Evidence-based policy making for health promotion to reduce the burden of non-communicable diseases in Moldova

Global Health Security Index not a proven surrogate for health systems capacity to respond to pandemics: The case of COVID-19

INTRODUCTION

Global Health Security borders on prevention, detection and response to public health threats like the novel coronavirus disease 2019 (COVID-19). Global Health Security Index (GHSI) of 2019 and 2021 revealed the world remains ill-prepared to deal with future pandemics, evident in the historic impact of COVID-19 on countries. As at 7th December 2022, COVID-19 has infected over 600 million people and claimed over six million lives, mostly in countries with higher GHSI scores.

OBJECTIVE

Determine whether the GHSI scores of countries have a correlation with COVID-19 cases, deaths and vaccination coverage, while adjusting for country level dynamics.

METHODS

This paper utilizes GHSI database of 195 countries. Data consists of 171 questions grouped into 37 indicators across six overarching categories on health security and COVID-19. Multivariate multiple regression analysis with robust standard errors was conducted to test the hypothesis that high GHSI ratings do not guarantee better COVID-19 outcomes like cases, deaths and vaccination coverage. Also, avplots STATA command was used to check outliers with potential negative effect on outcome and predictor variables.

RESULTS

Global average GHSI score for all 195 countries was 38.9. United States of America recorded the highest GHSI score of 75.9 but also recorded one of the highest COVID-19 cases and deaths; Somalia recorded the worst GHSI score of 16.0 and one of the lowest COVID-19 cases and deaths. High GHSI scores did not associate positively with reduction in COVID-19 cases (Coef=157133.4, p-value=0.009, [95%CI 39728.64 274538.15]) and deaths (Coef=1405.804, p-value=0.047, [95%CI 18.1 2793.508]). However, high GHSI ratings associated with increases in persons fully vaccinated per 100 population (Coef=0.572, p-value=0.000, [95%CI.272.873]).

CONCLUSION

It appears the world might still not be adequately prepared for the next major pandemic, if the narrative remains unchanged. Countries that recorded higher GHSI scores, counter-intuitively, recorded higher COVID-19 cases and deaths. Countries need to invest more in interventions towards attaining Universal Health Coverage (UHC) including integrated health systems and formidable primary health care to enhance preparedness and response to pandemics.

Concentration optimization of combinatorial drugs using Markov chain-based models

BACKGROUND

Combinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs.

RESULTS

In this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments.

CONCLUSION

This article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy.