Family Medicine & Community Health 2 PDF

Family Medicine & Community Health 2 NOEL M. LAXAMANA, MD, FPAFP Fellow, Philippine Academy of Family Physicians 1 Epidemiology Definition and Scope 2  LEARNING OBJECTIVES At the end of the session, students would be able to: Understand the B...

Family Medicine & Community Health 2 NOEL M. LAXAMANA, MD, FPAFP Fellow, Philippine Academy of Family Physicians 1 Epidemiology Definition and Scope 2  LEARNING OBJECTIVES At the end of the session, students would be able to: Understand the Basic Concepts of Epidemiology Learn about its Definition and Scope Connect Epidemiology and the Practice of Public Health 3  DEFINITION OF EPIDEMIOLOGY Epidemiology as defined as “the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the prevention and control of health problems” 4  DEFINITION OF EPIDEMIOLOGY Epidemiology is the basic science and most fundamental practice of public health and preventive medicine. The word “epidemiology” comes from epidemic, which translated literally from the Greek means “upon the people.” Historically, the earliest concern of the epidemiologist was to investigate, control, and prevent epidemics. 5  DEFINITION OF EPIDEMIOLOGY Greek roots epi = upon (as in “epidermis”) demos = the people (as in democracy) ology = “to speak of”, “to study” Modern definitions of epidemiology refer to distributions in populations (statistical) determinants of health and disease (pathophysiological, environmental, behavioral) control of health problems (biological, social, economic, political, administrative, legal) 6  DEFINITION OF EPIDEMIOLOGY Epidemiology was first applied to the control of communicable diseases and public health through quarantine and isolation, even though ideas about disease transmission and microbiology and epidemiology were rudimentary. 7  DEFINITION OF EPIDEMIOLOGY Careful clinical observation, precise counts of well- defined cases, and demonstration of relationships between cases and the populations in which they occur all combine in the method upon which epidemiology depends. 8  DEFINITION OF EPIDEMIOLOGY Epidem. compared to medicine Main unit of concern in epidem. population Main unit of concern in medicine individual Epidem. compared to public health Epidemiology “study of” Public health “organized effort” Epidem. is “methodologic backbone” of public health 9 10 11 12  Germ Theory Until the 19th century, germ theory played second fiddle to vague theories of pollution (e.g., miasma theory) Examples of early contagionists Fracastoro (16th century Italian) Henle & Koch (German physiologists) John Snow (epidemiologist’s hero) Pasteur (1865 experimental proof in silkworms) Daniel Salmon (vector borne transmission)  John Snow, Our Hero Snow’s cholera theory: Epidemics follow routes of commerce Agent is free-living & multiplies within the host Transmission is water-borne, spread via fecal contamination, ingested orally Pathophys: diarrhea fluid loss sludging of circulation asphyxiation death John Snow (1813–1858) Snow’s Methods Ecological design: compare cholera rates by region Cohort design: compare cholera rates in exposed and non-exposed individuals Case-control analysis: compare exposure history in cholera cases and non-cases Snow’s map Gerstman Chapter 1 16 Snow’s Case-Control Analysis Map shows high concentration of cases near Broad Street pump Among cases: 61 used Broad St. water, 6 did not, and 6 were uncertain Among non-cases, Broad St. water use was rare e.g., Among non-cases at the Brewery “the men …were allowed a certain quantity of malt liquor, and [the proprietor] believes they do not drink water at all” e.g., non-cases at workhouse had separate water source 18 Recent Developments in Epidemiology Richard Doll and Andrew Hill studied the relationship between tobacco use and lung cancer, beginning in the 1950s. Their work was preceded by experimental studies on the carcinogenicity of tobacco tars and by clinical observations linking tobacco use and other possible factors to lung cancer. By using long term cohort studies, they were able to establish the association between smoking and lung cancer. 19 20 21 22 23  Scope of Epidemiology A focus of an epidemiological study is the population defined in geographical or other terms; for example, a specific group of hospital patients or factory workers could be the unit of study. A common population used in epidemiology is one selected from a specific area or country at a specific time. This forms the base for defining subgroups with respect to sex, age group or ethnicity. The structures of populations vary between geographical areas and time periods. 24 25  Epidemiology and Public Health Public health refers to collective actions to improve population health. Epidemiology is one of the tools for improving public health. Early studies in epidemiology were concerned with the causes (etiology) of communicable diseases & such work continues to be essential since it can lead to the identification of preventive methods. Epidemiology is a basic medical science with the goal of improving the health of populations & especially the health of the disadvantaged. 26 Causation of Disease 27 Natural History of Disease 28  Health Status of Populations Epidemiology is often used to describe the health status of population groups. Knowledge of the disease burden in populations is essential for health authorities, who seek to use limited resources to the best possible effect by identifying priority health programs for prevention and care. In some specialist areas, such as environmental and occupational epidemiology, the emphasis is on studies of populations with particular types of environmental exposure. 29 Health Status of Populations 30 31 32 33 34 35  Evaluating Interventions Epidemiologists evaluate the effectiveness and efficiency of health services. This means determining things such as the appropriate length of stay in hospital for specific conditions, the value of treating high blood pressure, the efficiency of sanitation measures to control diarrheal diseases & the impact of reducing lead additives in petrol. 36 Evaluating Interventions 37 Achievements in Epidemiology 38 Smallpox Eradication 39 40 41 42  Smallpox Eradication When a ten-year eradication program was proposed by WHO in 1967, 10–15 million new cases and 2 million deaths were occurring annually in 31 countries. The number of countries reporting cases decreased rapidly in the period 1967–76 By 1976 smallpox was reported from only two countries, and the last naturally-occurring case of smallpox was reported in 1977 in a woman who had been exposed to the virus in a laboratory. Smallpox was declared to be eradicated on 8 May 1980. 43  Smallpox Eradication Several factors contributed to the success of the program: universal political commitment a definite goal a precise timetable well-trained staff flexible strategy. 44  Methyl Mercury Poisoning Mercury was known to be a hazardous substance in the Middle Ages, but has recently become a symbol of the dangers of environmental pollution. In the 1950s, mercury compounds were released with the water discharged from a factory in Minamata, Japan, into a small bay. This led to the accumulation of methyl mercury in fish, causing severe poisoning in people who ate them. 45 46 47  Methyl Mercury Poisoning This was the first known outbreak of methyl mercury poisoning involving fish, and it took several years of research before the exact cause was identified. Minamata disease has become one of the best- documented environmental diseases. A second outbreak occurred in the 1960s in another part of Japan. Less severe poisoning from methyl mercury in fish has since been reported from several other countries. 48 49  Rheumatic Fever & RHD Rheumatic fever and rheumatic heart disease are associated with poverty, and in particular, with poor housing and overcrowding, both of which favor the spread of streptococcal upper respiratory tract infections. In many rich countries, the decline in rheumatic fever started at the beginning of the twentieth century, long before the introduction of effective drugs such as sulfonamides and penicillin. 