Health Indicators - Kenya

Regional Disparities in Health Indicators: The health indicators domain reveals significant global disparities. Developed regions like North America, Western Europe, and parts of Asia have made substantial progress due to advanced healthcare systems and strong data infrastructure. However, developing regions struggle with data collection and infrastructure challenges, leading to uneven development and health outcomes.

Diversity in Health Systems: Health systems around the world are diverse, involving numerous organizations in indicator development. This diversity highlights the need for collaborative efforts to standardize measurements and ensure consistent, reliable data. Standardization is essential for comparing health outcomes across regions and guiding global health initiatives.

Challenges in Healthcare Financing and Resource Allocation: The healthcare sector faces significant challenges in financing and resource allocation. Insufficient funding and inefficient use of resources hinder efforts to achieve universal access to healthcare. Additionally, limited resources restrict investment in critical areas like infrastructure and technology, which are essential for improving health services.

Addressing Resource and Infrastructure Gaps: To optimize resource utilization, it is crucial to address key challenges such as human resource shortages, inadequate healthcare infrastructure, and affordability issues. Overcoming these obstacles will enhance the efficiency of healthcare systems, particularly in regions that are currently under-resourced, and improve access to quality healthcare for all.

Data coverage:

The dataset encompasses data from 152 countries. The charts depict the situation of each country in six different domains. These domains are socioeconomic indicators, macroeconomic indicators, health indicators, digital and connectivity indicators, consumption indicators, as well as logistics and transport indicators. Within these domains, various segments are covered, including demography, economic measures, economic inequality, employment, consumption, health determinants, and much more.

Modeling approach:

The composition of each domain follows a comprehensive approach that combines both top-down and bottom-up methodologies, with each domain and segment being guided by a specific rationale. To evaluate the situation of these six domains within each country, we rely on pertinent indicators and data from reputable international institutions, local national statistical offices, industry associations, and leading private institutions. Additionally, we undertake data processing procedures to address issues such as missing timelines, outliers, and data inconsistency. Our data processing incorporates advanced statistical techniques, including interpolation, exponential moving weighted average, and the Savitzky-Golay filter. These methods contribute to the refinement and enhancement of data quality.

Forecasts:

In our forecasting process, a wide range of statistical techniques is utilized based on the characteristics of the markets. For example, the S-curve function is employed to forecast the adoption of new technology, products, and services, aligning the forecast model with the theory of innovation adoption. Additionally, the data is forecasted using ARIMA with and without seasonality considerations, exponential trend smoothing, and the Compound Annual Growth Rate (CAGR), with the option to incorporate adjustment factors when necessary. These techniques enable accurate and reliable forecast methods tailored to the unique characteristics of the data in each market and country.

Additional notes:

The data is updated twice per year or every time there is a significant change in their dynamics. The impacts of the COVID-19 pandemic and of the Russia/Ukraine war are considered at a country-specific level.