Research in PHC

1. بسم الله الرحمن الرحيم Research in PHC Introduction to Primary Care: a course of the Center of Post Graduate Studies in FM PO Box 27121 – Riyadh 11417 Tel: 4912326 – Fax: 4970847

2. Objectives: • Appreciate the various uses of epidemiology in day to day practice • Define and distinguish between key measures of disease frequency • Explain the main features of study designs • Discuss measures of impact and association • Describe what is statistics and types of data. • Describe the average and spread • Explain what is p-value & confidence interval • Research Phobia in Family Medicine

3. Epidemiology: • Study of distribution & determinants of disease frequency in human population & application of this study to prevention & control health problems. Last’s Dictionary of Epidemiology.11 • Population : groups of people with common characteristics as age, gender, disease …etc. • Disease must be clearly defined in order to determine accurately who should be counted. • Disease definition: physical + pathological exam, diagnostic test results & S/S. e.g.H1N1 definition.

4. Epidemiology: Cases source: hospital registries, death certificates, surveys & reporting system – cancer, TB …etc Uses of epidemiology : Population or community health assessment - Measuring disease burden in a population. Investigating etiology (causation) Determining natural history and identifying predictors of outcome. Evaluation of intervention Measuring disease frequency: Health states : “disease present” or “disease absent” To establish disease presence criteria requires a definition of “normality” & “abnormality.”

5. Ratios, Proportions & Rates Ratios, Proportions & Rates : = x/y × 10n Ratio : values of x & y is completely independent, or x ise included in y : (1)male/female (2) female/all Proportion : ratio in which x is included in y - female/all Rate :is often a proportion, measured over time. Rate = population at risk during the same time period # of cases or events occurring during given time period × 10n Population at risk Correct testimate of number of people under study i.e. people who are susceptible to a given disease . e.g.. population at risk: identified by demographic, geographic or environmental factors. . e.g. 1- cervical disease - women 2- occupational injuries: brucellosis - working on farms & in slaughterhouses

6. Incidence & prevalence of diseases… Incidence : rate of occurrence of new cases arising in a given period in a population prevalence : frequency of existing cases in a population at a given point in time Incidence = # new events in a specified period __________________________________ 10n #persons exposed to risk during this period Prevalence = # of people with disease /condition at a specified time ________________________________________ ×10n # of people in the population at risk at specified time Relationship between incidence & prevalence Low incidence & a high prevalence – e.g. diabetes – or High incidence & a low prevalence – e.g. URTI. why? Prevalence = incidence × duration of disease

7. Factors influencing prevalence :- Age & disease severity, duration & number of cases..etc Prevalence increased by: Decreased by: Longer duration of disease Prolongation of life of patients without cure  new cases - incidence In-migration of cases Out-migration of healthy people In-migration of susceptible people Improved diagnostic facilities (better reporting) Shorter duration of disease High case-fatality rate from disease  new cases -incidence In-migration of healthy people Out-migration of cases Improved cure rate of cases Uses of prevalence 1- Assessing need for preventive action, healthcare & planning of health services. 2- Chronic diseases occurrence - DM, rheumatoid arthritis

8. Prevalence (unlike) can be influenced by factors unrelated to disease cause  not provide strong evidence of causality. Attack rate Often used instead of incidence during a disease outbreak in a narrowly-defined population over a short period of time. Attack rate= # of people affected/# of exposed For example, in the case of a food-borne disease outbreak (food poisoning) , the attack rate can be calculated for each type of food eaten, and then these rates compared to identify the source of the infection.

9. Types of study designs Descriptive studies Analytic studies Case report Case series Observational studies Cohort XCS CCS Ecological Experimental studies

10. Purposes of research 1-Essential prerequisite for all health care levels. 2-Establish priorities. 3-Determine health policy. 4-Identify causes of diseases 5-Identify risk factors of diseases 6-Identify pts health education needs. 7-Effective use of available resources 8-Adjustment of health strategies to situations changing. 9-Provide rational foundation for decisions & introducing objectivity into decision-making process (EBM).

