Improving Screening for Family Risks in Young Children in Primary Care
J Prim Care Community Health. 2022 Jan-December; 13: 21501319211062676.
Improving Child Development Screening: Implications for Professional Practice and Patient Equity
John Meurer
1Medical College of Wisconsin (MCW), Milwaukee, WI, U.s.a.
Robert Rohloff
2Children's Wisconsin, Milwaukee, WI, U.s.
Lisa Rein
iMedical College of Wisconsin (MCW), Milwaukee, WI, USA
Ilya Kanter
2Children'south Wisconsin, Milwaukee, WI, The states
Nayanika Kotagiri
iMedical College of Wisconsin (MCW), Milwaukee, WI, USA
Constance Gundacker
1Medical College of Wisconsin (MCW), Milwaukee, WI, U.s.a.
Sergey Tarima
1Medical College of Wisconsin (MCW), Milwaukee, WI, United states
Received 2021 Sep 6; Revised 2021 Nov two; Accustomed 2021 November nine.
Abstract
Introduction and Objectives:
A pediatric group with 25 clinics and 150 providers used multifaceted approaches to implement workflow processes and an electronic wellness tape (EHR) flowsheet to amend child developmental screening. The key issue was developmental screening done for every patient during 3 periods betwixt ages viii and 36 months. Identification of developmental concerns was the secondary written report upshot. Screening rates and referrals were hypothesized to be optimized for children regardless of demographic backgrounds.
Methods:
During preventive visits, developmental screens targeted patients in age groups 8 to 12, 13 to 24, and 25 to 36 months. EHRs were analyzed for screening documentation, results, and referrals by patient demographics. Fifteen pediatric professionals were interviewed about their qualitative experiences. Quality improvement interventions included appointing clinic champions, grooming staff about the screening process and responsibilities, using a standardized tool, employing plan-do-study-human action cycles, posting EHR prompts, providing financial incentives, and monitoring screening rates using control charts.
Results:
Inside 25 months, screening rates improved from 60% to >95% within the 3 preventive visit historic period groups for a full of more than xxx 000 children. Professionals valued the team process improvements. Children enrolled in Medicaid, black children, and those living in lower income zip codes had lower screening rates than privately insured, white children, and those living in higher income areas. Ages and Stages Questionnaire tertiary edition results were significantly different by gender, race/ethnicity, insurance, and income categories across all groups. Referral rates varied past race/ethnicity and zip code of residence.
Conclusions:
This projection resulted in an effective and efficient process to meliorate child developmental screening that was valued by pediatric professionals. Analyses of patient demographics revealed disparities in services for the nearly vulnerable families. Ongoing quality improvement, wellness services enquiry, and advocacy offer hope to meliorate health equity.
Keywords: child developmental screening, quality improvement, health equity
Introduction
During the past twenty years, pediatric and family medicine practices have been working to improve kid developmental screening. Universal screening during preventive visits has enabled early detection of developmental concerns and referral for specific diagnoses, therapeutic services, and support.
Despite more than a decade of initiatives, rates of developmental screening have remained low. 1 The American Academy of Pediatrics has published a policy statement to establish a universal system of developmental surveillance and screening for the early identification of weather condition that effect children's development and achievement. ii Developing a cohesive system to improve developmental screening is needed to optimize health outcomes for children through early intervention and referral to supportive services. 3
At our large, multi-site pediatric grouping, we have been interested in continually improving screening practices and ensuring equitable commitment of screening and referrals. In this article, nosotros first draw our process for improving developmental screening. We then summarize results of key informant interviews of pediatric professionals nearly the process changes. Finally, we address several questions of interest to master care practices and their community partners nationally working to improve delivery of developmental screening and health equity for vulnerable children and families. The objective of the report is to introduce the modified screening process and explore associations of kid demographics with Ages and Stages Questionnaire, tertiary edition (ASQ) screening rates, screening results, and referral rates for children historic period 8 to 36 months seen during preventive visits. The written report also describes implications of these findings to improve principal care practices, advance health disinterestedness, and guide further inquiry.
Methods
Practice Population
The project was done at Children's Medical Group, the main care division of Children'south Wisconsin, a private, non-profit, free-standing pediatric health arrangement in the Milwaukee area. The pediatric group is comprised of 25 clinics with 150 pediatricians and nurse practitioners serving urban and suburban locations in southeastern Wisconsin. Prior to 2017, the group lacked centralized guidance and clinics had wide variation in developmental screening champions, grooming, standardization, and monitoring. In February 2017, an improved procedure for developmental screening was implemented beyond all practices using the ASQ at patient ages ix, 18, and xxx months equally recommended by the American University of Pediatrics. 4 The quality comeback process followed the Model for Improvement framework. v We asked what are we trying to attain, how will we know that a alter is an improvement, and what change can we brand that will result in improvement. We used programme, do, study, deed cycles to continually improve processes.
Interventions
Quality improvement interventions included appointing clinic champions, grooming staff about the screening procedure and responsibilities, using standardized tool, employing program-do-report-act cycles, posting EHR prompts, providing financial incentives, and monitoring screening rates using control charts. Tactics included provider and staff engagement, educational activity, workflow refinement, information transparency, advice cascade to support total scale implementation and ongoing process review along with incentives to back up sustainment. The workflow process and potential risks in the process are shown in Effigy 1.
Clinic workflow for developmental screening process and risks in the process.
Blue text represents an efficient process. Ruddy text shows potential challenges in the procedure.
Data Sources
All ASQ results were documented in a flowsheet in the Epic electronic health record (EHR, Effigy 2). Information entered in the flowsheet and the demographic section of Ballsy was used to generate information in iNSIGHT Health Goad warehouse. Reporting was developed from the data warehouse and published to the Children'south SQL Server Reporting System. Ii reports were created to evaluate developmental screening. I measures rates of compliance to screening recommendations for a given measurement menses, the other displays data showing aggregated results of screens for a given menses.
Example of electronic tape ASQ flowsheet and referral reminder.
Measures
The logic for the project process measure was designed to address the Model for Comeback question—How will we know that change is an improvement? National measures use a yearly calculation—measuring whether a child, turning 1, 2, or 3 years of age, had a screen in the previous 12 months. five The yearly measure would not accept provided timely data. As screens are performed at 9, 18, and 30 months of historic period, an improvement might non be recognized for 3 to 6 months subsequently an intervention was made. To overcome this limitation, we created a more than complex logic to measure improvement quickly. The population beingness measured was divers by children with a preventive visit in 3 historic period groupings: 8 to 12 months, 13 to 24 months, and 25 to 36 months. A child appeared in the denominator and numerator immediately during the measurement flow for the advisable age range when a screen was documented. A child appeared in the denominator and not in the numerator, if the child aged out of an age range during a measurement period without a documented screen in the age range. If a child aged out of an historic period range during the measurement flow and had a screen in the age range merely prior to the measurement period, they were counted in the denominator and numerator. If a child had a preventive visit during the measurement flow simply did non have a screen and did non age out of the age range, they were excluded from the measure. If a child was present in more than 1 historic period range in a measurement menses, they were included in all appropriate age ranges. Thus, the measurement logic represented both an run across and patient level hybrid measure. The report could be filtered by measurement period, clinic location, primary provider, patient insurance, race/ethnicity, and zip code of residence.
In summary, the logic to determine rate of screening by age range was:
-
Inclusion criteria: preventive visit in the age range in the measurement period
-
Exclusion criteria: no ASQ documented AND non aged out of the age range in the measurement period
-
Numerator: documented ASQ in an age range during a measurement period
-
Denominator: preventive visit in age range during measurement period with documented ASQ or with no ASQ and aged out of historic period range during measurement period.
This information was displayed in the primary care quality application and viewable by all providers. ASQ rates were financially incentivized for individual pediatricians as a pay for performance bonus. Loftier screening rates take been maintained in the absence of the incentive. Screening rates were shared quarterly with each clinic. Average screening rates were chosen equally a primary care counterbalanced scorecard measure and communicated to the system board of directors.
Qualitative Data Assay
In society to sympathize the pediatric professional person experience with the procedure improvement, a medical student performed 30 to lx-min interviews with pediatricians, nurses, and practice managers most team fellow member'due south roles, their view of the strengths and weaknesses of the ASQ tool and screening process, training and motivation to perform screening, impact on the practice, and recommended changes to the process. Notes from the interviews were aggregated and summarized. A neutral enquiry assistant performed a standardized, open-ended interview of 17 personnel from 8 diverse practices to improve the reliability of the responses and rigor of the evaluation. The student noted answers to the questions and grouped positive and negative themes for each question. With the student, the master investigator and quality improvement director reviewed and named the themes and identified patterns for reporting.
Children's Wisconsin and the Medical College of Wisconsin (MCW) partnered in analyzing the patients' experiences of the developmental screening process. MCW is an affiliated private, non-profit, free-standing health science university and bookish medical center. The Children's Wisconsin Human Inquiry Review Lath accounted the written report protocol to be quality improvement.
Outcomes
The key outcome goal was ASQ screening done for every patient during each of the 3 age ranges. Information was extracted from the Children's Wisconsin warehouse, de-identified, and transferred to MCW for analysis. Data assay was performed separately for subgroups of children ages viii to 12 months, 13 to 24 months, and 25 to 36 months. Children with at least 1 preventive visit during each age range with complete follow-upwardly (meaning they had since aged out of this range) were included in each analytic subgroup. Compliance with ASQ screening was defined as at to the lowest degree i ASQ screening result documented in the Ballsy flowsheet during the age range or a reason reported by the pediatrician for non screening. Reasons for non screening included patients with known major developmental delays, enrolled in Medicaid fee-for-service due to neurodevelopment disabilities, or enrolled in early on intervention.
Quantitative Data Analysis
Kid characteristics were summarized inside each age group using frequencies and percentages for categorical variables and means, standard deviations, and medians for continuous variables. Demographic data included gender, race/ethnicity, wellness insurance, and median income for the zilch lawmaking of residence. Statistical comparisons were made inside each subgroup between child characteristics and ASQ compliance, ASQ results (all reassuring vs monitoring or concerns in at least 1 domain), and referral rates amidst children with any concerning domain. All tests were performed using unproblematic logistic regression, fitted using generalized estimating equations with exchangeable correlation structure to adjust for clustering within zip codes. All statistical analyses were performed using R version three.6.0 (2019-04-26) (R Foundation for Statistical Computing).
Results
Inside 2 years, the percent screening compliance improved from 60% of patients by historic period group to greater than 95% and the procedure became stable and predictable (Effigy three).
Plot of per centum ASQ-iii screening during preventive visits by calendar month.
Shaded ribbons stand for 95% conviction intervals.
Qualitative Results
10 pediatricians, three do managers, and ii nurses at 8 urban or suburban clinic sites participated in the interviews. Their responses reflected a universal positive view of the ASQ tool. Pediatricians were able to delve deeper into assessing a child's evolution and the process kept them accountable for milestones that may not otherwise have received as much attending. They felt that the tool allowed them to amend monitor a child's developmental progress past screening them consistently at unlike time points. Pediatricians were better able to educate parents on the phase of their child'southward development and potential concerns highlighted by the screening. Providers stated that the ASQ improved patient outcomes because the standardized screening enabled them to place developmental concerns and address them at a critical point. The vast majority were motivated to implement the screenings not for monetary compensation or recognition but because information technology was a direct style to provide better intendance for patients.
All interviewees felt developmental screening was implemented well into a team approach. Team members were confident as to who was responsible for different roles in the screening process although each site had customized methods. Pediatricians felt that parent engagement was adequate because there were steps in the process to follow up if a screening was not completed. Incorporating the ASQ results into the EHR allowed improved tracking of scores.
A common theme regarding improving the process was parent completion of the screening tool electronically prior to the preventive visit. Although pediatricians felt that most parents completed the ASQ when mailed prior to the visit, many felt a more efficient process could be implemented with electronic information submission directly into the EHR. A notification alerting parents to complete the ASQ prior to the visit might improve efficiency and ease of adding scores in the Epic flowsheet. Moreover, professionals would take access to results prior to the visit. Pediatricians felt some ASQ questions were unclear to parents and needed further caption and follow upwards. They too felt that the length of the screening tool hindered parents' ability to complete it during a visit.
Patient Population
Between April ane, 2017 and April 30, 2019, ten 430 children historic period 8 to 12 months (infants), 11 257 age xiii to 24 months, and ten 129 age 25 to 36 months (older toddlers) had at least 1 preventive visit when universal ASQ screening was promoted beyond the pediatric practice.
Approximately two-thirds of patients aged eight to 36 months were privately insured, ane-quaternary enrolled in a Medicaid wellness plan generally due to poverty, and v% enrolled in Medicaid fee-for-service (FFS) generally due to a inability (Tabular array 1). Approximately 60% of all patients were white, 20% blackness/African American, and 9% Hispanic/Latino.
Table one.
Demographics of Children With At Least 1 Preventive Intendance Visit During Each Age Range.
