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.

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.

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.

Classifications of epidemiologic research

1. Descriptive research —involves description, documentation, analysis, and interpretation of data to evaluate a current event or situation

a. incidence—number of new cases of a specific disease within a defined population over a period of time

b. Prevalence—number of persons in a population affected by a condition at any one time

c. Count—simplest sum of disease: number of cases of disease occurrence

d. Proportion—use of a count with the addition of a denominator to determine prevalence:

does not include a time dimension: useful to evaluate prevalence of caries in schoolchildren or tooth loss in adult populations

e. Rate— uses a standardized denominator and includes a time dimension. for example. the number of deaths of newborn infants within first year of life per 1000 births

2. Analytical research—determines the cause of disease or if a causal relationship exists between a factor and a disease

a. Prospective study—planning of the entire study is completed before data are collected and analyzed; population is followed through time to determine which members develop the disease; several hypotheses may be tested at on time

b. Cohort study—individuals are classified into groups according to whether or not they pos- sess a particular characteristic thought to be related to the condition of interest; observations occur over time to see who develops dis ease or condition

c. Retrospective study— decision to carry out an investigation using observations or data that have been collected in the past; data may be incomplete or in a manner not appropriate for study

d. Cross-sectional study— study of subgroups of individuals in a specific and limited time frame to identify either initially to describe current status or developmental changes in the overall group from the perspective of what is typical in each subgroup

e. Longitudinal study—investigation of the same group of individuals over an extended period of time to identify a change or devel opment in that group

3. Experimental research—used when the etiology of the disease is established and the researcher wishes to determine the effectiveness of altering some factor or factors; deliberate applying or withholding of the supposed cause of a condition and observing the result

Sampling methods are crucial in public health dentistry as they enable researchers and practitioners to draw conclusions about the oral health of a population based on a smaller, more manageable subset of individuals. This approach is cost-effective, time-saving, and statistically valid. Here are the most commonly used sampling methods in public health dentistry with their applications:

1. Simple Random Sampling: This is the most basic form of probability sampling, where each individual in the population has an equal chance of being selected. It involves the random selection of subjects from a complete list of all individuals (sampling frame). This method is applied when the population is homogeneous and the sample is expected to be representative of the entire population.

It is useful in studies that aim to determine prevalence of dental caries or periodontal disease in a community, assess the effectiveness of oral health programs, or evaluate the need for dental services.

2. Stratified Random Sampling: This technique involves dividing the population into strata (subgroups) based on relevant characteristics such as age, gender, socioeconomic status, or geographic location. Random samples are then drawn from each stratum. This method ensures that the sample is more representative of the population by reducing sampling error.

 It is often used when the population is heterogeneous, and there is a need to analyze the data separately for each subgroup to understand the impact of different variables on oral health.

Applications:

• Oral Health Disparities: Stratified sampling can be used to ensure representation from different socioeconomic groups when studying access to dental care.
• Age-Specific Studies: In research focusing on pediatric dental health, stratified sampling can help ensure that children from various age groups are adequately represented.

3. Cluster Sampling: In this method, the population is divided into clusters (e.g., schools, neighborhoods, or dental clinics) and a random sample of clusters is selected. All individuals within the chosen clusters are included in the study. This approach is useful when the population is widely dispersed, and it reduces travel and data collection costs. It is often applied in community-based dental health surveys and epidemiological studies.

Applications:

• School-Based Dental Programs: Cluster sampling can be used to select schools within a district to assess the oral health status of children, where entire schools are chosen rather than individual students.
• Community Health Initiatives: In evaluating the effectiveness of community dental health programs, clusters (e.g., neighborhoods) can be selected to represent the population.

4. Systematic Sampling: This technique involves selecting every nth individual from the sampling frame, where n is the sampling interval. It is a probability sampling method that can be used when the population has some order or pattern. For instance, in a school-based dental health survey, students from every third grade might be chosen to participate.

This method is efficient for large populations and can be representative if the sampling interval is appropriate.

Applications:

• Community Health Assessments: Systematic sampling can be used to select households for surveys on oral hygiene practices, where every 10th household is chosen from a list of all households in a neighborhood.
• Patient Records Review: In retrospective studies, systematic sampling can be applied to select patient records at regular intervals to assess treatment outcomes.

5. Multi-stage Sampling: This is a combination of different sampling methods where the population is divided into smaller and smaller clusters in each stage. It is particularly useful for large-scale studies where the population is not easily accessible or when the study requires detailed data from various levels (e.g., national to local levels).

