data systems, surveillance & analysis · data systems, surveillance and analysis collaborations...
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Data Systems, Surveillance & Analysis
Working Group
Zoe Hildon, Angel Dillip, Donat Shamba et al
Data systems, surveillance and analysis
collaborations
• Responsible for monitoring data collection of large clinical surveillance systems working with existing teams managing: CSS, HDSS, SPD
• Headed by Amani Mono
Data management
• Responsible for analysis and facilitating data sharing of these systems.
• Project consultancy.
• Provision of internal services and training to drive up quality
• Headed by Zoe Hildon
Data analysis cluster (DAC)
• Meaningfully archiving surveillance systems data
• Collating and archiving project data
• Headed by Sadiki Masomhe / Advo Kakorozya
DataCentral archiving
A multidisciplinary & multi-method (DAC)
analytic team
Qualitative cluster
Aloisia Shemdoe
Donat Shamba
Quantitative cluster
Francis Leviera (DSS)
Jeje William (CSS)
Juan Manuel Blanco (SPD)
Mixed method clusters
Dr Angel Dillip
Dr Zoe Hildon
Mixed
Qual
Quant
Building capacity while building output
Surveillance systems
CSS, DSS, SPD
• Information management
• Analyses (data completeness/ cleaning/ report writing)
• Project platforms
Project consultancy
• Feasibility and formative research
• Complex interventions/ evaluations
• Cohort studies
Training
• Seminars series and practical workshops
• Curriculum development
• Partner training events
Surveillance systems (CSS, DSS, SPD)
Progress
Clinical Surveillance Systems (CSS)
o Comparing changes in morbidity and mortality in under-five year
olds in Kilombero (2001-2010) & Bagamoyo (2006-2010) district
hospitals
Health and District Surveillance Systems (HDSS)
o Health and demographic surveillance: Ifakara and Rufiji (2000-
2011)
o Burden of Disease and Injuries for Coastal Regions in Tanzania
(2008-2011)
Sentinal Panel of Districts (SPD)
Facility based Information system (FBIS)
o Data completeness and way forward
Sample vital registration with verbal autopsy (SAVVY)
o Preliminary findings to follow
o Indentify the extent of the gaps
o Decide if retrospective data
collection / data retrieval are an
option
o Explore imputation possibilities
o If not, what are the most complete
subsets of data for analyses?
Clinical Surveillance Systems: Inpatient
facilities, Kilombero and Bagamoyo
More details can be found in January 2013 CSS report for: Comparing changes in morbidity and mortality in
under-five year olds in Kilombero (2001-2010) & Bagamoyo (2006-2010) district hospitals
Total admissions for under 5 year olds over time Saint Francis DD Hospital Bagamoyo D Hospital
Year Total N (%)
% female
Average age
% missing
diagnosis
Total N (%)
% female
Average age
% missing
diagnosis 2001 2,676(11.7) 42.4 2.17 0.4 ----- ----- ----- -----
2002 2,321(10.1) 44.4 2.27 0.3 ----- ----- ----- -----
2003 3,956(17.3) 44.7 2.39 0.4 ----- ----- ----- -----
2004 2,329(10.2) 46.8 2.24 1.6 ----- ----- ----- -----
2005 2,991(13.1) 42.8 2.17 2.1 ----- ----- ----- -----
2006 1,819(8.0) 41.4 2.11 13.6 1,250(29.8) 46.2 2.26 27.6
2007 1,345(5.9) 40.3 2.30 11.0 1,055(25.2) 44.3 2.23 7.5
2008 1,984(8.7) 43.3 2.40 1.1 984(23.5) 45.3 2.30 8.6
2009 2,113(9.2) 42.0 2.35 0.0 532(12.7) 45.1 2.30 9.8
2010 1,354(5.9) 43.0 2.20 0.0 371(8.9) 45.3 2.18 9.2
Total 22,888(100) 43.4 2.26 0.3 4,192(100) 45.3 2.26 14.1
Clinical Surveillance Systems: Data
completeness
St Francis DDH Bagamoyo DH
Diagnoses on admission data
Malaria lab confirmed
Anaemia lab confirmed
Diagnoses on admission data
Malaria lab confirmed
Anaemia lab confirmed
2001 ----- ----- ----- 2002 ----- ----- ----- 2003 ----- ----- ----- 2004 ----- ----- ----- 2005 ----- ----- ----- 2006 ~ ~ ~ ~
2007 ~ ~
2008 ~
2009 ~
2010 ~
Checklist for morbidity data usability
Morbidity trends for under 5 year olds at
St Francis DDH
Morbidity trends for under 5 year olds at
Bagamoyo DH
Health and Demographic Surveillance
Systems
Trends in crude death rate for Ifakara and Rufiji
More details can be found in Annual 2013 DSS report for: Health and demographic surveillance:
Ifakara and Rufiji (2000-2011).
