Roe v Wade, Crime and Confounding Factors

Roe v Wade, Crime and Confounding Factors

A confounding variable is an extraneous variable in a statistical model that correlates with both the dependent and independent variables. We discussed the correlation between pancreatic cancer, smoking and coffee intake. Does smoking and drinking coffee cause cancer independently of one another? Or is there a relationship between smoking and drinking coffee that can lead to pancreatic cancer? There is evidence that argues for either hypothesis. Confounding factors encourage epidemiologists to evaluate and understand all sides of disease transmission and analyze the relationship between necessary and sufficient cause of disease. 

The relationship between breast feeding and health benefits exemplifies confounding factors. Most educated women believe breast feeding is associated with healthy outcomes in both the child and mother but there is minimal scientific evidence that supports this claim leading me to believe that there is a confounding factor that plays a role. Breast feeding is typically associated with higher socioeconomic status and education level. Without adjusting for SES and education level, we cannot understand the impact that genetics, a safer household environment, a more conducive learning environment and increased access to books and vocabulary has on a child IQ level. There are many benefits to breast feeding including; increased nutrients and antibodies in milk, easier to digest than formula, protects against disease, more convenient than bottle formula and leads to a more secure, comforting relationship between mother and newborn. There is definitely an association between breast feeding and health outcomes but it might not necessarily be a causal link. 

Understanding the relationship between crime rates and police efforts involves an investigation into possible confounding factors. We need to consider city demographics, the type and severity of crime and policy changes. All of these variables can play a significant role in the rate of crime. If the demographics of a community are changing, we can expect to see a difference in crime rates. Additionally rates may fluctuate if there are changes made in the class and schedule of certain crimes. Policy also plays a roles in crime rates. Roe vs Wade is landmark decision by the United States Supreme Court in 1973 to legalize abortion. In the 15 years following Roe vs Wade states with high abortion rates and low abortion rates had nearly identical crime patterns but it is important to note that this is a period before generations exposed to legalized abortion are old enough to do much crime. But between the years of 1985-1977 when individuals born after Roe v Wade would be in peak crime ages, high abortion states experience a decline in crime rate of 30% relative to low abortion states.  When comparing arrest rates for individuals born in the same state, we also witness a decline in crime rates for those born after legalization. The legalization of abortion minimized the number of children born into broken, unloving homes. There was a greater percentage of children born into nurturing homes with conducive learning environments. Parents were more invested into teaching their children right from wrong. There is a lot of evidence that can lead an individual to believe that Roe v. Wade was a direct cause of declining crime rates. However, it is important to consider confounding factors such as increased policing efforts in major cities and community initiatives such as the war on drugs. Drug use is often associated with crime and programs such as DARE were introduced in elementary schools across the nation to teach children to say no to drugs. The impact that the war on drugs and DARE had on declining crime rates is debatable but there is reason to believe that it could have impacted the decline crime rates in 1985-1997. 

Confounding factors call in to question the correlation between Roe v Wade  and declining crime rates. This exemplifies the importance of adjusting for confounding variables and identifying direct causality between disease rate and transmission.