This cross-sectional study examines the association between quality of care for Latinx youths and the ethnic concordance of the mothers and clinicians.
Key Points
Question
Is maternal-clinician ethnic concordance associated with youth receipt of family-centered care?
Findings
In this cross-sectional study including 2515 Latinx youths, maternal-clinician Latinx ethnic concordance was found to have had a positive average association with maternal reports of their youths receiving family-centered care, after adjusting for maternal characteristics.
Meaning
The findings of this study suggest that clinicians from underrepresented minority backgrounds contribute to the attenuation of medical home provision disparities among Latinx youths in the US.
Abstract
Importance
Disparities in medical home provisions, including receipt of family-centered care (FCC), have persisted for Latinx youths in the US.
Objective
To examine the association between maternal-clinician ethnic concordance and receipt of FCC among US-born Latinx youths.
Design, Setting, and Participants
A cross-sectional secondary analysis of data from the Medical Expenditure Panel Survey from January 1, 2010, to December 31, 2017, was conducted. Data analysis was performed from January 6 to February 3, 2020. Latinx youths (age, ≤17 years) born in the US who had a usual source of care and used care in the past year, their Latina mothers (age, 18-64 years), and youths’ health care clinician characteristics (eg, race, ethnicity, and sex) were evaluated using χ2 tests and propensity-score matching methods.
Main Outcomes and Measures
Maternal reports on whether their youths’ clinician listened carefully to the parent, explained things in a way the parent could understand, showed respect, and spent enough time with the patient.
Results
There were 2515 US-born Latinx youths with linked maternal characteristics during the study period; 51.67% (95% CI, 48.87%-54.45%) of the youths were male, mean (SD) age was 8.48 (0.17) years (30.86% [95% CI, 28.39%-33.44%] were between ages 5 and 9 years), 61.53% (95% CI, 57.15%-65.74%) had public insurance coverage, and 39.89% (95% CI, 32.33%-47.89%) had mothers who were ethnically concordant with the youths’ medical care clinician. We found that for youths with maternal-clinician ethnic concordance, the probabilities of reporting FCC were significantly higher than they would have been in the absence of concordance: that the medical care clinician listened carefully to the parent (average treatment effect on the treated [ATET], 5.44%; 95% CI, 2.14%-8.74%), explained things in a way the parent could understand (ATET, 4.82%; 95% CI, 1.60%-8.03%), showed respect for what the parent had to say (ATET, 5.51%; 95% CI, 2.58%-8.45%), and spent enough time with the patient (ATET, 5.28%; 95% CI, 1.68%-8.88%).
Conclusions and Relevance
Given the increase of Latinx populations and the simultaneous shortage of underrepresented minority health care clinicians, the findings of this study suggest that increasing the number of clinicians from underrepresented minority backgrounds and ethnic-concordant parental-clinician relationships may help reduce disparities in receipt of medical home provision among US-born Latinx youths.
Introduction
In recent decades, there has been an influx of immigrants, asylum seekers, and refugees from Latin America to the US.1 Latinx individuals make up the largest ethnic group of youths in the US, and it is projected that by 2050 almost 30% of all youths in the US will be Latinx.2 Nearly all (94%) of young Latinx individuals are US born.3 At least half of all US-born Latinx youths have at least 1 immigrant parent,4 which is associated with social disadvantages compared with US-born Latinx youths with US-born parents, such as a greater likelihood of being uninsured and lacking a usual source of care (eg, health care professional or site).5,6
Medical homes are a model of primary care that is patient centered, is comprehensive, recognizes the family as a constant in a child's life, and emphasizes the partnership between health care professionals and families to improve health outcomes.7 In addition to access-to-care disparities, US-born Latinx youths face disparities in accessing a medical home. For instance, a study using national data found that only a quarter of Latinx children had a medical home compared with two-thirds of non-Latinx White children.8 A family’s experience during a health care visit contributes to and is an important aspect of health care quality and health outcomes.9 Third-generation (ie, US-born youths with US-born parents) Latinx youths have yet to reach parity with non-Latinx White youths in reports of the amount of time medical clinicians devote to their care and on clinicians’ sensitivities to their families’ values and customs.10 Latinx families headed by immigrant parents also reported receiving less-specific health-related information than families of non-Latinx White youths.10 Considering the demographic shifts discussed above, more attention is needed to explore health care experience disparities among US-born Latinx youths based on maternal and clinician characteristics.
Much of the parent-clinician concordance literature among Latinx youths has focused on language.11,12,13 Parent-clinician language concordance may be beneficial in communication, but language concordance alone may not equate to reports of higher-quality well-child care, whereas cultural competency has been associated with family-centered care (FCC).11 For instance, a clinician speaking Spanish alone may not be associated with the clinician spending more time with the patient, listening more carefully, or respecting familial values and customs—all constructs that are of importance for families. Two cross-sectional studies using regional data have examined the association between racial and ethnic parent-clinician concordance and FCC receipt and did not find that ethnic concordance contributed to ethnic differences in parent reports of youths receiving FCC.12,13 To our knowledge, maternal-clinician ethnic concordance among Latinx youths has not been examined since then nor has it been examined using a nationally representative database.
