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Record W4390617872 · doi:10.1186/s12889-023-17001-y

Impact of the COVID-19 pandemic on quality of tuberculosis care in private facilities in Bandung, Indonesia: a repeated cross-sectional standardized patients study

2024· article· en· W4390617872 on OpenAlexafffund
Angelina Sassi, Bony Wiem Lestari, Kuuni Ulfah Naila El Muna, Charity Oga‐Omenka, Nur Afifah, Rodiah Widarna, Lavanya Huria, Nathaly Aguilera Vasquez, Andrea Benedetti, Panji Fortuna Hadisoemarto, Benjamin Daniels, Jishnu Das, Madhukar Pai, Bachti Alisjahbana

Bibliographic record

VenueBMC Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of WaterlooMcGill University
FundersMcGill UniversityBill and Melinda Gates Foundation
KeywordsMedicineTuberculosisPandemicBiostatisticsCross-sectional studyOdds ratioPublic healthEnvironmental healthPrivate sectorConfidence intervalHealth careIncidence (geometry)Family medicineCoronavirus disease 2019 (COVID-19)Internal medicineDiseaseNursingInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

BACKGROUND: Indonesia has the second highest incidence of tuberculosis in the world. While 74% of people with tuberculosis in Indonesia first accessed the private health sector when seeking care for their symptoms, only 18% of tuberculosis notifications originate in the private sector. Little is known about the impact of the COVID-19 pandemic on the private sector. Using unannounced standardized patient visits to private providers, we aimed to measure quality of tuberculosis care during the COVID-19 pandemic. METHODS: A cross-sectional study was conducted using standardized patients in Bandung City, West Java, Indonesia. Ten standardized patients completed 292 visits with private providers between 9 July 2021 and 21 January 2022, wherein standardized patients presented a presumptive tuberculosis case. Results were compared to standardized patients surveys conducted in the same geographical area before the onset of COVID-19. RESULTS: Overall, 35% (95% confidence interval (CI): 29.2-40.4%) of visits were managed correctly according to national tuberculosis guidelines. There were no significant differences in the clinical management of presumptive tuberculosis patients before and during the COVID-19 pandemic, apart from an increase in temperature checks (adjusted odds ratio (aOR): 8.05, 95% CI: 2.96-21.9, p < 0.001) and a decrease in throat examinations (aOR 0.16, 95% CI: 0.06-0.41, p = 0.002) conducted during the pandemic. CONCLUSIONS: Results indicate that providers successfully identify tuberculosis in their patients yet do not manage them according to national guidelines. There were no major changes found in quality of tuberculosis care due to the COVID-19 pandemic. As tuberculosis notifications have declined in Indonesia due to the COVID-19 pandemic, there remains an urgent need to increase private provider engagement in Indonesia and improve quality of care.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.148
GPT teacher head0.478
Teacher spread0.330 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

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