2009年-世界发展银行全球_Measuring_Health_Workforce_Productivity___Application_of_a_Simple_Methodology_in_Ghana_32页_1mb

Summary of "Measuring Health Workforce Productivity: Application of a Simple Methodology in Ghana"

Core Content

This document presents a study on measuring health workforce productivity at the district level in Ghana using a simple methodology. The report outlines the development of a composite productivity index and explores its potential application in informing staffing decisions.

Main Objectives

• To develop an aggregate measure of health workforce productivity that can be monitored regularly at the district level.
• To explore factors correlated with workforce productivity at the district level.
• To examine trends in workforce productivity over time.
• To provide recommendations for further research, especially in improving data collection and quality.

Key Findings

• The study introduces a Composite Services Index (CSI) as the numerator and a Composite Human Resources for Health (CHRH) index as the denominator to calculate the Workforce Productivity Index (WPI).

• The WPI is defined as:

  $$
  WPI_i = \frac{CSI_i}{CHRH_i}
  $$

• The CSI is calculated using a weighted sum of services, including outpatient visits (OPD), inpatient days (IPD), antenatal care (ANC), supervised deliveries (SD), family planning (FP), and immunizations (Imm), with the following weights:

  $$
  CSI = OPD + 3 \times IPD + ANC + 3 \times SD + FP + 0.5 \times Imm
  $$

• The CHRH is calculated as the total wage bill (in millions of cedis) for all health workers in a district.

• The average WPI in Ghana is 12.7 CSI per 1 million cedis of salary expenditure, with significant variance across districts and regions.

• The Western region had the highest WPI (20.7), while Volta had the lowest (6.8).

• The distribution of WPI is right-skewed, indicating a few districts with very high productivity levels.

• The study found no significant correlation between workforce productivity and skill mix, or between productivity and the availability of health infrastructure.

• There is a negative relationship between health workers per capita and workforce productivity, suggesting that higher staff numbers may not necessarily lead to higher productivity.

• The analysis of two outlier districts (Cape Coast and Dangbe West) indicated that drastic changes in productivity were likely due to data quality issues rather than real changes in service delivery.

Limitations

• The data used in the study is limited, with only 20 districts having data for both 2004 and 2006.
• No suitable measures for case complexity or service quality were available.
• The study could not fully test all potential factors influencing productivity due to data constraints.

Recommendations

• Collect data on hypothesized predictors such as equipment availability, which were not included in the current analysis.
• Further explore the demand for services as a potential predictor of productivity.
• Conduct more rigorous statistical analysis using regression techniques.
• Improve data collection and quality in the health information system (HMIS) to support more accurate productivity assessments.

Methodology Overview

1. Define the Service Unit: The study uses districts as the unit of analysis due to data availability and the need to inform central ministry staffing decisions.
2. Define Service Categories: Selected based on their importance and coverage, including OPD, IPD, ANC, SD, FP, and Imm.
3. Aggregate Services into CSI: Using a weighted sum based on the relative importance and resource intensity of each service.
4. Define Human Resources Categories: Includes all health workers except those in regional health offices and central administration.
5. Aggregate HRH into CHRH: Using total salary expenditure as the composite measure.

Data and Analysis

• Data sources include the Centre for Health Information Management (CHIM), Ghana Health Service, and monthly payroll files.
• The analysis spans 2004 and 2006, with a final sample of 116 districts after adjusting for incomplete data.
• Trends in productivity show a slight decrease over time, but the small number of data points limits strong conclusions.

Conclusion

The report provides a simple and practical methodology for measuring health workforce productivity at the district level in Ghana. It highlights the importance of regular monitoring and the potential of using such an index to improve staffing decisions. However, due to data limitations, further research is needed to fully understand the factors influencing productivity and to refine the methodology for more accurate results.