50 Rheumatic Fever & RHD 51 Tobacco Use, Asbestos & Lung Cancer 52 Tobacco Use, Asbestos & Lung Cancer Lung cancer used to be rare, but since the 1930s, there has been a dramatic increase in the occurrence of the disease, initially in men. It is now clear that the main cause of increasing lung cancer death rates is tobacco use. The first epidemiological studies linking lung cancer and smoking were published in 1950; five case-control studies reported that tobacco use was associated with lung cancer in men. 53 Tobacco Use, Asbestos & Lung Cancer However, other exposures, such as to asbestos dust and urban air pollution also contribute to the increased lung cancer burden. Moreover, the combined effect of smoking and exposure to asbestos is multiplicative, creating exceedingly high lung cancer rates for workers who both smoke and are exposed to asbestos dust 54  1 MEASURES OF DISEASE FREQUENCY Feb 05, 2022 NOEL M. LAXAMANA, MD, FPAFP  Learning Objectives 2  Review the differences between rates, ratio and proportion  Identify objective parameters in assessing the health status of a community  Statistical indices of health and disease  Emphasize the importance of prevalence rate and incidence rate  References 3  Lecture-Handouts, Basic Epidemiology and Statistics (EPISTAT1). Institute of Tropical Medicine, Antwerp. August 2007.  Friis, Robert. Epidemiology 101. Maryland: Jones and Bartlett Publishers. 2010.  Mercado, Remigio D. Biostatistics for the Health Administrator. 2nd Edition. Quezon City: Academic Publishing Corporation. 2001.  Sanchez, F.S., Peralta, P.C., et. al. Research Methods in Health and Medicine. Volume 1. 3rd Edition. Manila: PCHRD. 1996.  Lecture-Handouts, Epidemiology, Biostatistics and Research Methods in Occupational Health. Quezon City: Phil. Coll. Of Occupational Medicine. 2011.  Definition of EPIDEMIOLOGY 4  the study of the distribution and determinants of disease frequency in specified populations  the three closely interrelated components – distribution, determinants and frequency – encompass all epidemiological principles and methods  Components of the definition 5  Specific population  Group (not individual)  Group can be defined (geographical, occupational, other characteristics)  Distribution of disease  “who is getting the disease within a population, where & when the disease is occurring”  Person, place and time  Determinants  Factors capable of bringing about change in health state  Goal of EPIDEMIOLOGY Identification of RISK FACTORS PREVENTION of DISEASE 6  Measures of Disease Frequency 7  involves quantification of the existence or occurrence of disease  the most basic measure of disease frequency is a simple count of affected individuals  the availability of such data is a prerequisite for any systematic investigation of patterns of disease occurrence in human population  Measures of Disease Frequency 8  The most basic measure of disease frequency is essential for public health planners and administrators who wish to determine the allocation of health care resources in a particular community  9 BASIC CONCEPTS: Ratio, Proportion, Rate  RATIO 10  a single number that represents the relative size of two numbers a RATIO = ______ x k b a = number of events/ number of persons with characteristics b = number of events/ number of persons with characteristics different from a k = a number with base 10 depending on magnitude of a and b  “k” is usually assigned a value of 100; 1,000; 10,000 and so on  Examples of RATIO 11 Sex Ratio = number of males X 100 number of females Age-Dependency Ratio = no. of persons aged 0 –14 y.o. + no. of persons aged 65 years and over X 100 no. of person 15 – 64 years old  RATIO 12  A ratio is the number of observations in a group with a given characteristic divided by the number of observations without the given characteristic  It is always defined as a part divided by another part  Obtained by simply dividing one quantity by another without implying any specific relationship between the numerator and denominator  RATIO 13  Expression of the relative frequency of occurrence of some event compared to some other event  Dividing one quantity by another  These two quantities are not necessarily related to each other  SEX RATIO 14 In 1990, there were 30,443,187 males and 30,115,929 females in the Philippines. Compute for the sex ratio. a ______ Sex ratio = x k b 30,443,187 ______________ Sex ratio = x 100 30,115,929 = 101.09 or 101  In the Philippines, there are 101 males for every 100 females. 15 https://www.doh.gov.ph/population 16 17 18 19  AGE DEPENDENCY RATIO 20 Table 1 Distribution of the Population by Age Group Phil, 1990 Age Group Number Percent 0 – 14 24,004,586 39.5 15 – 64 34,629,959 57.1 65 and over 2,063,445 3.4 Total 60,697,990 100.0 Age Dependency Ratio 24,004,586 + 2,063,445 _______________________________ = x 100 = 75.28 or 75 34,629,959  In 1990, every 100 persons in the economically productive group had to support 75 dependents. is the ratio of persons in the "dependent" ages (generally under age 15 and over age 64) to those in the "economically productive" ages (15-64 years) in the population. It is sometimes divided into the old-age dependency (the ratio of people aged 65 and older to those aged 15-64 years) and the child dependency (ratio of people under 15 to those aged 15-64 years) 21 22 23 PROPORTION  A special type of ratio in which the numerator is part of the denominator a ______ PROPORTION = x k a+b a = number of people in a group with the characteristic of interest a + b = number of people in the group k = any number with base 10 (percentage if k = 100) 24 PROPORTION  A proportion is always defined as a part divided by the whole, and is useful for ordinal and numerical data as well as nominal data, especially when the observations have been placed in frequency table  A percentage is simply the proportion multiplied by 100% 25  Negative for T2DM Positive for T2DM Assume: Population A, Time X Calculate for the Proportion. 26  RATE 27  the frequency of occurrence of events over a given interval of time  it measures the amount of changes  categorized into: I. Crude Rates - the denominator is the total population II. Specific Rates - events happening to a specified group are related only to the corresponding segment of the population - used when making comparisons between and among populations which differ in distribution according to the different variables  RATE 28  expresses the relationship between an event and a population at risk over a given interval of time a RATE = --- (k) x a = number of affected individuals in a given time x = “population time” or “person time” k = any number with base 10  CRUDE RATES 29 1. Crude Birth Rates 2. General Fertility Rates 3. Crude Death Rates 4. Cause-of-Death Rates  CRUDE BIRTH RATE 30  measures how fast people are added to the population through births no. of registered live births in a year ______________________________________________________ CBR = x 1,000 midyear population (or average population )  can be made specific by computing for the different variables  it is affected by: a) fertility/marriage patterns and practices of a place b) sex and age composition of a population c) birth registration practices  https://www.doh.gov.ph/sites/default/files/statistics/2015%20Philippines%20in%20Figures%20%28PIF%29.pdf 31 32  GENERAL FERTILITY RATES 33  births are related to the segment of the population deemed to be capable of giving birth no. of registered live births in one year _______________________________________________________________ GFR = x 1,000 midyear population of women 15 – 44 years of age  it can be made specific by computing it for different categories of maternal variables  CRUDE DEATH RATES 34  gives the rate with which mortality occurs in a given population no. of deaths in a year CDR = __________________________________ x 1,000 midyear population  it affected by: a. peace and order situation b. adverse environmental and occupational conditions c. age and sex composition  CAUSE-OF-DEATH RATES 35  gives the rate of dying secondary to specific causes no. of deaths from a certain cause in a year ___________________________________________________________ C of DR = x 100,000 midyear population  it can be made specific by relating the deaths from a specific cause and group to the midyear population of that specified group  it is affected by: a. completeness of registration of deaths b. composition of the population c. disease ascertainment level in the community  SPECIFIC RATES 36 Specific Mortality Rates  shows the rate of dying in specific population groups no. of deaths in a specified in a year ___________________________________________________________ SMR = x 1,000 midyear population of the same specified group  much more valid to use than CDR when comparing mortality experiences between groups  Types: 1. Infant Mortality Rates 4. Swaroop’s