Skip to main content
NCBI home page
Springer logoLink to Springer
. 2022 Jan 18;26(3):601–613. doi: 10.1007/s10995-021-03313-1

Risk Factors for Dual Burden of Severe Maternal Morbidity and Preterm Birth by Insurance Type in California

Alison M El Ayadi 1,8,✉, Rebecca J Baer 2,4, Caryl Gay 3,4, Henry C Lee 5,6, Juno Obedin-Maliver 7, Laura Jelliffe-Pawlowski 4,8, Audrey Lyndon 3,9
PMCID: PMC8917014  PMID: 35041142

Abstract

Objectives

Among childbearing women, insurance coverage determines degree of access to preventive and emergency care for maternal and infant health. Maternal-infant dyads with dual burden of severe maternal morbidity and preterm birth experience high physical and psychological morbidity, and the risk of dual burden varies by insurance type. We examined whether sociodemographic and perinatal risk factors of dual burden differed by insurance type.

Methods

We estimated relative risks of dual burden by maternal sociodemographic and perinatal characteristics in the 2007–2012 California birth cohort dataset stratified by insurance type and compared effects across insurance types using Wald Z-statistics.

Results

Dual burden ranged from 0.36% of privately insured births to 0.41% of uninsured births. Obstetric comorbidities, multiple gestation, parity, and birth mode conferred the largest risks across all insurance types, but effect magnitude differed. The adjusted relative risk of dual burden associated with preeclampsia superimposed on preexisting hypertension ranged from 9.1 (95% CI 7.6–10.9) for privately insured to 15.9 (95% CI 9.1–27.6) among uninsured. The adjusted relative risk of dual burden associated with cesarean birth ranged from 3.1 (95% CI 2.7–3.5) for women with Medi-Cal to 5.4 (95% CI 3.5–8.2) for women with other insurance among primiparas, and 7.0 (95% CI 6.0–8.3) to 19.4 (95% CI 10.3–36.3), respectively, among multiparas.

Conclusions

Risk factors of dual burden differed by insurance type across sociodemographic and perinatal factors, suggesting that care quality may differ by insurance type. Attention to peripartum care access and care quality provided by insurance type is needed to improve maternal and neonatal health.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10995-021-03313-1.

Keywords: Severe maternal morbidity, Preterm birth, Health disparities, Insurance coverage

Significance

What is already known on this topic? Risk for dual burden of severe maternal morbidity and preterm birth differs by insurance type. Insurance influences U.S. maternal and infant health care access and quality and may contribute to the relative importance of factors associated with dual burden.

What this study adds The differences identified in dual burden risk by insurance type may increase certain women’s risks of dual burden of severe maternal morbidity and preterm birth. Our findings point to the need to review care quality by insurance type and ensure high quality peripartum care regardless of insurance type to improve maternal and neonatal health.

Introduction

Insurance coverage is an important contributor to U.S. health disparities, with substantial differences in health status and outcomes observed not only among individuals without insurance coverage compared to individuals with coverage but also across different categories of insurance (Dickman et al., 2017; Freeman et al., 2008; Griffith et al., 2017; Hadley, 2007; McWilliams 2009). Among the insured, coverage influences health status, outcomes, and survival through determining access to health education, clinical and social preventive services, and clinical services for chronic and acute conditions (Chikani et al., 2015; Sommers et al., 2017; Woolhandler & Himmelstein, 2017). Uninsured non-elderly American adults are sicker, less likely to receive preventive services, and more likely to receive lower quality medical care than insured individuals when hospitalized for chronic and acute conditions (Institute of Medicine (US) Committee on the Consequences of Uninsurance, 2002; Woolhandler & Himmelstein, 2017). Even within the same facility, insurance type has been identified as an important factor determining quality of care (Spencer et al., 2013). Health insurance is a particularly critical factor for childbearing women, as it may limit preventive and emergency care for conditions that contribute to adverse maternal or infant outcomes (Johnson et al., 2006). Given the high costs and consequences of inadequate care access and low quality care during this vulnerable time, ensuring high quality coverage is of particular importance (Johnson et al., 2006; Sakala & Corry, 2008).

Insurance type is an important independent risk factor for two significant causes of poor maternal and neonatal health: severe maternal morbidity and preterm birth. Severe maternal morbidity (SMM) occurs in approximately 2.4% of U.S. births (Callaghan et al., 2012; Carmichael et al., 2021; Geller et al., 2018; Grobman et al., 2014; Lyndon et al., 2019). SMM has increased nearly 200% over recent decades, from 49.5 per 10,000 childbirth hospitalizations in the early 1990s to 146.6 in 2015 (Centers for Disease Control & Prevention, 2018; Fingar et al., 2018). SMM has profound physical, psychological, social, and financial consequences for women and their families.(Norhayati et al., 2015) Compared to women with private insurance, Californian women with Medi-cal or other insurance (Indian Health Service, CHAMPUS or TRICARE, other local, state, or federal insurance, or charity), or no insurance, have a 24–29% increased risk of SMM (Lyndon et al., 2012). Several studies have identified a significant relationship between SMM and preterm birth, with over one-quarter of women with SMM delivering preterm in national and state-level studies (Kilpatrick et al., 2016; Lyndon et al., 2019; Reddy et al., 2015).

Preterm birth (< 37 weeks gestational age) is the principal contributor to neonatal and child mortality, occurring in approximately one in ten births globally, including in the United States (Harrison & Goldenberg, 2016; Martin et al., 2017). Infants born preterm are at increased risk of respiratory, cardiovascular, neurologic, and gastrointestinal morbidities, and long-term respiratory, sensory, emotional, and neurocognitive challenges (Frey & Klebanoff, 2016; Manuck et al., 2016). In the National Inpatient Sample, the largest all-payor inpatient care database in the United States, Medicaid was the payor for over half of preterm births, and Medicaid-covered preterm births had double the neonatal rehospitalization of preterm births covered by commercial insurers (Barradas et al., 2016).

Women and infant dyads with dual burden of SMM and preterm birth are a uniquely vulnerable group, experiencing high physical morbidity combined with psychological and psychosocial concerns for families, such as increased risk of postpartum depression or post-traumatic stress disorder, and sequelae of these conditions (Elmir & Schmied, 2016; Elmir et al., 2012; Fenech & Thomson, 2014; Lefkovics et al., 2014). Prior research has highlighted differences in risk of the dual burden of SMM and preterm birth that exist by insurance type. Compared to women with private insurance, risk of dual burden was 20% higher for women with Medi-Cal, California’s Medicaid program, or other insurances, and 30% higher for uninsured women (Lyndon et al., 2019). Given the differences in maternal and infant health care access and quality by insurance type, we sought to understand how the associations between sociodemographic and perinatal factors and risk of dual burden of SMM and preterm birth differed by insurance type using a large in-patient administrative dataset from California.

Materials and Methods

We conducted a retrospective analysis of 3,059,156 California live births occurring between January 1, 2007, and December 31, 2012. Eligible births in the California Office of Statewide Health Planning and Development birth cohort database were births with gestation lengths between 20 and 44 weeks and a valid hospital identifier (Fig. 1). This birth cohort database contains data from linked birth and infant death certificates, detailed information on maternal and infant characteristics, and hospital discharge diagnoses and procedures recorded within one year prior to and nine months following birth. Diagnosis and procedure codes were based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9) (American Medical Association, 2008).

Fig. 1.