11. Descriptive studies : • Describing the characteristics of a particular situation, event or case. 2 types:- • Case report & case series: • Describe in-depth characteristics of one / limited number of ‘cases’. • A case may be, patient, a health centre, or a village. • It can provide quite useful insight into a problem. • Case studies are common in inclinical medicine. e.g., characteristics of a hitherto unrecognized illness may be documented as a case study. It is often first step toward building up a clinical picture of that illness. e.g. HIV diagnosis started as reported cases of similar unusual groups of symptoms on 1980 . • However, if to test whether findings pertain to a larger population, a more extensive, XCS is needed.

12. Descriptive studies : (2) Cross-sectional surveys (XCS) : Looking at present • XCS aim: describe & quantify distribution of certain • variables in a study population at one point of time. e.g.: • Physical characteristics: people, materials/environment as • — prevalence surveys ( bilharzias, HIV) • — evaluation of coverage ( immunization,) • SE characteristics of people as age, education, marital status, number of children & income • Behavior or practices of people & knowledge, attitudes, beliefs, (KAP studies), or • Eventsthat occurred in the population. • XCS cover a selected sample of the population. • If a XCS covers total population it is called a census. Small surveys can reveal associations between certain variables, as between having TB & SES. If describe +compare groups within study population  comparative / analytical studies.

13. Comparative or analytical studies Search for cause & effect Or why & how e.g. Smoking - lung ca, Salmonella outbreak-eat shawerma 1- XC comparative studies 2- CCS 3- Cohort Cross –sectional comparative studies • XC surveys focus on describing +comparing groups. • e.g. : a survey on malnutrition to establish: • % of malnourished children in a population; • SE, physical variables influence availability of food; • Feeding practices; & knowledge, beliefs & opinions • influence these practices. • The researcher will :- describe these variables &, by comparing malnourished & well-nourished children, he will determine which SE, behavioral & other independent variables may contributed to malnutrition.

14. Comparative or analytical studies In any comparative study, watch out for CONFOUNDING or INTERVENING variables. • Confounding is an apparent association between disease and exposure caused by a third factor not taken into consideration. • A confounder is a variable that is associated with the exposure & independent of that exposure, is a risk factor for the disease. Age is the strongest confounder. e.g. • Study A found an association between cigar smoking & baldness - The study was confounded by age • Study C found improved perinatal outcomes for birthing centers when compared to hospitals • The study may be confounded by highly motivated volunteers who select the birthing center option

15. An observational analytic study that identify & compare affected & non-affected subjectsto determine risk of association for investigated disease. Case-control studies: CCS : longitudinal • Enroll gp. of people with disease (or other outcome) (cases)& a gp. without this disease (controls) & compare their patterns of previous exposureto a risk factor Direction of inquiry CCS Design TIME Exposed Start with: Diseased (Cases) Not Exposed Target Population Not Diseased (Controls) Exposed Not Exposed

16. CCS provide a relatively simple way to investigate causes of diseases, especially rare diseases. e.g. CCS In a study of causes of neonatal death, investigator will first select ‘cases’ (children who died within first month of life) & ‘controls’ (children who survived their first month of life), then interviews their mothers to compare history of these 2 groups of children, to determine whether certain risk factors are more prevalent among children who died than among those who survived. Selection of cases 1- Selected cases should represent all cases in study population 2- Cases selected on basis of disease not exposure 3- Define diagnostic criteria for disease i.e. :Case definition - clinical criteria as restriction by time, place, & person. e.g. H1N1

17. Selection of control  Key is to identify appropriate control or comparison group. CCS Controls should come from same ‘source’ population. Of cases e.g. from same hospital If not they would not be comparable to cases. Classic e.g.: discovery of relationship between thalidomide & limb defects in babies born in Germany in 1959 and 1960. The study, done in 1961, compared affected children with normal children. Control confounding variables by matching the groups e.g. In a study on causes of malnutrition in children-3 yrs match well & malnourished on 1- age ( strongest confounder), 2- economic status of parents.

