\documentclass{beamer} %\documentclass[handout]{beamer} \usetheme{Warsaw} %\setbeameroption{show notes} \title{Epidemiology, by Example} \author{Joshua Naranjo} \institute{Department of Statistics, \\ Western Michigan University\\ } \date{} \begin{document} \frame{\titlepage} \begin{frame} \frametitle{Outline} \tableofcontents \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} \begin{frame} \frametitle{Epidemiology} Etymology of ``Epidemiology" \vspace{1ex} \begin{center} \begin{tabular}{ccc} {\em epi} & {\em demos} & {logos} \\ $\Downarrow$ & $\Downarrow$ & $\Downarrow$ \\ Upon & People & Study \\ \end{tabular} \end{center} \vspace{1ex} literally meaning "the study of what is upon the people" \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame} \frametitle{Epidemiology} Definition by the Centers for Disease Control (CDC): \pause \vspace{1ex} \begin{itemize} \item[-] ``the basic science of public health" \pause \item[-] ``the study of the distribution and determinants of health-related states in specified populations, and the application of this study to control health problems." \end{itemize} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame} Epidemiological studies may be \begin{itemize} \item Observational \begin{itemize} \item[-]<2-> descriptive \item[-]<2-> inferential \begin{itemize} \item<3->[-] cross-sectional \item<3->[-] cohort \item<3->[-] case control \end{itemize} \end{itemize} \item Randomized controlled \end{itemize} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Types of studies} \subsection{Descriptive studies} \begin{frame} \frametitle{Descriptive Studies} \begin{itemize} \item[-] Collect information to characterize and summarize the health event or problem \vspace{1em} Who? What? Where? When? \vspace{1em} \item[-]Example: 1854 London cholera outbreak \item[-]Example: Tractor related deaths in Georgia \end{itemize} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame} \frametitle{Example: 1854 London cholera outbreak} \vspace{1em} On 31 August 1854, an outbreak of cholera struck Soho, London. Over the next ten days, 500 people on or near Broad Street died. \vspace{2ex} John Snow studied the pattern of the disease and made a map showing the clusters of cholera cases. \vspace{2ex} In Snow's own words: \begin{quote} Nearly all the deaths had taken place within a short distance of the [Broad Street] pump. There were only ten deaths in houses situated decidedly nearer to another street-pump. \end{quote} %In five %of these cases the families of the deceased persons informed me that they always sent to the pump %in Broad Street, as they preferred the water to that of the pumps which were nearer. In three other %cases, the deceased were children who went to school near the pump in Broad Street... \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame} \includegraphics[width=3.6in]{Snow-map2.jpg} \end{frame} \begin{frame} \frametitle{Example: 1854 London cholera outbreak} Snow and his map \vspace{2ex} -persuaded the local council to disable the well pump by removing its handle, effectively ending the outbreak. \vspace{2ex} -convinced the scientific community that cholera was a waterborne illness, and not transmitted by air. \end{frame} \begin{frame} \frametitle{Example: Tractor related deaths in Georgia} \begin{center} \small{Figure 1: Deaths associated with tractor injuries, by month of death} \includegraphics[width=1.75in]{month.pdf} \end{center} \pause Peaks during spring and fall. Due to planting and harvest? \end{frame} \begin{frame} \frametitle{Example: Tractor related deaths in Georgia} \begin{center} \small{Figure 2: Deaths associated with tractor injuries, by time of day} \includegraphics[width=1.75in]{hour.pdf} \end{center} \pause Increasing before lunch. Fatigue? \pause Peak at 4-5. Fatigue? Hunger? Darkness? \pause \hspace{1em} Children home from school. \end{frame} \begin{frame} \frametitle{Example: Tractor related deaths in Georgia} \begin{center} \small{Figure 3: Deaths associated with tractor injuries, by age} \includegraphics[width=1.75in]{tractor-age.pdf} \end{center} Peak in older age group. Tractor users older? Less likely to survive an accident? Small peak for school-age group. \end{frame} %\begin{frame} %Example: Fatalities associated with the use of farm tractors (Georgia 1971-1981) % %\begin{center} %\small{Figure: Deaths associated with tractor injuries, by place} % %\includegraphics[width=1.5in]{tractor1.pdf} %\end{center} %\end{frame} \begin{frame} \frametitle{Inferential Studies} Inferential epidemiology test hypotheses using %\vspace{2ex} \begin{itemize} \item