Data and Methods for Monitoring and Decisions in Public Health

Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model’s performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes. Full article

The struggle for information and the hasty search for answers caused by the COVID-19 pandemic threatened the possibility of lowering study quality, as well as ethical committees’ review standards during the outbreak. Our investigation aimed to assess the impact of COVID-19 on the quality of clinical research studies submitted to Italian Ethics Committees in the period between April and July 2020. All 91 Italian ethics committees were contacted via email in order to collect anonymized information on the type and quality of COVID-19-related studies submitted to each committee during the study period. The present study summarizes the characteristics of the 184 study applications collected, pointing out, especially, how the quality of the study population and statistical analysis are crucial variables in determining the study approval. Nevertheless, despite the need for high-quality and open scientific information, especially exacerbated by this particular historical period, only a minority of the ethics committees (20.9%) agreed to share their data; such scarce participation, beyond biasing the representativeness of the results obtained by the present study, more importantly, hinders the broader goal of creating trust between researchers and the general public. Full article

Background: Various authors have validated scales to measure comorbidity. However, the prognosis capacity variation according to the comorbidity measurement index used needs to be determined in order to identify which is the best predictor. Aims: To quantify the differences between the Charlson (CCI), Elixhauser (ECI) and van Walraven (WCI) comorbidity indices as prognostic factors for in-hospital mortality and to identify the best comorbidity measure predictor. Methods: A retrospective observational study that included all hospitalizations of patients over 18 years of age, discharged between 2017 and 2021 in the hospital, using the Minimum Basic Data Set (MBDS). We calculated CCI, ECI, WCI according to ICD-10 coding algorithms. The correlation and concordance between the three indices were evaluated by Spearman’s rho and Intraclass Correlation Coefficient (ICC), respectively. The logistic regression model for each index was built for predicting in-hospital mortality. Finally, we used the receiver operating characteristic (ROC) curve for comparing the performance of each index in predicting in-hospital mortality, and the Delong method was employed to test the statistical significance of differences. Results: We studied 79,425 admission episodes. The 54.29% were men. The median age was 72 years (interquartile range [IQR]: 56–80) and in-hospital mortality rate was 4.47%. The median of ECI was = 2 (IQR: 1–4), ICW was 4 (IQR: 0–12) and ICC was 1 (IQR: 0–3). The correlation was moderate: ECI vs. WCI rho = 0.645, p < 0.001; ECI vs. CCI rho = 0.721, p < 0.001; and CCI vs. WCI rho = 0.704, p < 0.001; and the concordance was fair to good: ECI vs. WCI Intraclass Correlation Coefficient type A (ICC_A) = 0.675 (CI 95% 0.665–0.684) p < 0.001; ECI vs. CCI ICC_A = 0.797 (CI 95% 0.780–0.812), p < 0.001; and CCI vs. WCI ICC_A = 0.731 (CI 95% 0.667–0.779), p < 0.001. The multivariate regression analysis demonstrated that comorbidity increased the risk of in-hospital mortality, with differences depending on the comorbidity measurement scale: odds ratio [OR] = 2.10 (95% confidence interval [95% CI] 2.00–2.20) p > |z| < 0 using ECI; OR = 2.31 (CI 95% 2.21–2.41) p > |z| < 0 for WCI; and OR = 2.53 (CI 95% 2.40–2.67) p > |z| < 0 employing CCI. The area under the curve [AUC] = 0.714 (CI 95% 0.706–0.721) using as a predictor of in-hospital mortality CCI, AUC = 0.729 (CI 95% 0.721–0.737) for ECI and AUC = 0.750 (CI 95% 0.743–0.758) using WCI, with statistical significance (p < 0.001). Conclusion: Comorbidity plays an important role as a predictor of in-hospital mortality, with differences depending on the measurement scale used, the van Walraven comorbidity index being the best predictor of in-hospital mortality. Full article