18. Selection of cases & control Selected cases should represent all cases in studied population CCS Cases selected on basis of disease not exposure Controls should come from same ‘source’ population. Of cases e.g. from same hospital If not they would not be comparable to cases. CCS uses An estimateof the ratio of incidence rates or risks (relative risk) is obtained by calculating an odds ratio (OR) “2-by-2” table ad OR = bc

19. longitudinal / incidence/prospective studies An observational analytic study that identify exposed (E ) & unexposed (Ē) population & follow them prospectively over time to determine rate of specific disease event. Cohort Study Begin by categorizing subjects on basis of exposure to potential cause (risk factor) – they are free of disease- : study group(E) & control group (Ē). the whole cohort is followed up & observed over a period of time to if development of the disease (or other outcome) differs between the (E) & (Ē) groups . Smoking lung ca., bronchitis…etc So directly measure incidence in E & Ē Source of data : 1- Existing records:- medical, employment 2- study subjects :Interviews, Qers, physical exam. or a test

20. TIME Direction of inquiry Cohort Study Diseased Exposed Target Population Not Diseased Diseased People without the disease Not Exposed Not Diseased can be both prospective and retrospective depending on time of data collection Cohort study uses 1- best information about causation of disease 2- most direct measurement of risk of developing disease. Framingham Study • Since 1948, samples of residents of Framingham, followed up for risk factors related to occurrence of heart disease.

21. For cohort or CCS Example: Calculating the Relative Risk Disease Status CHD cases (Cases) No CHD (Controls) TOTAL Exposure Status Smoker 112 176 288 Non-smoker 88 224 312 Ie/Iu Relative Risk (RR) = a/(a+b) 112 / 288 RR = = = 1.38 b/(c+d) 88 / 312 112 224 If XCS : approximate RR/ OR OR =ad/bc = = 1.53 88  176

22. Fast • Cheap • Slow • Expensive • Ethical problems minimal • Ethical problems ± signf. • Rare dis. • Common dis. • Volunteers: no need • No attrition problems • Small sample • Large sample • Attrition problems • Volunteers needed • Selection , recall bias • Less bias susceptible • RR approximate (OR) • Undefined population # • No incidence calcult • Incidence determn • Defined population # • RR accurate Cohort CCS

23. Intervention / Experimental studies Reference pop. Involves a direct comparison of 2or more intervention The hallmark : investigator dictates each subject’s exposure. 1-Strongest/gold standard test to a hypothesis Experimental pop. Aims 2-To determine a causative factor Non- participants 3-To determine effective Rx. 1- Prospective in nature. Participants ( study pop.) Distinctions 2- Investigator manipulates /change / intervenes with E for one group a- time b-manpower 3- Feasibility problem: as 4- Cost. Rx allocation c- population selection 5- Ethical considerations: as: a- Harmful agents b- Useful Rx or vaccine. Rx gp. Comparison gp. Intervention conduction at PHC level :- 1- Staff training - as WBC nurses to improve their performance 2- Health education for obese patients to loss weight.

24. Study types power Strength of hypothesis testing Experimental followed by cohort, CCs then XCS.

25. Basic biostatistics: concepts and tools Needed for summarizing and analyzing data Summarizing data : Data are either numerical or categorical variables. • Numerical variables : 1- Counts - # children of a specific age 2-measurements - height & weight. • Categorical variables : 1- The result of classifying - individuals can be classified into categories according to their blood group; A, B, O/ AB. 2- Ordinal data – which express ranks – as cancer grading. • Summary numbersinclude medians, means, ranges, standard deviations, standard errors and variances.

26. Basic biostatistics: concepts and tools • Tables & graphs : important means of summarizing & displaying data, but seldom prepared with sufficient care. • Aim: to display data so quickly & easily understood. • Each table / graph should be self-expressing: contain enough information so that it can be interpreted without reference to text. Figure 1.1 :Distribution of cholera cases in London, August-September 1854 Table 1.1: Mortality from cholera in e London -July 1854 Districts with Water Supplied Cholera Death Rate /1,000 Southwark 5.0 0.9 Lambeth

27. Frequency distributions & histograms… Frequency distributions, measures of central location, and measures of dispersion are effective tools for summarizing numerical characteristics such as height, BP, & incubation period.. Frequency distribution : organization of a data set into contiguous mutually exclusive intervals. Displayed : 1- a histogram : no space between bars. 2- Bar chart. 3- Pie chart.