Index 2. Maternal Mortality Rates 5. Case Fatality Rates 3. Proportionate Mortality Rates  INFANT MORTALITY RATE 37  a sensitive index of the level of health in a community  it can be artificially lowered by improving the registration of births no. of deaths under 1 year old in a specified year IMR = _____________________________________________________________________ x 1,000 number of live births in the same year  affected by: a. maternal and child health care c. environmental sanitation b. malnutrition d. health service delivery  subdivisions: a. Neonatal Mortality Rate b. Post-neonatal Mortality Rate  Neonatal Mortality Rates 38  deaths are due to pre-natal or genetic factor no. of deaths among those under 28 days, in a year NMR = ________________________________________________________________ x 1,000 no. of live births in the same year  Post-neonatal Mortality Rates 39  deaths are due to environmental, genetic, nutritional and/or infectious factors no. of deaths among those 28 days to < 1 year of age in a year PnMR = ______________________________________________________________________________ x 1,000 no. of live births in the same year  MATERNAL MORTALITY RATE 40  measures the number of deaths among women due to diseases directly related to pregnancy, delivery and puerperium per 1,000 live births no. of deaths due to pregnancy, delivery, puerperium in a year MMR = ____________________________________________________________________________ x 1,000 no. of live births in the same year  affected by a. maternal health practices b. diagnostic ascertainment c. completeness of registration of births  PROPORTIONATE MORTALITY RATE 41  is the proportion of total deaths occurring in a particular population group or from a particular cause no. of deaths from a particular cause or population group in a year PMR = _______________________________________________________________________________ x 100 total deaths in same year  SWAROOP’S INDEX 42  a special kind of proportionate mortality ratio  a sensitive indicator of the standard of health care no. of deaths among those 50 years and over in a year S.I. = ____________________________________________________________________ x 100 total number of deaths in the same year  developed countries have higher Swaroop’s Index than less developed ones  the better the health condition, the nearer will the percentage approach 100  CASE FATALITY RATE 43  is the proportion of cases which end up fatally  it tells how much of the afflicted, die from the disease  formula: no. of deaths from a specified cause CFR = ______________________________________________ x 100 number of cases of the same disease  high CFR means more fatal disease  it depends on : a. nature of the disease itself b. diagnostic ascertainment c. level of reporting in the population 44 45  https://timesofindia.indiatimes.com/india/backward-states-register-progress-in-health/articleshow/57477406.cms 46  https://timesofindia.indiatimes.com/india/backward-states-register-progress-in-health/articleshow/57477406.cms 47  https://timesofindia.indiatimes.com/india/backward-states-register-progress-in-health/articleshow/57477406.cms 48  49 STANDARDIZING RATES  Rates 50  CRUDE  SPECIFIC  STANDARDIZED  CRUDE Rates 51  Single (summary) measure for an entire population  Obtained by dividing the number of events (diseases, death) by the size of the population from where the cases come from in a specified population  E.g. CDR, CBR  Cannot be used to compare events in different population affected by age-sex composition  Crude Mortality Rates 52 Population No. of deaths Total population Death rate per 1,000 population MANILA 18,700 1,700,000 11.0 QUEZON CITY 18,000 2,000,000 9.0 URBAN POOR 34,612 3,400,000 10.2  SPECIFIC Rates 53  Rates calculated for specific strata / segments of the population  Stratify population into homogenous groups (strata) based on demographic characteristics thought to be related to the outcome of interest  E.g. age-specific, sex-specific, race-specific  Comparison of Rates 54  Not valid to compare the crude rates of two populations whose population structures (distributions) are very different with respect to important confounders (e.g. age, sex, etc.)  One analytic technique of controlling for the effect of confounders is through standardization or adjustment of rates  Standardization means applying a common set of weights to the specific rates of the populations being compared  Standardized Rates 55  Also known as adjusted rates  Rate that has been standardized to a different population  Standard population – a population chosen to provide a common set of characteristics  Influence of some variables thought to affect the rate of the outcome of interest (e.g. age) has been removed  Standardized Rates 56  Allows comparison of rates between different population groups that differ in characteristics that influence the rate of interest  Standardized rates equalize the differences in the population at risk so that the rates are comparable  Standardized Rates 57  Adjusted Rate represents the hypothetical rate that would have been observed had each population had the same distribution on the variable or characteristic of interest  Statistical techniques are used to compute for summary rates for different populations  Requirement: demographic composition of the population  Standard Population 58  Any population, the distribution of which (with respect to the important confounder) serves as the weights applied to the specific rates of the populations being compared  They may be –  Combined population of the groups being compared  Population of another country  Distribution of one of the populations being compared  The exposed group (in the case of a cohort study)  Direct Method 59  Adjusted rates are derived by applying category-specific rates observed in each population to a single standard population  Requires a standard population, to which the estimated age- specific rates can be applied  Choice of the standard population may affect the magnitude of the age-adjusted rates, but not the ranking of the population  Direct Method 60 STEPS (to determine age-adjusted rates) 1. Obtain age-specific rates of the population being compared. 2. Choose a standard population.  Composite of all population being compared or a hypothetical pop 3. Calculate expected number of events [ E ].  Multiply age-specific rates by the chosen standard population in particular age group to determine the number of events that would have occurred [ E = age specific rates x standard population ] 4. Calculate standardized rate [Ra = E/N].  Divide total number of events by the standard population. EXAMPLE 1 COMMUNITY A COMMUNITY B Age Death Rate Death Rate Population Deaths Population Deaths (in year/s) (per 1,000) (per 1,000) Under 1 1,000 15 15.0 5,000 100 20.0 1-14 3,000 3 1.0 20,000 35 1.7 15-34 6,000 6 1.0 35,000 35 1.0 35-54 13,000 52 4.0 17,000 85 5.0 55-64 7,000 105 15.0 8,000 160 20.0 Over 64 20,000 1,600 80.0 15,000 1,350 90.0 All Ages 50,000 1,781 35.6 100,000 1,740 17.4 EXAMPLE 1 Standard Population: COMBINED POPULATION Age STANDARD (in year/s) Population Under 1 6,000 1-14 23,000 15-34 41,000 35-54 30,000 55-64 15,000 Over 64 35,000 TOTAL 150,000 EXAMPLE 1 Standard Population: COMBINED POPULATION Death Rate in Age STANDARD Community A (in year/s) Population (per 1,000) Under 1 6,000 15.0 1-14 23,000 1.0 15-34 41,000 1.0 35-54 30,000 4.0 55-64 15,000 15.0 Over 64 35,000 80.0 TOTAL 150,000 35.6 EXAMPLE 1 Standard Population: COMBINED POPULATION Death Rate in Expected Deaths Age STANDARD Community A at Community (in year/s) Population (per 1,000) A’s rates Under 1 6,000 15.0 90 1-14 23,000 1.0 23 15-34 41,000 1.0 41 35-54 30,000 4.0 120 55-64 15,000 15.0 225 Over 64 35,000 80.0 2,800 TOTAL 150,000 35.6 3,299 EXAMPLE 1 Standard Population: COMBINED POPULATION Death Rate in Expected Deaths Death Rate in Age STANDARD Community A at Community Community B (in year/s) Population (per 1,000) A’s rates (per 1,000) Under 1 6,000 15.0 90 20.0 1-14 23,000 1.0 23 1.7 15-34 41,000 1.0 41 1.0 35-54 30,000 4.0 120 5.0 55-64 15,000 15.0 225 20.0 Over 64 35,000 80.0 2,800 90.0 TOTAL 150,000 35.6 3,299 17.4 EXAMPLE 1 Standard Population: COMBINED POPULATION Expected Expected Death Rate in Death Rate in Age STANDARD Deaths at Deaths at Community A Community B (in year/s) Population Community Community (per 1,000) (per 1,000) A’s rates B’s rates Under 1 6,000 15.0 90 20.0 120.0 1-14 23,000 1.0 23 1.7 39.0 15-34 41,000 1.0 41 1.0 41.0 