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  • It is possible to identify three broad objectives that characterize the utility of descriptive epidemiology: To permit evaluation of trends in health and disease and comparisons among countries and subgroups within countries; the objective includes monitoring of known diseases as well as the identification of emerging problems To provide a basis for planning, service provision, and evaluation of health services; data needed for efficient allocation of resources often come from descriptive epidemiologic studies To identify problems to be studied by analytic methods and to suggest areas that may be fruitful for investigation
  • Exposure-based cohort studies.
  • This type of cohort includes either an entire population or a representative sample of the population. Here exposures are unknown until the first period of observation when exposure information is collected after administration of questionnaires, collection of biologic samples, etc. Then the cohort can be divided into exposed and non-exposed group.
  • Retrospective studies are quicker and cheaper and efficient for diseases with a long latency but you need good records of relevant exposure data, as well data on potential confounders (diet, smoking) may not be available.
  • Research methodology by hw

    1. 1. Research Methodology: Methods and Materials
    2. 2. Components of a research proposal Title Summary Introduction Objective Methods and materials Ethical consideration Dissemination and utilization of findings Work plan Budget References Annexes 2
    3. 3. Methods and Materials The methods or procedures section is really the heart of the research proposal Methods/procedures show how you will achieve the objectives and answer the research question Indicates the methodological steps you will take to answer every question, to test every hypothesis or to address every objective 3
    4. 4. Methods and Materials… • There should be a very clear link between the methods you describe in this section and the objectives you have previously defined. • Be explicit in your writing and state exactly how – the methods you have chosen will fulfil your objectives & – It will help to deal with the needs/problems on which your proposal is focused
    5. 5. Methods section may include: Study design Study area and period Populations Study variables Eligibility (Inclusion and exclusion) criteria Sample size calculation, sampling methods & procedures Data collection techniques & tools Data quality control measures Data management Plan for data processing and analysis Operational definitions Limitation of the study Ethical considerations Plan for dissemination and utilization of findings 5
    6. 6. 1. Study design Clearly state the study design to be used  Specify the approaches to carry out the research (either quantitative, qualitative, or mixed) Select the most appropriate and most feasible study design 6
    7. 7. Epidemiologic studies • Help to answer questions: – How big is the problem (magnitude)? – Who has the problem? When and where? – What causes the problem? – Are certain factors associated with the problem? – What will happen if the suspected factors are removed or reduced? – What is the effect of a particular intervention on the problem? New drug? Health education? – What are the possible solutions?
    8. 8. • The selection of study design depends on: • The type of problem • The current knowledge about the problem • Availability of resources • Different research questions may require different study designs o The selection of an appropriate study design for the study is the most important decision the investigator has to make.
    9. 9. 2. Population The population under consideration should be clearly defined in terms of place, time, and other relevant criteria In this sub-section, identify: – Source (Reference or target population) population – Study population – Sampling unit: unit measurement of source population – Study unit: a direct source of information
    10. 10. Study population  Sampling unit: unit measurement of source population Study unit: a direct source of information
    11. 11. Eligibility criteria  Inclusion criteria: – identify eligible subjects for the study Exclusion criteria: – to systematically exclude subjects from the study population –justify why exclusion is important –Exclusion is from the domain 12
    12. 12. Study Variables • A variable is a characteristic of a person, object or phenomenon, which can take on different values. • Variables may be: – Numerical (values can be expressed in numbers- eg. Age, income – Non-numerical characteristics (categorical) e.g. sex, treatment outcome
    13. 13. Variables • In health research we often look for associations. • Hence, it is important to make a distinction between dependent and independent variables.
    14. 14. Independent variable(X) : • Also known as predictor = explanatory variables • a variable that attempts to explain the variation in dependent variable (Y). • factor that influence the outcome variable
    15. 15. Dependent variable (Y)/outcome variable:Dependent variable (Y)/outcome variable:  It is the outcomeIt is the outcome ((response)response) of a study,of a study, vary in relation to the independent variables, and results can be predicted
    16. 16. Dependent… • Eg. Association between smoking and lung cancer • Dependent variable= developing lung cancer (yes, No) • Independent variable= smoking (yes, No)
    17. 17. »Study design
    18. 18. Case-controlCase-control CohortCohort IndividualsIndividuals InterventionalInterventional RetrospectiveRetrospective ProspectiveProspective DescriptiveDescriptive PopulationsPopulations AnalyticalAnalytical ObservationalObservational Case reports/case seriesCase reports/case series Cross-sectional Ecologic/correlationalEcologic/correlational Experimental (RCT) Types of Epidemiological Studies Quazi-Experimental Classical Case-coClassical Case-co Nested Case- control Nested Case- control Epidemiological study design Is there comparison group? Test hypothesis? Epidemiological study design Is there comparison group? Test hypothesis?