28. Measures of Central Location & dispersion Measures of central location are single values that represent center of observed distribution of values. Different ways :- Arithmetic Mean The most commonly used measure. It is arithmetic average - “mean” or “average.” In formulas Mean = x = Σ xi/n Mode : Value occuring most frequently in a set of data . Median i.e. middle Identifying median from individual data: 1. Arrange observations - increasing /decreasing order. 2. Find Middle rank = (n +1)/2 a. If # of observations (n) is odd: median = middle rank b. If n is even, middle rank falls between 2 observations & median is equal to average of values of those observations. e.g.

29. Measures of Central Location & dispersion 13+7+9+11++13+7/5 : Arithmetic Mean= = Σ xi/n = 60/5= 12. Mode = 7. Odd number of observations : set of data with n = 5: 13, 7, 9, 15, 11 1. Arrange observations in increasing or decreasing order : either: 7, 9, 11, 13, 15 or: 15, 13, 11, 9, 7. 2. Find Middle rank = (n +1)/2 = 5+1 /2=3  median lies at value of 3rdobservation -11. Even number of observations: set of data with n = 6: 15, 7, 13, 9, 10, 11 1. Arrange the observations :- increasing or decreasing order - 15,13,11,10,9,7 2. Find Middle rank = (n +1)/2 = 6+1 /2=3.5  median lies halfway between values of 3rd & 4th observations. = average of 13 & 9 = 13+2/2= 10.5.

30. p-value & confidence interval • Assessments of role of chance :- hypothesis testing, which produces a ‘p-value’ – i.e check that this is an unbiased study findings. • Assessment of whether or not findings are ‘significantly different’ or ‘not significantly different’ from some reference value . approach to statistical significance Threshold value is 0.05 or 0.01. • If the P-value is 0.05, there is a 95% probability that : • The results did not occur by chance • The null hypothesis is false • There really is a difference between the groups i.e. there is a significant effect.

31. Measures of Dispersion\ deviation Range, Minimum Values, and Maximum Values. Standard Deviation (SD) : measures of dispersion of observations around the mean of a distribution. Normal distributions bell-shaped Show relationships of mean & SD. • 68.3% the area under normal curve lies between the mean ± 1 SD. • 95.5% of the area lies between the mean ± 2 SD • 99.7% of area lies between mean ± 3 SD. • 95% of area lies between the mean and ± 1.96 SD.

32. Population parameter is interfered from sample statistic. Probability note: reality is that population mean is either inside or outside the range we have calculated. Point Estimate for population mean μ & Error : Sample mean x is a point estimate for population mean μ x for a random sample will not be exact same value as true μ.

33. 95% Confidence Intervals (CI) 95% of area under normal curve lies between ±1.96 SD on each side of the mean. So the 95% confidence limits= Lower 95% confidence limit = x −(1.96×SE) Upper 95% confidence limit = x +(1.96×SE) The true mean has a 95% probability of lying between the limits we find. • Confidence limits are also calculated for proportions, rates, risk ratios, odds ratios, and other measures when we wish to draw inferences from a sample to population at large. • The interpretation of the confidence interval remains the same: ( narrower interval, more precise our estimate of population value ( & more confidence we have in our study value as an estimate of population value);.

34. Research Phobia in Family Medicine • Historical background : Past decades : Family doctors (FD) involved in manual practice & distant from Ideas & Theory of Research. But now... • Frequent Discoveries & Health Authorities are often asking us to change our prescribing behavior • We need to study & to work in group with Research tools :Epidemiology. EBM, Qualitative Research Myths against Research • It is necessary to change but FDs still resist hard… • We often think:- • “We are inferior & very practical • “Research is high Theory for academic people” • “We have no time” • Too much Statistics • NO tools for research

35. Behavioral Therapy “cognitive-behavioral” therapy can be useful to break “mental walls” still surviving in our open world First small steps……The Idea.. Do not be afraid of the white empty page… Start from the richness of FM : Informal ideas,problems and feelings connected to daily practice are the real “steam-engine” of Research New Development in FM (Group practice, PC, Telematics, not expensive software) can facilitate a change First step: Follow steps of a flow chart

36. Steps of conducting a research project

37. تم بحمد الله Thank you