsia.

      Centers for Disease Control and Prevention (CDC) National Center for Chronic Disease Prevention and Health Promotion. Safe motherhood: advancing the health of mothers in the 21st century, 2015. Available at: http://www.cdc.gov/chronicdisease/resources/publications/aag/pdf/2015/safe-motherhood-aag-2015.pdf. Accessed April 30, 2016.

      We estimated 72 maternal deaths associated from preeclampsia. In one study using mixed methods to improve upon case identification and cause of death, 17% of maternal deaths were attributed to preeclampsia.
      • Mitchell C.
      • Lawton E.
      • Morton C.
      • McCain C.
      • Holtby S.
      • Main E.
      California pregnancy-associated mortality review: mixed methods approach for improved case identification, cause of death analyses and translation of findings.
      The number of predicted maternal deaths from preeclampsia from our model represents 14.6% of all maternal deaths. The overall infant death rate from our model was 6.17 per 1000 births. In 2012, the Centers for Disease Control and Prevention reported an infant mortality of 5.98 per 1000 births.

      United States Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, Division of Vital Statistics. Linked birth/infant death records, 2007–2013. Available at: http://wonder.cdc.gov/lbd-current.html. Accessed Oct. 13, 2016.

      Second, we examined the sensitivity of our results to certain assumptions. For example, we examined the sensitivity of our maternal cost burden estimates by comparing our results using the California OSHPD to estimates from insurance claims data. In the California OSHPD model, which is more population-based, preeclampsia was estimated to increase cost per birth by $6583. Using only insurance claims data, this effect was $8361. If we apply the cost correction factor to the estimate from the insurance data, the cost of preeclampsia for mothers is similar to our baseline estimate: $5468 per birth.
      Applying the cost-corrected estimate from the claims data decreases the maternal cost burden of preeclampsia from $1.03 billion to $813 million. One potential explanation is that the commercially insured population is healthier on average, although this is unlikely to completely explain the difference between the coefficients. It is further possible that in the overall population, preeclampsia is more likely to be correlated with costly negative health outcomes than in the claims data.
      We assessed the sensitivity of our infant results in a similar fashion. In particular, we regressed infant costs on maternal covariates and whether the infant’s mother had preeclampsia, which suggested that preeclampsia increases infant costs by $14,144. Across all infants in the United States, this implies an infant cost burden of approximately $2.2 billion, considerably higher than our baseline estimate.
      Both our model checks and sensitivity analyses suggest that our results are robust. Our prediction models and statistical methodology generate predictions that are similar to published findings, and our sensitivity analyses suggest that our burden estimates may, if anything, be underestimated. The primary reason that our predictions are similar to nationally reported statistics is that we used population-based data sets to ensure that our results were nationally representative, which is a strength of our study. Overall, our analysis is the most comprehensive effort to date to quantify the national epidemiological and economic burden of preeclampsia to both mothers and infants during the first 12 months after delivery.

      Clinical and research implications

      Although our findings suggest that preeclampsia has a significant burden on society, there are additional burdens that we did not consider. For example, we did not measure health burden. However, estimates of QALYs lost exist for several infant adverse outcomes associated with preeclampsia (see Appendix 7).
      • Feingold E.
      • Sheir-Neiss G.
      • Melnychuk J.
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      • Paul D.
      HRQL and severity of brain ultrasound findings in a cohort of adolescents who were born preterm.
      • Gough A.
      • Linden M.
      • Spence D.
      • Patterson C.C.
      • Halliday H.L.
      • McGarvey L.P.
      Impaired lung function and health status in adult survivors of bronchopulmonary dysplasia.
      • Kamholz K.L.
      • Cole C.H.
      • Gray J.E.
      • Zupancic J.A.
      Cost-effectiveness of early treatment for retinopathy of prematurity.
      Given the incidence of these conditions in preeclamptic pregnancies and the net loss of QALYs established in prior studies of these adverse outcomes, our cost burden estimate is likely conservative, capturing only a part of the overall burden of preeclampsia to society.
      Moreover, our study estimates costs up to the 12 months after birth for mothers and infants, but longer-term adverse outcomes associated with preeclampsia have been demonstrated.
      • Brown M.C.
      • Best K.E.
      • Pearce M.S.
      • Waugh J.
      • Robson S.C.
      • Bell R.
      Cardiovascular disease risk in women with pre-eclampsia: systematic review and meta-analysis.
      • Lykke J.A.
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      • Sibai B.M.
      • Funai E.F.
      • Triche E.W.
      • Paidas M.J.
      Hypertensive pregnancy disorders and subsequent cardiovascular morbidity and type 2 diabetes mellitus in the mother.
      • Bellamy L.
      • Casas J.P.
      • Hingorani A.D.
      • Williams D.J.
      Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis.
      • Doyle L.W.
      • Peter J.
      Adult outcome of extremely preterm infants.
      • Hennessy E.M.
      • Bracewell M.A.
      • Wood N.
      • et al.
      Respiratory health in pre-school and school age children following extremely preterm birth.
      • Ray J.G.
      • Vermeulen M.J.
      • Schull M.J.
      • Redelmeier D.A.
      Cardiovascular health after maternal placental syndromes (CHAMPS): population-based retrospective cohort study.
      Preeclampsia has been associated with an increased risk of cardiovascular disease.
      • Brown M.C.
      • Best K.E.
      • Pearce M.S.
      • Waugh J.
      • Robson S.C.
      • Bell R.
      Cardiovascular disease risk in women with pre-eclampsia: systematic review and meta-analysis.
      • Ray J.G.
      • Vermeulen M.J.
      • Schull M.J.
      • Redelmeier D.A.
      Cardiovascular health after maternal placental syndromes (CHAMPS): population-based retrospective cohort study.
      Women with a history of preeclampsia have twice the relative risk of developing ischemic heart disease or cerebrovascular disease and 3 times the risk of developing hypertension.
      • Brown M.C.
      • Best K.E.
      • Pearce M.S.
      • Waugh J.
      • Robson S.C.
      • Bell R.
      Cardiovascular disease risk in women with pre-eclampsia: systematic review and meta-analysis.
      • Bellamy L.
      • Casas J.P.
      • Hingorani A.D.
      • Williams D.J.
      Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis.
      In addition, end-stage renal disease occurs more often in women who have had preeclampsia compared with those with healthy pregnancies.
      • Vikse B.E.
      • Irgens L.M.
      • Leivestad T.
      • Skjaerven R.
      • Iversen B.M.
      Preeclampsia and the risk of end-stage renal disease.
      Other studies have shown increased risk of stroke in adult offspring of pregnancies with preeclampsia.
      • Kajantie E.
      • Eriksson J.G.
      • Osmond C.
      • Thornburg K.
      • Barker D.J.
      Pre-eclampsia is associated with increased risk of stroke in the adult offspring the helsinki birth cohort study.
      Future studies can improve upon this research by using cost data that are directly observable (rather than imputing costs). Furthermore, extending the time window beyond the first 12 months after birth will provide a better understanding of how preeclampsia has an impact on women and children in the long term.

