Immunity levels to poliovirus in Lao children and adults before the vaccine-derived poliovirus outbreak: A retrospective study

Factors associated with hepatitis B vaccination in Laos: findings from the multiple indicator cluster surveys in 2011/12 and 2017

Background

Within Laos, the vaccination coverage rates with the monovalent hepatitis B birth dose vaccine and hepatitis B antigen-containing combination vaccines remain stagnant with 75% and 64%, respectively, in 2021. In this study, we used data from the Multiple Indicator Cluster Surveys to identify possible factors that represent barriers for receiving these childhood vaccinations.

Methods

Data from the Multiple Indicator Cluster Surveys in 2011/12 and 2017 were analysed to examine factors associated with receiving the hepatitis B-containing vaccines using regression modelling. Data analyses were conducted in R.

Findings

In 2011/12, the weight-adjusted coverage rate for receiving the hepatitis B birth dose was 48%, while the coverage with the hepatitis B antigen-containing combination vaccine was 55.1% based on both vaccination documents and recall; compared to 69.3% and 59.4% respectively in 2017. Ethno-linguistic group, maternal education, healthcare utilization and wealth were associated with receiving the vaccinations against hepatitis B.

Interpretation

National estimates of vaccination coverage rates can conceal country-specific regional or socio-economic variations. Children from Hmong-Mien households, from less wealthier households and whose mothers were less educated and were not able to or did not utilize healthcare were identified as being less likely to receive the vaccinations. These findings indicate the need for improving access to healthcare, in particular for minority groups.

Funding

This work was supported by the Ministry of Foreign and European Affairs, Luxembourg and the Luxembourg Institute of Health (project "Luxembourg-Laos Partnership for Research and Capacity Building in Infectious Disease Surveillance").

Age-stratified seroprevalence of vaccine-preventable infectious disease in Saravan, Southern Lao People's Democratic Republic

BACKGROUND

Lao People's Democratic Republic has frequent outbreaks of vaccine-preventable diseases (VPD). This study aimed to determine susceptibility and exposure to VPD in Saravan, a rural province with high ethnic diversity and some of the poorest health indicators nationwide.

METHODS

Patients from three district hospitals and one provincial hospital were enrolled. Serum was tested by ELISA for IgG against hepatitis B virus (HBV), tetanus, diphtheria, measles, and rubella.

RESULTS

The study enrolled 2463 participants aged 5-90 years. Exposure to HBV was 33.2% and increased with age up to 62.4% of those aged >50 years. HBV surface antigen prevalence was 5.7% in males and 2.4% in females; 9.9% had serology compatible with vaccination. Seroprevalence of protective anti-tetanus antibodies was 46.3% overall. Protective anti-diphtheria seroprevalence was 40.5%. Anti-measles seroprevalence increased from 16.7% in those aged 5-10 years to 97.7% in those aged >50 years. Anti-rubella seroprevalence was 93.0% overall. There were differences in seroprevalences between sex, districts and ethnicity.

CONCLUSIONS

Routine infant vaccination needs strengthening in order to close the immunity gaps. High burden of HBV infection should be addressed by increasing birth dose vaccine coverage. Regional and ethnic differences need to be investigated to enable a targeted approach to vaccination.

Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach

Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method.