OECD Pharmaceutical Spending Analysis

Team Members

• 📈 Biswapriya Manna - @Bishudsi
• 📈 Hiroyoshi Hiratsuka - @pim61sit
• 📈 Melanie Ramnauth - @melram17
• 📈 Oleksandr Lebediuk - @olebediuk
• 📈 Vidhi Patel - @Patelvidhim
• 📈 Zeinab Sadrosadat - @zsadr2002

Explore our collaborative work on GitHub: Pharma Spending Pattern Repository

Strategic Pharmaceutical Market Expansion 🎯

Objective & Impact

This project helps pharmaceutical companies pinpoint and enter high-potential drug markets. We address the industry's inherently high-cost nature – driven by significant R&D expenses, the need for high profits, and monopoly tendencies – by analyzing OECD pharmaceutical spending to provide effective strategies for continuous market expansion. The target audience of this project is executives and sales teams from major pharmaceutical companies and startups. The DS approaches for this project will uncover high potential countries, clustering characteristics that point to market opportunities. The data from the OECD will be examined to achieve the business goal and project objectives.

Health spending and per capita GDP reflects a nation's commitment to well-being and economic productivity. Our analysis reveals where pharmaceutical investments yield the greatest impact, helping companies align growth with global health trends and prioritization.

Key Objectives

• Identify the high-potential countries for pharmaceutical expansion.
• Cluster countries with similar market traits for targeted strategies.
• Pinpoint drivers of high drug spending at the country level.

All insights leverage reliable OECD data from DataHub.io, ensuring strategic value for your teams.

Current State: Top Health Spending Countries 📊

Understanding the current landscape of health expenditure is crucial for identifying potential growth markets. This view highlights countries with the highest pharmaceutical drug spending, providing a snapshot of the global investment in health and pharmaceutical products. Our analysis began with an initial time series examination of pharmaceutical spending to identify existing top investment areas. This foundational view was crucial in predicting key factors and countries that would constitute high-potential markets. This view was completed with matplotlib and helps users to quickly digest global health trends.

Analyze Markets by Clustering 🧩

We've analyzed health investment across 37 countries, focusing on two key measures: Total Health Spending (USD) and USD Per Capita Growth. This allowed us to group them into three distinct market types.

Most countries (24) fall into "Saturated Markets." These show high Total Health Spending but low USD Per Capita Growth, indicating mature economies with established health investments.

In contrast, 13 countries are "High-Potential Markets." They currently have lower Total Health Spending but significant USD Per Capita Growth, suggesting exciting opportunities for new investments.

Finally, we identified just one "Stable Market," demonstrating a balanced and steady approach in both Total Health Spending and USD Per Capita Growth.

In summary, while many countries are already heavily invested, there are also numerous emerging markets where strategic investments could lead to substantial growth in health spending.

Market Entrance Recommendations 🚀

Visualizing these clusters on a world map reveals a significant concentration of high-potential markets across Europe. It suggests that while Europe may already host some 'Saturated Markets,' there are numerous countries within the continent that present considerable untapped growth. This regional concentration could also offer logistical and regulatory advantages for European expansion. Top countries include Austria, Bulgaria, Switzerland and Cypress.

Conclusion 📝

In summary, this case study provides actionable intelligence for pharmaceutical companies to pinpoint optimal market entry points. By understanding these diverse market landscapes, businesses can effectively align their expansion strategies with global health trends and prioritize investments where they are most likely to yield substantial growth. Below, is a complete table of high potential markets identified through this analysis.

Methodologies 🤖

Our approach to analyzing global pharmaceutical spending involves three core phases:

• 1. Data Preparation: We acquire OECD data from DataHub.io, integrating population figures, cleaning, and transforming features (e.g., growth, per capita spending). This is done in Python using pandas and NumPy within Jupyter Notebooks, with GitHub for management.
• 2. Clustering Analysis: To segment markets, we apply KMeans clustering (k=3). Data is aggregated by country (spending, growth), features are standardized with StandardScaler (scikit-learn), and the KMeans algorithm iteratively groups countries based on similarity.
• 3. Data Analysis & Visualization: Insights are generated and presented using Python libraries: NumPy for computation, pandas for data manipulation, seaborn and matplotlib.pyplot for static visuals, and plotly.express for interactive plots like choropleths.

Limitations ⚠️

This analysis has certain limitations, primarily due to data timeliness and historical gaps, with data only available up to 2022 and some missing entries. Consequently, our insights are based on pre-COVID economic and health spending trends. Furthermore, the scope is limited to OECD member nations, as not all countries are included in this dataset. For future work, we recommend updated data collection to capture post-COVID trends and an expanded analytical scope to include classification and regression models for deeper predictive insights.