Data Processing Topic 4 Health Management Information Systems João Carlos de Timóteo Mavimbe Oslo, April 2007

Collection Input Raw data Presenting Interpreting USE ANALYSIS Processing Data Collation & Accuracy

Learning objectives (1) Explain the use of the data handling process as a strategy to provide good data quality Explain the techniques for providing and ensuring good quality data Describe mechanisms for assessing data quality

Learning objectives (2) Examine the importance of good data quality Appreciate the importance of accuracy in health data Understand why errors occur Acquire the skills required to detect, correct and prevent future errors Establish and apply the logistics of error checking

Processing data in the information cycle Collection Input Raw data Presenting Interpreting USE ANALYSIS Processing Data Collation & Accuracy

Ensuring data accuracy Once data has been collected, it should be checked for any inaccuracies and obvious errors. Ideally this should be done as close to the point of data collection as possible. But also at all steps of the information cycle.

Why checking data is vital? Use of inaccurate data is DANGEROUS Producing data is EXPENSIVE Inaccurate data are USELESS data Producing inaccurate data is a WASTE of money and time

Why checking data is vital? Better to have NO data, than to have inaccurate data!!!

Common problems with data  large gaps  unusual month to month variations  duplication  Promoted by different vertical programs  inconsistencies  unlikely values  data is present where it should not be  typing errors  maths problems – poor calculation  data entered in wrong boxes

Good quality data WHAT?WHAT? data that are complete, correct and consistent (and timely) WHY?WHY? facilitates: good decision-making appropriate planning ongoing Monitoring & Evaluation improvement of coverage and quality of care HOW?HOW? provides an accurate picture of health programmes and services

Visual scanning (eyeballing)  checking for 3 C’s Completeness Correctness Consistency

Are data complete?  submission by all (most) reporting facilities  physical events observed = events registered (how?)  registered data = collated data (how?)  all data elements registered

Are data correct?  data within normal ranges  logical data  existing standardised definitions used adequately  legible handwriting  are there any preferential end digits used?

Are data consistent? data in the similar range as this time last year (last reporting period) no large gaps is the correct target population being used?

Accuracy enhancing principles Training User-friendly collection/collation tools Feedback on data errors Feedback of analysed Information Use of information (and prove it!)

How do you detect Errors? general accuracy checking measures specific accuracy checking measures

General accuracy checks Completeness Proper place Friendly tools Arithmetic

Specific Accuracy Checks Time-trend consistency Time-trend variation Minimum/maximum Realism Comparison Parts vs whole Preferential end-digits

PREFERENTIAL END-DIGITS JANFEBMARCHAPRILMAYJUNEJULY Other examples ?

Practical error checking procedures Check completeness of the data forms Set minimum and maximum values Examine a printout of data for errors using general and specific error checks Hold an error feedback session

What to do if you find errors? Find the cause Correct the error Prevent future errors

Good data quality 10 steps to achieve it

1. small, essential dataset - EDS 2. clear definitions - standardized 3. careful collection and collation of data – good tools 4. local analysis of data using relevant indicators 5. presentation of information to all collectors 6. regular feedback on both data and information 7. supportive supervision - at all levels 8. ongoing training and support 9. discussion of information at facility team meetings 10. monitoring use of information

Please remember…!

Data, in order to be locally useful, should be: AVAILABLE ON TIME fix dates for reporting AVAILABLE AT ALL LEVELS who reports to whom? - feedback mechanisms RELIABLE & ACCURATE check that all data is correct, complete, consistent COMPREHENSIVE collected from all possible data sources USABLE if no action, throw data away COMPARABLE same numerator and denominator definitions used by all

Controlling quality with DHIS Maximum / minimum values 13-month retrospective Regression line Validation rules: absolute statistical Validation reminders