Fig. 1

Open in a new tab

Flowchart describing analytic sample. Notes OSHPD: California Office of Statewide Health Planning and Development

Our composite outcome was dual burden of SMM in combination with preterm birth. We defined preterm birth as having occurred at less than 37 weeks gestation per best obstetric estimation recorded on the birth certificate. We categorized inpatient diagnosis and procedural codes associated with unexpected maternal outcomes of labor and birth as SMM using the published Centers for Disease Control and Prevention (CDC) algorithm(Centers for Disease Control & Prevention. Severe Maternal Morbidity Indicators & Corresponding ICD-9-CM Codes during Delivery Hospitalizations, 2016) (Supplemental Table 1). For comprehensive assessment, we followed the CDC definition and included SMM that occurred during childbirth hospitalization and SMM identified on postpartum readmission within 45 days of birth (Carmichael et al., 2021). We excluded morbidity cases that were not severe through restricting SMM during childbirth hospitalization to women with length of stay ≥ 90th percentile operationalized separately for vaginal birth (three days), primary cesarean (five days), and repeat cesarean (four days) (Callaghan et al., 2012). No length of stay restriction was applied to SMM on postpartum readmission.

Analyses were stratified by insurance type at childbirth (Medi-Cal, private, uninsured, and other1) to explore the relationships between maternal sociodemographic and perinatal characteristics and outcome of dual burden of SMM and preterm birth within each insurance group. We first estimated the rate of dual burden per 10,000 births overall and by maternal sociodemographic and perinatal characteristics. We then calculated risks of dual burden by maternal factors using modified Poisson regression models for binary outcomes with a robust error variance for hospital to obtain relative risks and 95% confidence intervals (CI). Predictors included sociodemographic and perinatal characteristics selected a priori from our conceptual model of predictors and consequences of preterm birth and severe maternal morbidity and predictors available within the administrative dataset (Table 1) (Lyndon et al., 2019).

Table 1.

Sociodemographic and perinatal covariates included in stratified analyses of dual burden of severe maternal morbidity and preterm birth, California 2007–2012

Category Measure Source
Sociodemographic characteristics
Age BC
Race/ethnicity (white non-Hispanic, Black non-Hispanic, Hispanic, Asian non-Hispanic, and other non-Hispanic) BC
Educational attainment (< 12 years, 12 years, > 12 years) BC
Level of country urbanity/ruralitya BC
Perinatal factors
Prenatal factors Pre-pregnancy body mass index (BMI), calculated from pre-pregnancy weight and height BC
Adequacy of prenatal care (inadequate: received < 50% of expected visits, intermediate: received 50–79% of expected visits, adequate/adequate plus: received 80% of expected visits or more)b BC
Smoking status (ICD-9 649.0) BC & PDD
Pregnancy factors Mode of birth (vaginal vs. cesarean) BC
Gestation (singleton or multiple gestation) BC
Obstetric comorbidities Preexisting hypertension without progression to preeclampsia (ICD-9 s 642.0 Benign essential hypertension; 642.1 Hypertension secondary to renal disease; 642.2 Other preexisting hypertension) PDD
Preexisting hypertension with progression to preeclampsia (ICD-9 642.7 Preeclampsia or eclampsia superimposed on preexisting hypertension) PDD
Gestational hypertension without progression to preeclampsia (ICD-9 642.3 Transient hypertension of pregnancy) PDD
Gestational hypertension with progression to preeclampsia (ICD-9 642.4 Mild or unspecified preeclampsia, 642.5 Severe preeclampsia) PDD
Preexisting diabetes (ICD-9 648.0 Diabetes mellitus; 250 Diabetes mellitus) PDD
Gestational diabetes (ICD-9 648.8 Abnormal glucose tolerance complicating pregnancy) PDD
Open in a new tab

BC birth certificate, PDD patient discharge data

aFederal Information Processing Standard (FIPS) county codes (1–6)

bKotelchuck Adequacy of Prenatal Care Utilization Index (Harrison & Goldenberg, 2016); ICD-9: International Classification of Diseases, 9th Revision, Clinical Modification

Sociodemographic and pregnancy-related characteristics were described within each payor type by dual morbidity burden (rate per 10,000). We estimated the unadjusted relative risks of dual burden of SMM and preterm birth for each predictor of interest, stratified by insurance type, and adjusted relative risks in stratified models. We conducted pairwise comparisons between insurance type using Wald Z-statistics.

All analyses were performed using Statistical Analysis Software version 9.4 (Cary, NC), and differences where p < 0.05 were considered statistically significant. The study was approved by the Committee for the Protection of Human Subjects within the Health and Human Services Agency of the State of California. Data used for the study were obtained by [Institute] at [University] in June 2016. We employ the terms woman and maternal referring to biological sex and related to pregnancy, while acknowledging that not all pregnant/birthing individuals will identify with these terms (Moseson et al., 2020).

Results

Our final analytic sample included 3,059,156 women, of which there were 45,427 cases of severe maternal morbidity (1.5%) and 267,585 cases of preterm birth (8.7%), with 11,417 cases representing dual burden of SMM and preterm birth (0.37%). Sociodemographic characteristics and perinatal factors of the overall sample are presented by insurance type in Table 2. Dual burden of SMM and preterm birth ranged from 0.36% of privately insured births and 0.38% of Medi-Cal-insured births to 0.41% among uninsured births and births covered by other insurance, respectively (Table 3). Distributions of births and preterm birth indications by gestational age category are presented for each insurance type in Table 4.

Table 2.

Sociodemographic and perinatal characteristics of births by insurance type, California, 2007–2012

