Epidemiology Introduction Epidemiology • The study of the distribution and determinants of the frequency of health-related outcomes in specified populations • Quantitative discipline • Measurement of disease / condition / risk factor frequency is central to epidemiology • Comparisons require measurements Much of epidemiological research is taken up trying • to establish associations between exposures and disease rates • to measure the extent to which risk changes as the level of exposure changes • to establish whether the associations observed may be truly causal (rather than being just consequence of bias or chance) • Epidemiology has a major role in developing appropriate strategies to improve public health through prevention ° public health has wider meaning in this sense; it is about the health of the whole population. ° it does not cover only classic areas, such as immunization or monitoring of diseases, it also covers factors such as poverty, smoking, nutrition • In this sense, epidemiology has a crucial role in trying to put into perspective the effects on population health of different risk factors. Variables (outcomes/risk factors) • Binary ° Deaths (y/n) ° Disease (y/n) • Categorical (ordinal or nominal) ° Frequency of drinking (never, 1-3 times a month, I -3 times a week, 4 times a week or more often) ° Severity of pain (none, some, a lot) • Continous 0 BMI, blood pressure etc What type of variable is... • Self-rated health ° Very poor, poor, average, good, very good • Total cholesterol concentration • Economic activity ° Employed, unemployed, housewife, pensioner • Risk of CVD death in the next 10 years (SCORE) • Ethnicity • Quartile of income • Sex • Marital status (married, divorced, ever single, widowed) Binary outcomes:"cases" vs. "non-cases" • Persons with disease = "cases" • Definition of case is crucial •E.g. ° Obesity: BMI>30 0 Hypertension: SBP> 140 mm Hg or DBP>90 mm Hg or treatment ° High cholesterol: ^6.2 mmol/L • Must always be clearly specified Epidemiological dataset ID Age Sex Disease Smoking • • • 1 54 1 0 0 2 65 2 0 1 3 47 1 1 1 4 53 1 0 0 • • • • • • • • • • • • • • • Measures of disease frequency • Used for binary outcomes • Require a numerator and denominator number of persons with disease number of persons examined • expressed as X per 1000 persons (or per 100,000 etc) Numerators and denominators • The number of cancer cases in the UK is 247,667 whereas in Belgium it is 47,948. • The UK has a bigger problem in numerical terms. • But do Belgians have lower risk of getting cancer? ° Numerators alone are meaningless ° We need both numerators AND denominators Numerators and denominators • The number of cancer cases in the UK is 247,667 whereas in Belgium it is 47,948. • The UK has a bigger problem in numerical terms. • But do Belgians have lower risk of getting cancer? ° Numerators alone are meaningless ° We need both numerators AND denominators • UK: 247 667 / 60 000 000 = 0.00413 = 413 per 100 000 • Belgium: 47 948 / 10 000 000 = 0.00479 = 479 per 100 000 Prevalence • number of existing cases / population of interest at a defined time Incidence • number of new cases in a given time period / total population at risk Prevalence • number of existing cases / population of interest at a defined time ° Unable to work now for health reasons ° Injury ever in the past ° Ever wheezing or whistling in the chest NOTE a denominator is needed for prevalence Adult prevalence by BMI status Health Survey for England (2008-2010 average) 24.9% Underweight 1.7% Women Overweight 32.2% Adult (aged 16+) BMI thresholds Underweight: <18.5kg/m2 Healthy weight: 18.5 to <25kg/m2 Overweight: 25 to <30kg/m2 Obese: >30kg/m2 2012 Incidence rates • In 2014, 55,222 new cases of breast cancer were diagnosed in the UK. • Approximately 65M people in the UK • Most cases in women (only 389 cases in men) • Population at risk? • Cumulative incidence of breast cancer in the UK in 2014 in females was ? 777 • • • 777 • • • Incidence rates • In 2014,55,222 new cases of breast cancer were diagnosed in the UK. • Approximately 65.5M people in the UK • Most cases in women (only 389 cases in men) • Population at risk? • Incidence of breast cancer in the UK in 2014 in females was ? 55222-389 54833 ....................=................