1. L09 Critical Review of Epidemiologic Studies

2. FORMAT OF JOURNAL ARTICLES Abstract – a short summary of the entire article Introduction – the context of the study, including prior research  Context – investigator’s motivation for the study, discussing the hypothesis and objectives Methods – the setting, design, data collection procedures*, and analysis* (which provides the most information on the study’s validity)  Clearly defined and appropriately measured collection of data in order to obtain an outcome  Depends on the study type, population and associated sample size, and level of comparability between the cases vs. controls  Major area for bias or confounding  Potential reduction: randomization, restriction, matching, stratification, standardization, multivariation  Measures of reported association (e.g., relative risk, odds ratio) and statistical stability (e.g., hypothesis testing) Results – characteristics of the study population and findings from the study Discussion – extrapolates the results, offering an interpretation* of the findings and trends while addressing limitations  Interpretation of the potential risk for bias or confounding based on the collected data Conclusion – summarizes the findings and points to possible future research  Determination of causation (in which the presence of association does not imply causation) References Targets areas for criticism*

3. CAUSATION Evidence-based information – identifies the strengths and weaknesses within scientific evidence, aiding in the making of informed clinical practice decisions Causal inference – the process of how epidemiologists determine causative and preventative disease factors  Steps:  Is the result valid and true (explicitly free from bias, confounding, or random error)?  Has the exposure actually caused the disease?  Characteristics:  Association: the cause and effect must be statistically dependent  Time: the cause must proceed the effect  Direction: an established asymmetrical relationship must exist between the cause and effect (in which the cause leads to the effect but the effect doesn’t lead to the cause)  Positive correlation – the presence of the cause induces the effect  Negative correlation – the absence of the cause induces the effects  Can be influenced by host or environmental factors in which the conditions are active (with the intervention causes a change) or static (with an unchanging given set of conditions)

4. CAUSATION Sufficient-Component Cause Model (K. J. Rothman – 1976):  Sufficient cause – a set of conditions in which the absence of one event prevents the disease from occurring  Component cause – any one of the set of conditions which are necessary for the completion of a sufficient cause  Necessary cause – a component cause that is a member of every sufficient cause SUFFICIENT CAUSE COMPONENT CAUSES NECESSARY CAUSE * * * * *

5. CAUSAL “GUIDELINES” FOR ESTABLISHING CAUSATION (SIR AUSTIN BRADFORD HILL): Temporal relationship – exposure to the factor occurs before the disease development (in which the interval length between the exposure and disease onset is important)  Easiest to establish in a prospective cohort study Strength of the association – demonstrates the direct association between the exposure and disease  Stronger associations are more likely to be causal, minimizing the effects of bias and confounding Biological Gradient – provides additional evidence that a direct causal relationship exists between the level of exposure and disease onset Replication of the findings – repeatability of the observed association in different scenarios and study designs Biologic plausibility – the existence of a biological or social model to explain the association supporting current knowledge of biology and natural history Consideration of alternate explanations (in the event a similar relationship is observable with other exposures or diseases) Experiment or cessation of exposure – a decline in the risk of disease after the reduction or elimination of the exposure Consistency with other knowledge – interpretation of the results coincides with other data, creating an observable association with repeatability Specificity of the association – a single disease stemming from a single exposure, providing additional support for causal interference when present

6. SURGEON GENERAL’S GUIDELINES FOR ESTABLISHING CAUSALITY Uses:  Denoting distinctions between association and causation in epidemiologic research (in which associations are observed while causation is inferred)  All of the evidence must be considered and the criteria must be weighed against each other in order to infer the causal relationship  Critically reading epidemiologic studies  Designing epidemiologic studies  Interpretation of results

7. L10 Analysis and Interpretation of Medical Literature

8. JOURNAL ARTICLE – SDL Somatic Dysfunction and Its Association With Chronic Low Back Pain, Back- Specific Functioning, and General Health: Results From the OSTEOPATHIC Trial – John C. Licciardone, DO, MS, MBA and Cathleen M. Kearns, BA  http://www.jaoa.osteopathic.org/content/112/7/420.full.pdf+html http://www.jaoa.osteopathic.org/content/112/7/420.full.pdf+html Objectives:  Identify the strengths and limitations of medical literature through critical analysis  Identify whether the methods applied to study subjects and collect and analyze data are appropriate and free from error  Interpret the research study to identify any errors conducted in the design, conduct, and interpretation of results  Understand the impact the results of a medical journal article have on current medical practice  Recall how to best apply evidence based medicine within clinical practice