There have been considerable publications on the association of patient-physician racial and ethnic concordance with satisfaction among adults with mixed findings.14,15,16 Most recently, a randomized clinical trial observed that clinicians who were Black improved preventive health behaviors among Black patients and reduced the Black-White gap in cardiovascular mortality by 19%.17 As the US becomes more racially and ethnically diverse, it faces a shortage of clinicians who identify as racial or ethnic minorities.18,19 For instance, among active physicians in the US, only 5% are Black and 5.8% are Hispanic, which does not reflect the proportion of Black (13.4%) and Hispanic (18.5%) individuals in the US population.20,21 This shortage has broader implications for the practice of medicine because clinicians from racial and ethnic minority groups are more likely to provide culturally competent care; provide care for underserved, underrepresented minority communities; and work in primary care settings.21,22,23,24,25
To our knowledge, the association between maternal-clinician ethnic concordance and youth receipt of FCC among Latinx youths has not been recently examined. We examined the association between maternal-clinician ethnic concordance and receipt of FCC owing to the current clinician shortage in the US and because mothers are more likely than fathers to attend office visits26 and respond to surveys on youth health care services.27 We used the Andersen Model of Health Services28 as a framework and hypothesized that maternal-clinician ethnic concordance would be associated with youth receipt of FCC, which has the potential to reduce disparities in the provision of medical homes for US-born Latinx youths.
Methods
Data Source
We obtained data on US-born Latinx youths, their mothers, and the youths’ medical care clinicians from January 1, 2010, to December 31, 2017, from the Medical Expenditure Panel Survey (MEPS) household data. The health insurance eligibility unit identifier in MEPS was used to link maternal characteristics to youth observations, resulting in a sample of 2515 US-born Latinx youths younger than 18 years whose mothers identified as Latina. The Agency for Healthcare Research and Quality administers MEPS, which is drawn from a nationally representative subsample of households that participated in the prior year’s National Health Interview Survey. MEPS is a panel survey that allows for the understanding of how changes in a respondent’s health status, income, and use of services are related and information on a person’s demographic characteristics, access to care, satisfaction with care, and insurance coverage are collected.29 The data for this study are publicly available and this study did not involve human participation; thus, the Dornsife School of Public Health, Drexel University, indicated that the investigation did not require institutional review board approval and was exempt from the need for informed consent. This cross-sectional study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.30
Measures
Our outcome measure of interest was FCC defined by 4 factors: (1) how often a clinician listened carefully to the parent, (2) how often a clinician explained things in a way the parent could understand, (3) how often a clinician showed respect for what the parent had to say, and (4) how often a clinician spent enough time with a patient. Responses for each of the variables were categorized on an ordinal scale of never, sometimes, usually, or always. Consistent with previous studies,31,32 we dichotomized always responses to always, and never, sometimes, and usually responses to not always. These variables were derived from the Consumer Assessment of Healthcare Providers and System survey instrument in MEPS, which is designed to measure the quality of care from a consumer’s perspective.29 Parents of youths who have a usual source of care and have received care within the past 12 months are asked to complete the Consumer Assessment of Healthcare Providers and System survey.
The main independent variable was maternal-clinician ethnic concordance, which was defined by the youth’s medical care clinician characteristic variables, such as race and ethnicity, reported by their mothers. Mothers self-reported their own racial and ethnic identity and that of their youths’ clinicians in MEPS.29 We considered mothers to be ethnically concordant with the youth’s clinicians if the clinician’s ethnicity was classified as Hispanic in MEPS (ie, concordant). Conversely, we classified youth observations with reports of non-Hispanic clinicians as not ethnically concordant with Latina mothers (ie, nonconcordant).
Predisposing variables we adjusted for included a dichotomized maternal language (English/English, Spanish, or Spanish/other), youth’s clinician’s sex (male or female), maternal marital status (married, divorced/separated, or never married), youth age (0-4, 5-9, 10-13, or 14-17 years), youth sex (male or female), maternal age (18-29, 30-39, 40-49, or 50-64 years), maternal non–US-born status (born in the US: yes or no), and US Census residential region (Northeast, North Central/Midwest, South, or West). Enabling variables included youth insurance coverage status (private, public, or uninsured), maternal insurance coverage status (private, public, or uninsured), and income as a percentage of the federal poverty level (≥400%, 300%-399%, 200%-299%, 100%-199%, or ≤99%). We also included survey year to model time-fixed effects.
Statistical Analysis
We used Stata, version 15 (StataCorp LLC), for all analyses.33 Data analysis was performed from January 6 to February 3, 2020. Descriptive statistics were analyzed using χ2 tests to assess differences in predisposing and enabling characteristics by maternal-clinician ethnic concordance. We assessed differences in FCC outcomes by concordance through χ2 tests and multivariable logistic regression. Some covariates may be associated with the likelihood of mothers being in concordant relationships; thus, we implemented propensity-score matching in attempts to minimize selection bias into the treatment category (eg, concordance)—a widely used method to estimate treatment outcomes when randomization is not feasible.34,35 We used a series of propensity-score matching models to match youths based on the probability of concordance. Specifically, we examined the average treatment effect on the treated (ATET) to estimate the potential association of concordance in youths with mothers who were ethnically concordant with clinicians. The ATET estimates differences in reports of FCC among youths with maternal-clinician concordance and what reports may have been had they not had concordant clinicians. Covariates that were balanced to estimate propensity scores were maternal language (English/English and Spanish or Spanish/other), maternal non–US-born status (born in the US: yes or no), youth insurance coverage status (private, public, or uninsured), and maternal insurance coverage status (private, public, or uninsured). We used 1:1 nearest-neighbor matching, and comparisons, associations, and average effects were considered significant at a 2-sided P < .05. All estimates were weighted to reflect the noninstitutionalized population of US-born Latinx youths and adjust for complex survey design.