| eight-12 Months (N = ten 430) | 13-24 Months (N = xi 257) | 25-36 Months (N = 10 129) | |
|---|---|---|---|
| Male | 5291 (50.7%) | 5742 (51.0%) | 5179 (51.1%) |
| Race/ethnicity | |||
| White | 6352 (sixty.9%) | 6701 (59.five%) | 6319 (62.4%) |
| Black/African American | 2118 (twenty.3%) | 2465 (21.nine%) | 2074 (20.5%) |
| Hispanic/Latino | 972 (nine.three%) | 1034 (9.2%) | 828 (viii.two%) |
| Asian | 458 (4.4%) | 483 (4.3%) | 430 (4.two%) |
| Other/Unknown | 530 (5.1%) | 574 (5.one%) | 478 (iv.7%) |
| Insurance | |||
| Commercial | 6778 (65.0%) | 7130 (63.3%) | 6773 (66.9%) |
| Medicaid Health Plan | 2728 (26.two%) | 3007 (26.7%) | 2455 (24.two%) |
| Medicaid Fee-for-Service | 434 (4.2%) | 604 (5.4%) | 483 (4.8%) |
| Self-Pay | 358 (iii.four%) | 386 (3.4%) | 306 (3.0%) |
| Medicare/Other Government | 132 (1.3%) | 130 (1.ii%) | 112 (one.1%) |
| Null-code median income quartile | |||
| Q1: Lowest | 2545 (24.5%) | 2837 (25.three%) | 2353 (23.3%) |
| Q2: Low-Medium | 2403 (23.1%) | 2571 (22.ix%) | 2186 (21.7%) |
| Q3: Medium-High | 2649 (25.five%) | 2798 (25.0%) | 2611 (25.9%) |
| Q4: Highest | 2796 (26.9%) | 3002 (26.eight%) | 2939 (29.1%) |
| Freq missing | 37 | 49 | 40 |
Quantitative Results
Compliance rates were significantly different past race/ethnicity, insurance, and median ZIP code income categories across all historic period groups (Tabular array ii). Nigh 73% of children historic period 8 to 12 months in Medicaid FFS had an ASQ screening done during a preventive visit compared with 84% with other health insurance. Approximately 75% of children historic period 25 to 36 months in a Medicaid wellness plan or FFS had a screening done compared with 84% of privately insured. Black children had lower screening rates than others, especially age 25 to 36 months when 75% of black and 83% of white patients were screened. Children residing in lower income zip codes also had lower screening rates.
Table 2.
Associations Between Demographics and ASQ Compliance Within Each Age Group.
| 8-12 Months | 13-24 Months | 25-36 Months | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Non-compliant (N = 1702) | Compliant (N = 8728) | P-value | Non-compliant (N = 2616) | Compliant (N = 8641) | P-value | Non-compliant (N = 1922) | Compliant (N = 8207) | P-value | |
| Gender | .235 | .987 | 0.238 | ||||||
| Male | 841 (15.ix%) | 4450 (84.ane%) | 1334 (23.2%) | 4408 (76.8%) | 1006 (19.4%) | 4173 (80.six%) | |||
| Female person | 861 (xvi.8%) | 4278 (83.2%) | 1282 (23.ii%) | 4233 (76.viii%) | 916 (eighteen.5%) | 4034 (81.5%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Race/ethnicity | <.001 | <.001 | <.001 | ||||||
| White. | 962 (15.1%) | 5390 (84.9%) | 1660 (24.eight%) | 5041 (75.2%) | 1051 (16.6%) | 5268 (83.4%) | |||
| Blackness/African American | 396 (18.7%) | 1722 (81.3%) | 527 (21.4%) | 1938 (78.six%) | 528 (25.5%) | 1546 (74.5%) | |||
| Hispanic/Latino | 136 (fourteen.0%) | 836 (86.0%) | 219 (21.2%) | 815 (78.8%) | 159 (nineteen.ii%) | 669 (80.8%) | |||
| Asian | 91 (xix.nine%) | 367 (80.1%) | 86 (17.8%) | 397 (82.2%) | 85 (19.8%) | 345 (80.2%) | |||
| Other/Unknown | 117 (22.one%) | 413 (77.9%) | 124 (21.6%) | 450 (78.4%) | 99 (twenty.7%) | 379 (79.3%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Insurance | <.001 | <.001 | <.001 | ||||||
| Commercial | 1052 (15.5%) | 5726 (84.v%) | 1745 (24.5%) | 5385 (75.5%) | 1107 (xvi.3%) | 5666 (83.7%) | |||
| Medicaid HMO | 460 (xvi.9%) | 2268 (83.i%) | 640 (21.3%) | 2367 (78.vii%) | 622 (25.three%) | 1833 (74.7%) | |||
| Medicaid | 118 (27.2%) | 316 (72.8%) | 124 (20.5%) | 480 (79.v%) | 98 (twenty.3%) | 385 (79.7%) | |||
| Self-pay | 55 (15.4%) | 303 (84.six%) | 88 (22.8%) | 298 (77.2%) | 71 (23.2%) | 235 (76.8%) | |||
| Medicare/Govt | 17 (12.nine%) | 115 (87.i%) | 19 (14.vi%) | 111 (85.4%) | 24 (21.4%) | 88 (78.6%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Goose egg-code median income quartile | <.001 | <.001 | <.001 | ||||||
| Q1: Everyman | 483 (nineteen.0%) | 2062 (81.0%) | 625 (22.0%) | 2212 (78.0%) | 536 (22.viii%) | 1817 (77.ii%) | |||
| Q2: Low-Med | 371 (15.4%) | 2032 (84.6%) | 547 (21.iii%) | 2024 (78.vii%) | 461 (21.ane%) | 1725 (78.9%) | |||
| Q3: Med-High | 389 (14.seven%) | 2260 (85.3%) | 657 (23.5%) | 2141 (76.5%) | 433 (sixteen.6%) | 2178 (83.4%) | |||
| Q4: Highest | 456 (16.3%) | 2340 (83.7%) | 769 (25.6%) | 2233 (74.4%) | 482 (16.4%) | 2457 (83.6%) | |||
| Freq missing | iii | 34 | 18 | 31 | x | 30 | |||
ASQ overall screening concerns decreased from fifteen% at age 8 to 12 months to 12% at age 25 to 36 months. Monitoring and concerning results were about prevalent for gross motor (xxx%) at historic period viii to 12 months, communication (22%) at age 13 to 24 months, and fine motor (xviii%) at age 25 to 36 months (Tabular array 3).