        Glossary of terms

      • administrative claims data:
        • data collected by government departments and other organizations for the purposes of registration, transaction, record keeping, billing, and payment, usually during the delivery of a service, which contain a comprehensive history of health care utilization.
      • cost episode:
        • the cost of all services provided to a patient with a medical problem within a specific period of time across a continuum of care in an integrated system.
      • counterfactual scenario:
        • a hypothetical scenario that captures what might happen to the treated sample had it not been subject to the exposure, keeping other factors constant.
      • generalized estimating equations:
        • the formula used in generalized linear models, which accounts for correlated data that can arise from longitudinal repeated measures or clustering among observations that share similar characteristics.
      • interaction:
        • a statistical and epidemiological phenomenon in which the magnitude of the association between the outcome and exposure variable differs according to a third variable.
      • linear regression:
        • a statistical approach for modeling relationships between variables, in which exposure variables and controls have an impact on outcomes in a linear fashion.
      • logistic regression:
        • a statistical technique for estimating the relationship between a binary outcome and 1 or more independent variables, based on the logistic function.
      • logit link:
        • link that specifies that the relationship between the linear predictor and the mean of the outcome variable follows a logit function, which is the natural log of the odds that the outcome equals 1 of the possible 2 outcome categories.
      • retrospective cohort study:
        • a study design that involves identification and follow-up of subjects who are identified only after the follow-up period under study has ended.
      • quality-adjusted life-year (QALY):
        • a measure of disease burden, including both the quality and the quantity of life lived used in economic evaluation to assess the economic value of medical interventions.
      • weighted multivariable linear regression:
        • an approach for estimating the relationship between an outcome variable and 1 or more explanatory variables, in which each data points is assigned a weight and data points with higher weights contribute more to the estimated relationship.

      Appendix

      Appendices: short-term costs of preeclampsia to the United States health care system

      Appendix 1. Data sets reviewed for analysis

      Health outcomes only: These data sources comprised of cohort or networks of research centers that focus primarily on health outcomes.
      • 1.
        California Maternal Quality Care Collaborative (CMQCC)
      • 2.
        California Office of Statewide Health Planning and Development (California OSHPD)
      • 3.
        California Perinatal Quality Care Collaborative (CPQCC)
      • 4.
        Child Health Development Study (CHDS): 1950–1970
      • 5.
        Danish Birth Cohort
      • 6.
        Early Childhood Longitudinal Study Birth Cohort (National Center for Education Statistics)
      • 7.
        European Birth Cohorts (many)
      • 8.
        Extremely Low Gestational Age Newborns (ELGAN)
      • 9.
        Pediatrix Center for Research, Education, and Quality (CREQ)
      • 10.
        Preeclampsia Foundation
      • 11.
        Preeclampsia International Network
      • 12.
        UK Millennium Cohort Study (UK MCS)
      • 13.
        Vermont Oxford Network (VON)
      • 14.
        National Bureau of Economic Research / National Center for Health Statistics
      • 15.
        National Perinatal Collaborative Project (PCP): 1959–1974
      • 16.
        National Survey of Family Growth (NSFG)
      • 17.
        Neonatal Research Network (NRN)
      • 18.
        NIAID Birth Cohorts (40 or more cohorts)
      • 19.
        Maternal-Fetal Medicine Units Network (MFMU)
      • 20.
        Regenstrief Institute
      Health and cost outcomes: These data sources include health maintenance organizations or claims-based administrative data sets from which both health and cost outcomes can be abstracted.
      • 21.
        HealthCore (WellPoint Anthem)
      • 22.
        Henry Ford Health Systems
      • 23.
        Kaiser Permanente of Northern California (KPNC)
      • 24.
        Athena HealthCare
      • 25.
        Geisinger (MedMining) and Quintiles
      • 26.
        Pharmetrics IMS
      • 27.
        Pharmetrics Plus
      • 28.
        Pregnancy to Early Life Longitudinal System (PELL)
      • 29.
        Premier Perinatal Safety Initiative
      • 30.
        Optum Stork
      • 31.
        Optum Touchstone
      • 32.
        Truven Marketscan
      • 33.
        Healthcare Cost and Utilization Project (HCUP)
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.

      Appendix 2. Data sources

      California Office of Statewide Health Planning and Development (California OSHPD)
      We, the investigators of this study, linked several California population-based data sets. The first was a linked California vital statistics/California Office of Statewide Health Planning and Development (California OSHPD) hospital discharge administrative database for maternal hospitalizations.

      Office of Statewide Health Planning and Development. https://www.oshpd.ca.gov/. Accessed November 29, 2016.

