NEET MDS Lessons
Public Health Dentistry
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
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
The null hypothesis is a fundamental concept in scientific research,
including dentistry, which serves as a starting point for conducting experiments
or studies. It is a statement that assumes there is no relationship, difference,
or effect between the variables being studied. The null hypothesis is often
denoted as H₀.
In dentistry, researchers may formulate a null hypothesis to test the efficacy
of a new treatment, the relationship between oral health and systemic
conditions, or the prevalence of dental diseases. The purpose of the null
hypothesis is to provide a baseline against which the results of the study can
be compared to determine if the observed effects are statistically significant
or not.
Here are some common applications of the null hypothesis in dentistry:
1. Comparing Dental Treatments: Researchers might formulate a
null hypothesis that a new treatment is no more effective than the standard
treatment. For example, "There is no significant difference in the reduction of
dental caries between the use of fluoride toothpaste and a new, alternative
dental gel."
2. Oral Health and Systemic Conditions: A null hypothesis could
be used to test if there is no correlation between oral health and systemic
diseases such as diabetes or cardiovascular disease. For instance, "There is no
significant relationship between periodontal disease and the incidence of
stroke."
3. Dental Materials: Studies might use a null hypothesis to
assess the equivalence of different materials used in dental restorations. For
example, "There is no difference in the longevity of composite resin fillings
compared to amalgam fillings."
4. Dental Procedures: Researchers may compare the effectiveness
of new surgical techniques with traditional ones. The null hypothesis would be
that the new procedure does not result in better patient outcomes. For instance,
"There is no significant difference in post-operative pain between
laser-assisted versus traditional scalpel gum surgery."
5. Epidemiological Studies: In studies examining the prevalence
of dental diseases, the null hypothesis might state that there is no difference
in the rate of cavities between different population groups or regions. For
example, "There is no significant difference in the incidence of dental caries
between children who consume fluoridated water and those who do not."
6. Dental Education: Null hypotheses can be used to evaluate
the impact of new educational methods or interventions on dental student
performance. For instance, "There is no significant improvement in the manual
dexterity skills of dental students using virtual reality training compared to
traditional methods."
7. Oral Hygiene Products: Researchers might hypothesize that a
new toothpaste does not offer any additional benefits over existing products.
The null hypothesis would be that "There is no significant difference in plaque
reduction between the new toothpaste and the market leader."
To test the null hypothesis, researchers conduct statistical analyses on the
data collected from their studies. If the results indicate that the null
hypothesis is likely to be true (usually determined by a p-value greater than
the chosen significance level, such as 0.05), they fail to reject it. However,
if the results suggest that the null hypothesis is unlikely to be true,
researchers reject the null hypothesis and accept the alternative hypothesis,
which posits a relationship, difference, or effect between the variables.
In each of these applications, the null hypothesis is essential for maintaining
a rigorous scientific approach to dental research. It helps to minimize the risk
of confirmation bias and ensures that conclusions are drawn from objective
evidence rather than assumptions or expectations.
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:
- 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).
- 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.
- 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:
- First Stage: The population is divided into clusters (e.g., geographic areas, schools, or communities). A random sample of these clusters is selected.
- 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.
- 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.
When testing a null hypothesis, two types of errors can occur:
-
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.
-
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.
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.
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.