Results
In our study sample of US-born Latinx youths, 48.33% (95% CI, 45.55%-51.13%) of the children were girls, 51.67% (95% CI, 48.87%-54.45%) were boys, and the mean (SD) age was 8.48 (0.17) years; 39.86% (95% CI, 32.33%-47.89%) of the sample was ethnically concordant between mothers’ and youths’ medical care clinicians, and 85.91% (95% CI, 79.75%-90.42%) of mothers who were ethnically concordant with their youth’s clinicians spoke Spanish or another language, compared with 69.16% (95% CI, 62.73%-74.92%) of mothers who were not ethnically concordant with their youth’s clinicians (Table 1). Slightly more than a third (33.63%; 95% CI, 28.53%-39.15%) of youths with concordant mothers had a female clinician compared with 45.61% (95% CI, 40.80%-50.49%) of youths with nonconcordant mothers. Most concordant mothers were not born in the US (73.60%; 95% CI, 64.10%-81.31%). Concordant mothers were more likely to report youth uninsurance (3.85%; 95% CI, 2.38%-6.18%) and public insurance coverage (70.94%; 95% CI, 64.64%-76.53%) compared with only 25.21% (95% CI, 19.91%-31.37%) reporting youths having private insurance coverage; noncorcordant mothers were more likely to report private insurance coverage (42.21%; 95% CI, 36.37%-48.27%) and were less likely to report public coverage (55.30%; 95% CI, 49.30%-61.15%) and uninsurance (2.5%; 95% CI, 1.61%-3.84%) for their youths compared with concordant mothers.
Table 1. Sample Characteristicsa.
| Characteristic | Participants, weighted column % (95% CI) | P valueb | ||
|---|---|---|---|---|
| Total | Maternal-clinician ethnic concordance | |||
| Not concordant | Concordant | |||
| No. | 2515 | 1512 | 1003 | NA |
| Weighted % | 100 | 60.14 (52.11-67.67) | 39.86 (32.33-47.89) | NA |
| Predisposing factors | ||||
| Maternal language | ||||
| English/English and Spanish | 24.16 (20.06-28.80) | 30.84 (25.08-37.27) | 14.09 (9.58-20.25) | <.001 |
| Spanish/other | 75.84 (71.20-79.94) | 69.16 (62.73-74.92) | 85.91 (79.75-90.42) | |
| Clinician sex | ||||
| Female | 40.82 (37.13-44.62) | 45.61 (40.80-50.49) | 33.63 (28.53-39.15) | .002 |
| Male | 59.18 (55.38-62.87) | 54.39 (49.51-59.20) | 66.37 (60.85-71.47) | |
| Maternal marital status | ||||
| Married | 65.33 (60.69-69.69) | 66.38 (60.90-71.45) | 63.77 (56.40-70.54) | .16 |
| Divorced/separated | 13.33 (11.19-15.81) | 11.08 (8.49-14.35) | 16.67 (13.17-20.89) | |
| Never married | 21.34 (17.59-25.64) | 22.54 (18.29-27.45) | 19.56 (13.67-27.18) | |
| Youth age, y | ||||
| 0-4 | 26.54 (23.59-29.71) | 27.59 (23.56-32.01) | 24.96 (21.66-28.57) | .12 |
| 5-9 | 30.86 (28.39-33.44) | 28.47 (25.41-31.76) | 34.46 (31.13-37.95) | |
| 10-13 | 22.48 (20.43-24.67) | 23.50 (20.71-26.54) | 20.94 (18.27-23.87) | |
| 14-17 | 20.12 (17.81-22.65) | 20.44 (17.18-24.13) | 19.65 (16.69-22.99) | |
| Youth sex | ||||
| Female | 48.33 (45.55-51.13) | 48.86 (44.89-52.85) | 47.53 (42.08-53.05) | .73 |
| Male | 51.67 (48.87-54.45) | 51.14 (47.15-55.11) | 52.47 (46.95-57.95) | |
| Maternal age, y | ||||
| 18-29 | 16.90 (14.56-19.53) | 17.78 (14.48-21.64) | 15.57 (12.21-19.64) | .25 |
| 30-39 | 48.67 (44.54-52.82) | 49.17 (44.02-54.35) | 47.92 (41.76-54.15) | |
| 40-49 | 30.30 (26.70-34.16) | 28.20 (23.66-33.22) | 33.47 (27.90-39.56) | |
| 50-64 | 4.13 (3.00-5.65) | 4.85 (3.15-7.38) | 3.04 (2.10-4.37) | |
| Mother not born in the US | 63.90 (59.39-68.18) | 57.48 (51.79-62.97) | 73.60 (64.10-81.31) | .006 |
| US Census region | ||||
| Northeast | 13.12 (9.62-17.64) | 15.27 (11.06-20.71) | 9.87 (5.83-16.22) | <.001 |
| North Central/Midwest | 9.00 (5.94-13.40) | 10.27 (6.15-16.65) | 7.08 (3.91-12.48) | |
| South | 43.71 (34.60-53.26) | 32.74 (26.96-39.10) | 60.27 (45.82-73.12) | |
| West | 34.17 (27.04-42.10) | 41.72 (34.44-49.38) | 22.79 (14.96-33.12) | |
| Enabling factors | ||||
| Youth insurance coverage | ||||
| Private | 35.43 (31.05-40.07) | 42.21 (36.37-48.27) | 25.21 (19.91-31.37) | <.001 |
| Public | 61.53 (57.15-65.74) | 55.30 (49.30-61.15) | 70.94 (64.64-76.53) | |
| Uninsured | 3.04 (2.15-4.27) | 2.50 (1.61-3.84) | 3.85 (2.38-6.18) | |
| Maternal insurance coverage | ||||
| Private | 43.91 (39.39-48.54) | 49.21 (43.05-55.40) | 35.91 (30.75-41.43) | .009 |
| Public | 24.45 (20.62-28.72) | 22.96 (18.10-28.68) | 26.68 (20.60-33.80) | |
| Uninsured | 31.64 (26.30-37.51) | 27.82 (22.83-33.44) | 37.41 (30.60-44.74) | |
| Income (% FPL) | ||||
| ≥400% | 9.77 (7.18-13.17) | 11.28 (7.65-16.33) | 7.50 (4.49-12.29) | .02 |
| 300%-399% | 7.76 (5.70-10.49) | 9.44 (6.50-13.51) | 5.22 (2.65-10.03) | |
| 200%-299% | 14.09 (11.44-17.25) | 15.96 (12.24-20.55) | 11.28 (8.02-15.64) | |
| 100%-199% | 25.51 (22.09-29.27) | 26.65 (22.62-31.10) | 23.80 (18.42-30.17) | |
| ≤99% | 42.86 (38.59-47.24) | 36.67 (32.02-41.59) | 52.19 (46.60-57.73) | |
| Contextual characteristic | ||||
| Survey year | ||||
| 2010-2012 | 28.97 (25.63-32.56) | 28.57 (24.34-33.21) | 29.58 (24.93-34.70) | .94 |
| 2013-2015 | 45.12 (41.47-49.13) | 45.60 (40.34-50.95) | 44.41 (39.41-49.52) | |
| 2016-2017 | 25.91 (22.32-29.85) | 25.83 (20.86-31.52) | 26.01 (22.06-30.41) | |
Abbreviations: FPL, federal poverty level; NA, not applicable.