      The California OSHPD data provide administrative and billing information on nearly all hospitalizations in California. Only 2% of births are not linked to vital statistics in the cases of home births without hospitalization, births in military hospitals, and freestanding birthing centers. This maternal data set was linked to the corresponding OSHPD hospital discharge database for infants, with 96% of observations linked. Both databases include diagnoses and procedures based on the International Classification of Disease, ninth revision (ICD-9), codes. Vital statistics data provide further sociodemographic and medical characteristics of the mother and infant. We used linked data between 2008 and 2011, constituting 2,021,013 linked maternal-infant births during this period. We used the California OSHPD data to identify all hospitalizations that occur to either the mother or the infant within 12 months of delivery.
      Natality data set from National Center for Health Statistics
      The National Center for Health Statistics (NCHS) holds a collection of data on births within the United States.
      • Hetzel A.M.
      History and organization of the vital statistics system.
      The natality data set has information obtained from all birth certificates in all states. Birth certificates have been revised over the years, but the basic information includes delivery events, selected maternal characteristics, and infant outcomes at delivery, with geographic information on births by state. In 2005, the NCHS withheld geographic information on the birth event from publicly available data sets. The most recent information with geographical data on the birth event available to the public is 2004. We used the 2004 data set to provide a distribution of mothers who gave birth within California or elsewhere in the country. To extrapolate to national counts of adverse outcomes, we used 3,960,796 total births in the United States in 2012 as the denominator.
      Commercial claims data
      The claims database is a private-sector health insurance claims-based data. A major advantage of the claims data set is that it combines information from a plethora of different insurance programs, in contrast to most other claims data sets that tend to be linked to only a single insurance program. As a result, the claims data set has greater representation than the data from a single insurance provider. The database captures all health care claims, including prescription drugs, inpatient, emergency, and ambulatory services for elderly and nonelderly individuals with employer-provided insurance from more than 50 large Fortune 500 firms. It contains a cumulative 82 million lives since 1993 and 2 million to 3 million covered lives per year for 1997–2008. Members reside in the Northeast, Midwest, South, and West regions of the United States. We utilized these data to estimate a cost model that predicts the medical costs from all health care claims by observed health outcomes or adverse events. For mothers, the model included the period 6 months prior to and 12 months after delivery, while for infants, the model included the period 12 months after delivery.
      Health care Cost and Utilization Project (HCUP)
      The HCUP contains a database called the National Inpatient Sample (NIS), which constitutes a 20% sample of discharges from all hospitals participating in HCUP.
      • Knight M.
      Eclampsia in the United Kingdom 2005.
      The NIS covers all patients, including individuals covered by Medicare, Medicaid, or private insurance as well as those who are uninsured. HCUP also provides the Nationwide Emergency Department Sample. We used HCUP to obtain nationally representative estimates for cost to supplement our findings using the claims database. We combined data on birth events from HCUP and the claims data to identify systematic differences in costs between the 2 data sets, controlling for health outcomes. We then estimated this systematic difference in cost to correct costs predicted using our claims-based cost model.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.

      Appendix 3

      Table 1ICD-9 codes for preeclampsia and adverse maternal outcomes
      Condition or outcomeICD-9 codeDescription
      Preeclampsia
       Preeclampsia without severe features642.4xMild or unspecified preeclampsia
       Preeclampsia with severe features642.5xSevere preeclampsia
      642.6xEclampsia, unspecified
      642.7xPreeclampsia or eclampsia superimposed on preexisting hypertension
      Renal failure584.xAcute renal failure
      586Renal failure, unspecified
      593.9Unspecified disorder of kidney and ureter
      669.3xAcute renal failure following labor and delivery
      Cerebrovascular accident431Intracerebral hemorrhage
      432.xOther and unspecified intracranial hemorrhage
      433.xxOcclusion and stenosis of precerebral arteries
      434.xxOcclusion of cerebral arteries
      436Acute, but ill-defined, cerebrovascular disease
      671.5xOther phlebitis and thrombosis
      674.0xCerebrovascular disorders in the puerperium
      Transient ischemic attack435.0xUnspecified transient cerebral ischemia
      435.1xVertebral artery syndrome
      435.8xOther specified transient cerebral ischemias
      435.9xUnspecified transient cerebral ischemia
      Eclamptic seizure780.3xConvulsions
      Myocardial infarction410.xxMyocardial infarction
      Thrombocytopenia287.3Primary thrombocytopenia
      287.33Congenital and hereditary thrombocytopenic purpura
      287.39Other primary thrombocytopenia
      287.4Secondary thrombocytopenia
      287.5Thrombocytopenia, unspecified
      289.84Heparin-induced thrombocytopenia
      446.6Thrombotic microangiopathy
      776.1Transient neonatal thrombocytopenia
      Severe intra- and post-partum hemorrhage666.0xThird-stage hemorrhage
      666.1xOther immediate postpartum hemorrhage
      666.2xDelayed and secondary postpartum hemorrhage
      641.9Unspecified antepartum hemorrhage
      Disseminated intravascular coagulation286.6Defibrination syndrome
      286.9Other and unspecified coagulation defects
      666.3xPostpartum coagulation defects
      ICD-9, International Classification of Disease, ninth revision.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Table 2ICD-9 codes for adverse infant outcomes
      OutcomeICD-9 codeDescription
      Fetal distress656.8xOther specified fetal and placental problems affecting management of mother
      656.3xFetal distress affecting management of mother
      768.2Fetal distress before onset of labor, in liveborn infant
      768.3Fetal distress first noted during labor and delivery, in liveborn infant
      768.4Fetal distress, unspecified as to time of onset, in liveborn infant
      Respiratory distress syndrome769Respiratory distress syndrome in newborn
      Bronchopulmonary dysplasia770.7Chronic respiratory disease arising in the perinatal period
      516.34Respiratory bronchiolitis interstitial lung disease
      516.69Other interstitial lung diseases of childhood
      517.8Lung involvement in other diseases classified elsewhere
      518.89Other diseases of lung not elsewhere classified
      Retinopathy of prematurity, stage >3362.25Retinopathy of prematurity, stage 3
      362.26Retinopathy of prematurity, stage 4
      362.27Retinopathy of prematurity, stage 5
      Necrotizing enterocolitis, Bell’s grade ≥2777.53, 777.52Stage 3 necrotizing enterocolitis in newborn
      Intraventricular hemorrhage, stage ≥3772.14, 772.13Intraventricular hemorrhage, grade 4
      Cystic periventricular leukomalacia779.7Periventricular leukomalacia
      Sepsis659.3xGeneralized infection during labor
      670.2xPuerperal sepsis
      771.81Septicemia [sepsis] of newborn
      995.91Sepsis
      995.92Severe sepsis
      Meningitis322.9Meningitis, unspecified
      320.xxBacterial meningitis
      321.xxMeningitis due to other organisms
      322.xxMeningitis of unspecified cause
      115.xxHistoplasmosis
      114.2Coccidioidal meningitis
      112.83Candidal meningitis
      100.81Leptospiral meningitis (aseptic)
      098.82Gonococcal meningitis
      094.2Syphilitic meningitis
      091.81Acute syphilitic meningitis (secondary)
      090.42Congenital syphilitic meningitis
      072.1Mumps meningitis
      054.72Herpes simplex meningitis
      053.0Herpes zoster with meningitis
      049.1Nonarthropod-borne meningitis due to adenovirus
      047.9Unspecified viral meningitis
      047.8Other specified viral meningitis
      047.1Meningitis due to echo virus
      047.0Meningitis due to coxsackie virus
      036.0Meningococcal meningitis
      013.xxTuberculosis of meninges and central nervous system
      003.21Salmonella meningitis
      Seizures780.31Febrile convulsions (simple), unspecified
      780.32Complex febrile convulsions
      780.33Posttraumatic seizures
      780.39Other convulsions
      345.40Localization-related (focal) (partial) epilepsy and epileptic syndromes with complex partial seizures, without mention of intractable epilepsy
      345.41Localization-related (focal) (partial) epilepsy and epileptic syndromes with complex partial seizures, with intractable epilepsy
      345.50Localization-related (focal) (partial) epilepsy and epileptic syndromes with simple partial seizures, without mention of intractable epilepsy
      345.51Localization-related (focal) (partial) epilepsy and epileptic syndromes with simple partial seizures, with intractable epilepsy
      345.80Other forms of epilepsy and recurrent seizures, without mention of intractable epilepsy
      345.81Other forms of epilepsy and recurrent seizures, with intractable epilepsy
      DeathDischarge status of death
      798.0Sudden infant death syndrome
      798.1Instantaneous death
      798.2Death occurring in less than 24 h from onset of symptoms not otherwise explained
      798.9Unattended death
      348.82Brain death
      ICD-9, International Classification of Disease, ninth revision.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Table 3ICD-9 and CPT codes for birth events
      VariableCode
      ICD-9 diagnosis640.81, 640.91,

