NEET MDS Lessons
Public Health Dentistry
Common tests in dental biostatics and applications
Dental biostatistics involves the application of statistical methods to the
study of dental medicine and oral health. It is used to analyze data, make
inferences, and support decision-making in various dental fields such as
epidemiology, clinical research, public health, and education. Some common tests
and their applications in dental biostatistics include:
1. T-test: This test is used to compare the means of two
independent groups. For example, it can be used to compare the pain levels
experienced by patients who receive two different types of local anesthetics
during dental procedures.
2. ANOVA (Analysis of Variance): This test is used to compare
the means of more than two independent groups. It is often used in dental
studies to evaluate the effectiveness of multiple treatments or to compare the
success rates of different dental materials.
3. Chi-Square Test: This is a non-parametric test used to
assess the relationship between categorical variables. In dental research, it
might be used to determine if there is an association between tooth decay and
socioeconomic status, or between the type of dental restoration and the
frequency of post-operative complications.
4. McNemar's Test: This is a statistical test used to analyze
paired nominal data, such as the change in the presence or absence of a
condition over time. In dentistry, it can be applied to assess the effectiveness
of a treatment by comparing the presence of dental caries in the same patients
before and after the treatment.
5. Kruskal-Wallis Test: This is another non-parametric test for
comparing more than two independent groups. It's useful when the data is not
normally distributed. For instance, it can be used to compare the effectiveness
of three different types of toothpaste in reducing plaque and gingivitis.
6. Mann-Whitney U Test: This test is used to compare the
medians of two independent groups when the data is not normally distributed. It
is often used in dental studies to compare the effectiveness of different
interventions, such as comparing the effectiveness of two mouthwashes in
reducing plaque and gingivitis.
7. Regression Analysis: This statistical method is used to
analyze the relationship between one dependent variable (e.g., tooth loss) and
one or more independent variables (e.g., age, oral hygiene habits, smoking
status). It helps to identify risk factors and predict outcomes.
8. Logistic Regression: This is used to model the relationship
between a binary outcome (e.g., presence or absence of dental caries) and one or
more independent variables. It is commonly used in dental epidemiology to assess
the risk factors for various oral diseases.
9. Cox Proportional Hazards Model: This is a survival analysis
technique used to estimate the time until an event occurs. In dentistry, it
might be used to determine the factors that influence the time until a dental
implant fails.
10. Kaplan-Meier Survival Analysis: This method is used to
estimate the probability of survival over time. It's commonly applied in dental
studies to evaluate the success rates of dental restorations or implants.
11. Fisher's Exact Test: This is used to test the significance
of a relationship between two categorical variables, especially when the sample
size is small. It might be used in a study examining the association between a
specific genetic mutation and the occurrence of oral cancer.
12. Spearman's Rank Correlation Coefficient: This is a
non-parametric measure of the correlation between two continuous or ordinal
variables. It could be used to assess the relationship between the severity of
periodontal disease and the patient's self-reported oral hygiene habits.
13. Cohen's kappa coefficient: This measures the agreement
between two or more raters who are categorizing items into ordered categories.
It is useful in calibration studies among dental professionals to assess the
consistency of their diagnostic or clinical evaluations.
14. Sample Size Calculation: Determining the appropriate sample
size is crucial for ensuring that dental studies are adequately powered to
detect significant differences. This is done using statistical formulas that
take into account the expected effect size, significance level, and power of the
study.
15. Confidence Intervals (CIs): CIs provide a range within
which the true population parameter is likely to lie, given the sample data.
They are commonly reported in dental studies to indicate the precision of the
results, for instance, the estimated difference in treatment efficacy between
two groups.
16. Statistical Significance vs. Clinical Significance: Dental
biostatistics helps differentiate between results that are statistically
significant (unlikely to have occurred by chance) and clinically significant
(large enough to have practical implications for patient care).
