Understanding Factors Contributing to Vaccine Hesitancy in a Large Metropolitan Area

Abstract

Vaccine hesitancy has become a major global concern, leading to a significant decrease in the vaccination rate, with the World Health Organization recognizing it as one of the top ten threats to public health. Moreover, the health cost generated is evaluated to be 27 billion dollars per year in the US alone. To investigate the association between demographic variables and knowledge, attitudes, and behaviours related to vaccination, a survey-based cross-sectional study was conducted with 1163 individuals. Three models were used to perform a multiple linear regression analysis. In Model I, knowledge about vaccinations was found to be associated with smoking habits, education, and marital status. In Model II, attitudes towards vaccinations were significantly associated with sex, smoking habits, education, marital status, and knowledge. In Model III, behaviours related to vaccination were associated with sex, smoking habits, having children, knowledge, and attitudes. One potential solution to improve behaviours related to vaccinations in the general population is to implement specific public health programs, which can be a cost-effective intervention. This study provides valuable insights into the determinants of knowledge, attitudes, and behaviours related to vaccinations in the general population.

Public Health Implications for Effective Community Interventions Based on Hospital Patient Data Analysis Using Deep Learning Technology in Indonesia

“…Evaluating the effectiveness of public health interventions based on patient data available in hospitals [9][10][11]: In this article, the effectiveness of several public health interventions conducted based on hospital patient data was evaluated. The study also compares treatment outcomes between the intervention group and the control group for some critical diseases.…”

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“…RMSE is often used to evaluate how accurately forecasting results fit historical data values based on the relative range of a dataset. Equation (10) explains that X i and X ′ i present the actual hotspot dataset compared with the forecasted data at time t, X i is the mean actual value of the hotspot dataset, and N is the total number of data points. When the RMSE value changes from a small number to zero, it implies that the LSTM algorithm produces reliable results:…”

“…The algorithm terminates when the error reaches the present accuracy or the number of learning is greater than the pre-specified set maximum number of times as set out in Equation (10). Otherwise, we select the next learning sample as well as the corresponding expected output and return to enter the next round of learning.…”