Improving maternity care: An evolutionary perspective

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Study Design | IRB | Survey Design | Ethnography | Statistical Modeling | Subject Recruitment | Problem Framing

This project sought to test the theory that women have evolved to seek help from other women when giving birth. To do this, I surveyed and interviewed 30 women to understand which parts of childbirth affect feelings of satisfaction. Findings indicated that a more evolutionarily grounded childbirth - one that had fewer medical interventions, included a midwife instead of a OB/GYN, and one that included a followed birth plan - all contributed to higher satisfaction outcomes. It also pointed to the importance of using quantitative and qualitative data to explore the human experience. Both were salient to the conclusion and added the right level of complexity to the analysis.

Prompt: None. This was a non-required, curiosity driven assignment.

Principal Investigator | University of Minnesota | Advisor: Michael Wilson | Timeline: 1.5 years

Neil C. Tappen Prize Recipient

Understanding the Problem

Most studies fail to address the emotional impacts of modern childbirth on the mother. Pairing an underutilized evolutionary perspective with ethnographic techniques, my research investigated mothers’ satisfaction outcomes with childbirth, thus allowing me to capture the richness of the human experience through qualitative and quantitative analysis. To test the evolutionary theory that women seek help from other women when giving birth due to potential dangers, I used the following predictor variables to explore the impact on woman's emotions while giving birth:

  • Presence of a doula
  • Choice of a midwife vs. an obstetrician as the provider
  • The number of medical interventions 
  • Presence, absence, and adherence to a birth plan 
  • Intersection of the type of provider and the number of medical interventions

Methods

30 women up to six months postpartum were recruited via posters, facebook, OB/GYNs, Midwives, and MeetUps. Participants completed a face-to-face interview and questionnaire about demographics, birth planning and components, and their feelings of satisfaction about the experience. Interviews were recorded and subsequently transcribed for thematic analysis.

 
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Spearman Rank Correlation tested the correlation between the number of medical interventions in each childbirth and satisfaction outcomes, and Wilcoxon Rank Sum tests compared the satisfaction outcomes between the groups of women who had a doula present at their birth and those who did not, women who had a midwife and women who had an obstetrician/gynecologist as their primary providers, and between women who had birth plans that were followed and women who did not.

Finally, seven generalized linear models were developed and compared using AIC scores.

insight

The Quantitative

I first tested the significance of different variables using simple linear regression to help me gain a clear picture of what was going on with the data. Once I had an understanding of which variables were affecting outcomes, I conducted Spearman Rank Correlation tests and Wilcoxon Rank Sum tests based on the recommendation of statisticians at the University of Minnesota. Once I had established statistical significance for a number of variables, I created a statistical model that could predict satisfaction outcomes based on specific variables.

 

Modeling Childbirth Satisfaction

Generalized Linear Model of factors predicting postpartum maternal satisfaction outcomes X indicates predictors included in model; K: number of estimable parameters in model (number of covariates plus intercept); AIC: Akaike Information Criteria; ΔAIC: AIC max – AIC min; Models are ordered from lowest to highest AIC scores. Model 1 was the best model, but Model 2 was still strongly supported.

Generalized Linear Model of factors predicting postpartum maternal satisfaction outcomes

X indicates predictors included in model; K: number of estimable parameters in model (number of covariates plus intercept); AIC: Akaike Information Criteria; ΔAIC: AIC max – AIC min; Models are ordered from lowest to highest AIC scores. Model 1 was the best model, but Model 2 was still strongly supported.

From this model, I deduced that the number of medical interventions and whether a woman had a followed birth plan were the most important predictors for satisfaction outcomes. The second model was also strongly supported, so midwives as providers also have an effect on satisfaction outcomes.

 

The Qualitative: Thematic Analysis

High Societal Pressure

Pressure from friends, providers, nurses, or ambient media to have "natural" childbirths. This type of cultural pressure often went beyond birth, with many mothers feeling it necessary to justify their use of infant formula and disposable diapers rather than the cloth alternative.

Providers Make or Break the Experience

In cases where subjects had continuity in their care and supportive providers, they were complimentary and grateful, but in cases where the subjects perceived that her caretakers were unsupportive, the interviews had a negative tone.

High Satisfaction Scores Did Not Always Correlate with Happy Interviews

Doing research simply with numeric data or simply with qualitative data is not sufficient to capture the richness of the human experience. 

 

Looking to the Future

Using human evolutionary history as a framework for analysis proved useful in investigating modern human childbirth procedures, and my hypothesis was mostly supported. Eventually, however, the most interesting ideas that came out of this research, and perhaps the most actionable in terms of design, was the idea that having a birth plan does not guarantee that your birth will go the way you want it to, and unfollowed birth plans were the most significant predictor of low satisfaction outcomes. 

Moving toward more satisfactory birth experiences requires an understanding of how mothers view birth plans, the complexities in the culture that surrounds birth, how providers and nurses should interact with mothers giving birth, and how we can better support new moms. It requires rigorous data analysis mixed with qualitative analysis to shed light on the whole picture.