Social cognitive, behavioral, and psychosocial predictors of young African American children s oral health in Detroit

Transcription

1 Social cognitive, behavioral, and psychosocial predictors of young African American children s oral health in Detroit Tracy L. Finlayson, PhD 1 Kristine A. Siefert, PhD, MPH 2 and Amid I. Ismail, BDS, DrPH, MPH 1,3 1 School of Public Health, 2 School of Social Work, 3 School of Dentistry Detroit Center for Research on Oral Health Disparities* The University of Michigan FUNDING SOURCES: The National Institute for Dental and Craniofacial Research, National Institutes of Health *(NIDCR NIH grant # U-54 DE ); NIDCR Minority Research Supplement to the first author; The Delta Dental Fund of Michigan; The University of Michigan Office of the Vice President for Research; NIMH pre-doctoral traineeship to the first author (grant # 5 T32 MH16806) 1

2 Abstract Objective: To advance knowledge of the social determinants of low income African American children s oral health, by examining how specific maternal beliefs, behaviors, and psychosocial factors relate to their children's dental outcomes and practices. Methods: A population-based sample of 719 African American children aged 1-5 and their mothers living in Detroit who participated in the Detroit Dental Health Project (DDHP) in were studied. Descriptive and multivariable regression analyses of survey and dental exam data were conducted in SUDAAN. Health belief scales (efficacy, fatalism, attitudes about dental disease and bottle knowledge) were constructed with exploratory factor analysis. Mothers beliefs, behaviors, background, and psychosocial characteristics (depression, stress, social support) were examined as predictors of children s brushing frequency, Early Childhood Caries (ECC) status, and subjective oral health ratings. Results: Some social cognitive and psychosocial characteristics are significantly associated with children s brushing and dental health in earliest childhood. Notably, mothers efficacy, attitudes, fatalism, and brushing behavior predicted children s brushing and higher parenting stress was inversely related to ECC. Conclusions: Behaviors and cognitions are potentially modifiable. Findings can inform the design of a tailored intervention which enhances efficacy and educates mothers about ECC, to boost her motivation and ability to positively affect young children s oral health. 2

3 Background Despite the overall decline in caries in the last thirty years, disparities exist and it remains prevalent among lower-income African-Americans (Eklund et al 1997; Mueller et al 1998; Vargas et al 1998; Brown et al 2000). These disparities are most striking among preschool children. NHANES III estimates: the proportion of 2-4 year olds with dental caries is 18%, and African Americans aged 2-4 with dental caries is 24% This study addresses the processes by which social location translates to poor oral health in earliest childhood. We sought to examine the relationship between specific and potentially modifiable maternal psychosocial factors and children s oral health status and brushing practices. 3

4 Social and Environmental Context Conceptual model Caregiver Social, Behavioral, and Family Functioning Health Beliefs, Values, Attitudes, and Knowledge Oral Health Behavior Oral Health Status Oral Health System Systems Level Individual Level Adapted from Chen (1995) 4

5 Population Studied The Detroit Center for Research on Oral Health Disparities (NIDCR U54 DE ) a.k.a. the Detroit Dental Health Project (DDHP) The DDHP is a population-based study examining intra-group disparities in 1,021 African-American children (under age 6) and their main caregivers living in the poorest 39 Census tracts in Detroit. Survey and dental exam data for children aged 1-5 and their mothers from the first phase of DDHP (2002-3) were used for this study (N=719). 5

6 METHODS Descriptive, bivariate, and regression analyses were conducted in SUDAAN, which accounts for the complex sample design and produces robust estimates. Multivariate regression models were driven by Social Cognitive Theory, an approach to understanding human behavior, motivation, affect, and thought processes (Bandura 1986). 6

7 Independent Variables Background characteristics: mom s age, education, income, brushing Child s dental history: age, past dental care use, insurance status, brushing frequency Mothers dental knowledge and beliefs: fatalism, self-efficacy, hygiene needs, bottle feeding practices (Finlayson, Siefert, Ismail, Delva, 2005) Mothers psychosocial resources: availability of social support (McLoyd et al,1994), depressive symptoms (CES-D) (Radloff, 1977), parenting stress (adapted PSI) (Abidin, 1995). 7

8 Dependent Variables Child s one-week brushing frequency: Reported total number of times the child s teeth were brushed in the last week, by the child or someone else. (Also included as predictor in other models) Mother s perception of her child s oral health status: Fair/Poor VS. Better. Early Childhood Caries (ECC) status: Severe ECC, ECC, no caries. Dental exams based on ICDAS criteria and age-specific case definition of disease severity used (Drury et al., 1999) 8