      641.01, 641.11, 641.21, 641.31, 641.81, 641.91,

      642.41, 642.42, 642.51, 642.52, 642.6-642.62, 644.2, 642.71, 642.72,

      643.01, 643.11, 643.21, 643.81, 643.91,

      644.20, 644.21,

      645.11, 645.21,

      646.01, 646.11, 646.12, 646.21, 646.22, 646.31, 646.41, 646.42, 646.51, 646.52, 646.61, 646.62, 646.71, 646.81, 646.82, 646.91,

      647.0-647.02, 647.1-647.12, 647.2-647.22, 647.3-647.32, 647.4-647.42, 647.5-647.52,

      647.6-647.62, 647.8-647.81, 647.9-647.92,

      648, 648.1-648.12, 648.2-648.22, 648.3-648.32, 648.4-648.42, 648.5-648.52, 648.6-648.62,

      648.7-648.72, 648.9-648.91
      650.xx
      651.01, 651.11, 651.21, 651.31, 651.41, 651.51, 651.61, 651.71, 651.81, 651.91,

      652.01, 652.11, 652.21, 652.31, 652.41, 652.51, 652.61, 652.71, 652.81, 652.91,

      654.01, 654.02, 654.11, 654.12, 654.14, 654.21, 654.31, 654.32, 654.41, 654.42, 654.51, 654.52, 654.61, 654.62, 654.71, 654.72, 654.74, 654.81, 654.82, 654.91, 654.92,

      655.01, 655.11, 655.21, 655.31, 655.41, 655.51, 655.61, 655.71, 655.81, 655.91,

      656.01, 656.11, 656.21, 656.31, 656.41, 656.51, 656.61, 656.71, 656.81, 656.91,

      657.01,

      658.01, 658.11, 658.20, 658.21, 658.23, 658.30, 658.31, 658.33, 658.41, 658.81, 658.91
      659.xx
      660.00, 660.01, 660.03, 660.10, 660.11, 660.13, 660.20, 660.21, 660.23, 660.30, 660.31, 660.40, 660.41, 660.43, 660.50, 660.51, 660.53, 660.60, 660.61, 660.63, 660.70, 660.71, 660.73, 660.80, 660.81, 660.83, 660.90, 660.91, 660.93, 661.01, 661.11, 661.21, 661.31, 661.41, 661.91,

      662.01, 662.11, 662.21, 662.31,

      663.01, 663.11, 663.21, 663.31, 663.41, 663.51, 663.61, 663.81, 663.91,

      664.01, 664.11, 664.21, 664.31, 664.41, 664.51, 664.61, 664.81, 664.91,

      665.01, 665.11, 665.22, 665.31, 665.41, 665.51, 665.61, 665.71, 665.72, 665.81, 665.82, 665.91, 665.92, 667.02, 667.12,

      668.01, 668.11, 668.21, 668.81, 668.91,

      669.01, 669.11, 669.21, 669.41, 669.50, 669.51, 669.60, 669.61, 669.70, 669.71, 669.81, 669.91
      670.02, 670.12, 670.22, 670.32, 670.82,

