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Beyond the Exam Room: Leveraging Perinatal Data to Increase Father Involvement and Improve Maternal-Child Health Outcomes Mark D. Thomas, PhD, MPA Sr.

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Presentation on theme: "Beyond the Exam Room: Leveraging Perinatal Data to Increase Father Involvement and Improve Maternal-Child Health Outcomes Mark D. Thomas, PhD, MPA Sr."— Presentation transcript:

1 Beyond the Exam Room: Leveraging Perinatal Data to Increase Father Involvement and Improve Maternal-Child Health Outcomes Mark D. Thomas, PhD, MPA Sr. Principal, Health and Human Services Portfolio Manager The Center for Transforming Health, a DHHS FFRDC The MITRE Corporation* *The views and opinions expressed are those of the author only. Collaborative Family Healthcare Association 16 th Annual Conference October 16-18, 2014 Washington, DC U.S.A. Session #G3a October 17, 2014

2 Faculty Disclosure I have not had any relevant financial relationships during the past 12 months.

3 Learning Objectives At the conclusion of this session, the participant will be able to: 1)Identify data elements within EMRs that can be leveraged to identify patients at greater risk for low paternal involvement/poor maternal-child health outcomes 2)Define data elements that could be added to those currently being collected by their EMR, enabling them to increase the health system’s ability to identify and address drivers of poor maternal-child health outcomes. 3)Describe ways in which providers in the medical and social service systems can collaborate to improve maternal-child health outcomes.

4 Learning Assessment A learning assessment is required for CE credit. A question and answer period will be conducted at the end of this presentation.

5 A New Era with New Opportunities With the proliferation of electronic health records (EHRs), providers have increased opportunities to leverage EHR data to improve health outcomes among the populations they care for Providers can often influence the data elements captured in an EHR Perinatal data contained in EHR can be leveraged to proactively identify and intervene with at-risk populations

6 An Example: Maternal-Child Health and Father Involvement An aspect of maternal-child health that has not been thoroughly explored or understood 1 Increasingly thought to be of significance and requires further examination 1 Insufficient collaboration between child and family scholars and public health/medical researchers 2 Continued need to better understand the experiences of low-income and minority fathers 3 1 Lu et al., 2010; Kotelchuck, 2003; Alio et al., Lu & Halfon, 2003; Kotelchuck, Lu et al., 2010; Toth & Xu, 1999; Coley, 2001; Jarret, Roy, & Burton, 2002; Tamis-LeMonda & McFadden,

7 Maternal-Child Health and Father Involvement, Cont’d This discussion will examine how maternal-child health data elements from EHRs were leveraged to generate insights into the processes that contribute to father involvement at birth and in infancy/early childhood An opportunity for ground-breaking inquiry and greater collaboration between public health researchers and child and family scholars

8 Hypotheses CDC Dataset: – Maternal sociodemographics (e.g. poverty, teen pregnancy, less than a high school education), maternal prenatal physical and mental health, maternal lifestyle/health behaviors predict father involvement at child birth. EHS Dataset: – Maternal sociodemographics (e.g. poverty, teen pregnancy, less than a high school education) and child physical health at birth; maternal physical and mental health, family emotional environment (maternal emotional responsivity and family conflict), child physical health and outcomes at 14 months predict father involvement at 36 months. 8

9 Methods This study explored the nature of father involvement at birth and in infancy/early childhood using two large scale maternal-child health datasets – Dataset #1: CDC dataset (Weisz, et al., 2011) – Dataset #2: Early Head Start Research and Evaluation Study (United States Department of Health and Human Services, Administration for Children and Families [DHHS/ACF], 2011) 9

10 Methods: CDC Dataset An amalgamation of linked perinatal and birth data, for each live birth at the major birth hospital in Syracuse, NY between January 2000 and March 2002 Full dataset included 2,909 mothers (38.2% White, 47% Black, 6.9% Latino, and 7.9% other racial and ethnic groups) Data cleaning/reduction: – Eliminated cases missing substantial maternal data (e.g. age) and non- singleton births – Eliminated 229 mothers belonging to other racial/ethnic groups (predominantly different Asian groups) Too few cases to include within the analyses Differences in historical and ecological contexts compared to White, Black and Latino mothers Final sample consisted of 2,569 mothers (38.5% White, 47.4% Black and 14.1% Latino) 10