Characteristic Medical Private Uninsured Othera
(n = 1,462,463) (n = 1,431,956) (n = 64,507) (n = 100,149)
n % n % n % n %
Sociodemographic characteristics
Maternal age
   < 18 65,973 4.5 15,583 1.1 2336 3.6 2865 2.9
  18–25 671,424 45.9 253,134 17.7 18,257 28.3 36,148 36.1
  26–35 560,762 38.3 799,134 55.8 31,568 48.9 45,873 45.8
   > 35 164,250 11.2 364,015 25.4 12,336 19.1 15,252 15.2
 Race/ethnicity
  White, non-Hispanic 190,618 13.0 579,269 40.5 8607 13.3 30,690 30.6
  Hispanic 985,156 67.4 420,775 29.4 32,857 50.9 39,322 39.3
  Black, non-Hispanic 93,543 6.4 58,687 4.1 2658 4.1 10,792 10.8
  Asian, non-Hispanic 82,926 5.7 266,518 18.6 17,330 26.9 11,852 11.8
  Other, non-Hispanic 110,220 7.5 106,707 7.5 3055 4.7 7493 7.5
 Education
  < 12 years 609,873 43.4 83,068 6 14,419 23.8 15,943 16.4
  12 years 468,283 33.3 264,557 19.2 13,705 22.6 30,490 31.4
  > 12 years 328,700 23.4 1,032,843 74.8 32,568 53.7 50,562 52.1
 Urban/rural statusb
  1 (most urban) 874,356 59.9 934,009 65.5 42,787 73.9 66,519 66.9
  2 150,251 10.3 207,198 14.5 4875 8.4 10,113 10.2
  3 333,390 22.9 220,665 15.5 6082 10.5 19,399 19.5
  4 71,031 4.9 46,845 3.3 3465 6.0 2646 2.7
  5 21,467 1.5 12,942 0.9 512 0.9 557 0.6
  6 (most rural) 8311 0.6 5356 0.4 186 0.3 221 0.2
Perinatal factors
 Prenatal factors
  Maternal pre-pregnancy BMI
   < 18.5 74,820 5.5 64,321 4.8 4574 8.5 4661 4.9
   18.5–24.9 577,474 42.8 741,382 54.8 29,650 55.1 47,716 50.1
   25.0–29.9 375,164 27.8 314,887 23.3 12,156 22.6 23,782 25.0
    ≥ 30 322,330 23.9 232,738 17.2 7459 13.9 19,136 20.1
  Prenatal care
   Inadequate 368,351 25.8 159,200 11.3 19,789 32.1 17,342 17.7
   Intermediate 130,321 9.1 158,831 11.3 6362 10.3 8147 8.3
   Adequate/adequate plus 930,921 65.1 1,090,559 77.4 35,404 57.5 72,441 74
 Smoked during pregnancy
   Yes 97,167 6.6 32,985 2.3 3223 5.0 5282 8.2
   No 1,365,296 93.4 1,398,971 97.7 61,284 95.0 94,867 147.1
 Pregnancy factors
  Parity and mode of birth
  Primiparous vaginal 364,947 25.0 410,145 28.7 20,997 31.5 21,794 23.0
  Multiparous vaginal 621,452 42.5 536,124 37.5 24,604 37 38,987 41.2
  Primiparous primary cesarean 151,392 10.4 206,884 14.5 8289 12.5 12,210 12.9
  Multiparous primary cesarean 90,416 6.2 84,814 5.9 3444 5.2 6356 6.7
  Repeat cesarean 233,835 16 192,926 13.5 9220 13.9 15,221 16.1
 Pregnancy type
  Singleton 1,430,425 97.8 1,373,532 95.9 62,789 97.3 97,034 96.9
  Multiple gestation 32,038 2.2 58,424 4.1 1718 2.7 3115 3.1
 Obstetric co-morbidities
  Hypertension
  No HTN 1,354,596 99 1,319,204 98.6 61,002 99.5 92,494 98.7
  Preexisting HTN without preeclampsia 13,440 1 18,356 1.4 333 0.5 1216 1.3
  Preexisting HTN with preeclampsia 6992 0.5 7506 0.6 195 0.3 559 0.6
  Gestational HTN without preeclampsia 31,293 2.3 32,960 2.5 1096 1.8 2403 2.6
  Gestational HTN with preeclampsia 47,448 3.5 46,050 3.4 1607 2.6 2729 2.9
 Diabetes
  No diabetes 1,329,739 90.9 1,288,453 90 60,881 94.4 92,519 92.4
  Preexisting diabetes 13,801 0.9 10,378 0.7 260 0.4 831 1.3
  Gestational diabetes 118,923 8.1 133,125 9.3 3366 5.2 6799 10.5
Open in a new tab

BMI body mass index, HTN hypertension

aOther insurance includes: Indian Health Service, CHAMPUS or TRICARE, other local, state, or federal insurance, or charity

bFederal Information Processing Standard categories

Table 3.

Dual burden of severe maternal morbidity and preterm birth by sociodemographic and perinatal characteristics and insurance type, California, 2007–2012

Characteristic Medi-cal Private Uninsured Other insurancea
n = 1,462,463 n = 1,431,956 n = 64,507 n = 100,149
n % n % n % n %
Overall 5541 0.38 5204 0.36 266 0.41 406 0.41
Sociodemographic characteristics
 Maternal age
   < 18 177 0.27 56 0.36 12 0.51 11 0.38
  18–25 1920 0.29 613 0.24 86 0.47 94 0.26
  26–35 2311 0.41 2429 0.30 95 0.30 187 0.41
   > 35 1130 0.69 2100 0.58 73 0.59 113 0.74
 Race/ethnicity
  White, non-Hispanic 714 0.37 1889 0.33 58 0.67 112 0.36
  Hispanic 3231 0.33 1458 0.35 124 0.38 141 0.36
  Black, non-Hispanic 814 0.87 412 0.70 33 1.24 95 0.88
  Asian, non-Hispanic 261 0.31 936 0.35 28 0.16 29 0.24
  Other, non-Hispanic 521 0.47 509 0.48 32 1.05 29 0.39
Education
  < 12 years 2201 0.36 338 0.41 87 0.60 67 0.42
  12 years 1772 0.38 927 0.35 77 0.56 121 0.40
   > 12 years 1267 0.39 3655 0.35 89 0.27 193 0.38
Urban/rural statusb
  1 (most urban) 3316 0.38 3368 0.36 141 0.33 275 0.41
  2 620 0.41 835 0.40 43 0.88 46 0.45
  3 1228 0.37 778 0.35 58 0.95 67 0.35
  4 242 0.34 150 0.32 13 0.38 8 0.30
  5 88 0.41 32 0.25 5 0.98 2 0.36
  6 (most rural) 24 0.29 15 0.28 3 1.61 2 0.90
Perinatal factors
 Prenatal factors
  Maternal pre-pregnancy body mass index
    < 18.5 330 0.44 289 0.45 20 0.44 22 0.47
   18.5–24.9 1986 0.34 2493 0.34 110 0.37 177 0.37
   25.0–29.9 1316 0.35 1090 0.35 56 0.46 87 0.37
    ≥ 30 1331 0.41 905 0.39 40 0.54 80 0.42
  Prenatal carec
   Inadequate 1443 0.39 556 0.35 122 0.62 82 0.47
   Intermediate 265 0.20 232 0.15 14 0.22 19 0.23
   Adequate/adequate plus 3495 0.38 4238 0.39 117 0.33 274 0.38
  Smoked during pregnancy
   Yes 657 0.68 162 0.49 49 1.52 44 0.83
   No 4884 0.36 5042 0.36 217 0.35 362 0.38
 Pregnancy factors
  Parity and mode of birth
   Primiparous vaginal 457 0.13 462 0.11 20 0.10 23 0.11
   Multiparous vaginal 783 0.13 540 0.10 41 0.17 43 0.11
   Primiparous primary cesarean 1022 0.68 1592 0.74 57 0.69 102 0.84
   Multiparous primary cesarean 1580 1.75 1597 1.88 69 2.00 132 2.08
   Repeat cesarean 1696 0.73 1072 0.56 77 0.84 100 0.66
  Pregnancy type
   Singleton 4285 0.30 3156 0.23 217 0.35 269 0.28
   Multiple gestation 1256 3.92 2048 3.51 49 2.85 137 4.40
Obstetric co-morbidities
 Hypertension
  No HTN 3186 0.24 2396 0.18 161 0.26 231 0.25
  Preexisting HTN without preeclampsia 214 1.59 159 0.87 10 3.00 16 1.32
  Preexisting HTN with preeclampsia 416 5.95 320 4.26 17 8.72 27 4.83
  Gestational HTN without preeclampsia 166 0.53 150 0.46 8 0.73 13 0.54
  Gestational HTN with preeclampsia 1226 2.58 1422 3.09 51 3.17 89 3.26
 Diabetes
  No diabetes 4531 0.34 4198 0.03 237 0.39 359 0.39
  Preexisting diabetes 270 1.96 172 1.66 8 3.08 7 0.84
  Gestational diabetes 740 0.62 834 0.63 21 0.62 40 0.59
Open in a new tab

Dual burden defined as concomitant severe maternal morbidity or maternal in-hospital death and preterm birth (< 37 weeks). Severe maternal morbidity was determined according to the CDC algorithm + birth certificate: 1) in-hospital deaths, regardless of length of stay; 2) #19–25 without regard to length of stay; 3) #1–18 and length of stay restriction, ≥ 90th percentile for type of birth with length of stay calculated separately for vaginal birth, primary cesarean, and repeat cesarean; and #1–25 on postpartum readmission regardless of length of stay)

BMI body mass index, HTN hyptertension

aOther insurance includes: Indian Health Service, CHAMPUS or TRICARE, other local, state, or federal insurance, or charity

bFederal Information Processing Standard categories

cKotelchuck Adequacy of Prenatal Care Utilization Index (Harrison & Goldenberg, 2016)

Table 4.

