International journal of epidemiology最新文献

Measurement error and information bias are ubiquitous in epidemiology, yet directed acyclic graphs (DAGs) are infrequently used to represent them, in contrast with confounding and selection bias. This represents a missed opportunity to leverage the full utility of DAGs to depict associations between the variables we actually analyse in practice: empirically measured variables, which are necessarily measured with error. In this article, we focus on applying causal diagrams to depict the data-generating mechanisms that give rise to the data we analyse, including measurement error. We begin by considering empirical data considerations using a general example, and then build up to a specific worked example from the clinical epidemiology of hearing health. Throughout, our goal is to highlight both the challenges and the benefits of using DAGs to depict measurement error. In addition to the application of DAGs to conceptual causal questions (which pertain to unmeasured constructs free from measurement error), which is common, we highlight the advantages associated with applying DAGs to also include empirically measured variables and-potentially-information bias. We also highlight the implications implied by this use of DAGs, particularly regarding the unblocked backdoor path causal structure. Ultimately, we seek to help increase the clarity with which epidemiologists can map traditional epidemiological concepts (such as information bias and confounding) onto causal graphical structures.

Background: Currently, most large-scale public health programs, such as immunization or anti-parasitic deworming, work in relative isolation. Integrating efforts across programs could potentially improve their efficiency, but identifying populations that could benefit from multiple programs has been an operational challenge.

Methods: We analyzed a nationally representative survey conducted in India between 2019 and 2021 to assess and map coverage of seven vaccines [Bacillus Calmette-Guérin (BCG), hepatitis B, polio, diphtheria-tetanus-pertussis (DTP), haemophilus influenza type b (Hib), rotavirus and measles-containing vaccine (MCV)], plus Vitamin A supplementation and anti-parasitic deworming treatment among 86 761 children aged 1-3 years old.

Results: National coverage varied widely by program, from 42% (rotavirus) to 95% (BCG). There was high correlation between district-level coverage estimates (r ≥ 0.7) and extensive spatial overlap in low-coverage populations. In simulated implementation strategies, we show that an integrated strategy that targets full immunization coverage for four core vaccines (BCG, polio, DTP, MCV) would achieve similar coverage to an optimal (but unrealistic) implementation strategy and far better coverage than multiple efforts focused on individual vaccines. Targeting the most under-vaccinated districts within states based on spatial clustering or coverage thresholds led to further improvements in full coverage per child targeted. Integration of anti-parasitic deworming or rotavirus vaccination into a core vaccine delivery mission could nearly double their coverage (from ∼45% to ∼85%).

Conclusions: Integrated delivery and geographic targeting across core vaccines could accelerate India's progress toward full immunization coverage. An integrated platform could greatly expand coverage of non-core vaccines and other child health interventions.