The sample comprised a total of 10,525 children who were <5 years of age. Characteristics of female parents of these children are given in Table 1. In Table 2, we present the results of the comparison of selected sociodemographic characteristics between black and white female parents of the children. Black mothers tended to be older, especially in the age group beyond 50, where the proportion of blacks was almost twice that of whites. Black households were larger in size and contained a greater number of children. Black mothers were also at a disadvantage in terms of level of educational attainment.
Figure 1. Rates (%) of preventive care utilization by age of the child.
The black-white gap was also apparent with respect to a number of indicators of financial barriers to healthcare access (Table 3). Black mothers earned, on average, much less than their white counterparts even though they tended to be more frequently employed than whites. Although the level of insurance coverage for mothers was similar for both racial groups (86.4% for blacks vs. 87.0% for whites), an important difference existed regarding the type of health insurance. The overwhelming majority of whites were privately insured, in contrast to only about half of blacks.
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Table 1. Maternal Characteristics of Study Sample (Estimates are Weighted)
| Characteristic | Mean | 95% Confidence Interval | Range |
| Maternal age (years) | 33.7 | 33.4-33.9 | 15-88 |
| Family size | 3.8 | 3.9-4.0 | 1-18 |
| Number of children | 4.0 | 3.9-4.1 | 1-19 |
| Total income (in U.S. $) | 19,195 | 18,606-19,784 | 0-222,560 |
| Education (years) | 12.7 | 12.6-12.8 | 0-17 |
Overall, the level of pediatric preventive services utilization was 15.4%. Up-to-date immunization coverage and dental visits were 76.1% and 18.2%, respectively. With the exception of up-to-date immunization receipt, significant differences were noted in these rates by age of the child (Figure 1). For dental visits, the prevalence of at least one visit during the year increased from 2.2% by the age of one completed year to 34.2% by the end of the fifth birthday in a dose-dependent pattern (p for trend <0.001). A similar rising trend with child’s age was observed for the composite variable of preventive care. Black children consistently lagged behind their white counterparts in all three indices (immunization: 74.1% vs. 76.6%; dental visit; 15.9% vs. 18.8%; preventive care: 13.0% vs. 16.0%; p value O.001 in all cases).
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Table 2. Maternal Characteristics by Race (Percentages Are Weighted)
| Characteristic | Black (N=2,090) [%] |
White (N=8,435) [%] |
P Value |
| Maternal Age (Years) Mean (±SE) | 34 (±0.4) |
33.5 (±0.1) |
O.0001 |
| <20 | 2.2 |
2.0 |
|
| 20-29 | 38.8 |
32.8 |
|
| 30-39 | 32.4 |
45.5 |
|
| 40-49 | 15.8 |
14.1 |
|
| 50+ | 10.8 |
5.6 |
|
| Marital Status | O.0001 | ||
| Married | 39.8 |
76.1 |
|
| Unmarried | 60.2 |
23.9 |
|
| Family Size | 0.0006 | ||
| <5 | 68.3 |
71.7 |
|
| 5-9 | 31.0 |
27.7 |
|
| 10-14 | 0.7 |
0.6 |
|
| 15+ | 0.0 |
0.1 |
|
| Number of Children | O.0001 | ||
| <5 | 64.7 |
71.1 |
|
| 5-9 | 29.5 |
26.2 |
|
| 10-14 | 5.5 |
2.3 |
|
| 15+ | 0.3 |
0.4 |
|
| Education (Years) Mean (±SE) | 12.5 (±0.1) |
12.8 (±0.1) |
O.0001 |
| Less than high school | 22.3 |
21.5 |
|
| High school and beyond | 77.7 |
78.5 |
Figure 2 illustrates the adjusted estimates for the association between preventive care utilization and maternal predictors. Maternal income was left out of the model because it correlated strongly with employment status and, for that reason only, the variable coding for the latter was retained. Apart from employment status (adjusted odds ratio = 0.96; 95% confidence interval = 0.84-1.09), all the other variables demonstrated an independent association with preventive care use. Visibly, the strongest predictors of preventive care utilization were maternal age and large family size (>10). From the figure, it is apparent that the relationship between maternal age and preventive care use was inverted U-shaped. As compared to those of younger mothers (<20-year-olds), children under the care of young mature mothers (20-29-year-olds) were almost twice as likely to be up-to-date for immunization schedules and to have had at least one dental visit during the year [adjusted odds ratio (AOR)=1.82; 95% confidence interval (CI)=1.06-3.12]. The likelihood more than tripled and described a plateau during the two subsequent decades [30-39 (AOR=3.50; 95% CI=2.05-6.00), and 40-49 (OR=3.30; 95% CI=2.00-5.90)]. Thereafter, the probability of preventive care utilization then dwindled but still remained above the reference threshold so that by around the maternal age of 50 and beyond, the likelihood for preventive care usage by the child was similar as for 20-29-year-old mothers (AOR=1.90; 95% СЫ.05-3.40). montelukast asthma
Table 3. Indicators of Financial Barriers to Healthcare Access by Race (Percentages Are Weighted)
| Barrier |
Black |
White |
P Value |
| Total Income (in U.S. $) Mean |
16,465 |
19,810 |
O.0001 |
| Employed | O.0001 | ||
| Yes |
67.1 |
63.0 |
|
| No/uncertain |
32.6 |
36.7 |
|
| Not stated |
0.3 |
0.3 |
|
| Insurance Coverage | |||
| Private |
54.3 |
73.2 |
|
| Public |
32.1 |
13.8 |
|
| Uninsured |
13.6 |
13.0 |
O.0001 |
Figure 2 also illustrates that a threshold effect exists between family size and the probability of usage of preventive care by the child. As compared to a family size of <5, preventive care did not depict a significant change up to a family size of nine (AOR=0.89; 95% CI =0.77-1.01). Thereafter (a family size of >10), the likelihood of utilization plummeted. Children in house holds with a family size of <5 members were almost five times as likely to benefit from preventive care as compared to their counterparts in households of >10 members (AOR=4.54; 95% CI=1.64-14.28). Other predictors of preventive care use were race [black mothers predicted less usage (AOR=0.78; 95% CI=0.64-0.94)], and insurance status of the mother and higher maternal education (predicted greater usage). cheap lexapro
Figure 2. Determinants of preventive care use in the U.S. population. Estimates are adjusted by introducing all the variables in the figure into the model.


































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