9. COLLECTION OF DATA 1.What was the context of the study?  Somatic dysfunction is diagnosed by the presence of any 4 TART criteria: tissue texture abnormality, asymmetry, restriction of motion, or tenderness 2.What were the objectives of the study?  To measure the prevalence of somatic dysfunction in patients with chronic low back pain (LBP) and to study the associations of somatic dysfunction with LBP severity, back-specific functioning, and general health. 3.What was the primary exposure of interest? Was this accurately measured?  Somatic dysfunction in the lumbar, sacrum/pelvis, and pelvis/innominate regions, including key lesions representing severe somatic dysfunction  Yes – 15 osteopathic physicians were enlisted to conduct baseline evaluations of the participants, using the Outpatient Osteopathic SOAP Note Form as an objective tool for measuring and recording the diagnosis and treatment of somatic dysfunction and for categorizing the severity of somatic dysfunction in each of 14 anatomic regions on the basis of TART criteria.  Baseline testing: LBP severity by a Visual Analog Scale; back-specific functioning and general health by Roland-Morris Disability Questionnaire and Medical Outcomes Study Short Form-36 Health Survey

10. COLLECTION OF DATA 4.What were the primary outcomes of interest? Was this accurately measured?  Statistical significant pairwise correlations for severe somatic dysfunctions: T10-12 with ribs, T10-12 with lumbar, lumbar with sacrum/pelvis, and sacrum/pelvis with pelvis/innominate  in any anatomic region, severe somatic dysfunction was correlated with the overall number of key lesions  The presence of severe somatic dysfunction in the lumbar region was associated with greater LBP severity and greater back-specific disability.  The presence of severe somatic dysfunction in the sacrum/pelvis region was associated with greater back-specific disability.  An increasing number of key lesions was associated with back-specific disability and poorer general health.  Yes – Multiple logistic regression analyses were applied to compute the adjusted odd ratios and 95% confidence intervals along with the Spearman rank correlation coefficient ( ρ ). Comparisons of the predominantly nonparametric methods were computed using the Mann-Whitney test and Kruskal- Wallis One-way Analysis of Variance to assess further relationships within the data. The data was stratified across a broad spectrum of characteristics (e.g., age brackets, gender, health status of various pre-existing conditions) to further separate the data when comparing p-values against the selected 0.05 level of statistical significance. 5.What type of study was conducted?  Cross-sectional study nested within a randomized controlled trial

11. COLLECTION OF DATA 6.Describe the source of the study population, process of subject selection, sample size, and number of controls compared to cases.  Randomized, double-blind, sham-controlled, 2 ×2 factorial design of 455 adult participants between the ages of 21-69 who reported having non-specific LBP constantly or on most days over the past 3 months  Exclusions: presence of a “red flag” condition; low back surgery in the past year; receipt of worker’s compensation benefits in the past 3 months; ongoing litigation involving back problems; medical conditions that might impede OMT or ultrasound protocol implementation; corticoid steroid use in the past month; or clinical evidence of lumbar radiculopathy (through specified testing) 7.Is there any potential for selection bias to have occurred? If so, how did it occur?  Potential need to exclude patients without somatic dysfunction in clinical trials of OMT (since a few patients did not present with any somatic dysfunction in specific regions during the baseline structural examination)  Unclear if the few patients without somatic dysfunction at the baseline would remain without somatic dysfunction during the entire course of the clinical trial

12. COLLECTION OF DATA 8.Is there any potential for bias in the collection of information? If so, how did it occur?  Potential limitation involving the possible interexaminer variability in diagnosing somatic dysfunction by using the musculoskeletal table of the OOSOAPNF, creating the potential for overlap between scoring  No formal assessment of provider performance or interexaminer reliability was made although fidelity training for the OMT physicians was provided. 9.What provisions were made to minimize the influence of confounding factors prior to the analysis of the data? Should other provisions have been made?  The data was stratified and compared across multiple logistic regression analyses to minimize the potential of confounding factors.  Yes - To further prevent possible confluence between the variables, a greater number of participants should be evaluated in order to accurately assess all aspects of somatic dysfunction and its possible to other variables evaluated in the study (even though no consistent statistical significance was found).

13. ANALYSIS OF DATA 1.What methods were used to control confounding bias during data analysis? Were these methods sufficient?  Yes – Predominantly relied on nonparametric methods for analysis before dichotomizing the severity of somatic dysfunction by combining the 3 lowest levels (none, mild, and moderate) and contrasting these with the highest level (severe), representing clinically significant, key lesions (which maintain a dysfunctional pattern that includes secondary dysfunctions)  multiple logistic regression analyses to compute odds ratios and 95% confidence intervals across the stratified data 2.What measures of association were reported in this study?  Spearman rank correlation coefficient ( ρ ) for severe somatic dysfunction in each anatomic region and the overall methods  Mann-Whitney test to compare LMP severity, back-specific functioning, and general health of patients with and without severe somatic dysfunction in each anatomic area  Kruskal-Wallis One-way Analysis of Variance by ranks to further assess the relationships between the number of key lesions and LBP severity, back-specific functioning, and general health 3.What measures of statistical stability were reported in this study? How you do interpret these measures?  Hypothesis testing conducted at the 0.05 level of statistical significance - To test for rejection of the H 0 : no association between non-specific LBP and somatic dysfunction within each anatomic area (p- value > 0.05)

14. INTERPRETATION OF THE DATA 1.What were the major results of the study?  Statistical significant pairwise correlations for severe somatic dysfunctions: T10-12 with ribs, T10-12 with lumbar, lumbar with sacrum/pelvis, and sacrum/pelvis with pelvis/innominate  in any anatomic region, severe somatic dysfunction was correlated with the overall number of key lesions  The presence of severe somatic dysfunction in the lumbar region was associated with greater LBP severity and greater back-specific disability.  The presence of severe somatic dysfunction in the sacrum/pelvis region was associated with greater back-specific disability.  An increasing number of key lesions was associated with back-specific disability and poorer general health. 2.How is the interpretation of these results affected by information bias, selection bias, and confounding? Discuss the magnitude and direction of the bias?  Possibility of inaccurate association between the specific anatomic regions mentioned and the level of somatic dysfunction by either over- or underestimating the effect of one of the other classified factors (e.g., key lesion or general health) 3.How is the interpretation of these results affected by non-differential misclassification? Discuss the magnitude and direction of the misclassification?  Misclassification bias during the baseline reporting (by the 15 osteopathic physicians) could potentially skew the computed p-values that were numerically close to the selected 0.05 level of statistical significance (e.g., age distributions more commonly affected by LBP and associated somatic dysfunction), overestimating the correlation between non-specific LBP and somatic dysfunction

15. INTERPRETATION OF THE DATA 4.Did the discussion section adequately address the limitation of the study?  Yes – Many of the potential limitations were extensively covered. 5.What were the author’s main conclusions? Were they justified by the findings?  The present study demonstrates that somatic dysfunction, particularly in the lumbar and sacrum/pelvis regions, is common in patients with chronic LBP. Severe somatic dysfunction in the lumbar region is directly associated with LBP severity and back- specific disability. Severe somatic dysfunction in the sacrum/pelvis region is directly associated with back-specific disability and is inversely associated with general health. An increasing number of key lesions were associated with greater back- specific disability and poorer general health.  Yes – Each of the conclusions coincided with the critical p-values and rejection of the H 0. 6.To what larger population can the results of this study be generalized?  Other sufferers of non-specific LBP with similar parameters can seek treatment using OMT techniques to relieve their associated somatic dysfunction.

