BACKGROUND AND OBJECTIVES:

For children with intrauterine opioid exposure (IOE), well-child care (WCC) provides an important opportunity to address medical, developmental, and psychosocial needs. We evaluated WCC adherence for this population.

METHODS:

In this retrospective cohort study, we used PEDSnet data from a pediatric primary care network spanning 3 states from 2011 to 2016. IOE was ascertained by using physician diagnosis codes. WCC adherence in the first year was defined as a postnatal or 1-month visit and completed 2-, 4-, 6-, 9-, and 12-month visits. WCC adherence in the second year was defined as completed 15- and 18-month visits. Gaps in WCC, defined as ≥2 missed consecutive WCC visits, were also evaluated. We used multivariable regression to test the independent effect of IOE status.

RESULTS:

Among 11 334 children, 236 (2.1%) had a diagnosis of IOE. Children with IOE had a median of 6 WCC visits (interquartile range 5–7), vs 8 (interquartile range 6–8) among children who were not exposed (P < .001). IOE was associated with decreased WCC adherence over the first and second years of life (adjusted relative risk 0.54 [P < .001] and 0.74 [P < .001]). WCC gaps were more likely in this population (adjusted relative risk 1.43; P < .001). There were no significant adjusted differences in nonroutine primary care visits, immunizations by age 2, or lead screening.

CONCLUSIONS:

Children <2 years of age with IOE are less likely to adhere to recommended WCC, despite receiving on-time immunizations and lead screening. Further research should be focused on the role of WCC visits to support the complex needs of this population.

What’s Known on This Subject:

Children of mothers with opioid use disorder have increased risk for adverse health and developmental outcomes. Well-child care visits provide an important opportunity to support these children and their families through screening, anticipatory guidance, and connection to resources.

What This Study Adds:

In this retrospective analysis of children <2 years of age, we found decreased well-child care adherence associated with intrauterine opioid exposure. Further research should be focused on pediatric engagement of mothers with opioid use disorder to support their complex needs.

In 2017, the opioid crisis was deemed a public health emergency by the US Department of Health and Human Services.1  Increasingly, pregnant women with opioid use disorder (OUD) and their children are part of this crisis, giving rise to a range of unique health care challenges related to intrauterine opioid exposure (IOE). It is now estimated that every 15 minutes in the United States, another infant is born experiencing the constellation of withdrawal symptoms resulting from IOE known as neonatal abstinence syndrome (NAS).2  A large and growing body of research has been focused on their perinatal care, including NAS prevention and treatment. However, for all children with IOE (regardless of whether they experienced NAS), important gaps remain in understanding health care use and outcomes after discharge from the hospital.35 

Within a primary care setting, well-child care (WCC) visits provide an important opportunity for providers to identify and address information gaps, behavioral and developmental concerns, growth and nutritional challenges, and medical and psychosocial issues.6  Previous studies reveal the benefits of a number of aspects of WCC, including, but not limited to, immunizations, developmental screening, anticipatory guidance, maternal depression screening, and reading promotion.712  Among children <2 years of age, WCC adherence is associated with improved health outcomes, including reduced hospitalizations and emergency department use, as well as improved kindergarten readiness.1315 

Unfortunately, numerous challenges for mothers with OUD threaten their engagement in pediatric primary care. In addition to traditional barriers, such as lack of transportation, time constraints, and family crises, additional challenges include perceived stigmatization and discrimination.16  Mothers with OUD may feel overwhelmed by parenting and by their own engagement in substance use treatment or may feel guilty, judged, or removed from the care of their child.17,18  One recent study of mothers in treatment for OUD suggests that one-third of their children did not adhere to recommended WCC during infancy, and this outcome worsened over time.19  To date, there is a lack of large-scale studies in which WCC adherence is evaluated among children with IOE. Our objective was to determine the association between IOE and WCC adherence over the first 2 years of life in a multistate primary care cohort. We hypothesized that, independent of other clinical and social risk factors, IOE would be associated with decreased WCC adherence.

Data for this retrospective cohort study were obtained from PEDSnet, a longitudinal electronic health record database comprising 8 of the largest US pediatric academic health centers. PEDSnet currently contains 191 data elements across 15 domains, examples of which include patient age, race, zip code, and encounter dates. Data quality processes are performed by the PEDSnet Coordinating Center on quarterly cycles.20  Our analysis included all children born January 1, 2011, to April 30, 2016, who completed a visit within 90 days of life at 1 of 35 primary care sites within a single children’s health system spanning Delaware, Pennsylvania, and Florida. Infants ≥90 days old at their first appointment were excluded because of concerns that they might have received initial primary care elsewhere. Although this had the effect of omitting those with a prolonged neonatal hospitalization, we anticipated that most children requiring NAS treatment would have been discharged from the hospital within this time frame.21,22  To help further ensure that the cohort represented those who had established care within this system, thereby minimizing missing data bias, we excluded children without any type of primary care visit after their first birthday. The sample derivation is depicted in Fig 1. This study was approved by the Nemours Children’s Health System Institutional Review Board.

FIGURE 1

Flow diagram of sample derivation. The arrow curving outward depicts observations excluded from the analysis. The gray box depicts the final cohort included for analysis.

FIGURE 1

Flow diagram of sample derivation. The arrow curving outward depicts observations excluded from the analysis. The gray box depicts the final cohort included for analysis.

Close modal

The primary outcome was WCC adherence based on American Academy of Pediatrics recommendations and derived from Current Procedural Terminology codes. The following time intervals were used to define WCC visits up to the second birthday: 0 to 14 days (postnatal), 15 to 41 days (1 month), 42 to 90 days (2 months), 91 to 150 days (4 months), 151 to 210 days (6 months), 211 to 335 days (9 months), 336 to 425 days (12 months), 426 to 517 days (15 months), and 518 to 730 days (18 months). WCC adherence in the first year was defined as a completed postnatal or 1-month visit and completed 2-, 4-, 6-, 9-, and 12-month visits. WCC adherence in the second year was defined as completing 15- and 18-month visits. Gaps in WCC throughout the first 2 years were also evaluated, defined as missing ≥2 consecutive WCC visits.