Table 3.
Summary of ASQ-3 Screening Results past Historic period Group.
| 8-12 Months (N = 8728) | thirteen-24 Months (North = 8641) | 25-36 Months (North = 8207) | |
|---|---|---|---|
| Overall | |||
| Reassuring | 4518 (52.1%) | 5361 (62.v%) | 5483 (67.4%) |
| Monitoring | 2880 (33.2%) | 2090 (24.4%) | 1665 (20.5%) |
| Concerns | 1269 (14.vi%) | 1131 (13.two%) | 981 (12.one%) |
| Freq missing | 61 | 59 | 78 |
| Gross motor | |||
| Reassuring | 6055 (69.nine%) | 7738 (90.4%) | 7462 (92.0%) |
| Monitoring | 2029 (23.four%) | 493 (5.8%) | 376 (four.half dozen%) |
| Concerns | 576 (half dozen.7%) | 333 (three.9%) | 274 (iii.4%) |
| Freq missing | 68 | 77 | 95 |
| Fine motor | |||
| Reassuring | 7451 (86.1%) | 7555 (88.1%) | 6641 (81.ix%) |
| Monitoring | 794 (9.2%) | 619 (vii.2%) | 1140 (xiv.1%) |
| Concerns | 411 (four.seven%) | 397 (iv.6%) | 323 (iv.0%) |
| Freq missing | 72 | seventy | 103 |
| Personal social | |||
| Reassuring | 7214 (83.five%) | 7640 (89.4%) | 6968 (86.0%) |
| Monitoring | 1169 (13.5%) | 621 (7.iii%) | 777 (nine.vi%) |
| Concerns | 261 (3.0%) | 289 (3.4%) | 359 (4.4%) |
| Freq missing | 84 | 91 | 103 |
| Communication | |||
| Reassuring | 7159 (82.vii%) | 6663 (77.viii%) | 7144 (88.0%) |
| Monitoring | 1287 (14.nine%) | 1469 (17.ii%) | 487 (6.0%) |
| Concerns | 210 (2.iv%) | 430 (5.0%) | 484 (6.0%) |
| Freq missing | 72 | 79 | 92 |
| Problem solving | |||
| Reassuring | 7450 (86.2%) | 7073 (82.eight%) | 7105 (87.seven%) |
| Monitoring | 731 (8.5%) | 925 (10.8%) | 607 (7.5%) |
| Concerns | 463 (5.4%) | 541 (half-dozen.3%) | 390 (4.eight%) |
| Freq missing | 84 | 102 | 105 |
ASQ results were significantly dissimilar by gender, race/ethnicity, insurance, and median ZIP code income categories across all age groups (Tabular array 4). Boys with increasing age had a higher prevalence of overall monitoring/concerning results than girls. Most children in Medicaid FFS had monitoring/apropos results. At age 8 to 12 months, children in Medicaid health plans had the highest prevalence (64%) of reassuring results. By ages 25 to 36 months, privately insured children had the highest (71%) reassuring results. 48% of white children had reassuring overall results at age eight to 12 months and improved to 70% by age 25 to 36 months. At 25 to 36 months, nearly 40% of black/African American and Hispanic/Latino children had monitoring/concerning results. With increasing age, both white and Hispanic children had increases in reassuring results, while Black children had decreases. From viii to 12 months to 25 to 36 months, children residing in the highest income zip codes improved from 47% to 71% reassuring overall results, while children in the lowest income areas persisted at approximately sixty% reassuring results.
Table 4.
