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:

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.

When testing a null hypothesis, two types of errors can occur:

1. 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.

2. 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.