Source: Agency for Healthcare Research and Quality.29
Determined using χ2 test.
There were no statistically significant differences in the bivariate association between FCC outcomes and maternal-clinician ethnic concordance (Table 2). Of nonconcordant mothers, 16.20% (95% CI, 12.75%-20.36%) reported that their youth’s clinician did not listen carefully to the parent and 18.32% (95% CI, 16.01%-20.74%) reported that their youth’s clinician did not explain things in a way the parent could understand. In addition, 23.41% (95% CI, 21.04%-25.12%) of nonconcordant vs 20.37% (95% CI, 15.43%-22.97%) of concordant mothers reported that their youth’s clinician did not show respect for what the parent had to say, and 17.81% (95% CI, 15.36%-20.01%) of nonconcordant mothers reported that the clinician did not spend enough time with them vs 14.63% (95% CI, 11.04%-17.11%) who were concordant.
Table 2. Descriptive Statistics of Family-Centered Carea.
| Characteristic | Participants, weighted column % (95% CI) | P valueb | ||
|---|---|---|---|---|
| Total | Maternal-clinician ethnic concordance | |||
| Not concordant | Concordant | |||
| No. | 2515 | 1512 | 1003 | |
| Weighted % | 100 | 60.14 (52.11-67.67) | 39.86 (32.33-47.89) | |
| Family-centered care | ||||
| Clinician listened carefully to parent | ||||
| No | 14.36 (11.51-17.78) | 16.20 (12.75-20.36) | 11.60 (8.00-16.51) | .06 |
| Yes | 85.64 (82.22-88.49) | 83.80 (79.64-87.25) | 88.40 (83.49-92.00) | |
| Clinician explained things in a way the parent could understand | ||||
| No | 17.08 (14.89-20.28) | 18.32 (16.01-20.74) | 16.83 (13.44-18.04) | .07 |
| Yes | 82.92 (79.34-85.00) | 81.68 (76.54-89.77) | 83.17 (78.54-86.69) | |
| Clinician showed respect for what the parent had to say | ||||
| No | 22.29 (19.84-25.36) | 23.41 (21.04-25.12) | 20.37 (15.43-22.97) | .06 |
| Yes | 77.71 (73.42-80.06) | 76.59 (74.05-81.62) | 79.63 (76.88-82.69) | |
| Clinician spent enough time with a person | ||||
| No | 15.92 (13.44-17.09) | 17.81 (15.36-20.01) | 14.63 (11.04-17.11) | .07 |
| Yes | 84.08 (81.74-86.93) | 82.19 (79.71-85.04) | 85.37 (76.68-88.16) | |
Source: Agency for Healthcare Research and Quality.29
Determined using χ2 test.
Maternal-clinician ethnic concordance was associated with all 4 FCC outcomes among US-born Latinx youths in multivariable models (Table 3). Concordance vs nonconcordance was associated with higher odds of reporting that the clinician listened carefully to the parent (adjusted odds ratio [aOR], 1.71; 95% CI, 1.08-2.69), explained things in a way the parent could understand (aOR, 1.75; 95% CI, 1.07-2.10), showed respect for what the parent had to say (aOR, 1.98; 95% CI, 1.20-2.56), and spent enough time with the patient (aOR, 1.45; 95% CI, 1.12-1.88).
Table 3. Logistic Regression Estimation of Maternal-Clinician Ethnic Concordance on Family-Centered Care Componentsa.
