In this study, Pregnancy outcome in women with previous one cesarean section, the authors did many statistical comparisons of the data. One comparison is easily understood with a chi-square – does having a previous vaginal delivery increase the success rate of VBAC?Read More →

Before we move into our example study we need to spend a few minutes on the difference between risk and odds.  It can be difficult to understand, but it is important for interpretation of studies. When research presents risk, what is being presented is the number of people with the selected characteristic divided by the total number of people. When research presents odds, what is being presented is the number of people with the selected characteristic divided by the number of people without the characteristic. Visualize this difference by thinking of a set of dice.  With six sides, each having a different number between 1-6,Read More →

  We have been talking about statistics, and how to understand the statistics piece of a research article. Today we will start looking at the most common statistical tests used in health-care research. We will not talk about how to do these tests. Instead we will focus on how and why these tests are used. We will begin with the Chi-square. Chi-square is used to analyze the difference in proportions for categorical data. If you remember, categorical data is information with discrete groups like race, sex, or the rating out of ten given to pain during pushing stage. A proportion is simply the number inRead More →

You’ve designed your study, recruited a sample and collected data. Maybe your study finds 20% of the women who gave birth at a birth center had at least a second degree tear, while 25% of the women who gave birth at a hospital had at least a second degree tear.  The next question you need to ask yourself is this: Does this difference represent a real difference, or is this difference due to the random variation I should expect when I sample and measure?Read More →

No doubt you’ve seen something like this hierarchy of studies from Duke University’s Introduction to Evidence-Based Practice site. In this hierarchy, the studies are classified by the type of sample used. Types of Samples In a case study or case report, the sample is one or two individuals who experienced something unusual or noteworthy. In a case-control study, the sample is a group of individuals with the characteristic of interest (usually diagnosed with something) called the cases and another set of individuals who do not have that characteristic called the controls.  The controls are matched to the cases on as many demographic and health criteria asRead More →

When we compare two populations and find a difference in a health outcome, we want to look more closely to determine if that outcome difference is due to a difference in health related characteristics or non-health related characteristics.  When the difference is due to something other than health characteristics, the difference is considered a disparity. Disparities are particularly disturbing because it means something about the health system or society is causing differences in health outcomes.  Globally, disparities in maternal health have to do with the distribution of health care resources. Industrialized countries have higher rates of cesarean delivery and lower rates of maternal mortality thanRead More →

Last time we talked about the difference between incidence and prevalence.  Today, we will look at how we can use these descriptive measures to understand differences in risk. Remember from Monday that the population is the group of individuals you would like to learn about. As midwives, we are often interested in specific groups of women.  These groups might be defined by age (e.g. reproductive aged women), by condition (e.g. women living with HIV), or any number of other ways we can divide women into groups. When you divide your population into two or more groups, you can look at the difference in incidence orRead More →

We need to talk about what might be considered the “back end” of statistics. That is, how did the data come to be? Types of Observations Data is simply a collection of variables, grouped by observation.  In health care, the observation is usually a “case” or a “person”. Researchers make each observation in a variety of ways. They may use a survey, asking individuals to report specific things. They may use biomarkers, taking blood or saliva samples to look for interesting components. They may use administrative data, looking at all hospital discharges or insurance charges for an organization. They may use surveillance data, examining collectionsRead More →

I ran into an infographic titled A Breakdown of Birth in the U.S.A. I wanted to like it, but the information on the poster is misleading. It does make a great little demonstration of  how to learn to question what you read. If you haven’t opened the infographic yet, put it in a new window and lets talk about the problems. Data Sourcing Always ask yourself first where the data is coming from. Notice the infographic provides only three sources for the data (which are nearly impossible to read), but I find that highly suspect given the variety of information the infographic shares.  This isRead More →

Most of the research we are interested in as midwives is inferential – meaning we draw conclusions about a group of people based on the results.  However, descriptive statistics are still very helpful. Descriptive statistics help us organize and summarize information.  For example, the number of births attended by midwives is a descriptive statistic. We can break down the data by country or state/province and see differences between groups. In experiments, the descriptive statistics help us ensure the two study groups are similar. In healthcare, statistics isn’t useful without epidemiology.  Epidemiology is the study of patterns of illness and conditions. We use epidemiology to determineRead More →