Utilization of the Modified Tanahashi Model for Assessing Universal Effective Health Coverage: The Nigeria Bottleneck Instrument and its’ Implications for Planning

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  • December 12, 2019
Creator
  • Ebhohon, Ebehiwele
    • Affiliation: Gillings School of Global Public Health, Public Health Leadership Program
Abstract
  • ABSTRACT Background The Sustainable Development Goal 3 (SDG-3) through evidence-based health coverage models; aim to mitigate the existing health disparities among populations. The Tanahashi model developed in 1978 and subsequently modified is one model that has been adapted to several countries to identify gaps and barriers in health systems performance. This modified Tanahashi model is referred to as the Bottleneck Analysis instrument for health systems delivery and highlights six determinants of effective coverage. This include Essential Commodities, Human Resources, Geographical Access, Initial Utilization, Continuous Coverage, and Quality. The first three determinants constitute the supply-side determinants and the other three, the demand-side determinants. The modified Tanahashi model was adapted to Nigeria context and is called the Nigeria Bottleneck analysis instrument. This study sort out to assess the nature of the relationships between these determinants that constitute this instrument, premised on the modified Tanahashi model, its ability to perform accurate bottleneck analyses in Nigeria Primary health care system and predict Universal Effective Health Coverage. Methods The study used cross-sectional data from 147 LGAs located in 11 states to examine the relevance of the Bottleneck Analysis instrument in UEHC planning. The measures include Commodity, Human Resources, Geographical Access, Utilization, Continuity and Quality and are continuous variables. Three analytic approaches—Pearson’s correlation, Frequency count and Ordinary Least Squares regression—were used to address the research questions. For select tracer interventions: Routine immunization, Integrated Management of Childhood Illnesses, ii Antenatal care and Skilled Birth Attendance and Newborn Care intervention, were evaluated in this study. Results Data consisted of Ninety-nine (68%) observations from Northern Nigeria and 47 (32%) from Southern Nigeria. Analysis to determine the correlation between the determinants for the select tracer interventions were predominantly not statistically significant on the supply-side. On the Demand side, the results showed linear relationships between Utilization and Continuity (p < .001) as with Continuity and Quality (p < .001). The Nigeria bottleneck analysis instrument collectively explained 26-50% of the total variance in the effectiveness of Routine Immunization, Integrated Management of Childhood Illnesses, Antenatal Care and Skilled Birth Attendance and Newborn Care coverage. Another notable finding in this study was that there is significant decrease in the quality of routine immunization coverage in the northern states in comparison with southern states. Conclusion In this study, the Nigeria bottleneck analysis instrument was not a good predictor of quality coverage for the select tracer interventions. Aside Utilization and Continuity, other determinants that make up the instrument, did not significantly predict effective coverage. Furthermore, the few diminished relationships observed in this study— a criteria to perform true bottleneck analyses, impedes proper planning and monitoring of the nation’s progress to Universal Effective Health Coverage. To ensure appropriateness of use, improved healthcare information systems for better data quality and well-structured proxy-indicators are required. Likewise, the government should provide the necessary resources to drive competency in data management and collection.
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  • In Copyright
Note
  • Track: Leadership
  • Paper type: Research or research design
Advisor
  • Ramaswamy, Rohit
Reviewer
  • Eboreime, Ejemai
Degree
  • Master of Public Health
Academic concentration
  • Public Health
Degree granting institution
  • University of North Carolina at Chapel Hill
Graduation year
  • 2018
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  • 02036113-ec38-496d-923f-2c0d106a6cbc

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