 For example, in a multi-stage design, a random sample of states might be selected in the first stage, followed by random samples of counties within those states, and then schools within the selected counties.

Applications in Public Dental Health:

• National Oral Health Surveys: Researchers may first randomly select states or regions (clusters) and then randomly select dental clinics or households within those regions to assess the prevalence of dental diseases or access to dental care.
• Community Health Assessments: In a large city, researchers might select neighborhoods as the first stage and then sample residents within those neighborhoods to evaluate oral health behaviors and access to dental services.
• Program Evaluation: Multi-stage sampling can be used to evaluate the effectiveness of community dental health programs by selecting specific program sites and then sampling participants from those sites.

6. Convenience Sampling: Although not a probability sampling method, convenience sampling is often used in public health dentistry due to practical constraints. It involves selecting individuals who are readily available and willing to participate. While this method may introduce bias, it is useful for pilot studies, exploratory research, or when the goal is to obtain preliminary data quickly and inexpensively. It is important to be cautious when generalizing findings from convenience samples to the broader population.

Applications:

• Pilot Studies: Convenience sampling can be used in preliminary studies to gather initial data on dental health behaviors among easily accessible groups, such as dental clinic patients.
• Focus Groups: In qualitative research, convenience sampling may be used to gather opinions from dental patients who are readily available for discussion.

7. Quota Sampling: This is a non-probability sampling method where the researcher sets quotas for specific characteristics of the population (e.g., age, gender) and then recruits individuals to meet those quotas. It is often used in surveys where it is crucial to have a representative sample regarding certain demographic variables.

However, it may not be as statistically robust as probability sampling methods and can introduce bias if the quotas are not met correctly.

Applications in Public Dental Health:

• Targeted Surveys: Researchers can use quota sampling to ensure that specific demographic groups (e.g., children, elderly, low-income individuals) are adequately represented in surveys assessing oral health knowledge and behaviors.
• Program Evaluation: In evaluating community dental health programs, quota sampling can help ensure that participants reflect the diversity of the target population, allowing for a more comprehensive understanding of program impact.
• Focus Groups: Quota sampling can be used to assemble focus groups for qualitative research, ensuring that participants represent various perspectives based on predetermined characteristics relevant to the study.

8. Purposive (Judgmental) ampling: In this approach, participants are selected based on specific criteria that the researcher believes are important for the study. This method is useful for studies that require in-depth understanding, such as qualitative research or when studying a rare condition. It is essential to ensure that the sample is diverse enough to provide a comprehensive perspective.

Applications:

• Expert Interviews: In studies exploring dental policy or public health initiatives, purposive sampling can be used to select key informants, such as dental professionals or public health officials.
• Targeted Health Interventions: When studying specific populations (e.g., individuals with disabilities), purposive sampling ensures that the sample includes individuals who meet the criteria.

9. Snowball Sampling: This is a non-probability method where initial participants are selected based on the researcher's judgment and then asked to refer others with similar characteristics. It is often used in studies involving hard-to-reach populations, such as those with rare oral conditions or specific behaviors.

While it can provide valuable insights, the sample may not be representative of the broader population.

Applications :

• Studying Marginalized Groups: Researchers can use snowball sampling to identify and recruit individuals from marginalized communities (e.g., homeless individuals, low-income families) to assess their oral health needs and barriers to accessing dental care.
• Behavioral Research: In studies examining specific behaviors (e.g., smoking and oral health), initial participants can help identify others who share similar characteristics or experiences, facilitating data collection from a relevant population.
• Qualitative Research: Snowball sampling can be effective in qualitative studies exploring the experiences of individuals with specific dental conditions or those participating in community dental health programs.

10. Time-Space Sampling: This technique is used to study populations that are not fixed in place, such as patients attending a dental clinic during specific hours. Researchers select random times and days and then include all patients who visit the clinic during those times in the sample.

This method can be useful for assessing the representativeness of clinic-based studies.

Applications

• Mobile Populations: Researchers can use time-space sampling to assess the oral health of populations that may not have a fixed residence, such as migrant workers or individuals living in temporary housing.
• Event-Based Sampling: Public health campaigns or dental health fairs can be used as time-space sampling points to recruit participants for surveys on oral health behaviors and access to care.
• Community Outreach: Time-space sampling can help identify individuals attending community events or clinics to gather data on their oral health status and service utilization.

The choice of sampling method in public health dentistry depends on the research question, the population's characteristics, the available resources, and the desired level of generalizability. Probability sampling methods are generally preferred for their scientific rigor, but non-probability methods may be necessary under certain circumstances. It is essential to justify the chosen method and consider its limitations when interpreting the results.