= 0.001674= 167.4/100,000 65.5M/2 32.75 Incidence rate example: 3-year study with a sample size of 100, outcome of interest was fatal heart disease. year 1 year 2 Study ends developed outcome 6 5 4 dropped out 4 10 - sample at risk 90 75 71 • 10 participants were followed for I year • 15 participants were followed for 2 years • 75 participants were followed for 3 years Total person-years: Rate = Incidence rate example: 3-year study with a sample size of 100, outcome of interest was fatal heart disease. year 1 year 2 Study ends developed outcome 6 5 4 dropped out 4 10 - sample at risk 90 75 71 • 10 participants were followed for I year • 15 participants were followed for 2 years • 75 participants were followed for 3 years Total person-years of follow up = (10x1) + (15x2) + (75x3) = 265 person-years at risk Incidence rate = 15 / 265 = 0.057 = 57 cases per 1000 person-years Relationship between prevalence and incidence • The prevalence of a health-related outcome depends both on the incidence rate and the time between onset and recovery or death. • Prevalence = Incidence x Average disease duration • E.g. volume of water in water tank depends on ° Inflow ° Outflow Mortality • number of deaths / total population • Rate (or risk) • the number of deaths in a specified population, divided by the number of that population, per unit time. • If the mortality rate is to be calculated in a given year, the mid-year population is usually used as the denominator. • Mortality rate is always expressed as deaths per X (e.g. 1000 persons per year). E.g. ° A city has a population of 900,000,30,000 deaths occur in a 3-year period. ° Mortality rate for the period = 30 000 / 900 000 = 0.0033 or 33 deaths per 1000 per 3 years ° = I I deaths per 1000 per year. Mortality rates can be • All-cause mortality rates: refers to the total number of deaths per 1000 people per year.This is also usually referred to just as all-cause mortality. • Cause-specific mortality rate refers to total number of deaths due to a specific cause. Mortality rates can be • Crude mortality rates - no care has been taken for age structure of the population • Standardised mortality rate refers to a mortality rate which is age-standardised in order to permit comparisons between different countries, regions etc. Other examples of specific mortality • Infant mortality (first year) • Neonatal (first 28 days) • Early neonatal (first 7 days) • Post-neonatal (29th day to I year) • Maternal (while pregnant or within 42 days of the end of pregnancy) • Stillbirth (baby is born dead after 24 completed weeks of pregnancy) • Child (usually under 5 years) Case fatality • Case fatality rate is the rate of death among people who already have a condition, usually in a defined period of time, usually measured as a decimal or as a percent. • Survival rate is the proportion of people who remain alive for a given period of time after diagnosis of disease. E.g. breast cancer has 5-year survival rate around 70%. Life expectancy LIFE EXPECTANCY THROUGH THE AGES Early humans did nnl generally N« long entrap to develop EicaH disuse, cancer«loss of meirtal Junction. A siitff>shot of hoY> life eipetlancy Has changed, and (he big killers of each era: 'years Neanderthal* Died of injuries caused by r«k falls, hunting atftfcflt* and conflicts, food scarcity tad to malnutrition. 1 iiese hunter .w: "■:■-!:■' diseases tlwt spread From animals. Rabies, tuberculosis, brucellosis, yellow fever and encephalitis #W widespread. 38 Hwlitlik CaSft> BC to 3500 BOiAjrietJrtUnfc ir i igalron and urbanization bf OURht problems associated wilfc stilled popufationsvsuchas fecal contamination ot wilti and diseases such as cholera, smallpox, typhoid, police* inftufijua. Malaga and other diseases carried hy rtl?lHgi^«JoAj insects, which fed m dome-stKated animals. ■Hjpfr.lrfiJ. 35 Ci.v.-kic.il C-.Uil! Hid RflTO (500 Btlo SWAP): Tutetfut&siSvtyplioid fever, smailpox and scarlet lever Spread among the denser urban populations, Malnutrition, gjisl ru-enteritis and violence were also b^Wlcrs 48^ 3fL 40 70 75 IFARLV ftCVE Medieval period (500 AD to 1500 AD): LiftiipeetoACytffcw with urbanization, but famine caused by crop tailing and hghfrlct plague were the big killers. The Black Death 0W-TJ51) wiped *jt2S million people in Europe and W million in fait, relor ning several times, culminate in (he Great HsgueofUridon 06&4-16rj6}.B/1500,Kle expectancy had dropped bacV Victorian (T&Osio im): TyphUS, typhoid fever, rickets, diphtheria, tuberculosis, ;eyfel fever and cholera Hged in crowded citte. RESEAROfBY rick HrWdDE*/T«Ültf0 STAJt lMWHV iOUCEi JftlRHK. Of KftlLAl £fl NESEXK*, MwCPDNUHwEfclT' STtfrfDIO UMVÖÜTY, rtÖAIrJItÖLTH 0t&*fc2ATtfN J9r)fc: tetter healthcare unilation and living conditions buuj.i-a expectancy to 70 for men jnd 75 Jgrwpmm by 1950. 82 85 Today. Cancer, heart disease and stroke are the biggest killers in the developed world. Qih longer lifespan also con-tfi with unprecedented I™ of mental function and mobility problems. Numbers of women expected to die at each age, out of 100,000 born, assuming mortality rates stay the same as 20IO-20l2.The expectation is 83 (mean), median 86, the most likely value (mode) is 90. 5000.0 4500.0 4000.0 3500.0 3000.0 2500.0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 5189515951 225975