Lecture 6 – July 16,  2003

	Linking populations, prevention, and risk assessment

Objectives

	To understand the operating premises of risk assessment
	To be familiar with the different types of risk factors and with risk predictors
	To understand the relationship between risk factors and levels of evidence
	To be able to differentiate between risk models and prediction models
	To be familiar with criteria for prediction models
	To understand the notion of targeting and how it applies to teeth as well as individuals

Operating premises of risk assessment

		For decades medicine has developed methods to identify individuals at high risk of diseases
			such as heart disease, stroke, and cancer
		High risk individuals then targeted for special programs
			such as early detection and treatment for various types of cancer
			risk reduction efforts for heart disease and stroke

Operating premises of risk assessment

	Majority of risk assessment efforts
		applied to the two major dental diseases, caries and periodontitis
		began in the early 1980’s
	Interest in risk assessment for dental conditions arose out of a change in prior paradigms
		regarding the etiology
		and progression of these diseases

Operating premises of risk assessment

	Under the old paradigm
		prevalence of the conditions was extremely high
		result was little error in prediction
		when everyone was categorized as having the disease
	Under the new paradigm
		prevalence of the disease not as high
		some people more likely to be affected by the condition than others

Operating premises of risk assessment

	Now from perspective of assigning risk
	Caries and periodontitis thought to be more like some common medical conditions
		Certain people or subgroups of the population at higher risk than others
		efforts at prevention and intervention involve a combination of personal behaviors and professional practices

Operating premises of risk assessment

	Studies undertaken:
	to develop models to identify populations at high risk prior to development of the disease
	Multivariate (multivariable) perspective

Operating premises of risk assessment

	That: Clinical, behavioral, and etiological factors can be used to determine caries risk
	That: Not all patients require the same level of prevention
	Thus:  The extent of prevention can be appropriate to the level of risk

Variables used in models

	Essentially two types of variables used in the development of multivariate models:
	1) risk factors
		those that can be modified (e.g., levels of pathogens)
		those that are immutable to change (background factors)
	2) risk predictors

Risk Factors

	Recently World Workshop on Periodontics adopted a working definition of the term risk factor:
		An environmental, behavioral, or biologic factor
		confirmed by temporal sequence
		usually in longitudinal studies
		if  present, directly increases probability of disease occurring
		and, if absent or removed, reduces the probability
	Risk factors part of the causal chain
		Or expose host to the causal chain

Example - variables in multivariate models( I)

	Clinical conditions - referral status, caries in primary teeth, orthodontic care
	Microbiological tests - S mutans, lactobacilli
	Social behavioral variables - snack consumption, dental health practices, fluoride history, antibiotic use

Demographic risk factor

	Demographic risk factors (background characteristics)
		Meet definition of a risk factor,
			But currently immutable to change
		More likely to expose host to causal chain
			than be part of causal chain
		Not useful for intervention
			But often useful as group characteristic when targeting people to apply another intervention

Example - Variables in multivariable models (I)

	Clinical/dental - age, water fluoridation, gingival recession, n teeth with perio pockets over 3 mm in depth, n teeth with calculus
	Measures of general health and physical function
	Behavioral and psychosocial - smoking, sugar consumption, anxiety, social integration, depression, stress

Risk predictor (risk marker)

	Risk predictor (risk marker)
		A characteristic associated with elevated risk of disease (i.e., it predicts well)
		Not thought to be part of the causal chain (e.g., tooth loss is a good predictor of future disease)
		Usually either a byproduct of the causal chain (usually called a risk marker)
		Or some historical measure of the outcome, such as number of missing teeth, or baseline caries
		Useful to identify who is at risk, but not useful in identifying likely interventions

Risk predictor (risk marker)

	The terms risk predictor and risk marker often used synonymously
		Risk marker tends to be used when describing biological predictors, such as C-reactive protein being a marker for inflammation
		Risk predictor often used for any non-etiologic variable that is a good predictor
		In dentistry tend to be alternative historical measures of the disease being studied,
			such as the number of missing teeth
			or past evidence of dental caries or periodontal disease

Example: Variables in multivariable models (I)

	Clinical/dental - age, water fluoridation, gingival recession, n teeth with perio pockets over 3 mm in depth, n teeth with calculus
	Measures of general health and physical function
	Behavioral and psychosocial - smoking, sugar consumption, anxiety, social integration, depression, stress