| Characteristic | Family-centered care, aOR (95% CI) | |||
|---|---|---|---|---|
| Clinician listened carefully to parent | Clinician explained things in a way the parent could understand | Clinician showed respect for what the parent had to say | Clinician spent enough time with patient | |
| Predisposing factors | ||||
| Maternal-clinician ethnic concordance | ||||
| No | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Yes | 1.71 (1.08-2.69)b | 1.75 (1.07-2.10)b | 1.98 (1.20-2.56)b | 1.45 (1.12-1.88)b |
| Maternal language | ||||
| English/English and Spanish | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Spanish/other | 0.83 (0.45-1.52) | 0.80 (0.47-1.36) | 0.66 (0.40-1.09) | 0.73 (0.43-1.25) |
| Clinician sex | ||||
| Male | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Female | 1.36 (0.98-2.09) | 1.21 (0.88-1.65) | 1.19 (0.86-1.66) | 1.07 (0.83-1.39) |
| Maternal marital status | ||||
| Married | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Divorced/separated | 1.07 (0.60-1.89) | 1.00 (0.67-1.50) | 0.73 (0.46-1.17) | 1.02 (0.72-1.45) |
| Never married | 1.31 (0.82-2.09) | 1.16 (0.80-1.68) | 1.03 (0.71-1.47) | 1.12 (0.80-1.56) |
| Youth age, y | ||||
| 0-4 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| 5-9 | 0.93 (0.59-1.47) | 0.84 (0.64-1.10) | 0.95 (0.66-1.38) | 0.90 (0.67-1.20) |
| 10-13 | 0.96 (0.61-1.54) | 0.88 (0.64-1.22) | 0.98 (0.64-1.49) | 0.86 (0.62-1.80) |
| 14-17 | 0.74 (0.42-1.29) | 0.61 (0.40-0.91)c | 0.76 (0.47-1.23) | 0.85 (0.57-1.25) |
| Youth sex | ||||
| Male | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Female | 1.09 (0.76-1.57) | 1.19 (0.92-1.52) | 1.19 (0.88-1.62) | 0.97 (0.75-1.26) |
| Maternal age, y | ||||
| 18-29 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| 30-39 | 1.65 (0.98-2.77) | 1.28 (0.84-1.94) | 1.11 (0.76-1.62) | 1.45 (0.99-2.12) |
| 40-49 | 1.12 (0.64-1.95) | 1.28 (0.80-2.03) | 1.02 (0.65-1.59) | 1.38 (0.91-2.10) |
| 50-64 | 2.62 (0.79-4.98) | 1.83 (0.76-4.46) | 1.71 (0.70-4.22) | 1.97 (1.02-3.80)c |
| Mother born in the US | ||||
| Yes | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| No | 1.56 (0.92-2.65) | 1.27 (0.85-1.91) | 1.44 (0.94-2.20) | 1.04 (0.71-1.52) |
| US Census region | ||||
| Northeast | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| North Central/Midwest | 3.14 (1.36-6.27)b | 2.87 (1.56-5.30)d | 1.91 (1.04-3.51)c | 1.78 (1.06-3.03)c |
| South | 2.36 (1.08-4.15)c | 2.11 (1.24-3.60)b | 1.66 (0.93-2.99) | 1.69 (1.09-2.62)c |
| West | 1.55 (0.78-3.10) | 1.59 (0.99-2.54) | 1.30 (0.79-2.14) | 1.20 (0.83-1.75) |
| Enabling factors | ||||
| Youth insurance coverage | ||||
| Private | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Public | 0.54 (0.30-0.97)c | 0.64 (0.42-0.96)c | 0.66 (0.39-1.11) | 0.90 (0.59-1.40) |
| Uninsured | 0.50 (0.18-1.38) | 1.03 (0.48-2.20) | 0.67 (0.27-1.65) | 0.68 (0.33-1.41) |
| Maternal insurance coverage | ||||
| Private | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Public | 1.57 (0.74-3.33) | 1.27 (0.72-2.22) | 1.19 (0.68-2.10) | 1.55 (0.97-2.48) |
| Uninsured | 1.05 (0.55-2.01) | 0.95 (0.61-1.49) | 1.08 (0.63-1.83) | 1.02 (0.67-1.55) |
| Income (% FPL) | ||||
| ≥400% | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| 300%-399% | 1.33 (0.26-3.72) | 1.25 (0.42-3.73) | 0.95 (0.26-3.48) | 1.51 (0.59-3.85) |
| 200%-299% | 0.83 (0.27-2.51) | 0.97 (0.43-2.18) | 0.83 (0.27-2.49) | 0.84 (0.40-1.76) |
| 100%-199% | 0.73 (0.25-2.51) | 1.08 (0.48-2.43) | 0.79 (0.28-2.22) | 0.65 (0.31-1.34) |
| ≤99% | 0.48 (0.17-1.41) | 0.89 (0.40-1.97) | 0.60 (0.21-1.73) | 0.48 (0.22-1.03) |
| Contextual characteristic | ||||
| Year | ||||
| 2010-2012 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| 2013-2015 | 2.77 (1.70-4.50)d | 2.32 (0.99-3.12) | 1.76 (1.05-2.97)c | 1.47 (0.88-2.47) |
| 2016-2017 | 1.71 (0.98-2.98) | 1.89 (0.89-2.65) | 1.30 (0.68-2.48) | 1.52 (0.84-2.79) |
Abbreviation: aOR, adjusted odds ratio.
Source: Agency for Healthcare Research and Quality.29
P < .01.
P < .05.
P < .001.
A box plot of propensity scores before and after matching showed a raw or initial propensity toward nonconcordance with balance after matching (Figure). The ATET findings are presented in Table 4. Propensity score–matching models showed findings similar to multivariable logistic regression results. We found that reports of youth receipt of FCC were higher in concordant vs nonconcordant settings: clinician listened carefully to the parent (ATET, 5.44%; 95% CI, 2.14%-8.74%), explained things in a way the parent could understand (ATET, 4.82%; 95% CI, 1.60%-8.03%), showed respect for what the parent had to say (ATET, 5.51%; 95% CI, 2.58%-8.45%), and spent enough time with the patient (ATET, 5.28%; 95% CI, 1.68%-8.88%).
Figure. Propensity Scores Toward Concordance vs Nonconcordance.
Propensity score balance box plot before and after matching. Whiskers represent the 5th and 95th percentiles, boxes represent the 25th and 75th percentiles, dots represent outliers, and horizontal lines inside the boxes are medians.
Table 4. ATET of Ethnic Concordance With Maternal-Clinician Ethnic Concordant Relationshipsa.
| Outcome | ATET, % (95% CI) | P value |
|---|---|---|
| Clinician listened carefully to parent | 5.44 (2.14-8.74) | .001 |
| Clinician explained things in a way the parent could understand | 4.82 (1.60-8.03) | .003 |
| Clinician showed respect for what the parent had to say | 5.51 (2.58-8.45) | <.001 |
| Clinician spent enough time with a person | 5.28 (1.68-8.88) | .004 |
Abbreviation: ATET, average treatment effect on the treated.
