Epidemiological Data Analysis
- 1 Data analysis using iSIKHNAS data case studies
- 1.1 Pre-requisites
- 1.2 Objectives of course
- 1.3 Learning approach
- 1.4 Data Analysis Facilitator material
- 1.5 Overview of data analysis
- 1.6 Case Studies
- 1.7 Concluding remarks
- 2 Appendix 1: Extension work using R.
- 3 Appendix 2: Answers to Exercises
Data analysis using iSIKHNAS data case studies
This course is for veterinarians within the Indonesian animal health system. It is assumed that participants will have completed the Excel, Basic Field Epidemiology and Surveillance training modules before this course. If you already know about epidemiology and how to use Excel, then these pre-requisite modules are not required.
Objectives of course
The broader aim of the course is help participants make evidence based animal health policy decisions. This will assist them to improve livestock production and health in Indonesia.
To do this, participants need to be able to access, understand and analyse information on animal health in Indonesia. Fortunately, a new initiative in Indonesia means that Indonesian animal health staff has access to one of the best animal health information systems in the world: iSIKHNAS. This provides staff with large amounts of high quality information (data) that they can use to make good animal health decisions. Therefore, the objectives of this course are to teach participants to download, understand, evaluate, analyse and interpret iSIKHNAS data.
This course will be taught by analysing real iSIKHNAS data. Three case studies will be presented. During each case study, a question will be asked. Then the question will be answered by participants during practical exercises. Spaces are included after each exercise where you can write your answers. Answers to exercises are provided as Appendix 2. It is generally recommended that you answer the question before reading the answers.
Sometimes, notes on core concepts will be presented before or during case studies to support learning. These are backgrounded with grey to enable you to distinguish these notes from the exercises.
The course is very applied and relevant to Indonesian animal health staff. The three case studies concentrate on: assessment of veterinary services (staff performance), disease management (diarrhoea in cattle) and livestock production (beef self-sufficiency).
The data used in this training course was downloaded in early 2014. This was when iSIKHNAS had been operating for approximately a year in a small part of Indonesia. This early data was used so that we could provide answers to exercises. You may wish to download newer and more complete data to analyse during exercises at the time of your course. Please be aware that if you do, you will not have answers to check your work.
The interim nature of the data means that no real conclusions can be made about the results of data-analyses conducted during this course. Instead, we conducted the analyses and made conclusions to demonstrate and teach data analysis. Over time, more complete data will be available. Then Indonesians will be able to conduct more complete and accurate analyses.
The course is delivered in Excel. Excel was chosen because it is cheap, available to most Indonesian staff and intuitive. Analyses in Excel will allow staff to make some useful conclusions about iSIKHNAS data.
If you intend to do a lot of important statistical work, you will need to learn how to use a complete statistical package instead of Excel. For this reason, we have also included some extension work for those participants who wish to extend their knowledge beyond Excel. This is presented in Appendix 1. Here R, a free online statistical package is introduced. R is one of the most useful software packages in the world. Better still it is free and downloadable from the internet. Appendix 1 repeats case study 1 in R.
Many screenshot videos will be used to assist you in understanding how to do exercises during the course. These can be played on several different software platforms including Windows media player.
Overview of data analysis
Relevance of data analysis to animal health policy
In order to make good animal health policy, a veterinarian needs to understand the animal health situation where they work. For example, how much disease is present? Or, what is causing disease and how are various interventions working?
To gain this understanding a veterinarian could guess at the situation or they could make assumptions based on their own experience. These are generally poor means of making decisions. Decisions made by guessing are made in the absence of information. Decisions made on their own experience can be useful but are generally based on a very small amount of experience. That is, decisions are based on the experience of only one veterinarian, even if that veterinarian is very experienced.
A better means of decision making for veterinarians is to make decisions based on information that reflects the broader animal health situation. This information can be received in several ways, such as in animal health data, publications, text books and reports. Fortunately, animal health information (data) is now being collected across much of Indonesia. This data is recorded in iSIKHNAS. This data can assist good decision making if it is analysed and interpreted appropriately. The broad objective of this course is to assist you to do this.
