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Exploring data quality in Community Health Information Systems in Kenya

Regeru Njoroge Regeru1st International Symposium on

Community Health Workers23rd February 2017Kampala, Uganda.

BackgroundCHWs have emerged as a means to achieve Universal Health Coverage

- Kok et al., 2016

CHWs collect data from the households they visit on a routine basis- Mireku et al., 2014

Data collection at community level is critical in assessing the performance of CHW programmes

- Perry et al., 2014

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Kenya’s Healthcare System

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National Referral Health Services

County Referral Health Services

Primary Care Services

Community Health ServicesMinistry of Health (Kenya), 2014

Referral

Referral

Referral

Structure of the Community Health Strategy

4Ministry of Health (Kenya), 2006

Community Health Services in Kenya

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Community Health Volunteer (CHV)

Community Health Extension Worker

(CHEW)

Facility In-Charge Link Healthcare Facility

Sub-County Community Health Strategy Focal

Person

Community Health Committee

Facility Health Management CommitteeMinistry of Health (Kenya), 2014

The structure of a Health Information System

6Pact Inc., 2014

Service Delivery Log Books

Community Health Extension

Worker Summary

National Health Information

System

Community Health Volunteers

Community Health Extension Worker

Sub-County Health Records Information

Officer 7

Health Information System performance

“data quality and continuous use of routine information for decision-making”

- Hotchkiss et al., 2012

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What does data quality refer to?

Accuracy Reliability Precision Completeness

Timeliness Integrity Confidentiality

9MEASURE Evaluation, 2008

Why explore data quality?

Data should be used for decision- and policy-making

Data collected at community level in Kenya is not used in decision-making because it is considered to be low quality

10Ekirapa et al., 2012

Research objectives

PERCEPTIONS OF ENABLERS AND BARRIERS TO

GENERATING HIGH QUALITY DATA

DATA QUALITY ASSESSMENT

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Methods

• Focus Group Discussions• In-depth Interviews• Calculation of data

verification ratios for 7 indicators; March – May

2016

• Purposive selection of FGD and IDI participants

• Data collection and reporting tools used for DQA

• 2 Counties – Nairobi (urban) and Kitui (rural)

• 4 Community Units

• Cross-sectional• Mixed Methods

Study Design

Study Sites

Data collection

Sample selection

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Qualitative Data Collection• Participants

Community Health Volunteers (FGDs), Community Health Extension Workers (IDIs) and Sub-County Health Records Information Officers (IDIs)

Key Informants: Facility In-Charges (IDIs) and Sub-County Community Health Strategy Focal Persons (IDIs)

• Topics explored Understanding of data quality Data flow Data source Data collection

Data collation Data analysis Data reporting Data use 13

Referrals

Data Quality Assessment – 7 indicators selected for calculation ofdata verification ratios

• Pregnant woman referred for ANC• Pregnant woman referred for skilled delivery• Delivered by skilled attendant• Child 0-11 months referred for immunization• Child 0-59 months participating in growth monitoring• Child 6-59 months with mid-upper arm circumference (Red) indicating severe

malnutrition• Child 6-59 months with mid-upper arm circumference (Yellow) indicating moderate

malnutrition 14

Data Quality Assessment – calculation of data verification ratios

Level 1:

Value reported in Community Health Extension Workers SummaryReaggregated total of values recorded in Service Delivery Log Books

Level 2:

Value reported in National Health Information SystemValue reported in Community Health Extension Workers Summary 15

Data Quality Assessment – interpretation of data verification ratios

100% = PERFECT MATCH

< 100% = UNDER-REPORTING

> 100% = OVER-REPORTING

* Admon et al., 2013, Otieno et al., 2012

90-110%*

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Results - Data Quality AssessmentCommunity Units

Township (Kitui)

Museve (Kitui)

Maili Saba (Nairobi)

Bangladesh (Nairobi)

Number of Community Health Workers at time of study

45 40 50 14

Number of Service Delivery Log Books reaggregted

0 0 33 13

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Results - Data Quality Assessment• No data reported for at least 12 months• Data Quality Assessment not possible• Partly attributed to devolution and establishment of a new

County community health programme

Kitui County

• Data verification ratios level 1: 0 – 260%• Data verification ratios level 2: 0 – 100%

Nairobi County

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Qualitative Results - Barriers

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• Data source Lack of data collection and reporting

tools/referral tools Sub-optimal design of tools

• Data collection Inadequate training on data

management Inconsistent understanding of

indicators• Data collation

Incomplete data collection Lack of guidelines for data verification

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• Data analysis Lack of guidelines for data analysis

• Data reporting Late submission or no submission of

data to the higher level • Data use

Lack of feedback to CHEWs and CHVs on performance

Lack of feedback to communities on their health status

• Referrals Poor linkage between Community Units

and Primary Healthcare facilities

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Conclusions and Recommendations• Availability of data collection and reporting tools is a

prerequisite- PROVIDE

• Regular training is necessary to increase reliability of data collection and reporting

- TRAIN

• Accountability and ownership is

fostered via regular feedback and supportive supervision

- REGULAR DATA QUALITY ASSESSMENTS

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Acknowledgements• Meghan Bruce-Kumar• Robinson Karuga• Millicent Kiruki• Maryline Mireku• Robbie Mulwa• Nelly Muturi• Dorothy Njeru• Lilian Otiso• Miriam Taegtmeyer• All CHVs, CHEWs, Sub-County CHS Focal Persons, Sub-County Health Record Information Officers and

Facility In-Charges that participated in this study

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Regeru Njoroge RegeruTechnical Officer

LVCT HealthRregeru@lvcthealth.org

@rnregeruThe USAID SQALE CHS Program is made possible by the generous support of the American people through the United States

Agency for International Development (USAID) and is implemented under cooperative agreement number AID-OAA-A-16-00018. The program is managed by prime recipient, Liverpool School of Tropical Medicine

www.lvcthealth.orgwww.reachoutconsortium.org

www.usaidsqale.reachoutconsortium.org