English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Local estimates of HIV-prevalence provide information that can be used totarget interventions and consequently increase the efficiency of the resources.This closer-to-optimal allocation can lead to better health outcomes, includingthe control of the disease spread, and for more people. Producing reliableestimates at smaller geographical levels can be challenging and carefulconsideration of the nature of the data and the epidemiologic rational isneeded. In this paper, we use the DHS data phase V to estimate HIV prevalenceat the first-subnational level in Kenya, Tanzania, and Mozambique. We fit thedata to a spatial random effect intrinsic conditional autoregressive (ICAR)model to smooth the outcome. We also use a sampling specification from amultistage cluster design. We found that Nyanza (P=14.2%) and Nairobi (P=7.8%)in Kenya, Iringa (P=16.2%) and Dar es Salaam (P=10.1%) in Tanzania, and Gaza(P=13.7%) and Maputo City (P=12.7%) in Mozambique are the regions with thehighest prevalence of HIV, within country. Our results are based onstatistically rigorous methods that allowed us to obtain an accurate visualrepresentation of the HIV prevalence in the subset of African countries wechose. These results can help in identification and targeting of high-prevalentregions to increase the supply of healthcare services to reduce the spread ofthe disease and increase the health quality of people living with HIV.

HIV-prevalence mapping using Small Area Estimation in Kenya, Tanzania, and Mozambique at the first sub-national level