Dental health records may be a fertile
source of data for life insurers.
Life insurers have
long recognized the value of oral fluids for risk assessment through screening
for HIV-1 and tobacco or drug use, but oral health itself is not routinely
evaluated as part of the underwriting process.
Oral health is recognized by the medical
community as an important determinant of overall health, and is associated with
other health conditions, socioeconomic indicators, and lifestyle behaviors that
are risk factors for mortality. As such, dental health records may be a fertile
source of data for life insurers.
Munich Re provides the industry with research,
introductions to new tools, and analytical support for carriers interested in
incorporating new sources of data in underwriting and risk assessment. We have
evaluated supplementary sources of evidence related to oral health in
underwriting, particularly for detecting tobacco use without collecting fluids.
This paper will explore some of the relationships between oral health and
overall health, the impact of poor oral health on mortality as mediated through
smoking status, and new sources of underwriting data related to dental records,
specifically the Tobacco Score from Sikka Software Corporation.
A holistic view of
oral health
The practice of dentistry is evolving towards
a multi-dimensional approach to assessing oral health which recognizes the role
that dental disease and disorders can play in well-being. This “oral
health-related quality of life” framework is recognized by the World Health
Organization; it links oral health (e.g. pain or gum issues), oral function
(such as chewing), social and environmental factors, emotional wellbeing, and
satisfaction with treatment into a measure that guides appropriate dental
treatment and improves overall health.1
While oral health problems can arise at any
age, they are generally of greater concern in an aging population. For example,
periodontal disease (gum damage) and dental caries (cavities) cause
inflammation, which is a known contributor to cardiovascular disease, and can
allow bacteria to pass into the bloodstream, potentially causing serious
life-threatening infections. The risk is greater in those with known
cardiovascular disease and may complicate the treatment of other systemic
diseases.2,3 Tooth loss can also accelerate gum disease and is
associated with poor nutrition.4 Other health conditions, such
as diabetes and obesity, and behavioral choices can contribute to poor oral
health.5 Notably, tobacco use is a significant risk factor for
oral cancers and gum disease.
Oral health and
differential mortality
Does variation in oral health relate to
differences in mortality outcomes? One study based on the National Health and
Nutrition Examination Survey (NHANES) reported on three traditional measures of
poor oral health: significant tooth loss, root caries (lesions on the tooth
root), and periodontal disease for individuals above the age of 40. It found
that each indicator, when considered individually, was associated with
significantly higher mortality (excluding deaths related to accidents or
violence and controlling for age and gender).6 However, the
relationship for each condition weakened when sociodemographic variables were
also included. For root caries and periodontal disease, the relationship with
mortality became statistically insignificant when controlling for age, gender,
and health behavior indicators (obesity/smoking), with smoking likely being the
cause of these oral conditions and also driving elevated mortality. The study
did find that when all three oral health conditions were present at the same
time, the rate of mortality was increased compared to individuals with no or
only one oral condition.
Oral health and smoker
detection: Sikka Software Tobacco Score
The American Dental Association states that,
“Because of the oral health implications of tobacco use, dental practices may
provide a uniquely effective setting for tobacco use recognition, prevention,
and cessation.”8 This suggests that there is novel information
to be gained from dentists’ observations of their patients’ health and behavior
in regards to tobacco use.
The NHANES study also supports other research
where oral health is correlated with sociodemographic indicators, many of which
are already considered during the underwriting process. But smoking status is a
very important lurking variable, with smoking and smokeless tobacco use
contributing to adverse oral health and higher mortality. Besides increasing
the risk of oral cancer, gum disease, and tooth loss and decay, tobacco use
causes other observable changes to the mouth, including stained teeth,
darkening of the gums, bad breath, and longer wound healing time.7
The Tobacco Score offered by Sikka Software
Corporation (Sikka)9 incorporates HIPAA-compliant dental
clinical notes into a categorical score reflecting an individual’s likelihood
of being a smoker. Sikka develops applications for the retail healthcare
industry, covering dental, veterinary, chiropractic, vision, orthodontics, and
hearing care markets in the United States, with over 110 million patients on
its platform. This platform allows Sikka to perform healthcare analytics across
a diverse population and develop tools relevant to the life insurance process.