Risk factors - criteria

	Three criteria need to be satisfied in order to identify a characteristic as a risk factor:
		1.  the factor must be observed to covary with the disease
			i.e., the factor must be statistically associated with the development of the disease
			or equivalently, the frequency of disease must be observed to differ by category or value of the factor
		2.  the presence of the risk factor ( or a relevant change in the risk factor) must precede occurrence of the disease

Risk factor – criteria:

	3 – the observed association must not be entirely due to any source of error
		including chance or sampling error
		the involvement of other (extraneous) risk factors
		or other problems with the study design or data analysis
		E.g., study should reflect design and analytic methods
			that make it less likely to produce:
			biased findings
			or associations unadjusted for potential confounders

Risk factors – level of evidence

	If the criteria for identifying a risk factor are to be met, longitudinal studies must be used
		However longitudinal studies are expensive to conduct and may take many years to complete
		Consequently variables thought to be risk factors frequently are uncovered through associations seen in prevalence (cross-sectional) studies

Risk factors – level of evidence

	Indicator for Caries Management  -
	From the patient history:
	past and present fluoride availability

Risk factors – Level of evidence

	Level 1 -
	Strong evidence from at least one published systematic review of multiple well-designed randomized controlled trials
	(multiple,reviewed) ( rct’s) (pop-based)

Risk factors – level of evidence

	Indicator for Caries Management -
	From the patient history:
	dietary component in smooth surface caries

Risk factors – level of evidence

	Level 3 -
	Evidence from published well-designed trials without randomization
	single group pre-, post- comparisons
	cohort, time series or matched case controlled studies
	(no randomization) (population-based)

Risk factors – level of evidence

	Term “risk indicator”
	used to differentiate factors that have only been identified by means of prevalence data and can be defined as a probable or putative risk factor
		Often detected in cross-sectional studies, that has not been confirmed by longitudinal studies
		Other terms used to label factors derived from prevalence studies are putative (or potential) risk factors

Example - variables in multivariable models (I)

	Clinical/dental - age, water fluoridation, gingival recession, n teeth with perio pockets over 3 mm in depth, n teeth with calculus
	Measures of general health and physical function
	Behavioral and psychosocial - smoking, sugar consumption, anxiety, social integration, depression, stress

An example: caries risk assessment (CRA) model

	First, screen populations for model risk variables
	Use the model to predict risk

An example: caries risk assessment (CRA) model

	A total of nine variables considered
	Each variable has assigned weighting score
	Weights summed to arrive at a CRA score: 0-3, low; 4-8, moderate; 9, high risk

An example: caries risk assessment (CRA) model

	The following series of variables are considered:  Presence of active caries
	1.  Presence of frank lesions in mouth (3)
	2.  Frank carious lesions = 3 or more  (5)
	3.  Incipient caries surfaces = 3 or more (4)

An example: caries risk assessment (CRA) model

	Evidence of Prior Infectious Disease
	4.  Number of filled surfaces = 5 or >   (2)
	5.  Last filling for caries was placed less              than 1 year ago   (1)

An example: caries risk assessment (CRA)

	Variables consistent with the current paradigm of the cause and progression of caries
	6.  Mutans streptococcus count in saliva is   high (>= 106 cfu/ml)   (2)
	7.  Sugar/diet history (2)

An example: caries risk assessment (CRA) model

	Secondary variables that influence the rate of progression of the carious lesion
	8.  Fluoride exposure is/was adequate  (1/2)
	9.  Unstimulated saliva flow is below     normal (<0.2 ml/min)  (2)

An example: caries risk assessment (CRA) model

	Three risk categories: high, middle, low
	Weights summed to arrive at a CRA score:
	0-3,  low
	4-8,  moderate
	9,  high risk

Risk model vs. prediction model

	Distinction based on the intended use of the model:
	prediction of people at high risk
	or prediction of people at high risk and delineation of risk factors

Risk model

	If the purpose is prediction and delineation of risk factors in order to develop the most effective prevention or treatment interventions
	then a “risk” model should be developed
	Risk factors part of the causal chain
		Or expose host to the causal chain

Prediction model

	If are mainly interested in identifying who is at high risk
		If the main goal is to maximize sensitivity and specificity of the prediction
		any good predictor may be included in the model