13.7
8.8
10.9
6.9
56789
101112131415
Death
s p
er
1000
po
pu
lati
on
Year
Rufiji Ifakara
41.7
27.8
36.3
36.1
0
5
10
15
20
25
30
35
40
45
Bir
ths p
er
10
00 w
om
an
Rufiji Ifakara
Trends in crude birth rate for Ifakara and Rufiji
Health and Demographic Surveillance
Systems
Data completeness for DSS physician coded
Verbal Autopsy (VA) - cause of death - data
Ifakara HDSS 2008-2011
Year of death All deaths
VA interviews (%)
2008 819 768(93) 2009 794 695(87) 2010 863 618(65) 2011 804 494(61) Total 3,280 2575(78)
Rufiji HDSS 2008-2011
Unknown cause of death Year of death All
deaths Total
Included in analyses (%)
Process- ing
(%)
Incomplete
(%)
Undeter-mined
(%)
Total Missing
(%) 2008 792 566(71) 52(7) 118(15) 56(7) 226(29) 2009 750 573(76) 30(4) 94(13) 53(7) 177(24) 2010 816 640(79) 82(10) 53(6) 41(5) 176(21) 2011 843 650(77) 131(16) 17(2) 45(5) 193(23) Total 3,201 2429(76) 295(9) 282(9) 195(6) 772(24)
o
o
o Clearing back log of
existing VA coding,
underway
o Retrospective
Ifakara VA data
collection planned
Burden of disease and injuries
More details can be found in: Burden of disease and injuries for coastal regions in
Tanzania (2008-2011).
Major causes of death by sex for all ages in Rufiji
Burden of disease and injuries
Distribution of deaths among under-five in Rufiji
Examine 3 sources of inequality:
- maternal educational attainment
- household economics and
- health service accessibility
18
Are they important factors of child mortality in an african context?
SOCIO-DETERMINANTS OF
CHILD SURVIVAL
Health and Demographic Surveillance
system (Rufiji and Ifakara)
• Continuous monitoring data collection on pregnancy,
birth, death, cause of death, migration,
• Thrice / year (every 4 months)
• Other information collected once a year
• Education
• Durable assets Socioeconomic status (SES)
• Geo-location of households and health facilities (GIS)
19
Map of Demographic surveillance area
hospital
health center Limits of DSS area 20
Data and Methods
• Data from Ifakara & Rufiji DSS
(Tanzania): 2000-2010 • Individual: child and parents characteristics
• Household: SES and time travel to health
facilities
• Method • All children born within the DSS area
• Univariate & Multivariate analysis
21
22
0
50
100
150
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
P
r
o
b
a
b
i
l
i
t
y
o
f
d
y
i
n
g
(
5
q
0)
(
p
e
r
1
0
0
0)
Source: Database of Ifakara and Rufiji HDSSs, 2011
5q0
CI 95%
Trend line
5q0: 122 to 75
per 1,000 40%
reduction
Trends in Ifakara and Rufiji, 2000-2010
Average annual
decrease ~ 4.4%
with only 2% in
2000-05 and 7%
decrease in 2006-
10
2000 and 2010
Multivariate analysis of child mortality (U5), 2000-2010
Variable Type OR
Gender Boy 1.09*
Girl Ref.
Birth order
1 to 2 1.34***
3 to 4 1.15*
More than 5 Ref.