35-54 30,000 4.0 120 5.0 150.0 55-64 15,000 15.0 225 20.0 300.0 Over 64 35,000 80.0 2,800 90.0 3,150.0 TOTAL 150,000 35.6 3,299 17.4 3,800  Age-Adjusted Death Rate 67 Age-Adjusted Death Rate (per 1,000) Community A = 22.0 Community B = 25.0  Crude versus Age-Adjusted Death Rate 68 CRUDE DEATH AGE-ADJUSTED POPULATION RATE DEATH RATE Community A 35.6 22.0 Community B 17.4 25.0  Exercise 1 69 Calculate for age-adjusted rates of Example 1 data using population A as the standard population. EXAMPLE 1 COMMUNITY A COMMUNITY B Age Death Rate Death Rate Population Deaths Population Deaths (in year/s) (per 1,000) (per 1,000) Under 1 1,000 15 15.0 5,000 100 20.0 1-14 3,000 3 1.0 20,000 35 1.7 15-34 6,000 6 1.0 35,000 35 1.0 35-54 13,000 52 4.0 17,000 85 5.0 55-64 7,000 105 15.0 8,000 160 20.0 Over 64 20,000 1,600 80.0 15,000 1,350 90.0 All Ages 50,000 1,781 35.6 100,000 1,740 17.4 ANSWER TO EXERCISE 1: Standard Population: POPULATION/COMM. A Expected Expected Death Rate in Death Rate in Age STANDARD Deaths at Deaths at Community A Community B (in year/s) Population Community Community (per 1,000) (per 1,000) A’s rates B’s rates Under 1 1,000 15.0 15 20.0 20.0 1-14 3,000 1.0 3 1.7 5.1 15-34 6,000 1.0 6 1.0 6.0 35-54 13,000 4.0 52 5.0 65.0 55-64 7,000 15.0 105 20.0 140.0 Over 64 20,000 80.0 1,600 90.0 1,800.0 TOTAL 50,000 35.6 1,781 17.4 2,036.1  Age-Adjusted Death Rate 72 Age-Adjusted Death Rate (per 1,000) Community A = 35.6 Community B = 40.7  Crude versus Age-Adjusted Death Rate 73 AGE-ADJUSTED AGE-ADJUSTED CRUDE DEATH DEATH RATE POPULATION DEATH RATE RATE (Std = Combined (Std = Pop A) Pop A + B) Community A 35.6 22.0 35.6 Community B 17.4 25.0 40.6  Measures of Disease Frequency 74 Incidence Rate  Measures the occurrence / appearance of a disease in a population over a period of time Prevalence Rate  Measures existing (prevalent) cases of a disease at a “point” in time  Measures of Disease Frequency 75 POPULATION AT RISK  Calculation of measures of disease frequency depends on correct estimates of the number of people under consideration  Ideally, these figures should include only people who are potentially susceptible to the diseases being studied  Clearly, for instance, men should not be included in calculations of the frequency of carcinoma of the cervix!  Measures of Disease Frequency 76 0-25 years 25-69 years 70+ years  INCIDENCE RATES (INCIDENCE DENSITY) 77  measures the development of a disease in a group exposed to the risk of the disease in a period  useful than PR in determining etiologic factors of diseases no. of new cases of a disease developing in a period of time ____________________________________________________________________________________ IR = x F population at risk of developing the disease during the same period of time CUMULATIVE INCIDENCE RATES  measures the proportion of persons in a population who are initially free of the disease of interest and who develop the disease within specified time interval  PREVALENCE RATES 78  measures the proportion of existing cases of a disease in a population  more useful than the incidence rate in describing the occurrence of chronic conditions and as an indicator or basis for making decisions in the administration of health services no. of existing cases of a specified disease ________________________________________________________ PR = x F population examined  influenced by: a. diagnostic capabilities b. levels of notifications c. age and sex composition of the population  PREVALENCE RATES 79  Provides an indication of the amount of disease prevailing in a given point in time, thus allowing assessment of the burden of a particular disease in a community.  FACTORS INFLUENCING OBSERVED PREVALENCE RATE 80 DECREASED BY: INCREASED BY: Shorter duration of the disease High case-fatality rate from disease Longer duration of the disease Decrease in new cases Prolongation of life of patients with cure In-migration of healthy people Increase in new cases Out-migration of cases Out-migration of healthy people Improved cure rate of cases In-migration of susceptible people Improved diagnostic facilities  Negative for T2DM Positive for T2DM Assume: Population A, Time X Prevalence = 5/20 x 100 = 25/100 Incidence = ? 81  Time X 15 5 Follow-up 3 Time Y 12 82  Measures of Disease Frequency 83 Incidence Rate  Measures the occurrence of a disease in a population over a period of time  3/15 x 100 = 20% Prevalence Rate  Measures existing (prevalent) cases of a disease at a “point” in time  8/20 x 100 = 40%  INCIDENCE 84  has a longitudinal component  measurement of incidence requires at least two sets of observations to determine the number of new cases of disease that have occurred among the initial disease free individuals during some specified period of time  2 observation points  START - assemble disease-free individuals  END - count the number of affected individuals  INCIDENCE 85  Fundamental measure for etiologic studies since they are direct indicators of risk of disease over a period of time  High incidence measures are synonymous with high risk of disease.  OTHER MEASURES USED IN PUBLIC HEALTH 86 Life Expectancy - a very useful and commonly used method of describing a population trait. - defined as the average of years an infant is expected to live under the mortality conditions for a given year. - life expectancy of a female child is always higher that her male counterpart for the years indicated. Average Length of Stay in the Hospital - measures the mean length of time a specific group of patients spent in the hospital continuously. - computed by adding up the duration of stay of all patients and dividing the sum by the number of patients.  SUMMARY 87  Pointed out the differences among rates, ratio and proportion  Enumerated the different statistical measures affecting health and disease  Highlighted the importance of standardizing rates when making comparisons  Clearly identified the differences between incidence versus the prevalence Basics of Biostatistics Noel M. Laxamana, MD, FPAFP 02 March 2019  BIOSTATISTICS Basic epidemiology requires a knowledge of biostatistics. STATISTICS: science that deals with collection of data, organization of data, analysis of data & interpretation of data BIOSTATISTICS: applications of statistical methods to the life sciences like medicine and public health VARIATION: refers to tendency of a measurable characteristic to change from one individual or one setting to another  BIOSTATISTICS DATA: the observed values of a variable VARIABLE: a characteristic of population or sample that is of interest for us; values or categories cannot be predicted with certainty Independent variable Dependent variable Stimuli that researchers manipulate Effect of the action of independent; to create an effect Responding behaviour that a Varied by and under the control of researcher wants to explain the experimenter Respond to experimental manipulation  BIOSTATISTICS SAMPLING: it is the act of studying or examining only a segment of the population to represent the whole SAMPLE: subset of a population POPULATION: refers to entire group of individuals or items of interest in the study; all members of a specified group PARAMETER: measure of a characteristic of a population STATISTIC: measure of a sample CONFOUNDERS: potentially confuses the results  BIOSTATISTICS CONSTANT: value of a characteristic that remains the same from person to person, from time to time or from place to place VARIABLE: characteristic takes on different values  BIOSTATISTICS POPULATION: refers to entire group of individuals or items of interest in the study; all members of a specified group The group from which representative information is desired and to Target Population which interference will be made Sampling population Population from which a sample will actually be taken Units that are chosen in selecting the sample and may be made up of Sampling unit non-overlapping collection of elements or elementary units; a collection of sampling units is referred to as sampling frame An object or a person on which a measurement is actually taken or an Elementary unit observation is made  BRANCHES OF BIOSTATISTICS Descriptive statistics Inferential statistics Refers to the different methods Methods involved in order to make applied to summarize and present generalizations and conclusions about data in a form to make them easier to a target population, based on analyze and interpret by using result/knowledge from a sample; methods of tabulation, graphical includes estimation of parameters representation, and so on. and testing of hypothesis.  