16. L11 Outbreak Investigation

17. OUTBREAKS Epidemic – the occurrence of more disease cases than expected in a given area or among a specific group of people over a particular period of time  Pandemic – an epidemic occurring over a widespread area, typically affecting a substantial proportion of the population  Outbreak – an epidemic limited to a localized increase in the incidence of disease Cluster – an aggregation of cases in a given area over a particular period of time without regard to whether the number of cases is more than expected (with no inclination of chance) Reasons for outbreaks:  Agent: increases in the amount of virulence and the introduction into a previously unsettled area that allows it to thrive  Host: a change in susceptibility and influence by factors that increase possible exposure  Environment: increased interaction between host and agent  Enhanced mode of transmission Main reasons for investigation: identify possible causes for prevention and control Challenges: credibility of data sources, limited participation, specimen collection, publicity

18. STEPS OF AN OUTBREAK INVESTIGATION 1.Establish existence of an outbreak by determining if the observed numbers exceed the expected levels  Artifactual causes for increases or decreases of reporting cases: changes in reporting practices, changes in case definition, availability of new diagnostic tests, recent media coverage 2.Verify the diagnosis by confirming the clinical diagnosis with laboratory techniques 3.Define a case based on its standard elements (e.g., clinical information, time, place, affected individuals) and varying degrees of certainty (with associated risk factors)  Can vary depending on the purpose 4.Identify additional cases linked by similarities to the case definition 5.Perform descriptive epidemiology by orienting the data through graphing  Time: plotting a graph that illustrates the number of cases (y-axis) over the time of disease onset (x- axis) using an appropriate interval  Place: geographic distribution of cases to identify possible sources and modes of transmission  Individual: examining case characteristics based on personal exposures to establish relationships  Determines additional information: the size of the epidemic, outliers, time course, the pattern of spread and associated progression

19. STEPS OF AN OUTBREAK INVESTIGATION 6.Develop a hypothesis using descriptive epidemiology (e.g., person, place, and time with the clinical and laboratory findings) and test the new hypothesis using analytic epidemiology (e.g., retrospective cohort or case-control study) 7.Reconsider hypothesis by “squaring” the hypothesis to the clinical, laboratory, and epidemiologic facts  Development of a new hypothesis for re-testing may occur 8.Perform additional studies (if needed) by better defining the extent of the epidemic, evaluating new laboratory methods and case-finding techniques (for improved sensitivity and specificity), or conducting an environmental investigation 9.Implement control measures to prevent exposure and associated infection, disease, and possible death 10.Communicate findings to the media and community for raised awareness  Risk communication – effectively provide information about the expected type and magnitude of an outcome from associated behaviors or exposures (in order to empower decision-making)  Principles: prevent fear by emphasizing the establishment of a process for management of the epidemic; acknowledge uncertainty and answer questions while being regretful of the associated events; be a role model and assign tasks

20. L14 Public Health Databases, Resources, and Disease Surveillance

21. SURVEILLANCE Surveillance - the ongoing systematic collection, analysis, and interpretation of health data that is essential to the planning, implementation, and evaluation of public health practice (which is closely integrated with the timely dissemination to those who need to know)  Passive – regularly reporting of cases based on a standard case definition of each particular disease (e.g., death certificates)  Active – initiation of information collection from local or state health departments in order to achieve more complete and accurate reporting  Syndromic – the ongoing, systematic collection, analysis, interpretation, and application of real-time indicators for disease, allowing for detection before public health authorities would otherwise identify them Uses: estimate the magnitude of the problem, determine the geographic distribution of an illness, portray the natural history of a disease, detect epidemics and define possible problems, generate hypotheses to stimulate research, evaluate control measures, monitor changes in infectious agents, detect changes in health practices, and facilitate planning

22. SOURCES OF DATA Birth data – used for the calculation of community health indicators (through the National Health Statistics System)  Birth outcomes (e.g., delivery methods, delivery complications, birth weight)  Demographic and socioeconomic data of the parents (e.g., race, education level)  Reproductive history of the mother  Prenatal care record  Medical risk factors (e.g., gestational diabetes, previous miscarriages) Family practices data – gathers national data on marriage/divorce, family planning, and infertility in both men and women Mortality data – used for the calculation of many population-based measures (through the National Vital Statistics System)  Demographic and socioeconomic data (e.g., age, gender, employment status and type)  Time, place, and manner of death  Causes of death and contributing factors