Secondary outcomes included the number of nonroutine visits over the first 2 years of life, immunization status by age 2 years, and lead level screening at 1 year.23  On-time immunization status was defined as receipt of the combined 7-vaccine series (4:3:1:3:3:1:4) by age 2 per the Centers for Disease Control and Prevention, which includes ≥4 doses of the diphtheria-tetanus-acellular pertussis vaccine, ≥3 doses of the poliovirus vaccine, ≥1 dose of the measles-containing vaccine, ≥3 or ≥4 doses (depending on product type) of the Haemophilus influenzae type b vaccine, ≥3 doses of the hepatitis B vaccine, ≥1 dose of the varicella vaccine, and ≥4 doses of the pneumococcal conjugate vaccine.24  On-time lead screening was defined as screening performed by 425 days of life or 2 months after the first birthday.

IOE was measured by using physician-recorded diagnosis data, which, in PEDSnet, include visit and problem list diagnoses. In PEDSnet, diagnosis data are standardized by using the Systematized Nomenclature of Medicine–Clinical Terminology (SNOMED-CT), a terminology with greater granularity than the International Classification of Diseases (ICD). For this study, IOE was ascertained by using a combination of SNOMED-CT and ICD codes because correspondence between these 2 terminologies is often not 1:1, with some ICD codes mapping to multiple, more specific SNOMED-CT codes and, conversely, some SNOMED-CT codes mapping to multiple, more specific ICD codes (see Supplemental Table 5).

On the basis of existing literature on WCC use, we assessed clinical and sociodemographic covariates, including child race, ethnicity, sex, insurance type, birth weight, and gestational age.2527  An indicator variable identifying complex chronic conditions was derived by using a previously established classification system.28  Given the known association between community-level effects and health care use, patient zip code was linked to US Census Bureau data to derive an area-level measure of the percentage of residents living below the federal poverty level.29,30  Publicly available rural-urban commuting area codes from the US Department of Agriculture were also used to assign metropolitan zip codes of residence. On the basis of distribution of this variable, zip codes were classified as nonmetropolitan if designated as micropolitan, a small town, or rural.31 

Bivariate comparisons were conducted by using χ2 (proportions) and Wilcoxon rank tests (nonnormally distributed count measures). Next, multivariable regression modeling was used to test the association between IOE and a binary outcome of WCC adherence in the first year of life, with adjustment for all covariates. Because WCC adherence was not a rare outcome (occurring in >10% of the sample), we used generalized linear models with a log link and Poisson distribution to produce an unbiased estimate of the adjusted relative risk (aRR).32,33  To account for the increased possibility of prolonged birth hospitalization during the first months of life among infants with IOE, we also conducted a sensitivity analysis by relaxing the definition of WCC adherence in the first year, measuring only those visits at 4, 6, 9, and 12 months of age. We then performed a separate analysis to test the association between IOE and WCC adherence in the second year to determine if findings attenuated or were consistent over time. We used this same approach to test the association between IOE and ≥1 WCC gap over the first 2 years. Next, we tested the association between IOE and the count of nonroutine visits using multivariable negative binomial regression (a likelihood ratio test of overdispersion was used to confirm that Poisson distribution was not appropriate). Finally, we used generalized linear models as above to evaluate on-time immunization status and lead screening. For all regression models, multicollinearity was assessed by using variance inflation factors, and clustering by primary care site was adjusted by using robust variance estimation. The percentage of missing data across all analytic variables was low (<5%); therefore, a case-complete analysis, rather than imputation methods, was performed.34  All analyses were conducted by using Stata 11.0 (Stata Corp, College Station, TX).

In total, 11 334 children were included in the analytic cohort (Fig 1). Of this sample, 236 (2.1%) had a diagnosis of IOE, the majority of whom (80.1%) were classified as having NAS. As shown in Table 1, those with IOE were significantly more likely to be white and insured by Medicaid compared with children without IOE. Children with IOE also had a lower average birth weight and were more likely to live in higher-poverty areas. No differences were observed in gestational age, diagnosis of complex chronic condition(s), sex, or metropolitan residence.