11 Methods: CDC Dataset, cont’d Measures – Maternal Sociodemographics: Maternal age Mother’s years of education Poverty (constructed variable based receipt of: AFDC, Medicaid or WIC) Marital status – Maternal Lifestyle/Health Behaviors: Positive urine screen for illegal drugs Smoking at time of first prenatal care visit Alcohol consumption during pregnancy. Maternal lifestyle/health behaviors risk score: 0 = no lifestyle/health behaviors risks were present and 1= one or more of the risks were present. 11

12 Methods: CDC Dataset, cont’d Measures, cont’d – Maternal Prenatal Physical Health Mother diagnosed with Chlamydia Mother was re-infected with chlamydia following initial treatment Mother experienced a prenatal trauma that required medical attention (e.g. an assault) Mother reported to her health care provider that she was a victim of domestic violence Maternal prenatal physical health risk score: 0= no physical health risks were present and 1 = any one of the risks were present. 12

13 Methods: CDC Dataset, cont’d Measures, cont’d – Maternal Prenatal Mental Health Prenatal antidepressant use Intendedness of pregnancy – Mothers not wanting to be pregnant at all (vs. becoming pregnant sooner or later than was desired) are significantly at-risk of experiencing feelings of hopelessness or of feeling overwhelmed (Bouchard, 2005; Leathers & Kelley, 2000; Claridge &Fisch, 2008) – Child Physical Health (at Birth) Preterm delivery Low birth weight Congenital anomaly or abnormality Child physical health score: 0 = no negative child physical health outcomes were present and 1 = any one of the negative child physical health outcomes were present. 13

14 Methods: CDC Dataset, cont’d Measures, cont’d – Father Involvement (at Birth) Based on marital status and paternity declaration Father involvement variable was created with 3 possible values – 1 = biological parents were not married to each other and no paternity declaration was made within 48 hours after birth/prior to hospital discharge – 2 = biological parents were not married but the father signed a paternity declaration prior to hospital discharge – 3 = biological parents of the child were married 14

15 Methods: EHS Dataset Early Head Start Research and Evaluation Study: A longitudinal impact evaluation of the EHS program that was conducted between 1996 and 2010 Public-use data files of this dataset are available via the ICPSR Data collected longitudinally from both intervention and control groups, in 3 separate waves – Birth to 3 years (data for this study drawn from this wave) – Pre-kindergarten follow-up – Elementary school follow-up (approximately, 5th grade) A nationally representative sample 15

16 Methods: EHS Dataset, cont’d Study only included families in the control group (n=1,474), who did not receive any services from the EHS program Data cleaning/reduction: – Sample was further restricted to where the respondent was the biological mother across each wave of data collection (36.6% White, 34.1% Black, 22.3% Latino, 4.8% other racial and ethnic groups, and 2.2% missing) – Eliminated 50 mothers belonging to other racial/ethnic groups (predominantly different Asian groups) Too few cases to include within the analyses Differences in historical and ecological contexts compared to White, Black and Latino mothers Maintained congruence with the analysis of the CDC dataset – Eliminated 23 mothers missing racial data Final sample: 965 mothers (39.4 % White, 36.7 % Black and 23.9 % Latino) 16

17 Methods: EHS Dataset, cont’d Single dataset created using variables of interest from each wave of data collection waves (14, 24 and 36 months) Missing data common in longitudinal studies: – Many variables missing significant amounts of data—ranging from 2.2% to 50.5% – Little's Missing Completely at Random (MCAR) Test with expectation-maximization (EM) was performed (Little, 1988; IBM Corporation, 2011). Results indicated that the data was missing at random (χ2 (63, N= 965) = 58.03, p =.654) Multiple imputation was used to correct for missing data in analyses Only one difference in significant findings when using multiple imputation vs. without 17

18 Methods: EHS Dataset, cont’d Measures Assessments at Birth – Maternal Sociodemographics Whether the mother was a high school graduate/GED Teen pregnancy Marital status Poverty (constructed variable based on receipt of AFDC Medicaid or food stamps) – Child Physical Health Preterm birth (more than 3 weeks early) Low birth weight (less than 2500 grams) Child physical health score: 0 = no negative child physical health outcomes present and 1 = any one of the negative child physical health outcomes present 18

