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).
 

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.

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.

Factors Considered for Prescribing Fluoride Tablets

Child's Age:

• Different age groups require different dosages.
• Children older than 4 years may receive lozenges or chewable tablets, while those younger than 4 are typically prescribed liquid fluoride drops.

Fluoride Concentration in Drinking Water:

• The fluoride level in the child's drinking water is crucial.
• If the fluoride concentration is less than 1 part per million (ppm), systemic fluoride supplementation is recommended.

Risk of Dental Caries:

• Children at higher risk for dental decay may need additional fluoride supplementation.
• Regular dental assessments help determine the need for fluoride.

Overall Health and Dietary Needs:

• Consideration of the child's overall health and any dietary restrictions that may affect fluoride intake.

Recommended Doses of Fluoride Tablets

For Children Aged 6 Months to 4 Years:

• Liquid drops are typically prescribed in doses of 0.125, 0.25, and 0.5 mg of fluoride ion.

For Children Aged 4 Years and Older:

• Chewable tablets or lozenges are recommended, usually at doses of 0.5 mg to 1 mg of fluoride ion.

Adjustments Based on Water Fluoride Levels:

• Doses may be adjusted based on the fluoride content in the child's drinking water to ensure adequate protection against dental caries.

Duration of Supplementation:

• Fluoride supplementation is generally continued until the child reaches 16 years of age, depending on their fluoride exposure and dental health status.

Berkson's Bias is a type of selection bias that occurs in case-control studies, particularly when the cases and controls are selected from a hospital or clinical setting. It arises when the selection of cases (individuals with the disease) and controls (individuals without the disease) is influenced by the presence of other conditions or factors, leading to a distortion in the association between exposure and outcome.

Key Features of Berkson's Bias

1. Hospital-Based Selection: Berkson's Bias typically occurs in studies where both cases and controls are drawn from the same hospital or clinical setting. This can lead to a situation where the controls are not representative of the general population.

2. Association with Other Conditions: Individuals who are hospitalized may have multiple health issues or risk factors that are not present in the general population. This can create a misleading association between the exposure being studied and the disease outcome.

3. Underestimation or Overestimation of Risk: Because the controls may have different health profiles compared to the general population, the odds ratio calculated in the study may be biased. This can lead to either an overestimation or underestimation of the true association between the exposure and the disease.

Example of Berkson's Bias

Consider a study investigating the relationship between smoking and lung cancer, where both cases (lung cancer patients) and controls (patients without lung cancer) are selected from a hospital. If the controls are patients with other diseases that are also related to smoking (e.g., chronic obstructive pulmonary disease), this could lead to Berkson's Bias. The controls may have a higher prevalence of smoking than the general population, which could distort the perceived association between smoking and lung cancer.

Implications of Berkson's Bias

• Misleading Conclusions: Berkson's Bias can lead researchers to draw incorrect conclusions about the relationship between exposures and outcomes, which can affect public health recommendations and clinical practices.
• Generalizability Issues: Findings from studies affected by Berkson's Bias may not be generalizable to the broader population, limiting the applicability of the results.

Mitigating Berkson's Bias

To reduce the risk of Berkson's Bias in research, researchers can:

1. Select Controls from the General Population: Instead of selecting controls from a hospital, researchers can use population-based controls to ensure a more representative sample.

2. Use Multiple Control Groups: Employing different control groups can help identify and account for potential biases.

3. Stratify Analyses: Stratifying analyses based on relevant characteristics (e.g., age, sex, comorbidities) can help to control for confounding factors.

4. Conduct Sensitivity Analyses: Performing sensitivity analyses can help assess how robust the findings are to different assumptions about the data.

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.