Objectives

1. Content Analysis Process A Theory-Based Content Analysis of Women’s Beliefs about the Causes of Birth Defects Amy Case (TX), Marjorie Royle (NJ), Angela Scheuerle (TX), Suzan Carmichael (CA),TunuRamadhani (TX), Karen Moffitt (TX), and the National Birth Defects Prevention Study Objectives Results • To obtain a description of beliefs about the causes of birth defects that are held by a representative sample of American women. • To compare how those beliefs differ by ethnicity. • To organize and analyze that information in the context of an appropriate health behavior theory in a way that can support the development of effective preventive interventions. Background • Health beliefs are an important component in predicting and changing health behaviors. • American women’s beliefs about health and the causes of disease have been shown to vary by ethnicity. • Most research about women’s beliefs concerning the causes of birth defects are ethnographic studies about isolated, non-modern, “traditional” cultures. • Little is known about the beliefs of modern, ethnically diverse American women. • Identifying erroneous health beliefs can help in communicating effectively about health behaviors. • Health communication theories provide constructs for organizing, analyzing, and operationalizing open-text responses. • Attribution Theory is especially applicable in situations (such as birth defects) that are: • Negative • Disruptive • Threatening • Attribution Theory presents a potential framework for classifying response themes into helpful constructs. • Attribution Theory constructs include: • Locus of causality: Internal or external (inherently to self or outside of self) • Stability: Mutability; could change over time • Controllability: Amenable to manipulation by self or others Ethnic Variation Hispanics Blacks • Compared to responses from white controls (referent), MORE likely to mention: • Smoking/Tobacco • Stress/Emotions • Taking Care of Self • Compared to responses from white controls (referent), LESS likely to mention: • Gas/fuel • Living quarters • Paint/Varnish • Pesticides/Agriculture • Pollution (Other or Unspecified) • Compared to responses from white controls (referent), MORE likely to mention: • Childbirth-Related • Consanguinity • Contraceptives • Drugs (Illicit) • Eclipse/Sun/Moon • Taking Care of Self • Compared to responses from white controls (referent), LESS likely to mention: • Age • Chemicals (Unspecified) • Food (Specific Foods or Toxins) • Gas/Fuel • Hyperthermia (not Fever) • Living Quarters • Paint/Varnish • Pesticides/Agriculture • Pollution (Other or Unspecified) • Artificial Sweeteners • Vitamins/Folic Acid Methods National Birth Defects Prevention Study Figure 1: Content Analysis Sequence • Multicenter population-based interview and biologic case-control study funded by the CDC • This project analyzes control women’s open-text responses to the following interview questions: • “Is there anything, including some of the factors we’ve talked about that you think might cause birth defects?” • “Can you tell me about some of those factors?” All open-text responses Discussion & consensus to select logical categories & associated keywords (e.g. alcohol, pollution) Asian/Pacific Islanders B = Non-Hispanic Black; H = Hispanic; A = Asian/Pacific Islander Note • Compared to responses from white controls (referent), MORE likely to mention: • Alcohol • Genetics/heredity • Lead/heavy metals • Smoking/tobacco 3 authors assign sample of responses to categories;inter-rater reliability check Categories refined as needed; conflicts resolved by consensus Search responses; count “hits” based on keywords; check for missed &dup responses Conclusions/Discussion 3 authors assign theoretical construct to each category; inter-rater reliability check Content Analysis • Content analysis is a process of combining a potentially infinite universe of open text responses into logical groupings suitable for quantitative analysis. • An iterative process was followed to categorize responses while reducing subjective biases (Figure 1). • Unit of analysis: Number of “hits” in any category (one woman’s response could be assigned to multiple categories). • Crude odds ratios were calculated for hits in response categories; non-Hispanic white women as referent. • Percentages were calculated for responses corresponding to each theoretical construct, and by ethnicity. • Nearly 40%of responses referred to Illicit drugs, alcohol, or tobacco-related exposures. Only 6% mentioned genetics or heredity as possible causes. • Two-thirds of responses indicated a belief that causes of birth defects to be internal, unstable, and of medium-high controllability. This may indicate receptiveness to information about the causes and prevention of birth defects (i.e. non-fatalistic attitude). • There are many statistically significant differences when responses are stratified by ethnicity. Since women’s social support networks are likely to reflect and reinforce these ethnic variation, birth defects prevention materials and interventions will be more effective if they acknowledge or incorporate women’s existing beliefs. • Possible limitations include: • Homogeneity of coders’ age, ethnicity or education leading to mis-categorization of responses. • Introduction of social desirability bias, as the question of interest followed a1-hour interview about potential causes of birth defects. Stratify response counts by 1) ethnicity; 2) theoretical constructs. Figure 1: Steps in Preparing Open Text for Content Analysis Data Analysis Calculate odds ratios for response categoriesby ethnicity. Calculate percentages for theoretical constructs. Notes: Percentages = Category responses/Total responses. Acknowledgements • The authors would like to thank the participating families, staff and scientists from all NBDPS sites; and Dr. Deanna Hoelscher, University of Texas Health Science Center School of Public Health for her assistance in selecting an appropriate health behavior theory. • This study was funded through Cooperative Agreement No. U50/CCU613232 from the Centers for Disease Control. • Corresponding author: Amy Peterson Case, Birth Defects Epidemiology & Surveillance Branch, Texas Department of State Health Services, amy.p.case@uth.tmc.edu. *Percentages by column = proportion of total answers corresponding to that construct. Controllability does not sum to 100 due to rounding error. The findings and conclusions in this report have not been formally disseminated by the CDC and should not be construed to represent any agency determination or policy.