COVID-19 causes acute respiratory illness in humans. The direct consequence of the spread of the virus is the need to find appropriate and effective solutions to reduce its spread. Similar to other countries, the pandemic has spread in Algeria, with noticeable variation in mortality and infection rates between regions. We aimed to estimate the proportion of people who died or became infected with SARS-CoV-2 in each provinces using a Bayesian approach. The estimation parameters were determined using a binomial distribution along with an a priori distribution, and the results had a high degree of accuracy. The Bayesian model was applied during the third wave (1 January–15 August 2021), in all Algerian’s provinces. For spatial analysis of duration, geographical maps were used. Our findings show that Tissemsilt, Ain Defla, Illizi, El Taref, and Ghardaia (Mean = 0.001) are the least affected provinces in terms of COVID-19 mortality. The results also indicate that Tizi Ouzou (Mean = 0.0694), Boumerdes (Mean = 0.0520), Annaba (Mean = 0.0483), Tipaza (Mean = 0.0524), and Tebessa (Mean = 0.0264) are more susceptible to infection, as they were ranked in terms of the level of corona infections among the 48 provinces of the country. Their susceptibility seems mainly due to the population density in these provinces. Additionally, it was observed that northeast Algeria, where the population is concentrated, has the highest infection rate. Factors affecting mortality due to COVID-19 do not necessarily depend on the spread of the pandemic. The proposed Bayesian model resulted in being useful for monitoring the pandemic to estimate and compare the risks between provinces. This statistical inference can provide a reasonable basis for describing future pandemics in other world geographical areas. Full article

Vaccination is one of the most effective medical measures for preventing infectious diseases. Even though there are recommendations for specific occupational groups that have an increased risk of infection, e.g., armed forces personnel, there are gaps in the vaccination rates of this personal. We conducted a randomised and controlled cohort study to examine whether a computerised reminder system would increase the vaccination rates of active soldiers over a period of twelve months. A total of 506 soldiers with a mean age of 27.7 ± 6.5 years (experimental group (EG)) and 27.9 ± 6.3 years (control group (CG)) were included in our study. Only 26.2% of the EG and 31.3% of the CG had received the required vaccinations at the beginning of our study. The vaccination rates for influenza (50.5% and 49.1%) and tick-borne encephalitis (57.1% and 60.7%) were particularly low, for measles, mumps, and rubella they were high (94.3% and 97.8%). A highly significant increase (p < 0.001) in vaccination rates was observed for both groups during our study. The results revealed considerable vaccination gaps among German armed forces soldiers. Despite a highly significant increase in vaccination rates during the study, there is still a clear need for action. Full article

Italy was one of the nations most affected by SARS-CoV-2. During the pandemic period, the national government approved some restrictions to reduce diffusion of the virus. We aimed to evaluate changes in in-hospital mortality and its possible relation with patient comorbidities and different restrictive public health measures adopted during the 2020 pandemic period. We analyzed the hospital discharge records of inpatients from public and private hospitals in Apulia (Southern Italy) from 1 January 2019 to 31 December 2020. The study period was divided into four phases according to administrative restriction. The possible association between in-hospital deaths, hospitalization period, and covariates such as age group, sex, Charlson comorbidity index (CCI) class, and length of hospitalization stay (LoS) class was evaluated using a multivariable logistic regression model. The risk of death was slightly higher in men than in women (OR 1.04, 95% CI: 1.01–1.07) and was lower for every age group below the >75 years age group. The risk of in-hospital death was lower for hospitalizations with a lower CCI score. In summary, our analysis shows a possible association between in-hospital mortality in non-COVID-19-related diseases and restrictive measures of public health. The risk of hospital death increased during the lockdown period. Full article

Previous studies indicated that optimal care for pneumonia during hospitalization might reduce the risk of in-hospital mortality and subsequent readmission. This study was a retrospective observational study using Japanese administrative claims data from April 2010 to March 2019. We analyzed data from 167,120 inpatients with pneumonia ≥15 years old in the benchmarking project managed by All Japan Hospital Association. Hospital-level risk-adjusted ratios of 30-day readmission for pneumonia were calculated using multivariable logistic regression analyses. The Spearman’s correlation coefficient was used to assess the correlation in each consecutive period. In the analysis using complete 9-year data including 54,756 inpatients, the hospital standardized readmission ratios (HSRRs) showed wide variation among hospitals and improvement trend (r = −0.18, p = 0.03). In the analyses of trends in each consecutive period, the HSRRS were positively correlated between ‘2010–2012’ and ‘2013–2015’ (r = 0.255, p = 0.010), and ‘2013–2015’ and ‘2016–2018’ (r = 0.603, p < 0.001). This study denoted the HSRRs for pneumonia could be calculated using Japanese administrative claims data. The HSRRs significantly varied among hospitals with comparable case-mix, and could relatively evaluate the quality of preventing readmission including long-term trends. The HSRRs can be used as yet another measure to help improve quality of care over time if other indicators are examined in parallel. Full article