23. SOURCES OF DATA Morbidity data:  Centralized Cancer Registries – collects tumor type and associated stages of malignancy (with possible exposures) for cancer rates  National Notifiable Disease Surveillance System – collects exposure information and disease symptomology with associated dates for infectious disease rates  National Health Interview Survey – self-reporting telephone interview for the reporting of chronic disease and medical risk factors  Associated behavioral risk factors:  National Health and Nutrition Examination Survey (NHANES) – gather information on the health and diet of the population through interviews and health tests  Behavioral Risk Factor Surveillance System – telephone interview for information on exercise and associated weight management, nutrition intake, smoking and drinking, preventative care, and mental health  Youth Behavioral Risk Factor Surveillance System – form-based survey administered in schools for information on sexual activity, smoking and drinking, domestic violence, risk of suicide, and personal safety  National Health Care Surveys – a set of surveys about the use and quality of health care and the impact of medical technology in a variety of settings (including hospital inpatient and outpatient departments, emergency rooms, hospices, home health agencies, and physician’s offices) Demographic and socioeconomic data:  US Census Bureau – administration of a decennial census and annual American Community Survey for the enumeration and estimation of the population in order to provide the basis for population-based rates (e.g., poverty levels, health insurance, employment/disability, family structure)

24. NOTIFIABLE DISEASE SURVEILLANCE National Notifiable Disease Surveillance System (NNDSS) – a foundation for the state and local application of the reportable infectious and noninfectious diseases, voluntarily passing reports from the local and state health departments (as the role of the Council of State and Territorial Epidemiologists (CSTE)) to the CDC  Collects a list of disease and laboratory findings of public health interest for the creation of case definitions before disseminating the surveillance data through the Morbidity and Mortality Weekly Report (MMWR) and Annual Summary of Notifiable Diseases  Case definition – uniform criteria for reporting cases

25. COMMUNITY HEALTH ASSESSMENT Community Health Assessment – produces information about the health status and needs of the community via the ongoing and systematic process of data collection, data analysis, interpretation of results, and the distribution of findings Purpose:  To help inform stakeholders in the community health for decision-making (e.g., planning and implementing of interventions, setting priorities, allocating resources)  To document the need for resources and to bolster the community’s commitment and political will to intervene Functions:  Compare the rates from similar localities to the state and national rates  Compare the benchmark rates or Health Plan 2020 targets to the local reports  Consider the demographic and socioeconomic comparability of the populations from which the comparison rates were derived  Examine both recent and trending data  Examine subpopulation rates to reveal local issues that may be masked on a larger scale

26. LIMITATIONS OF SURVEILLANCE Limitations:  Incomplete or overwhelming volumes of data from various sources  Uneven application of information technology  Timeliness of reporting  Fallible completeness due to unreported cases and incomplete reports Reasons for the failure to report:  Lack of awareness of legal requirement  Lack of knowledge of which conditions are reportable  Lack of knowledge of how or whom to report  Assumption that someone else will report the case  Intentional failure to report to protect patient privacy  Insufficient reward for reporting  Insufficient penalty for not reporting

27. L15 – L17 Biostatistics

28. POPULATION Population – all items in a study or group Sample – a subset of the population (as a representative subgroup)  Simple random – every member of the population has an equal chance of being selected  Systemic sampling – samples are selected over a fixed pattern or time interval  Stratified – when known differences (categories) exist in a population, samples can be taken from each category that are proportional to their volume in the total population  Cluster – only used when a population is known to be relatively unvarying  Convenience – selects the most readily available members of a population  Quota – takes a percentage of the population Uses of sampling: time limitation, cost of the study, representation of the population

