Quantifying Chronic Obstructive Pulmonary Disease

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Chronic Obstructive Pulmonary Disease, or COPD, is a chronic disease characterized by the inflammation of the lungs. This inflammation causes air to be obstructed from the lungs and can result in difficulty breathing, coughs, mucus and wheezing (Mayo Clinic). It affects at least 16 million Americans and 250 million people globally – it is a leading cause of death nationally and globally (Healthline). There are three main causes of COPD – the primary cause is exposure to tobacco smoke, the secondary is exposure to air pollution or fumes and the tertiary cause is due to asthma. Most COPD cases are the result of the first two causes and, as a result, are somewhat preventable; however, there is more to the story. COPD can also be the result of genetics and is correlated with the presence of other diseases which the field of analytics can help take into account in trying to predict COPD risk. As analytics continues to impact nearly every aspect of our lives, it is hopeful that it can also be a tool to help those suffering from Chronic Obstructive Pulmonary Disease.

Key Data Points

The main pieces of data needed to evaluate risk of COPD are lung function tests, the results of a chest x-ray, arterial blood gas analysis, sputum (mucus) test and the results of an Alpha-1-antitrypsin blood test. The Alpha-1-antitrypsin test is a genetic test that tells a patient whether or not they are deficient in the protein that protects the lungs from irritants; those who are deficient are likely develop COPD at a young age. This piece of information can be key to prevention because once COPD is present, it is irreversible (Healthline). Additionally, because COPD can cause hypertension, heart disease, diabetes and other health problems, it can be useful to look at the general metabolic panel.

New Technology and Relevant Studies

Another way to look at COPD from a data standpoint is through geocoding which looks at health conditions as the result of a surrounding environment. Geocoding is not a new form of data visualization but can be immensely helpful. For example, take a look at the image below:

This image shows us where prevalence of COPD is highest. Researchers can use this information to find commonalities between these cities to identify causes of COPD that may have been overlooked or not even considered. For example, one study found that in their studied population, lower winter ambient temperatures could be associated with increased COPD hospital admissions (Serra-Picamal). This is not surprising, because asthma symptoms worsen with colder air so one could expect to see similar statistics for COPD, however, it is not an assumption one can make without data-based proof. Of course, this is just one study, but it goes to show that data can pick up trends that we as humans cannott validate without proof.

Aside from diagnosis and progression predictions, analytics can also be used to improve care for COPD patients. At Intermountain Healthcare, a scoring system called Laboratory-based Intermountain Validated Exacerbation (LIVE) predicts mortality, morbidity and hospitalization rates for patients with COPD. The score is calculated by using hemoglobin, albumin, creatinine, chloride and potassium values to determine which patients are at risk of progression or death and to identify which patients need to move onto advanced care. In the first test of the LIVE scoring system, researchers found that it was able to successfully identify which patients were low or high risk at time of hospital admission and could produce a score that matched to the appropriate plan of care (Kent).

Prevention 

The best thing someone can do to prevent COPD is to stop smoking or stop exposure to secondhand smoke and air pollution. Following that, the best way to prevent COPD is to live a healthy lifestyle by maintaining good hygiene, keeping up to date with flu and pneumonia vaccines, eating a healthy diet and staying active.

Conclusion

Chronic Obstructive Pulmonary Disease is a disease that is highly preventable by maintaining a healthy lifestyle, however, there are factors that make certain groups more at risk than others. By combining the power of data with medicine, we can continue to compile a list of those factors to help those who are at risk prevent the disease before they have it. Analytics can also help in disease and care monitoring to improve hospital care for patients. As this field continues to develop, we can hope to see lower rates of incidence of COPD in the future and continually improving care for those who do have it.

Works Cited

“CDC – Data and Statistics – Chronic Obstructive Pulmonary Disease (COPD).” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 5 June 2018, www.cdc.gov/copd/data.html.

“COPD.” Mayo Clinic, Mayo Foundation for Medical Education and Research, 15 Apr. 2020, www.mayoclinic.org/diseases-conditions/copd/symptoms-causes/syc-20353679.

Kent, Jessica. “Predictive Analytics, Risk Scores Improve Care for COPD Patients.” HealthITAnalytics, 9 Aug. 2019, healthitanalytics.com/news/predictive-analytics-risk-scores-improve-care-for-copd-patients.

Roland, James. “COPD Diagnosis: Spirometry, X-Ray, and 6 More Tests for COPD.” Healthline, Healthline Media, 17 Nov. 2018, www.healthline.com/health/copd/tests-diagnosis#takeaway.

Serra-Picamal, Xavier, et al. “Hospitalizations Due to Exacerbations of COPD: A Big Data Perspective.” Respiratory Medicine, W.B. Saunders, 16 Jan. 2018, www.sciencedirect.com/science/article/abs/pii/S095461111830009X.

Thomas, Jen. “COPD: Facts, Statistics, and You.” Healthline, Healthline Media, 14 May 2019, www.healthline.com/health/copd/facts-statistics-infographic#8.

Improving Predictive Healthcare Models by Filtering for Racial Differences in Data

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Risk calculators are at the forefront of analytics in healthcare – using ranges to understand who might be at risk of contracting a disease and when they might contract it, is a powerful tool but it doesn’t stop there. Analytics can also help us to better understand disease progression and manage symptoms, however, these tools often underserve racial and ethnic minorities because of the lack of inclusion of race-adjusted ranges in the metabolic and blood panels. To better serve the general population, racial differences in health data must be taken into consideration and used where applicable to make analytics a beneficial tool to all. In recent years, several studies have explored this topic and we will take a look at some of them in this article.

Current Problem

The biggest challenge in this research space is collecting data – for example, the link between breast cancer and race in women proves that at least some disparities in cancer diagnoses boil down to racial differences. One of the contributing factors to this disparity is many randomized clinical trials become stalled due to lack of enrollment (Zewde)[1].

Consequently, data segmented by racial differences can be difficult to obtain. The next biggest challenge is identifying when race is the impactful variable. Many people of the same race and ethnicity often share similar cultural practices so relationship, lifestyle, location, and other variables can influence the interaction of panel data and race. One-way that the National Center for Biotechnology

Information (NCBI) suggests tackling this is by administering more comprehensive questionnaires so that such parameters can be factored out to identify the root cause of a disparity.

Emerging Technology and Studies

This field of research is central to our mission at Altheia Predictive Health. Our proprietary predictive health models take race into account when creating risk ranges to ensure that each individual receives information that is personalized to their background. We can see in much of our research that risk ranges vary among race and ethnic groups with many minorities being classified at a higher risk than Caucasian Americans even with the same variable being measured. By including race as a parameter in predictive algorithms, we can train machines to better interpret and apply the most accurate data possible and, as a result, increase the accuracy of these algorithms.

There is more to this area of research; outside of diagnosing and managing diseases, analytics also identifies racial disparities in care management programs. In a study at Portland State University, researchers observed patients in a hospital emergency room and studied the way nurses and physicians interacted with people of varying races and ethnicities. Researchers found that “Black patients were 32 percent less likely to receive pain medication than white patients, while Hispanic patients were 21 percent less likely to receive pain medication than their white counterparts. Asian patients were 24 percent less likely to receive pain medication than white individuals. This was despite the fact that black and Hispanic patients reported higher average pain scores than white patients.” [2]

Works Cited

[1] Zewde, Makda. “Tracking Health Disparities with Big Data.” NPHR Blog, 20 Oct. 2017,  nphr.wordpress.com/2017/10/19/tracking-health-disparities-with-big-data/#prettyPhoto.

[2] Kent, Jessica. “EHR Data Reveals Racial Disparities in Emergency Pain Treatment.” HealthITAnalytics, 20 Dec. 2019, healthitanalytics.com/news/ehr-data-reveals-racial-disparities-in-emergency-pain-treatment.