    19. 19. 1. Descriptive Epidemiology Describe patterns of disease occurrence within a population in relation to person, place and time ♣ Used to identify any health problems that may exist ♣ Generates idea(s)/ hypothesis for presence of association between risk factor and illness ♣Frequently encountered approach
    20. 20. 1. Population as study subject o Correlational /ecological studies Individual as study subjects o Case report (Single case) o Case series (few cases) o Cross-sectional study (survey) Categories of descriptive statistics
    21. 21. 2. Analytical Epidemiology  Uses comparison groups to establish an association between risk factors and illness in the two groups ♣ Identify the cause (s) of the problem ♣ Concerned with determinants of disease, • the reasons for low or high frequency of a disease ♣ Tests hypotheses
    22. 22. 2.1 Observational studies = The investigator simply observes the natural course of events or an outcome of interest o Case-control study o Cohort study 2.2 Experimental / intervention studies = The investigator allocates the exposure and then follow the subjects for the subsequent development of disease Categories of analytic epidemiological studies
    23. 23. WHO? WHERE? WHEN? Descriptive Epidemiology
    24. 24. Person Place Time Cases 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 0 200 400 600 800 1000 1200 0-4 '5-14 '15- 44 '45- 64 '64+ Age Group Who? Where? When? Descriptive Studies
    25. 25. Characteristics of Persons  “Who is getting the disease?”  Age, sex, religion, socio-economic status, race • Young vs old, males vs females, rich vs poor, more educated vs less educated, black vs white, etc
    26. 26.  Characteristics of Place  “Where are the rates of disease highest/ lowest?” • Urban vs rural, some regions more affected than others? • National vs international? • High altitude or low altitude? • Polluted areas or unpolluted areas? • Mountainous vs valley • Adequate rainfall or little rainfall areas?  Differences in frequency of diseases are related to variations in climate, altitude, topography, geology and in general environment.
    27. 27.  Characteristics of Time  “When does the disease occur commonly/ rarely?”  Was there a sudden increase over a shorter period of time?  Is the problem greater during rainy or dry season?  “Is the frequency of the disease now different from the corresponding frequency in the past?”  Is the problem gradually increasing/ decreasing?
    28. 28. Uses of Descriptive Studies Describe the pattern of disease occurrence Describe the problem in terms of person, place and time Generate numbers of events (frequency) Help to calculate ratio, proportion and rates Program planning / resource allocation Identify problems to be studied by analytic methods Generate hypothesis
    29. 29. Types of Descriptive Studies • Ecological / Correlational studies – The unit of observation is the entire population to compare disease frequency between different groups • During the same period of time among different populations or in the same population at different points in time. – Comparison of rates (morbidity or mortality) across geographical areas (or regions).
    30. 30. Ecologic / Correlational • Advantages: – useful for the formulation of hypotheses – Quick and inexpensive – Often use already available information (secondary data)
    31. 31. Ecologic … – Disadvantages: • Based on averages and may miss actual contributing factors • Unable to link exposure with disease at individual level • Lack of ability to control for potential confounders • Presence or absence of correlation does not imply valid statistical association
    32. 32. Types of Descriptive …Cont’d • Case report or case series – Detailed report of a single patient (case report) or a group of patients (case series) with a given disease – Document unusual medical occurrences – Gives the first clues in the identification of new disease and adverse effects of exposures – An important link between clinical medicine and epidemiology – Most common types of studies
    33. 33. Types of Descriptive …Cont’d • Case reports and case series played a role in the early recognition of AIDS – In 1980 and 1981, five cases of Pneumocystis carini were reported among young homosexual men in Los Angeles. Previously, it occurred in older cancer patients with compromised immunity – In 1981, large number of cases of Kaposi’s sarcoma happened in young homosexual men. Previously this exclusively occurred in elderly men and women equally
    34. 34. Case reports / case series  Advantages – Simple, quick, inexpensive – Formulate hypothesis  Disadvantages – Can’t be used to test hypotheses – Based on the experience of one or few people (small sample size), it can be coincidence – Lacks comparison group
    35. 35. Cross-Sectional Studies • Often called prevalence study. E.g., KAP, DHS, etc • Collection of data at one point in time at individual level • Presence or absence of both exposure and disease is assessed at the same time • Provide “snapshot” of health experience • Used to assess the health care status and health care needs of a population
    36. 36. Cross-Sectional Studies Advantages Disadvantages • Quick and inexpensive • Used for planning • Initial step • Multiple factors/ outcomes • Provide early clues for hypothesis generation • Temporal relationship of exposure and disease not distinguishable (whether exposure or disease came first unknown) • Bias in measuring exposure • No incidence/ relative risk • No hypothesis testing
    37. 37. Why? How? Analytical Epidemiology
    38. 38. Analytical Studies • Purpose/aim – Focus on determinants of cause – Search for cause and effect. – Answer questions like: Why? How? – To test whether certain factors are associated with disease or not – Test hypothesis about causal relationship • Proof – Quantify the association between exposure and outcome
    39. 39. Analytical Studies • Basic features – Appropriate comparison group needed • Exposed Vs Control – It is the use of comparison group that allows testing of epidemiologic hypotheses
    40. 40. Two types of Analytic Studies Difference lies in the role of the investigator - Observational studies • The investigator simply observes the natural course of an event • The investigator measures but does not intervene. – Interventional studies • The investigator assigns study subjects to exposure and non- exposure, then follows to measure for disease occurrence. • The investigator manipulates the intervention or exposure.
