Maternal and Child Health Journal

, Volume 16, Issue 1, pp 83–91 | Cite as

Mother–Child Interactions and the Associations with Child Healthcare Utilization in Low-Income Urban Families

  • Margaret L. Holland
  • Byung-Kwang Yoo
  • Harriet Kitzman
  • Linda Chaudron
  • Peter G. Szilagyi
  • Helena Temkin-Greener


Studies have demonstrated that low-income families often have disproportionately high utilization of emergency department (ED) and hospital services, and low utilization of preventive visits. A possible contributing factor is that some mothers may not respond optimally to their infants’ health needs, either due to their own responsiveness or due to the child’s ability to send cues. These mother–child interactions are measurable and amenable to change. We examined the associations between mother–child interactions and child healthcare utilization among low-income families. We analyzed data from the Nurse-Family Partnership trial in Memphis, TN control group (n = 432). Data were collected from child medical records (birth to 24 months), mother interviews (12 and 24 months postpartum), and observations of mother–child interactions (12 months postpartum). We used logistic and ordered logistic regression to assess independent associations between mother–child interactions and child healthcare utilization measures: hospitalizations, ED visits, sick-child visits to primary care, and well-child visits. Better mother–child interactions, as measured by mother’s responsiveness to her child, were associated with decreased hospitalizations (OR: 0.51; 95% CI: 0.32, 0.81), decreased ambulatory-care-sensitive ED visits (OR: 0.65, 95% CI: 0.44, 0.96), and increased well-child visits (OR: 1.55, 95% CI: 1.06, 2.28). Mother’s responsiveness to her child was associated with child healthcare utilization. Interventions to improve mother–child interactions may be appropriate for mother–child dyads in which child healthcare utilization appears unbalanced with inadequate primary care and excess urgent care. Recognition of these interactions may also improve the care clinicians provide for families.


Mother–child interactions Healthcare utilization Low-income Pediatric Hospitalization Well-child care 


Child healthcare utilization is an important measure, because it may both predict and reflect child health. Insufficient utilization of health services represents lost opportunities to provide immunizations, screening, health behavior counseling [1, 2], and other recommended preventive services [3, 4]. Increased utilization of urgent care may be due to poor child health [5], but may also reflect inconsistent access to care [6, 7, 8] and family characteristics.

Multiple studies have attempted to identify family characteristics that can predict inefficient child healthcare utilization, such as disproportionate use of the emergency department (ED) or suboptimal use of preventive services. For example, studies have noted that maternal depression is related to suboptimal immunization and low preventive care rates [9]. In addition, high household social risk (based on parents’ educational attainment, parents’ marital status, and household income) has been associated with greater child healthcare utilization [10]. However these family characteristics are not always apparent in a clinical setting,

One characteristic that may be more readily identifiable involves the quality of mother–child interactions. Mother–child interactions are the sending and receiving of cues or feedback between the mother and child that allow each individual to either adapt their own behavior or modify the other’s behavior [11]. The communication of cues, which can be observed and measured, are the visible manifestation of a complex interaction that requires a mother to assess her child’s needs and determine a proper course of action [12]. Likewise, the child learns to respond to the mother to fulfill needs [12]. Therefore, mother–child interactions can be measured from the perspective of either the mother (responsiveness to child) or the child (responsiveness to mother).

Mother–child interactions have been suggested as a possible mechanism to explain decreased prevention practices among mothers with elevated depressive symptoms [13]. A mother with poor responsiveness to her child may take longer to recognize developing behaviors (such as interest in electrical outlets) that put the child at risk of injury unless preventive measures are taken (installing outlet covers). McCarthy et al. [14] reported that poor mother–child interactions are associated with increased per-visit resource use, defined as the cost of tests, prescriptions, over-the-counter medications, and additional visits or hospitalizations, related to a primary care visit. Thus, mother–child interactions may be associated with a number of child healthcare measures.

