Related Experiment Video
Updated: Jul 6, 2026

14:43
A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
Programme budgeting and marginal analysis: an approach to priority setting in need of refinement
Journal of Health Services Research & Policy
|June 6, 1996
Summary
Programme budgeting with marginal analysis is being revived in the UK's National Health Service for strategic planning. However, current methods for identifying evaluation options are flawed, requiring a more systematic approach for better results.
Area of Science:
- Health economics
- Healthcare management
- Public health policy
Background:
- Programme budgeting and marginal analysis (PBMA) is gaining traction in the UK's National Health Service (NHS).
- PBMA aims to map healthcare activities and expenditures to populations.
- It seeks to improve strategic planning by identifying optimal resource reallocation within healthcare programmes.
Purpose of the Study:
- To evaluate the effectiveness of current PBMA methods in the NHS.
- To identify methodological deficiencies in the application of PBMA.
- To propose a more systematic technique for identifying options for evaluation within PBMA.
Main Methods:
- Analysis of the coupling of programme budgeting with marginal analysis in the NHS.
- Critique of the current approach to identifying options for evaluation.
- Development of a systematic technique to address methodological flaws.
Main Results:
- PBMA is being revived for strategic planning and resource reallocation in the NHS.
- Current methods for identifying evaluation options are flawed, particularly the reliance on 'expert' groups.
- Existing approaches have yielded mixed results due to methodological limitations.
Conclusions:
- The current application of PBMA in the NHS requires refinement.
- A more systematic approach is needed to identify evaluation options, moving beyond reliance on expert groups.
- Improved methodologies can enhance the effectiveness of PBMA in increasing net benefits through resource reallocation.
More Related Videos
Related Concept Videos
Decision Making: P-value Method
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Kaplan-Meier Approach
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Introduction to Partial Derivatives
In many real-world situations, an output depends on more than one input. In a high-tech assembly plant, total production may depend on technician labor and machine capacity at the same time. This relationship can be represented by a continuous function P(T, M), where T denotes technician labor input, and M denotes machine capacity. When demand increases, but the budget remains fixed, the manager must determine which input will improve production more efficiently.Partial derivatives provide a...
Lagrange Multipliers: Two Constraints
The method of Lagrange multipliers with two constraints is used to optimize a function subject to two independent constraints. In many applications, the objective function represents a quantity to be maximized or minimized, such as cost, area, distance, or energy. The two constraints represent requirements that the solution must satisfy, such as fixed volume, limited resources, or prescribed dimensions.For a function of three variables, each constraint forms a surface in three-dimensional space.
Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