TABLE 1

Study Population by IOE

IOE, n = 236No IOE, n = 11 098P
Patient characteristics    
 Race, % (n  <.001 
  White 69.1 (163) 34.8 (3865)  
  African American 15.7 (37) 44.4 (4925)  
  Asian American 0 (0) 2.9 (323)  
  Other 13.6 (32) 13.8 (1532)  
  Unknown 1.7 (4) 4.1 (453)  
 Ethnicity, % (n  .01 
  Hispanic 11.0 (26) 12.5 (1387)  
  Non-Hispanic 88.6 (209) 83.5 (9265)  
  Unknown 0.4 (1) 4.0 (446)  
 Insurance, % (n  <0.001 
  Medicaid 97.9 (231) 66.7 (7404)  
  Private 1.4 (4) 32.8 (3645)  
  Other or unknown 0.4 (1) 0.4 (49)  
 Birth wt, g, mean 3048.4 3353.1 <.001b 
 Gestational age, wk, mean 38.0 38.2 .14b 
 Complex chronic condition, % (n16.5 (39) 15.9 (1762) .78a 
 Child sex male, % (n50.0 (118) 51.9 (5763) .56a 
 Area-level poverty, % 17.5 15.9 .02a 
 Metropolitan versus nonmetropolitan zip code, % (n97.5 (230) 95.2 (10 566) .11a 
Outcomes    
 WCC adherence, % (n   
  Birth to 12 mo 25.9 (61) 54.7 (6068) <.001a 
  4–12 mo 39.8 (94) 58.2 (6459) <.001a 
  12–24 mo 41.5 (98) 57.5 (6377) <.001a 
 ≥1 WCC gap, % (n42.8 (101) 27.8 (3085) <.001a 
 No. nonroutine visits, median (IQR) 9.5 (7–14) 11 (8–15) .005b 
 On-time immunization status,c % (n83.9 (198) 85.8 (9526) .40a 
 Lead screening at 1 y, % (n62.3 (147) 52.9 (5875) .004a 
IOE, n = 236No IOE, n = 11 098P
Patient characteristics    
 Race, % (n  <.001 
  White 69.1 (163) 34.8 (3865)  
  African American 15.7 (37) 44.4 (4925)  
  Asian American 0 (0) 2.9 (323)  
  Other 13.6 (32) 13.8 (1532)  
  Unknown 1.7 (4) 4.1 (453)  
 Ethnicity, % (n  .01 
  Hispanic 11.0 (26) 12.5 (1387)  
  Non-Hispanic 88.6 (209) 83.5 (9265)  
  Unknown 0.4 (1) 4.0 (446)  
 Insurance, % (n  <0.001 
  Medicaid 97.9 (231) 66.7 (7404)  
  Private 1.4 (4) 32.8 (3645)  
  Other or unknown 0.4 (1) 0.4 (49)  
 Birth wt, g, mean 3048.4 3353.1 <.001b 
 Gestational age, wk, mean 38.0 38.2 .14b 
 Complex chronic condition, % (n16.5 (39) 15.9 (1762) .78a 
 Child sex male, % (n50.0 (118) 51.9 (5763) .56a 
 Area-level poverty, % 17.5 15.9 .02a 
 Metropolitan versus nonmetropolitan zip code, % (n97.5 (230) 95.2 (10 566) .11a 
Outcomes    
 WCC adherence, % (n   
  Birth to 12 mo 25.9 (61) 54.7 (6068) <.001a 
  4–12 mo 39.8 (94) 58.2 (6459) <.001a 
  12–24 mo 41.5 (98) 57.5 (6377) <.001a 
 ≥1 WCC gap, % (n42.8 (101) 27.8 (3085) <.001a 
 No. nonroutine visits, median (IQR) 9.5 (7–14) 11 (8–15) .005b 
 On-time immunization status,c % (n83.9 (198) 85.8 (9526) .40a 
 Lead screening at 1 y, % (n62.3 (147) 52.9 (5875) .004a 

Totals for some variables may not add to 100% because of missing data. IQR, interquartile range.

a

χ2P value.

b

Wilcoxon rank test P value.

c

Receipt of the combined 7-vaccine series (4:3:1:3:3:1:4) by age 2 y per the Centers for Disease Control and Prevention, which includes ≥4 doses of the diphtheria-tetanus-acellular pertussis vaccine, ≥3 doses of the poliovirus vaccine, ≥1 dose of the measles-containing vaccine, ≥3 or ≥4 doses (depending on the product type) of the Haemophilus influenzae type b vaccine, ≥3 doses of the hepatitis B vaccine, ≥1 dose of the varicella vaccine, and ≥4 doses of the pneumococcal conjugate vaccine.

Overall, 54.1% of all children in the study adhered to recommended WCC in the first year, and 57.1% in the second year. As shown in Table 1, there were significant differences between children with and without IOE (25.9% vs 54.7% in the first year and 41.5% vs 57.5% in the second year; P < .001). The median number of WCC visits was lower among children with IOE compared with those without (median [interquartile range]: 6 [5–7] vs 8 [6–8]; P < .001). Attendance was highest in both groups for the 2-month WCC visit (see Fig 2).

FIGURE 2

WCC visit completion over time. Gray bars represent children with IOE. Black bars represent children without IOE. The y-axis represents the percentage who completed at least 1 WCC visit during each age interval, shown on the x-axis. * P < .05.

FIGURE 2

WCC visit completion over time. Gray bars represent children with IOE. Black bars represent children without IOE. The y-axis represents the percentage who completed at least 1 WCC visit during each age interval, shown on the x-axis. * P < .05.

Close modal

In the multivariable regression, IOE was associated with decreased WCC adherence in the first year (aRR 0.54; 95% confidence interval [CI] 0.39–0.74; Table 2). Additional factors associated with WCC adherence included race, ethnicity, insurance, low birth weight, and area-level poverty. In the sensitivity analysis for evaluating WCC adherence starting at 4 months, the effect size associated with IOE was smaller but still significant (aRR 0.74; 95% CI 0.66–0.83; results not shown); however, the association with low birth weight was no longer significant (aRR 1.01; 95% CI 0.96–1.16). Other model parameters were substantively unchanged in the sensitivity analysis.

TABLE 2

aRRs for WCC Adherence Over the First and the Second Year of Life

WCC Adherence Over the First Year, aRR (95% CI)aWCC Adherence Over the Second Year, aRR (95% CI)a
IOE 0.54 (0.39–0.74)* 0.77 (0.68–0.87)* 
Child race   
 White Reference Reference 
 African American 0.79 (0.70–0.89)* 0.84 (0.77–0.92)* 
 Asian American 0.93 (0.85–1.02) 0.96 (0.87–1.06) 
 Other 0.88 (0.80–0.96)* 0.94 (0.89–0.99) 
 Unknown 1.02 (0.89–1.17) 1.01 (0.87–1.17) 
Ethnicity   
 Non-Hispanic Reference Reference 
 Hispanic 1.10 (1.02–1.18)* 1.13 (1.09–1.18)* 
 Unknown 1.05 (0.89–1.22) 0.94 (0.82–1.08) 
Insurance   
 Private Reference Reference 
 Medicaid 0.71 (0.65–0.78)* 0.77 (0.74–0.80)* 
 Other or unknown 0.88 (0.70–1.09) 0.82 (0.66–1.03) 
Low birth wt <2500 g 0.85 (0.79–0.91)* 1.06 (1.00–1.12)* 
Complex chronic condition 1.05 (0.98–1.13) 1.06 (1.02–1.11)* 
Child sex   
 Female Reference Reference 
 Male 1.01 (0.99–1.03) 1.01 (0.98–1.03) 
Area-level povertyb 0.99 (0.98–0.99)* 0.99 (0.99– <1.00)* 
Metropolitan residence 0.97 (0.85–1.11) 1.02 (0.97–1.08) 
WCC Adherence Over the First Year, aRR (95% CI)aWCC Adherence Over the Second Year, aRR (95% CI)a
IOE 0.54 (0.39–0.74)* 0.77 (0.68–0.87)* 
Child race   
 White Reference Reference 
 African American 0.79 (0.70–0.89)* 0.84 (0.77–0.92)* 
 Asian American 0.93 (0.85–1.02) 0.96 (0.87–1.06) 
 Other 0.88 (0.80–0.96)* 0.94 (0.89–0.99) 
 Unknown 1.02 (0.89–1.17) 1.01 (0.87–1.17) 
Ethnicity   
 Non-Hispanic Reference Reference 
 Hispanic 1.10 (1.02–1.18)* 1.13 (1.09–1.18)* 
 Unknown 1.05 (0.89–1.22) 0.94 (0.82–1.08) 
Insurance   
 Private Reference Reference 
 Medicaid 0.71 (0.65–0.78)* 0.77 (0.74–0.80)* 
 Other or unknown 0.88 (0.70–1.09) 0.82 (0.66–1.03) 
Low birth wt <2500 g 0.85 (0.79–0.91)* 1.06 (1.00–1.12)* 
Complex chronic condition 1.05 (0.98–1.13) 1.06 (1.02–1.11)* 
Child sex   
 Female Reference Reference 
 Male 1.01 (0.99–1.03) 1.01 (0.98–1.03) 
Area-level povertyb 0.99 (0.98–0.99)* 0.99 (0.99– <1.00)* 
Metropolitan residence 0.97 (0.85–1.11) 1.02 (0.97–1.08) 

