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Public Health Dentistry - NEETMDS- courses
Public Health Dentistry

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.

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.

Multiphase and multistage random sampling are advanced sampling techniques used in research, particularly in public health and social sciences, to efficiently gather data from large and complex populations. Both methods are designed to reduce costs and improve the feasibility of sampling while maintaining the representativeness of the sample. Here’s a detailed explanation of each method:

Multiphase Sampling

Description: Multiphase sampling involves conducting a series of sampling phases, where each phase is used to refine the sample further. This method is particularly useful when the population is large and heterogeneous, and researchers want to focus on specific subgroups or characteristics.

Process:

  1. Initial Sampling: In the first phase, a large sample is drawn from the entire population using a probability sampling method (e.g., simple random sampling or stratified sampling).
  2. Subsequent Sampling: In the second phase, researchers may apply additional criteria to select a smaller, more specific sample from the initial sample. This could involve stratifying the sample based on certain characteristics (e.g., age, health status) or conducting follow-up surveys.
  3. Data Collection: Data is collected from the final sample, which is more targeted and relevant to the research question.

Applications:

  • Public Health Surveys: In a study assessing health behaviors, researchers might first sample a broad population and then focus on specific subgroups (e.g., smokers, individuals with chronic diseases) for more detailed analysis.
  • Qualitative Research: Multiphase sampling can be used to identify participants for in-depth interviews after an initial survey has highlighted specific areas of interest.

Multistage Sampling

Description: Multistage sampling is a complex form of sampling that involves selecting samples in multiple stages, often using a combination of probability sampling methods. This technique is particularly useful for large populations spread over wide geographic areas.

Process:

  1. First Stage: The population is divided into clusters (e.g., geographic areas, schools, or communities). A random sample of these clusters is selected.
  2. Second Stage: Within each selected cluster, a further sampling method is applied to select individuals or smaller units. This could involve simple random sampling, stratified sampling, or systematic sampling.
  3. Additional Stages: More stages can be added if necessary, depending on the complexity of the population and the research objectives.

Applications:

  • National Health Surveys: In a national health survey, researchers might first randomly select states (clusters) and then randomly select households within those states to gather health data.
  • Community Health Assessments: Multistage sampling can be used to assess oral health in a large city by first selecting neighborhoods and then sampling residents within those neighborhoods.

Key Differences

  • Structure:

    • Multiphase Sampling involves multiple phases of sampling that refine the sample based on specific criteria, often leading to a more focused subgroup.
    • Multistage Sampling involves multiple stages of sampling, often starting with clusters and then selecting individuals within those clusters.
  • Purpose:

    • Multiphase Sampling is typically used to narrow down a broad sample to a more specific group for detailed study.
    • Multistage Sampling is used to manage large populations and geographic diversity, making it easier to collect data from a representative sample.

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.

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).
 

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

 

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).

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