Dr. Semmelweis and the Discovery of Handwashing - Python

Childbed fever was a significant threat to women giving birth in the mid-19th century, with Dr. Ignaz Semmelweis pioneering the idea of handwashing to reduce mortality rates. This project reanalyzes the historical data from Vienna General Hospital to understand the impact of handwashing on childbed fever.

Objective:

• Investigate the alarming number of childbed fever deaths at the hospital.
• Analyze the difference in mortality rates between clinics with and without mandatory handwashing.
• Quantify the impact of handwashing on reducing childbed fever deaths .

Data Analysis Initiation:

• Imported necessary modules.
• Loaded and displayed the yearly data reflecting births, deaths, and clinics.

Alarming Death Rates

Calculated the proportion of deaths per number of births.

Focused on Clinic 1 to expose the severity of childbed fever.

Death at the clinics

The dataset reveals the shocking number of deaths during childbirth between 1841 and 1846 at Clinic 1 and Clinic 2. I calculated the proportion of deaths concerning the number of births, emphasizing Clinic 1's consistently higher mortality rates.

Clinic Disparity Unveiled

The analysis unveils a peculiar pattern, with Clinic 1 consistently reporting higher death rates. Dr. Semmelweis attributes this to medical students serving at Clinic 1, who also spend time in autopsy rooms. This leads to a hypothesis connecting post-mortem examinations to childbed fever.

The effect of handwashing

In a bold move, Dr. Semmelweis mandates handwashing to curb mortality rates. I analyzed monthly data from Clinic 1, before and after handwashing initiation in June 1847, using Matplotlib for visualization.

In the plot below we haven't marked where obligatory handwashing started, but it reduced the proportion of deaths to such a degree that you should be able to spot it!

The effect of handwashing highlighted

Visualizes the reduction in deaths by comparing data before and after obligatory handwashing. The effect of handwashing is made even more clear if we highlight this in the graph.

Quantifying the Impact:

Calculated the mean difference, showcasing an 8% reduction in monthly proportion of deaths.

Bootstrap Analysis:

Utilized bootstrap analysis to estimate a 95% confidence interval for the reduction in deaths.

Key Takeaways:

• Clinic 1 exhibited alarming mortality rates, prompting further investigation.
• The handwashing initiative, initiated by Dr. Semmelweis, led to a significant reduction in deaths.
• Statistical analysis, including mean differences and bootstrap analysis, quantifies the impact and provides confidence intervals.
• Dr. Semmelweis's story underscores the importance of effective communication of statistical findings in the medical field.

Conclusion

This project demonstrates the power of data analysis tools and techniques in uncovering insights and making data-driven decisions. Dr. Semmelweis's story serves as a testament to the transformative potential of statistical analysis in healthcare practices.