We speculate that mother–child interactions represent the phenotypic expression of the complex underlying relationship between mother and child. Although mother–child interactions may not identify the underlying factors, they are nevertheless observable. If studies find that mother–child interactions predict subsequent healthcare utilization patterns, clinicians could use these interactions as signals to help care for families.

To our knowledge, the relationship between mother–child interactions and child healthcare utilization has not been previously reported. Poor mother–child interaction and suboptimal child healthcare utilization may be associated in several ways. Poor maternal responsiveness may be associated with poor parental supervision [15], delays in recognizing symptoms, and mothers’ overestimating a child’s severity of illness [16], all of which may increase ED visits and hospitalizations and reduce willingness to wait for primary care appointments. Further, physicians’ decisions regarding hospital admission are influenced by perceptions of care the child could receive at home [17], thus poor mother–child interactions may lead to more hospitalizations. Physicians may also overestimate the severity of the problem due to the child’s diminished ability to interact with adults [16]. Finally, poor mother–child interactions may serve as proxies for family characteristics that impede attendance of scheduled appointments such as well-child visits. Overall, poor maternal-child interactions, while likely being a signal of underlying problems, may be associated with increased hospitalizations and ED visits and decreased sick-child and well-child visits.

We examined the associations between mother–child interactions and rates of four types of child healthcare encounters (hospitalizations; ED visits; primary care sick-child and well-child visits) in a low-income population. We hypothesize that poor mother–child interactions will be associated with increased child hospitalizations and ED visits, but decreased sick-child and well-child visits to primary care.


Study Sample

We used data from the Nurse-Family Partnership (NFP) trial, in which pregnant women were recruited at the regional obstetrical clinic for Medicaid recipients in Memphis, TN from 1990 to 1991. Eligible women had no previous children, and had 2 of 3 high-risk criteria: (a) were unmarried, (b) were unemployed, (c) had less than 12 years of education. Eighty-eight percent of those eligible enrolled. Participants were randomly assigned to 4 groups: (1) control group followed until child birth, (2) control group followed through the child’s second birthday, (3) nurse-visitation intervention through child birth, and (4) nurse-visitation intervention through the child’s second birthday. Nurses visited Group 4 (228 women) from pregnancy until children were 24 months old to help mothers and other caregivers “improve the physical and emotional care of their children” [18]. Groups 2 and 4 were interviewed at intake, at 36 weeks of pregnancy, and at 6, 12, and 24 months after birth. We used only the control group followed after child birth (Group 2) for this study due to the potential influence of the intervention on key variables in our current study [18, 19]. The final sample was 432 after applying exclusion criteria to reduce potential bias: twins (n = 17); children who died before 24 months (n = 7); children with very low birthweight (n = 12), malformations (n = 3), or incomplete medical records (n = 37); and mothers who did not live with the study child for 2 months or more during the study period (n = 15). We also excluded an additional 24 children from the sick- and well-child analyses due to missing primary care data from 2 physicians, resulting in a final sample size of 408 for these analyses. This dataset represents a fairly homogeneous group of high-risk, low-income mother–child dyads (Table 1).
Table 1

Independent variables



Mean or percent (SD; range)

Predisposing characteristics

 Child’s sex



 Household density

People/room at 12 months


(0.51; 0.20, 5.0)

 Insurance coverage

Number of months enrolled in Medicaid from birth to 24 months (Note: 96% of children were enrolled in Medicaid for at least 2 months.)


