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We estimated excess mortality in Medicare recipients in the United States with probable and confirmed Covid-19 infections in the general community and amongst residents of long-term care (LTC) facilities. We considered 28,389,098 Medicare and dual-eligible recipients from one year before February 29, 2020 through September 30, 2020, with mortality followed through November 30th, 2020. Probable and confirmed Covid-19 diagnoses, presumably mostly symptomatic, were determined from ICD-10 codes. We developed a Risk Stratification Index (RSI) mortality model which was applied prospectively to establish baseline mortality risk. Excess deaths attributable to Covid-19 were estimated by comparing actual-to-expected deaths based on historical (2017–2019) comparisons and in closely matched concurrent (2020) cohorts with and without Covid-19. Overall, 677,100 (2.4%) beneficiaries had confirmed Covid-19 and 2,917,604 (10.3%) had probable Covid-19. A total of 472,329 confirmed cases were community living and 204,771 were in LTC. Mortality following a probable or confirmed diagnosis in the community increased from an expected incidence of about 4.0% to actual incidence of 7.5%. In long-term care facilities, the corresponding increase was from 20.3% to 24.6%. The absolute increase was therefore similar at 3–4% in the community and in LTC residents. The percentage increase was far greater in the community (89.5%) than among patients in chronic care facilities (21.1%) who had higher baseline risk of mortality. The LTC population without probable or confirmed Covid-19 diagnoses experienced 38,932 excess deaths (34.8%) compared to historical estimates. Limitations in access to Covid-19 testing and disease under-reporting in LTC patients probably were important factors, although social isolation and disruption in usual care presumably also contributed. Remarkably, there were 31,360 (5.4%) fewer deaths than expected in community dwellers without probable or confirmed Covid-19 diagnoses. Disruptions to the healthcare system and avoided medical care were thus apparently offset by other factors, representing overall benefit. The Covid-19 pandemic had marked effects on mortality, but the effects were highly context-dependent.

Background In the 1930s, the Home Owners’ Loan Corporation categorized neighborhoods by investment grade along racially discriminatory lines, a process known as redlining. Although other authors have found associations between Home Owners’ Loan Corporation categories and current impacts on racial segregation, analysis of current health impacts rarely use these maps. Objective To study whether historical redlining in Baltimore is associated with health impacts today. Approach Fifty-four present-day planning board-defined community statistical areas are assigned historical Home Owners’ Loan Corporation categories by area predominance. Categories are red (“hazardous”), yellow (”definitely declining”) with blue/green (“still desirable”/”best”) as the reference category. Community statistical area life expectancy is regressed against Home Owners’ Loan Corporation category, controlling for median household income and proportion of African American residents. Conclusion Red categorization is associated with 4.01 year reduction (95% CI: 1.47, 6.55) and yellow categorization is associated with 5.36 year reduction (95% CI: 3.02, 7.69) in community statistical area life expectancy at baseline. When controlling for median household income and proportion of African American residents, red is associated with 5.23 year reduction (95% CI: 3.49, 6.98) and yellow with 4.93 year reduction (95% CI: 3.22, 6.23). Results add support that historical redlining is associated with health today.

Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning. However, very few studies rely solely on unstructured clinical notes from the EHR for mortality prediction. In this work, we propose a framework to predict short, mid, and long-term mortality in adult ICU patients using unstructured clinical notes from the MIMIC III database, natural language processing (NLP), and machine learning (ML) models. Depending on the statistical description of the patients’ length of stay, we define the short-term as 48-hour and 4-day period, the mid-term as 7-day and 10-day period, and the long-term as 15-day and 30-day period after admission. We found that by only using clinical notes within the 24 hours of admission, our framework can achieve a high area under the receiver operating characteristics (AU-ROC) score for short, mid and long-term mortality prediction tasks. The test AU-ROC scores are 0.87, 0.83, 0.83, 0.82, 0.82, and 0.82 for 48-hour, 4-day, 7-day, 10-day, 15-day, and 30-day period mortality prediction, respectively. We also provide a comparative study among three types of feature extraction techniques from NLP: frequency-based technique, fixed embedding-based technique, and dynamic embedding-based technique. Lastly, we provide an interpretation of the NLP-based predictive models using feature-importance scores.

The coronavirus disease 2019 (COVID-19) has had devastating consequences worldwide, including a spike in global mortality. Residents of long-term care homes have been disproportionately affected. We conducted a retrospective cohort study to determine the scale of pandemic-related deaths of long-term care residents in the province of Ontario, Canada, and to estimate excess mortality due to a positive COVID-19 test adjusted for demographics and regional variations. Crude mortality rates for 2019 and 2020 were compared, as were predictors of mortality among residents with positive and negative tests from March 2020 to December 2020. We found the crude mortality rates were higher from April 2020 to June 2020 and from November 2020 to December 2020, corresponding to Wave 1 and Wave 2 of the pandemic in Ontario. There were also substantial increases in mortality among residents with a positive COVID-19 test. The significant differences in excess mortality observed in relation to long-term care home ownership category and geographic region may indicate gaps in the healthcare system that warrant attention from policymakers. Further investigation is needed to identify the most relevant factors in explaining these differences.

Long lasting insecticidal nets (LLINs) are a proven tool to reduce malaria transmission, but in Africa efficacy is being reduced by pyrethroid resistance in the major vectors. A previous study that was conducted in Muleba district, Tanzania indicated possible involvement of cytochrome P450 monooxygenases in a pyrethroid resistance in An. gambiae population where pre-exposure to piperonyl butoxide (PBO) followed by permethrin exposure in CDC bottle bioassays led to partial restoration of susceptibility. PBO is a synergist that can block pyrethroid-metabolizing enzymes in a mosquito. Insecticide resistance profiles and underlying mechanisms were investigated in Anopheles gambiae and An. funestus from Muleba during a cluster randomized trial. Diagnostic dose bioassays using permethrin, together with intensity assays, suggest pyrethroid resistance that is both strong and very common, but not extreme. Transcriptomic analysis found multiple P450 genes over expressed including CYP6M2, CYP6Z3, CYP6P3, CYP6P4, CYP6AA1 and CYP9K1 in An. gambiae and CYP6N1, CYP6M7, CYP6M1 and CYP6Z1 in An. funestus. Indeed, very similar suites of P450 enzymes commonly associated with resistant populations elsewhere in Africa were detected as over expressed suggesting a convergence of mechanisms across Sub-Saharan African malaria vectors. The findings give insight into factors that may correlate with pyrethroid PBO LLIN success, broadly supporting model predictions, but revision to guidelines previously issued by the World Health Organization is warranted.

