๐ Public Health Dentistry
Dental Indices
Public Health DentistryPlaque 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).
Distribution and determinants of disease
Public Health Dentistry1. 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.
Bias in Public Health Dentistry
Public Health DentistryHere are some common types of bias encountered in public health dentistry, along with their implications:
1. Selection Bias
Description: This occurs when the individuals included in a study are not representative of the larger population. This can happen due to non-random sampling methods or when certain groups are more likely to be included than others.
Implications:
- If a study on dental care access only includes patients from a specific clinic, the results may not be generalizable to the broader community.
- Selection bias can lead to over- or underestimation of the prevalence of dental diseases or the effectiveness of interventions.
2. Information Bias
Description: This type of bias arises from inaccuracies in the data collected, whether through measurement errors, misclassification, or recall bias.
Implications:
- Recall Bias: Patients may not accurately remember their dental history or behaviors, leading to incorrect data. For example, individuals may underestimate their sugar intake when reporting dietary habits.
- Misclassification: If dental conditions are misdiagnosed or misreported, it can skew the results of a study assessing the effectiveness of a treatment.
3. Observer Bias
Description: This occurs when the researcherโs expectations or knowledge influence the data collection or interpretation process.
Implications:
- If a dentist conducting a study on a new treatment is aware of which patients received the treatment versus a placebo, their assessment of outcomes may be biased.
- Observer bias can lead to inflated estimates of treatment effectiveness or misinterpretation of results.
4. Confounding Bias
Description: Confounding occurs when an outside variable is associated with both the exposure and the outcome, leading to a false association between them.
Implications:
- For example, if a study finds that individuals with poor oral hygiene have higher rates of cardiovascular disease, it may be confounded by lifestyle factors such as smoking or diet, which are related to both oral health and cardiovascular health.
- Failing to control for confounding variables can lead to misleading conclusions about the relationship between dental practices and health outcomes.
5. Publication Bias
Description: This bias occurs when studies with positive or significant results are more likely to be published than those with negative or inconclusive results.
Implications:
- If only studies showing the effectiveness of a new dental intervention are published, the overall understanding of its efficacy may be skewed.
- Publication bias can lead to an overestimation of the benefits of certain treatments or interventions in the literature.
6. Survivorship Bias
Description: This bias occurs when only those who have "survived" a particular process are considered, ignoring those who did not.
Implications:
- In dental research, if a study only includes patients who completed a treatment program, it may overlook those who dropped out due to adverse effects or lack of effectiveness, leading to an overly positive assessment of the treatment.
7. Attrition Bias
Description: This occurs when participants drop out of a study over time, and the reasons for their dropout are related to the treatment or outcome.
Implications:
- If patients with poor outcomes are more likely to drop out of a study evaluating a dental intervention, the final results may show a more favorable outcome than is truly the case.
Addressing Bias in Public Health Dentistry
To minimize bias in public health dentistry research, several strategies can be employed:
- Random Sampling: Use random sampling methods to ensure that the sample is representative of the population.
- Blinding: Implement blinding techniques to reduce observer bias, where researchers and participants are unaware of group assignments.
- Standardized Data Collection: Use standardized protocols for data collection to minimize information bias.
- Statistical Control: Employ statistical methods to control for confounding variables in the analysis.
- Transparency in Reporting: Encourage the publication of all research findings, regardless of the results, to combat publication bias.
Procedure for Test of Significance
Public Health DentistryA 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.