Group age of
mother (year)
Under 20 1.18**
20 - 35 Ref.
More than 35 1.29***
Education of
mother
No education 1.14*
Primary 1.12*
Secondary/college Ref.
*** (p<0.001); ** (p<0.01); * (p<0.5) 23
24
Variable Type OR
SES of household
(wealth quintiles)
Poorest 1.22**
second 1.18**
Middle 1.06
Fourth 1.08
Least poor Ref.
Travel time from household to
nearest health facility (hospital
or health center) (hour)
Less than 1 Ref.
1 to 2 1.22***
More than 2 1.28***
Number of children 72520
Number of events 5528
*** (p<0.001); ** (p<0.01); * (p<0.5)
Multivariate analysis of child mortality (U5), 2000-2010
25
Travel time (hour)
Less than 1 More than
1
Education No education 1.0 2.8***
Education Ref. 1.0
SES of
household
Poorest 3.6*** 5.6*** Poor 5.0*** 5.9*** Less poor Ref. 6.8***
N = 72520; Event = 5528
* p<0.001
26
Sentinel Panel of Districts (District Observatory)
Time
Variables
Location
Variables:
• Horizontal HMIS
• Vertical programs
Time:
• Monthly
• Quarterly
• Yearly
Location:
• Facility A+B…Z
District 1
Country
Facility Based Information System (FBIS)
District Heath Information System
OPV 1 Vaccination coverage in Pwani
Region
ANC: 1st and 4th visits – Pwani Region
Data collection challenges
Challenges
• Stock out of HMIS forms
• Changes of HMIS forms associated with
upgrading the DHIS
• Power problems
• Network problems
• Transport problems
• Hardware breakdowns in some districts
• Managerial challenges:
o changes in key personnel – training / re-
training
o communication and feedback 33 Is a powerful information platform to generate facility based information
What is SAVVY?
• SAmple Vital registration with Verbal AutopSY
• Sample of districts to provide nationally representative
estimates of mortality
• Age
• Sex
• Residence (rural/urban)
• Conduct baseline to get denominators
• Established vital events reporting for numberators
• Collaborate with Ministry of Health, National Bureau of
Statistics, and NIMR
• Funding is from Center for Disease Control (2009-2014)
Phase I: March-July 2011; Bagamoyo,Kinondoni,Geita, Kahama
Phase II: March - June 2012; Sumbawanga, Mbozi,Songea (U), Iringa (U),Muleba & Musoma(R).
Phase III: Sept – Dec, 2012; Mtwara (U),Ruangwa & Kilosa.
Phase IV: Jan-March 2013; Babati, Kondoa, Singida (U), Arusha, Tanga (U) &Moshi
Phase V: Apr-Jun 2013
SAVVY Implementation timeline
Population characteristics of 10 SAVVY
Districts
Characteristic Value
Number of districts 10
Total Population 302,224
Males 145,982
Number of Births 12,540
Number of Deaths 3,426
No. of deaths with VA 3,151
No. of newborn deaths 402
No. of infant deaths 630
No. of under 5 deaths 1,149
Basic Mortality Indicators
*per 1000
Ratio per 1000 live births
Indicators
Rate /
Ratio
Crude Birth Rate* 41.5
Crude Death Rate* 11.3
Neonatal Mortality Ratio** 32.1
Infant Mortality Rate** 50.2
Under Five Mortality Ratio** 91.6
Top ten causes of deaths in < 5 years old
in 10 SAVVY districts (n=1,149)
3
3
3
4
4
4
6
8
16
24
HIV
Other intestinal infectious
Other respiratory and CVD
Birth asphyxia, or other respiratory di
SIDS
Prematurity and LBW
Fetus/newborn affected by maternal…
Pneumonia
Still birth
Malaria
Percent of deaths children < 5 years old
Top ten cause of deaths in adults 15-64
years from 10 SAVVY districts (n=139)
4
4
4
5
5
6
6
6
7
14
Cerebrovascular diseases
All other external causes
Other intestinal infectious diseases
Epilepsy
HIV
Tuberculosis
Unspecified/Undetermined
Hypertensive diseases
Transport accidents
Malaria
Percent of deaths in adults 15-64 years
Top ten cause of deaths in adults 65+
years from 10 SAVVY districts (n=587)
3
4
4
5
5
6
7
11
11
11
HIV
Other intestinal infectious
Other neoplasms
Unspecified/Undetermined
TB
Diabetes mellitus
Cerebrovascular diseases
Hypertensive diseases
Malaria
Senility/Oldage
Percent of deaths in adults 60 year and above
Key findings
• Crude birth rate 42 per 1000 (38 per 1000 DHS 2010)
