Related Experiment Video
Updated: Jul 16, 2026
14:32
Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
Published on: February 16, 2011
From data to policy: good practices and cautionary tales
Carla AbouZahr1, Sam Adjei, Churnrurtai Kanchanachitra
1Health Metrics Network, World Health Organization, 27 Avenue Appia, 1211 Geneva 27, Switzerland. abouzahrc@who.int
Lancet (London, England)
|March 27, 2007
Summary
Effective health statistics are crucial for evidence-based policy. This study outlines a framework to overcome barriers, promoting data use for better health outcomes globally.
Area of Science:
- Public Health
- Health Policy
- Biostatistics
Background:
- Sound statistical data is essential for evidence-based policymaking.
- Numerous institutional, political, and practical barriers hinder the effective utilization of health data.
- The relationship between health statistics and policy development at national and international levels requires examination.
Purpose of the Study:
- To analyze the interplay between health statistics and policymaking.
- To propose a framework facilitating the transition from data to policy.
- To identify good practices for enhancing the use of health statistics in policy.
Main Methods:
- Review of existing literature and practices on health statistics and policymaking.
- Development of a fourfold framework for data-to-policy transition.
- Identification of key challenges and facilitators in utilizing health data for policy.
Main Results:
- A fourfold framework is proposed to bridge the gap between data and policy.
- Key good practices include data reconciliation, enhanced communication, country ownership, and managing conflicts of interest.
- Significant investments are needed in data collection and capacity building for data analysis and interpretation.
Conclusions:
- Overcoming barriers to health statistics use requires a multi-faceted approach.
- Implementing the proposed framework and good practices can improve evidence-based policymaking.
- Strengthening national capacity in data analysis and presentation is vital for effective health policy development.
Related Concept Videos
01:12Hindsight Biases
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
01:52Persuasion Strategies
Researchers have tested many persuasion strategies, including the foot-in-the door and the door-in-the-face techniques, in a variety of contexts. Ultimately, the principles are effective in selling products and changing people’s attitude, ideas, and behaviors (Cialdini & Goldstein, 2004).
01:12What Are Outliers?
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
01:16Unusual Results
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...
01:20Actuarial Approach
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:09Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...