Background We retrospectively data-mined the case records of Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmed COVID-19 patients hospitalized to a tertiary care centre to derive mortality predictors and formulate a risk score, for prioritizing admission. Methods and findings Data on clinical manifestations, comorbidities, vital signs, and basic lab investigations collected as part of routine medical management at admission to a COVID-19 tertiary care centre in Chengalpattu, South India between May and November 2020 were retrospectively analysed to ascertain predictors of mortality in the univariate analysis using their relative difference in distribution among ‘survivors’ and ‘non-survivors’. The regression coefficients of those factors remaining significant in the multivariable logistic regression were utilised for risk score formulation and validated in 1000 bootstrap datasets. Among 746 COVID-19 patients hospitalised [487 “survivors” and 259 “non-survivors” (deaths)], there was a slight male predilection [62.5%, (466/746)], with a higher mortality rate observed among 40–70 years age group [59.1%, (441/746)] and highest among diabetic patients with elevated urea levels [65.4% (68/104)]. The adjusted odds ratios of factors [OR (95% CI)] significant in the multivariable logistic regression were SaO23; 3.01 (1.61–5.83), Age ≥50 years;2.52 (1.45–4.43), Pulse Rate ≥100/min: 2.02 (1.19–3.47) and coexisting Diabetes Mellitus; 1.73 (1.02–2.95) with hypertension and gender not retaining their significance. The individual risk scores for SaO2Urea ≥50 mg/dl-15, NLR >3–11, Age ≥50 years-9, Pulse Rate ≥100/min-7 and coexisting diabetes mellitus-6, acronymed collectively as ‘OUR-ARDs score’ showed that the sum of scores ≥ 25 predicted mortality with a sensitivity-90%, specificity-64% and AUC of 0.85. Conclusions The ‘OUR ARDs’ risk score, derived from easily assessable factors predicting mortality, offered a tangible solution for prioritizing admission to COVID-19 tertiary care centre, that enhanced patient care but without unduly straining the health system.

Background Racial inequities in Coronavirus 2019 (COVID-19) have been reported over the course of the pandemic, with Black, Hispanic/Latinx, and Native American individuals suffering higher case rates and more fatalities than their White counterparts. Methods We used a unique statewide dataset of confirmed COVID-19 cases across Missouri, linked with historical statewide hospital data. We examined differences by race and ethnicity in raw population-based case and mortality rates. We used patient-level regression analyses to calculate the odds of mortality based on race and ethnicity, controlling for comorbidities and other risk factors. Results As of September 10, 2020 there were 73,635 confirmed COVID-19 cases in the State of Missouri. Among the 64,526 case records (87.7% of all cases) that merged with prior demographic and health care utilization data, 12,946 (20.1%) were Non-Hispanic (NH) Black, 44,550 (69.0%) were NH White, 3,822 (5.9%) were NH Other/Unknown race, and 3,208 (5.0%) were Hispanic. Raw cumulative case rates for NH Black individuals were 1,713 per 100,000 population, compared with 2,095 for NH Other/Unknown, 903 for NH White, and 1,218 for Hispanic. Cumulative COVID-19-related death rates for NH Black individuals were 58.3 per 100,000 population, compared with 38.9 for NH Other/Unknown, 19.4 for NH White, and 14.8 for Hispanic. In a model that included insurance source, history of a social determinant billing code in the patient’s claims, census block travel change, population density, Area Deprivation Index, and clinical comorbidities, NH Black race (OR 1.75, 1.51–2.04, p Conclusions In Missouri, COVID-19 case rates and mortality rates were markedly higher among NH Black and NH Other/Unknown race than among NH White residents, even after accounting for social and clinical risk, population density, and travel patterns during COVID-19.

Extensive research shows that residential segregation has severe health consequences for racial and ethnic minorities. Most research to date has operationalized segregation in terms of either poverty or race/ethnicity rather than a synergy of these factors. A novel version of the Index of Concentration at the Extremes (ICERace-Income) specifically assesses racialized economic segregation in terms of spatial concentrations of racial and economic privilege (e.g., wealthy white people) versus disadvantage (e.g., poor Black people) within a given area. This multidimensional measure advances a more comprehensive understanding of residential segregation and its consequences for racial and ethnic minorities. The aim of this paper is to critically review the evidence on the association between ICERace-Income and health outcomes. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a rigorous search of academic databases for papers linking ICERace-Income with health. Twenty articles were included in the review. Studies focused on the association of ICERace-Income with adverse birth outcomes, cancer, premature and all-cause mortality, and communicable diseases. Most of the evidence indicates a strong association between ICERace-Income and each health outcome, underscoring income as a key mechanism by which segregation produces health inequality along racial and ethnic lines. Two of the reviewed studies examined racial disparities in comorbidities and health care access as potential explanatory factors underlying this relationship. We discuss our findings in the context of the extant literature on segregation and health and propose new directions for future research and applications of the ICERace-Income measure.

Evolutionary computing (EC) is an area of computer sciences and applied mathematics covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively study all the variety of methods which were originally based on the principles of selectionism. As a result, many new algorithms and approaches, significantly more efficient than classical selectionist schemes, were found. This is especially true for some families of special problems. There are strong arguments to believe that EC approaches are quite suitable for modeling and numerical analysis of those methods of synthetic biology and biotechnology that are known as in vitro evolution. Therefore, it is natural to expect that the new algorithms and approaches developed in EC can be effectively applied in experiments on the directed evolution of biological macromolecules. According to the John Holland’s Schema theorem, the effective evolutionary search in genetic algorithms (GA) is provided by identifying short schemata of high fitness which in the further search recombine into the larger building blocks (BBs) with higher and higher fitness. The multimodularity of functional biological macromolecules and the preservation of already found modules in the evolutionary search have a clear analogy with the BBs in EC. It seems reasonable to try to transfer and introduce the methods of EC, preserving BBs and essentially accelerating the search, into experiments on in vitro evolution. We extend the key instrument of the Holland’s theory, the Royal Roads fitness function, to problems of the in vitro evolution (Biological Royal Staircase, BioRS, functions). The specific version of BioRS developed in this publication arises from the realities of experimental evolutionary search for (DNA-) RNA-devices (aptazymes). Our numerical tests showed that for problems with the BioRS functions, simple heuristic algorithms, which turned out to be very effective for preserving BBs in GA, can be very effective in in vitro evolution approaches. We are convinced that such algorithms can be implemented in modern methods of in vitro evolution to achieve significant savings in time and resources and a significant increase in the efficiency of evolutionary search.