Gestational age and preterm indication among births with dual burden of severe maternal morbidity and preterm birth by insurance type, California 2007–2012

Characteristic Medi-cal Private Uninsured Othera
n = 1,462,463 n = 1,431,956 n = 64,507 n = 100,149
n % n % n % n %
 < 28 weeks gestational age 7,742 0.53 6,670 0.47 552 0.86 597 0.60
 PPROM 2,566 0.18 2,344 0.16 176 0.27 164 0.16
 Preterm labor without PPROM 4,557 0.31 3,703 0.26 316 0.49 391 0.39
 Medically indicated 457 0.03 522 0.04 43 0.07 31 0.03
 Unknown 162 0.01 101 0.01 17 0.03 11 0.01
28 to < 32 weeks gestational age 11,240 0.77 11,411 0.80 617 0.96 819 0.82
 PPROM 3,039 0.21 3,411 0.24 171 0.27 211 0.21
 Preterm labor 6,506 0.44 6,389 0.45 335 0.52 515 0.51
 Medically indicated 1,362 0.09 1,432 0.10 86 0.13 80 0.08
 Unknown 333 0.02 179 0.01 25 0.04 13 0.01
32 to < 37 weeks gestational age 106,640 7.29 108,643 7.59 4,972 7.71 7,674 7.66
 PPROM 19,358 1.32 25,344 1.77 985 1.53 1,295 1.29
 Preterm labor 58,227 3.98 56,327 3.93 2,576 3.99 4,468 4.46
 Medically indicated 20,578 1.41 21,509 1.50 858 1.33 1,400 1.40
 Unknown 8,477 0.58 5,463 0.38 553 0.86 511 0.51
37 to 38 weeks gestational age 408,302 27.90 377,844 26.39 18,506 28.69 28,362 28.32
39 to 42 weeks gestational age 927,227 63.40 926,575 64.71 39,795 61.69 62,610 62.52
43 to 44 weeks gestational age 1,312 0.09 812 0.06 65 0.10 87 0.09
Open in a new tab

Dual burden defined as concomitant severe maternal morbidity or maternal in-hospital death and preterm birth (< 37 weeks). Severe maternal morbidity was determined according to the CDC algorithm + birth certificate: (1) in-hospital deaths, regardless of length of stay; (2) #19–25 without regard to length of stay; (3) #1–18 with length of stay restriction (≥ 90th percentile for type of birth with length of stay calculated separately for vaginal birth, primary cesarean, and repeat cesarean); or (4)#1–25 on postpartum readmission regardless of length of stay). Gestational age and prematurity categories are provided for descriptive purposes only and are not included in subsequent tables or as covariates in adjusted analyses. PPROM: Preterm premature rupture of the membranes

aOther insurance includes: Indian Health Service, CHAMPUS or TRICARE, other local, state, or federal insurance, or charity

Dual Burden of Severe Maternal Morbidity and Preterm Birth by Maternal Characteristics across Insurance Type

Women with preexisting and obstetric comorbidities had the highest rates of dual burden across insurance types, including women with preexisting hypertension with preeclampsia (range 4.3–8.7%), gestational hypertension with preeclampsia (range 2.6–3.3%), preexisting hypertension without preeclampsia (range 0.9–3.0%), and preexisting diabetes (range 0.8–3.1%). Similarly high rates of dual burden were identified in women with multiple gestation and cesarean birth.

Maternal characteristics exhibiting the largest differences in the rate of dual burden of SMM and preterm birth rate across insurance type included preexisting and obstetric comorbidities, multiple gestations, smoking during pregnancy, other race/ethnicity, and urban/rural status. The highest rates of dual burden were generally observed among uninsured women. For example, women with preexisting hypertension with and without preeclampsia, dual burden rates were lowest for women with Medi-Cal (6.0% and 1.6%, respectively) and highest for uninsured women (8.7% and 3.0%, respectively). Among the characteristics with relatively large differentials across payor type, multiple gestation was the only characteristic in which uninsured women had the lowest rates, at 2.9%, with the highest rate at 4.4% among other-insured women (Table 2).

Relative Risk of Dual Burden of Severe Maternal Morbidity and Preterm Birth by Maternal Characteristics Across Insurance Type

Obstetric comorbidities, pregnancy type, parity, and mode of birth conferred the largest adjusted relative risks of dual burden across all delivery payor types and showed variation in effect magnitude across payor existed (Table 5). Elevated risks associated with preexisting hypertension, both with and without preeclampsia, were lowest among privately insured women and highest among uninsured women. For example, preexisting hypertension without preeclampsia was associated with a 2.7-fold (aRR 2.65, 95% CI 2.21–3.19) increased dual burden risk among privately insured women to a 7.3-fold (aRR 7.27, 95% CI 3.64–14.51) risk increase among uninsured women. The range for preexisting hypertension with preeclampsia ranged from ninefold to 16-fold among private and uninsured women, respectively. Increased risk of dual burden associated with gestational hypertension without preeclampsia had a smaller effect and range across insurance payor, from aRR 1.45 (95% CI 1.15–1.82) among privately insured women to 1.89 (95% CI 0.86–4.24) among uninsured women. Increased risk of dual burden associated with gestational hypertension with preeclampsia ranged from 6.9-fold among privately insured women (aRR 6.93, 95% CI 6.28–7.65) to 8.1-fold (aRR 8.13, 95% CI 5.35–12.35) among uninsured women; no significant differences in effect size by insurance type were identified.

Table 5.

Adjusted relative risk of dual burden of severe maternal morbidity and preterm birth by sociodemographic, pregnancy, and obstetric factors, stratified by insurance Payor, California 2007–2012

