Evidence map›Paper›PMID 32938614›Full record

ArticleBMJ global health2020

Antibiotic overuse in the primary health care setting: a secondary data analysis of standardised patient studies from India, China and Kenya.

Giorgia Sulis, Benjamin Daniels, Ada Kwan, Sumanth Gandra, Amrita Daftary, Jishnu Das, Madhukar Pai

Abstract read
In one paragraph

Article in BMJ global health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

75 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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15 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Giorgia SulisEpidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.ORCID 0000-0001-6641-0094
Benjamin DanielsMcCourt School of Public Policy, Georgetown University, Washington, District of Columbia, USA.ORCID 0000-0001-9652-6653
Ada KwanSchool of Public Health, University of California Berkeley, Berkeley, California, USA.ORCID 0000-0003-4889-9433
Sumanth GandraDivision of Infectious Diseases, Department of Medicine, Washington University in Saint Louis, Saint Louis, Missouri, USA.
Amrita DaftaryDahdaleh Institute of Global Health Research, York University, Toronto, Ontario, Canada.ORCID 0000-0003-2275-3540
Jishnu Das *McCourt School of Public Policy, Georgetown University, Washington, District of Columbia, USA.ORCID 0000-0002-5909-3585
Madhukar Pai *Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada madhukar.pai@mcgill.ca.ORCID 0000-0003-3667-4536

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDetermining whether antibiotic prescriptions are inappropriate requires knowledge of patients' underlying conditions. In low-income and middle-income countries (LMICs), where misdiagnoses are frequent, this is challenging. Additionally, such details are often unavailable for prescription audits. Recent studies using standardised patients (SPs) offer a unique opportunity to generate unbiased prevalence estimates of antibiotic overuse, as the research design involves patients with predefined conditions.

methodsSecondary analyses of data from nine SP studies were performed to estimate the proportion of SP-provider interactions resulting in inappropriate antibiotic prescribing across primary care settings in three LMICs (China, India and Kenya). In all studies, SPs portrayed conditions for which antibiotics are unnecessary (watery diarrhoea, presumptive tuberculosis (TB), angina and asthma). We conducted descriptive analyses reporting overall prevalence of antibiotic overprescribing by healthcare sector, location, provider qualification and case. The WHO Access-Watch-Reserve framework was used to categorise antibiotics based on their potential for selecting resistance. As richer data were available from India, we examined factors associated with antibiotic overuse in that country through hierarchical Poisson models.

resultsAcross health facilities, antibiotics were given inappropriately in 2392/4798 (49.9%, 95% CI 40.8% to 54.5%) interactions in India, 83/166 (50.0%, 95% CI 42.2% to 57.8%) in Kenya and 259/899 (28.8%, 95% CI 17.8% to 50.8%) in China. Prevalence ratios of antibiotic overuse in India were significantly lower in urban versus rural areas (adjusted prevalence ratio (aPR) 0.70, 95% CI 0.52 to 0.96) and higher for qualified versus non-qualified providers (aPR 1.55, 95% CI 1.42 to 1.70), and for presumptive TB cases versus other conditions (aPR 1.19, 95% CI 1.07 to 1.33). Access antibiotics were predominantly used in Kenya (85%), but Watch antibiotics (mainly quinolones and cephalosporins) were highly prescribed in India (47.6%) and China (32.9%).

conclusionGood-quality SP data indicate alarmingly high levels of antibiotic overprescription for key conditions across primary care settings in India, China and Kenya, with broad-spectrum agents being excessively used in India and China.

Indexed as

Anti-Bacterial AgentsData AnalysisChinaHumansIndiaKenyaPrimary Health CareAnti-Bacterial Agentsepidemiologyother study designtreatment

Identifiers

PMID32938614
PMCPMC7493125

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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.