Background The Covid-19 pandemic in the United Kingdom has seen two waves; the first starting in March 2020 and the second in late October 2020. It is not known whether outcomes for those admitted with severe Covid were different in the first and second waves. Methods The study population comprised all patients admitted to a 1,500-bed London Hospital Trust between March 2020 and March 2021, who tested positive for Covid-19 by PCR within 3-days of admissions. Primary outcome was death within 28-days of admission. Socio-demographics (age, sex, ethnicity), hypertension, diabetes, obesity, baseline physiological observations, CRP, neutrophil, chest x-ray abnormality, remdesivir and dexamethasone were incorporated as co-variates. Proportional subhazards models compared mortality risk between wave 1 and wave 2. Cox-proportional hazard model with propensity score adjustment were used to compare mortality in patients prescribed remdesivir and dexamethasone. Results There were 3,949 COVID-19 admissions, 3,195 hospital discharges and 733 deaths. There were notable differences in age, ethnicity, comorbidities, and admission disease severity between wave 1 and wave 2. Twenty-eight-day mortality was higher during wave 1 (26.1% versus 13.1%). Mortality risk adjusted for co-variates was significantly lower in wave 2 compared to wave 1 [adjSHR 0.49 (0.37, 0.65) p Conclusion There has been substantial improvements in COVID-19 mortality in the second wave, even accounting for demographics, comorbidity, and disease severity. Neither dexamethasone nor remdesivir appeared to be key explanatory factors, although there may be unmeasured confounding present.

Background In resource-limited settings, underlying causes of death (UCOD) often are not ascertained systematically, leading to unreliable mortality statistics. We reviewed medical charts to establish UCOD for decedents at two high volume mortuaries in Kisumu County, Kenya, and compared ascertained UCOD to those notified to the civil registry. Methods Medical experts trained in COD certification examined medical charts and ascertained causes of death for 456 decedents admitted to the mortuaries from April 16 through July 12, 2019. Decedents with unknown HIV status or who had tested HIV-negative >90 days before the date of death were tested for HIV. We calculated annualized all-cause and cause-specific mortality rates grouped according to global burden of disease (GBD) categories and separately for deaths due to HIV/AIDS and expressed estimated deaths per 100,000 population. We compared notified to ascertained UCOD using Cohen’s Kappa (κ) and assessed for the independence of proportions using Pearson’s chi-squared test. Findings The four leading UCOD were HIV/AIDS (102/442 [23.1%]), hypertensive disease (41/442 [9.3%]), other cardiovascular diseases (23/442 [5.2%]), and cancer (20/442 [4.5%]). The all-cause mortality rate was 1,086/100,000 population. The highest cause-specific mortality was in GBD category II (noncommunicable diseases; 516/100,000), followed by GBD I (communicable, perinatal, maternal, and nutritional; 513/100,000), and III (injuries; 56/100,000). The HIV/AIDS mortality rate was 251/100,000 population. The proportion of deaths due to GBD II causes was higher among females (51.9%) than male decedents (42.1%; p = 0.039). Conversely, more men/boys (8.6%) than women/girls (2.1%) died of GBD III causes (p = 0.002). Most of the records with available recorded and ascertained UCOD (n = 236), 167 (70.8%) had incorrectly recorded UCOD, and agreement between notified and ascertained UCOD was poor (29.2%; κ = 0.26). Conclusions Mortality from infectious diseases, especially HIV/AIDS, is high in Kisumu County, but there is a shift toward higher mortality from noncommunicable diseases, possibly reflecting an epidemiologic transition and improving HIV outcomes. The epidemiologic transition suggests the need for increased focus on controlling noncommunicable conditions despite the high communicable disease burden. The weak agreement between notified and ascertained UCOD could lead to substantial inaccuracies in mortality statistics, which wholly depend on death notifications.

Objectives Among older adults in China and the US, we aimed to compare the biomarkers of chronic-kidney-diseases (CKD), factors associated with CKD, and the correlation between CKD and mortality. Setting China and the US. Study design Cross-sectional and prospective cohorts. Participants We included 2019 participants aged 65 and above from the Chinese Longitudinal Healthy Longevity Study (CLHLS) in 2012, and 2177 from US National Health and Nutrition Examination Survey (NHANES) in 2011–2014. Outcomes Urinary albumin, urinary creatinine, albumin creatinine ratio (ACR), serum creatinine, blood urea nitrogen, plasma albumin, uric acid, and estimated glomerular filtration rate (eGFR). CKD (ACR ≥ 30 mg/g or eGFR< 60 ml/min/1.73m2) and mortality. Analytical approach Logistic regression and Cox proportional hazard models. Covariates included age, sex, race, education, income, marital status, health condition, smoking and drinking status, physical activity and body mass index. Results Chinese participants had lower levels of urinary albumin, ACR, and uric acid than the US (mean: 25.0 vs 76.4 mg/L, 41.7 vs 85.0 mg/g, 292.9 vs 341.3 μmol/L). In the fully-adjusted model, CKD was associated with the risk of mortality only in the US group (hazard ratio [HR], 95% CI: 2.179, 1.561–3.041 in NHANES, 1.091, 0.940–1.266 in CLHLS). Compared to eGFR≥90, eGFR ranged 30–44 ml/min/1.73m2 was only associated with mortality in the US population (HR, 95% CI: 2.249, 1.141–4.430), but not in the Chinese population (HR, 95% CI: 1.408, 0.884–2.241). Conclusions The elderly participants in the US sample had worse CKD-related biomarker levels than in China sample, and the association between CKD and mortality was also stronger among the US older adults. This may be due to the biological differences, or co-morbid conditions.

In the paper the costs of Polish county hospitals in 2015–2018 are studied using behavioral cost function. The set of variables combines hospitals’ characteristics which may determine their level of costs, such as the form of ownership, bed turnover rate, number of patient-days and share of beds in emergency department with environment characteristics which may influence both outsourcing costs and patients’ health. In 2017 the system of basic hospital service provision (hospital network) was introduced in Poland. Dummy variables included in the model represent the category of hospital in the system. The results show that the costs may be described using fixed effect panel model. Positive impact of percentage of emergency department patients transferred to other departments and of wages is found. Higher ratio of residents and interns to doctors is found to decrease costs. Dummy variable for the period after the introduction of hospital network assumed a negative sign with costs, but the parameter remained insignificant.

We designed and performed a participatory sensing initiative to explore the reliability and effectiveness of a distributed network of citizen-operated smartphones in evaluating the impact of environmental noise in residential areas. We asked participants to evaluate the comfort of their home environment in different situations and at different times, to select the most and least comfortable states and to measure noise levels with their smartphones. We then correlated comfort ratings with noise measurements and additional contextual information provided by participants. We discuss how to strengthen methods and procedures, particularly regarding the calibration of the devices, in order to make similar citizen-science efforts effective at monitoring environmental noise and planning long-term solutions to human well-being.