    41. 41. Observational Studies • Temporal relationship between observations of Exposure (E) and Disease (D): • Direction – Forward: starts with E – Backward: starts with D • Chronological relationship between onset of study and occurrence of D: • Timing – Prospective: study onset -----> D – Retrospective: D <--------- study onset
    42. 42. Observational Studies Case Control Vs Cohort – Case-control = Both the exposure and disease have already occurred at the time of the study – Cohort = Disease free exposed and non- exposed people are followed up to measure the outcome
    43. 43. Disease No disease Exposure ? ? Retrospective Nature Case-Control Studies (Case) (Control) Present Past
    44. 44. • Case-Control Study: – Compares people with disease (case) and without disease (control) – to determine the exposure status by looking backward in time. • Data are analyzed whether exposure was different for cases and for controls • Higher proportion of risk factor among cases than controls suggests association or lesser proportion of risk factors among case • Very common type of epidemiologic studies
    45. 45. Source: partially adapted from WHO, 1993 Design of a Case-Control Study
    46. 46. can
    47. 47. Selection of cases • Subjects selected on the basis of disease • A case should be clearly defined with regard to specific characteristic of disease • Needs standard diagnostic criteria • Sources of Cases: – hospital setting = hospital-based case-control – defined general population = population-based – disease registries with complete records
    48. 48. Selection of Controls • Be comparable to the cases: controls should have the same characteristics as the cases (except for the disease of interest) • Must have the same opportunity for exposure as a case • Must be subject to the same inclusion and exclusion criteria as cases • Involves consideration of a number of issues: scientific, economic and practical considerations
    49. 49. • Selection of controls may involve matching: – Cases and controls have the same (or similar) characteristics other than the disease – Ensures comparability – Age, sex, race, socio-economic status, etc. • These factors are associated with the incidence of most diseases
    50. 50. Sources of Controls • Hospital controls: patients attending or admitted to the same institution for other diseases • Relatives, friends or neighborhood • Community (population) controls: selected from the same source population as the cases – More expensive
    51. 51. Data collection • Interviews, questionnaires and/or examination; or surrogates (spouses or mothers of children) or from medical records • Should be objective or well standardized • Better not to know cases or controls (blinding) • Same procedure for cases and controls should be applied
    52. 52. Case-Control Studies • Advantages – Rare disease, e.g., cancer of a specific organ – Suitable for the evaluation of diseases with long latent periods – Quick and inexpensive – Relatively efficient, small sample size – Little problem with attrition – Can examine multiple etiologic exposures – No ethical problems
    53. 53. Case-Control Studies • Disadvantages – Inefficient for rare exposures – No calculation of rates and risks – In some situations, the temporal relationship between exposure and disease may be difficult to establish  Temporal E – D uncertainty – Prone to selection and recall bias – Selection of control difficult
    54. 54. Cohort Studies • Disease free exposed and non-exposed people are followed up and then outcome events are picked up when they occur • Measure and compare the incidence of disease in two or more study cohorts • Usually prospective or forward looking. • Are also called longitudinal studies.