Associations Betwixt Kid Demographics and ASQ-3 Overall Reassuring Results.
| 8-12 Months | 13-24 Months | 25-36 Months | |||||||
|---|---|---|---|---|---|---|---|---|---|
| All reassuring (N = 4518) | Monitoring/concerns (N = 4149) | P-value | All reassuring (North = 5361) | Monitoring/concerns (N = 3221) | P-value | All reassuring (N = 5483) | Monitoring/concerns (North = 2646) | P-value | |
| Gender | .088 | <.001 | <.001 | ||||||
| Male | 2266 (51.ii%) | 2157 (48.8%) | 2530 (57.9%) | 1839 (42.1%) | 2521 (61.one%) | 1607 (38.9%) | |||
| Female | 2252 (53.1%) | 1992 (46.9%) | 2831 (67.2%) | 1382 (32.8%) | 2962 (74.0%) | 1039 (26.0%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Race/ethnicity | <.001 | <.001 | <.001 | ||||||
| White | 2578 (48.1%) | 2777 (51.9%) | 3328 (66.four%) | 1682 (33.six%) | 3681 (70.4%) | 1544 (29.6%) | |||
| Black/African American | 1130 (66.1%) | 580 (33.9%) | 1091 (56.9%) | 827 (43.1%) | 932 (61.i%) | 593 (38.9%) | |||
| Hispanic/Latino | 437 (52.vii%) | 393 (47.3%) | 445 (54.7%) | 369 (45.iii%) | 401 (lx.5%) | 262 (39.5%) | |||
| Asian | 175 (48.1%) | 189 (51.9%) | 233 (58.7%) | 164 (41.3%) | 215 (62.7%) | 128 (37.3%) | |||
| Other/Unknown | 198 (48.5%) | 210 (51.5%) | 264 (59.half-dozen%) | 179 (40.4%) | 254 (68.one%) | 119 (31.9%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Insurance | <.001 | <.001 | <.001 | ||||||
| Commercial | 2724 (47.nine%) | 2967 (52.1%) | 3550 (66.iii%) | 1804 (33.7%) | 3986 (70.9%) | 1635 (29.1%) | |||
| Medicaid HMO | 1435 (63.7%) | 818 (36.3%) | 1408 (59.ix%) | 943 (forty.1%) | 1140 (62.6%) | 682 (37.iv%) | |||
| Medicaid | 116 (37.five%) | 193 (62.5%) | 148 (31.3%) | 325 (68.7%) | 145 (39.five%) | 222 (60.5%) | |||
| Self-pay | 178 (59.1%) | 123 (xl.9%) | 183 (62.5%) | 110 (37.5%) | 152 (65.five%) | eighty (34.v%) | |||
| Medicare/Govt | 65 (57.five%) | 48 (42.v%) | 72 (64.nine%) | 39 (35.i%) | sixty (69.0%) | 27 (31.0%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Zip-code median income quartile | <.001 | <.001 | <.001 | ||||||
| Q1: Everyman | 1259 (61.5%) | 789 (38.5%) | 1239 (56.iv%) | 956 (43.6%) | 1095 (61.i%) | 698 (38.9%) | |||
| Q2: Low-Med | 1097 (54.iv%) | 920 (45.6%) | 1212 (sixty.4%) | 795 (39.6%) | 1136 (66.4%) | 575 (33.6%) | |||
| Q3: Med-High | 1061 (47.1%) | 1191 (52.ix%) | 1352 (63.3%) | 783 (36.vii%) | 1494 (69.2%) | 666 (30.8%) | |||
| Q4: Highest | 1079 (46.6%) | 1237 (53.4%) | 1540 (69.half dozen%) | 674 (30.4%) | 1734 (71.2%) | 701 (28.viii%) | |||
| Freq missing | 22 | 12 | eighteen | 13 | 24 | 6 | |||
At ages 8 to 12 months, children living in the lowest income areas had the highest referral charge per unit (26%) for concerns and the everyman rate (16%) for standing electric current early intervention therapy (Table 5). At ages xiii to 24 months, blackness/African American children had the highest referral charge per unit (39%) for concerns and the lowest rate (15%) for continuing electric current therapy. Regardless of age group, there were no pregnant differences in continuing electric current therapy and referral rates by child gender or health insurance (Tabular array 5).
Table 5.
Associations Between Child Demographics and Referral Rates for ASQ-iii Overall Concerning Results.