The pharmacological management of COVID-19 has evolved significantly and various immunomodulatory agents have been repurposed. However, the clinical efficacy has been variable and a search for cure for COVID-19 continues. A retrospective cohort study was conducted on 916 patients hospitalized with polymerase chain reaction (PCR)-confirmed COVID-19 between February 2020 and October 2020 at a tertiary care academic medical center in Karachi, Pakistan. The median age was 57 years (interquartile range (IQR) 46–66 years). The most common medications administered were Methylprednisolone (65.83%), Azithromycin (50.66%), and Dexamethasone (46.6%). Majority of the patients (70%) had at least two or more medications used in combination and the most frequent combination was methylprednisolone with azithromycin. Overall in-hospital mortality was 13.65% of patients. Mortality was found to be independently associated with age greater than or equal to 60 years (OR = 4.98; 95%CI: 2.78–8.91), critical illness on admission (OR = 13.75; 95%CI: 7.27–25.99), use of hydrocortisone (OR = 12.56; 95%CI: 6.93–22.7), Ferritin> = 1500(OR = 2.07; 95%CI: 1.18–3.62), Creatinine(OR = 2.33; 95%CI: 1.31–4.14) and D-Dimer> = 1.5 (OR = 2.27; 95%CI: 1.26–4.07). None of the medications whether used as monotherapy or in combination were found to have a mortality benefit. Our study highlights the desperate need for an effective drug for the management of critical COVID-19 which necessitates usage of multiple drug combinations in patients particularly Azithromycin which has long term implications for antibiotic resistance particularly in low-middle income countries.

Background The unprecedented coronavirus disease 2019 (COVID-19) pandemic has caused millions of infections worldwide and represents a significant challenge facing modern health care systems. This study was conducted to investigate the impact of lockdown measures in a tertiary Children’s Hospital in southwest China, which might be used to predict long-term effects related to health-seeking behavior of parents/caregivers. Methods This study included newborns enrolled over a span of 86 weeks between January 4, 2019, and August 27, 2020. We designated two time periods for analysis purposes: a stable pre-COVID period(55 weeks between January 4, 2019, and January 23, 2020) and a COVID-impacted period (31 weeks between January 24, 2020, and August 27, 2020). An interrupted time-series analysis was employed to compare changes and trends in hospital admissions and disease spectra before and after the period of nonpharmaceutical interventions (NPIs). Furthermore, this study was conducted to evaluate whether the health-seeking behavior of parents/caregivers was influenced by pandemic factors. Results Overall, 16,640 infants were admitted to the neonatology department during the pre-COVID period (n = 12,082) and the COVID-impacted period (n = 4,558). The per week neonatal admissions consistently decreased following the first days of NPIs (January 24, 2020). The average weekly admission rates of 220/week pre-COVID period and 147/week COVID-impacted period. There was an evident decrease in the volume of admissions for all disease spectra after the intervention, whereas the decrease of patients complaining about pathological jaundice-related conditions was statistically significant (p Conclusions In summary, there was a significant decline in neonatal admissions in a tertiary care hospital during the COVID-19 Pandemic and the associated NPIs. Additionally, this situation had a remarkable impact on disease spectra and health-seeking behavior of parents/caregivers. We, therefore, advise continuing follow-ups and monitoring the main health indicators in vulnerable populations affected by this Pandemic over time.

Background Prior studies have established those elderly patients with chronic obstructive pulmonary disease (COPD) are at elevated risk for developing influenza-associated complications such as hospitalization, intensive-care admission, and death. This study sought to determine whether influenza vaccination could improve survival among elderly patients with COPD. Materials/Methods This study included Veterans (age ≥ 65 years) diagnosed with COPD that received care at the United States Veterans Health Administration (VHA) during four influenza seasons, from 2012–2013 to 2015–2016. We linked VHA electronic medical records and Medicare administrative files to Centers for Disease Control and Prevention National Death Index cause of death records as well as influenza surveillance data. A multivariable time-dependent Cox proportional hazards model was used to compare rates of mortality of recipients of influenza vaccination to those who did not have records of influenza vaccination. We estimated hazard ratios (HRs) adjusted for age, gender, race, socioeconomic status, comorbidities, and healthcare utilization. Results Over a span of four influenza seasons, we included 1,856,970 person-seasons of observation where 1,199,275 (65%) had a record of influenza vaccination and 657,695 (35%) did not have a record of influenza vaccination. After adjusting for comorbidities, demographic and socioeconomic characteristics, influenza vaccination was associated with reduced risk of death during the most severe periods of influenza seasons: 75% all-cause (HR = 0.25; 95% CI: 0.24–0.26), 76% respiratory causes (HR = 0.24; 95% CI: 0.21–0.26), and 82% pneumonia/influenza cause (HR = 0.18; 95% CI: 0.13–0.26). A significant part of the effect could be attributed to “healthy vaccinee” bias as reduced risk of mortality was also found during the periods when there was no influenza activity and before patients received vaccination: 30% all-cause (HR = 0.70; 95% CI: 0.65–0.75), 32% respiratory causes (HR = 0.68; 95% CI: 0.60–0.78), and 51% pneumonia/influenza cause (HR = 0.49; 95% CI: 0.31–0.78). However, as a falsification study, we found that influenza vaccination had no impact on hospitalization due to urinary tract infection (HR = 0.97; 95% CI: 0.80–1.18). Conclusions Among elderly patients with COPD, influenza vaccination was associated with reduced risk for all-cause and cause-specific mortality.

Recent work has unearthed many empirical regularities in mortality trends, including the inverse correlation between life expectancy and life span inequality, and the compression of mortality into older age ranges. These regularities have furnished important insights into the dynamics of mortality by describing, in demographic terms, how different attributes of the life table deaths distribution interrelate and change over time. However, though empirical evidence suggests that the demographically-meaningful metrics these regularities involve (e.g., life span disparity and life table entropy) are correlated to the moments of the deaths distribution (e.g., variance), the broader theoretical connections between life span inequality and the moments of the deaths distribution have yet to be elucidated. In this article we establish such connections and leverage them to furnish new insights into mortality dynamics. We prove theoretical results linking life span disparity and life table entropy to the central moments of the deaths distribution, and use these results to empirically link statistical measures of variation of the deaths distribution (e.g., variance, index of dispersion) to life span disparity and life table entropy. We validate these results via empirical analyses using data from the Human Mortality Database and extract from them several new insights into mortality shifting and compression in human populations.

Background Adolescent fertility in Kenya is vital in the development and execution of reproductive health policies and programs. One of the specific objectives of the Kenyan Adolescent Sexual Reproductive Health (ASRH) policy developed in 2015 is to decrease early and unintended pregnancies in an attempt to reduce adolescent fertility. We aimed to establish determinants of adolescent fertility in Kenya. Methods The Kenya Demographic and Health Survey (KDHS) 2014 data set was utilized. Adolescent’s number of children ever born was the dependent variable. The Chi-square test was utilized to determine the relationship between dependent and independent variables. A Proportional-odds model was performed to establish determinants of adolescent fertility at a 5% significance level. Results Over 40% of the adolescent girls who had sex below 17 years had given birth i.e, current age 15–17 years (40.9%) and Conclusion The study established that age at first sex, current age, marital status, and contraceptive use are the main determinants of adolescent childbearing. The stated determinants should be targeted by the government to control the adolescent birth rate in Kenya. Consequently, delaying the age at first sex, discouraging adolescent marriage, and increasing secondary school enrollment among adolescent girls are recommended strategies to control adolescent fertility in Kenya.