29. DESCRIPTIVE STATISTICS Descriptive statistics – simple graphical numerical techniques to summarize information about the data Statistical inference – how the information contained in a sample can be used to draw conclusions about the population Random variables – anything capable of being measured  Measures of centers – represents the location of data  Mean ( μ) – the calculated average from the sum of the observations divided by the number of observations  Median – the middle number in a set of numbers ordered from smallest to largest  Even number of data? Calculate the mean of the two center values  Mode – the most frequently occurring number in a set of numbers  Measures of dispersion – represents the variability of the data observed as the spread of the distribution  Standard deviation – a measure of the variation in a set of data away from its mean  Standard error – estimates the probable error (or variation) of the sample mean against the estimated population mean  95% confidence intervals – defines a range of values likely to contain the true population mean  What does this mean? We are 95% confident that the true mean value in the population should be between ( μ - SE) and (μ + SE).  Range – difference between the minimum and maximum value  Quartile – splitting of the data into four evenly subdivided categories around the median (in intervals of 25%)  Minimum – lowest value in a set of data  Maximum – highest value in a set of data s = standard deviation n = sample size

30. MEASURES OF DISPERSION Standard deviation:  Example: The average on the biostatics exam is 85 with a standard deviation of 5. Using the understood standard deviation percentiles, calculate the standard deviation ranges.  68% of the class: (85-5) and (85+5) thus 68% of the students received an 80-90  95% of the class: (85-(2×5)) and (85+(2×5)) thus 95% of the students received a 75- 95  99.7% of the class: (85-(3×5)) and (85+(3×5)) thus 99.7% of the students received a 70-100

31. SUMMARIZING DATA Categorical random variables – data capable of being counted and recorded (as a frequency or percentage) Continuous random variables – data with no finite end (through the use of summary statistics)  Summary statistics (e.g., mean and associated standard deviations, minimums and maximums, median and mode) l

32. DATA Quantitative data – used to determine the relationship between an independent variable (the predictor variable) and a dependent variable (the outcome variable) in a population  Descriptive – subjects are measured once, establishing associations between the variables  Experimental – subjects are measured before and after the treatment Qualitative data – involves an in-depth understanding of human behavior and the reasons that govern it, relying on the reasons behind the various aspects of behavior (used as a research methodology in the social sciences) Categorical data:  Nominal data – counted data (that is not measured on a scale) with no ranking order (e.g., blood groups)  Ordinal data – ordered data with 2+ categories of classification (which can be used to create a ranking order) (e.g., severity of illness)  Dichotomous (binary) data – data that consists of counting in whole numbers (with favourably implied direction) (e.g., healthy vs. sick) Continuous (dimensional) data – measured data that can take on any value along a continuous scale (with no finite value) (e.g., birth weight)

33. Mode Median Mean Mode Mean Median CONTINUOUS DATA Normal distribution:  Data is continuous  The mean, median, and mode are equal to each other, existing as unimodal data  A certain percentage of the data falls within a specific standard deviation (68%, 95%, and 99.7%) j Left skewed distribution – the mean falls to the left of the median and mode Right skewed distribution – the mean falls to the right of the median and mode

34. HYPOTHESIS TESTING Hypothesis testing – a specified claim or theory used to compare treatments, acting as a scientific inquiry into the connection between cause and effect  Uses the scientific method to test the validity of the hypothesis based on observed data Goal: to see if there is sufficient statistical evidence to reject a presumed null hypothesis in favor of an alternate hypothesis  Null hypothesis (H 0 ) – the claim or theory currently presumed to be true in which there is no true underlying difference between the groups  Alternate hypothesis (H 1 ) – the claim or theory to be proved in which there is a difference between the groups Steps: 1.Develop the null and alternate hypotheses 2.Establish an appropriate α-level 3.Perform a suitable test of statistical significance on the appropriately collected data 4.Compare the p-value from the test with the established α-level 5.Conclude the result by either rejecting the null hypothesis in favor of the alternative (p- value α)

35. STATISTICAL SIGNIFICANCE Statistical significance:  P-value > α? There is no statistically significant difference.  P-value < α? There is a statistically significant difference. Type I error ( α) – a difference exists when there really is not a difference Type II error ( β) – a difference does not exist where there really is a difference H 0 IS TRUEH 0 IS FALSE REJECT H 0 TYPE I ERROR CORRECT FAIL TO REJECT H 0 CORRECT TYPE II ERROR