Generalized linear models with a log link and Poisson distribution.

a

Adjusted for all variables listed in the table, year of birth as a fixed effect, and clustering by practice by using robust variance estimation.

b

The coefficient represents a change in the relative risk per 1% increase.

*

P < .05.

In the second year, IOE was associated with decreased WCC adherence (aRR 0.58; 95% CI 0.46–0.73; Table 2). In contrast to the model of WCC adherence in the first year, low birth weight and complex chronic conditions were significantly associated with increased WCC adherence (Table 2).

TABLE 3

aRRs for ≥1 WCC Gap Over the First 2 Years of Life

≥1 WCC Gap Over the First 2 y, aRR (95% CI)a
IOE 1.43 (1.20–1.71)* 
Child race  
 White Reference 
 African American 1.36 (1.18–1.56)* 
 Asian American 1.24 (1.04–1.48)* 
 Other 1.26 (1.09–1.46)* 
 Unknown 0.93 (0.69–1.26) 
Ethnicity  
 Non-Hispanic Reference 
 Hispanic 0.84 (0.72–0.98)* 
 Unknown 1.08 (0.84–1.39) 
Insurance  
 Private Reference 
 Medicaid 2.00 (1.58–2.53)* 
 Other or unknown 1.53 (0.90–2.63) 
Low birth wt <2500 g 0.90 (0.81– <1.00)* 
Complex chronic condition 0.82 (0.75–0.90)* 
Child sex  
 Female Reference 
 Male 0.97 (0.91–1.02) 
Area-level povertyb 1.02 (1.01–1.02)* 
Metropolitan residence 1.01 (0.81–1.27) 
≥1 WCC Gap Over the First 2 y, aRR (95% CI)a
IOE 1.43 (1.20–1.71)* 
Child race  
 White Reference 
 African American 1.36 (1.18–1.56)* 
 Asian American 1.24 (1.04–1.48)* 
 Other 1.26 (1.09–1.46)* 
 Unknown 0.93 (0.69–1.26) 
Ethnicity  
 Non-Hispanic Reference 
 Hispanic 0.84 (0.72–0.98)* 
 Unknown 1.08 (0.84–1.39) 
Insurance  
 Private Reference 
 Medicaid 2.00 (1.58–2.53)* 
 Other or unknown 1.53 (0.90–2.63) 
Low birth wt <2500 g 0.90 (0.81– <1.00)* 
Complex chronic condition 0.82 (0.75–0.90)* 
Child sex  
 Female Reference 
 Male 0.97 (0.91–1.02) 
Area-level povertyb 1.02 (1.01–1.02)* 
Metropolitan residence 1.01 (0.81–1.27) 

Generalized linear models with a log link and Poisson distribution.

a

Adjusted for all variables listed in the table, year of birth as a fixed effect, and clustering by practice by using robust variance estimation.

b

The coefficient represents a change in the relative risk per 1% increase.

*

P < .05.

As shown in Fig 1, 42.8% of children with IOE, versus 27.8% of children who were not exposed, had at least 1 WCC gap during the first 2 years (χ2P < .001). Gaps in WCC most commonly occurred during the 15- to 18-month period (27.3% of the entire cohort), with significant differences between children with IOE and children who were not exposed at 4 to 6, 6 to 9, and 15 to 18 months (see Fig 3). In the multivariable analysis, IOE was associated with an increased likelihood of ≥1 WCC gap during the first 2 years (aRR 1.43; 95% CI 1.20–1.71, Table 3).

FIGURE 3

Gaps in WCC visits over time. Gray bars represent children with IOE. Black bars represent children without IOE. The y-axis represents the percentage with ≥2 consecutive missing WCC visits during each age interval, shown on the x-axis. * P < .05.

FIGURE 3

Gaps in WCC visits over time. Gray bars represent children with IOE. Black bars represent children without IOE. The y-axis represents the percentage with ≥2 consecutive missing WCC visits during each age interval, shown on the x-axis. * P < .05.

Close modal

As shown in Table 1, the median number of nonroutine visits was lower among children with IOE, and the percentage of children with on-time immunizations was ∼85% in both groups. The percentage with lead screening was higher among children with IOE (62% vs 53% of children who were not exposed). However, in the multivariable regression, there was no significant difference in these secondary outcomes on the basis of IOE status (Table 4).