Source: Agency for Healthcare Research and Quality.29
Discussion
To our knowledge, the association between maternal-clinician ethnic concordance and youth receipt of FCC among US-born Latinx youths has not been examined in nearly 2 decades.12,13 We observed an association between maternal-clinician ethnic concordance and reports of youths receiving FCC. To protect against selection bias in concordance, we used propensity score–matching methods. After adjusting for maternal characteristics, we noted that concordance was associated with reports that the youth’s clinician listened carefully to the parent, explained things in a way the parent could understand, showed respect for what the parent had to say, and spent enough time with a patient. Previous research noted that receipt of FCC was associated with health care clinicians eliciting important youth health and developmental information.36 For instance, parents of Latinx youths who received FCC had almost twice the odds of clinician elicitation of developmental concerns, which is vital for child development and long-term health outcomes.
Although language use and levels of acculturation among Latinx populations may be important predisposing and enabling factors to examine in health services research, there is a gap in understanding about clinician characteristics in the provision of health care services. Previous literature rooted in frameworks that suggested individuals from minoritized groups have poorer health, health care access, and health services quality because they are not acculturated enough or are limited in their English language abilities contributes to larger population health disparities37 and may be perceived to indirectly blame the patient. Furthermore, patient-clinician racial concordance can reduce disparities in infant mortality among Black newborns38 by focusing efforts on changing health care system business models among the most marginalized populations,39 including Latinx youths. In addition, clinical practices can implement appointment-booking practices to ensure patient-clinician concordance based on race and ethnicity, language, or sex.
Although bivariate analyses of FCC by concordance may appear insignificant, FCC improvements are still meaningful within a population health context. For instance, between 2010 and 2017, the average youth Latinx population in the US was 17 851 82240; thus, the change from 1.8% to 5.5% equates to 321 333 to 981 850 US-born Latinx youths receiving FCC. Moreover, the aORs showed an association between concordance and receipt of FCC, and these study findings have health policy and pediatric practice implications. Because Latinx individuals make up the largest racial and ethnic group among all youths in the US,2 our study supports the need for more clinicians from underrepresented minority groups. The most recent report from the Association of American Medical Colleges indicates that, among active physicians, only 5.0% are Black and 5.8% are Hispanic.41 These proportions of Black and Hispanic clinicians are less than half of the proportion of Black (13.4%) and less than one-third of the proportion of Hispanic (18.5%) individuals in the US population.20 Although there was a slight increase in the number of physicians from underrepresented minority groups between 2012 and 2017,42 efforts focused on improving these changes have failed to achieve equity in representation. To substantially increase the proportion of clinicians from underrepresented minority backgrounds, we argue that systemic racism must be confronted because it contributes to racial and ethnic educational attainment disparities (eg, equitable public-school funding to achieve high-quality kindergarten through high school education that enables students of racial and ethnic minority groups to competitively apply to health-related higher education and training). It would also be beneficial to increase the 1997 Medicare funding cap for residency training in US medical schools,43 reduce university and medical school tuition for underrepresented minorities from lower socioeconomic backgrounds, eliminate the requirement of standardized testing (eg, Medical College Admission Test), and increase antiracist cultural competency training for health care clinicians during medical school and continuing medical education. In addition, federal payers (eg, Medicare, Medicaid/Children's Health Insurance Program) can require clinicians to provide improved care (eg, FCC) for racial and ethnic minority populations.44
Limitations and Strengths
Our study has limitations. First, clinician race and ethnicity were reported by the mothers, which is not optimal and can contribute to information bias, misidentification, and recall bias. For instance, a maternal respondent may have reported their youth’s clinician as being non-Hispanic White, but the clinician may identify themselves as Hispanic. To date, certain sections in MEPS have undergone validity and reliability assessments but have not included the clinician’s race and ethnicity measure,45,46,47,48 and work is warranted in assessing this measure further with other data (eg, claims data that include clinician-reported race and ethnicity). Second, we do not have state identifiers and cannot control for state fixed effects that could explain whether there were any improvements in receipt of FCC for youths based on policy changes (eg, Medicaid expansion and increased insurance coverage for youths). Third, although we observed significant findings in maternal-clinician concordance, this study may be limited in power to determine differences in FCC component outcomes by clinician sex, which could also be associated with youth receipt of FCC.49,50 Fourth, there may be measurement error in the FCC variables given that mothers may not remember the experience they had when their youths saw a clinician; thus, there may be an underestimation in the number of youths receiving FCC.
Although there are several limitations, the study also has strengths. First, to our knowledge, this was the first study in almost 20 years examining the association of maternal-clinician ethnic concordance and youth receipt of FCC. Second, the bivariate comparisons of FCC by concordance may appear insignificant; however, we believe that although small, these improvements are meaningful within a population health context.
Conclusions
This cross-sectional study noted an association between maternal-clinician ethnic concordance and receipt of FCC among US-born Latinx youths with Latina mothers. Clinicians from underrepresented racial and ethnic minority backgrounds may contribute to the attenuation of medical home provision disparities among youths in this population of the US. The number of Latinx and Black clinicians has not been commensurate with the population growth of Latinx youths in the US. Increased effort to recruit, train, and hire clinicians from underrepresented minority populations would be beneficial.