    55. 55. What is a cohort? • A group of persons – sharing the same experience – followed for a specified period of time • Examples – birth cohort – workers at a chemical plant – graduating university class – attendants of this course
    56. 56. Disease among exposed? Disease among non- exposed? Usually prospective Cohort study Population at risk Exposed Not Exposed
    57. 57. Types of Cohort Studies • Based on the starting point of the study – Prospective (classical) – Retrospective (historical)
    58. 58. Prospective Cohort Study + - + - ill exp + - exp Disease occurrence Study startsExposure occurrence Prospective assessment of disease Selection based on exposure Time
    59. 59. Exposure assessment Study starts Disease occurrence Prospective Cohort Study Time + - + - ill exp + - exp Prospective assessment of exposure and disease Selection of population
    60. 60. Rétrospective cohort studies Exposure time Disease occurrence • Disease outbreak following a gathering • Occupational exposure in mine workers Study starts
    61. 61. Rétrospective Cohort Study Study takes place Disease occurrence Exposure occurrence Retrospective assessment of exposure and disease + - + - ill exp
    62. 62. • Limitations of retrospective cohort: – All relevant variables may not be available in the original records – Difficult to ascertain that the study population was free from the disease at the start – Loss of records, incomplete data
    63. 63. Data collection for Cohort Studies • Interview with follow-up • Medical records monitored over time • Medical examinations and laboratory testing • Apply equally to exposed and non-exposed
    64. 64. Advantages of cohort studies • Directly measure relative risk or rate • Measures of effect have clear meaning and are easily understandable • Temporal relationship between exposure & disease is clear • Prospective cohort studies less susceptible to selection bias because outcome not known • Well suited to rare exposures
    65. 65. Disadvantages of cohort studies • Large sample size • Inefficient for disease with latency period • Loss to follow-up • Exposure can change over time • Multiple exposures = difficult • High cost • Time consuming
    66. 66. Summary • Cohort studies allow measure of risk • Case-control studies are rapid, but not measure risk; only estimate RR • In the ideal world: prefer cohort to case- control study • In the real world: case-control studies usually do the job
    67. 67. Experimental/Intervention Studies o Investigator assigns subjects to exposure and non-exposure and makes follow up to measure for the occurrence of a disease. o It is usually prospective. o Provides high quality data o Random allocation o Assign E randomly, follow for D
    68. 68. Source: partially adapted from WHO, 1993 Design of an Experimental Study Investigator determines exposure status through Random allocation
    69. 69. When to choose an experimental design? • Generally reserved for relatively "mature” research questions • A lot has to be done before embarking on an experimental study
    70. 70. When to choose an experimental design? • When: – the research question cannot be answered by observational studies – earlier observational studies have not answered the research question – existing knowledge is not sufficient to determine clinical or public health policy – an experiment is likely to provide an important extension of this knowledge
    71. 71. Types of Experimental Studies • Randomized Clinical Trial (RCT) • Community Intervention Trial (CIT)
    72. 72. Randomized Clinical Trial (RCT) • Randomization is done on individuals – Each patient is given an equal chance of being assigned to either group (e.g., treatment vs. placebo) • Blinding (masking) possible: – Double-blind = Neither the patients nor the investigators responsible for outcome assessment know what treatment she/he is getting – Single-blind = The investigator alone is aware of the group to which a participant has been assigned – Un-blinded = Both the investigator and patient are aware of the treatment assignment. • Most common
    73. 73. Direction of inquiry Manipulation
    74. 74. Community Intervention Trials (CITs) • Randomization is done on groups or communities rather than individuals – E.g., New drug or vaccine testing (some communities receive vaccine others placebo through random assignment) • Blinding not possible • Contaminations and co-interventions serious problems
    75. 75. Problems of Intervention Studies • More difficult to design and conduct • Ethical issues – Withholding – Exposing • Feasibility – Very large sample size required • Cost – Very expensive
    76. 76. Advantages of Intervention Studies • GOLD STANDARD = Randomized, placebo controlled, blinded clinical trials • The ability to assign exposure • The ability to control confounding • Findings can be replicated = Generalizability
    77. 77. The hierarchy of epidemiological research
    78. 78. Summary Points on Study Designs • Two types of epidemiological studies – Descriptive – Analytic • Descriptive – Case reports/Case series – Ecological – Cross-sectional • Analytical – Observational: Case-control & Cohort – Experimental
    79. 79. Operational definition For some variables it is sometimes not possible to find meaningful categories unless the variables are made operational with one or more precise indicators Operationalizing variables means that you make them measureable 81
    80. 80. Operational Versus standard definition Standard definitions are widely/universally accepted definitions of the variable E.g. “Obesity”-excessive fatness”, “overweight” However, operational definition is heavily influenced by considerations of practicability during measuring 82
    81. 81. In general, operational definitions of variables are used in order to: – Avoid ambiguity – Make the variables to be more measurable  Justification is needed for setting cut-off points Example: Knowledge 83
    82. 82. Sample size and sampling techniques Describe how the sample size is determined Describe the methods of sample selection If needed, use diagrams to simplify the sample selection process (sampling procedures) 84
    83. 83. Sample size and sampling...con’d The key reason for being concerned with sampling is validity (internal and external validity) The key word in sampling is representativeness A representative sample has all the important characteristics of the population from which it is drawn A sample is a representative of the population under study 85
    84. 84. SAMPLE SIZE Depending on: 1) Variability in the target population. (If unknown, assume maximum variability) 2) Desired precision in the estimate 3) Desired confidence in the estimate 4) Feasibility
    85. 85. αand Confidence Level  α: The significance level of a test: the probability of rejecting the null hypothesis when it is true (or the probability of making a Type I error). It is usually 5% (0.05)  Confidence level: The probability that an estimate of a population parameter is within certain specified limits of the true value; (commonly denoted by “1- α”, and is usually 95%).