(6.5; 0, 24)


Black (vs. White)



Mother had second child before first child was 24 months old


Enabling characteristics

 Mother’s responsiveness

Score ranging from 0 to 11; measured at 12 months; “high” if >10


(1.9; 2, 11)

 Child’s responsiveness

Score ranging from 0 to 13; measured at 12 months; “high” if >10


(2.6; 2, 13)

 Child care by mother’s male partner

Husband/boyfriend/father of child cares for child at 12 months


 Consistently high maternal depressive symptoms

≥40 on MHI-5 at both 12 and 24 months


 Lives with grandmother

Mother lives with her mother at 12 months


 Marital status

Mother married at any time in 24 month period


 Mother’s age

Years at enrollment


(3.2; 12, 33)

 Mother’s education

Mother “on-track” for completing high school at 12 months


 Mother’s employment

Mother employed for any length of time in 24 month period


 Mother’s social support

Score ranging from 0.6 to 11.2; measured at 12 months


(1.7; 3.1, 10.3)

 Very low income

At or below $3000 annual household income at 12 months



 Adult-Adolescent Parenting Inventory

Score ranging from 32 to 150 (below 91 suggests risk for abuse [42]); measured at 12 months


(7.8; 79.6, 124.2)

 Child’s chronic condition

Any diagnosis code for chronic health condition from birth to 24 months (from medical records)a


 Child’s birthweight



(497; 1585, 4540)

aChronic conditions in this sample include: pulmonary artery anomaly, epilepsy, atrial septal defect, mental retardation, and sickle-cell anemia


The primary dependent variables were the number of child healthcare encounters: hospitalizations, ED visits, sick-child visits, and well-child visits. We obtained numbers of encounters from medical records for the children from birth through 24 months. Encounters were classified based on the type of encounter and facility but not provider specialty. We used two secondary dependent variables to examine potentially avoidable encounters: hospitalizations for ambulatory-care-sensitive conditions (ACS) and ED visits for ACS conditions (ACS-ED). ACS conditions are defined as “conditions that can potentially be managed with timely and effective ambulatory care” [20]. These conditions were identified using diagnoses from the child’s medical records and established classifications as modified for children by Logan et al. [20]. The primary ACS conditions in this sample were: otitis media, fever, gastroenteritis, and upper respiratory infection.

Anderson’s Health Behavior Model identifies 3 categories of factors that influence receipt of healthcare: need, predisposing characteristics, and enabling resources [21]. For children, these factors include the characteristics of their caregiver(s). Our primary explanatory variable, mother–child interactions, was considered an enabling factor, because the social relationship between the mother and her child can “facilitate or impede health services’ use” [21]. We controlled for other enabling factors, predisposing characteristics, and need.

Mother–child interactions were evaluated using two validated measures. Trained observers implemented both the Home Observation for Measurement of the Environment (HOME) and the Nursing Child Assessment Satellite Training (NCAST) when the child was 12 months old. Interviewers were trained to administer these measures and attained at least 90% interrater reliability for each measure [22]. We found reasonable reliability in our sample for both subscales described below (Cronbach’s alpha = 0.73 for the mother’s responsivity from HOME and 0.71 for the child’s responsiveness from NCAST). We dichotomized both subscales to improve model fit (see Table 1).

The mother’s responsiveness to her child was measured using the Emotional & Verbal Responsivity of Mother subscale of the HOME, which consists of 11 items and has been used successfully in low-income and high-risk populations [23]. The observer used a checklist to evaluate the interactions, which were used as the measure of mother’s responsiveness [24].

The child’s responsiveness to the mother was measured using the Child’s Responsiveness to Parent subscale of the NCAST, which consists of 13 items and has been used successfully in low-income populations [25]. The mother was instructed to teach her child a task, while the observer used a checklist to evaluate the interactions; this was the measure of child’s responsiveness [11, 26].

We chose predisposing characteristics based on literature review (Table 1), as well as some less common characteristics as appropriate for this population. We coded maternal education as “on-track for age” based on the number of years of schooling completed compared to the number expected for her age when her child was 12 months old. The eligibility criterion of having less than 12 years of education did not consider if younger mothers were on-track to complete high school or if they progressed after enrollment. Mother’s employment at baseline was an eligibility criterion, but mother’s employment during the study period was used in the models as a concurrent measure. We included the presence of a grandmother in the household, because she may substantially contribute to child rearing in a three-generation household [27]. To account for the influence of a male partner, we included the percentage of time he cared for the child. The presence of a second child may affect the care of the first child [28] and was therefore included.

