Understanding the Difference Between Association and Prediction in Epidemiology Health Studies

Many clients approach us with the question, “Which markers most reliably indicate our health risks?” Although no test can offer absolute certainty, some biomarkers can provide substantial predictive insights. 

 

By understanding these key indicators, we can better assess potential health risks and take proactive measures. Let’s take a closer look at the markers that offer the greatest certainty and discuss how they can help in predicting health outcomes.

 

 

 

 

 

 

 

 

 

 

 

Introduction to Epidemiology

 

 

Epidemiology is the study of how diseases affect the health and illness of populations. It involves investigating the causes, distribution, and control of diseases in groups of people. Epidemiologists use this information to improve public health outcomes by identifying risk factors and implementing strategies to prevent disease.

 

In nutritional and wellness studies, epidemiology is especially important because it allows researchers to study the relationships between diet, lifestyle factors, and health outcomes in large populations. Directly testing certain things on humans can be unethical or impractical, so epidemiologists rely on observational studies to gather data.

 

The Common Misconception

Many people believe that a significant link between a risk factor and an outcome means the risk factor can predict the outcome. For example, if a new biomarker, which we will call the Unicorn Factor, is strongly linked to a higher risk of death, people might think that measuring the Unicorn Factor will help predict who will die. This seems logical but often isn’t the case.

 

A Hypothetical Example: Unicorn Factor

 

 

Let’s imagine a study where we follow 10,000 people for 10 years, and 2,000 of them die. At the beginning of the study, we measure the Unicorn Factor in everyone. The Unicorn Factor can be either 0 or 1. Our analysis shows that people with a positive Unicorn Factor are 40 times more likely to die than those with a negative Unicorn Factor. This result is highly statistically significant (P value < 0.001), indicating a strong association.

 

Given this strong link, we might expect the Unicorn Factor to be a powerful predictor of death. To test this, we use a metric called the area under the receiver operating characteristic curve (AUC or C-statistic). This metric tells us, given two people – one who dies and one who doesn’t – how often we can correctly identify the one who will die based on their Unicorn Factor.

 

A C-statistic of 0.5 means the Unicorn Factor is no better than a coin flip, while a C-statistic of 1 means perfect prediction. Given the strong association, we might expect a high C-statistic, perhaps 0.9 or 0.95. However, the actual C-statistic is only 0.5024, which is barely better than chance. This shows the difference between association and prediction.

 

Why the Discrepancy?

To understand why a strong association does not always lead to strong predictive power, we need to look at the data more closely. In our example, only 11 people have the Unicorn Factor, and 10 of those 11 people die. While the Unicorn Factor is highly linked to death, it is very rare in the population. This rarity makes it almost useless to predict who will die because it does not help distinguish who will die among the vast majority who do not have the Unicorn Factor.

 

Real-World Example: Cardiovascular Disease Prediction

 

 

This concept is important in real-world health predictions, such as predicting cardiovascular disease (CVD), the leading cause of death worldwide. Current predictive models for CVD use risk scores based on well-known factors such as cholesterol levels and blood pressure. The most common risk score in the United States, the pooled cohort risk equation, uses nine variables, including a cholesterol panel and blood pressure measurement. 

 

This model achieves a C-statistic as high as 0.82 in Black women and as low as 0.71 in Black men, indicating good but not perfect predictive power.

 

In the era of big data and personalized medicine, researchers have tried adding various biomarkers to these risk scores to improve prediction. 

 

A study published in JAMA included 164,054 patients from 28 cohort studies across 12 countries, measuring key biomarkers like troponin (a marker of heart muscle stress), NT-proBNP (a marker of heart muscle stretch), and C-reactive protein (CRP, a marker of inflammation). Higher levels of these biomarkers were linked to a higher risk of cardiovascular events. 

 

Despite these strong links, adding these biomarkers to the existing model only slightly improved the C-statistic from 0.812 to 0.819. The best improvement was seen in predicting heart failure within one year, where the AUC improved by 0.04. However, this small improvement suggests that these biomarkers, despite their strong associations with cardiovascular events, add little to the predictive power of existing models.

 

Why Strong Associations Don’t Always Improve Predictions

 

 

There are several reasons why strong associations do not always improve predictive models. including:

 

• Rarity in the Population: If a biomarker is rare, it won’t significantly help in predicting outcomes for most people.
• Overlap with Existing Factors: New biomarkers may correlate with factors already included in existing models. For example, high levels of NT-proBNP might reflect a patient’s blood pressure or cholesterol levels, which are already part of the risk equation.
• Need for Unique Information: Improvement in prediction requires new and independent information. If the new biomarkers do not provide unique insights beyond what is already known, they will not significantly enhance the model’s predictive power.

 

The Role of Epidemiology in Nutrition and Wellness Studies

Epidemiology plays a crucial role in nutritional and wellness studies because it allows researchers to observe and analyze health outcomes in large populations without exposing individuals to potentially harmful interventions. 

 

For example, it would be unethical to conduct a study where participants are forced to consume a potentially harmful substance to observe its effects. Instead, epidemiologists study people who have naturally been exposed to certain factors and compare their health outcomes to those who have not.

 

Case Study: Diabetes Prediction Model

Another example can be seen in diabetes prediction models. While many biomarkers and genetic factors have been identified that are associated with diabetes, integrating these into a practical and accurate predictive model remains challenging. 

 

A comprehensive study by the American Diabetes Association highlighted that even though specific genetic markers are strongly associated with diabetes risk, their addition to traditional risk factors (such as BMI, age, and family history) only marginally improved the predictive power of models.