    86. 86. Power and β Power: The probability of correctly rejecting the null hypothesis when it is false; commonly denoted by “1- β”.  β : The probability of failing to reject the null hypothesis when it is false (or the probability of making a Type II error).
    87. 87. Null hypothesis
    88. 88. Precision A measure of how close an estimate is to the true value of a population parameter. It may be expressed in absolute terms or relative to the estimate. It is denoted by d in sample size determination
    89. 89. SAMPLE SIZE Sample Size Required for Estimating Population Mean • The objective in interval estimation is to obtain narrow intervals with high reliability • The width of the interval is determined by the magnitude of the quantity
    90. 90. Sample Size Required for Estimating Proportions • The formula requires the knowledge of p, the proportion in the population possessing the characteristic of interest. – A pilot or preliminary sample. Observations used in the pilot can be counted as part of the final sample – Estimates may be available from previous studies and the upper bound of p can be used in the formula – If impossible to come with a better estimate, set p = 0.5 in the formula to yield the maximum value of n
    91. 91. Sample Size Required for Estimating Proportions Assuming random sampling and approximate normality in the distribution of p, Where q = 1 – p n Zα/2 P q = 2 2 d
    92. 92. Finite Population Correction • Finite Population Correction (FP) • – N = population size – n = sample size • Can be ignored when sample size is small in comparison with the population size
    93. 93. Finite Population Correction N n n/N (N-n)/(N-1) nFPC 100000 384 0.00384 0.996 383 50000 384 0.00768 0.992 381 20000 384 0.0192 0.981 377 10000 384 0.0384 0.962 369 5000 384 0.0768 0.923 355 1000 384 0.384 0.617 237
    94. 94. Definition of sampling Procedure by which some members of the population are selected as representatives of the entire population
    95. 95. Why sampling? • Due to the variability of characteristics among items in the population, researchers apply scientific sample designs in the sample selection process to reduce the risk of a distorted view of the population, and they make inferences about the population based on the information from the sample survey data.
    96. 96. Advantages of sampling: • Feasibility: Sampling may be the only feasible method of collecting the information. • Reduced cost: Sampling reduces demands on resource such as finance, personnel, and material. • Greater accuracy: Sampling may lead to better accuracy of collecting data • Sampling error: Precise allowance can be made for sampling error • Greater speed: Data can be collected and summarized more quickly
    97. 97. Disadvantages of sampling: • There is always a sampling error. • Sampling may create a feeling of discrimination within the population. • Sampling may be inadvisable where every unit in the population is legally required to have a record.
    98. 98. Errors in sampling • i) Sampling error: • Errors introduced due to errors in selection of a sample. • They cannot be avoided or totally eliminated. ii) Non-sampling error: - Observational error - Respondent error - Lack of preciseness of definition - Errors in editing and tabulation of data
    99. 99. Concept of representativity • Time • Seasonality • Day of the week • Time of the day • Place • Urban • Rural • Persons • Age • Sex • Other demographic characteristics
    100. 100. Definition of sampling terms • Sampling unit – Basic sampling unit (bsu) around which sampling is planned • Sampling frame – Any list of all the sampling units in the population • Sampling scheme – Method of selecting sampling units from sampling frame
    101. 101. Why do we sample populations? • Get information from large populations • Study efficiency • Obtain more accurate information
    102. 102. Type of samples • Non-probability samples – Convenience samples • Biased • Best or worst scenario – Subjective samples • Based on knowledge • Time/resources constraints • Probability samples – Only sampling method that allows to draw valid conclusions about population
    103. 103. Probability sampling: • It is a sample obtained in a way that ensures that every member of the population has a known, non zero probability of being included in the sample. • Probability sampling involves the selection of a sample from a population, based on chance. • Probability sampling is more complex, more time-consuming and usually more costly than non-probability sampling.