Objectives To quantify changes in adherence to mask and distancing guidelines in outdoor settings in Philadelphia, PA before and after President Trump announced he was infected with COVID-19. Methods We used Systematic Observation of Masking Adherence and Distancing (SOMAD) to assess mask adherence in parks, playgrounds, and commercial streets in the 10 City Council districts in Philadelphia PA. We compared adherence rates between August and September 2020 and after October 2, 2020. Results Disparities in mask adherence existed by age group, gender, and race/ethnicity, with females wearing masks correctly more often than males, seniors having higher mask use than other age groups, and Asians having higher adherence than other race/ethnicities. Correct mask use did not increase after the City released additional mask guidance in September but did after Oct 2. Incorrect mask use also decreased, but the percentage not having masks at all was unchanged. Conclusions Vulnerability of leadership appears to influence population behavior. Public health departments likely need more resources to effectively and persuasively communicate critical safety messages related to COVID-19 transmission.

Biocapacity of a region exhibits spatial differences owing to the limitations of regional scale and natural conditions. Based on the multi-scale perspective, we comprehensively studied and analyzed the temporal and spatial differences of the biocapacity of a region in an attempt to establish the groundwork for optimizing urban development and its utilization framework. By adopting the ecological footprint model along with multi-scale difference evaluation method, the municipal and county scales are incorporated into a unified analysis framework in this paper, thereby facilitating the exploration of the temporal and spatial differences in the biocapacity of Shenyang—a city in China—from 2005 to 2019. The results demonstrated that: 1) At the municipal scale, the biocapacity per capita fluctuated between 1.35 hm2/person and 2.22 hm2/person. It revealed an “up-down-up” trend, which appeared consistent with the Kuznets cycle; at the county scale, the biocapacity depicted spatial differences, while those of downtown and surrounding districts/counties developed a two-level ascending hierarchical structure. 2) The time series of footprint size and depth first ascended and then declined, and can be classified into four types: closed type, inverted U-type, S-type, and M-type. Among them, S-type and M-type have the phenomenon of over-utilizing the stock capital. 3) For a long time, the regional difference of biocapacity has mostly dwelt on two scales with an evident scale effect, and the biocapacity of Liaozhong District was the worst.

Background Data on outcomes of children with severe acute malnutrition (SAM) following treatment are scarce with none described from any upper-middle-income country. This study established mortality, clinical outcomes and anthropometric recovery of children with SAM six months following hospital discharge. Methods A prospective cohort study was conducted in children aged 3–59 months enrolled on discharge from two hospitals in the Tshwane district of South Africa between April 2019 and January 2020. The primary outcome was mortality at six months. Secondary outcomes included relapse rates, type(s) and frequency of morbidities experienced and the anthropometric changes in children with SAM following hospital discharge. Standard programmatic support included nutritional supplements. Results Forty-three children were enrolled with 86% of participants followed up to six months. Only a third of the participants had normal anthropometry at hospital discharge–a quarter still had ongoing SAM. There were no deaths, although four children (9%) were re-hospitalised including two for complicated SAM. Mean weight-for-length z-scores (WLZ) and wasting rates improved at one month but deteriorated by three months. At three months, six children (14%) either had ongoing or relapsed SAM–a SAM incidence rate of 20 per 1000 person-months despite more than half of the participants still receiving nutritional supplements at the time. Risk factors associated with persistent malnutrition at three months included a low WLZ on admission (relative risk [RR] 3.3, 95% confidence interval [95%CI] 1.2–9.2), being discharged from hospital before meeting WHO SAM treatment discharge criteria (RR 5.3, 95%CI 1.3–14.8) or having any illness by three months (RR 8.6, 95%CI 1.3–55.7). Conclusion Post-discharge mortality and morbidity was lower than in other less resourced settings. However, anthropometric recovery was poorer than expected. Modifying discharge criteria, optimising the use of available nutritional supplements and better integration with community-based health and social services may improve outcomes for children with SAM post-hospitalisation.

We aimed to assess the epidemiological, clinical, and laboratory characteristics associated with mortality among hospitalized Egyptian patients with COVID-19. A multicenter, retrospective study was conducted on all polymerase chain reaction (PCR)-confirmed COVID-19 cases admitted through the period from April to July 2020. A generalized linear model was reconstructed with covariates based on predictor’s statistical significance and clinically relevance. The odds ratio (OR) was calculated by using stepwise logistic regression modeling. A total of 3712 hospitalized patients were included; of them, 900 deaths were recorded (24.2%). Compared to survived patients, non-survived patients were more likely to be older than 60 years (65.7%), males (53.6%) diabetic (37.6%), hypertensive (37.2%), and had chronic renal insufficiency (9%). Non-survived patients were less likely to receive azithromycin (p 60 years, comorbidities, tachycardia, hypoxemia, and altered consciousness level are independent predictors of mortality among Egyptian hospitalized patients with COVID-19. On the other hand, the use of anticoagulants and azithromycin is associated with reduced mortality.

Objective Bangladesh achieved the fourth Millennium Development Goal well ahead of schedule, with a significant reduction in under-5 mortality between 1990 and 2015. However, the reduction in neonatal mortality has been stagnant in recent years. The purpose of this study is to explore the association between place of delivery and newborn care with early neonatal mortality (ENNM), which represents more than 80% of total neonatal mortality in Bangladesh. Methods In this study, 2014 Bangladesh Demographic and Health Survey data were used to assess early neonatal survival in children born in the three years preceding the survey. The roles of place of the delivery and newborn care in ENNM were examined using multivariable logistic regression models adjusted for clustering and relevant socio-economic, pregnancy, and newborn characteristics. Results Between 2012 and 2014, there were 4,624 deliveries in 17,863 sampled households, 39% of which were delivered at health facilities. The estimated early neonatal mortality rate during this period was 15 deaths per 1,000 live births. We found that newborns who had received at least 3 components of essential newborn care (ENC) were 56% less likely to die during the first seven days of their lives compared to their counterparts who received 0–2 components of ENC (aOR: 0.44; 95% CI: 0.24–0.81). In addition, newborns who had received any postnatal care (PNC) were 68% less likely to die in the early neonatal period than those who had not received any PNC (aOR: 0.32; 95% CI: 0.16–0.64). Facility delivery was not significantly associated with the risk of early newborn death in any of the models. Conclusion Our study findings highlight the importance of newborn and postnatal care in preventing early neonatal deaths. Further, findings suggest that increasing the proportion of women who give birth in a healthcare facility is not sufficient to reduce ENNM by itself; to realize the theoretical potential of facility delivery to avert neonatal deaths, we must also ensure quality of care during delivery, guarantee all components of ENC, and provide high-quality early PNC. Therefore, sustained efforts to expand access to high-quality ENC and PNC are needed in health facilities, particularly in facilities serving low-income populations.