      671.01, 671.11, 671.21, 671.81, 671.82,

      672.02,

      674.12, 674.22, 674.32, 674.42, 674.51, 674.52, 674.82, 674.92,

      675.01, 675.02, 675.11, 675.12, 675.21, 675.22, 675.81, 675.82, 675.91, 675.92,

      676.01, 676.02, 676.11, 676.12, 676.21, 676.22, 676.31, 676.32, 676.41, 676.42, 676.51, 676.52, 676.61, 676.62, 676.81, 676.82, 676.91, 676.92,

      677
      763.xx
      V27.xx
      V30.xx-V39.xx
      ICD-9 procedure72.xx-73.1; 73.22, 73.3, 73.4, 73.51, 73.59, 73.6, 73.8, 73.91, 73.92, 73.93, 73.94, 73.99, 74.0, 74.1, 74.2, 74.4, 74.99
      CPT

      procedure
      59400-59410
      59510-59515
      59610, 59612, 59614, 59618, 59620, 59622
      CPT, current procedural terminology; ICD-9, International Classification of Disease, ninth revision.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.

      Appendix 4. Epidemiological methods

      Sampling weights
      To obtain nationally representative estimates, we derived sampling weights for the California data using the NCHS. Geographic information available in 2004 in the NCHS was used to identify whether a mother delivered in the State of California. Specifically, we developed statistical models to predict the probability of giving birth in California (vs non-California) based on shared demographic and risk factor variables available between the NCHS and California OSHPD. These predictors included maternal age, race, education, diabetes, hypertension, renal disease, and number of prenatal visits. We estimated model parameters using the NCHS data and then applied the estimates to the California OSHPD to obtain a predicted probability P of giving birth in California for each mother and infant in our California data set.
      Weighted logistic regression models
      We conducted analyses to assess the association between preeclampsia and maternal and infant outcomes. Each outcome was assessed in a separate model. We used logistic models to assess the association between preeclampsia and dichotomous outcomes (maternal and infant) that occurred within the 12 month period following delivery. All models were conducted first in California OSHPD data sets and adjusted for maternal age, infant sex, nulliparity, prenatal care, multiples, maternal body mass index, maternal race, insurance status, cigarette smoking, alcohol consumption, and prior diabetes. An interaction term between gestational age and preeclampsia was included in each model. We used inverse probability weighting techniques using sampling weights derived from the NCHS to conduct weighted regressions in California data. All logistic regressions were thus weighted, with weights equal to 1/P for each observation in the sample,
      • Mansournia M.A.
      • Altman D.G.
      Inverse probability weighting.
      • Solon G.
      • Haider S.J.
      • Wooldridge J.M.
      What are we weighting for?.
      thereby reweighting the individuals in the OSHPD data to match the overall US population, based on the demographic and risk variables described previously. These models were then used to predict the probability of each outcome for those with preeclampsia in the United States. These national probabilities were then applied to the number of US mother-infant–linked records in 2012 from the NCHS to obtain the absolute count of annual predicted outcomes nationally during the 12 months after birth.
      NCHS, National Center for Health Statistics; OSHPD, Office of Statewide Health Planning and Development.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.

      Appendix 5. Sources and description of cost estimates

      To predict the medical costs of health care for mothers and infants in the California OSHPD data, we first estimated models of maternal and infant costs in the commercial claims data. We estimated these models using a sample of all women 18 years and older who had a delivery episode between 2004 and 2014 and were continuously enrolled for 6 months before and 12 months after birth. Birth events were identified by an ICD-9 code or CPT code for delivery event. Birth events occurring within 30 days of each other were considered to be the same event.
      Total costs were defined as the sum of medical (including inpatient, emergency, and ambulatory services) and pharmacy claims from all payers. Medical costs were adjusted for inflation using the annual CPI data published by the Bureau of Labor Statistics. Medical costs were adjusted using the following equation:
      CPIAdjustedCost=CostinServiceYearCPIReferenceYearCPIServiceYear


      For example, if the CPI in the reference year of 2014 is 435.292, and the CPI in the service year 2007 is 351.054, then a $100 medical cost incurred in 2007 will be adjusted as follows:
      CPIAdjustedCost(2014dollars)=$100.00435.292351.054=$124.00