Do Analytics Discriminate? Disparities in Algorithms Across Various Racial and Ethnic Groups

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Patients who come in with concerns about kidney function are always tested for their glomerular filtration rate(GFR). It is interesting to note, the GFR is factored into the Kidney Failure Risk Equation and other algorithms differently for African Americans but remains standard across all other ethnic and racial minority groups. However, across several other predictive health algorithms and risk calculators, racial differences in the metabolic panel are typically not considered. Given that different racial and ethnic groups have different predispositions to and progressions of diseases, this is an area that needs improvement. As the field of medicine moves towards analytics to predict patient risk, medication needs and other factors, it is important to consider the benefits of increased accuracy by taking race into consideration when creating algorithms, as well as the implications of biased algorithms.

Current Problem

In 2019, NBC reported on an algorithm written by Optum that was heavily biased. The algorithm was used to rank patients that would most benefit from a care program that aimed to manage responsible use of medication and to keep patients out of the hospital. In a group of 6,000 black patients and 44,000 white patients, only 17.5% of people recommended for the program were black despite the fact that the black patients had 26.3% more chronic conditions. Developers had to ask themselves why this was the case and found that their cost-based algorithm left behind black patients because of monetary biases – black patients spend $1,800 less per year than white patients with the same conditions. This means that black patients with the same or worse chronic conditions than white patients were left behind because of their inability to pay. Once Optum balanced that aspect of the algorithm, they saw the number of black patients recommended for the program jump from 17.5% to 46.5% (Gawronski). This example shows disparities in algorithms that we can not always anticipate but that still negatively affect certain racial groups.

Analytics As A Solution

In October of 2019, George Washington University received a grant to study these types of disparities. The four-year study is still in its earliest stages, but it will help researchers better identify the relationships between race and disease prediction. Yan Ma, who is the Vice Chair of the Department of Bioinformatics at George Washington University, says that one of the biggest and easiest steps that can be taken to improve this situation is for large databases to include a patient’s ethnic group and/or race in their information. In fact, it’s surprising that many data bases do not already have this data.  If they did, it would make machine learning and artificial intelligence even more powerful tools than they are currently (Kent).

The Centers for Medicare & Medicaid Services echo the same sentiment in their studies of using racial data to improve healthcare treatment for all patients, citing lack of clear and reliable data as the biggest roadblock to their research and the area most in need of improvement. Currently, Medicare uses (and has been using for quite some time) a method called geocoding to target at risk communities. Geocoding makes predictions about a person’s health based on the characteristics of the areas in which they live. It works in the way that many would like algorithms to account for racial differences;  it takes into consideration the population health and trends of a community to make more targeted predictions about a person’s health. This method is limited because it is not exact and is highly dependent on areas that self-segregate. It does however lay out the groundwork for how a study could separate certain groups to identify their specific risk factors and general health.

Conclusion

While data science tools such as machine learning and artificial intelligence have significantly advanced the field of medicine, there are many people that are left behind because of biased equations. In order to best serve all communities it is important that healthcare providers and companies that are creating predictive algorithms take differences in racial predispositions into account and adjust metabolic and blood panels in the same way to provide the best care to each unique patient.

Works Cited

Gawronski, Quinn. “Racial Bias Found in Widely Used Health Care Algorithm.” NBCNews.com, NBCUniversal News Group, 7 Nov. 2019, www.nbcnews.com/news/nbcblk/racial-bias-found-widely-used-health-care-algorithm-n1076436.

Kent, Jessica. “Machine Learning to Uncover Racial Disparities in Healthcare.” HealthITAnalytics, 31 Oct. 2019, healthitanalytics.com/news/machine-learning-to-uncover-racial-disparities-in-healthcare.

Llanos, Karen. “Using Data on Race and Ethnicity to Improve Health Care Quality for Medicaid Beneficiaries.” CHCS, 2006, www.chcs.org/media/Using_Date_to_Reduce_Health_Disparities.pdf.