Evidence map›Paper›PMID 33720030›Full record

ArticleJMIR public health and surveillance2021

Hybrid Approach to Estimation of Underreporting of Tuberculosis Case Notification in High-Burden Settings With Weak Surveillance Infrastructure: Design and Implementation of an Inventory Study.

Ellen M H Mitchell, Olusola Adedeji Adejumo, Hussein Abdur-Razzaq, Chidubem Ogbudebe, Nkem Chukwueme, Samson Bamidele Olorunju, Mustapha Gidado

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

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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.

Ellen M H Mitchell *Tropical Infectious Diseases, Department of Public Health, Institute for Tropical Medicine, Antwerp, Belgium.ORCID 0000-0002-2824-3499
Olusola Adedeji Adejumo *Mainland Hospital, Yaba Lagos, Nigeria.ORCID 0000-0001-8946-3244
Hussein Abdur-RazzaqHealth Research Unit, Lagos State Ministry of Health, Lagos, Nigeria.ORCID 0000-0002-4946-3415
Chidubem OgbudebeKNCV TB Foundation Nigeria, Abuja, Nigeria.ORCID 0000-0002-9616-0695
Nkem ChukwuemeNew York Medical College, New York, NY, United States.ORCID 0000-0001-7848-375X
Samson Bamidele OlorunjuDepartment of Epidemiology and Medical Statistics, Faculty of Public Health, University of Ibadan, Ibadan, Nigeria.ORCID 0000-0001-5486-5964
Mustapha GidadoKNCV TB Foundation, Koninklijke Centrale Vereniging tot bestrijding der Tuberculose (KNCV), The Hague, Netherlands.ORCID 0000-0002-1798-7281

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe greatest risk of infectious disease undernotification occurs in settings with limited capacity to detect it reliably. World Health Organization guidance on the measurement of misreporting is paradoxical, requiring robust, independent systems to assess surveillance rigor. Methods are needed to estimate undernotification in settings with incomplete, flawed, or weak surveillance systems. This study attempted to design a tuberculosis (TB) inventory study that balanced rigor with feasibility for high-need settings.

objectiveThis study aims to design a hybrid TB inventory study for contexts without World Health Organization preconditions. We estimated the proportion of TB cases that were not reported to the Ministry of Health in 2015. The study sought to describe TB surveillance coverage and quality at different levels of TB care provision. Finally, we aimed to identify structural-, facility-, and provider-level barriers to notification and reasons for underreporting, nonreporting, and overreporting.

methodsRetrospective partial digitalization of paper-based surveillance and facility records preceded deterministic and probabilistic record linkage; a hybrid of health facilities and laboratory census with a stratified sampling of HFs with no capacity to notify leveraged a priori knowledge. Distinct extrapolation methods were applied to the sampled health facilities to estimate bacteriologically confirmed versus clinical TB. In-depth interviews and focus groups were used to identify causal factors responsible for undernotification and test the acceptability of remedies.

resultsThe hybrid approach proved viable and instructive. High-specificity verification of paper-based records in the field was efficient and had minimal errors. Limiting extrapolation to clinical cases improved precision. Probabilistic record linkage is computationally intensive, and the choice of software influences estimates. Record absence, decay, and overestimation of the private sector TB treatment behavior threaten validity, meriting mitigation. Data management demands were underestimated. Treatment success was modest in all sectors (R=37.9%-72.0%) and did not align with treatment success reported by the state (6665/8770, 75.99%). One-fifth of TB providers (36/178, 20%) were doubtful that the low volume of patients with TB treated in their facility merited mastery of the extensive TB notification forms and procedures.

conclusionsSubnational inventory studies can be rigorous, relevant, and efficient in countries that need them even in the absence of World Health Organization preconditions, if precautions are taken. The use of triangulation techniques, with minimal recourse to sampling and extrapolation, and the privileging of practical information needs of local decision makers yield reasonable misreporting estimates and viable policy recommendations.

Indexed as

Disease NotificationHumansNigeriaPublic Health SurveillanceRetrospective StudiesTuberculosisWorld Health Organizationepidemiologyinfectious disease notificationinfectious disease reportingintegrated disease surveillance reportinginventory studynotificationprivate sectorpublic health surveillancetuberculosis

Identifiers

PMID33720030
PMCPMC8088841

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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.