ArticlePloS one2020
Impacts on knowledge and testing on HIV in waves of Mozambique surveys with Bayes estimates.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Bayesian Spatial Modelling of HIV Prevalence in Jimma Zone, Ethiopia.Diseases (Basel, Switzerland) · 2023Article
- Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches.BMC infectious diseases · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is well known that it is more reliable to investigate the effects of several covariates simultaneously rather than one at time. Similarly, it is more informative to model responses simultaneously, as more often than not, the multiple responses from the same subject are correlated. This is particularly true in the analysis of Mozambique survey data from 2009 and 2018.
methodA multiple response predictive model for testing positive for HIV and having sufficient HIV knowledge is modeled to 2009 and 2018 survey data with the use of Bayes estimates. These data are obtained through a hierarchical data structure. The model allows one to address the change in the response to HIV, as it relates to morbidity and to HIV knowledge in Mozambique in the fight against the disease in the last decade.
resultsA more affluent resident is more likely to test positive, more likely to be more knowledgeable about the disease. Whereas, individuals practicing the Islam faith are less likely to test positive but also less likely to be knowledgeable about the disease. Education, while still a factor, has declined in its impact on testing positive for HIV or being knowledgeable about HIV. Females are more likely to test positive but more likely to be knowledgeable about the disease than men. The rate of impact of affluence on knowledge has increased in the past decade. Marital status (cohabitating or married) showed no impact on the knowledge of the disease. Age had no impact on knowledge suggesting that the message is getting to resident.
conclusionsA joint Bayes modeling of correlated binary (testing positive and knowledge about the disease) responses, while accounting for the hierarchy of the data collection, presents an opportunity to extract the extra variation before allocating the variation on the responses as the due of the covariates. The fight against HIV in Mozambique seems to be succeeding. Some knowledge is common among all ages, and Islam religion has a positive effect. While education still shows an influence on the binary responses, it has declined over the last decade.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.