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Record W2179475848 · doi:10.1186/s12913-015-1194-4

Population level usage of health services, and HIV testing and care, prior to decentralization of antiretroviral therapy in Agago District in rural Northern Uganda

2015· article· en· W2179475848 on OpenAlexaff
George Abongomera, Sylvia Kiwuwa-Muyingo, Paul Revill, Levison Chiwaula, Travor Mabugu, Andrew Phillips, Elly Katabira, Victor Musiime, Charles F. Gilks, Adrienne K. Chan, James Hakim, Robert Colebunders, Cissy Kityo, Diana M. Gibb, Janet Seeley, Deborah Ford

Bibliographic record

VenueBMC Health Services Research · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Toronto
FundersMedical Research CouncilDepartment for International DevelopmentImperial College London
KeywordsMedicinePublic healthHealth carePopulationHealth administrationDecentralizationCommunity healthNursing researchRural areaFamily medicineDemographyEnvironmental healthNursingEconomic growthSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Decentralization of ART services scaled up significantly with the country wide roll out of option B plus in Uganda. Little work has been undertaken to examine population level access to HIV care particularly in hard to reach areas in rural Africa. Most work on ART scale up has been done at health facility level which omits people not accessing healthcare in the community. This study describes health service usage, particularly HIV testing and care in 2/6 parishes of Lapono sub-county of northern Uganda, prior to introduction of ART services in Lira Kato Health Centre (a local lower-level health centre III), as part of ART decentralization. METHODS: Household and individual questionnaires were administered to household members (aged 15-59 years). Logit random effects models were used to test for differences in proportions (allowing for clustering within villages). RESULTS: 2124 adults from 1351 households were interviewed (755 [36%] males, 1369 [64 %] females). 2051 (97%) participants reported seeking care locally for fever, most on foot and over half at Lira Kato Health Centre. 574 (76%) men and 1156 (84%) women reported ever-testing for HIV (P < 0.001 for difference); 34/574 (6%) men and 102/1156 (9%) women reported testing positive (P = 0.04). 818/850 (96%) women who had given birth in the last 5 years had attended antenatal care in their last pregnancy: 7 women were already diagnosed with HIV (3 on ART) and 790 (97%) reported being tested for HIV (34 tested newly positive). 124/136 (91%) HIV-positive adults were in HIV-care, 123/136 (90 %) were taking cotrimoxazole and 74/136 (54%) were on ART. Of adults in HIV-care, most were seen at Kalongo hospital (n = 87), Patongo Health Centre (n = 7) or Lira Kato Health Centre (n = 23; no ART services). 58/87, 5/7 and 20/23 individuals walked to Kalongo hospital (56 km round-trip, District Health Office information), Patongo Health Centre (76 km round-trip, District Health Office information) and Lira Kato Health Centre (local) respectively. 8 HIV-infected children were reported; only 2 were diagnosed aged <24 months: 7/8 were in HIV-care including 3 on ART. CONCLUSIONS: Higher proportions of women compared to men reported ever-testing for HIV and testing HIV-positive, similar to other surveys. HIV-infected men and women travelled considerable distances for ART services. Children appeared to be under-accessing testing and referral for treatment. Decentralization of ART services to a local health facility would decrease travel time and transport costs, making care and treatment more easily accessible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.115
GPT teacher head0.436
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2015
Admission routes1
Has abstractyes

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