Improving the Quality of Health Data and

Weekly Surveillance Reporting Rates in

Mityana District

By Dr.Lwasampijja Fred

District Health Officer

Mityana District Local Government

ACADEMIC MENTOR:

John Kissa (Ministry of Health)

INSTITUTIONAL MENTOR:

Nkata B. James

(Mityana District Local Government)

Grand Dissemination. Golf Course Hotel Kampala, 14/07/2016

July 2016

1

Presentation outline

• Introduction and background

• Problem statement

• Project objectives

• Project implementation & Results

• Challenges experienced and how they were overcome

• Conclusion & Way forward

• Acknowledgement

2

Introduction and background

• Mityana district is located in the central region 74kms West

of Kampala and has 59 health facilities

• HMIS is one of the core functions of the district and serves

as a source of health information

• Health information is used for planning and management of

services hence a need to be credible

• During extraction of data from the registers to reporting tools

errors and omissions occur which affect data quality

Problem identification

• The DHT reviewed a report on the district capacity

assessment survey done by CDC/MOH/MakSPH in 2014

• Among the 8 domains assessed: HMIS, Service delivery,

Human resources and supply chain were performing poorly

and needed urgent attention

• DHT scored on the urgency, feasibility and effect of the above

gaps on service delivery and came up with HMIS

• During brainstorming on HMIS, the issue of poor quality data

and low rates of weekly reporting were considered to be

critical. Hence the project.

4

Baseline Situation

Parameter HMIS Tool Baseline

Completeness

(Financial summary)

105 (Outpatient) 42%

Accuracy

(New clients started on

ART)

106a (HIV/ART

Quarterly)

52.3%

Timeliness 108 (Inpatient) 87%

Health facility Weekly

reporting rate

033b (Weekly

surveillance)

57.3%

5

**Source: July-Sept 2015 District HMIS Report

Root cause analysis

The DHT used a fish bone analysis and identified

the following:

• Health workers not oriented on issues of quality data and

use of data for decisions

• Health workers do not prioritize tools during procurement

planning and ordering

• DHT do not prioritize review and feedback meetings

• DHT do not prioritize supervision and DQA

6

Problem Statement

• Health data collected and reported in Mityana district was of poor quality;

• 58% of HMIS 105 reports were incomplete-Financial

summary section

• 48% inaccuracy in reporting for the selected ART indicator

from 106a was noted.

• 43% Health facilities were not submitting weekly reports as

required.

All the above are below the national standard of 80%

• Timeliness for HMIS 108 of 87% is also below the standard

of 100%

7

Problem Statement...

Identified causes

• Low knowledge and skills

• Attitude of health staffs

• Inadequate support supervision

• Limited financial resources for M&E related activities

Addressing the above would strengthen systems

for generating quality data

8

Objectives

Overall

Objective

To improve the quality of health data generated

and rates of weekly surveillance reported from

the health facilities in Mityana district by June,

2016.

Objective 1 To improve completeness of HMIS 105 reporting

from 42% to 80%

Objective 2 To improve the accuracy of HMIS 106a reporting

from 52.3% to 80%

Objective 3 To improve timely submission of HMIS 108 from

87% to 100%

Objective 4 To improve health facility weekly surveillance

reporting rates from 57.3% to 65% in 22 health

facilities

9

Project Implementation

Activities undertaken

Oriented 158 health staff on the values and dimensions of quality

data, extraction and reporting

Developed data improvement plans

Conducted support supervision and on-job mentorship

Carried out data validation at the health facilities

Conducted monthly and quarterly review and feedback meetings

Supported In-charges and records assistants to develop SOPs for

submission of reports

10

Project Implementation

Activities undertaken…

Provided internet data on 4 modems to ease entry of data and

timely submission

Registered 35 alternative phone numbers (for 22 facilities)

