NEET MDS Lessons
Public Health Dentistry
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.
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.
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
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.
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.
Factors Considered for Prescribing Fluoride Tablets
Child's Age:
- Different age groups require different dosages.
- Children older than 4 years may receive lozenges or chewable tablets, while those younger than 4 are typically prescribed liquid fluoride drops.
Fluoride Concentration in Drinking Water:
- The fluoride level in the child's drinking water is crucial.
- If the fluoride concentration is less than 1 part per million (ppm), systemic fluoride supplementation is recommended.
Risk of Dental Caries:
- Children at higher risk for dental decay may need additional fluoride supplementation.
- Regular dental assessments help determine the need for fluoride.
Overall Health and Dietary Needs:
- Consideration of the child's overall health and any dietary restrictions that may affect fluoride intake.
Recommended Doses of Fluoride Tablets
For Children Aged 6 Months to 4 Years:
- Liquid drops are typically prescribed in doses of 0.125, 0.25, and 0.5 mg of fluoride ion.
For Children Aged 4 Years and Older:
- Chewable tablets or lozenges are recommended, usually at doses of 0.5 mg to 1 mg of fluoride ion.
Adjustments Based on Water Fluoride Levels:
- Doses may be adjusted based on the fluoride content in the child's drinking water to ensure adequate protection against dental caries.
Duration of Supplementation:
- Fluoride supplementation is generally continued until the child reaches 16 years of age, depending on their fluoride exposure and dental health status.