DESCRIPTIVE STATISTICS DATA COLLECTION Source Primary – data obtained first hand by the investigator for his or her specific purpose Secondary – already existing data; data that have been obtained by other people for purposes not necessarily those of the investigator’s  DESCRIPTIVE STATISTICS Primary Data Secondary Data Information in its original form Reflect the view point of participant or Provide analysis & interpretation of observer of an event or phenomenon event or phenomenon Has not been previously interpreted or Subsequent interpretations or studies translated that are based on primary sources Can also be sets of data which have been tabulated, but not interpreted  CENSUS Complete enumeration of a population In the Philippines, a population census is conducted every 5 years De Facto census De Jure census Physical presence is important Assign individuals in their usual regardless of where they usually live residence regardless of where they were during the census  TYPES OF VARIABLES  QUALITATIVE: categorical observation; provide depth and detail through direct quotation and careful description of situations, events interactions (nominal or ordinal)  QUANTITATIVE: numerical observations; computed thru arithmetic calculations (numerical)  DISCRETE: finite number of values possible; use of whole number  CONTINUOUS: usually associated with physical measurement; take on values that are fractions or decimals  ELEMENTS NEEDED IN ANALYSIS OF INTERVAL/RATIO DATA 1. Identification of Data in General – “What?” 2. Collection of Data 3. Organization and Summarization of Data 4. Descriptive Analysis 5. Inferential Analysis and Hypothesis Testing 6. Conclusions 7. Recommendations 1. Identify Variables and SCALES OF MEASUREMENT 1. NOMINAL Male/ Female 2. ORDINAL Black/ White Urban/Suburban/Rural 3. INTERVAL Red /Green 4. RATIO DICHOTOMOUS DATA SCALES OF MEASUREMENT 1. NOMINAL First, Second, Third 2. ORDINAL Stage of Cancer 3. INTERVAL Pain Rating 4. RATIO SCALES OF MEASUREMENT 1. NOMINAL have meaningful intervals 2. ORDINAL However, ratios of scores are not meaningful 3. INTERVAL the zero point is arbitrary 4. RATIO  Scores in an intelligence test  Fahrenheit and Celsius  Time in a calendar SCALES OF MEASUREMENT Has an absolute zero 1. NOMINAL 2. ORDINAL Meaningful ratios exist 3. INTERVAL 4. RATIO Kelvin scale, Length, Width, Capacity, Loudness IMPORTANT POINTS:  WHICH ARE QUALITATIVE?  Nominal and Ordinal Data  WHICH ARE QUANTITATIVE?  Interval and Ratio Data  LEAST TO MOST SUPERIOR SCALE OF MEASUREMENT  Nominal  Ordinal  Interval  Ratio  ELEMENTS NEEDED IN ANALYSIS OF INTERVAL/RATIO DATA 1. Identification of Data in General – “What?” 2. Collection of Data 3. Organization and Summarization of Data 4. Descriptive Analysis 5. Inferential Analysis and Hypothesis Testing 6. Conclusions 7. Recommendations 2. METHODS OF DATA COLLECTION COLLECTION METHODS 1. Observation 2. Review of documents – diseases prevalent, chart reviews 3. Enumeration – census or survey 4. Questionnaires 5. Interview – formal or informal 6. Experimental 7. Registration 8. Case record 2. METHODS OF DATA COLLECTION CHARACTERISTICS IMPORTANT IN DATA COLLECTION A. ACCURACY B. PRECISION C. VALIDITY D. RELIABILITY E. OBJECTIVITY F. COMPLETENESS  ACCURACY The closeness of a measured or computed value to its true value Trueness of test measurements Related with: Positive predictive value Negative predictive value  PRECISION Consistency and reproducibility of a test (reliability) Absence of random variation in a test Random error entails reduced precision in a test Systematic error entails reduced accuracy in a test  VALIDITY The data / technique should measure what is supposed to measure Related with: Sensitivity Specificity False Positivity False Negativity  VALIDITY Internal validity – the study’s ability to determine if a causal relationship exists between one or more independent variables, and one or more dependent variables External validity – the extent to which you can generalize your findings to a larger group or other contexts  RELIABILITY Refers to consistency, reproducibility, repeatability of results; similar information is supplied when a measurement is performed more than once VALIDITY versus RELIABILITY Valid Measurements = Reliable Measurements Reliable Measurements NOT ALWAYS Valid Measurements OBJECTIVITY & COMPLETENESS Adequacy and representativeness of the sample size  SCREENING Secondary level of prevention A screening test identifies asymptomatic individuals who may have the disease A diagnostic test is used to determine the presence or absence of a disease  SCREENING PROGRAMS Condition screened must be a vital or important health condition that affects the majority of the population The disease must have a well-developed natural history There are means to detect the early stages of the disease There must be a difference between treatment during the early stage to that of the late stage  SCREENING PROGRAMS The screening test should be acceptable, inexpensive, easy to administer, would cause minimal discomfort, reliable and valid The cost of the test should be outweighed by its benefits Adequate health service provision should be made Interval for repeating testing is determined  SCREENING TEST VALIDITY SENSITIVITY – probability of detecting disease SPECIFICITY – probability that those without the disease will screen negative POSITIVE PREDICTIVE VALUE – probability that subjects with a positive screening test truly have the disease NEGATIVE PREDICTIVE VALUE – probability that subjects with a negative screening test truly do not have the disease EVALUATION OF A SCREENING TEST EVALUATION OF A SCREENING TEST  DISEASE TOTAL TEST a b a+b True Positives (TP) False Positives (FP) c d c+d False Negatives (FN) True Negatives (TN) TOTAL a+c b+d a+b+c+d  SENSITIVITY Ability of the test to label positive those who really have the disease Number of true positives (TP) divided by number of all people with the disease High sensitivity is desirable for a screening test! Sensitivity (Sn) = TP x 100 = a x 100 TP + FN a +c  “SnOUT” Sn-N-Out = highly sensitive test, when negative, rules out the disease Value approaching 100% is desirable for ruling out disease and indicates a low false negative rate  SPECIFICITY Ability of the test to label negative those who do not have the disease Number of true negatives (TN) divided by number of all people without the disease High specificity is desirable for a confirmatory test! Specificity (Sp) = TN x 100 = d x 100 TN + FP d+b  “SpIN” Sp-I-IN = highly specific test, when positive, rules in the disease Value approaching 100% is desirable for ruling in disease and indicates a low false positive rate POSITIVE PREDICTIVE VALUE (+,+) The probability of having a condition, given a positive test Number of true positives divided by number of people who tested positive for the disease PPV = TP x 100 = a x 100 TP + FP a+b NEGATIVE PREDICTIVE VALUE (-,-) The probability of not having a condition, given a negative test Number of true negatives divided by number of people who tested negative for the disease NPV = TN x 100 = d x 100 FN + TN c+d  LIKELIHOOD RATIOS Likelihood ratios are used for assessing the value of performing a diagnostic test Likelihood Ratio of a POSITIVE TEST LR+ = Probability that the test (+) among diseased = Sn__ Probability that the test is (+) among non-diseased 1-Sp Likelihood Ratio of a NEGATIVE TEST LR- = Probability that the test (-) among diseased = 1-Sn__ Probability that the test is (-) among non-diseased Sp Example  A researcher develops a new tumor marker for pancreatic cancer, which he then compares it to tissue histology. There were a total of 300 patients. 100 patients were found to have pancreatic cancer, of whom 70 tested positive for the tumor marker. The tumor marker was also positive in 15 patients without pancreatic cancer.  