Effect of Health Literacy on Antiviral Treatment of Hepatitis B: Instrumental Variable Analysis
Bibliographic record
Abstract
Background: China is a country with a high burden of hepatitis B (Hep B) but a low treatment rate. One of the key reasons for the low treatment rate is the inadequate health literacy (HL) of the people, which may affect the awareness and knowledge of Hep B and its treatment, as well as the ability to actively and correctly seek medical resources. Objective: This study analyzed how HL contributed to the scale-up of antiviral treatment of Hep B in China. We expect that the findings of this study could be used to inform resource allocation for health education and other approaches intending to improve the HL of the Chinese population, thus facilitating the nationwide scale-up of Hep B treatment and contributing to the achievement of the 2030 goal of eliminating viral hepatitis as a major public health threat in China. Methods: We used the two-stage least squares regression method and adopted the mobile phone penetration rate as the instrumental variable to estimate the effect of improved HL on the number of 12-month standard Hep B antiviral treatments in China based on the panel data of 31 provinces from 2013 to 2020. Results: In the cross-sectional dimension, the higher the HL, the higher the number of treatments in the provinces in a specific year. In the time series dimension, the number of treatments in a specific province increased with the improvement of HL over time. After controlling the time-invariant inherent attributes of provinces, the instrumental variable estimation with two-stage least squares regression based on the province fixed effect model found that for every 1% increase of HL in each province, the number of treatments increased by 7.15% (0.0715 = e0.0691 - 1; P<.001). Such an increase turned to 5.19% (0.0519 = e0.0506 - 1; P<.001) for the analysis targeting the observation time from 2013 to 2019, as the data of 2020 were removed when the COVID-19 pandemic started. The study found no statistically significant effect of HL on the number of Hep B treatments in the provinces with higher newly reported Hep B incidence and lower gross domestic product per capita. Conclusions: Our findings suggest that improved HL of the population is an important favorable facilitator for the scale-up of Hep B treatment in China. Building awareness and knowledge of Hep B and its treatment can help individuals understand their health status, ensuring a healthier lifestyle and appropriate health care-seeking behaviors and health care service utilization, so that people can be diagnosed and treated timely and appropriately. Enhancing resource allocation to improve the overall HL of the population and sending Hep B-specific messages to the infected people would be a feasible and effective approach to scale-up the treatment of Hep B in low- and middle-income settings with limited resources, and contribute to achieving the 2030 global goal of eliminating viral hepatitis as a major public health threat.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".