 

Advances in Machine Learning

 

 

In recent years, advances in big data and machine learning have promised better predictive models by leveraging vast amounts of health data. These technologies can identify complex patterns and interactions among variables that traditional statistical methods might miss. However, the challenge remains to ensure that the identified patterns provide new and useful information for prediction.

 

For instance, machine learning models have been used to analyze electronic health records (EHRs) to predict hospital readmissions. These models incorporate a wide range of data, including patient demographics, medical history, lab results, and medication records. Despite the potential, studies have shown that while these models improve prediction accuracy slightly, they often do not drastically outperform simpler models that use fewer variables.

 

The Importance of Context in Predictive Models

Predictive models in epidemiology must account for context to be truly effective. This includes understanding the population being studied and the specific health outcomes of interest. For example, a biomarker that is a strong predictor in one population may not be as effective in another due to genetic, environmental, or lifestyle differences.

 

Real-World Applications in Nutritional Studies

Epidemiology has been instrumental in understanding the impact of diet on health. For example, long-term studies have shown that eating non-processed, real foods is associated with a reduced risk of chronic diseases such as heart disease and cancer. However, translating these associations into predictive models for individual dietary recommendations remains challenging.

 

Case Study: Framingham Heart Study

The Framingham Heart Study, initiated in 1948, is one of the most influential epidemiological studies in understanding cardiovascular disease. Researchers followed over 5,000 participants in Framingham, Massachusetts, to identify common factors contributing to heart disease. The study identified several key risk factors, including high blood pressure, high cholesterol, smoking, obesity, and diabetes.

 

The Framingham Risk Score, developed from this study, is a predictive model that estimates an individual’s risk of developing heart disease based on these factors. While the model has been widely used and validated, it is not without limitations. It does not account for all possible risk factors and may not be as accurate for populations different from the original study group. This further validates the need for continuous refinement and validation of predictive models in diverse populations.

 

The Role of Genetic and Environmental Factors

Both genetic and environmental factors play significant roles in health outcomes. For instance, genetic predispositions can influence how individuals respond to certain diets or environmental exposures. 

 

However, the interactions between genes and the environment are complex and are not fully understood. This complexity makes it challenging to develop predictive models that accurately account for these interactions.

 

Personalized Nutrition and Health

Personalized nutrition, which tailors dietary recommendations based on an individual’s genetic makeup, lifestyle, and health status, is an emerging field. While promising, it faces several challenges, including the need for robust predictive models that integrate genetic, environmental, and lifestyle data. Current research is exploring how personalized nutrition can improve health outcomes, but more studies are needed to validate these approaches.

 

The Future of Predictive Models in Epidemiology Health Studies

 

 

Predictive models in epidemiology continue to evolve with advancements in technology and data analysis. The integration of big data, machine learning, and personalized medicine holds the potential to significantly improve the accuracy and utility of these models. 

 

Future predictive models can benefit from integrating a wide range of data sources, including genetic information, environmental exposures, social determinants of health, and behavioral data. Personalized predictive models that account for individual differences in genetics, lifestyle, economic status, insurance access, and environment are needed. These models should provide personalized health recommendations and interventions, improving overall preventive measures and treatments.

 

The increasing availability of real-time data from wearable devices, electronic health records (EHRs), and other sources may support predictive models to provide more timely and actionable insights. Real-time data analysis can help identify emerging health risks and support early interventions. 

 

Ethical Considerations and Data Privacy

The use of big data and advanced analytics in health prediction raises important ethical considerations. Ensuring data privacy and security is critical, especially given the sensitive nature of health information. Additionally, predictive models must be used responsibly to avoid potential harm, such as discrimination or stigmatization of individuals based on predicted health outcomes.

 

Privacy and individualized care must take precedence over leveraging predictive models for pharmaceutical or financial gain.

 

Closing Thoughts On Association vs. Prediction in Epidemiology Health Studies

While a strong association between a risk factor and an outcome can provide valuable insights, it does not guarantee predictive power. Understanding this distinction is crucial for managing an individual’s health. Bloodwork is not the only predictive power measure for health or disease. Medical history, diet, lifestyle, environmental factors, daily symptoms, overall wellness, access to healthcare, and community support must all be considered.

 

So, if you ask our practice if there’s a definitive way to determine cardiovascular risk, the answer is sort of. It’s because there is a stark difference between association and prediction, and why certain promising “predictive” biomarkers don’t always lead to better predictive health outcomes. 

 

And while the advancements in big data and machine learning sound promising, there are many things we’ll need to take into consideration if and when we implement these tools within society. Better predictive models may improve diagnosing outcomes, but we know that many in our community have been disillusioned by experiences including standard care pushing prescriptions based on existing predictive models used without the context and nuances required. When predictive models begin to dictate our treatment and care for a patient, you can see how this puts patients at risk.      

 

What we do know is instead of simply chasing bloodwork, it’s essential to consider all facets of diet, lifestyle, community, environment, and stress management. We can do our best with the tools we have in order to enjoy lives nearly symptom-free, but ultimately, we will never know predictive risk with absolute certainty.   

 

With all the details and nuances in each of our health cases, it requires both data and a trusted practitioner in order to put together these puzzle pieces to have a holistic understanding and create personalized treatment protocols.  

 

Rather than falling down the research rabbit hole or chasing numbers on blood work based on predictive models, we recommend finding a practitioner who’s able to interpret and balance all of your health data while working toward root-cause healing. Leave the rest and have faith.