Tissue histology marker Tumor  Tissue histology marker Tumor Total + - + - Total  Tissue histology marker Tumor Total + - + 70 15 - Total 100 300  Tissue histology marker Tumor Total + - + 70 (a) 15 (b) 85 - 30 (c) 185 (d) 215 Total 100 200 300  ELEMENTS NEEDED IN ANALYSIS OF INTERVAL/RATIO DATA 1. Identification of Data in General – “What?” 2. Collection of Data 3. Organization and Summarization of Data 4. Descriptive Analysis 5. Inferential Analysis and Hypothesis Testing 6. Conclusions 7. Recommendations 3.ORGANIZATION / SUMMARIZATION OF DATA 1. FREQUENCY DISTRIBUTION 2. THE NORMAL CURVE 3. AREA UNDER THE CURVE 4. APPLICATIONS OF THE GAUSSIAN DISTRIBUTION 5. PARAMETERS OF FREQUENCY DISTRIBUTION Measures of Central Tendency – Mean, Median, Mode Measures of Dispersion/Variation/Variability – Range, Variance, Standard Deviation, CV ORGANIZATION / SUMMARIZATION OF DATA PARAMETERS OF FREQUENCY DISTRIBUTION  MEASURES OF CENTRAL TENDENCY  – Mean, Median Mode  MEASURES OF DISPERSION / VARIATION / VARIABILITY  – Range, Variance, Standard Deviation, CV ORGANIZATION/SUMMARIZATION OF DATA I. RANGE  Difference between the largest and smallest observations in the data set  Simplest  Greatly influenced by extreme scores  Range = HS-LS  SET A: 6, 9, 15, 17, 24  SET B: 6, 9, 15, 17, 24, 500 ORGANIZATION/SUMMARIZATION OF DATA Range Problem:  Benedict took 7 Prev. Med tests in one marking period. What is the range of his test scores? 89, 73, 84, 91, 87, 77, 94 Solution: Ordering the test scores from least to greatest, we get: 73, 77, 84, 87, 89, 91, 94 highest - lowest = 94 - 73 = 21 Answer: The range of these test scores is 21 points. RANGES DERIVED FROM PERCENTILES  Suppose we arrange our data of magnitude with the smallest value of the variable x  The value of x that has 1% of the observations in the ordered set lying below it (and 99% of the observations lying above it) is called the FIRST PERCENTILE 99 % (and so on…) x  The value of x that is the 10th, 20th, 30th…. 90th percentiles are called DECILES 1%  The value of x that divides into four equally sized groups that is 25 th, 50th, 75th percentiles are called QUARTILES.  The 50th percentile is the MEDIAN ORGANIZATION/SUMMARIZATION OF DATA II. VARIANCE  average of the sum of squared deviations from the mean  mean of the squares of all the deviation scores in the distribution  symbolized by s for a population, and S for a sample 2 2 ORGANIZATION/SUMMARIZATION OF DATA III. STANDARD DEVIATION  Square root of the variance  Denoted by s for a population, and S for a sample  Most common & useful measure because it is the average distance of each score from the mean or how much each data value deviates from the mean ORGANIZATION/SUMMARIZATION OF DATA IV. COEFFICIENT OF VARIATION  expresses the standard deviation as a percentage of the mean  used when the units of measurement of the variables being compared are different, e.g., height in cm and weight in kg, or when the means differ markedly, e.g. mean weight of adults and mean weight of infants CV = s x 100 x ORGANIZATION/SUMMARIZATION OF DATA I. FREQUENCY DISTRIBUTION  most convenient way of organizing and summarizing data  includes a set of intervals and the number of observations in each interval  shows the proportion of the population having certain characteristics  varying shapes: bell-shaped, skewed, symmetric  The Normal Distribution (Gaussian distribution or curve)  The Normal Distribution (Gaussian distribution or curve) Characteristics  bell-shaped and symmetrical about the mean  mean, median and mode are equal  total area under the curve & above the x-axis is equal to one  tapering tails extend infinitely in either direction but never touch the x-axis  it is determined by its parameters: mean & standard deviation  the standard deviation becomes a more meaningful quality than merely being a measure of dispersion  ELEMENTS NEEDED IN ANALYSIS OF INTERVAL/RATIO DATA 1. Identification of Data in General – “What?” 2. Collection of Data 3. Organization and Summarization of Data 4. Descriptive Analysis 5. Inferential Analysis and Hypothesis Testing 6. Conclusions 7. Recommendations 4. Steps of descriptive Data Analysis for Interval-Ratio Variables  Organize the Data into a Frequency Distribution  Display the Data in a Graph  Describe What Is Average or Typical of a Distribution  Describe Variability Within a Distribution  Describe the Relationship Between Two Variables  ELEMENTS NEEDED IN ANALYSIS OF INTERVAL/RATIO DATA 1. Identification of Data in General – “What?” 2. Collection of Data 3. Organization and Summarization of Data 4. Descriptive Analysis 5. Inferential Analysis and Hypothesis Testing 6. Conclusions 7. Recommendations 5. Statistical Inference and Hypothesis Testing I. Definitions  Statistical inference – process of generalizing or drawing conclusions about the target population on the basis of results obtained form a sample.  Hypothesis Testing – a type of statistical inference which involves a process of determining whether or not the data provide evidences against the null hypothesis in favor of the alternative hypothesis.  Definitions  Hypothesis – a statement of belief used in the evaluation of population values o Null hypothesis – a claim that there is no difference in the statistic computed between the groups or populations being compared o Alternative hypothesis – statement of difference  Definitions  Test statistic – a numerical variable used to determine the relative position of the statistic (e.g. mean, proportion, standard deviation, etc.) in the sampling distributions; ex. T, z, chi-square, F  Critical Region – the region on the far end of the distribution; If only one end of the distribution is involved, the region is known as one-tailed. If both ends are involved, it is a two-tailed test.  Definitions  Significance Level ( )– the probability that a test statistic falls in the critical region; represents the error that an investigator allows himself in rejecting a null hypothesis when it is true (Type I or alpha error); by convention, a small  is set at either 0.05, 0.01 or 0.1 level. o Type I or alpha error – error of rejecting a true hypothesis o Type II or beta error – error of not rejecting a false hypothesis  Steps in Hypothesis Testing 1. state the null and alternative hypotheses 2. set the level of significance,  3. compute the test statistic 4. determine the critical region 5. consult standard tables for the possible areas (p value) in the distribution corresponding to the value of the test statistic 6. make a statistical decision whether or not to reject the null hypothesis - if p value from the table is less than or equal to the pre-stated  level, the null hypothesis is rejected and vice- versa 7. draw conclusions about the population COMPARISON OF SINGLE POPULATION : Z-test vs T- test  T test = compare the difference between the means  Z test = compare the difference between proportions (sensitivity, specificity, PPV, risks, percentages of people with symptoms, illness, or recovery) STANDARD ERROR  The standard error is dependent on the size of the samples  Standard error is inversely related to the square root of the sample size so that the larger n becomes, the more closely will the sample means represent the true population mean  This is the mathematical reason why the results of large studies or surveys are more trusted than the results of small ones STANDARD DEVIATION VS STANDARD ERROR  STANDARD DEVIATION  Tells us how much variability can be expected among individuals  STANDARD ERROR  Standard deviation of the means in a sampling distribution  It tells us how much variability can be expected among means in future samples REPRESENTATION OF DATA NOEL M. LAXAMANA, MD, FPAFP  Types of Numbers Nominal represents name or identifier/s of a person’s status, category, or attribute that does not represent quantity or amount Ordinal represents an ordered series of relationship Interval scale represents quantity; meaningful comparison of one number to the another number is possible; no meaningful zero Ratio scale represents quantity; meaningful comparison of one number to the another number is possible; it has meaningful zero  Types of Variables Variables used interchangeably with attribute; are specific characteristics of anything which can be assessed 1. Qualitative - are attributes that yield observations that can be categorized according to some characteristics or quality 2. Quantitative - are variables that yield observations that can be measured a. Discrete – expressed as integers b. Continuous – expressed as fraction  Types of Data Presentation 1. Narrative Presentation 2. Tabular Presentation 3. Graphical Presentation  1. Narrative Presentation – also known as the Textual Method of data presentation – the data is simply narrated in a story-like fashion – tendency to get confused – usually used as research “abstract” or summaries of study/research  I. Narrative Presentation A census conducted in Barangay X in Angeles City the International Institute of Rural Reconstruction in 1988 showed that there were 272 females aged between 15 and 44 years old. Of these women, 75 or 28% were less than 20 years old, 106 or 39% were between 20 and 29 while 91 or 33% were between 30 and 44. Approximately sixty six percent (66%) or 181 of the women had married at least once. Among the 15-19 age group, only 6 women or 8% were ever married. The proportion of ever married women increased sharply in the 20-29 age group to 81% or 86 women. For 30-44 age group, the number of ever married women was 89 or 98% of the women in this age bracket.  