Background Heart failure (HF) and chronic kidney disease (CKD) frequently coexist, and the combination is linked to poor outcomes, but limited data exist to guide optimal management. We evaluated the outcome of dialysis therapy in older patients with HF and advanced CKD. Methods We examined adults aged ≥70 years with HF and eGFR ≤20 ml/min/1.73 m2 between 2008–2012 and no prior renal replacement therapy, cancer, cirrhosis or organ transplant. We identified patients who initiated chronic dialysis through 2013 and matched patients who did not initiate dialysis on age, gender, diabetes status, being alive on dialysis initiation date, and a high-dimensional propensity score for starting dialysis. Deaths were identified through 2013. We used Cox regression to evaluate the association of chronic dialysis and all-cause death. Results Among 348 adults with HF and advanced CKD who initiated dialysis and 947 matched patients who did not start dialysis, mean age was 80±5 years, 51% were women and 33% were Black. The crude rate of death was high overall but lower in those initiating vs. not initiating chronic dialysis (26.1 vs. 32.1 per 100 person-years, respectively, P = 0.02). In multivariable analysis, dialysis was associated with a 33% (95% Confidence Interval:17–46%) lower adjusted rate of death compared with not initiating dialysis. Conclusions Among older adults with HF and advanced CKD, dialysis initiation was associated with lower mortality, but absolute rates of death were very high in both groups. Randomized trials should evaluate net outcomes of dialysis vs. conservative management on length and quality of life in this high-risk population.

Background Most studies on rapid response system (RRS) have simply focused on its role and effectiveness in reducing in-hospital cardiac arrests (IHCAs) or hospital mortality, regardless of the predictability of IHCA. This study aimed to identify the characteristics of IHCAs including predictability of the IHCAs as our RRS matures for 10 years, to determine the best measure for RRS evaluation. Methods Data on all consecutive adult patients who experienced IHCA and received cardiopulmonary resuscitation in general wards between January 2010 and December 2019 were reviewed. IHCAs were classified into three groups: preventable IHCA (P-IHCA), non-preventable IHCA (NP-IHCA), and inevitable IHCA (I-IHCA). The annual changes of three groups of IHCAs were analyzed with Poisson regression models. Results Of a total of 800 IHCA patients, 149 (18.6%) had P-IHCA, 465 (58.1%) had NP-IHCA, and 186 (23.2%) had I-IHCA. The number of the RRS activations increased significantly from 1,164 in 2010 to 1,560 in 2019 (P = 0.009), and in-hospital mortality rate was significantly decreased from 9.20/1,000 patients in 2010 to 7.23/1000 patients in 2019 (P = 0.009). The trend for the overall IHCA rate was stable, from 0.77/1,000 patients in 2010 to 1.06/1,000 patients in 2019 (P = 0.929). However, while the incidence of NP-IHCA (P = 0.927) and I-IHCA (P = 0.421) was relatively unchanged over time, the incidence of P-IHCA decreased from 0.19/1,000 patients in 2010 to 0.12/1,000 patients in 2019 (P = 0.025). Conclusions The incidence of P-IHCA could be a quality metric to measure the clinical outcomes of RRS implementation and maturation than overall IHCAs.

Background/Aims Organ failure in patients with acute decompensation (AD) is a defining characteristic of acute-on-chronic liver failure (ACLF). However, the clinical features of AD during the long-term clinical course of hepatocellular carcinoma (HCC) are still poorly understood. This study aimed to clarify features and impact of AD/ACLF on the prognosis of patients after treatment for HCC. Methods This retrospective study enrolled 556 consecutive patients who were initially diagnosed with HCC, and analyses were conducted taking into account HCC treatment type, HCC stage, and presence or absence of cirrhosis. Results During follow-up, 299 patients with AD were hospitalized. AD occurrence is closely related to prognosis, regardless of the presence or absence of cirrhosis and HCC stage, and early-onset AD (within 90 days after HCC treatment) has negative impact on prognosis. In the intermediate-advanced–stage group, surgical resection had a positive impact on AD incidence post-treatment. After systemic therapy for HCC, renal impairment was the predictive factors for AD development. The 28/90-day mortality rate was higher among 41 cases (13.7%) with AD who exhibited ACLF as compared with cases without ACLF. AD without cirrhosis had similar ACLF incidence and short-term mortality, compared to AD with cirrhosis. The prognostic model using a decision-tree–based approach, which includes ACLF, bilirubin level, HCC progression, and MELD score is useful for predicting 90- or 28-day mortality after AD diagnosis. Conclusions Careful management of patients with HCC who are hospitalized with AD is necessary, considering ACLF, HCC progression, and liver function.

Introduction Sepsis is a public health problem due to its high prevalence and mortality. Mean platelet volume (MPV), a biomarker reported in routine blood counts, has been investigated and shows promise for determining fatal outcomes in septic patients. Objective Evaluate whether the mean platelet volume (MPV) and mean platelet volume-to-platelet count (MPV/P) ratio are predictors of clinical severity and mortality in patients with sepsis. Methods A prospective population cohort of 163 patients aged 18–97 years was recruited at the Intensive Care Unit of Pablo Arturo Hospital, Quito, Ecuador from 2017–2019 and followed up for 28 days. Patients were diagnosed with sepsis based on SEPSIS-3 septic shock criteria; in which the MPV and the MPV/P ratio were measured on days 1, 2, and 3. Sequential organ failure assessment (SOFA) score and presence of septic shock assessed clinical severity. Mortality on day 28 was considered the fatal outcome. Results The average age of the patients was 61,15 years (SD 20,94) and female sex was predominant. MPV cutoff points at days 1, 2 and 3 were >9,45fL, >8,95fL and >8, 85fL; and (MPV/P) ratio >8, 18, >4, 12 y >3, 95, respectively. MPV at days 2 (9,85fL) and 3 (8,55fL) and (MPV/P) ratio at days 1 (4,42), 2 (4,21), and 3 (8,55), were predictors of clinical severity assessed by septic shock, which reached significance in the ROC curves. MPV and (MPV/P) ratio were also predictors of clinical severity determined by SOFA at days 1, 2, and 3, where higher values were observed in non-survivors reaching significance in all categories. MPV and MPV/P ratio at days 1, 2 and 3 were independent predictor factors of mortality using Cox proportional hazards model (HR 2,31; 95% CI 1,36–3,94), (HR 2,11; 95% CI 1,17–3,82), (HR 2,13; 95% CI 1,07–4,21) and (HR 2,38; 95% CI 1,38–4,12), (HR 2,15; 95% CI 1,14–4,06), (HR 4,43; 95% CI, 1,72–11,37) respectively. Conclusions MPV and the MPV/P ratio are predictors of clinical severity and mortality in sepsis. The MPV and its coefficient are indicators of the biological behavior of platelets in sepsis. They should be considered as a cost-effective and rapidly available tool that guides the treatment.