19 Methods: EHS Dataset, cont’d Measures, cont’d Assessments at 14 months – Maternal Physical Health (continuous measure of the mother’s perception of her health status on a 5-point scale, where 1 indicates poor health and 5 indicates excellent health) – Maternal Mental Health (14 Months) CESD-LF, higher scores indicate more symptoms of depression (Zich et al., 1990; Ensel, 1986). Maternal distress via PSI-SF, higher scores = higher levels of distress (feeling overwhelmed, trapped or alone) (DHHS/ACF, 2011b) – Family emotional environment HOME, maternal warmth toward infant, scores highly correlated with child achievement, higher scores = higher responsivity (Bradley & Caldwell, 1984) Conflict dimension of FES, higher scores = higher levels of conflict (DHHS/ACF, 2011b; Fowler, 1981). 19

20 Methods: EHS Dataset, cont’d Measures, cont’d Assessments at 14 months, cont’d – Child Physical Health Child has biological/medical risks Child has ever visited the emergency room Child health status is fair or poor Child has been hospitalized Child physical health score: 0 = no negative child physical health outcomes present and 1 = any one of the negative child physical health outcomes present – Child Outcomes Mental development – Bayley Mental Development Index (cognitive, language, and personal-social development of children under age 3 ½), higher scores = appropriate development (DHHS/ACF, 2011b; Niccols & Latchman, 2002; United States Department of Health and Human Services, Assistant Secretary for Planning and Evaluation, n.d.) 20

21 Methods: EHS Dataset, cont’d Measures, cont’d Assessments at 36 months – Father Involvement At 14, 24 and 36 months, mothers asked how often child saw their father If mothers indicated that the child’s biological father was present, meaning that he saw the child: 1) every day or almost every day, 2) a few times a week, or 3) a few times a month—across all three interview periods—then he was deemed to be continuously involved in the child’s life (DHHS/ACF, 2011b). 21

22 Results: CDC Dataset Chi square/ANOVA analyses – Significant relationship between maternal race and each measure, with the exception of child physical health outcomes Father involvement: Multinomial logistic regression models – Predictors of 3 levels of father involvement: 1) parents were unmarried and no paternity declaration had been made, 2) parents were unmarried but had a paternity declaration, and 3) married parents – Group 1 (low father involvement) used as the reference/comparison group 22

23 Results: CDC Dataset, cont’d Father involvement: Multinomial logistic regression models cont’d – Model #1 contained four independent maternal sociodemographic variables (high school completion, teen pregnancy, poverty and race) [Table #11] – Model #2 added maternal lifestyle/health behaviors risk (positive urine screen for illegal drugs, smoking and/or alcohol consumption during pregnancy) and maternal prenatal physical health risk (chlamydia infection, chlamydia re-infection, prenatal trauma, victim of domestic violence) [Table #12] – Model #3 (full model) added maternal antidepressant use during pregnancy and pregnancy intendedness [Table #13] 23

24 Results: CDC Dataset, cont’d 24

25 Results: CDC Dataset, cont’d Significant predictors of low father involvement at birth – Mothers with less than high school education – Teen pregnancy – Maternal poverty – Maternal race—for Black mothers compared to Latino mothers – Maternal risky lifestyle/health behaviors – Maternal prenatal health risks – Maternal depression/antidepressant use – Unintended pregnancy 25

26 Results: EHS Dataset Chi square/ANOVA analyses – Significant relationship between maternal race and each measure, with the exception of maternal depression, family conflict, child physical health at birth and 14 months, and child development/outcomes Father involvement: Logistic regression model – Predictors of low father involvement (mother report that father was not present in the child’s life at 14, 24 and 36 months) – Low father involvement used as the reference/comparison group 26

27 Results: EHS Dataset cont’d Father involvement: Logistic regression model cont’d – Step #1 contained five sociodemographic independent variables (marital status, maternal high school completion, teen pregnancy, maternal poverty and maternal race) – Step #2 added child physical health (at birth) – Step #3 added maternal physical health, maternal mental health and maternal distress – Step #4 added family emotional environment measures (maternal emotional responsivity toward infant and family conflict) – Step #5 (full model) added child physical health at 14 months and child mental development [Table #31] 27