    104. 104. Probability samples • Random sampling • Removes possibility of bias in selection of subjects • Each subject has a known probability of being chosen • Allows application of statistical theory to results
    105. 105. Sampling error • No sample is a perfect mirror image of the population • Magnitude of error can be measured in probability samples • Expressed by standard error – of mean, proportion, differences, etc • Function of – sample size – amount of variability in measuring factor of interest
    106. 106. Methods used in probability samples • Simple random sampling • Systematic sampling • Stratified sampling • Cluster sampling • Multistage sampling
    107. 107. Simple random sampling • Principle – Equal chance for each statistical unit • Procedure – Number all units – Randomly draw units • Advantages – Simple – Sampling error easily measured • Disadvantages – Need complete list of units – Does not always achieve best representatively when there is minority
    108. 108. Example: Simple random sampling 1 Albert D. 2 Richard D. 3 Belle H. 4 Raymond L. 5 Stéphane B. 6 Albert T. 7 Jean William V. 8 André D. 9 Denis C. 10 Anthony Q. 11 James B. 12 Denis G. 13 Amanda L. 14 Jennifer L. 15 Philippe K. 16 Eve F. 17 Priscilla O. 18 Frank V.L. 19 Brian F. 20 Hellène H. 21 Isabelle R. 22 Jean T. 23 Samanta D. 24 Berthe L. 25 Monique Q. 26 Régine D. 27 Lucille L. 28 Jérémy W. 29 Gilles D. 30 Renaud S. 31 Pierre K. 32 Mike R. 33 Marie M. 34 Gaétan Z. 35 Fidèle D. 36 Maria P. 37 Anne-Marie G. 38 Michel K. 39 Gaston C. 40 Alain M. 41 Olivier P. 42 Geneviève M. 43 Berthe D. 44 Jean Pierre P. 45 Jacques B. 46 François P. 47 Dominique M. 48 Antoine C.
    109. 109. Systematic sampling • Principle – A unit drawn every k units – Equal chance of being drawn for each unit • Procedure – Calculate sampling interval (k = N/n) – Draw a random number (≤ k) for starting – Draw every k units from first unit • Advantages – Ensures representativity across list – Easy to implement • Disadvantages – Dangerous if list has cycles
    110. 110. Example: Systematic sampling Example: systematic sampling
    111. 111. Stratified sampling • Principle – Classify population into homogeneous subgroups (strata) – Draw sample in each strata – Combine results of all strata • Advantage – More precise if variable associated with strata – All subgroups represented, allowing separate conclusions about each of them • Disadvantages – Sampling error difficult to measure – Loss of precision if very small numbers sampled in individual strata
    112. 112. Example: Stratified sampling • Determine vaccination coverage in a country • One sample drawn in each region • Estimates calculated for each stratum • Each strata weighted to obtain estimate for country
    113. 113. Cluster sampling • Principle – Random sample of groups (“clusters”) of units – In selected clusters, all units or proportion of units included • Advantages – Simple as no list of units required – Less travel/resources required • Disadvantages – Imprecise if clusters homogeneous (large design effect) – Sampling error difficult to measure
    114. 114. CLUSTER SAMPLING The sampling unit is not a subject, but a group (cluster) of subjects. It is assumed that the variability among clusters is minimal, while within each cluster is representing the general population 1. Define the number of clusters to be included 2. Compute a cumulative list with the populations per each unit and a grand total 3. Divide the grand total by the number of clusters and obtain the sampling interval
    115. 115. CLUSTER SAMPLE 6. By repeating the same procedure, identify all the clusters 7. In each cluster select a random sample using a sampling frame of subjects (e.g. residents) or households. 4. Choose a random number and identify the first cluster 5. Add the sampling interval and identify the second cluster Advantage: easy to perform Disadvantage: design effect
    116. 116. CLUSTER SAMPLING in EPI Procedure: list of all villages (areas) with total population village inhabitants Cumulative 1 34 34 2 60 94 3 30 124 4 76 200 5 315 515 . . 4,715 divide the cumulative total by 30 clusters we wish to select 4,715 : 30= 157.1
    117. 117. EPI CLUSTER SAMPLING choose from the cumulative distribution the clusters by adding 157 (sampling interval) 4 124 124 * 1st cluster 5 76 200 6 315 515 ** 2nd 123+157=280 3th 280+157=437 . . in each village (area) choose 7 children Total sample 30 X 7= 210 find a random number with three digits (= sampling interval) e.g. 123
    118. 118. Design effect Global variance p(1-p) Var srs = ---------- n Cluster variance p= global proportion pi= proportion in each stratum n= number of subjects k= number of strata Σ (pi-p)² Var cluster = ------------- k(k-1) Design effect = ------------------ Var srs Var clust
    119. 119. Example: Cluster sampling Section 4 Section 5 Section 3 Section 2Section 1
    120. 120. Multistage sampling • Principle – Several chained samples – Several statistical units • Advantages – No complete listing of population required – Most feasible approach for large populations • Disadvantages – Several sampling lists – Sampling error difficult to measure
    121. 121. Data collection techniques and tools Describe: What are data collection techniques and tools? Who will collect the data? Who will supervise the data collection process? How long will take the data collection? etc… 123
    122. 122. Data quality control measures Be aware of possible sources of error to which your design exposes you  You will not produce a perfect, error free design (no one can!)  However, you should anticipate possible sources of error and attempt to overcome them or take them into account in the analysis 124