Background Better understanding of the determinants of national life expectancy is crucial for economic development, as a healthy nation is a prerequisite for a wealthy nation. Many socioeconomic, nutritional, lifestyle, genetic and environmental factors can influence a nation’s health and longevity. Environmental degradation is one of the critical determinants of life expectancy, which is still under-researched, as the literature suggests. Objectives This study aims to investigate the determinants of life expectancy in 31 world’s most polluted countries with particular attention on environmental degradation using the World Bank annual data and British Petroleum data over the period of 18 years (2000–2017). Methods The empirical investigation is based on the model of Preston Curve, where panel corrected standard errors (PCSE) and feasible general least square (FGLS) estimates are employed to explore the long-run effects. Pairwise Granger causality test is also used to have short-run causality among the variables of interest, taking into account the cross-sectional dependence test and other essential diagnostic tests. Results The results confirm the existence of the Preston Curve, implying the positive effect of economic growth on life expectancy. Environmental degradation is found as a threat while health expenditure, clean water and improved sanitation affect the life expectancy positively in the sample countries. The causality test results reveal one-way causality from carbon emissions to life expectancy and bidirectional causalities between drinking water and life expectancy and sanitation and life expectancy. Conclusion Our results reveal that environmental degradation is a threat to having improved life expectancy in our sample countries. Based on the results of this study, we recommend that: (1) policy marker of these countries should adopt policies that will reduce carbon emissions and thus will improve public health and productivity; (2) environment-friendly technologies and resources, such as renewable energy, should be used in the production process; (3) healthcare expenditure on a national budget should be increased; and (4) clean drinking water and basic sanitation facilities must be ensured for all people.

Background The large amount of glucose in the dialysate used in peritoneal dialysis (PD) likely affects the glycemic control. The aim of this study was to investigate the association between HbA1c variability, as a measure of long-term glycemic variability, and the risk of all-cause mortality in diabetic patients with PD. Methods 325 patients with diabetes and ESRD were followed (2008–2018) in the Swedish Renal Registry. Patients were separated in seven groups according to level of HbA1c variability. The group with the lowest variability was denoted the reference. The ratio of the standard deviation (SD) to the mean of HbA1c, HbA1c (SD)/HbA1c (mean), i.e. the coefficient of variation (CV), was defined as HbA1c variability. Hazard ratios (HR) and 95% confidence intervals (CI) were examined using Cox regression analyses. Results During follow-up, 170 (52%) deaths occurred. The highest mortality was among patients with the second highest HbA1c variability, CV≥2.83 [n = 44 of which 68% patients died]. In the multivariate analyses where lowest HbA1c variability (CV≤0.51) was used as the reference group, HbA1c CV 2.83–4.60 (HR 3.15, 95% CI 1.78–5.55; p 4.6 (HR 2.48, 95% CI 1.21–5.11; p = 0.014) were associated with increased risk of death. Conclusion The high risk of all-cause mortality in patients with diabetes and PD increased significantly with elevated HbA1c variability, as measure of long-term glycemic control. This indicates that stable glycemia is associated with an improvement of survival; whereas more severe glycemic fluctuations, possibly caused by radical changes in dialysis regimes or peritonitis, are associated with a higher risk of mortality in diabetic patients with PD.

Understanding the rise, spread, and fall of large-scale states in the ancient world has occupied thinkers for millennia. However, no comprehensive mechanistic model based on their insights has emerged, leaving it difficult to evaluate empirically or quantitatively the different explanations offered. Here I present a spatially- and temporally-resolved agent-based model of large-scale (>200 thousand km2) agrarian states and steppe nomadic confederations in Afro-Eurasia between the middle Bronze and the end of the Medieval era (1500 BCE - 1500 CE). The model tracks the spread of agrarian states as they expand, conquer the territory of other states or are themselves conquered, depending on their relative modeled population size and military efficiency. To accurately retrodict the historical record, several key regional technological advances in state military and agricultural efficiencies are identified. Modifying the location, scale, and timing of these innovations allows quantitative investigation of historically-plausible alternative trajectories of state growth, spread, and fragmentation, while demonstrating the operation and limits of the model. In particular, under nominal assumptions, the historically rapid increase of agrarian state sizes, first in southwest Asia after 600 CE and then in east Asia after 200 BCE, is accounted for by modeling increasing military efficiency improvements in response to intense extortion pressure from nomadic tribes and subsequent ‘mirror’ confederations following the invention and slow diffusion of horse cavalry tactics across the steppe after 1000 BCE. However, in spite of various technological advances, throughout the period the modeled creation and spread of new agrarian states is a fundamental consequence of state collapse and internal civil wars triggered by rising 'demographic-structural' pressures that occur when territorial growth is checked yet (elite) population growth continues. Together the model's underlying mechanisms substantially account for the number of states, their duration, location, spread rate, overall occupied area, and population size for three millennia.

Background The rapid advancement of mobile technology has fueled the use of mobile devices for health interventions and for improving healthcare provision in underserved communities. Despite the potential of mHealth being used as a health information and surveillance tool, its scale-up has been challenging and, in most cases, unable to advance beyond the pilot stage of implementation. The purpose of this study was to explore user experiences of a mobile phone-based interactive voice response (IVR) system among caregivers of children under-five in rural communities in the Asante Akim North District of Ghana. Methods The study adopted an exploratory design. A convenience sampling technique was used to recruit 35 participants who had used an IVR system for at least six months for the study. About 11 in-depth interviews and three focus group discussions were conducted among participants using a semi-structured interview guide. Thematic content analysis was utilized for the analysis of data in this study. Result The system was found to be acceptable, and the attitude of caregivers towards the system was also positive. The study discovered that the mobile phone-based Health Information and Surveillance System (mHISS) was useful for improving access to healthcare, communicating with health professionals, served as a decision support system, and improved caregivers’ awareness about self-management of childhood illnesses. Poor network quality, unstable electricity power supply, and dropped/cut calls served as significant barriers to using the mHISS system. Conclusion The mHISS system was generally acceptable and could help improve access to healthcare and identify children with severe health conditions during outbreaks of diseases.