TABLE 4

Secondary Outcomes, Adjusted Incident Rate Ratios, and aRRs

IOE, aIRR (95% CI)
Nonroutine primary care visits by age 2 y 1.00 (0.95–1.05) 
On-time immunization status by age 2 y 1.00 (0.95–1.05)a 
Lead screening at age 1 y 1.13 (0.99–1.28)a 
IOE, aIRR (95% CI)
Nonroutine primary care visits by age 2 y 1.00 (0.95–1.05) 
On-time immunization status by age 2 y 1.00 (0.95–1.05)a 
Lead screening at age 1 y 1.13 (0.99–1.28)a 

The multivariable negative binomial regression was adjusted for race, ethnicity, insurance, low birth weight status, complex chronic condition, sex, area-level poverty, metropolitan residence, and year of birth; clustering by practice was adjusted by using robust variance estimation. aIRR, adjusted incident rate ratio.

a

The multivariable generalized linear regression with a log link and Poisson distribution was adjusted for race, ethnicity, insurance, low birth wt status, complex chronic condition, sex, area-level poverty, metropolitan residence, and year of birth; clustering by practice was adjusted by using robust variance estimation.

Among children <2 years of age, IOE was associated with decreased WCC adherence. Other child characteristics, such as low birth weight and complex chronic conditions, were associated with a neutral or negative effect on WCC adherence in the first year, potentially because of prolonged birth hospitalization or other competing acute care needs. However, in the second year, these characteristics were associated with increased WCC adherence, suggesting a protective effect against delays in care after early infancy. Conversely, the association between IOE and decreased WCC adherence was persistent over time. Our findings suggest that children with IOE are often “catching up” on WCC components, such as immunizations and lead screening, during nonroutine visits when their families seek care for more urgent conditions. Findings from this birth cohort from areas of the United States that have been significantly impacted by the opioid crisis provide new information regarding opportunities to improve preventive care for this population.35 

For children affected by IOE, WCC visits are an important opportunity for primary care providers to address parental knowledge gaps, assess illness and injury risk, and evaluate child growth and development.4,6,3638  These opportunities include surveillance and management of ongoing opioid withdrawal symptoms such as feeding difficulty, fussiness, and sleeplessness.39  Tailored discussions about opioid use while breastfeeding may play an important role.40  Other key topics for children with IOE include barriers to implementing safe sleep precautions, screening for strabismus and other visual disturbances, and, when exposed, testing for hepatitis C seroconversion.4144  Among mothers with OUD, previous research has demonstrated low responsiveness to child cues, heightened tendency toward physical provocation, low ability to promote child learning, and limited understanding of basic development.4548  WCC visits are a time to review motor and cognitive developmental expectations, encourage positive parenting strategies that optimize development, and connect families to early intervention services (including hearing and vision supports and speech, physical, and occupational therapy). Perhaps most importantly, WCC visits are an opportunity to assess parenting stress, coping skills, and quality of parent-child interaction and to refer to other community resources as needed.45,46,49  The benefits of such WCC services for young children have been previously described and extend beyond immunizations or other procedures that could be added on during a nonroutine visit.12,50 

There is limited research on WCC adherence among children impacted by the opioid crisis.3,19  One recent retrospective study using a national insurance database revealed higher rates of all health care use (admissions, ED visits, and outpatient visits) in this population, with an overall lower proportion of care attributed to WCC visits.51  Our findings are also consistent with previous research revealing socioeconomic disparities in WCC adherence.27  Some of the key drivers hypothesized to impact WCC adherence include transportation issues, time constraints, family crisis events, and low perceived value of primary care.26,52  Mothers with OUD may also encounter challenges such as stigmatization and discrimination, legal and child custody concerns, and mental health issues.4,53  Furthermore, mothers with OUD are often burdened by a significant history of trauma, which is associated with lower satisfaction with and more negative attitudes toward parenting.47,54,55 

Given these challenges, increasing WCC visits for this population may require changes to current models of care that increase perceived value for families, maternal empowerment, and trust in the health care system. Research from 1 maternal substance use treatment facility suggests that less than half of mothers feel their child’s pediatrician spends sufficient time with their child, and less than one-third feel they were asked about their viewpoints as a mother.56  One approach to address these concerns may be group-based WCC visits, which would afford more provider time, peer-to-peer interaction, and in-depth discussion.5759  Previous research of a group-based mindful parenting intervention for this population demonstrated promising results in improving maternal stress and parenting.60,61  Patient-centered medical homes may improve WCC adherence through better patient outreach, care coordination, and provider continuity.62  Home visiting programs have also shown potential to increase WCC adherence among low-income families.63  Further research is needed to determine the impact these approaches may have in reducing stigma, increasing WCC engagement, and ultimately improving outcomes for children affected by IOE.

Although strengths of this study include a large recent study cohort spanning multiple states and the inclusion of both publicly and privately insured children, there are several limitations. First, we relied on billing codes for ascertainment of opioid exposure, which may lead to misclassification bias. We anticipated some underestimation of IOE on the basis of variation in maternal drug testing as well as physician documentation of known exposure in the pediatric electronic medical record. In 1 population-based cohort study with universal maternal urine drug testing at delivery, the reported IOE rate was slightly higher than that in our sample.64  As reflected in the high rate of NAS (80%) among those in our cohort with IOE, undercoding of IOE is more likely to occur for children not requiring treatment of NAS. These codes also provide limited insight into the type of opioid exposure, severity of NAS if diagnosed, or disposition of the child into maternal custody versus foster or kinship care. Furthermore, we lacked information on other known maternal predictors such as health literacy and social support, education level, adherence to prenatal care, marital status, and employment. However, we hypothesize that many of these factors are colinear with maternal substance use during pregnancy, and so although we could not discern the independent effects of each of these per se, the diagnosis of IOE is sufficient to serve as a marker of high-risk status for children in a pediatric office setting. We also lacked data from outside the primary care network; therefore, it is possible we underestimated WCC adherence among families who transferred care elsewhere. To minimize this, we used a conservative approach of restricting the analysis to infants with at least 1 visit within the network during the second year of life. When this restriction is removed, the negative effect sizes for IOE are slightly increased. This suggests that families of infants affected by IOE may be more likely to relocate their care. Finally, in this study, we did not evaluate adverse outcomes that may have resulted from decreased WCC adherence. Future work may focus on the impact of improved WCC adherence on salient outcomes for this population (eg, hospitalizations, subspecialty referrals, infant sleep safety, and parental approaches to discipline).