To measure social support, 5 items were collected regarding social support from the mother’s male partner (if any), the mother’s mother, and 2 additional support people. The highest single score among these individuals was used. The questions included the support person’s level of interest in the child; the respondent’s comfort discussing personal issues with the support person; and the frequency of: contact, discussions about the child, and advice regarding the child.

Another predisposing characteristic is maternal depression [9], which has been shown to increase problem-oriented child healthcare utilization and decrease well-child utilization [20, 29, 30, 31, 32]. Therefore, we included the Mental Health Inventory-5 (MHI-5), which has been psychometrically validated [33, 34, 35] as a measure of depressive symptoms [36]. Consistently elevated depressive symptoms were defined as MHI-5 scores above the literature threshold (60 [33]) at both 12 and 24 months postpartum. This repeated measure was used instead of a single time point, because its association with healthcare utilization is stronger [30] and it may indicate more severe, chronic depression.

The enabling characteristics in our model were income, insurance (measured as the number of months the child was covered by Medicaid), and household density. Household density was included because crowded conditions have been associated with changes in parenting behaviors [37] and is defined as the number of people per room; 1 or more is considered “crowded” [38].

We controlled for need using several measures of, or proxies for, child health. Birthweight was used as a predictor of future healthcare needs [39, 40]. We identified chronic conditions using ICD-9 codes from medical records and a list established by Perrin et al [41]. The Adult-Adolescent Parenting Inventory was used as a proxy for the risk of child abuse [42], because actual rates of state-verified child abuse or neglect were very low in this sample (<4%) [18].


We utilized ordered logistic regression to assess the independent associations between mother–child interactions and utilization of each category of child healthcare for all outcomes except ACS hospitalizations. The assumption of proportional odds was met for all other outcomes. We used logistic regression to assess ACS hospitalizations, because of the small number of children (n = 14) with more than one ACS hospitalization. We included all covariates in the final models, regardless of bivariate significance, because they are each theoretically associated with utilization. All correlations between covariates were below 0.5, all correlations between covariates and primary explanatory variables were less than 0.2, and all variance inflation factors were below 2. Interactions between mother/child responsiveness and the covariates were explored by adding one interaction term to the model at a time and testing for significance at a level of 0.05. Because of the large number of comparisons, these interaction results were considered exploratory.

We utilized imputation by chained equations to reduce potential bias from missing values in primary predictors and covariates [43, 44]. We excluded observations with missing values for the outcome. Across covariates, 2.4% of data were missing. Mother’s employment during the study period was the most common missing value (11%). Child’s responsiveness had 6.7% missing data and mother’s responsiveness had 4.6%. We created 20 imputations with the “ice” command in Stata 10 and combined them in analyses using “micombine” [43].


Table 2 shows the distribution of each type of child healthcare encounter in this sample. The most frequent healthcare encounter was sick-child visits to primary care and the least frequent was ACS hospitalizations. Table 3 displays child healthcare utilization according to the mother’s and the child’s responsiveness. Without adjustment for covariates, higher mother’s responsiveness was significantly associated with fewer ACS-ED visits and more well-child visits; there was a trend toward fewer hospitalizations and ED visits, as hypothesized. Higher child’s responsiveness was associated with significantly more well-child visits, supporting our hypotheses.
Table 2

Distribution of dependent variables



Ambulatory-care sensitive (ACS)

Number of encounters

Number of observations (%)

Number of encounters

Number of observations (%)



292 (68)


346 (80)


94 (22)


72 (17)

2 or more

46 (11)

2 or more

14 (3)

Emergency department (ED) visits


87 (20)


149 (34)


91 (21)


147 (34)


82 (19)


67 (16)


