PREVNEXTSUBMITMenuResourcesGlossaryTranscriptClear and return to MenuSearch ResultsFilterSlide TextTranscriptSearch in:Check to includeSearch...TermsDefinitionFINISHfinishdefinitionresourcessearchpauseplayreplaysubmitnextpreviousvolumeSkip Navigation. Hit enter to return to the slide.SUBMIT ALLsubmit allQuestion ListMore infoSend an emailCloseBioExit<html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Welcome to Part 4 of Module 1. In this part, we’ll learn about the data cycle and the patient pathway.</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>For all types of health issues, there’s a pathway by which disease prevention, diagnosis, care, and treatment is followed. In the story of Nomzamo Dlamini, the grandmother with tuberculosis, or the story of Nelson, the young patient with diarrhoeal disease, we saw one part of this patient pathway. When we think about the role and importance of data in health care and public health, it’s useful to talk about the data cycle in relation to this patient pathway. </textformat></html><html><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The data cycle involves the following 4 steps: </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>1. Collection and storage </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>2. Processing </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>3. Reporting </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>4. Analysis, interpretation, and use</textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>For each step in the data cycle, we will cover the goals of the data system and common challenges and best practices. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The first step in the data cycle involves collection and storage of data. When we think about a patient pathway, such as for tuberculosis or diarrhoeal disease care, we think about collecting data by questions to the patient, measurements such as vital signs, weight, physical examination, and clinical observations at each step.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>As we go through the patient pathways, it’s important to note the who, what, when, where, how, and why of data collection. Click on each box to read more about each aspect of data collection. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>When we talk about data collection in health care, we must think about patient pathways and data pathways. By patient pathways we mean the ways that patients move through health care services. The patient pathway starts when the patient identifies a health need, such as a cough, and proceeds in going to the clinic for an evaluation and finishes by the receiving treatment. By data pathways we mean the ways that data moves in order to document the patient pathway.</textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Patient pathways and data pathways may vary from one community and one health facility to another. Some health facilities may not have community health workers who link patients in the community to services in the health facility. Other health facilities may not have data analysts who are available to assist in certain parts of the data cycle (instead clerks, nurses, or other staff often provide the analysis). We discuss patient pathways and data pathways in a general sense, but we must keep in mind that there are many reasons that specifics can vary from one place to the next. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The link between the patient and data pathways generally begin in the community. At the request of a community health worker or on his or her own initiative, a patient who has an illness or condition visits a facility. This solid line represents the patient’s pathway. The blue box shows a few of the tasks the CHWs do while in the local communities, such as conducting surveillance, screening for conditions and diseases, referring sick patients to clinics, conducting health promotion and education, and monitoring minor ailments. CHWs also collect data during their visits (as seen in the green box), including household demographics, data on the burden of chronic disease, and how many people they referred to clinics. </textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>In fact, each one of the tasks the CHW does while visiting a household will be reflected as a data element in their collection. They report these data in their role as a member of the WBPHCOT. The data from the community health worker working within the ward-based primary health care outreach team, as well as the data from his or her colleagues on the team, are passed along to the facility by the outreach team leader, who also collects the number and types of referrals from the service providers. And in this graphic, the data pathway is represented by a dashed line.</textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The latest national policy documents refer to an additional health worker included in the WBPHCOT, namely the home-based carer. This important health cadre is not yet working as part of the established teams. For this reason, the content of this course focuses only on the work of the community health worker. The graphics, in turn, don’t detail the tasks and relevant data related to the home-based carer. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The community patient data pathway can be represented like this. The red person icon represents an ill person (male or female). The facility is at the bottom of the graphic. Notice that the patient pathway, the solid line, takes a different path than the data. </textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The green dots represent points where data are collected (step 1) and transferred. As mentioned in the previous slide, data collected in the community include demographics, referrals, and number and type of interventions, to name just a few. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>In the facility, Thembi at the help desk documents the patient’s chief complaint and demographics, using the primary health care, or PHC, head count register. The triage nurse records the patient’s vital signs in the clinical record. The patient’s history, physical exam results, and referral information is also recorded in the clinical records, by either the nurse or the doctor. The nurse documents the laboratory results. Either the nurse or physician will document the treatment plan and patient education provided. Finally, the pharmacist records what medicine he or she dispensed in the clinical records. Data is collected for each follow up visit as well.</textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The facility patient data pathway can be represented like this. Notice that the patient (solid line) and data (dashed line) pathways follow the same path. The green dots represent points where data are collected (step 1) and transferred. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>There are several types of data collection tools used in the public health sector in South Africa. These include clinical consultation cards, registers, tally sheets, and electronic data systems. We will learn more about these in Module 5. </textformat></html><html><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>After data collection, the next step in the data cycle is data processing. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Think of the data as originally collected as “raw” data. When we prepare the data for reporting and analysis, we must process the “raw” data. One way of processing data is to collate data, or merge together data. We can do this in two ways:</textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 22, 48, 96, 144]' blockindent='22' indent='-22'>· Summarising data from the same data elements but from different sources. For example, we may provide a summary of all voluntary HIV testing and counselling done within the health facility by collating data from HCT registers on tests done in the antenatal clinic, the outpatient clinic, the laboratory, the inpatient ward, and any other site within the health facility. </textformat><textformat leading='0' tabstops='[0, 22, 48, 96, 144]' blockindent='22' indent='-22'>· Summarising data from the same source but over a period of time. For example, we may summarize chief complaints in the outpatient clinic over the past year by collating PHC headcount register data from each month in the past year.</textformat><textformat leading='0' tabstops='[0, 22, 48, 96, 144]' blockindent='22' indent='-22'></textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>For a definition of 'collating' or other terms, remember to click on the Glossary tab on the upper left hand side of your screen.</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Processing of data can also mean summarising the data by aggregation. Aggregating the data means combining and forming a sum, mass, or total. For example, we may aggregate the data collected from individual patients to represent data for the total group of patients seen. We may also group the data by sex or by age group for reporting purposes. This grouping of the data is part of data processing. </textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>When electronic data systems exist, a step in data processing may involve entering the “raw” data into the electronic system so that aggregations and indicator calculations may happen in an automated manner. Examples of electronic data systems used in data processing in South Africa are the Tier.net and ETR.net systems. </textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Click on the tip box for information about standard definitions. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>After processing, the next step in the data cycle is reporting. Reporting of data happens at all levels of the health system, from facilities to districts, from districts to provinces, and from provinces to the national level. There are even certain health indicators which South Africa reports as a country to the international level, such as certain HIV/AIDS indicators reported to UNAIDS.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Multiple facilities send data along to the district, which compiles these data into reports.</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The district sends these reports on to the province. Information from other districts about the number of tests done and positive cases are compiled into reports, which are sent on to the national level. At the national level, this data may be compared to national targets. It can influence planning; it may impact upon resource allocation, and can influence policy decisions. Each level has its role to play in receiving the reported data and monitoring it for trends.</textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Frequently, there may be missing data that should have been recorded but isn’t. Often, missing data is the result of a systems error, rather than the fault of an individual. For example, a CHW may be recording data related to his or her household visits on the incorrect form. There are sometimes as many as three ward-based primary health care outreach teams linked to a facility. Each will then potentially be using the wrong form to collect the data. The impact, therefore, of this error is magnified many times in the system. </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The ultimate goal of the data cycle is to analyse, interpret, and use the data. Without analysis and use of the data, the previous steps of the data cycle do not have value.</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Reviewing reports may help to understand if, say, a disease is becoming more or less frequent over time, to know how much medication to order, and to plan activities. Analysis is also critical at the district, province, and national levels. It’s only by completing the data cycle through the step of analysis that it’s possible to fulfil the public health functions. </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Click on the buttons to read about each type of analysis.</textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Note that in the data cycle the process that doesn’t stop with analysis, interpretation, and use. Analysis should feed back into our data collection processes. This feedback forms part of a Quality Improvement (QI) exercise that will be covered in Module 3. Click each level to read some examples of how the results of the analysis step could affect the data collection step. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Now let’s explore the patient pathway for Nomzamo Dlamini, our suspected TB patient. As we do so, please note that it’s important to be aware of what interventions are required at each of the steps along the patient pathway. Each intervention is reported as data points. The data collected must be accurate and comprehensive, reflecting the realities of each step in the pathway. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Nomzamo has night sweats, is losing weight and has been coughing for three weeks. Thabo is a community health worker who visits her home on a routine surveillance visit. Upon hearing about her symptoms, Thabo suspects tuberculosis or another serious health issue and refers her to the clinic. Two days later, Nomzamo travels to the clinic…</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>…and is directed to the help-desk to collect her clinic file. She then goes to the triage team, where her vitals are taken. Nomzamo is then seen by the professional nurse, Sister Zingy. On history-taking and examination, Nomzamo is suspected for a diagnosis of pulmonary tuberculosis. She is referred to the doctor, in light of the fact that she has co-morbidities of chronic disease, namely hypertension and diabetes. In some facilities a doctor may not always be available; so the nurse and doctor consults may occur on different days. She has certain blood tests taken, including an HIV test. She is asked to provide a sputum specimen for GeneXpert testing. She is sent to Xray department for a chest X-ray. Nomzamo then returns to the doctor’s consult room. She sees the doctor again, who reviews the immediate results of the investigation. She is GeneXpert positive (Rifampicin sensitive). A diagnosis of pulmonary TB is confirmed. The nurse provides a management plan and gives her a follow-up appointment date. Nomzamo's follow-up appointment is made by the file clerk. Nomzamo is then directed by Thembi, the clerk at the help-desk, to visit to the pharmacy before returning her file and leaving the clinic. She departs for home. It has been a long day.</textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Nomzamo is one of six patients diagnosed with TB in the clinic during that week. </textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>This type of scenario is familiar. While the specific set-up and details of the tuberculosis patient pathway may differ a bit depending upon the patient (such as for adult or pediatric patients) or depending upon the clinic (such as based upon staffing mix and laboratory diagnostic capacity), these overall steps in the patient pathway are typical in the South African context. </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Let’s look at the first step: collection and storage.</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Now let's talk about the data pathway as it follows Nomzamo's patient pathway. Data is collected at each point in the patient pathway. For example, when he first visits the home, Thabo uses his CHW household registration and assessment form and individual adult assessment form to indicate that he encountered a person with signs and symptoms of TB and that he made a referral. The CHW tracking tools allow Thabo to cross check with the clinic to see if Nomzamo completed the referral, and follow up with another home visit if she failed to come to the clinic. If Thabo fails to capture the referral as data, this could result in a missed opportunity to follow up with the client in the community because there would be nothing on record to flag the need to follow up. This followup is especially important for Nomzamo as a TB suspect.</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>At the help desk, Nomzamo’s chief complaint is recorded in the PHC head count register. The triage nurse records Nomzamo’s vitals in her clinical record. Sister Zingy completes the patient consultation, collecting data on Nomzamo’s medical history and the results of the exam and records it in the clinical record. In their own record keeping system, the lab records the results for her blood tests, including her HIV status. The lab also records that she is GeneXpert positive. In addition to the laboratory and TB investigations, Sister Zingy records the results of Nomzamo’s blood tests, including the HIV results. </textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>If available to assist, a doctor will conduct a consultation with Nomzamo. He confirms Sister Zingy’s suspected diagnosis of TB. The doctor takes into account Nomzamo’s age, how far she lives from the clinic, and other aspects of her life when creating a management plan. He includes pharmacological and non-pharmacological interventions. Each of the steps in the management plan will be reflected as a data point along the continuation of Nomzamo’s pathway. Should Sister Zingy be working on her own in the clinic without a doctor’s support, complete and accurate recording is all the more crucial, even despite her busy schedule. Two of the interventions in the management plan are reflected in the pathway above. Firstly, the file clerk records the follow-up appointment. Secondly, the pharmacy records the mediations Nomzamo receives. </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Managing the patient through this pathway is a team effort by the health care team. Thabo played a critical role in referring Nomzamo, while the doctor (if available) was critical to diagnosis, and the nurse was critical to planning for treatment and follow up, amongst others. Ensuring that the right data are recorded and used is also a team process. This is critical in the handover of the patient from one health care worker to another, as they together support her in moving through the patient pathway. </textformat></html><html><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The answers to the who, what, when, how, and why of data collection can depend on the health issue or area. Click on each box to see how these elements work with the TB example we went through earlier. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Of course, we know that a tuberculosis case requires treatment over an extended period of many months, meaning that there will be multiple repetitions of this patient pathway, including encounters between Nomzamo and various health workers both in the community and in the clinic. Failure to collect the appropriate data each time can compromise patient care. </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>In addition to having multiple repetitions for one patient, facilities will collect data on other patients. Thabo will conduct TB contact screening for everyone in Nomzamo’s household and refer them to the clinic. Sister Zingy’s clinic will also collect data on other TB suspects and patients. Note that the graphic shown can be multiple visits for one patient or visits of many patients. This data is, in turn, collated with the same data of other patients. A single facility can aggregate data for all of its patients.</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>If these facilities send along data that’s missing or inaccurate, it can result in poor decisions being made at the district, provincial, and national levels. For example, if a facility reports fewer than actual number of patients being treated for TB, the facility manager or district decision maker could order or send less TB medication than the facility really needs. </textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>In moving through the tuberculosis patient pathway, we have covered the first part of the data cycle, data collection. Before discussing further about the impact on data reporting, let’s talk about what happens with Nomzamo’s patient data after she leaves the clinic and as the data on Nomzamo’s TB case moves through the data cycle. The next stage of the data cycle is data processing. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>We saw that there were six other cases of TB diagnosed in the same week at Mzansi clinic. Data reflecting Nomzamo’s journey along the patient pathway is collated to reflect her multiple interactions with the health system. This data is, in turn, collated with the same data of other patients. A single facility can aggregate data for all of its patients.</textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>Nomzamo’s data are recorded in a TB register along with each of the other cases diagnosed that week, with one line for each patient. The data are also entered into the relevant computer programme. The register and the computer database represent tools for storing and processing the collected data. These tools support Sister Zingy to tally the number of TB tests done and positive cases detected each week or month. Processing the data involves collating the data from different sources, such as bringing together data on CHW referrals made with actual clinic visits recorded with a referral slip. Processing the data also involves aggregating or pooling the data across the many patients served. </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The third stage in the data cycle is reporting. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>At the end of each month, Mzansi Clinic must collate the number of TB tests done and the number of positive TB cases diagnosed in the clinic and then report this information to the district. As we saw before, in addition to receiving data from Mzansi Clinic, the district receives reports on TB tests and positive cases from other facilities. It then compiles these data into reports that are sent on to the province. The information is then compiled at the provincial and national levels, leading to a complete picture of TB testing and TB positivity rates for the country. Each level has its role to play in receiving the reported data and monitoring it for trends. </textformat><textformat leading='0' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'></textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>The last step in the data cycle is analysis, interpretation, and use. It is only by completing the data cycle through the step of analysis that it is possible to fulfil the public health functions. </textformat></html><html><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>In Part 4 we talked about the steps in the data cycle and what happens to data at each step. We also covered patient and data pathways and looked at the tasks healthcare workers perform and the data they collect along these pathways. </textformat><textformat leading='2' tabstops='[0, 48, 96, 144]' blockindent='0' indent='0'>In the next part, we’ll look at how data can be used as indicators for monitoring and evaluation to improve health care.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>A total amount; thought of as a whole, all together.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>To gather together information from different sources in order to study it carefully.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>Individual facts, statistics, measurements, or a collective of the same. Data can refer to any characteristic, event or other descriptor. It is usually used to calculate, analyse or plan something.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>The pathway in the life of data, which involves the following steps:</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>1. Collection and storage</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>2. Processing </textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>3. Reporting</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>4. Analysis, interpretation, and use</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>The goal of descriptive analysis is to describe what is happening in a sample or target population. An example of descriptive analysis could be the average number of patients seen at a clinic per month, how this number may vary over the course of the year, and the patterns by sex and age group. It does not attempt to explain why the number may increase or decrease, or why a particular pattern by age and sex exists.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>Epidemiology means "the study of what is upon the people". It is the study of what health problems exist in the population, how health problems change over time, what factors affect health, and how diseases and illnesses can be controlled. </textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>To judge the value/condition of someone/something in a careful and thoughtful way.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>Involves a systematic, objective analysis of a project’s outcome and impact in relation to its objectives:</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Are the activities enabling the program to meet its objectives?</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Have the expected outcomes been achieved?</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Is the project having the desired impact?</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Is it successful?</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Is it needed?</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Should it continue?</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>The goal of explanatory analysis is to explore relationships and/or identify causes. Explanatory analysis may involve testing a hypothesis or experiment. For example, when testing a new outreach strategy to increase HIV testing uptake among adolescents, an explanatory analysis would compare the trend in HIV testing in this target group before and after implementation of the new outreach strategy to find out if the strategy worked, and why or why not.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>A measure used to quantify, characterize, or assess a given situation. Indicators are often standardized and expressed as an event (numerator) over a population (denominator) for a given period of time. Tracking indicators helps us to observe relative changes over time.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>Knowledge about someone or something; facts or details about a subject. Data becomes information when it can be used to make decisions.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>When information and evidence are brought together and confirmed with time and experience.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>To watch, observe, listen to, or check something for a special purpose over a period of time.</textformat></html><html><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>Process of routinely gathering information on key aspects of a program and utilizing this information in management and decision-making.</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>Monitoring involves:</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Recording what a program has done – activities and outputs</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Comparing actual outputs with planned outputs</textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'></textformat><textformat leading='0' tabstops='[0, 48, 96]' blockindent='0' indent='0'>• Making adjustments if the difference between the plan and what is actually achieved is too great</textformat></html>