28 Results: EHS Dataset cont’d 28

29 Results: EHS Dataset cont’d Significant predictors of low father involvement included: – Being unmarried at the time of birth – Maternal poverty – Race for both Black and White mothers, compared to Latino mothers, but with Black mothers having a 1.6 times greater likelihood of low father involvement Significant predictor of father involvement Family conflict (between mothers and fathers) Was not significant prior to imputation for missing values 29

30 Discussion Partial validation of Doherty, Kouneski, and Erickson’s (1998) responsible fathering conceptual framework using novel sources of large scale MCH data 30

31 Discussion cont’d Using large-scale maternal-child health datasets to look at family processes a departure from typical use of small, often convenience, samples in child and family research Results presented in this study can be generalized to other low-income, racially diverse populations – CDC dataset is a population dataset that captured data on nearly all births in the city of Syracuse, New York – EHS dataset is a nationally representative, randomly selected sample 31

32 Discussion cont’d Key predictors of low father involvement from across the CDC and EHS datasets – Unmarried Parents – Mothers without a high school education – Teen pregnancy – Maternal poverty – Race for both Black and White mothers, compared to Latino mothers, but with Black mothers having the greatest likelihood of low father involvement – Maternal risky lifestyle/health behaviors and maternal prenatal health risks – Maternal depression/antidepressant use – Unintended pregnancy The anomaly – Family conflict 32

33 Contributions to the Father Involvement Literature Use of large secondary maternal-child health datasets to gain insights into family processes Identification of maternal characteristics/behaviors that increase risk of low father involvement – Largely absent from the current knowledgebase/literature The identification of these characteristics/behaviors creates the opportunity for much earlier intervention— long before conception Adds to the body of knowledge on father involvement in low-income and racially diverse families Generalizable findings 33

34 Limitations Use of proxy measures in secondary datasets – Constructed measures of father involvement in CDC dataset – Maternal report of father involvement in EHS data (dichotomous presence vs. absence) Use of datasets to examine phenomena (e.g. family processes) that the datasets were not designed for Missing data on fathers No information on the fathers’ perspective on involvement Cross sectional vs. longitudinal design Restricted to White, Black and Latino mothers Datasets lack information on exogenous influences on predictors 34

35 Future Directions Continued need to focus on the experiences of low-income fathers, especially racial and ethnic minorities Need for father-reported data Need for better measures of involvement for low-income, racial and ethnic minority fathers Need for early intervention/research, to move intervention to earlier phases of the life course Need to invest in girls—focusing on those characteristics/behaviors that increase their risk for low father involvement Expanded collaboration between child and family scholars and public health (MCH) researchers 35

36 Back up slides 36

37 Father Involvement Literature Limited in scope to heterosexual, biological fathers 1 Conceptualization of Father Involvement – An evolution that continues 2 – Social roles: ambiguity, conflictual 3 – Feminist perspectives 4 – Role-inadequacy perspective 5 1 Doherty, Kouneski, & Erickson, Marsiglio, 1993; Lamb, LaRossa, 1993; Gerson, Silverstein, 1996; Walters, Carter, Papp, & Silverstein, Doherty,

38 Father Involvement Literature, cont’d. Conceptualization of Father Involvement, cont’d. – Primary components 1 : Positive engagement activities Warmth and responsiveness Control – Auxiliary domains 1 : Direct care Process responsibility – Limitations for the study of racial, ethnic or economically disadvantaged fathers 2 – Generative fathering 3 – Behavioral, cognitive and affective domains 4 1 Lamb, Pleck, Charnov, & Levine, 1985; Pleck, Jarret, Roy, & Burton, 2002; Townsend, 2002; Roopnarine & Hossain, Dollahite & Hawkins, Palkovitz,

39 Father Involvement Literature cont’d Influences on Responsible Fathering: A Conceptual Model Doherty, Kouneski, & Erickson, 1998