References
- 1.Cohn DP, Passel JS, Gonzalez-Barrera A. Rise in US immigrants from El Salvador, Guatemala and Honduras outpaces growth from elsewhere. Pew Research Center. December 7, 2017. Accessed February 6, 2020. https://www.pewhispanic.org/2017/12/07/rise-in-u-s-immigrants-from-el-salvador-guatemala-and-honduras-outpaces-growth-from-elsewhere/
- 2.Vespa J, Armstrong DM, Medina L. Demographic turning points for the United States: population projections for 2020 to 2060. February 2020. Accessed October 6, 2021. https://www.census.gov/library/publications/2020/demo/p25-1144.html
- 3.Patten E. The nation’s Latino population is defined by its youth. PEW Research Center. April 20, 2016. Accessed February 7, 2020. https://www.pewresearch.org/hispanic/2016/04/20/the-nations-latino-population-is-defined-by-its-youth/
- 4.Clarke W, Turner K, Guzman L. One quarter of Hispanic children in the United States have an unauthorized immigrant parent. October 2017. Accessed October 5, 2021. https://www.hispanicresearchcenter.org/wp-content/uploads/2019/08/Hispanic-Center-Undocumented-Brief-FINAL-V21.pdf
- 5.DeCamp LR, Bundy DG. Generational status, health insurance, and public benefit participation among low-income Latino children. Matern Child Health J. 2012;16(3):735-743. doi: 10.1007/s10995-011-0779-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Weathers AC, Novak SP, Sastry N, Norton EC. Parental nativity is an important factor associated with where children usually go for health care. Matern Child Health J. 2008;12(4):499-508. doi: 10.1007/s10995-007-0278-0 [DOI] [PubMed] [Google Scholar]
- 7.American Academy of Pediatrics . What is medical home? National Center for Medical Home Implementation. May 2020. Accessed October 20, 2019. https://medicalhomeinfo.aap.org/overview/Pages/Whatisthemedicalhome.aspx
- 8.DeCamp LR, Choi H, Davis MM. Medical home disparities for Latino children by parental language of interview. J Health Care Poor Underserved. 2011;22(4):1151-1166. doi: 10.1353/hpu.2011.0113 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Committee on Hospital Care. American Academy of Pediatrics . Family-centered care and the pediatrician’s role. Pediatrics. 2003;112(3, pt 1):691-697. doi: 10.1542/peds.112.3.691 [DOI] [PubMed] [Google Scholar]
- 10.Calvo R, Hawkins SS. Disparities in quality of healthcare of children from immigrant families in the US. Matern Child Health J. 2015;19(10):2223-2232. doi: 10.1007/s10995-015-1740-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Arauz Boudreau AD, Fluet CF, Reuland CP, Delahaye J, Perrin JM, Kuhlthau K. Associations of providers’ language and cultural skills with Latino parents’ perceptions of well-child care. Acad Pediatr. 2010;10(3):172-178. doi: 10.1016/j.acap.2010.01.002 [DOI] [PubMed] [Google Scholar]
- 12.Stevens GD, Mistry R, Zuckerman B, Halfon N. The parent-provider relationship: does race/ethnicity concordance or discordance influence parent reports of the receipt of high quality basic pediatric preventive services? J Urban Health. 2005;82(4):560-574. doi: 10.1093/jurban/jti125 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Stevens GD, Shi L, Cooper LA. Patient-provider racial and ethnic concordance and parent reports of the primary care experiences of children. Ann Fam Med. 2003;1(2):105-112. doi: 10.1370/afm.27 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Blanchard J, Nayar S, Lurie N. Patient-provider and patient-staff racial concordance and perceptions of mistreatment in the health care setting. J Gen Intern Med. 2007;22(8):1184-1189. doi: 10.1007/s11606-007-0210-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Phillips KL, Chiriboga DA, Jang Y. Patients’ perceptions of the interpersonal sensitivity of their healthcare providers: the potential role of patient-provider racial/ethnic concordance. Patient. 2012;5(3):175-183. doi: 10.1007/BF03262490 [DOI] [PubMed] [Google Scholar]
- 16.Street RL Jr, O’Malley KJ, Cooper LA, Haidet P. Understanding concordance in patient-physician relationships: personal and ethnic dimensions of shared identity. Ann Fam Med. 2008;6(3):198-205. doi: 10.1370/afm.821 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Alsan M, Garrick O, Graziani G.. Does diversity matter for health? experimental evidence from Oakland. Am Econ Rev. 2019;109(12):4071-4111. doi: 10.1257/aer.20181446 [DOI] [Google Scholar]
- 18.Emery C, Boatright D, Culbreath, K. Stat! an action plan for replacing the broken system of recruitment and retention of underrepresented minorities in medicine. September 10, 2018. Accessed February 20, 2020. https://nam.edu/stat-an-action-plan-for-replacing-the-broken-system-of-recruitment-and-retention-of-underrepresented-minorities-in-medicine/
- 19.Khullar D. Even as the US grows more diverse, the medical profession is slow to follow. Washington Post. September 23, 2018. Accessed October 6, 2021. https://www.washingtonpost.com/national/health-science/even-as-the-us-grows-more-diverse-the-medical-profession-is-slow-to-follow/2018/09/21/6e048d66-aba4-11e8-a8d7-0f63ab8b1370_story.html
- 20.US Census Bureau . QuickFacts statistics on United States population by race and Hispanic origin. 2019. Accessed February 7, 2020. https://www.census.gov/quickfacts/fact/table/US/PST045218
- 21.Williams DR, Mullan F. Why we need more black doctors. Stat. January 16, 2017. Accessed February 20, 2020. https://www.statnews.com/2017/01/16/black-doctors-shortage-education/