| 8-12 Months | thirteen-24 Months | 25-36 Months | |||||||
|---|---|---|---|---|---|---|---|---|---|
| No response or echo screen (Due north = 792) | Keep electric current therapy or referral (N = 477) | P-value | No response or repeat screen (N = 602) | Continue current therapy or referral (N = 529) | P-value | No response or repeat screen (Due north = 407) | Continue current therapy or referral (N = 574) | P-value | |
| Gender | .146 | .316 | .377 | ||||||
| Male | 400 (60.five%) | 261 (39.five%) | 343 (52.0%) | 317 (48.0%) | 254 (40.4%) | 374 (59.vi%) | |||
| Female person | 392 (64.five%) | 216 (35.v%) | 259 (55.0%) | 212 (45.0%) | 153 (43.3%) | 200 (56.7%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Race/Ethnicity | .059 | .044 | .279 | ||||||
| White | 470 (62.0%) | 288 (38.0%) | 272 (56.viii%) | 207 (43.2%) | 186 (38.0%) | 303 (62.0%) | |||
| Blackness/African Amer. | 154 (58.1%) | 111 (41.9%) | 180 (47.four%) | 200 (52.6%) | 128 (45.4%) | 154 (54.half-dozen%) | |||
| Hispanic/Latino | fourscore (63.0%) | 47 (37.0%) | 90 (56.two%) | 70 (43.eight%) | 51 (42.nine%) | 68 (57.1%) | |||
| Asian | 47 (75.8%) | fifteen (24.two%) | 30 (60.0%) | twenty (40.0%) | 20 (45.v%) | 24 (54.5%) | |||
| Other/Unknown | 41 (71.nine%) | 16 (28.1%) | 30 (48.4%) | 32 (51.6%) | 22 (46.eight%) | 25 (53.2%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Insurance | <.001 | <.001 | — | ||||||
| Commercial | 515 (64.7%) | 281 (35.iii%) | 300 (62.1%) | 183 (37.9%) | 209 (41.1%) | 299 (58.9%) | |||
| Medicaid HMO | 202 (67.3%) | 98 (32.7%) | 192 (53.3%) | 168 (46.vii%) | 125 (43.1%) | 165 (56.ix%) | |||
| Medicaid | 42 (36.2%) | 74 (63.viii%) | 75 (33.9%) | 146 (66.1%) | 51 (36.four%) | 89 (63.6%) | |||
| Self-pay | 27 (57.iv%) | twenty (42.six%) | 26 (48.i%) | 28 (51.ix%) | twenty (54.1%) | 17 (45.9%) | |||
| Medicare/Govt | 6 (threescore.0%) | four (40.0%) | ix (69.2%) | four (30.8%) | 2 (33.3%) | iv (66.7%) | |||
| Freq missing | 0 | 0 | 0 | 0 | 0 | 0 | |||
| Aught-code median income quartile | .643 | .050 | .065 | ||||||
| Q1: Everyman | 199 (61.half-dozen%) | 124 (38.iv%) | 198 (48.three%) | 212 (51.7%) | 153 (46.2%) | 178 (53.viii%) | |||
| Q2: Low-Med | 178 (sixty.iii%) | 117 (39.7%) | 152 (54.3%) | 128 (45.7%) | 85 (37.0%) | 145 (63.0%) | |||
| Q3: Med-High | 199 (65.2%) | 106 (34.8%) | 133 (55.two%) | 108 (44.8%) | 95 (43.4%) | 124 (56.6%) | |||
| Q4: Highest | 214 (62.6%) | 128 (37.iv%) | 117 (59.7%) | 79 (40.3%) | 73 (36.vii%) | 126 (63.three%) | |||
| Freq missing | two | ii | 2 | 2 | ane | 1 | |||
Discussion
Developmental surveillance and screening are of import activities to integrate into pediatric primary care teams. 4 During a 25-month period, more than than 30 000 various children aged eight to 36 months had at least 1 preventive visit when universal ASQ screening was promoted at a big, multi-site pediatric group. Our pediatric practice achieved excellent (>95%) developmental screening rates for children age viii to 36 months during preventive visits using demonstrated effective quality improvement processes.6-16 Specific improvements included: appointing a champion of an interprofessional projection team; training clinicians and staff about a consequent screening procedure with specific responsibilities; using the standardized ASQ-3 screening tool; employing a Model for Improvement framework; posting EHR prompts; providing financial incentives; and monitoring screening rates using command charts. Pediatric primary care practices tin can implement these practices to improve quality of care.
Fifteen professionals at 8 sites were interviewed and reported appreciation for the team-based developmental screening process and ASQ to enhance pediatric preventive intendance. Information technology has become well integrated in practice. Nationally pediatricians' reported use of a standardized developmental screening tool has tripled from 2002 to 2016, and more pediatricians are making referrals for children with concerns in developmental screening. 10 The EHR flowsheet enabled efficient and reliable documentation of screenings which could exist easily reviewed at subsequent visits. Pediatricians recommended increased electronic data entry by parents to amend efficiency.
