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 Tylleskär 26th July 2017 Ministry of Health

No conflicts of interest to declare. Conflict of Interest No conflicts of interest to declare.

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.

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.

Aims Assess district level trends and factors associated with MTCT. Model MTCT rates over time and space.

methods

Study setting

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

All infant and children samples Analysis data set Over 1 year old n=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 samples

RESULTS

Raw MTCT rates and early infant diagnosis Testing by 8 weeks/2 months considered “early”

Factors associated with MTCT Characteristic Total (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 treatment 22,389 1,171 (5.2%) Covariates: year of diagnosis, sex, infant’s age, age at diagnosis, maternal regimen, breastfeeding, and mother ARV status

Models comparison 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* The best fitting model was spatial-temporal model with covariates (age at diagnosis, breastfeeding, sdNVP use, infant’s age)

Spatial-temporal MTCT trend

Case rates /100,000 births 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 2 Homa Bay 43,423 13,159 1,257 1,968 163 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 2,276 652 8 Suba 9,662 7,381 900 1,440 86 890 12 * Kenya population estimates 2010-2018 † PEPFAR annual progress report (APR 2013) data ‡ Transmission rate per 100,000 live births = Absolute transmission in 2013 Estimated live births in 2013 x 100,000

Summary of findings Early 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.

CONCLUSION

Does spatial-temporal modeling help us tell the story? Limitations Routine data from programs are often incomplete. Did not take into account the underlying population. Strengths May be better than other models. Offers a visual tool to help program planners focus efforts.

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.

Thank you Acknowledgements Ministry of Health Kenya medical research institute (KEMRI) – field work & laboratory U.S. Centers for Disease Control and Prevention (CDC)/PEPFAR - funding University of Washington/University of Nairobi – GIS training 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.