17. Meta-Analysis: This technique combines the results of
multiple studies to obtain a more precise estimate of the effectiveness of a
treatment or intervention. It is frequently used in dental research to summarize
the evidence for various treatments and to guide clinical practice.
These tests and applications are essential for designing, conducting, and
interpreting dental research studies. They help ensure that the results are
valid and reliable, and can be applied to improve the quality of oral health
care.
Decayed-Missing-Filled Index ( DMF ) which was introduced by Klein, Palmer and Knutson in 1938 and modified by WHO:
1. DMF teeth index (DMFT) which measures the prevalence of dental caries/Teeth.
2. DMF surfaces index (DMFS) which measures the severity of dental caries.
The components are:
D component:
Used to describe (Decayed teeth) which include:
1. Carious tooth.
2. Filled tooth with recurrent decay.
3. Only the root are left.
4. Defect filling with caries.
5. Temporary filling.
6. Filled tooth surface with other surface decayed
M component:
Used to describe (Missing teeth due to caries) other cases should be excluded these are:
1. Tooth that extracted for reasons other than caries should be excluded, which include:
a- Orthodontic treatment.
b- Impaction.
c- Periodontal disease.
2. Unerupted teeth.
3. Congenitally missing.
4. Avulsion teeth due to trauma or accident.
F component:
Used to describe (Filled teeth due to caries).
Teeth were considered filled without decay when one or more permanent restorations were present and there was no secondary (recurrent) caries or other area of the tooth with primary caries.
A tooth with a crown placed because of previous decay was recorded in this category.
Teeth restored for reason other than dental caries should be excluded, which include:
1. Trauma (fracture).
2. Hypoplasia (cosmatic purposes).
3. Bridge abutment (retention).
4. Seal a root canal due to trauma.
5. Fissure sealant.
6. Preventive filling.
1. A tooth is considered to be erupted when just the cusp tip of the occlusal surface or incisor edge is exposed.
The excluded teeth in the DMF index are:
a. Supernumerary teeth.
b. The third molar according to Klein, Palmer and Knutson only.
2. Limitations - DMF index can be invalid in older adults or in children because index can overestimate caries record by cases other than dental caries.
1. DMFT: a. A tooth may have several restorations but it counted as one tooth, F. b. A tooth may have restoration on one surface and caries on the other, it should be counted as D . c. No tooth must be counted more than once, D M F or sound.
2. DMFS: Each tooth was recorded scored as 4 surfaces for anterior teeth and 5 surfaces for posterior teeth. a. Retained root was recorded as 4 D for anterior teeth, 5 D for posterior teeth. b. Missing tooth was recorded as 4 M for anterior teeth, 5 M for posterior teeth. c. Tooth with crown was recorded as 4 F for anterior teeth, 5 F for posterior teeth.
Calculation of DMFT \ DMFS:
1. For individual
DMF = D + M + F
2. For population
Minimum score = Zero
Primary teeth index:
1. dmft / dmfs Maximum scores: dmft = 20 , dmfs = 88
2. deft / defs, which was introduced by Gruebbel in 1944: d- decayed tooth. e- decayed tooth indicated for extraction . f- filled tooth.
3. dft / dfs: In which the missing teeth are ignored, because in children it is difficult to make sure whether the missing tooth was exfoliated or extracted due to caries or due to serial extraction.
Mixed dentition:
Each child is given a separate index, one for permanent teeth and another for primary teeth. Information from the dental caries indices can be derived to show the:
1. Number of persons affected by dental caries (%).
2. Number of surfaces and teeth with past and present dental caries (DMFT / dmft - DMFS / dmfs).
3. Number of teeth that need treatment, missing due to caries, and have been treated ( DT/dt, MT/mt, FT/f t).
The null hypothesis is a fundamental concept in scientific research,
including dentistry, which serves as a starting point for conducting experiments
or studies. It is a statement that assumes there is no relationship, difference,
or effect between the variables being studied. The null hypothesis is often
denoted as H₀.
