Information Cycle Data Handling in Information Cycle: Collection and Collation University of Oslo Department of Informatics Oslo Facilitator: Gertrudes Macueve 11th April 2007

Learning objectives (1) Define what is data and what is information Identify the different stages of the information cycle Explain how to handle data Recognize the difference between collecting data and gathering data Identify data collection tools

Learning objectives (2) Explain the need for flexibility and standardization in data collection Explain the rationale for use of an essential dataset Explain the correlation between data elements and indicators Define what is data collation Indicate common data collation methods and problems

Data and information Data – observations and measurements about the world, e.g. –Representation of observations or concepts suitable for communication, interpretation, and processing by humans or machines. –May or may be not useful to a particular task. Information –facts extracted from a set of data (interpreted data), Meaningful and useful –Data brought together in aggregate to demonstrate facts; –It is useful to a particular task.

Information Cycle What do we collect? What do we do with it? How do we present it? How do we use it? Quality information

Information Cycle Data converted to information What do we do with it? How do we present it? How do we use it? data sources & tools Process & Analysis Reports & graphs Interpretation of information Good quality data What do we collect? Decision-making for effective management feedback Stages Tools Outputs Quality at every stage EDS

Data Handling in the Information Cycle: 1. Data collection

The starting point… Feeding the information cycle Collection Input Raw data Presenting Interpreting USE ANALYSIS Processing Output INFORMATION

Data collection Two ways to obtain data 1.Collect data: Physical counting of elements 2.Gather data: if data have already been collected ; Requirements : The definitions of the data are the same as ours The format in which the data are collected, is the same Data are collected reasonably accurately We are able to negotiate access to the data

Data collection/gathering guiding principles WHO health care workers at all levels WHAT Essential Data Set WHEN daily – collated weekly & processed monthly WHERE work sites, facilities, districts (info filter) HOW data sources (tally sheets, registers etc) WHY To monitor progress towards goals & targets To Plan new policies and changes To evaluate current services To assist health management processes

What data elements should be collected? Can provide useful information (affecting the management decisions) Cannot be obtained elsewhere Are easy to collect Do not require much work or time Can be collected relatively accurately  ESSENTIAL DATA SET based on indicators reflecting the health status of the community

Essential data set MUST KNOW The % of children under one year who are fully immunised Drop out rate DPT 1- 3; measles coverage The % of children under two years who are fully immunised Other programme vaccines given

Essential data set at each level Standardised Usefulness Address the needs of all stakeholders User-friendly Dynamic

Where do we get data from? Routine data collection –Routine health unit and community data Activity data about patients seen and programmes run, routine services and epidemiological surveillance; e.g. Semi-permanent data about the population served, the facility itself and staff that run it –Civil registration

Where do we get data from? Non-routine data collection –Surveys –Population census (headcounts proportion/facility catchment’s area) –Quantitative or qualitative rapid assessment methods

Example: data collected at PHC facilities Special programme activities Mental & reproductive health Child health & nutrition HIV/AIDS, STI and TB Chronic diseases Routine Service Activities Minor ailments Non-priority activities Epidemiological surveillance Notifiable diseases Environmental health Administrative Systems Infrastructure, equipment Human resources Drugs, transport, labs, finances, budget, staff Population Census: age, sex, place Births & deaths registration

Requirement for data collection: Standardised definitions Essential standardised definitions of both data elements and indicators: –To ensure comparability between different facilities, districts and provinces –To ensure comparability across years

Data collection tools A.Client Record Cards B.Tally Sheets C.Registers

A. Client Record Cards Record details of the client’s interaction with the health service, e.g.: –Health facility record system (traditional) Associated with misfiling and loss vs –Client-held record system (Road to Health Card, Child Health Booklet, Women’s Health Book, TB patient treatment card); Associated with efficiency of the individual concern, suitable for mobile community

Road to Health card

Family planning consultation card

B. Tally sheets Easy way of counting identical events that do not have to be followed-up (e.g. headcounts, children weighed)

C. Registers Record data that need follow-up over long periods such as ANC, immunisation, FP, TB

Assessment of data collection tools (Using SOURCE criteria)  conduct an information audit of all tools – type & number  Ssimple – ease of use (layout)  Ooverlap – duplication (no overlap)  Uuseful for – indicators (relevance)  Rrelevance  Cclear – ease of use (layout)  Eeffective – decisions used for (purpose)

Data collection Tools criteria for appropriateness TOOLPURPOSELAYOUT RELEVANCEOVERLAP How many? client cards tally sheets registers reports Effective decision-making for: Public health Management Supervision/ support monitoring evaluation Simple, Clear, Easy to understand Priority actions No useless data Missing actions evident Useful for: Output/ Outcome/imput/ Process coverage/ Quality incidence/ prevalence no Overlap with other forms What When Where Why How

Data Collation

1.summarising data from the same data elements but from different sources 2. summarising data from the same source but over a period of time. Ways of collating data

Common collation problems Incorrect grouping of data Data are incorrectly added Missing data forms Double counting of data

Data collation practical methods Unities method

Data collation practical methods Rectangles method

Data collation practical methods Zeros Method (Tally sheet)