56 (13)


36 (8)


37 (9)


15 (3)


25 (6)

5 or more

18 (4)


17 (4)



15 (3)


8 or more

22 (5)


Sick-child visits


96 (24)


1, 2, 3

87 (21)


4, 5

67 (16)


6, 7, 8, 9

93 (23)


10, 11, 12, 13, 14

45 (11)


15 or more

20 (5)


Well-child visits


1 (0.3)



18 (4)



20 (5)



58 (14)



67 (16)



93 (23)



72 (18)



40 (10)



26 (6)


9 or more

13 (3)

Table 3

Number of visits by quality of mother–child interactions, unadjusted

Encounter type

Total number of encounters (SD)

Mother’s responsiveness

Child’s responsiveness

Number of visits (SD)

P value

Number of visits (SD)

P value






 All hospitalizations

0.48 (0.85)

0.54 (0.82)

0.40 (0.87)


0.51 (0.87)

0.45 (0.81)


 ACS hospitalizations

0.24 (0.55)

0.27 (0.57)

0.20 (0.50)


0.24 (0.54)

0.24 (0.56)


ED visits

 All ED

2.59 (2.58)

2.78 (2.55)

2.32 (2.59)


2.67 (2.68)

2.51 (2.46)



1.31 (1.54)

1.46 (1.66)

1.08 (1.31)


1.38 (1.62)

1.23 (1.43)


Primary care visits


2.48 (1.10)

2.43 (1.13)

2.56 (1.06)


2.50 (1.12)

2.46 (1.09)



4.91 (1.89)

4.71 (1.80)

5.21 (2.00)


4.70 (1.79)

5.14 (1.98)


SD standard deviation; Both mother–child interaction measures were dichotomized to “low” or “high” as shown in Table 1. P-value of unadjusted logistic regression

Bold signifies P value < 0.05

P < 0.10; ** P < 0.05

Results of the multivariate analyses were similar to the bivariate findings (Table 4). Greater mother’s responsiveness was associated with fewer hospitalizations and ACS-ED visits and more well-child visits; a trend toward fewer total ED visits (P < 0.09) was suggested. Mother’s responsiveness was not associated with ACS hospitalizations or sick-child visits to primary care. Greater child’s responsiveness was associated with more well-child visits, but no other outcomes.
Table 4

Mother–child interaction measures as predictors of healthcare utilization, adjusted


Key explanatory variable in model

Mother’s responsiveness

odds ratio (95% CI)

Child’s responsiveness

odds ratio (95% CI)


 All hospitalizations

0.51 (0.32, 0.81)***

1.14 (0.73, 1.79)

 ACS hospitalizations

0.64 (0.36, 1.13)

0.86 (0.50, 1.46)

ED visits

 All ED

0.72 (0.50, 1.05)*

0.88 (0.62, 1.25)


0.65 (0.44, 0.96)**

0.86 (0.60, 1.24)

Primary care visits


1.17 (0.80, 1.73)

0.92 (0.64, 1.33)


1.55 (1.06, 2.28)**

1.43 (1.00, 2.04)*

For each outcome, two models are presented and all include control variables. The left column shows the odds of an additional healthcare visit resulting from a one-unit increase in mother’s responsiveness. The right column shows the odds of an additional healthcare visit resulting from a one-unit increase in child’s responsiveness. Ordered logistic regression used the levels shown in Table 2 for each outcome variable

P < 0.10; ** P < 0.05; *** P < 0.01

Seven interaction terms between measures of mother–child interactions and covariates were found to be significant. The results of models including these interactions are shown in Table 5. Fewer hospitalizations and ACS-hospitalizations were associated with high mother’s responsiveness for girls, but not for boys. Fewer ED visits and ACS-ED visits was associated with high mother’s responsiveness for children who received child care from their mother’s male partner, but not for children who did not receive this child care. More ACS-ED visits were associated with high mother’s responsiveness for children with chronic conditions, while children without chronic conditions were predicted to have fewer ACS-ED visits if their mother had high responsiveness. Fewer ACS-ED visits were associated with high mother’s responsiveness for children who had younger siblings born during the study period, but not for those without younger siblings. More hospitalizations were associated with low child responsiveness for children of married parents, but hospitalizations were not associated with child responsiveness for children of single mothers.
Table 5