Children with IOE are less likely to adhere to WCC as recommended by the American Academy of Pediatrics. Use of nonroutine primary care is similar to that of children who were not exposed, as are rates of on-time immunization and lead screening. Given the growing awareness of the child health, safety, and developmental risks associated with maternal OUD, further research may focus on identifying and implementing health system interventions to promote the engagement of this population in preventive care to support their complex medical and psychosocial needs.

Dr Goyal conceptualized and designed this study, oversaw all aspects of the data collection and analysis, and drafted and critically revised the manuscript; Drs Rohde and Short contributed to the study design, assisted in interpretation of findings, edited the manuscript, and provided critical revisions for important scientific content; Dr Patrick contributed to the study design, assisted in interpretation of findings, and critically revised the manuscript for important scientific content; Dr Abatemarco supervised the study design and analysis and critically revised the manuscript for important scientific content; Dr Chung supervised the study design and analysis, assisted in interpretation of findings, edited the manuscript, and critically revised the manuscript for important scientific content; and all authors approved the final manuscript as submitted.

FUNDING: Supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health under grant U54-GM104941 (principal investigator: Binder-Macleod). Funded by the National Institutes of Health (NIH).

     
  • aRR

    adjusted relative risk

  •  
  • CI

    confidence interval

  •  
  • ICD

    International Classification of Diseases

  •  
  • IOE

    intrauterine opioid exposure

  •  
  • NAS

    neonatal abstinence syndrome

  •  
  • OUD

    opioid use disorder

  •  
  • SNOMED-CT

    Systematized Nomenclature of Medicine–Clinical Terminology

  •  
  • WCC

    well-child care

1
US Department of Health and Human Services
.
HHS acting secretary declares public health emergency to address national opioid crisis
.
2
Winkelman
TNA
,
Villapiano
N
,
Kozhimannil
KB
,
Davis
MM
,
Patrick
SW
.
Incidence and costs of neonatal abstinence syndrome among infants with Medicaid: 2004-2014
.
Pediatrics
.
2018
;
141
(
4
):
e20173520
3
Pryor
JR
,
Maalouf
FI
,
Krans
EE
,
Schumacher
RE
,
Cooper
WO
,
Patrick
SW
.
The opioid epidemic and neonatal abstinence syndrome in the USA: a review of the continuum of care
.
Arch Dis Child Fetal Neonatal Ed
.
2017
;
102
(
2
):
F183
F187
4
Spehr
MK
,
Coddington
J
,
Ahmed
AH
,
Jones
E
.
Parental opioid abuse: barriers to care, policy, and implications for primary care pediatric providers
.
J Pediatr Health Care
.
2017
;
31
(
6
):
695
702
5
Maguire
DJ
,
Taylor
S
,
Armstrong
K
, et al
.
Long-term outcomes of infants with neonatal abstinence syndrome
.
Neonatal Netw
.
2016
;
35
(
5
):
277
286
6
Kocherlakota
P
.
Neonatal abstinence syndrome
.
Pediatrics
.
2014
;
134
(
2
). Available at: www.pediatrics.org/cgi/content/full/134/2/e547
7
Guevara
JP
,
Gerdes
M
,
Localio
R
, et al
.
Effectiveness of developmental screening in an urban setting
.
Pediatrics
.
2013
;
131
(
1
):
30
37
8
Mendelsohn
AL
,
Mogilner
LN
,
Dreyer
BP
, et al
.
The impact of a clinic-based literacy intervention on language development in inner-city preschool children
.
Pediatrics
.
2001
;
107
(
1
):
130
134
9
Nelson
CS
,
Higman
SM
,
Sia
C
,
McFarlane
E
,
Fuddy
L
,
Duggan
AK
.
Medical homes for at-risk children: parental reports of clinician-parent relationships, anticipatory guidance, and behavior changes
.
Pediatrics
.
2005
;
115
(
1
):
48
56
10
O’Connor
E
,
Rossom
RC
,
Henninger
M
,
Groom
HC
,
Burda
BU
.
Primary care screening for and treatment of depression in pregnant and postpartum women: evidence report and systematic review for the US Preventive Services Task Force
.
JAMA
.
2016
;
315
(
4
):
388
406
11
Pease
A
,
Ingram
J
,
Blair
PS
,
Fleming
PJ
.
Factors influencing maternal decision-making for the infant sleep environment in families at higher risk of SIDS: a qualitative study
.
BMJ Paediatr Open
.
2017
;
1
(
1
):
e000133
12
Regalado
M
,
Halfon
N
.
Primary care services promoting optimal child development from birth to age 3 years: review of the literature
.
Arch Pediatr Adolesc Med
.
2001
;
155
(
12
):
1311
1322
13
Tom
JO
,
Mangione-Smith
R
,
Grossman
DC
,
Solomon
C
,
Tseng
CW
.
Well-child care visits and risk of ambulatory care-sensitive hospitalizations
.
Am J Manag Care
.
2013
;
19
(
5
):
354
360
14
Pittard
WB
 III
,
Laditka
JN
,
Laditka
SB
.
Early and periodic screening, diagnosis, and treatment and infant health outcomes in Medicaid-insured infants in South Carolina
.
J Pediatr
.
2007
;
151
(
4
):
414
418
15
Pittard
WB
 III
,
Hulsey
TC
,
Laditka
JN
,
Laditka
SB