Prediction models

	Some situations favor the use of a prediction model
	When the appropriate interventions are known
		the objective may be to maximize the ability of the model to identify high-risk and low-risk individuals
		i.e., maximize sensitivity and specificity
			the proportions of people who have
			and do not have the disease, respectively, who are correctly classified by the model

Prediction models

	While prediction models may predict more accurately (greater sensitivity and specificity) than risk models
		they contain predictor variables that will not influence the incidence of disease if changed
		or are characteristics that are immutable to change

An example: caries risk assessment (CRA)

	The following series of predictors are considered:  Presence of active caries
	1.  Presence of frank lesions in mouth (3)
	2.  Frank carious lesions = 3 or more  (5)
	3.  Incipient caries surfaces = 3 or more (4)

Prediction models

	These same predictors (such as past history of disease or number of teeth) may be powerful predictors that are easy and inexpensive to obtain
		In contrast, risk factors, such as microbiologic activity, salivary buffering capacity, and immune status, are more expensive to determine and increase the cost of assessment

An example: caries risk assessment (CRA)

	Factors consistent with the current paradigm of the cause and progression of caries
	6.  Mutans streptococcus count in saliva is   high (>= 106 cfu/ml)   (2)
	7.  Sugar/diet history (2)

Prediction models

	Thus prediction models often may be the models of choice
	However when using prediction models, one should always remember that the models may not make much explanatory sense
	because the presence of powerful predictors in a model may mask the effects of related risk factors
		E.g., the presence of “number of teeth” or “baseline DMF score” in a model may mask the role of microorganisms in diseases known to be infectious in nature

Prediction model criteria

	Criteria for predictive model for high-caries   risk:
		quick
		inexpensive
		easy to use
		limited equipment needed
		readily acceptable

Prediction model criteria

	Needs to be within acceptable parameters of accuracy, precision:
	sensitivity, specificity
	positive and negative predictive values

Prediction model criteria -

	Assessing Model Utility
	Compare actual and predicted scores
	Model sensitivity - The % of people that actually have the disease and were correctly predicted to get it
	Model specificity - The % of people that did not get the disease and were correctly predicted to be disease free

Prediction model criteria

	Choices between sensitivity and specificity must be made:
	Where to draw the line?
	What are the costs of misclassifying             patients?
	Answer is not statistical

Prediction model criteria

	When we attempt to improve the sensitivity of procedures, we become less selective
		include people who do not have the disease
	Can be more restrictive
		by raising the specificity of the test …
		reduce the # of false-positive determinations

An example: caries risk assessment (CRA) model

	Three risk categories: high, middle, low
	Weights summed to arrive at a CRA score:
	0-3,  low
	4-8,  moderate
	9,  high risk

Result: targeting

	Directing preventive services to those at risk
	1) For the clinician:
	Tailor the preventive program to the patient
	2) For the community planner:
	Target populations

Result: targeting - for the clinician

	The CRA score and risk determination help to determine:
			an individualized preventive plan incorporated into the overall tx plan
			a reevaluation interval determined

Result: Targeting – for a population

	Direct preventive services to those at risk
	Appropriate levels of care
	Greater effectiveness of preventive procedures
	Economic efficiency and cost containment

Example: targeting sealant use

	Changes in epidemiology of caries during the past decades:
		decrease in prevalence
		decrease in rate of progression
		distribution on different tooth surfaces
	Implications
		predicting risk
		conducting caries preventive programs

Example: targeting sealant use

	If  caries levels keep declining...
	smaller amounts of disease for a sealant program to prevent...
	procedure more costly per surface of caries prevented...
	unless susceptible surfaces identified

Example: targeting sealant use

	Tooth & Surface-Specific Patterns of Caries Attack

Slide 54

Slide 55

Slide 56

Example: targeting sealant use

	Figure 3 and 4: occlusal of first molars is the most caries-prone site
	Figure 5: increased caries rate of pit & fissure surfaces compared to other surfaces

Example: targeting sealant use

	Differences in risk among tooth surfaces
		allow effective targeting of disease prevention
		without incurring cost of population screening

Example: targeting sealant use

	Identifying teeth at highest risk of dental caries is more efficient than identifying high-risk individuals

Example: targeting sealant use

	With limited resources
		Need to find ways to predict and target children at higher risk for sealant application
		Sealant placement on all sound pit and fissure surfaces
		of primary and permanent teeth
		on all high risk children is not feasible