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).

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.

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

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

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.