.
School readiness among children insured by Medicaid, South Carolina
.
Prev Chronic Dis
.
2012
;
9
:
E111
16
Gressler
LE
,
Shah
S
,
Shaya
FT
.
Association of criminal statutes for opioid use disorder with prevalence and treatment among pregnant women with commercial insurance in the United States
.
JAMA Netw Open
.
2019
;
2
(
3
):
e190338
17
Cleveland
LM
,
Bonugli
R
.
Experiences of mothers of infants with neonatal abstinence syndrome in the neonatal intensive care unit
.
J Obstet Gynecol Neonatal Nurs
.
2014
;
43
(
3
):
318
329
18
Cleveland
LM
,
Gill
SL
.
“Try not to judge”: mothers of substance exposed infants
.
MCN Am J Matern Child Nurs
.
2013
;
38
(
4
):
200
205
19
Chung
EK
,
Brumbley
M
,
Hand
D
, et al
. Receipt of prenatal care and well-child care among drug-dependent women and their young children. In:
Pediatric Academic Societies Annual Meeting
; May 6–9,
2017
;
San Francisco, CA
20
PEDSnet
.
PEDSnet data quality program
.
Available at: https://pedsnet.org/data/data-quality/. Accessed March 1, 2019
21
Corr
TE
,
Hollenbeak
CS
.
The economic burden of neonatal abstinence syndrome in the United States
.
Addiction
.
2017
;
112
(
9
):
1590
1599
22
Hall
ES
,
Isemann
BT
,
Wexelblatt
SL
, et al
.
A cohort comparison of buprenorphine versus methadone treatment for neonatal abstinence syndrome
.
J Pediatr
.
2016
;
170
:
39
44.e1
23
Hagan
JF
Jr,
Shaw
JS
,
Duncan
PM
, eds.
Bright Futures: Guidelines for Health Supervision of Infants, Children, and Adolescents
. 4th ed.
Elk Grove Village, IL
:
American Academy of Pediatrics
;
2017
24
Hill
HA
,
Elam-Evans
LD
,
Yankey
D
,
Singleton
JA
,
Kang
Y
.
Vaccination coverage among children aged 19-35 months - United States, 2017
.
MMWR Morb Mortal Wkly Rep
.
2018
;
67
(
40
):
1123
1128
25
Goyal
NK
,
Folger
AT
,
Sucharew
HJ
, et al
.
Primary care and home visiting utilization patterns among at-risk infants
.
J Pediatr
.
2018
;
198
:
240
246.e2
26
O’Donnell
HC
,
Trachtman
RA
,
Islam
S
,
Racine
AD
.
Factors associated with timing of first outpatient visit after newborn hospital discharge
.
Acad Pediatr
.
2014
;
14
(
1
):
77
83
27
Wolf
ER
,
Hochheimer
CJ
,
Sabo
RT
, et al
.
Gaps in well-child care attendance among primary care clinics serving low-income families
.
Pediatrics
.
2018
;
142
(
5
):
e20174019
28
Feudtner
C
,
Feinstein
JA
,
Zhong
W
,
Hall
M
,
Dai
D
.
Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation
.
BMC Pediatr
.
2014
;
14
:
199
29
US Census Bureau
. 2013–2017 American Community Survey 5-year estimates. Available at: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF. Accessed November 27, 2019
30
Jones
MN
,
Brown
CM
,
Widener
MJ
,
Sucharew
HJ
,
Beck
AF
.
Area-level socioeconomic factors are associated with noncompletion of pediatric preventive services
.
J Prim Care Community Health
.
2016
;
7
(
3
):
143
148
31
US Department of Agriculture Economic Research Service
. Rural-urban commuting area codes. Available at: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/. Accessed January 15, 2018
32
McNutt
LA
,
Wu
C
,
Xue
X
,
Hafner
JP
.
Estimating the relative risk in cohort studies and clinical trials of common outcomes
.
Am J Epidemiol
.
2003
;
157
(
10
):
940
943
33
Zou
G
.
A modified Poisson regression approach to prospective studies with binary data
.
Am J Epidemiol
.
2004
;
159
(
7
):
702
706
34
Marshall
A
,
Altman
DG
,
Royston
P
,
Holder
RL
.
Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study
.
BMC Med Res Methodol
.
2010
;
10
:
7
35
Scholl
L
,
Seth
P
,
Kariisa
M
,
Wilson
N
,
Baldwin
G
.
Drug and opioid-involved overdose deaths - United States, 2013-2017
.
MMWR Morb Mortal Wkly Rep
.
2018
;
67
(
5152
):
1419
1427
36
Merhar
SL
,
McAllister
JM
,
Wedig-Stevie
KE
,
Klein
AC
,
Meinzen-Derr
J
,
Poindexter
BB
.
Retrospective review of neurodevelopmental outcomes in infants treated for neonatal abstinence syndrome
.
J Perinatol
.
2018
;
38
(
5
):
587
592
37
Nygaard
E
,
Moe
V
,
Slinning
K
,
Walhovd
KB
.
Longitudinal cognitive development of children born to mothers with opioid and polysubstance use
.
Pediatr Res
.
2015
;
78
(
3
):
330
335
38
McGlone
L
,
Mactier
H
.
Infants of opioid-dependent mothers: neurodevelopment at six months
.
Early Hum Dev
.
2015
;
91
(
1
):
19
21
39
Hudak
ML
,
Tan
RC
;
Committee on Drugs
;
Committee on Fetus and Newborn
;
American Academy of Pediatrics
.
Neonatal drug withdrawal
.
Pediatrics
.
2012
;
129
(
2
). Available at: www.pediatrics.org/cgi/content/full/129/2/e540
40
Reece-Stremtan
S
,
Marinelli
KA
.
ABM clinical protocol #21: guidelines for breastfeeding and substance use or substance use disorder, revised 2015
.
Breastfeed Med
.
2015
;
10
(
3
):
135
141
41
Spiteri Cornish
K
,
Hrabovsky
M
,
Scott
NW
,
Myerscough
E
,
Reddy
AR
.
The short- and long-term effects on the visual system of children following exposure to maternal substance misuse in pregnancy
.
Am J Ophthalmol