II. Tabular Presentation of Data – numerical data are presented in a logical fashion usually in form of table/s – provide a compact and an orderly way of presenting large sets of detailed information – can readily point out trends and comparison – show the interrelationships among variables  Parts of a Table 1. Table number - consecutively placed on the leftmost portion of the table 2. Title - give the “what”, “who”, “where”, and “when” of a table - a headnote may be placed as a secondary caption and serves to clarify items in the main title or body 3. Column headings - indicate the basis of classification of columns or vertical series of figures 4. Row headings - indicate the basis of classification of the rows or horizontal series of figures 5. Body - composed of cell/s (intersection of a row and a column) 6. Footnotes - placed immediately below the bottom rule of the table 7. Source/s of data - placed immediately after the footnotes of unoriginal data  Example of a Table Table 1 Ten Leading Causes of Deaths (Rate per 100,000 population) 5 – Year Averagea and 1997 Philippines CAUSES 5 – Year Average 1997 1. Disease of the Heart 74.2 76.6 2. Disease of the Vascular System 56.6 60.5 3. Pneumonia 50.9 45.0 4. Malignant Neoplasm 40.1 41.5 5. Accidents 21.2 41.1 6. Tuberculosis, All forms 16.4 36.5 7. COPD and Allied Conditions 38.2 18.6 8. Diabetes Mellitus 10.1 10.9 9. Nephritis, Nephrotic Syndrome, & Nephrosis 9.6 10.3 10. Other Diseases of the Respiratory System 10.1 10.3 Footnote: a Year 1992 to 1996 Source :National Statistics Office, Statistical Handbook of the Philippines, Philippines Health Statistics 2000  Characteristics of a Properly Constructed Table 1. Simplicity - a table should exhibit a clean, professional and uniform look 2. Clarity - table should jive with the textual discussion - can be achieved by: clear, concise headings or captions uncluttered footnotes minimum variables present well spaced columns and rows 3. Directness - implies that, what is only necessary should be included in the table  Pointers in Table Construction A. Positioning of the table - a table should be placed immediately after the text where it was first cited B. Uniformity in style - standardize a particular style of a table format for a single report C. Number of variables presented - minimize the variables presented on the table - if the data on a master table be presented, it should be broken down into simple tables with a maximum of 3 variables presented D. Every table should be self explanatory - the reader should be able to understand the content of the table without referring to textual explanations Pointers in Constructing a Self-Explanatory Table 1. Title must be complete but concise 2. All units of measure should be indicated in the table 3. Uncommon abbreviations should be explained in the footnote 4. Each row and column should have a clear and concise heading 5. Double-ruled lines should only be used in the top rule; succeeding rule lines should be single-ruled 6. Ruled lines should always be used for closely-spaced figures  Classification of tables according to number of variables 1. One-way table - a table which present distribution for a single variable 2. Contingency table (two-way table) - a table which shows the distribution of two variables 3. Multi-way table - a table which shows the distribution of three or more variables  1. One-way table Table 1. Selected Reportable Diseases, Philippines 1997 Diseases Number of Cases 1. Tuberculosis 240,509 2. Influenza 574,748 3. Malaria 69,248 4. Diarrhea 899,409 5. Measles 37,857 6. Diphtheria 53 7. Malignant Neoplasm 4,723 Source:1997 Annual Report of the Health Intelligence Center, Department of Health 2. Contingency table (two-way/2 X 2) Disease E + - x p + a b o s - c d u r e  3. Multi-way table (Master/Dummy) Table 1 Total Number of First Year Medical Students, AUF-SOMa, A.Y. 2007-2008 Age Sex Filipino Foreigner Male Cash Installment Cash Installment < 20 Female Male 21-30 Female Male 31-40 Female Male >41 Female Footnote: aAngeles University Foundation-School of Medicine  III. Graphic Presentation of Data Advantages 1. Simpler to read 2. Appeal to a greater number of people 3. Large complex masses of data can be presented in a simpler language 4. Significant trends or patterns can be made, to stand out more clearly 5. Offers a wider point of view of the data set, when precision is not required Disadvantages 1. Can be used to misrepresent facts 2. Twisted facts 3. Oversimplify situations  General Principles 1. Should be completely self-explanatory. 2. The scales should be properly labeled. 3. Trend lines and curves in the chart should be properly identified by labels or a legend 4. Grid or guide rulings may be used in a graph to guide the eye, but they should be kept at a minimum 5. Graphs should be simple, neat, and business-like 6. Basis of classification is generally represented on the horizontal scale, while, frequencies are placed along the vertical scale 7. The vertical scale should always start with zero. 8. Use color for emphasis or to differentiate between items in a diagram 9. On an arithmetic scale, equal distances between tick marks on the axis should represent equal numerical units Most Common Graphics used in Presentation of Data 1. Bar Graph 2. Pie Chart 3. Component Bar Diagram 4. Histogram 5. Frequency Polygon 6. Line Graph 7. Scatterpoint Diagram  1. Bar Graphs – for comparison of absolute or relative counts and rates between categories of a qualitative or discrete quantitative variable – Qualitative variables are represented using the horizontal bar graph – Discrete quantitative variables are represented using the vertical bar graph  1. Bar Graph Fig 1 Ten Leading Causes of Mortality Philippines, 1991 Dseases of the Heart Pneumonia Vacular Sytem Tuberculosis Malignant Neoplasm Accidents Septicemia Diarrheal Disease Nephritis Fetus and Infant Resp. Diseases 0 10 20 30 40 50 60 70 80 Rate per 100,000 Population  2. Pie Chart – shows the breakdown of a group or total where the number of categories of qualitative variable is not too many – the percentage of contribution of each component is multiplied by 3.6 – the area of each “sliced” is proportional to the relative contribution of the component to the whole pie  2. Pie Charts Fig 2 Maternal Deaths by Main Causes, Philippines, 1991 23% Other complicatons Hypertension Abortion Hemorrhage 42% 25% 10%  3. Component Bar Diagram - shows the breakdown of a group or total where there are several number of categories of qualitative variables  3. Component Bar Diagram Fig. 3 Births by Type of Attendant: NCR vs. Region 10, 1984 100% 90% Others 80% Hilot 70% Midwife 60% Nurse 50% Physician 40% 30% 20% 10% 0% NCR Region 10 Source: Health Intelligence Service, DOH, Philippine Health Statistics, 1984  4. Histogram – graphic representation of the frequency distribution of a continuous quantitative variable or measurement including age group – horizontal axis is a continuous scale showing the units of measurement of variable under consideration – vertical scale shows absolute or relative frequencies – rectangle are drawn over the true limits of the groupings – comparisons between groupings is made on the basis of the areas of rectangle and not the height  Frequency Distribution Table 4 Lengths in Centimeters of 84 Infants at Birth Lengths (cms) No of Infants 43 1 44 3 45 6 46 11 47 12 48 16 49 14 50 8 51 6 52 4 53 2 54 1  4. Histogram Fig. 4 Length of 84 Infants at Birth in Centimeters 20 18 16 No. of 14 Infants 12 10 8 6 4 2 0 43 44 45 46 47 48 49 50 51 52 53 54 55 Length in Centimeters  5. Frequency Polygon - same function as the histogram - use to depict more than one distribution in a single graph - the frequencies are plotted against the corresponding midpoint of the classes - can be constructed from a histogram by simply connecting the midpoints of the upper bases of each bar and closing the figure  5. Frequency Polygon Fig. 5 Length of 84 Infants at Birth in Centimeters 20 18 16 No. of 14 Infants 12 10 8 6 4 2 0 43 44 45 46 