Sikka’s Tobacco Score is generated with an individual’s consent, using his or
her dental clinical notes and completed oral health evaluations and dental
procedures, which have been parsed and fed through a machine-learning model.
Individuals who are matched to Sikka’s patient records are assigned to one of
three classes: tobacco user (T), not a tobacco user (NT), or no evidence of
tobacco use (N). The tobacco user class (T) is further segmented into five
subgroups (T1-T5) based on the age of the clinical note, the frequency of
dental procedures related to the periodontal therapies and surgeries, and
tobacco counseling procedures, with a higher score indicating a higher degree
of confidence about the current tobacco use status.
The NT class signifies that the clinical notes
indicate that this individual doesn’t use tobacco, while the N class denotes
that while this individual was found in the records, clinical notes provide no
evidence for tobacco use.
Munich Re performed a validation of Sikka’s
Tobacco Score using insured records. The match rate was approximately 25
percent using name and date of birth only. This confusion matrix shows the
results from the Sikka Tobacco Score model versus the actual underwritten
tobacco class, with the total of matched individuals standardized to equal
1,000 individuals.* We quantified the performance of this model using the
statistical measures of sensitivity and specificity. The sensitivity, equal to
the percent of smokers correctly identified as smokers (34) out of the total
number of actual tobacco class individuals (346), is 10 percent. That means
that the model was able to correctly identify 10 percent of true tobacco class
members. The specificity, equal to the percent of individuals correctly
identified as non-smokers or no-evidence (641) out of the total number of
actual non-tobacco class individuals (654), is 98 percent. High specificity
means that this model performed very well on correctly identifying non-tobacco
class members. Both of these performance measures are independent of the
prevalence of smoking in the population to which the score is being applied.
The Tobacco Score is likely best used to augment other information gathered in
the underwriting process; although the sensitivity in this sample is low, the
specificity is quite high.
Accelerated
underwriting: oral health and smoking
A primary source of additional mortality risk
in accelerated underwriting programs is smoker non-disclosure; smokers are
motivated to misrepresent their smoking behavior because they expect to
avoid lab (cotinine) tests. Smoker prediction models and tools identify
some of the smokers who self-disclose as non-smokers and route them to full
underwriting. Smoker models can also be easily integrated with other models for
triage and risk selection, as well as other underwriting tools for a
comprehensive risk assessment program.
We believe there is support for assessing
Sikka’s Tobacco Score in the context of an accelerated process to better manage
risk. Sikka’s Tobacco Score can offer protective value alongside smoker
prediction models, as it can be used to refine a list of “likely” smokers to
“highly likely” smokers. The Tobacco Score’s high specificity means that the
model can also be used alongside existing smoker models to correctly identify
most of those who are not smokers.
Research confirms that information about
dental health can be informative about overall health, and products such as
Sikka’s tap into this alternative source of information. Munich Re recommends
each carrier perform a retrospective study to best assess the value and
application of the Sikka Tobacco Score on its company-specific insured
population.
Carriers should also consult their legal team
concerning the use of third-party data. Munich Re can provide assistance in
program development and monitoring for carriers considering incorporating
third-party data sources such as the Sikka Tobacco Score in the life insurance
process.
References:1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3318061/2. https://onlinelibrary.wiley.com/doi/full/10.1111/j.1834-7819.2009.01144.x3. https://www.cdc.gov/nchs/data/ahcd/agingtrends/03oral.pdf4. https://data.web.health.state.mn.us/oa-tooth-loss5. https://www.mayoclinic.org/healthy-lifestyle/adult-health/in-depth/dental/art-200474756. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885153/;
(1999–2004 with follow-up on mortality through 2006)7. https://www.publichealth.va.gov/docs/smoking/oral-health-tobacco-use.pdf8. https://www.ada.org/en/member-center/oral-health-topics/smoking-and-tobacco-cessation9. https://www.sikkasoft.com/
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