• Under five mortality 92 deaths per 1000 live births
• Malaria is still the number one cause of deaths in under-fives, adults
• A quarter of deaths in under five is due to malaria
• Stillbirths and pneumonia are second and third cause of death in under fives
• Hypertension ranks third in adults and the elderly
• HIV, Tuberculosis and Malaria are among top ten three age groups
Project consultancies overview
o Formative research: INSIST, Emollients, HSSE, HAS,
Outdoor mosquito traps, Innovating spatial repellents
• An example from INSIST: Thermal Care for Newborn Babies
o Complex interventions/ evaluations: Ageing & NCDs,
PRAC-TZ, Impact of clinical trials on Maternal Health
Services.
o Cohort studies (TB cohort study)
o Surveillance systems as project platforms
Thermal care for new born babies in rural
Tanzania: barriers and facilitators for
behaviour change • In preparation for BMC Pregnancy and Childbirth
• Study objectives: Exploring the stated popularity of
• Timing of drying and wrapping the baby after delivery
• Timing and conditions of the first bath
• Day to day care such as wrapping and carrying the
baby skin-to-skin
• Methods
- multi-method qualitative study
Delaying the first bath for at least 6 hours
Popularity Why ‘yes’ - to
delay bathing?
Facilitating
change
Why not delay
bathing? Barriers to change
Women were just as
likely to report
having bathed the
baby within 6 hours
of birth as not
Taking on board the
health worker advice
Sensitization and
accurate / consistent
dissemination of
information
Belief that the birth
process is dirty and that
the baby is dirty after
birth
To shock the baby
To warm the baby
The vernix coating
linked to sperm
Social pressure - rather
than the belief that the
dirt can harm the baby
Birth attendants
encourage mothers to
bath babies
Nurses’ messages were
not consistent in terms
of the recommended
delay
When vernix was visible
– it’s cultural meaning
could lead to
stigmatization of the
mother
Secrecy surrounding
mothers overturning the
advice they were given
Beliefs in traditional
medicine and baths in
herbs for the first bath
45
Results
• This was fairly consistent with survey data on facility births which
showed that 45% of these had reported waiting at least up to 6
hours before bathing.
• Yet, for home births only 19% reported waiting.
• The findings suggest the survey reports of delays to bathing in
facilities may be over reported at 45%
• These data are derived from a household survey conducted in our
study area that contrasted delivery and childcare practices in home
versus facility (n=22,243 mothers).
Data Systems training partnerships
Participation includes:
o ALPHA network (Analysis Longitudinal Population based
HIV/AIDS data on Africa): Training and participation in the
analysis of HV/AIDS cohort
o INDEPTH (International Network for Demographic Evaluation of
Populations and their Health): Training on mortality / cause of
death analysis and data management
o Social Determinants of Health (SDH): working group aimed at
strengthening social science research in HDSS for all INDEPTH
sites
In house lunchtime seminars on analyses and dissemination
o Bi monthly work in progress seminar series /journal clubs
Contribution to MSc curriculum development
Capacity to run as short courses for interested parties
o Introduction to Public Health theory and methods
o Theories of behaviour change across disciplines
o Qualitative data collection tools: a practical workshop
o Introduction to qualitative analyses: Framework Analyses
o Participant and Structured Observation: qualitative and quantitative ways of seeing
o An example of mixing methods in Mixed Methods Analyses
o Software packages : NVIVO / STATA
Data Systems training capacity
Contact details
Dr Angel Dillip
Research Scientist
Dr Zoe Hildon
Principal Research Scientist