36. POWER ANALYSIS Power (1- β) – the chance of finding a significant effect when one does exist  If the null hypothesis is false, what is the probability that the data from the experiment will reject the null hypothesis?  Goal: to stroke a balance among the balance in order to achieve the most sensitive test given with the available resources Factors affecting power:  Effect size – the minimum signal that is required for detection (in which the larger the size of the effect, the better the chances are of finding it)  Significance level – direct relationship between α and β (and subsequently α and power)  Appropriate sample size (in which as the sample size increase so does the power of the associated test)  Population standard deviation – variability in the process which can obscure the signal, rendering the test less powerful (in which greater data variability requires a larger sample size to compensate)

37. PARAMETRIC STATISTICS Parametric statistics – assuming the distributions of the variables being assessed belong to the known parameterized families of probability distributions Student T-test – comparison between independent samples (in which one sample in no way affects the other sample) in order to evaluate the difference in means between the groups  Mann-Whitney U-test – comparison between independent samples of ordinal (ranked) data, combining the groups to rank the entire data set (e.g., when data is not normally distributed)  H 0 : the populations are from the same data set  H 1 : one population is larger than the other  Paired T-test – comparison within dependent paired (or matched) samples (in which an observation from one sample determines an observation taken from the other sample), evaluating the differences in means within groups Analysis of Variance (ANOVA) – comparison of independent samples with continuous data, evaluating the differences in 2+ categorical groups, (in which additional testing can be done to ascertain where (if any) the differences are)  Kruskal-Wallis test (One-way ANOVA) – comparison of independent samples from 2+ groups to test whether the continuous data samples are from the same distribution

38. NON-PARAMETRIC STATISTICS Non-parametric statistics – used when nothing is known about the parameters of the variable of interest in the population (e.g., Wilcoxon Signed-Rank Test, Pearson Χ 2 ) Wilcoxon Signed Rank test – comparison within dependent samples (as an alternative to the Paired T-test) in which there are repeated measurements of the data (but it is not normally distributed), assessing whether the population mean ranks differ Chi-Square Test ( Χ 2 ) – comparison of the proportions for treatments and outcomes using a ratio of actual-to- expected, using a 2 ×2 contingency table, based on the H 0 assumption that the expected values from the classifying values (factors) are true  Fisher’s Exact test – comparison of the proportions for treatments and outcomes using a ratio of actual-to-expected when the sample size is small, using a 2×2 contingency table, based on the H 0 assumption that the expected values from the classifying values (factors) are true DISEASE NO DISEASE TOTAL EXPOSUREacm NO EXPOSURE bdn TOTALrsN

39. CORRELATION Correlation – provides a visual display of the relationship between 2 variables for prediction, showing how one variable directly or indirectly increases or decreases with another variable Interpretation of the correlation statistic (in which correlation does not equal causation due to confounding variables): i NO CORRELATION STRONG-POSITIVE CORRELATION STRONG-NEGATIVE CORRELATION EXACT LINEAR CORRELATION PRESENCE OF AN OUTLIER WEAK CORRELATION: values between 0.0 – 0.3 MODERATE CORRELATION: values between 0.31 – 0.7 STRONG CORRELATION: values > 0.7

40. CORRELATION Spearman Rank Correlation Coefficient ( ρ) – assesses the relationship between 2 continuous variables (in which at least one is not normally distributed) in a single sample Kendal Rank Correlation Coefficient (Τ) – measures the ranked correlation between 2 variables from smaller sample sizes (in which the order of the data are ranked by quantity)

41. L18 – L19 Applied Biostats

42. RACIAL AND ETHNIC DISPARITY IN LOW BIRTH WEIGHT IN WAYNE COUNTY, NC Abstract: Low birth weight is a leading cause of infant mortality. Unfortunately, despite declining rates of infant mortality, racial and ethnic disparities in both low birth weight and infant mortality rates persist. In this teaching case, a clinical vignette is used to draw attention to this public health priority in Wayne County, NC. Students learn essential epidemiological skills, such as identifying limitations of sources of data and calculating relative risks, using the example of low birth weight. In performing these skills, students also identify etiologies for such disparity. Finally, students discuss interventions that, when implemented, may decrease infant mortality rates.