In dentistry, researchers may formulate a null hypothesis to test the efficacy
of a new treatment, the relationship between oral health and systemic
conditions, or the prevalence of dental diseases. The purpose of the null
hypothesis is to provide a baseline against which the results of the study can
be compared to determine if the observed effects are statistically significant
or not.
Here are some common applications of the null hypothesis in dentistry:
1. Comparing Dental Treatments: Researchers might formulate a
null hypothesis that a new treatment is no more effective than the standard
treatment. For example, "There is no significant difference in the reduction of
dental caries between the use of fluoride toothpaste and a new, alternative
dental gel."
2. Oral Health and Systemic Conditions: A null hypothesis could
be used to test if there is no correlation between oral health and systemic
diseases such as diabetes or cardiovascular disease. For instance, "There is no
significant relationship between periodontal disease and the incidence of
stroke."
3. Dental Materials: Studies might use a null hypothesis to
assess the equivalence of different materials used in dental restorations. For
example, "There is no difference in the longevity of composite resin fillings
compared to amalgam fillings."
4. Dental Procedures: Researchers may compare the effectiveness
of new surgical techniques with traditional ones. The null hypothesis would be
that the new procedure does not result in better patient outcomes. For instance,
"There is no significant difference in post-operative pain between
laser-assisted versus traditional scalpel gum surgery."
5. Epidemiological Studies: In studies examining the prevalence
of dental diseases, the null hypothesis might state that there is no difference
in the rate of cavities between different population groups or regions. For
example, "There is no significant difference in the incidence of dental caries
between children who consume fluoridated water and those who do not."
6. Dental Education: Null hypotheses can be used to evaluate
the impact of new educational methods or interventions on dental student
performance. For instance, "There is no significant improvement in the manual
dexterity skills of dental students using virtual reality training compared to
traditional methods."
7. Oral Hygiene Products: Researchers might hypothesize that a
new toothpaste does not offer any additional benefits over existing products.
The null hypothesis would be that "There is no significant difference in plaque
reduction between the new toothpaste and the market leader."
To test the null hypothesis, researchers conduct statistical analyses on the
data collected from their studies. If the results indicate that the null
hypothesis is likely to be true (usually determined by a p-value greater than
the chosen significance level, such as 0.05), they fail to reject it. However,
if the results suggest that the null hypothesis is unlikely to be true,
researchers reject the null hypothesis and accept the alternative hypothesis,
which posits a relationship, difference, or effect between the variables.
In each of these applications, the null hypothesis is essential for maintaining
a rigorous scientific approach to dental research. It helps to minimize the risk
of confirmation bias and ensures that conclusions are drawn from objective
evidence rather than assumptions or expectations.
Case-Control Study and Cohort Study are two types of epidemiological studies
commonly used in dental research to identify potential risk factors and
understand the causality of diseases or conditions.
1. Case-Control Study:
A case-control study is a retrospective analytical study design in which
researchers start with a group of patients who already have the condition of
interest (the cases) and a group of patients without the condition (the
controls) and then work backward to determine if the cases and controls have
different exposures to potential risk factors. It is often used when the
condition is relatively rare, when it takes a long time to develop, or when it
is difficult to follow individuals over time.
In a case-control study, the cases are selected from a population that already
has the disease or condition being studied. The controls are selected from the
same population but do not have the disease. The researchers then compare the
two groups to see if there is a statistically significant difference in the
frequency of exposure to a particular risk factor.
Example in Dentistry:
Suppose we want to investigate whether there is a link between periodontal
disease and cardiovascular disease. A case-control study might be set up as
follows:
- Cases: Patients with a diagnosis of periodontal disease.
- Controls: Patients without a diagnosis of periodontal disease but otherwise
similar to the cases (same age, gender, socioeconomic status, etc.).
- Exposure of Interest: Cardiovascular disease.
