Application Of Principal Component Analysis (PCA) To Health Indicator Data In North Sumatra In 2020

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Introduction

Health indicators are variables used to determine changes in conditions in the health sector. The number of indicators that explains the characteristics of health encourages the author to conduct this research with the aim of reducing these indicators to be more dominant in North Sumatra in 2020. This study takes 15 variables based on 33 districts/cities in North Sumatra and using the Principal Component Analysis method (PCA) to reduce these indicators.

Methodology

In this study, the first stage carried out was factor analysis, consisting of variable testing using the KMO Bartlett's Test of Sphericity method and MSA (Measure of Sampling Adequacy) measurements. The results of this test indicate the values ​​of KMO and MSA which are greater than 0.5, which indicate that these variables are feasible to be further tested. Furthermore, the factor and rotation process is carried out to get a clearer factor structure.

From the analysis conducted, found four factors formed, with each of the following factors values: 7,314; 1,747; 1,379; and 1,102. The final result shows that of the 15 variables available, there are 14 variables that can be grouped into four new factors. The four factors are:

Factor 1: Population Health Services

  • Includes indicators related to access and quality of health services provided to the community.
  • This factor is crucial in understanding the health services available to the population, including the accessibility, quality, and effectiveness of these services.

Factor 2: Health Conditions and Services

  • Describe the general conditions of public health and health services available in the area.
  • This factor provides insights into the overall health status of the population, including the prevalence of diseases, health outcomes, and the availability of health services.

Factor 3: Health Promotion and Education

  • Includes indicators related to health promotion and education programs available to the community.
  • This factor is essential in understanding the efforts made to promote healthy behaviors and educate the population about health-related issues.

Factor 4: Healthcare Infrastructure

  • Describe the availability and quality of healthcare infrastructure in the area.
  • This factor provides insights into the physical infrastructure of healthcare facilities, including the availability of equipment, staff, and resources.

Additional Analysis and Explanation

The application of PCA in this study provides a deeper insight about health indicators in North Sumatra. By reducing 15 variables into 4 main factors, researchers can more easily analyze and interpret the health conditions of the community in the area. PCA helps identify which variables are the most influential and classify data in a more efficient way.

This grouping is important for the formulation of health policies that are more targeted. For example, if the factors related to "population health services" indicate low value, the local government can allocate more resources to improve access and quality of health services. In addition, by knowing the behavior of the community in healthy living, educational programs and health promotion can be designed better to motivate the community.

Conclusion

Overall, the application of PCA not only provides a clear picture of health conditions in North Sumatra, but also becomes an effective tool in data-based decision making. With this approach, it is expected that the resulting health policy will be more responsive to the real needs of the community and have a positive impact on improving health in the area.

Recommendations

Based on the findings of this study, the following recommendations are made:

  • The local government should allocate more resources to improve access and quality of health services, particularly in areas with low values of "population health services".
  • Health promotion and education programs should be designed to target specific health-related issues and behaviors, based on the insights gained from the PCA analysis.
  • The healthcare infrastructure in the area should be improved, including the availability of equipment, staff, and resources.

Limitations

This study has several limitations, including:

  • The use of secondary data, which may not be up-to-date or accurate.
  • The limited number of variables included in the analysis, which may not capture the full range of health indicators.
  • The reliance on PCA, which may not be the most suitable method for all types of data.

Future Research Directions

Future research should aim to:

  • Validate the findings of this study using primary data collection.
  • Explore the use of other statistical methods, such as cluster analysis or decision trees, to identify patterns and relationships in the data.
  • Investigate the impact of health policies and interventions on health outcomes in North Sumatra.

Q: What is Principal Component Analysis (PCA)?

A: PCA is a statistical method used to reduce the dimensionality of a dataset by identifying the most important variables and grouping them into new factors. In this study, PCA was used to reduce 15 health indicators into 4 main factors.

Q: Why was PCA used in this study?

A: PCA was used to simplify the analysis of health indicators in North Sumatra, making it easier to identify patterns and relationships between variables. By reducing the number of variables, PCA helped to identify the most important factors influencing health outcomes in the region.

Q: What are the four factors identified by PCA in this study?

A: The four factors identified by PCA are:

  1. Population Health Services: Includes indicators related to access and quality of health services provided to the community.
  2. Health Conditions and Services: Describe the general conditions of public health and health services available in the area.
  3. Health Promotion and Education: Includes indicators related to health promotion and education programs available to the community.
  4. Healthcare Infrastructure: Describe the availability and quality of healthcare infrastructure in the area.

Q: How can PCA be used in health policy-making?

A: PCA can be used to identify the most important factors influencing health outcomes in a region, allowing policymakers to target interventions and resources more effectively. By understanding the relationships between variables, policymakers can develop more targeted and effective health policies.

Q: What are the limitations of PCA in this study?

A: The limitations of PCA in this study include:

  • The use of secondary data, which may not be up-to-date or accurate.
  • The limited number of variables included in the analysis, which may not capture the full range of health indicators.
  • The reliance on PCA, which may not be the most suitable method for all types of data.

Q: What are the recommendations for future research?

A: Future research should aim to:

  • Validate the findings of this study using primary data collection.
  • Explore the use of other statistical methods, such as cluster analysis or decision trees, to identify patterns and relationships in the data.
  • Investigate the impact of health policies and interventions on health outcomes in North Sumatra.

Q: How can PCA be applied in other fields?

A: PCA can be applied in various fields, including:

  • Marketing: to identify the most important factors influencing consumer behavior.
  • Finance: to analyze the relationships between financial variables and identify the most important factors influencing stock prices.
  • Social sciences: to analyze the relationships between social variables and identify the most important factors influencing social outcomes.

Q: What are the benefits of using PCA in data analysis?

A: The benefits of using PCA in data analysis include:

  • Simplifying complex datasets by reducing the number of variables.
  • Identifying the most important factors influencing outcomes.
  • Improving the accuracy of predictions and forecasts.
  • Enhancing the understanding of relationships between variables.

Q: What are the challenges of using PCA in data analysis?

A: The challenges of using PCA in data analysis include:

  • Choosing the right method and parameters for the analysis.
  • Interpreting the results and identifying the most important factors.
  • Dealing with multicollinearity and other issues that can affect the accuracy of the results.
  • Ensuring that the results are generalizable to other populations and contexts.