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

Abstract

Background: The 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.

Objective: This 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.

Methods: Retrospective 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.

Results: The 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.

Conclusions: Subnational 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.

Keywords: epidemiology; infectious disease notification; infectious disease reporting; integrated disease surveillance reporting; inventory study; notification; private sector; public health surveillance; tuberculosis.

Figure 1

TB surveillance systems in Nigeria in 2015. TB: tuberculosis; TBLS: tuberculosis and leprosy supervisor.

Figure 2

Map of primary data sources for the estimation of the magnitude of underreporting. B+: bacteriologically confirmed TB; DOTS: Directly Observed Treatment Short-Course; DSNO: disease surveillance and notification officer; LGA: local government area; PPM: public-private mix; S+ : smear positive; STBLCO: state tuberculosis and leprosy control officer; TB: tuberculosis.

Figure 3

Behavioral assumptions driving the decision to census or sample health facility strata. DOTS: directly observed therapy short-course; HF: health facilities; TB: tuberculosis.

Figure 4

Overview of the sequence of data management and analysis steps. TB: tuberculosis.

Figure 5

Process used to estimate the total number of tuberculosis cases treated in the unengaged private sector. B+: bacteriologically confirmed tuberculosis; HF: health facility; TB: tuberculosis.

Figure 6

Comparison of TB data sets to discern the level of misreporting of TB notifications. LGA: local government area; TB: tuberculosis.

Figure 7

Schematic of cross-border TB diagnosis and treatment. TB: tuberculosis.