Whilst you will learn a lot in the next several days, it is important that soon after completing this course you begin to download and analyse your own iSIKHNAS data. This will ensure that you consolidate your learning, improve your skills and at the same time improve your evidence based decision making. So please, set a day aside next week to do some of your own data analyses using the skills you learn here. Then regularly do some analysis of iSIKHNAS data. Over time your skills will improve.
Introduction to the basic steps of data analysis
There are several recognised steps to analyse and interpret data. These steps are determining an objective for your analyses, data management, describing data and testing hypotheses. Each of these will be briefly introduced here. Then the rest of the manual uses the four steps in the case studies.
It is important to have a clear and concise objective for your analysis. For example, what is the prevalence of diarrhoea in cattle for 2014? This then allows you to be focused in your efforts and to source appropriate data to address the objective. An objective is then translated into a hypothesis and tested.
It is important that veterinarians know how to access iSIKHNAS data and use it. We will help you to download iSIKHNAS data. We will also help you to preserve, error check, create and evaluate the iSIKHNAS data.
Description of data
The next step is to describe the data. One purpose of this step is to further check data for errors. Another purpose is to understand the structure and nature of data and the relationships between different parts of the data. In this step, single variable summaries, summaries of relationships between variables and plots are used. This helps you to start hypothesis testing (step 4).
In order to comprehensively address an animal health question it is important to develop a testable question (or hypothesis) from your objective. This hypothesis can then be tested using appropriate statistical tests and you can decide whether the data supports your idea.
- Case study 1: Performance measures for veterinary services
- Case study 2: Seasonal prevalence of diarrhoea (Mencret) in cattle
- Case study 3: Beef self-sufficiency (based on slaughter statistics)
Data analysis is critical to good animal health management and policy formation. Indonesia is fortunate to have an excellent and newly functional animal health information system, iSIKHNAS. However, it is no use having one of the world's best information systems if no-one uses the data. Hence, this course has focused on helping you to begin to use iSIKHNAs data. These approaches may help you to improve animal health decision making.
There are several standard steps to data analysis, including developing a research question of interest (objective), data management, description and hypothesis testing. All of these steps were used in this course. In order to consolidate your new skills it is recommended that you apply these steps to questions of interest to you immediately upon your return to your workplace. For example if you spend a day or two per month for the next several months you will consolidate your new skills.
We have only had time to use a fraction of the available statistical approaches (e.g. measures of association and a chi-squared test). You may choose to expand your knowledge beyond what we have learnt in this course. There are a number of useful text books that can assist you in developing your statistical skills. Veterinary Epidemiologic Research (Dohoo et al., 2009) is a useful text book that covers veterinary epidemiology and presents some statistics in this field. Statistics for Veterinary and Animal Science (Petrie and Watson, 2006) is a useful text book that applies statistics more generally to veterinary science. If you decide that you really want to learn both R and expand your statistical knowledge you should complete the appendix on R. Introductory Statistics with R (Dalgaard, 2008) is a very useful text book that introduces both statistics and R. If you start using R, then the statistical world will be at your figure tips!
This appendix introduces the reader to R. It is for those course participants who do a substantial amount of statistical analyses (or aim to) and wish to improve their capability beyond what Excel allows. There may not be time to complete this appendix during the course, but it can be completed later.
R is a statistical and graphics software environment. It is widely recognised and used throughout the world. R provides a very powerful and flexible environment in which to conduct simple or complex statistics or to produce publication ready graphics. It is also free, and constantly updated. It is widely supported by a range of scientists who are constantly contributing new packages that expand the capability of R.
This is where you will find the answers to the exercises in this Data Analysis course.
Dalgaard, P., 2008. Introductory Statistics with R. Springer.
Dohoo, I., Martin, W., Stryhn, H., 2009. Veterinary Epidemiologic Research. VER Charlottetown.
Petrie, A., Watson, P., 2006. Statistics for Veterinary and Animal Science. Wiley.
- The Oxford dictionary definition of data is: Facts and statistics collected together for reference or analysis