If you’ve just found out you’re pregnant, or if you’re on your fifth baby, chances are you’ve had plenty of claims and numbers thrown at you. Perhaps to sell you on a specific type of childbirth, maybe in earnest hopes of helping you out. You know the articles. Claims such as “being induced doubles your risk of a cesarean” or “epidurals greatly increase the risk of additional childbirth intervention.” The info that makes your buttcheeks clench. This post is NOT going to debunk or prove any specific study wrong. Instead, it will arm you with the information you need to dive into the sources and decide for yourself if the information presented is accurate and/or applicable to your situation. Or, alternately, you’ll have more tools to make sure you’re right on the internet in the next Facebook debate. The choice is yours. Some basic notes:

  • We’ll first discuss the types of studies that exist: how they are set up, what they are used for as well as their potential limitations
  • Studies and research can only ever truly reflect data of a specific population. That means numbers may never be able to fully reflect your own personal situation, boundaries or hard limits when it comes to decision making.
  • Studies will be listed in order of what’s referred to as Evidence Level, or the rating that determines how well a study is designed in order to ensure applicable results. See the University of Wisconsin’s guide here to learn more.

Meta Analysis and Systematic reviews

Meta Analysis A meta analysis is an excellent tool for determining the outcomes of specific actions, as it is essentially an aggregate, or a collection, of large points of data from multiple studies. Essentially, it’s a way to take a huge pool of evidence, collect it all, and then make sense of the results in terms of coinciding statistics and outcomes. The meta analysis is considered a “gold standard” when it comes to evidence based medicine, as it shows overall trends in public health, rather than one data set that could prove less useful for a whole population. That said, it definitely can have some pitfalls. One large concern in the analysis of multiple studies, when it comes to publishing results, is the potential for positive results to be published at a higher rate than negative outcomes, especially if it could have implications for the use of medical equipment or substances. Likewise, a meta analysis could also provide some level of biased information due to the dropout rate of participants whose final outcomes aren’t always documented. In order to negate these issues, one to two professionals in the specified field are assigned to try and account for potential bias and researching the outcomes of those who left the study. It should also be noted that a meta analysis is a subset of a systematic review, but focuses more on outcomes than making certain all methodology is the same.   SystematicReview Have you heard of the Cochrane Review? A popular form of systematic review, this research point collects all relevant data points from individual studies and from there, only includes studies that have the same types of statistical collection, study setup and results which can always be reproduced. They don’t always necessarily come to a definitive conclusion, but rather, show all of the information collected from extremely similar studies. This can be extremely helpful when you are looking to identify trends in a topic without having to account for differences in how studies found their data. So the obvious limitation? A systematic review may very well exclude data that could have been pertinent to your personal research.    

An observational study collects information that does not have a control group. These studies could be based on events reported in the past by a subject or events that occur to a subject going forward. Observational studies generally seek to find instances of correlation and potential causes rather than a definitive risk assessment. They’re great for understanding the decision making model from a more public health and social perspective.