Mother–child interaction measures as predictors of healthcare utilization, adjusted and including significant interactions between covariates and mother/child responsiveness


Key explanatory variable in model

Mother’s responsiveness (MR)

Child’s responsiveness (CR)

Mother’s responsiveness direct effect OR (95% CI)

Covariates with significant interactions OR (95% CI)

Child’s responsiveness direct effect OR (95% CI)

Covariates with significant interactions OR (95% CI)




0.28 (0.14, 0.57)***

Sex X MR: 2.92 (1.18, 7.23)**

Sex: 1.03 (0.61, 1.75)

1.14 (0.73, 1.79)

Married X CR: 0.23 (0.06, 0.92)**

Married: 2.59 (0.99, 6.75)*



0.28 (0.12, 0.67)***

Sex X MR: 4.54 (1.47, 14.0)***

Sex: 0.66 (0.34, 1.28)


Emergency department (ED) visits

 All ED

1.04 (0.67, 1.62)

Partner X MR: 0.26 (0.12, 0.60)***

Partner: 1.68 (0.93, 3.03)*



0.61 (0.36, 1.02)*

Partner X MR: 0.27 (0.12, 0.63)***

Partner: 1.78 (1.02, 3.11)**

Sibling X MR: 2.79 (1.25, 6.19)**

Sibling: 0.52 (0.32, 0.84)***

Chronic X MR: 6.94 (1.49, 32.4)**

Chronic: 1.25 (0.44, 3.56)


Ordered logistic model using the levels shown in Table 2 for each outcome variable. Each model includes the control variables listed in Table 1

OR Odds ratio, Sex Child’s sex, Partner Child care by mother’s male partner, Sibling study child has sibling born within study period, Chronic Child’s chronic condition

P < 0.10; ** P < 0.05; *** P < 0.01

Post hoc power was estimated using simulation methods [45], and included all covariates and actual correlations. The estimated power for the point estimates found between mother’s responsiveness and healthcare utilization was below 0.5 for ACS hospitalizations, ED visits, and sick-child visits, and well-child visits; 0.64 for ED-ACS visits; and 0.87 for hospitalizations. The estimated power for the point estimates found between child’s responsiveness and healthcare utilization was below 0.3 for all outcomes. The minimum detectable odds ratio for these associations ranged from 1.6 for ED visits to 2.2 for ACS hospitalizations.


Our study found that among these low-income mother–child dyads, greater mother’s responsiveness to her child was associated with fewer hospitalizations and fewer ED visits for conditions that might be prevented with appropriate primary care. Both measures of mother–child interactions were positively associated with well-child care visits. Of note, the children in this study had high rates of ED visits [46] and hospitalizations [32] and low rates of preventive visits [47] compared to the US general pediatric population.

Although we cannot rule out other explanations, the statistically significant findings are consistent with the hypothesized mechanisms. The stronger relationship for ACS-ED visits than for all ED visits supports the possibility that mothers with poor responsiveness to their child may delay recognizing symptoms and/or seeking care. The finding of increased hospitalizations is consistent with delayed care and also supports the hypothesis that physicians may be more likely to hospitalize a child when the mother has poor responsiveness. The increased likelihood of well-child care among dyads with better mother–child interactions is consistent with the hypothesis that better interactions may be correlated with family characteristics that support attending scheduled well-child appointments.

