trend of mother to child HIV transmission in western Kenya 20072013 Anthony Waruru Thomas Achia Hellen Muttai Lucy Nganga Abraham Katana Peter Young Jim Tobias Peter Juma ID: 625608
Download Presentation The PPT/PDF document "Are we there yet? Spatial-temporal" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Are we there yet? Spatial-temporal trend of mother to child HIV transmission in western Kenya, 2007-2013
Anthony Waruru, Thomas Achia, Hellen Muttai, Lucy Ng’ang’a, Abraham Katana, Peter Young, Jim Tobias, Peter Juma, Thorkild Tylleskär26th July 2017
1
Ministry of HealthSlide2
Conflict of InterestNo conflicts of interest to declare.Slide3
Background (1)Elimination
of mother to child transmission of HIV (e-MTCT) can be achieved through PMTCT efforts to reduce HIV transmission.PMTCT services are often prioritized by district-level service planning.Early infant diagnostic testing (EID) is a strategy of identifying HIV status for infants and assess PMTCT impact.3Slide4
Background (2)Measuring e-MTCT progress can be done through:
Measuring proportion of infected infants [<5%] Measuring case rates / 100,000 births [<50]Putting these measures in a spatial context is important.4Slide5
AimsAssess
district level trends and factors associated with MTCT.Model MTCT rates over time and space. 5Slide6
methodsSlide7
Study setting7Slide8
Data, laboratory procedures, analyses and mapping
Dried blood spot (DBS) samples collected accompanied by a submission form and sent to a regional laboratory.HIV testing performed using PCR. Analysis and mapping done using: Stata v.14. R – Integrated Nested Laplace Approximation (INLA) Quantum GIS (QGIS) to map fitted MTCT rates
8Slide9
Analysis data set9
Over 1 year oldn=5,186 (5.1%)*
Missing age
n=1715
(
1.7%)
Included in analyses
n=95,215
(
93.2%)
All
records
N=102,116
Infants ≤12 months old
Exclusions
All infant and children samplesSlide10
RESULTSSlide11
Raw MTCT rates and early infant diagnosis
11Testing by 8 weeks/2 months considered “early”Slide12
Factors associated with MTCT
12CharacteristicTotal (n)
Positive, n (%)
Adjusted
aOR
[95% CI]
Total
95
,
215
10,095
Age at diagnosis
Under/= 8 weeks
52,504
3,307 (
6.3%)
ref
.
Over 8 weeks
42,711
6,788 (
15.9%)
1.17
(1.08,1.26)
Maternal regimen
SdNVP only
2,763
279 (
10.1%)
2.51
(2.32,2.72)
AZT+NVP+3TC |
short course
11,634
871 (
7.5%)
1.51
(1.33,1.72)
ART
for prophylaxis
4,551
328 (
7.2%)
1.71
(1.49,1.97)
ART
for treatment22,3891,171 (5.2%)ref.
Covariates
:
year of
diagnosis
,
sex
, infant’s age,
age at diagnosis
,
maternal regimen
, breastfeeding, and
mother
ARV statusSlide13
Models comparison13
The best fitting model was spatial-temporal model with covariates (age at diagnosis, breastfeeding, sdNVP use, infant’s age)
Model
type
DIC
Effective parameters
Model choice
Model
1:- A
generalized linear model (non-spatial)
1,153
4.0
Fourth
Model
2:-
Spatial model without covariates
1,319
11.8
Fifth
Model
3:- Spatial-temporal
model without covariates
306
59.7
Second
Model
4:-
Spatial non-temporal model with covariates
325
62.3
Third
Model
5:-
Spatial-temporal model with covariates
305
58.8
First*Slide14
Spatial-temporal MTCT trend
14Slide15
Case rates /100,000 births
15* Kenya population estimates 2010-2018† PEPFAR annual progress report (APR 2013) data
District
Estimated live births in 2013*
Women tested for HIV in 2013
†
HIV+ women in 2013
Infants tested
in
2013
Absolute transmission (number infected)
Transmission rates per 100,000 live births
‡
Rank (low to high)
All
275,169
203,069
15,136
17,129
1,231
447
-
Bondo
13,262
9,925
1,372
1,739
116
875
11
Kisii
36,841
25,143
622
701
46
125
3
Gucha
17,231
17,316
375
293
20
116
2
Homa
Bay
43,423
13,159
1,257
1,968
163
375
5
Kisumu
24,931
29,599
2,882
2,469
167
670
9
Kuria
11,696
13,774
214
473
35
299
4
Migori
30,193
26,391
2,503
2,582
190
629
7
Nyamira
26,640
15,827
354
445
22
83
1
Nyando
19,063
10,307
1,208
1,286
102
535
6
Rachuonyo
17,243
12,658
1,451
1,457
121
702
10
Siaya
24,984
21,589
1,998
2,276
163
652
8
Suba
9,662
7,381
900
1,440
86
890
12Slide16
Summary of findingsEarly
testing rate has improved over time.Significant drop in mother to child transmission of HIV in 7-year period.Case rate per 100,000 live births is still high.Spatial-temporal model with covariates was best in explaining MTCT geographical variation.16Slide17
CONCLUSIONSlide18
Does spatial-temporal modeling help us tell the story?
LimitationsRoutine data from programs are often incomplete. Did not take into account the underlying population. StrengthsMay be better than other models. Offers a visual tool to help program planners focus efforts. 18Slide19
Are we there yet?
Improvement in uptake of infant testing and reduction of MTCT rates ~ growth of the PMTCT program.Overall, the PMTCT program coverage has contributed to reduction in MTCT rates in western Kenya.Geographical disparities may signify gaps in distribution of e-MTCT efforts.More spatial and spatial temporal analyses should be considered as additional tools for planning. 19Slide20
Thank you
AcknowledgementsMinistry of Health Kenya medical research institute (KEMRI) – field work & laboratory U.S. Centers for Disease Control and Prevention (CDC)/PEPFAR - fundingUniversity of Washington/University of Nairobi – GIS training 20
Attribution of Support: This evaluation was supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through U.S. Centers for Disease Control and Prevention, Division of Global HIV/TB (CoAg
# GH000041).Disclaimer: The findings and conclusions in this presentation are those of the authors and do not necessarily represent the official position of the funding agencies.