NEET MDS Lessons
Public Health Dentistry
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.
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.
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
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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.
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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.
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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:
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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.
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Use Multiple Control Groups: Employing different control groups can help identify and account for potential biases.
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Stratify Analyses: Stratifying analyses based on relevant characteristics (e.g., age, sex, comorbidities) can help to control for confounding factors.
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Conduct Sensitivity Analyses: Performing sensitivity analyses can help assess how robust the findings are to different assumptions about the data.
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
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:
- 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
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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.
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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.
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