The researchers would collect data on the medical and dental histories of both
groups, looking for a history of cardiovascular disease. They would compare the
proportion of cases with a history of cardiovascular disease to the proportion
of controls with the same history. If a significantly higher proportion of cases
have a history of cardiovascular disease, this suggests that there may be an
association between periodontal disease and cardiovascular disease. However,
because the study is retrospective, it does not prove that periodontal disease
causes cardiovascular disease. It merely suggests that the two are associated.
Advanatages:
- Efficient for studying rare diseases.
- Relatively quick and inexpensive.
- Can be used to identify multiple risk factors for a condition.
- Useful for generating hypotheses for further research.
Disadvantages:
- Can be prone to selection and recall bias.
- Cannot determine the temporal sequence of exposure and outcome.
- Cannot calculate the incidence rate or the absolute risk of developing the
disease.
- Odds ratios may not accurately reflect the relative risk in the population if
the disease is not rare.
2. Cohort Study:
A cohort study is a prospective longitudinal study that follows a group of
individuals (the cohort) over time to determine if exposure to specific risk
factors is associated with the development of a particular disease or condition.
Cohort studies are particularly useful in assessing the risk factors for
diseases that take a long time to develop or when the exposure is rare.
In a cohort study, participants are recruited and categorized based on their
exposure to a particular risk factor (exposed and non-exposed groups). The
researchers then follow these groups over time to see who develops the disease
or condition of interest.
Example in Dentistry:
Let's consider the same hypothesis as before, but this time using a cohort study
design:
- Cohort: A group of individuals who are initially free of
cardiovascular disease, but some have periodontal disease (exposed) and others
do not (non-exposed).
- Follow-up: Researchers would follow this cohort over a
certain period (e.g., 10 years).
- Outcome Measure: Incidence of new cases of cardiovascular
disease.
The researchers would track the incidence of cardiovascular disease in both
groups and compare the rates. If the exposed group (those with periodontal
disease) has a higher rate of developing cardiovascular disease than the
non-exposed group (those without periodontal disease), this would suggest that
periodontal disease may be a risk factor for cardiovascular disease.
Advanatges:
- Allows for the calculation of incidence rates.
- Can determine the temporal relationship between exposure and outcome.
- Can be used to study the natural history of a disease.
- Can assess multiple outcomes related to a single exposure.
- Less prone to recall bias since exposure is assessed before the outcome
occurs.
Disdvanatges:
- Can be expensive and time-consuming.
- Can be difficult to maintain participant follow-up, leading to loss to
follow-up bias.
- Rare outcomes may require large cohorts and long follow-up periods.
- Can be affected by confounding variables if not properly controlled for.
Both case-control and cohort studies are valuable tools in dental research.
Case-control studies are retrospective, quicker, and less costly, but
may be limited by biases. Cohort studies are prospective, more robust for
establishing causal relationships, but are more resource-intensive and require
longer follow-up periods. The choice of study design depends on the
research question, the availability of resources, and the nature of the disease
or condition being studied.
1. Disease is multifactorial in nature; difficult to identify one particular cause
a. Host factors
(1) Immunity to disease/natural resistance
(2) Heredity
(3) Age, gender, race
(4) Physical or morphologic factors
b. Agent factors
(1) Biologic—microbiologic
(2) Chemical—poisons, dosage levels
(3) Physical—environmental exposure
c. Environment factors
(1) Physical—geography and climate
(2) Biologic—animal hosts and vectors
(3) Social —socioeconomic, education, nutrition
2. All factors must be present to be sufficient cause for disease
3. Interplay of these factors is ongoing: to affect the disease, attack at the weakest link
Some Terms
1. Epidemic—a disease of significantly greater prevalence than normal; more than the expected number of cases; a disease that spreads rapidly through a demographic segment of a population
2. Endemic—continuing problem involving normal disease prevalence; the expected number of cases; indigenous to a population or geographic area
3. Pandemic—occurring throughout the population of a country, people, or the world
4. Mortality—death
5. Morbidity—disease
6. Rate—a numerical ratio in which the number of actual occurrences appears as the numerator and number of possible occurrences appears as the denominator, often used in compilation of data concerning the prevalence and incidence of events; measure of time is an intrinsic part of the denominator.