      We utilized the variables listed in Tables 2 and 3 (in the text) and Appendix 3 as predictors in the cost model; however, we restricted the covariates to include those measures available in both the claims data and the California OSHPD data. Because our goal was to simply predict the actual cost of medical care for each mother and infant, given the available data, we did not focus on methods that would allow for causal identification. We excluded variables with P values such that P > .1 in an iterative fashion to reduce potential error because of multicollinearity. Although a variable for gestational age was available in the claims data, it was missing for a large majority of the data and was therefore excluded from the model. Because gestational age likely drives costs through the need for costly health outcomes, the inclusion of the health outcomes themselves in the model minimizes any error introduced by omitting gestational age as a variable. For the infant model, maternal covariates were linked to the infant data with the family identification variable.
      For both the maternal and infant models, we assessed the appropriateness of a log transformation for the cost variable. Because there appeared to be skew both in the raw cost data and in the residuals of the untransformed infant cost model, we applied a log transformation to the dependent variable, cost. However, there did not appear to be asymmetry in the maternal data that would be corrected by a log transformation. Furthermore, we assessed the appropriateness of a generalized linear model with gamma errors and a log link by fitting these models to our claims data and then comparing the root mean squared error between the predicted and observed values. The results are provided in Appendix 5 Table 1 below. The results of the regression models used to predict maternal costs are provided in Appendix 5 Table 2 below.
      CPI, Consumer Price Index; CPT, current procedural terminology; ICD-9, International Classification of Disease, ninth revision; OSHPD, Office of Statewide Health Planning and Development.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Table 1Comparison of predictive accuracy of OLS vs GLM in claims data
      VariableRMSE MaternalRMSE Infant
      Model
       OLS (log transformation for infant costs)16,583150,997
       GLM (gamma, with log link)21,726660,303
      GLM, generalized linear model; OLS, ordinary least squares; RMSE, root-mean-square error.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Table 2Regression model of maternal costs
      Total cost, $βRobust SEP value
      Preeclampsia4188487.000
      Age19012.000
      Multiparity33841677.044
      Hypertension5888373.000
      Gestational diabetes3749264.000
      Diabetes5529538.000
      Cardiovascular disease14,5735468.008
      Acute chronic lung14,1483785.000
      Poor fetal growth4377234.000
      Excess fetal growth2066204.000
      Antepartum hemorrhage abruption7730493.000
      Cesarean delivery9326157.000
      Assisted vaginal delivery3094140.000
      Fetal death38331354.005
      Meconium61831676.000
      Premature rupture6396476.000
      Multigestational birth15,774795.000
      Eclamptic seizure21,3452754.000
      Cerebrovascular accident or transient ischemic attack27,3924728.000
      Thrombocytopenia99212861.001
      Disseminated intravascular coagulation12,2733275.000
      Pulmonary edema33,4249690.001
      Intrapostpartum hemorrhage6710574.000
      Pulmonary embolism22,8525325.000
      Deep venous thrombosis17,3043406.000
      Renal failure32,5136705.000
      Myocardial infarction41,07616,442.012
      Shock27,87114,614.056
      Sepsis24,9305195.000
      Acute liver failure79941107.000
      Acute fatty liver75502102.000
      Blood transfusion5176295.000
      Hysterectomy28,8987399.000
      Death28431556.068
      Constant1954363.000
      n83,846
      R20.2415
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Table 3Regression model of infant costs
      Log of total costsβRobust SEP value
      Preeclampsia0.1760.017.000
      Maternal age at birth event0.0050.001.000
      Hypertension0.0250.012.043
      Acute chronic lung disease0.2640.083.002
      Poor fetal growth0.0260.009.005
      Excess fetal growth–0.0410.008.000
      Antepartum hemorrhage abruption0.0700.015.000
      Vaginal delivery–0.1480.022.000
      Cesarean delivery0.1470.006.000
      Assisted vaginal delivery–0.0460.006.000
      Meconium–0.2610.103.011
      Premature rupture0.2650.019.000
      Cord prolapse0.1560.078.044
      Multigestational birth0.5290.020.000
      Fetal distress0.1500.025.000
      Respiratory distress syndrome1.5160.026.000
      Bronchopulmonary dysplasia1.2240.046.000
      Necrotizing enterocolitis >grade 20.8610.292.003
      Intraventricular hemorrhage >stage 30.7910.120.000
      Cystic periventricular leukomalacia1.0610.176.000
      Sepsis0.6050.028.000
      Meningitis0.5150.060.000
      Seizure0.8010.035.000
      Postbirth death0.8820.159.000
      NICU/critical care admission0.9250.012.000
      Constant8.1410.019.000
      n76,335
      R20.4298
      CPT, current procedural terminology; HCUP, Health care Cost and Utilization Project; ICD-9, International Classification of Disease, ninth revision; KID, Kids’ Inpatient Database; NICU, neonatal intensive care unit; NIS, National Inpatient Sample; OSHPD, Office of Statewide Health Planning and Development.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      The above models were applied to the California OSHPD data to predict the cost of each mother and infant observed in these data. We used Duan’s smearing estimate to correct predicted infant costs for the log transformation of the cost variable.
      • Duan N.
      Smearing estimate: a nonparametric retransformation method.
      For infant costs, following expert input, we replaced the cost in observations for which the predicted cost would exceed $12 million to be truncated at $12 million because these costs were unrealistically high. This change had an impact on only one observation in the data.
      The predicted costs obtained through the process previously mentioned assume all individuals in the data have costs similar to a privately insured population. However, these costs may not be nationally representative because the demographics, risk factors, and service utilization patterns of the privately insured population may not match those of the overall US population. Therefore, we used cost correction factors to the predicted costs to adjust for national representativeness.
      To estimate cost correction factors, we appended maternal or infant birth event commercial claims data with maternal or infant birth event data from the 2014 HCUP NIS for mothers and KID for infants. Birth events were again isolated using ICD-9 and CPT codes in the NIS and KID. In addition to the hospital cost data, we included all variables from Appendix 5 Tables 2 and 3 if they were also available in the HCUP data. We merged the HCUP birth event data with the yearly cost-to-charge files by hospital identification number corrected hospital charges using cost-to-charge ratios.
      • Afana M.
      • Brinjikji W.
      • Cloft H.
      • Salka S.
      Hospitalization costs for acute myocardial infarction patients treated with percutaneous coronary intervention in the United States are substantially higher than medicare payments.
      • Agarwal S.
      • Menon V.
      • Jaber W.A.
      Outcomes after acute ischemic stroke in the United States: Does residential ZIP code matter?.
      • Chan T.
      • Kim J.
      • Minich L.L.
      • Pinto N.M.
      • Waitzman N.J.
      Surgical volume, hospital quality, and hospitalization cost in congenital heart surgery in the United States.
      • Finkler S.A.
      The distinction between cost and charges.
      To calculate the cost correction factors in our analysis, we performed a linear regression of costs on the demographics, covariates, and outcomes, interacted with a dummy for whether the observation was from the claims data (vs the HCUP data). National sampling weights from HCUP were also utilized in the regression. Claims data were simply given a sampling weight of 1. Using these regression estimates, we then estimated the impact of the claims data dummy on costs by estimating the (weighted) predictive margin, or average predicted response, in a scenario in which the dummy was equal to 0 for everyone in the sample and a scenario in which the dummy was equal to 1 for everyone. The resulting predicted costs are provided in Appendix 5 Table 4. Costs for claims data correspond to predicted mean costs with the dummy equal to 1 and costs for HCUP correspond to predicted mean costs with the dummy equal to 0. Cost correction factors were calculated as the ratio of the margins for HCUP data vs the claims data.
      • Graubard B.I.
      • Korn E.L.
      Predictive margins with survey data.
      • Williams R.
      Using the margins command to estimate and interpret adjusted predictions and marginal effects.
      The predicted costs in the California OSHPD data were multiplied by this ratio to correct for national representativeness.
      We then used the model to predict costs for each mother and infant in the California OSHPD data. Because of the small sample sizes, we assumed, conservatively, that costs for infants born less than 23 weeks were equivalent to costs for infants born between 23 and 27 weeks and that costs for infants born at 40 weeks or later were equivalent to costs for infants born between 37 and 40 weeks.
      Table 4Impact of claims data on predicted mean costs
      VariablePredicted costSEt statisticP value
      Maternal
       HCUP4613.94.11123.1.000
       Claims data7054.636.2195.0.000
       Cost correction factor0.654
      Infant
       HCUP3612.118.2198.2.000
       Claims data5093.877.665.7.000
       Cost correction factor0.709
      HCUP, Health care Cost and Utilization Project; OSHPD, Office of Statewide Health Planning and Development.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Nationally corrected predicted costs in California OSHPD were then used in linear regressions to estimate the relationship between preeclampsia and maternal and infant costs using California OSHPD mother-infant–linked data. We used linear regression models to estimate the impact of preeclampsia on nationally corrected imputed costs for mothers and between gestational age and nationally corrected imputed costs for infants, adjusting the economic models and applying inverse probability weights identically as in the analysis estimating the impact of preeclampsia on adverse outcomes.
      Predictive margins were estimated to measure the average cost per mother by gestational age and preeclampsia status. Predictive margins were estimated for infants only by gestational age.