.
2013
;
156
(
1
):
190
194
42
Gill
AC
,
Oei
J
,
Lewis
NL
,
Younan
N
,
Kennedy
I
,
Lui
K
.
Strabismus in infants of opiate-dependent mothers
.
Acta Paediatr
.
2003
;
92
(
3
):
379
385
43
Committee on Infectious Diseases American Academy of Pediatrics
. Hepatitis C. In:
Kimberlin
DW
,
Brady
MT
,
Jackson
MA
,
Long
SS
, eds.
Red Book: 2015 Report of the Committee on Infectious Diseases
, 30th ed.
Elk Grove Village, IL
:
American Academy of Pediatrics
;
2015
:
423
430
44
Hoffman
HJ
,
Damus
K
,
Hillman
L
,
Krongrad
E
.
Risk factors for SIDS. Results of the National Institute of Child Health and Human Development SIDS Cooperative Epidemiological Study
.
Ann N Y Acad Sci
.
1988
;
533
:
13
30
45
Barnard
M
,
McKeganey
N
.
The impact of parental problem drug use on children: what is the problem and what can be done to help?
Addiction
.
2004
;
99
(
5
):
552
559
46
Burns
KA
,
Chethik
L
,
Burns
WJ
,
Clark
R
.
The early relationship of drug abusing mothers and their infants: an assessment at eight to twelve months of age
.
J Clin Psychol
.
1997
;
53
(
3
):
279
287
47
Rizzo
RA
,
Neumann
AM
,
King
SO
,
Hoey
RF
,
Finnell
DS
,
Blondell
RD
.
Parenting and concerns of pregnant women in buprenorphine treatment
.
MCN Am J Matern Child Nurs
.
2014
;
39
(
5
):
319
324
48
Suchman
NE
,
Luthar
SS
.
Maternal addiction, child maladjustment and socio-demographic risks: implications for parenting behaviors
.
Addiction
.
2000
;
95
(
9
):
1417
1428
49
McGlade
A
,
Ware
R
,
Crawford
M
.
Child protection outcomes for infants of substance-using mothers: a matched-cohort study
.
Pediatrics
.
2009
;
124
(
1
):
285
293
50
Fiks
AG
,
Hunter
KF
,
Localio
AR
,
Grundmeier
RW
,
Alessandrini
EA
.
Impact of immunization at sick visits on well-child care
.
Pediatrics
.
2008
;
121
(
5
):
898
905
51
Liu
G
,
Kong
L
,
Leslie
DL
,
Corr
TE
.
A longitudinal healthcare use profile of children with a history of neonatal abstinence syndrome
.
J Pediatr
.
2019
;
204
:
111
117.e1
52
Coker
TR
,
Chung
PJ
,
Cowgill
BO
,
Chen
L
,
Rodriguez
MA
.
Low-income parents’ views on the redesign of well-child care
.
Pediatrics
.
2009
;
124
(
1
):
194
204
53
Sutter
MB
,
Gopman
S
,
Leeman
L
.
Patient-centered care to address barriers for pregnant women with opioid dependence
.
Obstet Gynecol Clin North Am
.
2017
;
44
(
1
):
95
107
54
Conroy
E
,
Degenhardt
L
,
Mattick
RP
,
Nelson
EC
.
Child maltreatment as a risk factor for opioid dependence: comparison of family characteristics and type and severity of child maltreatment with a matched control group
.
Child Abuse Negl
.
2009
;
33
(
6
):
343
352
55
Sansone
RA
,
Whitecar
P
,
Wiederman
MW
.
The prevalence of childhood trauma among those seeking buprenorphine treatment
.
J Addict Dis
.
2009
;
28
(
1
):
64
67
56
Short
VL
,
Goyal
NK
,
Chung
EK
,
Hand
DJ
,
Abatemarco
DJ
.
Perceptions of pediatric primary care among mothers in treatment for opioid use disorder
.
J Community Health
.
2019
;
44
(
6
):
1127
1134
57
DeLago
C
,
Dickens
B
,
Phipps
E
,
Paoletti
A
,
Kazmierczak
M
,
Irigoyen
M
.
Qualitative evaluation of individual and group well-child care
.
Acad Pediatr
.
2018
;
18
(
5
):
516
524
58
Johnston
JC
,
McNeil
D
,
van der Lee
G
,
MacLeod
C
,
Uyanwune
Y
,
Hill
K
.
Piloting CenteringParenting in two Alberta public health well-child clinics
.
Public Health Nurs
.
2017
;
34
(
3
):
229
237
59
Mittal
P
.
Centering parenting: pilot implementation of a group model for teaching family medicine residents well-child care
.
Perm J
.
2011
;
15
(
4
):
40
41
60
Gannon
M
,
Mackenzie
M
,
Kaltenbach
K
,
Abatemarco
D
.
Impact of mindfulness-based parenting on women in treatment for opioid use disorder
.
J Addict Med
.
2017
;
11
(
5
):
368
376
61
Short
VL
,
Gannon
M
,
Weingarten
W
,
Kaltenbach
K
,
LaNoue
M
,
Abatemarco
DJ
.
Reducing stress among mothers in drug treatment: a description of a mindfulness based parenting intervention
.
Matern Child Health J
.
2017
;
21
(
6
):
1377
1386
62
Zutshi
A
,
Peikes
D
,
Smith
K
, et al
.
The Medical Home: What Do We Know, What Do We Need to Know? A Review of the Earliest Evidence on the Effectiveness of the Patient-Centered Medical Home Model
.
Rockville, MD
:
Agency for Healthcare Research and Quality, US Department of Health and Human Services
;
2014
. Available at: https://pcmh.ahrq.gov/page/medical-home-what-do-we-know-what-do-we-need-know-review-earliest-evidence-effectiveness-of-the-patient-centered-medical-home-model. Accessed January 16, 2019
63
Goyal
NK
,
Ammerman
RT
,
Massie
JA
,
Clark
M
,
Van Ginkel
JB
.
Using quality improvement to promote implementation and increase well child visits in home visiting
.
Child Abuse Negl
.
2016
;
53
:
108
117
64
Hall
ES
,
Wexelblatt
SL
,
Greenberg
JM
.
Surveillance of intrauterine opioid exposures using electronic health records
.
Popul Health Manag
.
2018
;
21
(
6
):
486
492

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data