40 Father Involvement Literature cont’d Determinants of Father Involvement 1 – Intrapersonal factors 2 – Interpersonal factors 3 – Neighborhood and community factors 4 – Cultural and societal factors 5 – Policies 6 – Life course factors 7 1 Lu et al., 2010; Cooley, Rhein, et al., 1997; Doherty, Kouneski, Erickson, De Luccie, 1995; Fagan & Barnett, 2003; Gaunt, 2008; Krishnakumar & Black, Lane et al., 2004; Lopo & Western, Doherty, Kouneski, Erickson, 1998; Lu et al., 2010; Hale, Carlson, Garfinkel, McLanahan, Mincy, & Primus, 2004; McLanahan, Doherty, Kouneski, Erickson, 1998; Misra, Guyer, & Allston, 2003; Lu & Halfon,

41 Father Involvement Literature cont’d Outcomes of Father Involvement – Note of caution: one factor in a large and diverse array of possible contributors to outcomes 1 – Cognitive Development 2 – Emotional/Social Development 3 – Maternal and Child Health 4 Less widely studied 5 Fathers historically omitted 6 Increasing interest, largely from medical/public health disciplines 7 – Largely focused on birth outcomes 1 Palkovitz, Dubowitz et al., 2001; Nugent, 1991; Bronte-Tinkew, Carrano, Horowitz, & Kinukawa, 2008; Rowe, Coker, & Pan, Cox, Owen, Henderson, & Margand, 1992; Lamb, 2002; Formoso, Gonzales, Barrera, & Dumka, Kielcolt-Glasser & Newton, 2001; Chang, Halpern, & Kauffman, 2007; Masho, Chapman, & Ashby, 2010; Weiz et al., Kotelchuck, Alio et al, 2009; Lamb, 2010; Lu et al., Lu et al., 2010; Kotelchuck, 2003; Alio et al., 2009; Alio et al.,

42 Shortcomings in the Literature Child and family studies perspective – Strengths: Decades of research on fathers and father involvement Rich theoretical and conceptual perspectives – Weaknesses: Largely focused on white, middle-income fathers 1 Misapplication of methods developed on white, middle-income fathers to minority, low-income fathers 1 Comparatively small datasets Public health/medical perspective – Strengths: Large sample sizes Attends to a domain largely overlooked by child and family studies research (maternal-child research) – Weaknesses Small but growing body of literature 2 Largely focused on birth outcomes 2 Reliance on proxy variables for father involvement/absence of family process variables 3 1 Coley, 2001; Tamis-Lemonda & McFadden, Kotelchuck, 2003; Lu et al., 2010; Alio et al., Alio et al., 2009; Alexander & Korenbrot, 1995; Kotelchuck,

43 Theoretical Framework Ecological/Ecosocial Theories 1 Developmental/Life-Course Theories 2 – Well suited to the study of minority, low- income fathers 3 Biopsychosocial Model 4 1 Bubolz & Sontag, 1993; Macintyre & Ellaway, 2000; Emmons, 2000; Meadows, McLanahan, & Brooks-Gunn, Kotelchuck, 2003; Krieger, 2001; Lu & Halfon, 2003; Lu et al., 2010; Dollahite, Hawkins, & Brotherson, 1997; Palkovitz, 1997; 3 Toth & Xu, 1999; Tamis-LeMonda & McFadden, Engel, 1977; Engel, 1980; Hoffman & Hatch, 2000; Orr, James, & Prince, 2002; 43

44 Expanding and Refining the Theoretical Frameworks Generally, data were explored to examine the theoretical assumptions that: – Father involvement is an interactive, individualized and systemic process that is influenced by individual, relational, and environmental factors 1 – Maternal characteristics and behaviors, and child characteristics can predict family processes (father involvement) 2 1 Doherty, Kouneski, Erickson, Doherty, Kouneski, Erickson, 1998; Engel, 1977; Engel, 1980; Lu et al.,

45 Expanding and Refining the Theoretical Frameworks Exploring the nature of father involvement at birth and in infancy/early childhood using two large scale datasets – Dataset #1: CDC dataset 1 – Dataset #2: Early Head Start Research and Evaluation Study 2 1 Weisz, et al., United States Department of Health and Human Services, Administration for Children and Families,

46 Session Evaluation Please complete and return the evaluation form to the classroom monitor before leaving this session. Thank you!


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