The addition of interactions between covariates and the measures of mother–child interaction were exploratory, but suggest some insights into how healthcare utilization decisions may be made in these families. For instance, based on the effect of the involvement of a male partner, we speculate that he may help prevent the need for urgent healthcare visits when the mother has high responsiveness, but improve access to healthcare (e.g., transportation) when she does not. Also, it may be that mothers of children with chronic conditions have been instructed to bring their children to care whenever symptoms begin and that those with higher responsiveness can act on these instructions more consistently.

The lack of an association between mother–child interactions and sick-child visits may either be due to a smaller effect size for this type of visit or to potential mechanisms that have contrary directions of association with sick-child visits. One potential mechanism is delay in recognition of imminent injury or symptoms of minor illness that could be managed at home, which could increase sick-child visits. A second potential mechanism is delay in seeking primary care. If the condition is not treated until it is perceived to require emergency care, visits may shift to the ED and sick-child visits could decrease. Therefore, the same factor (poor mother–child interactions) could result in increased sick-child visits in some situations, but decreased sick-child visits in others. Unfortunately, clarifying which sick-child or ED visits result from each mechanism requires information not available in our dataset and difficult to collect in general.

The difference in results for mother’s responsiveness and child’s responsiveness may provide a clue to the mechanisms underlying the relationship between maternal-child interactions and child healthcare utilization. Urgent care visits may be dependent on the mother’s perception of her child, while well-child visits are also dependent on an overall supportive, stable environment that leads to both a well-adjusted child and appropriate preventive care. If future research replicates this finding, interventions to improve the environment or outreach to help mothers follow the well-child care recommendations may be particularly helpful for dyads with poor interactions.

This study has several limitations. We studied a sample of low-income, mostly minority women, and our findings may not be generalizable to other populations. Previous work on the association between another maternal psychosocial characteristic (maternal depression) and child healthcare utilization has suggested different patterns for different socioeconomic groups [48]. These data were collected in the early 1990s, which also may limit the generalizability of our findings. However, we believe that the relationships among mother–child interactions, parenting, and healthcare seeking remain similar and that the basic patterns of healthcare need are also similar since the early 1990s. The measure of insurance is limited, because it only indicates whether or not the child was covered by Medicaid; for the months without Medicaid, we do not know if the child had private coverage or was uninsured. However, rates of private insurance are expected to be low in this population.

To our knowledge this is the first study to suggest a relationship between mother–child interactions and child healthcare utilization. These results are consistent with previous literature in related areas [14]. Further studies are needed to explicitly demonstrate if mother–child interactions themselves directly affect healthcare utilization, through the mechanisms suggested above, or if they are markers for other family characteristics known to influence healthcare decision making [8, 20, 29, 30, 31, 32, 49].

In conclusion, our findings suggest that poor mother–child interactions are associated with higher use of urgent care and lower use of preventive care among a low-income population. Interventions to better identify dyads with poor interactions, or to improve interactions between dyads at high risk for either high child healthcare utilization or poor mother–child interactions, may result in more optimal use of healthcare (fewer emergency visits, fewer hospitalizations, and more preventive care) and may also improve other child outcomes.



M.L. Holland acknowledges support from an NRSA Institutional Research Training Grant (T32 HS000044-16) and a Health Services Research Dissertation Award (R36 HS017737), both from the Agency for Healthcare Research and Quality.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Margaret L. Holland
    • 1
    • 2
  • Byung-Kwang Yoo
    • 3
  • Harriet Kitzman
    • 2
  • Linda Chaudron
    • 4
  • Peter G. Szilagyi
    • 1
  • Helena Temkin-Greener
    • 3
  1. 1.Department of PediatricsUniversity of Rochester School of Medicine and DentistryRochesterUSA
  2. 2.School of NursingUniversity of RochesterRochesterUSA
  3. 3.Department of Community & Preventive MedicineUniversity of Rochester School of Medicine and DentistryRochesterUSA
  4. 4.Department of PsychiatryUniversity of Rochester School of Medicine and DentistryRochesterUSA

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