47 48 49 50 51 52 53 54 55 Length in Centimeters  5. Frequency Polygon Fig. 5 Length of 84 Infants at Birth in Centimeters 20 18 16 No. of 14 Infants 12 10 8 6 4 2 0 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 51.5 52.5 53.5 54.5 55.5 Length in Centimeters  6. Line Diagram - shows trend data or changes with time or age with respect to some other variable - changes in growth of population, temperature readings, birth and death rates, morbidity and mortality rates are best portrayed using line graph 6. Line Diagram  7. Scatterpoint Diagram - show correlation between two quantitative variables - gives a rough estimate of the type and degree of correlation between two variables - usually made as a preliminary step towards more detailed mathematical analysis  7. Scatterpoint Diagram Fig. 7 Scatterplot of Age vs. Diastolic Blood Pressure 100 90 mmHg 80 70 60 15 20 25 30 35 40 45 50 Age in years Noel M. Laxamana, MD, FPAFP Family Medicine  Describe various diseases with respect to temporal occurrence  Recognition on how measurement & quantification of health outcomes by time: ◦ Provide clues to etiology of health-related events ◦ Assist in planning health services  Identify factors related to the different temporal variations of disease distribution How does disease frequency change over time, and what factors are temporally associated with those changes?  Variations in the pattern of disease associated with time may permit important insights into the pathogenesis of disease or the recognition of emerging epidemics  Comparing measures of disease frequency between two populations or within a population over time, the timing of data collection needs to be considered if there are seasonal or cyclic variations in the rate of disease  SECULAR TRENDS ◦ Refer to gradual changes in the frequency of a disease over long time periods, as illustrated by changes in the rates of chronic diseases Communicable Diseases Diseases of the Heart Malignant Neoplasms  Examples of identified factors affecting the trend: ◦ Long-term impact of PUBLIC HEALTH PROGRAMS ◦ DIET improvements ◦ Better DIAGNOSIS, leading to better TREATMENT ◦ In the case of Lung Cancer, more teenagers are exposed to health risks such as smoking ◦ Other unknown factors  Defined as increases and decreases in the frequency of diseases and health conditions over a period of years or within each year  Examples: ◦ Seasonal trends in birth rates, depressive symptoms (U.S. Data) ◦ Seasonal variations among diseases such as influenza, drownings, accidents & mortality from heart attacks occurring within each year  Seasonal variations may be caused by seasonal changes in the behavior of persons that place them at greater risk for certain diseases, changes in exposure to infectious or environmental agents or endogenous biologic factors  Many diseases demonstrate cyclic increases & decreases related to changes in the lifestyle of the host, seasonal climatic changes and virulence of the infectious agent for a communicable disease  Other examples of health phenomena that may show cyclic variation are responses of persons to temporary stressors SOURCE: http://commons.wikimedia.org/wiki/File:Trend_for_A(H1N1)_in_the_Philippines.png Source: Morbidity Week 30, National Epidemiology Center, Department of Health  This may indicate the response of a group of people circumscribed in place to a common source of infection, contamination or other etiologic factor to which they were exposed almost simultaneously  Acute infectious diseases & enteric infections manifest this type of relationship with time as does mass illness due to exposure to chemical agents and noxious gases  Despite exposure at a common point in time, the actual time of disease onset may vary. Table shows the number of cases per city in Metro Manila after Ondoy hit the region. GMANews.TV  **Morbidity Trend of Leptospirosis Marikina, Philippines Jan-Dec 2009 40 35 30 25 20 15 Leptospirosis 10 5 0 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec '09 '09 **mock chart Usually heart attacks tend to occur more frequently in the early morning hours, and on Mondays. What are some possible etiological factors associated with this phenomenon?  Daily hormonal fluctuations  Conditioned responses (i.e. stress associated with return to work on Monday) COHORT EFFECT  Long-term variation in disease occurrence among a group of persons who share something in common. Examples:  Occupational exposures during a specific time period.  Birth year or era, and changes in lifestyle characteristics such as acquiring smoking habits. http://www.asbestosclaimsolicitors.co.uk/asbestos-statistics-trends.html CASE CLUSTERING  refers to an unusual aggregation of health events grouped together in space or time INFECTIOUS DISEASE CLUSTERING  Outbreak of Legionnaires’ Disease in the late 1970’s NON-INFECTIOUS DISEASE CLUSTERING  Development of vaginal adenoCA among daughters whose mothers were prescribed diethylstilbestrol (DES) Examples:  Post-vaccination reactions / adverse reactions to vaccines  Early 1980’s – high number of cases of Kaposi Sarcoma in younger homosexual males Noel M. Laxamana, MD, FPAFP Family Medicine  Familiarize and describe variations in pattern of disease causation according to its geographic characteristics  Recognition on how measurement & quantification of health outcomes by place: ◦ Provide clues to etiology of health-related events ◦ Assist in planning health services  Identify factors related to distribution of diseases by their location / place REGIONS  NCR  Cordillera Administrative Region  Region 1 Ilocos  Region 2 Cagayan Valley  Region 3 Central Luzon  Region 4a Calabarzon  Region 4b Mimaropa  Region 5 Bicol  Region 6 Western Visayas  Region 8 Eastern Visayas  Region 9 Zamboanga Peninsula  Region 10 Northern Mindanao  Region 11 Davao  Region 12 Soccsksargen  Region 13 CARAGA  ARMM  It is a geographic concept described technically in terms of latitude, longitude, and altitude relative to sea level.  Epidemiologically, it is of interest to the extent that, it is occupied by man and is assumed to mean location of residence.  It is the basis for describing spatial distribution of population and diseases in terms of hemisphere, region, nation or smaller political subunits.  It is a variable in disease causation that influences the occurrence of a particular disease  Climate  Geologic and other physical features  Type of vegetation and fauna  Level and type of economic development  Type of population ◦ Race ◦ Cultural factors ◦ Diet ◦ Genetics NATURAL BOUNDERIES  composed usually of homogenous areas  more likely to be useful for understanding the etiology of disease  its characteristics may influence the disease frequency  affects economic activities, patterns of transportation  access to medical care facilities POLITICAL BOUNDARIES  it may join heterogeneous areas  offers convenience in dealing with disease statistics  International Variations  National Variations (a.k.a. Geographic/ Within-country)  Urban / Rural Variations  Local Patterns of Diseases TYPE OF DISEASE  Infectious ◦ usually high frequency rates difference  Non-infectious ◦ usually low frequency rates difference ACCURACY AND COMPLETENESS OF REPORTING  directly proportional on the level of development of a country GEOGRAPHY  influences the prevalence of particular disease CULTURE  influences the behavior of inhabitants that results in the development of a particular disease DIET Differences in disease occurrence may be attributed to: ◦ Climate ◦ Geology ◦ Latitude ◦ Environmental pollution ◦ Race/ethnicity *Geographic delineation **Spatial contiguity Differences in may be attributed to: ◦ Diet ◦ Physical activity ◦ Housing conditions (i.e. lead paint) ◦ Crowding (i.e. spread of infection) ◦ Pollution URBAN DISEASES - high contact transmitted diseases RURAL DISEASES - high vector transmitted diseases The nearer the inhabitant to the source of causative agent the higher the disease frequency. Some localized differences in disease occurrence may be attributed to:  Carcinogenic exposure  Geologic formations  Lifestyle Regarding cross-country variation in disease occurrence, what is a likely impact of migrating from one’s native land to a geographically and culturally different location? For many disorders, particularly chronic diseases, migrants begin to assume disease rates of the host country in just a few generations. This provides strong evidence for the influence of environmental factors, since genetics are relatively stable over time.