- 22.Elliott AM, Alexander SC, Mescher CA, Mohan D, Barnato AE. Differences in physicians’ verbal and nonverbal communication with Black and White patients at the end of life. J Pain Symptom Manage. 2016;51(1):1-8. doi: 10.1016/j.jpainsymman.2015.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Hoffman KM, Trawalter S, Axt JR, Oliver MN. Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proc Natl Acad Sci U S A. 2016;113(16):4296-4301. doi: 10.1073/pnas.1516047113 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Walker KO, Moreno G, Grumbach K. The association among specialty, race, ethnicity, and practice location among California physicians in diverse specialties. J Natl Med Assoc. 2012;104(1-2):46-52. doi: 10.1016/S0027-9684(15)30126-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ly DP, Seabury SA, Jena AB. Differences in incomes of physicians in the United States by race and sex: observational study. BMJ. 2016;353:i2923. doi: 10.1136/bmj.i2923 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Yogman M, Garfield CF; Committee on Psychosocial Aspects of Child and Family Health . Fathers’ roles in the care and development of their children: the role of pediatricians. Pediatrics. 2016;138(1):138. doi: 10.1542/peds.2016-1128 [DOI] [PubMed] [Google Scholar]
- 27.Grimberg A, Cousounis P, Cucchiara AJ, Lipman TH, Ginsburg KR. Parental concerns influencing decisions to seek medical care for a child’s short stature. Horm Res Paediatr. 2015;84(5):338-348. doi: 10.1159/000440804 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Andersen RM. National health surveys and the behavioral model of health services use. Med Care. 2008;46(7):647-653. doi: 10.1097/MLR.0b013e31817a835d [DOI] [PubMed] [Google Scholar]
- 29.Agency for Healthcare Research and Quality . Medical Expenditure Panel Survey. April 22, 2019. Accessed September 30, 2019. https://meps.ahrq.gov/mepsweb/about_meps/survey_back.jsp2019
- 30.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative . The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-1457. doi: 10.1016/S0140-6736(07)61602-X [DOI] [PubMed] [Google Scholar]
- 31.Saha S, Arbelaez JJ, Cooper LA. Patient-physician relationships and racial disparities in the quality of health care. Am J Public Health. 2003;93(10):1713-1719. doi: 10.2105/AJPH.93.10.1713 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Wallace LS, DeVoe JE, Rogers ES, Malagon-Rogers M, Fryer GE Jr. The medical dialogue: disentangling differences between Hispanic and non-Hispanic whites. J Gen Intern Med. 2007;22(11):1538-1543. doi: 10.1007/s11606-007-0368-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.StataCorp LP. Stata Statistical Software, release 15. 2017. Accessed January 4, 2020. https://www.stata.com
- 34.Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41-55. doi: 10.1093/biomet/70.1.41 [DOI] [Google Scholar]
- 35.Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Statistical Assoc. 1984;79(387):516-524. doi: 10.1080/01621459.1984.10478078 [DOI] [Google Scholar]
- 36.Guerrero AD, Rodriguez MA, Flores G. Disparities in provider elicitation of parents’ developmental concerns for US children. Pediatrics. 2011;128(5):901-909. doi: 10.1542/peds.2011-0030 [DOI] [PubMed] [Google Scholar]
- 37.Zambrana RE, Carter-Pokras O. Role of acculturation research in advancing science and practice in reducing health care disparities among Latinos. Am J Public Health. 2010;100(1):18-23. doi: 10.2105/AJPH.2008.138826 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Greenwood BN, Hardeman RR, Huang L, Sojourner A. Physician-patient racial concordance and disparities in birthing mortality for newborns. Proc Natl Acad Sci U S A. 2020;117(35):21194-21200. doi: 10.1073/pnas.1913405117 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Hardeman RR, Medina EM, Boyd RW. Stolen breaths. N Engl J Med. 2020;383(3):197-199. doi: 10.1056/NEJMp2021072 [DOI] [PubMed] [Google Scholar]
- 40.Kids Count Data Center . Child population by race in the United States. The Annie E. Casey Foundation. September 2021. Accessed September 2, 2021. https://datacenter.kidscount.org/data/tables/103-child-population-by-race?loc=1&loct=1#detailed/1/any/false/871,870,573,869,36,868,867,133/12,72/423,424
- 41.Association of American Medical Colleges . Diversity in medicine: facts and figures 2019: percentage of all active physicians by race/ethnicity, 2018. Accessed February 20, 2020. https://www.aamc.org/data-reports/workforce/interactive-data/figure-18-percentage-all-active-physicians-race/ethnicity-2018
- 42.Boatright DH, Samuels EA, Cramer L, et al. Association between the liaison committee on medical education’s diversity standards and changes in percentage of medical student sex, race, and ethnicity. JAMA. 2018;320(21):2267-2269. doi: 10.1001/jama.2018.13705 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Association of American Medical Colleges . The role of GME funding in addressing the physician shortage. Accessed February 20, 2020. https://www.aamc.org/news-insights/gme
- 44.Centers for Medicare and Medicaid Services . Equity initiatives. 2021. Accessed October 8, 2021. https://www.cms.gov/About-CMS/Agency-Information/OMH/equity-initiatives
- 45.Zuvekas SH, Olin GL. Validating household reports of health care use in the medical expenditure panel survey. Health Serv Res. 2009;44(5 Pt 1):1679-1700. doi: 10.1111/j.1475-6773.2009.00995.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Olaisen RH, Flocke SA, Smyth KA, Schluchter MD, Koroukian SM, Stange KC. Validating the new primary care measure in the Medical Expenditure Panel Survey. Med Care. 2020;58(1):52-58. doi: 10.1097/MLR.0000000000001220 [DOI] [PubMed] [Google Scholar]
- 47.Hill SC, Zuvekas SH, Zodet MW. Implications of the accuracy of MEPS prescription drug data for health services research. Inquiry. 2011;48(3):242-259. doi: 10.5034/inquiryjrnl_48.03.04 [DOI] [PubMed] [Google Scholar]
- 48.Zuvekas S, Olin G. Validating the collection of separately billed doctor expenditures for hospital services: results from the Medicare-MEPS Validation Study; working paper No. 08004. Agency for Healthcare Research and Quality; March 2008.
- 49.Bertakis KD, Azari R. Patient-centered care: the influence of patient and resident physician gender and gender concordance in primary care. J Womens Health (Larchmt). 2012;21(3):326-333. doi: 10.1089/jwh.2011.2903 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Bertakis KD, Franks P, Epstein RM. Patient-centered communication in primary care: physician and patient gender and gender concordance. J Womens Health (Larchmt). 2009;18(4):539-545. doi: 10.1089/jwh.2008.0969 [DOI] [PubMed] [Google Scholar]