    123. 123. Data quality…con’d Describe/provide: Selection and training of field staffs Translation of the data collection tool to the local language Pre testing the research methods and tools Standardization and/or calibration of data collection tools Strict supervision of field staffs Clarify the purpose of study to respondents Double data entry  Re-interviewing of randomly (e.g. 5%) etc… 125
    124. 124. Pretesting Versus Pilot study Describe where the pretesting will be conducted How many study subjects will be included in the pretesting Will that be undertaken in the same area and/or the same population. If the collected data is going to be analysed or included in the study. 126
    125. 125. Data management Data processing refers to: – data entry onto a computer, and – data checks and corrections The aim of this process is to produce a relatively ‘clean’ data set 127
    126. 126. Data management… con’d Data coding: In general computers are at their best with numbers Some statistical packages cannot analyze alphabetic codes, some cannot understand open ended responses 128
    127. 127. Data management… con’d Coding is assigning a separate (non-overlapping) numerical code for separate answers and missing values Example:  Instead of using ‘Male’ and ‘Female’ for the variable sex, it can be indicated as 1=Male & 2= Female 129
    128. 128. Data management… con’d Data Cleaning: Once data have been gathered, they need to be entered into a computer data file and checked for errors No matter how carefully the data have been entered some errors are inevitable The aim of this process is to produce a clean set of data for statistical analysis 130
    129. 129. Plan for data processing and analysis A plan for data analysis should include the following information: Identification of the analysis tasks to be completed – Z test, Chi-square test , t-test, correlation, regression… – Confidence interval (CI) and P-value A schedule or work plan for the analysis of the data Identification of the statistical software to be used for the analysis 131
    130. 130. Limitation of the study State anticipated and inevitable limitations of the study be methodological and/or logistical 132
    131. 131. Ethical consideration  Professional obligation to safeguard the safety of study subjects  Describe potential ethical concerns and mechanisms to minimize harm and maximize benefits  Every research can potentially cause ethical concerns!  Research Ethics principles  Respect person  Benefit /no harm  Justice 133
    132. 132. Plan for dissemination and utilization of findings Briefly describe the dissemination plan: – Feedback to the community – Feedback to local authorities – Identify relevant agencies that need to be informed – Scientific publication in a reputable journal – Presentation in meetings/conferences/ symposium Briefly describe how the study findings can be best translated into application 134
    133. 133. Work plan The work plan is the timeline that shows when specific tasks will have been accomplished A work plan informs the reader how long it will take to achieve the objectives/answer the questions 135
    134. 134. Work plan …con’d It is a schedule, chart or graph that summarizes the different components of a research proposal and how they will be implemented in a coherent way within a specific time-span Work plan includes: – Tasks to be performed – When these tasks will be performed – Who will perform the task 136
    135. 135. Work plan …con’d The GANTT Chart It is a planning tool which depicts graphically the order in which various tasks must be completed and their duration of activity A typical Gantt chart includes the following information: The tasks to be performed Who is responsible for each task The time each task is expected to take 137
    136. 136. A work plan can serve as: A management tool A tool for monitoring and evaluation A visual illustration of the sequence of the project operations 138
    137. 137. GANTT Chart
    138. 138. Budget To conduct research, it is necessary to obtain funding for the research project When drawing up a budget, be realistic! Do no attempt to be too economical to demonstrate how cheaply you can run the project At the same time, do not be too expensive so as not to discourage the fund providers 140
    139. 139. Budget …con’d How should a budget be prepared? It is necessary to use the work plan as a starting point Specify, for each activity in the work plan, what resources are required Determine for each resource needed the unit cost and the total cost 141
    140. 140. Budget …con’d The budget format and justification The type of budget format to be used may vary Most donor organizations have their own special project forms, which include a budget format Include 5%-10% contingency fund for market inflation 142
    141. 141. Annexes Annexes may include the following:  Data collection tools and procedures  Consent form and information sheet Dummy table Conceptual frame work Sampling procedure Map of the study area Letter of support (cooperation letter)  Copy of the ethical approval letter, etc… Curriculum vitae (CV) of the principal investigator 147
    142. 142. Referencing  Referencing is a standard way of acknowledging the sources of information It is important to be consistent when you are referencing 149
    143. 143. Major sources of literatures –Books –Journals –Report paper –Conference paper –Website etc... 150
