Analyzing the Effectiveness of Digital Technology in Vaccine Supply Management in Kano State, Nigeria

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Research ID 1PDOR

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Abstract

Background Poor vaccine stock management is a major challenge to targeted immunization coverage in Nigeria. Digital health tools are used to promote public health interventions. This study evaluated the impact of LoMIS Stock, a digital health tool, on vaccine stock management in Kano State. We deployed the quasi-experimental design using a mixed method to generate data for impact. We interviewed 109 stakeholders (66 in Kano; the state exposed to LoMIS; 43 in Bauchi; the comparison state). We measured LoMIS Stock usage rate, timeliness of reporting, facility stock-outs, and data-driven decision-making. The Analysis of Variance and T-tests were used to determine effect and compare difference of means. Using time series analysis (expert modeler), we forecasted the trend of stock-outs in Kano State if LoMIS Stock had not been introduced. All qualitative data was analyzed with Atlas Ti software.

Results We found that 62% of participants reported using the tool for stock reports three years after its deployment. Twenty-four (24) hour response to stock-out alerts and Cold Chain Equipment breakdown improved by 67% and 49% respectively. Significantly, stock-out alerts reduced by 88% as compared to only 29% in Bauchi State within the same period. It was found that stock-out events would have increased if LoMIS Stock had not been introduced in Kano State. All partners interviewed affirmed the impulse of LoMIS on improved vaccine visibility that led to timely response.
Conclusions LoMIS stock has significantly reduced stock-out experiences and improved vaccine visibility and accountability in health facilities. There are more opportunities for LoMIS Stock optimization.

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Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

Not applicable

Data Availability

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

Funding

This work did not receive any external funding.

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  • Classification

    NLM Code: W 84.4

  • Version of record

    v1.0

  • Issue date

    06 July 2023

  • Language

    en

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