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).

A test of significance in dentistry, as in other fields of research, is a statistical method used to determine whether observed results are likely due to chance or if they are statistically significant, meaning that they are reliable and not random. It helps dentists and researchers make inferences about the validity of their hypotheses.

The procedure for conducting a test of significance typically involves the following steps:

1. Formulate a Null Hypothesis (H0) and an Alternative Hypothesis (H1): The null hypothesis is a statement that assumes there is no significant difference between groups or variables being studied, while the alternative hypothesis suggests that there is a significant difference. For example, in a dental study comparing two different toothpaste brands for their effectiveness in reducing plaque, the null hypothesis might be that there is no difference in plaque reduction between the two brands, while the alternative hypothesis would be that one brand is more effective than the other.

2. Choose a significance level (α): This is the probability of incorrectly rejecting the null hypothesis when it is true. Common significance levels are 0.05 (5%) or 0.01 (1%).

3. Determine the sample size: Depending on the research question, power analysis or literature review may help determine the appropriate sample size needed to detect a clinically significant difference.

4. Collect data: Gather data from a sample of patients or subjects under controlled conditions or from existing databases.

5. Calculate test statistics: This involves calculating a value that represents the magnitude of the difference between the observed data and what would be expected if the null hypothesis were true. Common test statistics include the t-test, chi-square test, and ANOVA (Analysis of Variance).

6. Determine the p-value: The p-value is the probability of obtaining the observed results or results more extreme than those observed if the null hypothesis were true. It is calculated based on the test statistic and the chosen significance level.

7. Compare the p-value to the significance level (α): If the p-value is less than the significance level, the result is considered statistically significant. If the p-value is greater than the significance level, the result is not statistically significant, and the null hypothesis is not rejected.

8. Interpret the results: Based on the p-value, make a decision about the null hypothesis. If the p-value is less than the significance level, reject the null hypothesis and accept the alternative hypothesis. If the p-value is greater than the significance level, fail to reject the null hypothesis.

Here is a simplified example of a test of significance applied to dentistry:

Suppose you are comparing two different toothpaste brands to determine if there is a significant difference in their effectiveness in reducing dental plaque. You conduct a study with 50 participants who are randomly assigned to use either brand A or brand B for a month. After a month, you measure the plaque levels of all participants.

1. Null Hypothesis (H0): There is no significant difference in plaque reduction between the two toothpaste brands.
2. Alternative Hypothesis (H1): There is a significant difference in plaque reduction between the two toothpaste brands.
3. Significance Level (α): 0.05

Now, let's say you collected the data and found that the mean plaque reduction for brand A was 25%, with a standard deviation of 5%, and for brand B, the mean was 30%, with a standard deviation of 4%. You could use an independent samples t-test to compare the two groups' means.

4. Calculate the t-statistic: t = (Mean of Brand B - Mean of Brand A) / (Standard Error of the Difference)
5. Find the p-value associated with the calculated t-statistic. If the p-value is less than 0.05, you reject the null hypothesis.

If the p-value is less than 0.05, you can conclude that there is a statistically significant difference in plaque reduction between the two toothpaste brands, supporting the alternative hypothesis that one brand is more effective than the other. This could lead to further research or a change in dental hygiene recommendations.

In dental applications, tests of significance are commonly used in studies examining the effectiveness of different treatments, materials, and procedures. For instance, they can be applied to compare the success rates of different types of dental implants, the efficacy of various tooth whitening methods, or the impact of oral hygiene interventions on periodontal health. Understanding the statistical significance of these findings allows dentists to make evidence-based decisions and recommendations for patient care.

When testing a null hypothesis, two types of errors can occur:

1. Type I Error (False Positive):

   • Definition: This error occurs when the null hypothesis is rejected when it is actually true. In other words, the researcher concludes that there is an effect or difference when none exists.
   • Consequences in Dentistry: For example, a study might conclude that a new dental treatment is effective when it is not, leading to the adoption of an ineffective treatment.

2. Type II Error (False Negative):

   • Definition: This error occurs when the null hypothesis is not rejected when it is actually false. In this case, the researcher fails to detect an effect or difference that is present.
   • Consequences in Dentistry: For instance, a study might conclude that a new dental material is not superior to an existing one when, in reality, it is more effective, potentially preventing the adoption of a beneficial treatment.

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.

Terms

Health—state of complete physical, mental, and social well-being where basic human needs are met. not merely the absence of disease or infirmity; free from disease or pain

Public health — science and art of preventing disease. prolonging life, and promoting physical and mental health and efficiency through organized community efforts

1. Public health is concerned with the aggregate health of a group, a community, a state, a nation. or a group of nations

2. Public health is people’s health

3. Concerned with four broad areas

a. Lifestyle and behavior

b. The environment

c. Human biology

d. The organization of health programs and systems

Dental public health—science and art of preventing and controlling dental diseases and promoting dental health through organized community efforts; that form of dental practice that serves the community as a patient rather than the individual; concerned with the dental education of the public, with applied dental research, and with the administration of group dental care programs. as well as the prevention and control of dental diseases on a community basis

Community health—same as public health full range of health services, environmental and personal, including major activities such as health education of the public and the social context of life as it affects the community; efforts that are organized to promote and restore the health and quality of life of the people

Community dental health services are directed to ward developing, reinforcing, and enhancing the oral health status of people either as individuals or collectively as groups and communities

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.