      Appendix 6. Impact of premature birth due to preeclampsia on health care costs

      Table 1Estimated impact of premature birth due to preeclampsia on total infant health care costs, by gestational age at birth (2012) using California OSHPD and commercial claims data
      Gestational ageMean costsCost burden due to premature birthCost burden fractionBirthsTotal cost burden
      ObservedBorn 2 weeks later
      23$243,016$285,289-$42,273−0.17352-$14,880,096
      24$365,604$299,404$66,2000.18477$31,577,591
      25$285,289$230,911$54,3780.19547$29,744,493
      26$299,404$146,809$152,5940.51663$101,169,955
      27$230,911$109,372$121,5390.53742$90,182,235
      28$146,809$80,110$66,6990.451763$117,590,989
      29$109,372$63,323$46,0490.422125$97,853,445
      30$80,110$42,435$37,6750.472895$109,068,054
      31$63,323$29,244$34,0790.543728$127,045,244
      32$42,435$20,509$21,9260.525312$116,472,771
      33$29,244$12,067$17,1770.597801$134,001,443
      34$20,509$7153$13,3560.657891$105,394,437
      35$12,067$5841$62260.5212,273$76,406,151
      36$7153$5841$13110.1821,692$28,443,071
      37$5841$5841$00.008486$0
      38$5875$5875$00.0016,233$0
      39$6112$6112$00.0029,482$0
      40$6067$6067$00.0019,368$0
      Total141,830$1,150,069,783
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.
      Table 2Linear regression analysis of the association between adverse outcomes and maternal and infant costs
      VariableClaims dataCost-corrected
      EstimateLower 95% CIUpper 95% CIEstimate
      Maternal
      ARF37,47924,21150,74724,511
      CVA/TIA32,20222,35342,05221,060
      Eclamptic seizures25,58820,13931,03616,734
      Thrombocytopenia12,888712918,6468429
      Hemorrhage8680750998515677
      DIC21,79515,06128,52914,254
      Infant
      Fetal distress80-3278343957
      RDS89,47882,54096,41763,440
      IVH376,579201,149552,009266,994
      PVL263,32737,851488,802186,699
      BPD187,786164,301211,271133,140
      Sepsis29,14120,98637,29620,661
      Seizure48,51933,88363,15434,400
      Death133,95043,094224,80794,971
      Note: Coefficient estimates from multivariable linear regressions of cost on preeclampsia, the adverse outcomes reported in the table, and maternal covariates. Separate models were run for mothers and infants. Maternal covariates include age at birth and indicators for obesity, smoking, alcohol use, renal disease, diabetes, gestational diabetes, hypertension, cardiovascular disease, and chronic lung disease. Costs were not log transformed. Coefficients are only reported for adverse outcomes for which the number of observations was sufficiently large. ARF = acute renal failure, CVA/TIA = cerebrovascular and transient ischemic accidents, DIC = disseminated intravascular coagulation, RDS = respiratory distress syndrome, IVH = intraventricular hemorrhage, PVL = cystic periventricular leukomalacia, BPD = bronchopulmonary dysplasia.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.

      Appendix 7. Sources and description of social value estimates

      Although preeclampsia has immediate effects on child health after birth, a large part of its burden occurs over longer time periods. This is especially true because preterm infants are likely to develop a number of health conditions with long-lasting effects. Table 1 below produces rough estimates of lifetime QALYs (with a 3% discount rate) for infants with some of these conditions and compares them to expected QALYs for a healthy baby.
      A healthy full-term infant is assumed to have a life expectancy of 78 years and to live all years in full health. Although it is not realistic to live all years in perfect health, future health is discounted so most QALYs are accrued early in life. A second case in which all life years are weighted at 0.93—based on evidence from Gough et al. (2014)—is also shown in the table.
      One of the most devastating conditions caused by preterm birth is necrotizing enterocolitis (NEC), which has a case fatality rate of 15-30%.
      • Lin P.W.
      • Stoll B.J.
      Necrotising enterocolitis.
      NEC survivors are also at risk for a number of long-term complications including short-bowel syndrome (SBS), growth abnormalities, and neurodevelopmental disabilities.
      • Hintz S.R.
      • Kendrick D.E.
      • Stoll B.J.
      • et al.
      Neurodevelopmental and growth outcomes of extremely low birth weight infants after necrotizing enterocolitis.
      • Stanford A.
      • Upperman J.S.
      • Boyle P.
      • Schall L.
      • Ojimba J.I.
      • Ford H.R.
      Long-term follow-up of patients with necrotizing enterocolitis.
      SBS is a significant morbidity that occurs in one-fourth of NEC survivors.