Characteristic Medical Private Uninsured Othera
n = 1,462,463 n = 1,431,956 n = 64,507 n = 100,149
aRR (95% CI) aRR (95% CI) aRR (95% CI) aRR (95% CI)
Sociodemographic characteristics
 Maternal age
  < 18 1.12 (0.95–1.33) 1.63 (1.16–2.29) 1.50 (0.78–2.87) 1.75 (1.01–3.01)
  18–25 Reference Reference Reference Reference
  26–35b 1.11 (1.03–1.19) 0.99 (0.91–1.08) 0.62 (0.43–0.89) 1.23 (0.90–1.69)
   > 35c,e,g 1.45 (1.31–1.60) 1.30 (1.17–1.46) 0.86 (0.60–1.25) 1.62 (1.10–2.37)
 Race/ethnicity
  White non-Hispanic Reference Reference Reference Reference
  Hispanic b,c,e,g 1.03 (0.93–1.14) 1.24 (1.13–1.37) 0.72 (0.52–1.01) 1.24 (0.92–1.68)
  Black, non-Hispanic 1.91 (1.66–2.18) 1.93 (1.66–2.25) 1.55 (1.00–2.44) 2.16 (1.55–3.01)
  Asian, non-Hispanicc,d,e 1.09 (0.89–1.33) 1.26 (1.11–1.43) 0.46 (0.20–1.05) 0.83 (0.53–1.33)
  Other, non-Hispanicc 1.28 (1.12–1.46) 1.47 (1.29–1.67) 1.08 (0.68–1.71) 1.11 (0.67–1.84)
  Education
   < 12 yearsb,d 0.95 (0.87–1.03) 1.22 (1.06–1.41) 1.12 (0.85–1.49) 1.05 (0.75–1.46)
  12 years Reference Reference Reference Reference
  > 12 years 0.96 (0.88–1.05) 0.85 (0.77 -0.93) 0.69 (0.45–1.04) 0.80 (0.60–1.05)
 Urban/rural statush
  1 (most urban) Reference Reference Reference Reference
  2c,d 1.10 (0.89–1.35) 1.13 (1.00–1.29) 1.91 (1.16–3.15) 1.15 (0.64–2.04)
  3c,d 1.01 (0.84–1.20) 1.01 (0.84–1.21) 1.86 (1.11–3.10) 1.05 (0.72–1.54)
  4 0.89 (0.71–1.11) 0.92 (0.68–1.24) 0.89 (0.42–1.92) 0.79 (0.32–1.90)
  5 1.07 (0.66–1.74) 0.79 (0.46–1.37) 1.91 (0.59–6.14) h
  6 (most rural) 0.72 (0.45–1.16) 0.82 (0.38–1.76) h h
Prenatal factors
Maternal pre-pregnancy BMI
   < 18.5 1.33 (1.17–1.52) 1.44 (1.23–1.69) 1.22 (0.77–1.93) 1.49 (0.96–2.31)
  18.5–24.9 Reference Reference Reference Reference
  25.0–29.9 0.81 (0.75–0.87) 0.87 (0.79–0.96) 0.96 (0.69–1.34) 0.78 (0.60–1.02)
   ≥ 30 0.68 (0.63–0.74) 0.77 (0.69–0.87) 0.81 (0.54–1.20) 0.72 (0.53–0.98)
 Prenatal carei
  Inadequatec,e 1.12 (1.02–1.21) 1.05 (0.93–1.19) 1.58 (1.14–2.21) 1.37 (0.99–1.89)
  Intermediate 0.74 (0.60–0.91) 0.66 (0.55–0.78) 0.65 (0.29–1.48) 0.95 (0.59–1.55)
  Adequate/adequate plus Reference Reference Reference Reference
  Smoked during pregnancyc,e,g 1.58 (1.40–1.80) 1.20 (0.91–1.57) 2.93 (1.96–4.37) 1.51 (1.03–2.21)
Pregnancy factors
 Parity and mode of birth
  Primiparous vaginal Reference Reference Reference Reference
  Multiparous vaginalb,c,e 0.90 (0.80–1.01) 0.76 (0.68–0.86) 1.70 (1.00–2.88)g 1.03 (0.64–1.64)
  Primiparous primary cesareanf 3.07 (2.72–3.47) 3.41 (3.01–3.87) 4.29 (2.31–7.97) 5.38 (3.53–8.21)
  Multiparous primary cesareanc,e,f 8.21 (7.05–9.57) 7.03 (5.95–8.31) 19.35 (10.33–36.33) 12.77 (8.24–19.78)
  Repeat cesareanc,e 3.92 (3.47–4.42) 3.33 (2.91–3.80) 6.89 (4.22–11.26) 4.43 (2.88–6.80)
 Pregnancy type
  Singleton Reference Reference Reference Reference
  Multiple gestationc,e,g 6.00 (5.28–6.82) 6.93 (6.24–7.70) 3.80 (2.54–5.67) 7.94 (5.99–10.52)
Obstetric co-morbidities
 Hypertension
  No HTN Reference Reference Reference Reference
  Preexisting HTN without preeclampsiab,e 3.84 (3.28–4.51) 2.65 (2.21–3.19) 7.27 (3.64–14.51) 3.26 (1.92–5.53)
  Preexisting HTN with preeclampsiab 12.98 (11.21–15.03) 9.12 (7.62–10.92) 15.87 (9.14–27.57) 9.13 (5.76–14.46)
  Gestational HTN without preeclampsia 1.86 (1.56–2.22) 1.45 (1.16–1.82) 1.89 (0.84–4.24) 1.57 (0.79–3.12)
  Gestational HTN with preeclampsia 7.60 (6.79–8.50) 6.93 (6.28–7.65) 8.13 (5.35–12.35) 8.02 (5.64–11.42)
 Diabetes
  No diabetes Reference Reference Reference Reference
  Gestational diabetesd 2.54 (2.16–2.99) 2.62 (2.21–3.11) 3.43 (1.76–6.68) 1.30 (0.50–3.38)
  Preexisting diabetesd 1.14 (1.05–1.24) 1.16 (1.06–1.27) 1.20 (0.74–1.97) 1.00 (0.72–1.39)
Open in a new tab

BMI body mass index, HTN hypertension, aRR adjusted relative risks; Relative risks adjusted by all other variables in the table

aOther insurance includes: Indian Health Service, CHAMPUS or TRICARE, other local, state, or federal insurance, or charity

bpairwise comparison Medi-Cal vs. private p < 0.05

cpairwise comparison Medi-Cal vs. uninsured p < 0.05

dpairwise comparison Medi-Cal vs. other p < 0.05

epairwise comparison private vs. uninsured p < 0.05

fpairwise comparison private vs. other p < 0.05

gpairwise comparison uninsured vs. other p < 0.05

hFederal Information Processing Standard categories

iKotelchuck Adequacy of Prenatal Care Utilization Index (Harrison & Goldenberg, 2016)

Multiple gestation was associated with increased risk of dual burden across all insurance payors; however, the effect associated with multiple gestation was significantly lower among uninsured women (aRR 3.80, 95% CI 2.54–5.67) than among women covered by Medi-Cal, private, and other insurance (aRR range 6.0–7.94).

Cesarean births were associated with significantly higher risk of dual burden, particularly multiparous primary cesarean delivery which ranged from sevenfold (aRR 7.03, 95% CI 5.95–8.31) among privately insured women to 19-fold (aRR 19.35, 95% CI 10.33–36.33) among uninsured women. Dual burden associated with repeat cesarean was also significantly higher among uninsured women, at 6.89 (95% CI 4.22–11.26) compared to primiparous women with vaginal births.

Risk differences according to insurance type, smoking during pregnancy, and maternal race/ethnicity were present but attenuated. Risk of dual burden for Black women compared to white non-Hispanic women was significantly elevated among women with Medi-Cal, private insurance, and other insurance. However, no statistically significant increased risk was identified among uninsured women.

Discussion

Risk of dual burden of SMM and preterm birth was highest among women with no health insurance or other health insurance. Within each insurance type, the primary independent predictors of dual burden were generally consistent with previous studies (Lyndon et al., 2019). However, in this first known study to explore the magnitude of effect of sociodemographic and perinatal factors across insurance types, we identified important differences in magnitude of dual burden risk. Other literature investigating disparities in maternal morbidity by insurance status has been largely limited to comparisons between Medicaid versus privately insured women. The results of the study support the need to focus on the broader range of insurance types (Brandon et al., 2009; Greiner et al., 2018; Fingar et al., 2018; Lipkind et al., 2009).