Observational study \vspace{1ex} \begin{itemize} \item cross-sectional -data represent a point in time \item cohort -subjects selected according to exposure %, then investigators look at outcome patterns %\uncover<2->{(may be prospective or retrospective)} \item case-control -subjects selected according to outcome: cases and controls %, then investigators look back to determine exposure patterns (necessarily restrospective) \end{itemize} \vspace{1ex} \item Randomized experiment \end{itemize} \end{frame} \subsection{Cross sectional studies} \begin{frame} \frametitle{Cross-sectional studies} \begin{itemize} \item Cross-sectional studies are primarily surveys \item intended to look at prevalence rates and risk factors \item Example: National Health and Nutrition Examination Survey (NHANES) \item Example: Wisconsin Epidemiologic Study of Diabetic Retinopathy \item Example: Baltimore Eye Survey \end{itemize} \end{frame} \begin{frame} \frametitle{ Example: NHANES} \begin{itemize} \item[-] to assess the health and nutritional status of adults and children in the US \item[-] combines interviews and physical examinations (including lab tests) \item[-] responsible for producing vital and health statistics for the US \item[-] sample of about 5,000 persons from 15 counties each year \item[-] determine the prevalence of major diseases and risk factors \item[-] the basis for national standards of height, weight, blood pressure, etc. \end{itemize} \end{frame} \begin{frame} \frametitle{ Example: NHANES} Major Findings: \begin{itemize} \item[-] pediatric growth charts \item[-] Federal nutrition recommendations, school lunch programs \item[-] iron fortification of grain and cereal products (1973) \item[-] iodine fortification of salt has virtually eliminated goiter and stillbirths \item[-] Recommended Daily Allowance (RDA) of vitamins and minerals \item[-] vaccine policy (e.g. 1-in-4 females aged 14-59 infected with HPV, 2003-04) \end{itemize} \end{frame} \begin{frame} \frametitle{ Example: NHANES} Major Findings: \begin{itemize} \item[-] prevalence estimates of \begin{itemize} \item[-] malnutrition, obesity \item[-] cholesterol, hypertension \item[-] diabetes, arthritis, osteoporosis \item[-] hepatitis, HPV, other infectious diseases \item[-] dental health, visual health \item[-] exposures to lead, mercury, asbestos \end{itemize} \end{itemize} \end{frame} \begin{frame} Smaller, more targeted cross-sectional studies: \begin{itemize} \item[-] Wisconsin Epidemiologic Study of Diabetic Retinopathy \begin{itemize} \item[-]studied prevalence of retinopathy among diabetics \item[-]identified risk factors such as hyperglycemia or hypertension \end{itemize} \item[-] Baltimore Eye Survey \begin{itemize} \item[-] confirmed that rate of primary open-angle glaucoma in black Americans was found to be four to five times higher than whites \end{itemize} \item[-] European Youth Heart Study \begin{itemize} \item[-] physical activity levels should be higher than current guidelines to prevent CVD risk factors. \end{itemize} %\item[-] Clinical study: Persistent organochlorine pollutants (POPs) and Diabetes %\begin{itemize} %\item[-] In 196 men and 184 women (Swedish fishermen and their wives, %with high consumption of fatty fish from the Baltic Sea), serum %concentration of pollutants %CB-153 and pp-DDE were associated with higher odds of diabetes. %\end{itemize} \end{itemize} \end{frame} \subsection{Cohort studies} \begin{frame} \frametitle{Cohort studies} \begin{itemize} \item A {\em cohort} is a group of people who share something in common \begin{itemize} \item[-] students enrolled in Stat 2160 in Spring 2012 \item[-] premenopausal women in Kalamazoo 20 years and older \item[-] baby boomers \item[-] adult men and women residents of Framingham, Massachusetts \end{itemize} \item the cohort may be chosen according to exposure patterns, but must be identified {\em before} disease status has been determined (this is crucial) \item determination of disease status may be prospective or retrospective \item allows calculation of relative risk \end{itemize} \end{frame} \begin{frame} \frametitle{Cohort studies} \begin{itemize} \item Example: A Cohort Study of Childhood Asthma Followed to Adulthood \begin{itemize} \item[-] children born from April 1972 through March 1973 in Dunedin, New Zealand \item[-] assess risk factors for persistence and relapse \end{itemize} \item Example: A Retrospective Cohort Study of Measles, Mumps, and Rubella Vaccination and Autism \begin{itemize} \item[-] 537,303 children born in Denmark from January 1991 through December 1998 \item[-] risk of