    144. 144. Methods of citations in preparing LR:  Vancouver system  Harvard system 151
    145. 145. The Vancouver system In the Vancouver style, citations within the text of the essay/paper are identified by Arabic numbers in round brackets or Arabic numbers in superscript. Example: Although an increasing number of countries have succeeded in improving the health and well being of mothers and children, some countries with the highest burden of mortality made little progress during the 1990s (1). More than 10 million children die each year, most from preventable causes and almost all in poor countries, but the causes of death may differ from one country to another (2). 152
    146. 146. Vancouver…con’d Example: Human ascariasis occurs both in temperate and tropical environments. The prevalence is low in arid climates, but high where conditions are wet and warm as these conditions are ideal for egg survival and embryonation. In addition, crowding, low socioeconomic status, poor environmental hygiene, and water supply contribute to the increased risk of infections due to helminthes (3-6) . 153
    147. 147. Vancouver…con’d The original number assigned to the reference is reused each time the reference is cited in the text, regardless of its previous position in the text 154
    148. 148. Vancouver…con’d For a book Author(s)’ Surname followed by initials. Title of book. Place: Publisher; Year, Edition. Example: Abramson H. Survey methods in community Health. Edinburgh: Churchill Livingstone ; 1990, 4th ed. 155
    149. 149. Vancouver…con’d For a chapter in a book: Author(s) of chapter (Surname(s) followed by initials. Chapter title. In: Editor(s) of book, (Surname(s) followed by initials) (eds). Title of book. Place: Publisher, Year: Page numbers of chapter. Example: Jennifer D. Epidemiological methods. In: Ng’weshemi J, Boerma T, Bennett J and Schapink D (eds). HIV prevention and AIDS care in Africa; A district level approach. Amsterdam: KIT Press, 1997: 51-68. 156
    150. 150. Journal: Author(s) Family name and initials. Title of article. Title of journal abbreviated Publication year, month, day (month and day if available); volume(issue): pages. Example: Paul K. Maternal mortality in Africa from1980-87. Social Science and Medicine 1993;37(2):745-52. 157
    151. 151. Two Authors Example Haile A, Enqueselassie F. Influence of women's autonomy on couple's contraception use in Jimma town, Ethiopia. Ethiop. J. Health Dev 2006;20(3):145-151. 158
    152. 152. More than Six authors: Write the first three authors, and et al. Example: Tsega E, Mengesha B, Nordenfelt E, et al. Serological survey of HIV infection in Ethiopia. Ethiop Med J 1998;26(4):179-84. 159
    153. 153. Reports and other organizational publications Author(s). Title of report. Place of publication: Publisher; Date of publication (year and month if applicable). Example: WHO. Lay Reporting of Health Information. Geneva, Switzerland: World Health Organization; 1978. 160
    154. 154. Conference papers Author(s) of paper – Family name and initials. Title of paper. In: Editor(s) Family name and initials, editor(s). Title of conference; Date of conference; Place of conference. Place of publication: Publisher’s name; Year of publication. Example: Kimura J, Shibasaki H. Recent advances in clinical neurophysiology. Proceedings of the 10th International Congress of EMG and Clinical Neurophysiology; 1995 Oct 15-19; Kyoto, Japan. Amsterdam: Elsevier; 1996. 161
    155. 155. Websites Example World Health Organization. Deployment at community level of artemether-lumefantrine and rapid diagnostic tests, Raya Valley, Tigray, Ethiopia. 2009. ( ) (Accessed October 15, 2009). 162
    156. 156. The Harvard System In other journals and books it is common to put the year, between brackets, straight after the name of the author(s) If this system of citation is used, the references at the end of the proposal, should be listed in Alphabetical order In Harvard System, put the surname of the author, year of publication and number(s) of page(s) referred to between brackets, (E.g. Shiva 1998) 163
    157. 157. Harvard…con’d Example : Many patients with malaria have limited access to the new recommended first-line treatment because of poor communication, lack of knowledge, as well as distance and transport costs to reach the health services (Whitty et al. 2008). 164
    158. 158. Harvard…con’d Thus, WHO recommends combination therapies, preferably those including an artemisinin derivative, as treatment for uncomplicated P. falciparum malaria for achieving a rapid cure, reducing parasiteinfectivity (WHO 2008) and countering the threat of resistance to P. falciparum (CDC 2006). 165
    159. 159. Harvard…con’d  Name of the author(s) (year). Title. Place of Publication: Publisher Example: Abramson JH (1990), 4th ed. Survey methods in community medicine. Edinburgh: Churchill Livingstone. World Health Organization (1963). Terminology of Malaria and of Malaria Eradication. Columbia University Press, New York. 166
    160. 160. Tips! When you use the Vancouver system, you will use consecutive numbers in the text to indicate your references At the end, you will then list your references in that order, using the format described above  In Harvard system, put the surname of the author , year of publication and number(s) of page(s) referred to between brackets  If this system of citation is used, the references at the end of the proposal, should be listed in alphabetical order of the authors name 167
    161. 161. Student project work: Develop health research proposal! 168
    162. 162. Thank you! Be healthy graduate!!!