Established a system of reminding the contact persons at facilities

to submit the reports

Used the monitoring tool to track the performance of facilities

regarding submission of reports and recognizing good performers

at joint meetings

11

12

Workshops were conducted to build the

capacity of health staff to use their own

data for management & monitoring health

service delivery indicators

Health workers were engaged in

developing standards procedures for

information flow

Relevance for consistent collection and

documentation of good quality data for

decisions was emphasized

Orientation of the health workers in data use and management

Health workers were making data improvement plans

Health workers draw data flow charts as part of SOPs

Results: Completeness of data from high volume

facilities Oct 2015 to May 2016

0

20

40

60

80

100

120

Oct Nov Dec Jan Feb March April May

Mityana Hospital

Kyantungo HCIV

Ssekanyonyi HCIV

Mwera HCIV

Bulera HCIII

13

%

Reporting

Intervention point

Results: Accuracy of data on new clients started

on ART

14

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

MityanaHospital

SsekanyonyiHC IV

Kyantungo HCIV

Mwera HC IV Bulera HC III

Oct-Dec 2015 Accuracy 90% 100% 100% 100% 93%

Jan-Mar 2016 Accuracy 96% 100% 100% 97% 50%

April- June 2016 Accuracy 100% 100% 100.0% 100.0% 100.0%

Ne

w c

lie

nts

sta

rte

d o

n A

RT

Results: Timely Submission of Inpatient reports

(HMIS 108) in 9 facilities

15

33.3

62.9

66.7

96.3

0

20

40

60

80

100

120

% t

ime

lin

es

Intervention facilities

July - Sept 2015

Jan-Mar 2016

Results: Average reporting for the each sub-

counties before and after interventions

16

0102030405060708090

100

Bbanda

Su

b C

ounty

Bule

ra S

ub

county

Busi

mbi S

ubcounty

Buta

yunja

Subcounty

Kaki

ndu S

ubcounty

Kala

ngaalo

Sub

county

Kik

andw

a S

ubco

un

ty

Maa

nyi

Subcounty

Mala

ngala

Subco

unty

Mitya

na T

ow

n C

ouncil

Nam

ungo

Su

b C

ounty

Sse

kanyo

nyi

Subcounty

48

31

53 62

79

53

72

51

71

33

100

69 73

46

70 71

88

62

86

57

86

54

100

88

Av

era

ge

Re

po

rtin

g R

ate

average surveillance reporting by sub-county Before and After

Average Reporting Before Intervention Average Reporting After Intervention

Before period: 2015w1-2016w3 After period: 2016w4-2016w25

Results: Trend in district reporting rates over the

weeks in 2015 and 2016

17

33.9

45.8

25.4

62.7

57.6

66.1 69.5

83.1 83.1

20

30

40

50

60

70

80

90

100

201

5W

1

201

5W

3

201

5W

5

201

5W

7

201

5W

9

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

2015…

201

6W

1

201

6W

3

201

6W

5

201

6W

7

201

6W

9

2016…

2016…

2016…

2016…

2016…

2016…

2016…

2016…

Re

po

rtin

g R

ate

Mityana: Weekly Surveillance Reporting Rate for 2015w1 to 2016w25

Intervention

point

80%- Minimum Standard

Lessons Learnt

• Having more than one person registered for mTrac at the

facility strengthens team work and reporting.

• Presence of data improvement plans at the health facilities

encourages health workers to improve their recording,

compilation of reports and data use.

• Presence of the Standard Operating Procedure guides the

right flow of data from the facilities

• Review and feedback on performance encourages reporting

• DQA helps health workers to appreciate their weaknesses

18

Challenges and solutions

Challenges Solutions

Lack of funds to support

data management

Inclusion of data activities in Work

plans for funding

High attrition of staff in

private facilities

Support supervision and mentorship

Inadequacy of new updated

HMIS tools in some

facilities

Work with Mildmay and NMS for

provision and in charges to place

orders

Lack of teamwork at

facilities

Orientation, mentorship, supervision

and collective responsibility towards

reporting.

19

Conclusions

• Completeness of 105, accuracy of 106b and timeliness of

108 improved from 42% to 100%, 52.7 to 80% and 87%

to 100% respectively

• Generation of quality health data entails having skilled

and competent health staff who know the importance of

data

• Inclusion of HMIS activities in work plan is key for

collection and submission of quality data

20

Next steps

• Routine support supervision and mentorship

• New staff will be oriented and mentored on HMIS tools and

reporting

• Liaise with Implementing Partners to support DQA, support

supervision, mentorships and supply of tools

• Inclusion of HMIS activities in work plans at district and

facilities

• HMIS reporting to be an assessable area for annual

performance for in-charges and records assistants

21

Recommendations

• Inclusion of data management activities in work plans at

district and facilities

• Sustainable provision of new updated registers and other

HMIS tools

• Regular supervision, mentorship and data quality

assessment

22

Acknowledgement

• CDC

• Ministry of Health

• Makerere University School of Public Health

• Academic and Institutional Mentors

• Fellows in the programme

• Mityana District Leadership

• Implementing Partners

• District Health Team

• The health workers

23