Risk factors consistently influencing dual burden risk across insurance type of obstetric comorbidities, parity, and mode of birth were generally the highest among uninsured women, followed by women with Medi-Cal. However, the statistical significance of these differences was inconsistent. This pattern was different for multiple gestation where risks were significantly lower for uninsured women compared to all other insurance categories. This finding is puzzling and warrants further inquiry into profiles of multiple gestation patients across insurance categories. This finding may be partially explained by the links between multiple gestation, insurance type, and assisted reproductive technology. In California, 16% of multiple gestation births are through assisted reproductive technology (Sunderam et al., 2017), and access to assisted reproductive technology differs by insurance coverage (Kulkarni et al., 2013; Provost et al., 2016). Similarly, uninsured women were also the only payor group without an advanced maternal age-related increase in dual burden (Lean et al., 2017).

Substantial disparities in dual burden by race and ethnicity have been identified elsewhere, with significantly higher risk among racially and ethnically minoritized groups compared to non-Hispanic white women, including a two-fold increased risk for non-Hispanic Black women (Lyndon et al., 2019). Our current findings are consistent with the literature linking structural racism to adverse perinatal outcomes (Burris & Parker, 2021; Chambers et al., 2018; Lyndon et al., 2019; Shrimali et al., 2020; Taylor, 2020), with significantly increased risk of dual burden for Black compared to non-Hispanic white women for women with all insurance types except women with no insurance. The absence of a statistically significant Black-white disparity among uninsured women may reflect the greater level of disadvantage among this population overall.

Interpreting these findings for clinical intervention is not straightforward; instead, the findings suggest areas for additional research to further disentangle these results. Insurance status likely represents a variety of characteristics or exposures that may themselves be linked to risk of dual burden. For example, insurance status may be used as a rough proxy measure for socioeconomic status and is patterned by other characteristics such as race/ethnicity and health status (Artiga et al., 2019; Barnett et al., 2017; Sohn 2017), which are patterned by racism and socioeconomic discrimination that increase risk of adverse perinatal outcomes (Chambers et al., 2018, 2019; Shrimali et al., 2020). Furthermore, continuity of insurance coverage may be unstable during the perinatal period, with interruptions in coverage resulting from changing eligibility status (Daw & Sommers 2018). Severe pre-existing or prenatal morbidities may influence an insurance payor at birth through structuring a woman’s eligibility for public insurance (Martin & Bellux, 2019). These observations suggest that baseline pre-pregnancy and pregnancy health status may differ in important ways across insurance payor; although we adjusted for a variety of conditions and potential contributors to pre-pregnancy and pregnancy health, our analysis remains limited by the administrative data available.

While we have no direct comparison for the findings of this analysis, research in other areas that have examined health outcomes by payor status generally support improved health outcomes for individuals covered by private insurance, with individuals with public insurance considered to reflect greater disease severity. For example, a retrospective analysis of preventive care in a non-Medicaid expansion state found Medicaid-insured or uninsured women had fewer well-woman visits prior to giving birth and higher emergency department visits during pregnancy, whereas privately insured women were more likely to initiate prenatal care in the first trimester, receive adequate-plus- prenatal care, and have a postpartum checkup within six weeks of delivery (Taylor et al., 2002). Similarly, among older adults undergoing cancer-related surgery, Medicaid covered care was associated with longer lengths of stay and higher inpatient costs for colorectal, non-small cell lung cancer, and breast cancer (Bradley et al., 2012). Some have postulated that different inpatient experiences and outcomes across insurance payor may be due to differences in the clinical care provided. For example, the Kaiser Family Foundation hypothesizes that hospitals may compensate for rising costs by performing certain discretionary services for financially lucrative patients while economizing on services provided to low-income patients, for example, discretionary procedures such as breast construction; however, this literature is limited (Bradley et al., 2012). It is likely that the differences observed in our study and others reflect some combination of these two explanations. Further research on variation in quality of maternity care across insurance type would help us better understand the differences found in risk factors across insurance type.

The current analysis is limited by the administrative nature of the dataset used. For example, the potential for differential misclassification of morbidity predictors or status exists (Dietz et al., 2003; Kane & Sappenfield, 2014; Mallinson & Ehrenthal, 2019), and a variety of potentially important behavioral risk factors or care processes were unable to be precisely measured or included. Any misclassification in morbidity status is likely to result in a conservative analysis through underreporting (Main et al., 2016), particularly due to our use of a 90th percentile length of stay restriction,(Callaghan et al., 2012) and SMM algorithm validated for population-level measurement (Lydon-Rochelle et al., 2005; Yasmeen et al., 2006). Our findings are based on population-level data from a single large state accounting for > 12% of all U.S. births. They may not be generalizable to areas with differing demographics, risk profiles, and health care systems. Due to the limited descriptors of the other insurance category, the results of this group may be harder to interpret.

The observed differences in the magnitude of the effects of sociodemographic, pregnancy-related, and obstetric factors by insurance type raise concerns that important differences in care quality exist by insurance payor, which may increase women’s risks of the dual burden of SMM and preterm birth. Our findings are consistent with previous studies demonstrating differences in care quality by insurance type for other conditions and suggest that attention to ensuring that all birthing people and infants receive high-quality peripartum care regardless of insurance payor is needed to improve maternal and neonatal health.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 47 kb) (47.1KB, docx)

Acknowledgements

This study was supported by the University of California, San Francisco, California Preterm Birth Initiative, funded by Marc and Lynne Benioff, with continuing analytic work funded through the Hellman Fellows Fund. The authors would like to thank Dr. Charles McCullough and the UCSF CTSI K-scholars program for their contributions to this manuscript.

Funding

Funding was provided by UCSF Preterm Birth Initiative.

Declarations

Conflict of interest

The authors report no conflict of interest.