9 Case definition of ECC & S-ECC Age (months) ECC S-ECC < or more dmf surfaces * 1 or more dmf surfaces * 1 or more dmf surfaces * 1 or more dmf surfaces * 1 or more dmf surfaces * 1 or more dmf surfaces * 1 or more dmf smooth surfaces * 1 or more dmf smooth surfaces * 1 or more dmf smooth surfaces * 1 or more cavitated, filled, or missing (due to caries) smooth surfaces in primary maxillary anterior teeth OR dmfs score 4 1 or more cavitated, filled, or missing (due to caries) smooth surfaces in primary maxillary anterior teeth OR dmfs score 5 1 or more cavitated, filled, or missing (due to caries) smooth surfaces in primary maxillary anterior teeth OR dmfs score 6 * Any carious lesion, noncavitated (d 1 ) or cavitated (d 2 ), missing tooth due to caries (m), or filled (f) surface. Includes primary teeth only. (Drury et al., 1999) 9

10 Early Childhood Caries (ECC) status by child's age, weighted % 100% % % % 60% % 40% S-ECC ECC No cavitie 30% % % % total Child's age 10

11 RESULTS: Distribution of children s one-week brushing frequency

12 Descriptive highlights Most children have Medicaid and many have seen a dentist 49% <high school education, 46% earn <$10,000/yr Self-efficacy and knowledge scores positively skewed, so most moms are confident and well-informed 57% moms brushed at bedtime in the last week Parenting stress scores fairly normally distributed, most report sometimes feel stressed 79% endorse fatalistic health belief that most children eventually develop dental cavities Depressive symptoms highly prevalent, 35% scored CES-D 16 Most report having social support for transportation, childcare, a loan, or help with errands available 12

13 Association between child s ECC status and mother s perception of oral health Fair/Poor Good Very Good Excellent S-ECC (n=144) ECC (n=242) No caries (n=333) N= χ 2 (6) = 72.2, p= Similar pattern by child s age 13

14 Abbreviated Table. Logistic regression. DV= Fair/Poor oral health rating Age 1-3 (N=446) Ages 4-5 (N=273) OR 95% CI OR 95% CI_ Social Cognitive Theory variables Self-efficacy Fatalistic Knowledge ** Bottle 0.54*** * Mom brushes Psychosocial factors CES-D * Parent Stress Errands help *** Background High school+ 1.86* * Insurance ** Checkup Problems 8.43**** *p<.10; ** p<.05; *** p<.01; **** p<.001. Selected OR from full model that also included additional social support (help with $, childcare, ride) and background characteristics (age, income, brushing) 14

15 Abbreviated Table. Logistic: ECC vs sound & Cum. Logit: S-ECC, ECC, sound Age 1-3 (N=446) Ages 4-5 (N=273) OR 95% CI OR 95% CI_ Social Cognitive Theory variables Self-efficacy Fatalistic belief ** Knowledge ** Bottle Mom brushes Psychosocial factors CES-D Parent Stress 0.62** * Background High school ** $20, * Child s age 2.99**** * Insurance * Checkup Problems 11.50*** *p<.10; ** p<.05; *** p<.01; **** p<.001. Selected OR from full model that also included 4 social support variables and background characteristics (age, income, brushing, insurance) 15

16 Abbreviated Table. Poisson regression. DV= one-week brushing frequency. IDR=incidence density ratio (for count data) Age 1-3 (N=446) Ages 4-5 (N=273) IDR 95% CI IDR 95%CI Social Cognitive Theory variables Self-efficacy 1.18**** * Fatalistic belief 0.83* Knowledge 1.22**** ** Bottle Mom brushes 1.34*** **** Psychosocial factors Money help 0.80** Transportation 1.28* Background $10,000-19, ** $20, *** Child s age 1.23**** ** Insurance *** *p<.10; ** p<.05; *** p<.01; **** p<.001. Selected IDR from full model that also included more psychosocial variables and background characteristics (age, income, brushing, past dental visits) 16

17 Main Findings Dental caries was widespread in this group 21% S-ECC, 33% ECC, 45% caries free Some specific maternal beliefs and psychosocial characteristics were significantly associated with children s dental outcomes and practices Efficacy was positively related to brushing frequency; however, neither was related to ECC or mom s ratings Efficacy was inversely associated with depressive symptoms (CES-D 16) and parenting stress 17

18 Findings continued Children were expected to brush more often if: Mothers had higher levels of efficacy, brushed, were knowledgeable of children s hygiene needs and not fatalistic, help with transportation, no help with money Fair/poor oral health ratings were more likely if: Age 1-3: moms were depressed, more educated, lacked bottle knowledge, child had dental problems Age 4-5: moms were less educated, less hygiene knowledge, more bottle knowledge, help with errands Mothers oral health ratings were congruent with caries status based on dental exams Mothers with higher parenting stress had children who were less likely to have ECC surprising! 18

19 Contributions and potential limits CONTRIBUTIONS: Determinants of intra-group disparities and the complex links between social factors, dental behaviors, and ECC were examined. It s important to include psychosocial characteristics! *Future research should continue to contextualize risk factors Implications for intervention in this population *easy to assess and potentially modify beliefs and behaviors *tailored educational programs and cognitive-behavioral based interventions can be developed LIMITS: Cross-sectional data Possible social desirability bias in self-reported data Not include some traditional caries risk factors 19