Developmental screening rates and results varied by kid demographics. Children enrolled in Medicaid, black children, and children living in lower income zip codes had lower screening rates than privately insured and white children and those living in higher income areas. A broad array of factors within and across the health care system drive disparities in health and health intendance. These disparities are driven by social and economical inequities. 17 Overall developmental screening concerns decreased from 15% among infants to 12% for older toddlers with higher rates among boys than girls. The gender differences are consistent with other studies. 18
Our developmental screening results by kid demographics are consistent with other published findings.19,twenty In our study, the highest rates of reassuring results were infants in Medicaid health plans and older toddlers with individual insurance. Nearly xl% of older toddlers of color and those living in lowest income areas had monitoring/concerning results compared with thirty% of white children and those in college income areas. Children residing in the highest income cypher codes had a higher percent of reassuring results at 25 to 36 months of age than children in the lowest income zippo codes. This is consequent with other studies finding higher rates of developmental delay for children in poverty becoming apparent by age ii years. 20 Higher income and white children are more probable to be immunized and receive other preventive services such equally developmental screening than lower income and black children. 21 Neighborhoods matter for children's experiences, educational activity, norms, wellness, development, and outcomes. 22
Racial, indigenous, and language disparities accept been well documented in early childhood developmental and behavioral evaluations. 23 African American children growing up poor with toxic stress are at greater chance of academic, behavioral, and health problems. 24 Extensive information shows the punishing achieve of racism, especially for black boys. In our study, blackness/African American children ages 13 to 24 months had the highest referral rate (or continue electric current therapy) for concerns and the everyman rate for no response/repeat screen. Depression-income and children of color are less likely to receive early intervention services. 25 With this awareness, our pediatric group may have over-compensated past referring these patients with developmental concerns for therapy services, particularly at fundamental metropolis clinics that serve more vulnerable families. While we did not track actual referral follow-upwards in this study, we program to practise so in the future. After families are referred for Birth to 3 evaluation, therapy or community support services, interagency systems are needed to monitor and ensure children with developmental concerns tin easily access needed services. 26 Cross-sector coordination, particularly between wellness and early on childhood teaching, is essential to advancing wellness equity. Information technology will be of import to make up one's mind family-centered, culturally, and linguistically relevant tools and desired means of receiving early intervention services every bit prior studies have found black children are less probable to receive these services than children from other racial and ethnic groups. 27
Further inquiry, advocacy and continuing education will better the ability of pediatric professionals to form effective partnerships with community agencies to address the social determinants of health when caring for children who alive in poverty. 28 Principles and strategies have been identified to guide policy, practice and advocacy to help infants, toddlers and families at higher risk for poor outcomes. 29 Both high quality medical care and community policies and systems to accost social determinants of health are needed to ensure kid well-being. 30
This study has implications for further research. Nosotros did not study utilization of preventive visits by patient demographics; we intend to do then to understand access to and use of preventive services by kid demographics. This projection did not evaluate follow up practices which are usually not equally strong as screening practices. 27 We are establishing partnerships with county Birth to 3 programs and early babyhood didactics centers to strengthen collaboration including data sharing. To advance developmental screening processes, added efforts are needed to enhance referral systems, better early intervention programs, and provided improve tracking of kid outcomes. 30 Local speech, concrete, and occupational therapy services are first-class but financially constrained. Addressing social/emotional needs is more challenging in traditional early on intervention programs. We plan a pre/mail service time tendency analysis of efforts to reduce disparities in screening processes, testing, and referral rates. We besides plan to establish data sharing agreements with county Birth to Three programs for feedback almost referred patients.
Conclusion
This project resulted in an constructive and efficient procedure to improve child developmental screening that was valued past pediatric professionals. Analyses of patient demographics revealed disparities in services for the nearly vulnerable families. Children enrolled in Medicaid, black children, and children living in lower income zip codes had lower screening rates than privately insured and white children and those living in higher income areas. Nearly 40% of older toddlers of color and those living in lowest income areas had monitoring/apropos results compared with 30% of white children and those in higher income areas. Ongoing quality improvement, wellness services research, and advocacy offer hope to improve wellness equity.
Acknowledgments
We gratefully admit Seth Workentine, MD, for leading a literature review of how to amend childhood developmental screening processes.
Footnotes
Contributed past
Writer Contributions: Dr Meurer conceptualized and designed the study, performed literature reviews, drafted, reviewed, and revised the manuscript. Dr Rohloff conceptualized the process improvement, designed the study, reviewed, and critically revised the manuscript. Mr Kanter designed the data extraction and reviewed and critically revised the manuscript. Ms Kotagiri contributed to the design, conducted the interviews, summarized those findings, reviewed, and critically revised the manuscript. Ms Rein conducted the data analyses and created the tables and reviewed the manuscript. Dr Gundacker performed literature reviews, analyzed, and interpreted the data, discussed the findings, reviewed, and critically revised the manuscript. Dr Tarima designed the study, interpreted the data analyses, reviewed, and critically revised the manuscript. All authors approved the final manuscript every bit submitted and concord to be answerable for all aspects of the work.
Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this commodity.
Funding: The author(s) disclosed receipt of the following financial support for the enquiry, authorship, and/or publication of this article: Drs Meurer and Tarima, Ms Rein and Kotagiri were funded by the Research and Education Program Fund, a component of the Advancing a Healthier Wisconsin Endowment at the Medical College of Wisconsin (MCW). Dr Rohloff and Mr Kanter were supported by Children's Wisconsin. Dr Gundacker was supported by the MCW Department of Pediatrics.
ORCID iD: John Meurer 
https://orcid.org/0000-0001-7139-1289
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8743928/
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