autism was similar in MMR vaccinated and unvaccinated children \end{itemize} \end{itemize} \end{frame} \begin{frame} \begin{itemize} \item Example: Framingham Heart Study \begin{itemize} \item[-] began in 1948 with 5,209 adults from Framingham, Mass. \item[-] now on its third generation of participants (1971 and 2002) \item[-] assess risk factors for cardiovascular disease \end{itemize} \item Example: Nurses' Health Study \begin{itemize} \item[-] began in 1976, has followed 121,700 female registered nurses \item[-] assess risk factors for cancer and cardiovascular disease \end{itemize} \end{itemize} \end{frame} \begin{frame} \frametitle{Example: Framingham Heart Study} Major Findings: \begin{itemize} \item[1960s] Smoking, high cholesterol and BP increase risk of coronary heart disease (CHD). Exercise decreases risk, obesity increases it. \item[1970s] Elevated BP increases risk of stroke. \item[1980s] High levels of HDL cholesterol {\em reduces} risk of heart disease. \item[1990s] Framingham Risk Score is published, and correctly predicts 10-year risk of future CHD events. %At 40 years of age, the lifetime risk for CHD is 50\% for men and 33\% for women. \item[2000s] Lifetime risk of developing elevated BP is 90\%. Lifetime risk for obesity is approximately 50\%. Social contacts are relevant to whether a person is obese. % and whether smokers decide to quit smoking. Four risk factors for a precursor of heart failure are discovered. %30-year risk for serious cardiac events can be calculated. Some genes increase risk of atrial fibrillation. Parent dementia increases risk of poor memory. \end{itemize} \end{frame} \begin{frame} \frametitle{Example: Nurses' Health Study} Major Findings: \vspace{1em} \begin{tabular}{|l|l|l|} \hline & Breast Cancer & CHD/Stroke \\ \hline Smoking & No association & Strong positive association\\ \hline Oral & Current use & Current use \\ Contraceptives & increases risk & increases risk \\ \hline Alcohol & Increases risk & Reduces CHD risk\\ \hline Diet & Red meat & Fish reduces risk of stroke. \\ & increases risk & Nut/wholegrain reduce CHD risk\\ & & Trans fats increase risk \\ \hline \end{tabular} \end{frame} \subsection{Case-control studies} \begin{frame} \frametitle{Case-control studies} \begin{itemize} \item Case-control studies select subjects according to disease outcome (cases and controls) \item then the investigator looks back to determine exposure or risk factors \item necessarily retrospective (there is no waiting for disease outcome) \item relative risk is not valid \end{itemize} \end{frame} \begin{frame} \frametitle{Example: Effectiveness of Bicycle Safety Helmets} %Robert S. Thompson, M.D., Frederick P. Rivara, M.D., M.P.H., and Diane C. Thompson, M.S. %N Engl J Med 1989; 320:1361-1367May 25, 1989 %Example: A Case-Control Study of the Effectiveness of Bicycle Safety Helmets Thompson et al. (1989): \begin{itemize} \item Cases: 235 persons with bicycling head injuries, who sought emergency care at one of five hospitals \item Controls: \pause 433 persons who received emergency care at the same hospitals for bicycling injuries not involving the head \end{itemize} \vspace{2ex} \pause Results: \begin{itemize} \item[]Head Injury: 7 percent were wearing helmets \item[]No head injury: 24 percent were wearing helmets \end{itemize} \end{frame} \begin{frame} \frametitle{Example: Effectiveness of Bicycle Safety Helmets} How effective are helmets in preventing head injury? \[ \mbox{RR}=\frac{.07}{.24}=.29= \frac{P[ \mbox{Helmet} | \mbox{Head Inj}]} {P[ \mbox{Helmet} | \mbox{No Head Inj}]} \] \begin{center} ``Head injury reduces your risk of wearing a helmet by 71\%" \end{center} \pause We want: \[ \mbox{RR}^*= \frac{P[ \mbox{Head Inj} | \mbox{Helmet}]} {P[ \mbox{Head Inj} | \mbox{No Helmet}]} \] \pause But $\mbox{RR}^* \neq \mbox{RR}$. \end{frame} \begin{frame} Recall: \[ \mbox{Odds}(E | D ) = \frac{P(E|D)}{1-P(E|D)} \] It is easy to show \[ \frac{\mbox{Odds}(E |D)}{\mbox{Odds}(E | \mbox{not D} )} = \frac{P(E \cap D)\cdot P(E^c \cap D^c) }{P(E^c \cap D)\cdot P(E \cap D^c) } = \frac{\mbox{Odds}(D |E)}{\mbox{Odds}(D | \mbox{not E} )} \] \end{frame} \begin{frame} \frametitle{Example: Effectiveness of Bicycle Safety Helmets} Implication? \[ \frac{\mbox{Odds}[ \mbox{Head Inj} | \mbox{Helmet}]} {\mbox{Odds}[ \mbox{Head Inj} | \mbox{No Helmet}]} = \frac{\mbox{Odds}[ \mbox{Helmet} | \mbox{Head Inj}]} {\mbox{Odds}[ \mbox{Helmet} | \mbox{No Head Inj}]} \] \[ = \frac{.07/(1-.07) }{.24/(1-.24) } = .25 \] \begin{center} ``Wearing a helmet reduces your odds of head injury by 75\%" \end{center} \end{frame} \end{document}