      Berman L, Moss RL. Necrotizing enterocolitis: an update. Paper presented at: Seminars in fetal and neonatal medicine2011.

      We used results from these studies to provide back-of-the-envelope calculation of QALYs lost from the disease. In particular, if we assume (1) that 25% of infants with NEC die from the disease and the life expectancy for these infants is 1 month, and (2) a quality-of-life weight of 0.8 for the 25% of NEC survivors with SBS, then infants with NEC can expect to obtain 21.9 QALYs over a lifetime.
      We could not find a quality-of-life weight for SBS in the literature. The score of 0.8 was chosen based on preference scores for disorders of the digestive system reported in Bell et al. (1997). All calculations use a 3% discount rate.
      Retinopathy of prematurity (ROP)—the second leading cause of childhood blindness—is another serious condition often found in premature babies. Studies have found that infants born with ROP have significant visual impairment later in childhood.
      • O’Connor A.R.
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      • Palmer E.
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      Other research has examined the impact of loss of visual acuity on quality-of-life and shown that infants with ROP accrue, on average, 21.3 QALYs over a lifetime.
      • Kamholz K.L.
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      Premature birth also increases the risk of respiratory distress syndrome (RDS), a condition that usually develops shortly after birth and makes it hard for infants to breath. Some infants fully recover from RDS while others develop chronic lung disease (CLD). As a result, adult CLD survivors have worse quality-of-life in adulthood: mean utility based quality-of-life scores for adults with CLD as infants in a recent study were 0.84 while scores for full-term infants were 0.93.
      • Gough A.
      • Linden M.
      • Spence D.
      • Patterson C.C.
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      • McGarvey L.P.
      Impaired lung function and health status in adult survivors of bronchopulmonary dysplasia.
      If we extrapolate the utility score of 0.84 over a lifespan of 78 years, then total discounted QALYs are 26.0. By contrast, discounted QALYs are 28.7 for a full-term infant with a utility score of 0.93. There are consequently fairly large QALY losses from bronchopulmonary dysplasia (BPD) even if we assume that BPD has no effect on mortality.
      Grade III or IV intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL) are brain injuries that are more common in preterm newborns. It is common for babies with IVH to also have areas with PVL. Both IVH and PVL are positively associated with functional and cognitive abnormalities in childhood as well as poor elementary school outcomes.
      • Elbourne D.
      • Ayers S.
      • Dellagrammaticas H.
      • Johnson A.
      • Leloup M.
      • Lenoir-Piat S.
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      • Gross S.J.
      • Mettelman B.B.
      • Dye T.D.
      • Slagle T.A.
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      • Piecuch R.E.
      • Leonard C.H.
      • Cooper B.A.
      • Sehring S.A.
      Outcome of extremely low birth weight infants (500 to 999 grams) over a 12-year period.
      Fiengold et al.
      • Feingold E.
      • Sheir-Neiss G.
      • Melnychuk J.
      • Bachrach S.
      • Paul D.
      HRQL and severity of brain ultrasound findings in a cohort of adolescents who were born preterm.
      show that these impairments lead to significant losses in quality-of-life in early adulthood. They use a measure—the CDC HRQOL -14 “Healthy Days Measure”—that is not designed for creating a preference-based measure of quality-of-life needed to calculate QALYs, but their results are comparable to those QALY losses seen in BPD studies. Lifetime QALY losses due to premature birth are substantial and could total as much as 9 QALYs for infants with NEC or ROP.
      If we were to assume the value of a QALY of $150,000 in the US, this would mean a health burden of $20 billion per year, on top of the health care costs of $7 billion. Our use of $150,000 is based on a recent series of studies and reviews on the value of a life-year, or health-adjusted life-year, in a US setting.
      • Braithwaite R.S.
      • Meltzer D.O.
      • King Jr., J.T.
      • Leslie D.
      • Roberts M.S.
      What does the value of modern medicine say about the $50,000 per quality-adjusted life-year decision rule?.
      • Neumann P.J.
      • Cohen J.T.
      • Weinstein M.C.
      Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold.
      • Weinstein M.C.
      How much are Americans willing to pay for a quality-adjusted life year?.
      Looking more widely, we also accounted for the guidelines suggested by the World Health Organization's Macroeconomic Commission on Health, which was set up over a decade ago to evaluate the cost-effectives of global health interventions. This report suggested a health care intervention was cost effective if it saved a health-adjusted life-year at a cost of under three times a nation’s GDP per capita.

      World Health Organization. Report of the Commission on Macroeconomics and Health-Macroeconomics and Health: Investing in Health for Economic Development. Geneva. 2001.

      For the US, this is currently $160,000, so our estimate may be considered slightly conservative.
      Table 1Estimates of lifetime QALYs lost by outcome
      OutcomeLifetime discounted QALYsNet QALYs lostCases in preeclamptic pregnanciesQALYs lostSocial burden
      Healthy infant30.9
      NEC228.99768686$1303 m
      ROP21.39.6191818,413$2762 m
      BPD264.9218910,726$1609 m
      IVH / PVL273.924569578$1437 m
      Death030.9240774,376$11,156 m
      Total121,780$18,267 m
      Notes: Social burden estimate values a QALY at $150,000. NEC = necrotizing enterocolitis, ROP = retinopathy of prematurity, IVH = intraventricular hemorrhage, PVL = cystic periventricular leukomalacia, BPD = bronchopulmonary dysplasia.
      Stevens. Short-term costs of preeclampsia in the US. Am J Obstet Gynecol 2017.

      Supplementary Data

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