43. L20 Evidence-based Prevention: USPSTF

44. USPSTF United Sates Preventative Services Task Force (USPSTF) – the leading independent panel of private-sector experts in primary care and prevention sponsored by the Agency for Healthcare Research (AHRQ)  Mission: to improve the quality, safety, efficiency, and effectiveness of health care for all Americans  Framework: screening for early detection and treatment of at-risk individuals decreases morbidity and/or mortality  Functions: conducts rigorous and impartial assessments on the scientific evidence covering effective preventative services (e.g., screening, counseling, preventative medications), creating the “gold standard” recommendations (for asymptomatic children and adults) that should be routinely incorporated into clinical preventative services  Does not conduct the research—only evaluates the benefits and harms of existing evidence  Topic nominations: suggestions for new preventative services from the public; existing topic reconsiderations due to the availability of new evidence, changes in the public health burden of the condition, or availability of new screening tests with new evidence  Members: 16 volunteers represented by a chair and vice-chair that serve 4 year terms (as appointed by the AHRQ Director)

45. RECOMMENDATION GRADES Levels of certainty regarding net benefits:  High: the available evidence from well-designed and well-conducted representative studies is consistent  unlikely to be strongly affected by the results of future studies  Moderate: the available evidence is sufficient but confidence in the preventative service effects is limited by the quality of the study  the magnitude or direction of the observed preventative effect could change as more information is made readily available  Low: the available evidence is inefficient to assess the effects on the preventative health outcomes due to severe limitations of the study  requires more information for a better estimation of the effects on the preventative health outcomes ; GradeDefinition AThe USPSTF recommends the service. There is high certainty that the net benefit is substantial. B The USPSTF recommends the service. There is high certainty that the net benefit is moderate, or there is moderate certainty that the net benefit is moderate to substantial. C Clinicians may provide this service to selected patients depending on individual circumstances. However, for most individuals without signs or symptoms there is likely to be only a small benefit from this service. D The USPSTF recommends against the service. There is moderate or high certainty that the service has no net benefit or that the harms outweigh the benefits. I StatementThe USPSTF concludes that current evidence is insufficient to assess the balance of benefits & harms of the service.

46. PUBLIC HEALTH BURDEN Tobacco use is the leading preventable cause of death in the U. S., resulting in 400,000 deaths annually  USPSTF recommendations:  Clinicians ask all adults about tobacco use and provide tobacco cessation interventions for those who use tobacco products – A  Clinicians ask all pregnant women about tobacco use and provide augmented, pregnancy-tailored counseling for those who smoke – A  Tobacco counseling: 1.Ask about tobacco use 2.Advise to quit through clear personalized messages 3.Assess willingness to quit 4.Assist to quit 5.Arrange to follow-up and support Alcohol misuse is the 3 rd leading cause of preventable death in the U. S., causing 85,000 deaths annually  USPSTF recommendations:  Screen adults aged 18 years or older for alcohol misuse and provide persons engaged in risky or hazardous drinking with brief behavioral counseling interventions to reduce alcohol misuse – B  Current evidence is insufficient to assess the balance of benefits and harms of screening and behavioral counseling interventions in primary care settings to reduce alcohol misuse in adolescents – I  Screening for alcohol misuse: AUDIT (Alcohol Use Disorders Identification Test), Abbreviated AUDIT-C (Alcohol Use Disorders Identification Test - Consumption), Single question screening

47. PUBLIC HEALTH BURDEN Illicit drug use is ranked among the top 10 preventable risk factors for years of healthy life lost  USPSTF recommendation: current evidence is insufficient to assess the balance of benefits and harms of screening adolescents, adults, and pregnant women for illicit drug use – I Heart disease is the leading cause of death in the U. S. (which can be reduced by healthy diet and exercise)  USPSTF recommendation (for the general adult population without a known associated diagnosis): since the evidence indicating the health benefit of counseling is small, clinicians may choose to selectively provide preventative services based on risk factors, patient readiness for change, social support, community resources, etc. – C Approximately 69.2% of adult men and women are overweight (with 35.9% being obese), contributing to known associated health risks  USPSTF recommendation: screening all adults for obesity such that clinicians offer or refer patients with a body mass index (BMI) of 30 kg/m 2 or higher to intensive, multicomponent behavioral interventions – B