EPIDEMIOLOGY
Epidemiology is the study of the Distribution and determinants of disease frequency in Humans.
Epidemiology— study of health and disease in human populations and how these states are influenced by the environment and ways of living; concerned with factors and conditions that determine the occurrence and distribution of health. disease, defects. disability and deaths among individuals
Epidemiology, in conjunction with the statistical and research methods used, focuses on comparison between groups or defined populations
Characteristics of epidemiology:
1. Groups rather than individuals are studied
2. Disease is multifactorial; host-agent-environment relationship becomes critical
3. A disease state depends on exposure to a specific agent, strength of the agent. susceptibility of the host, and environmental conditions
4. Factors
- Host: age, race, ethnic background, physiologic state, gender, culture
- Agent: chemical, microbial, physical or mechanical irritants, parasitic, viral or bacterial
- Environment: climate or physical environment, food sources, socioeconomic conditions
5. Interaction among factors affects disease or health status
Uses of epidemiology
I. Study of patterns among groups
2. Collecting data to describe normal biologic processes
3. Understanding the natural history of disease
4. Testing hypotheses for prevention and control of disease through special studies in populations
5. Planning and evaluating health care services
6. Studying of non disease entities such as suicide or accidents
7. Measuring the distribution of diseases in populations
8. Identifying risk factors and determinants of disease
Plaque index (PlI)
0 = No plaque in the gingival area.
1 = A thin film of plaque adhering to the free gingival margin and adjacent to the area of the tooth. The plaque is not readily visible, but is recognized by running a periodontal probe across the tooth surface.
2 = Moderate accumulation of plaque on the gingival margin, within the gingival pocket, and/or adjacent to the tooth surface, which can be observed visually.
3 = Abundance of soft matter within the gingival pocket and/or adjacent to the tooth surface.
Gingival index (GI)
0 = Healthy gingiva.
1= Mild inflammation: characterized by a slight change in color, edema. No bleeding observed on gentle probing.
2 = Moderate inflammation: characterized by redness, edema, and glazing. Bleeding on probing observed.
3 = Severe inflammation: characterized by marked redness and edema. Ulceration with a tendency toward spontaneous bleeding.
Modified gingival index (MGI)
0 = Absence of inflammation.
1 = Mild inflammation: characterized by a slight change in texture of any portion of, but not the entire marginal or papillary gingival unit.
2 = Mild inflammation: criteria as above, but involving the entire marginal or papillary gingival unit.
3 = Moderate inflammation: characterized by glazing, redness, edema, and/or hypertrophy of the marginal or papillary gingival unit.
4 = Severe inflammation: marked redness, edema, and/or hypertrophy of the marginal or papillary gingival unit, spontaneous bleeding, or ulceration.
Community periodontal index (CPI)
0 = Healthy gingiva.
1 = Bleeding observed after gentle probing or by visualization.
2 = Calculus felt during probing, but all of the black area of the probe remains visible (3.5-5.5 mm from ball tip).
3 = Pocket 4 or 5 mm (gingival margin situated on black area of probe, approximately 3.5-5.5 mm from the probe tip).
4 = Pocket > 6 mm (black area of probe is not visible).
Periodontal screening and recording (PSR)
0 = Healthy gingiva. Colored area of the probe remains visible, and no evidence of calculus or defective margins is detected.
1 = Colored area of the probe remains visible and no evidence of calculus or defective margins is detected, but bleeding on probing is noted.
2 = Colored area of the probe remains visible and calculus or defective margins is detected.
3 = Colored area of the probe remains partly visible (probe depth between 3.5-5.5 mm).
4 = Colored area of the probe completely disappears (probe depth > 5.5 mm).