A case-control study was conducted to investigate the effectiveness of the Edueat^® Method, through experiential workshops focused on the use of all 5 senses. In two different primary schools in the same city, questionnaires were administered in two months with a follow-up one year later. Participants: 119 children (age 8.2–9.0) chosen randomly; control group 66 (55.5%). Seven lessons of 2 h each were held in the schools by experts of the Edueat^® method and seven extra lessons by the teachers. The main outcome measures were the children’s changes in their approach and attitude towards their eating habits. The answers were grouped with factor analysis and summarized through scores. Repeated-measures analysis of variance was conducted in order to identify the relationships between scores and treatment over time. At the end of treatment, the intervention group showed a significant appreciation towards healthy foods (+4.15 vs. −0.05, p = 0.02) and a greater capacity in identifying foods which are very good for the health (+15.6 vs. +14.4, p = 0.02). In conclusion, the Edueat^® method was found to be particularly promising in transmitting knowledge of those foods which are healthy. Greater involvement of teachers and parents is crucial. Full article

In terms of the number of respondents, Survey on Well-Being among Foreign Born Population (FinMonik) is so far the most extensive survey carried out among the population with foreign background in Finland. It comprises a wide range of self-reported data, including information on the respondent’s health, well-being and access to care, which can be widely utilized in planning and assessing integration, health and welfare policies. A mixed-method approach (an electronic questionnaire, a paper questionnaire and phone interviews) was used in collecting the data which consists of responses by 6836 respondents aged 18–64 years. All response types included, the response rate was 53.1% (n = 6836). This study describes in detail the methods used in the FinMonik survey. In addition, we describe the demographics of the respondents partaking in each response format. The aim of the study is to promote the development of mixed-method survey as a way of collecting reliable data that can be used to enhance foreign-born people’s health, well-being and access to health care. The survey responses will be used as a baseline in observing the respondents’ well-being through the register-based data available in several national registers on health, medicine use and access to care as well as the data collected in the study Impact of Coronavirus Epidemic on Well-Being among Foreign Born Population Study (MigCOVID). Furthermore, the FinMonik study protocol will be repeated every four years. Full article

Urbanization and climate change have been rapidly occurring globally. Evidence-based healthy city development is required to improve living quality and mitigate the adverse impact of the outdoor neighborhood environment on public health. Taking Guangzhou as an example to explore the association of neighborhood environment and public health and preferably to offer some implications for better future city development, we measured ten environmental factors (temperature (T), wind-chill index (WCI), thermal stress index (HSI), relative humidity (RH), average wind speed (AWS), negative oxygen ions (NOI), PM2.5, luminous flux (LF), and illuminance (I)) in four seasons in four typical neighborhoods, and the SF-36 health scale was employed to assess the physical and mental health of neighborhood residents in nine subscales (health transition(HT), physiological functions (PF), general health status (GH), physical pain (BP), physiological functions (RP), energy vitality (VT), mental health (MH), social function (SF), and emotional functions (RE)). The linear mixed model was used in an analysis of variance. We ranked the different environmental factors in relation to aspects of health and weighted them accordingly. Generally, the thermal environment had the greatest impact on both physical and mental health and the atmospheric environment and wind environment had the least impact on physical health and mental health, respectively. In addition, the physical health of the resident was more greatly affected by the environment than mental health. According to the results, we make a number of strategic suggestions for the renewal of the outdoor neighborhood environment in subtropical monsoon climate high-density cities and provide a theoretical basis for improving public health through landscape architecture at the neighborhood scale. Full article