Footnotes

1

Other insurance includes: Indian Health Service, CHAMPUS or TRICARE, other local, state, or federal insurance, or charity.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. American Medical Association . International classification of diseases, ninth revision, clinical modification. Americal Medical Association; 2008. [PubMed] [Google Scholar]
  2. Artiga S, Orgera K, Damico A. Changes in health coverage by race and ethnicity since implementaiton of the ACA, 2013–2017. Kaiser Family Foundation; 2019. [Google Scholar]
  3. Barnett ML, Song Z, Rose S, Bitton A, Chernew ME, Landon BE. Insurance transitions and changes in physician and emergency department utilization: An observational study. Journal of General Internal Medicine. 2017;32(10):1146–1155. doi: 10.1007/s11606-017-4072-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barradas DT, Wasserman MP, Daniel-Robinson L, et al. Hospital utilization and costs among preterm infants by payer: Nationwide inpatient sample, 2009. Maternal and Child Health Journal. 2016;20(4):808–818. doi: 10.1007/s10995-015-1911-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bradley CJ, Dahman B, Bear HD. Insurance and inpatient care: Differences in length of stay and costs between surgically treated cancer patients. Cancer. 2012;118(20):5084–5091. doi: 10.1002/cncr.27508. [DOI] [PubMed] [Google Scholar]
  6. Brandon GD, Adeniyi-Jones S, Kirkby S, Webb D, Culhane JF, Greenspan JS. Are outcomes and care processes for preterm neonates influenced by health insurance status? Pediatrics. 2009;124(1):122–127. doi: 10.1542/peds.2008-1318. [DOI] [PubMed] [Google Scholar]
  7. Burris HH, Parker MG. Racial and ethnic disparities in preterm birth outcomes: A call to action for neonatal providers. Journal of Perinatology. 2021;41(3):365–366. doi: 10.1038/s41372-021-00917-x. [DOI] [PubMed] [Google Scholar]
  8. Callaghan WM, Creanga AA, Kuklina EV. Severe maternal morbidity among delivery and postpartum hospitalizations in the United States. Obstetrics and Gynecology. 2012;120(5):1029–1036. doi: 10.1097/aog.0b013e31826d60c5. [DOI] [PubMed] [Google Scholar]
  9. Carmichael, S.L., Abrams, B., El Ayadi, A.M., et al. (2021). Ways forward in preventing severe maternal morbidity and maternal health inequities: Conceptual frameworks, definitions, and data, from a population health perspective. Women's Health Issues. 10.1016/j.whi.2021.11.006. [DOI] [PMC free article] [PubMed]
  10. Centers for Disease Control and Prevention. (2016). Severe maternal morbidity indicators and corresponding ICD-9-CM codes during delivery hospitalizations. Centers for Disease Control and Prevention.
  11. Centers for Disease Control and Prevention. (2018). Severe Maternal Morbidity in the United States. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/severematernalmorbidity.html.
  12. Chambers BD, Baer RJ, McLemore MR, Jelliffe-Pawlowski LL. Using index of concentration at the extremes as indicators of structural racism to evaluate the association with preterm birth and infant mortality-California, 2011–2012. Journal of Urban Health. 2019;96(2):159–170. doi: 10.1007/s11524-018-0272-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chambers BD, Erausquin JT, Tanner AE, Nichols TR, Brown-Jeffy S. Testing the association between traditional and novel indicators of county-level structural racism and birth outcomes among black and white women. Journal of Racial and Ethnic Health Disparities. 2018;5(5):966–977. doi: 10.1007/s40615-017-0444-z. [DOI] [PubMed] [Google Scholar]
  14. Chikani V, Brophy M, Vossbrink A, et al. Association of insurance status with health outcomes following traumatic injury: Statewide multicenter analysis. Western Journal of Emergency Medicine. 2015;16(3):408–413. doi: 10.5811/westjem.2015.1.23560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Daw JR, Sommers BD. Association of the affordable care act dependent coverage provision with prenatal care use and birth outcomes. JAMA. 2018;319(6):579–587. doi: 10.1001/jama.2018.0030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Dickman SL, Himmelstein DU, Woolhandler S. Inequality and the health-care system in the USA. Lancet (london, England) 2017;389(10077):1431–1441. doi: 10.1016/S0140-6736(17)30398-7. [DOI] [PubMed] [Google Scholar]
  17. Dietz P, Bombard J, Mulready-Ward C, et al. Validation of selected items on the 2003 US standard certificate of live birth: New York City and Vermont. Public Health Reports. 2015;130(1):60–70. doi: 10.1177/003335491513000108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Elmir R, Schmied V. A meta-ethnographic synthesis of fathers experiences of complicated births that are potentially traumatic. Midwifery. 2016;32:66–74. doi: 10.1016/j.midw.2015.09.008. [DOI] [PubMed] [Google Scholar]
  19. Elmir R, Schmied V, Wilkes L, Jackson D. Separation, failure and temporary relinquishment: Women's experiences of early mothering in the context of emergency hysterectomy. Journal of Clinical Nursing. 2012;21(7–8):1119–1127. doi: 10.1111/j.1365-2702.2011.03913.x. [DOI] [PubMed] [Google Scholar]
  20. Fenech G, Thomson G. Tormented by ghosts from their past': A meta-synthesis to explore the psychosocial implications of a traumatic birth on maternal well-being. Midwifery. 2014;30(2):185–193. doi: 10.1016/j.midw.2013.12.004. [DOI] [PubMed] [Google Scholar]
  21. Fingar KR, Hambrick MM, Heslin KC, Moore JE. Trends and Disparities In Delivery Hospitalizations Involving Severe Maternal Morbidity, 2006–2015: Statistical brief #243. Healthcare cost and utilization project (HCUP) Statistical briefs. Rockville, MD: Agency for Healthcare Research and Quality; 2018. [PubMed] [Google Scholar]
  22. Freeman JD, Kadiyala S, Bell JF, Martin DP. The causal effect of health insurance on utilization and outcomes in adults: A systematic review of US studies. Medical Care. 2008;46(10):1023–1032. doi: 10.1097/MLR.0b013e318185c913. [DOI] [PubMed] [Google Scholar]
  23. Frey HA, Klebanoff MA. The epidemiology, etiology, and costs of preterm birth. Seminars in Fetal and Neonatal Medicine. 2016;21(2):68–73. doi: 10.1016/j.siny.2015.12.011. [DOI] [PubMed] [Google Scholar]
  24. Geller SE, Koch AR, Garland CE, MacDonald EJ, Storey F, Lawton B. A global view of severe maternal morbidity: Moving beyond maternal mortality. Reproductive Health. 2018;15(Suppl 1):98. doi: 10.1186/s12978-018-0527-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Greiner KS, Speranza RJ, Rincón M, Beeraka SS, Burwick RM. Association between insurance type and pregnancy outcomes in women diagnosed with hypertensive disorders of pregnancy. The Journal of Maternal-Fetal & Neonatal Medicine. 2020;33(8):1–7. doi: 10.1080/14767058.2018.1519544. [DOI] [PubMed] [Google Scholar]
  26. Griffith, K., Evans, L., & Bor, J. (2017). The Affordable Care Act reduced socioeconomic disparities in health care access. Health Affairs (Millwood) [DOI] [PMC free article] [PubMed]
  27. Grobman WA, Bailit JL, Rice MM, et al. Frequency of and factors associated with severe maternal morbidity. Obstetrics and Gynecology. 2014;123(4):804–810. doi: 10.1097/AOG.0000000000000173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hadley J. Insurance coverage, medical care use, and short-term health changes following an unintentional injury or the onset of a chronic condition. JAMA. 2007;297(10):1073–1084. doi: 10.1001/jama.297.10.1073. [DOI] [PubMed] [Google Scholar]
  29. Harrison MS, Goldenberg RL. Global burden of prematurity. Seminars in Fetal and Neonatal Medicine. 2016;21(2):74–79. doi: 10.1016/j.siny.2015.12.007. [DOI] [PubMed] [Google Scholar]
  30. Institute of Medicine (US) Committee on the Consequences of Uninsurance. (2002). Care without coverage: Too little, too late. National Academy Press
  31. Johnson K, Posner SF, Biermann J, et al. Recommendations to improve preconception health and health care—United States: A report of the CDC/ATSDR preconception care work group and the select panel on preconception care. MMWR Recommendations and Reports. 2006;55(RR06):1–23. [PubMed] [Google Scholar]
  32. Kane DJ, Sappenfield WM. Ascertainment of medicaid payment for delivery on the iowa birth certificate: Is accuracy sufficient for timely policy and program relevant analysis? Maternal and Child Health Journal. 2014;18(4):970–977. doi: 10.1007/s10995-013-1325-7. [DOI] [PubMed] [Google Scholar]
  33. Kilpatrick SJ, Abreo A, Gould J, Greene N, Main EK. Confirmed severe maternal morbidity is associated with high rate of preterm delivery. American Journal of Obstetrics and Gynecology. 2016;215(2):233.e1–7. doi: 10.1016/j.ajog.2016.02.026. [DOI] [PubMed] [Google Scholar]
  34. Kulkarni AD, Jamieson DJ, Jones HW, Jr, Kissin DM, Gallo MF, Macaluso M, Adashi EY. Fertility treatments and multiple births in the United States. The New England Journal of Medicine. 2013;369:2218–2225. doi: 10.1056/NEJMoa1301467. [DOI] [PubMed] [Google Scholar]
  35. Lean SC, Derricott H, Jones RL, Heazell AE. Advanced maternal age and adverse pregnancy outcomes: A systematic review and meta-analysis. PLoS ONE. 2017;12(10):e0186287. doi: 10.1371/journal.pone.0186287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lefkovics E, Baji I, Rigo J. Impact of maternal depression on pregnancies and on early attachment. Infant Mental Health Journal. 2014;35(4):354–365. doi: 10.1002/imhj.21450. [DOI] [PubMed] [Google Scholar]
  37. Lipkind HS, Duzyj C, Rosenberg TJ, Funai EF, Chavkin W, Chiasson MA. Disparities in cesarean delivery rates and associated adverse neonatal outcomes in New York City hospitals. Obstetrics and Gynecology. 2009;113(6):1239–1247. doi: 10.1097/AOG.0b013e3181a4c3e5. [DOI] [PubMed] [Google Scholar]
  38. Lyndon A, Lee HC, Gilbert WM, Gould JB, Lee KA. Maternal morbidity during childbirth hospitalization in California. Journal of Maternal-Fetal and Neonatal Medicine. 2012;25(12):2529–2535. doi: 10.3109/14767058.2012.710280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lyndon A, Baer RJ, Gay CL, El Ayadi AM, Lee HC, Jelliffe-Pawlowski L. A population-based study to identify the prevalence and correlates of the dual burden of severe maternal morbidity and preterm birth in California. Journal of Maternal-Fetal and Neonatal Medicine. 2019;34(8):1–9. doi: 10.1080/14767058.2019.1628941. [DOI] [PubMed] [Google Scholar]
  40. Lydon-Rochelle MT, Holt VL, Nelson JC, et al. Accuracy of reporting maternal in-hospital diagnoses and intrapartum procedures in Washington State linked birth records. Paediatric and Perinatal Epidemiology. 2005;19(6):460–471. doi: 10.1111/j.1365-3016.2005.00682.x. [DOI] [PubMed] [Google Scholar]
  41. Main EK, Abreo A, McNulty J, et al. Measuring severe maternal morbidity: Validation of potential measures. American Journal of Obstetrics and Gynecology. 2016;214(5):643e1-e10. doi: 10.1016/j.ajog.2015.11.004. [DOI] [PubMed] [Google Scholar]
  42. Mallinson DC, Ehrenthal DB. Classification of medicaid coverage on birth records in Wisconsin, 2011–2012. Public Health Reports. 2019;134(5):542–551. doi: 10.1177/0033354919860503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Manuck TA, Rice MM, Bailit JL, et al. Preterm neonatal morbidity and mortality by gestational age: A contemporary cohort. American Journal of Obstetrics and Gynecology. 2016;215(1):103e1-e14.4921282. doi: 10.1016/j.ajog.2016.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Martin JA, Hamilton BE, Osterman MJK. Births in the United States, 2016. NCHS Data Brief. 2017;287:1–8. [PubMed] [Google Scholar]
  45. Martin, N., & Bellux, J. (2019). The extraordinary danger of being pregnant and uninsured in Texas. 2019. Retrieved January 25, 2020 from https://www.propublica.org/article/the-extraordinary-danger-of-being-pregnant-and-uninsured-in-texas.
  46. McWilliams JM. Health consequences of uninsurance among adults in the United States: Recent evidence and implications. Milbank Quarterly. 2009;87(2):443–494. doi: 10.1111/j.1468-0009.2009.00564.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Moseson H, Zazanis N, Goldberg E, et al. The Imperative for transgender and gender nonbinary inclusion: beyond women's health. Obstetrics and Gynecology. 2020;135(5):1059–1068. doi: 10.1097/AOG.0000000000003816. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Norhayati MN, Surianti S, Nik Hazlina NH. Metasynthesis: Experiences of women with severe maternal morbidity and their perception of the quality of health care. PLoS ONE. 2015;10(7):e0130452. doi: 10.1371/journal.pone.0130452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Provost MP, Thomas SM, Yeh JS, Hurd WW, Eaton JL. State insurance mandates and multiple birth rates after in vitro fertilization. Obstetrics & Gynecology. 2016;128(6):1205–1214. doi: 10.1097/AOG.0000000000001733. [DOI] [PubMed] [Google Scholar]
  50. Reddy UM, Rice MM, Grobman WA, et al. Serious maternal complications after early preterm delivery (24–33 weeks' gestation) American Journal of Obstetrics and Gynecology. 2015;213(4):538e1-9. doi: 10.1016/j.ajog.2015.06.064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Sakala C, Corry MP. Evidence-based maternity care: What it is and what it can achieve. Milbank Memorial Fund; 2008. [Google Scholar]
  52. Shrimali BP, Pearl M, Karasek D, Reid C, Abrams B, Mujahid M. Neighborhood privilege, preterm delivery, and related racial/ethnic disparities: An intergenerational application of the index of concentration at the extremes. American Journal of Epidemiology. 2020;189(5):412–421. doi: 10.1093/aje/kwz279. [DOI] [PubMed] [Google Scholar]
  53. Sohn H. Racial and ethnic disparities in health insurance coverage: Dynamics of gaining and losing coverage over the life-course. Population Research and Policy Review. 2017;36(2):181–201. doi: 10.1007/s11113-016-9416-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Sommers BD, Gawande AA, Baicker K. Health insurance coverage and health - What the recent evidence tells us. New England Journal of Medicine. 2017;377(6):586–593. doi: 10.1056/NEJMsb1706645. [DOI] [PubMed] [Google Scholar]
  55. Spencer CS, Gaskin DJ, Roberts ET. The quality of care delivered to patients within the same hospital varies by insurance type. Health Affairs (millwood) 2013;32(10):1731–1739. doi: 10.1377/hlthaff.2012.1400. [DOI] [PubMed] [Google Scholar]
  56. Sunderam S, Kissin DM, Zhang Y, et al. Assisted reproductive technology surveillance - United States, 2017. MMWR Surveillance Summaries. 2020;69(9):1–20. doi: 10.15585/mmwr.ss6909a1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Taylor JK. Structural racism and maternal health among black women. The Journal of Law, Medicine & Ethics. 2020;48(3):506–517. doi: 10.1177/1073110520958875. [DOI] [PubMed] [Google Scholar]
  58. Taylor YJ, Liu T-L, Howell EA. Insurance differences in preventive care use and adverse birth outcomes among pregnant women in a Medicaid Nonexpansion State: a retrospective cohort study. Journal of Women's Health. 2019 doi: 10.1089/jwh.2019.7658. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Woolhandler S, Himmelstein DU. The relationship of health insurance and mortality: Is lack of insurance deadly? Annals of Internal Medicine. 2017;167(6):424–431. doi: 10.7326/M17-1403. [DOI] [PubMed] [Google Scholar]
  60. Yasmeen S, Romano PS, Schembri ME, Keyzer JM, Gilbert WM. Accuracy of obstetric diagnoses and procedures in hospital discharge data. American Journal of Obstetrics and Gynecology. 2006;194(4):992–1001. doi: 10.1016/j.ajog.2005.08.058. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 47 kb) (47.1KB, docx)

Articles from Maternal and Child Health Journal are provided here courtesy of Springer

RESOURCES