Objective To describe the impact of different doses of corticosteroids on the evolution of patients with COVID-19 pneumonia, based on the potential benefit of the non-genomic mechanism of these drugs at higher doses. Methods Observational study using data collected from the SEMI-COVID-19 Registry. We evaluated the epidemiological, radiological and analytical scenario between patients treated with megadoses therapy of corticosteroids vs low-dose of corticosteroids and the development of complications. The primary endpoint was all-cause in-hospital mortality according to use of corticosteroids megadoses. Results Of a total of 14,921 patients, corticosteroids were used in 5,262 (35.3%). Of them, 2,216 (46%) specifically received megadoses. Age was a factor that differed between those who received megadoses therapy versus those who did not in a significant manner (69 years [IQR 59–79] vs 73 years [IQR 61–83]; p < .001). Radiological and analytical findings showed a higher use of megadoses therapy among patients with an interstitial infiltrate and elevated inflammatory markers associated with COVID-19. In the univariate study it appears that steroid use is associated with increased mortality (OR 2.07 95% CI 1.91–2.24 p < .001) and megadose use with increased survival (OR 0.84 95% CI 0.75–0.96, p 0.011), but when adjusting for possible confounding factors, it is observed that the use of megadoses is also associated with higher mortality (OR 1.54, 95% CI 1.32–1.80; p < .001). There is no difference between megadoses and low-dose (p .298). Although, there are differences in the use of megadoses versus low-dose in terms of complications, mainly infectious, with fewer pneumonias and sepsis in the megadoses group (OR 0.82 95% CI 0.71–0.95; p < .001 and OR 0.80 95% CI 0.65–0.97; p < .001) respectively. Conclusion There is no difference in mortality with megadoses versus low-dose, but there is a lower incidence of infectious complications with glucocorticoid megadoses.

Environmental process assessment based on the environmental depletion index (EDI) is an important part of the long-term monitoring and early warning mechanism of China’s resources and environmental carrying capacity. The EDI aims to realize the unified environmental impact assessment of economic and environmental systems through the ratio relationship between economic growth and pollutant emission growth. However, in terms of pollutant emissions, the EDI ignores the environmental capacity (EC), which means that the effectiveness and objectivity of environmental impact assessment must be verified. In this study, with Xinjiang as an example and based on the EDI, Sulfur dioxide (SO2), Nitrogen oxide (NOx), Chemical oxygen demand (COD) and Ammonia nitrogen (NH3-N) were selected for calculation and assessment both without and with consideration of EC and for discussion of the suitability of the environmental depletion method for resources and environmental carrying capacity. The results indicated that ① the percentages of SO2, NOx, COD, NH3-N and CEDI in counties and cities that tend to be poor and lack EC were 32.98%, 29.79%, 30.85%, 28.72% and 38.30%, respectively, while the percentages in counties and cities with EC were 10.64%, 3.19%, 13.83%, 8.51% and 10.64%, respectively. ② When EC was included, the number of counties and cities where changes in SO2, NOx, COD, NH3-N and CEDI tended to be “poor → good” were 23, 26, 17, 21 and 28, respectively, and the number of counties and cities where such changes tended to be “good → poor” were 2, 1, 1, 2 and 2, respectively. ③ EC inclusion corrected overestimated or underestimated EDI results, making the evaluation results more objective and reasonable. This understanding provides a scientific reference for the coordinated development of the regional economy and environment in Xinjiang and worldwide.

Introduction Child mortality has been linked to infectious diseases, malnutrition and lack of access to essential health services. We investigated possible predictors for death and patients lost to follow up (LTFU) for paediatric patients at the inpatient department (IPD) and inpatient therapeutic feeding centre (ITFC) of the Anka General Hospital (AGH), Zamfara State, Nigeria, to inform best practices at the hospital. Methods We conducted a retrospective cohort review study using routinely collected data of all patient admissions to the IPD and ITFC with known hospital exit status between 2016 and 2018. Unadjusted and adjusted rate ratios (aRR) and respective 95% confidence intervals (95% CI) were calculated using Poisson regression to estimate the association between the exposure variables and mortality as well as LTFU. Results The mortality rate in IPD was 22% lower in 2018 compared to 2016 (aRR 0.78; 95% CI 0.66–0.93) and 70% lower for patients coming from lead-affected villages compared to patients from other villages (aRR 0.30; 95% CI 0.19–0.48). The mortality rate for ITFC patients was 41% higher during rainy season (aRR 1.41; 95% CI 1.2–1.6). LTFU rates in ITFC increased in 2017 and 2018 when compared to 2016 (aRR 1.6; 95% CI 1.2–2.0 and aRR 1.4; 95% CI 1.1–1.8) and patients in ITFC had 2.5 times higher LTFU rates when coming from a lead-affected village. Conclusions Our data contributes clearer understanding of the situation in the paediatric wards in AGH in Nigeria, but identifying specific predictors for the multifaceted nature of mortality and LTFU is challenging. Mortality in paediatric patients in IPD of AGH improved during the study period, which is likely linked to better awareness of the hospital, but still remains high. Access to healthcare due to seasonal restrictions contributes to mortalities due to late presentation. Increased awareness of and easier access to healthcare, such as for patients living in lead-affected villages, which are still benefiting from an MSF lead poisoning intervention, decreases mortalities, but increases LTFU. We recommend targeted case audits and qualitative studies to better understand the role of health-seeking behaviour, and social and traditional factors in the use of formal healthcare in this part of Nigeria and potentially similar settings in other countries.

Background Patients with acute pulmonary embolism (PE) exhibit a wide spectrum of clinical and laboratory features when presenting to hospital and pathophysiologic mechanisms differentiating low-risk and high-risk PE are poorly understood. Objectives To investigate the prognostic value of clinical, laboratory and radiological information that is available within routine tests undertaken for patients with acute PE. Methods Electronic patient records (EPR) of patients who underwent Computed Tomography Pulmonary Angiogram (CTPA) scan for the investigation of acute PE during 6-month period (01.01.2016–30.06.2016) were examined. Data was gathered from EPR for patients that met inclusion criteria and all CTPA scans were re-evaluated. Biochemical thresholds of low-grade and high-grade inflammation, serum CRP >10mg/L and >150mg/L and serum albumin concentrations Results Of the total CTPA reports (n = 2129) examined, 245 patients were eligible for inclusion. Of these, 20 (8%) patients had died at 28-days and 43 (18%) at 6-months. Of the 197 non-cancer related presentations, 28-day and 6-month mortality were 3% and 8% respectively. Of the 48 cancer related presentations, 28-day and 6-month mortality were 29% and 58% respectively. On univariate analysis, age ≥65 years (p Conclusion PESI score >100, poGPS≥1, NLR ≥3 and CAC score ≥6 were associated with 28-day and 6-month mortality. PESI score ≥100, poGPS≥1 and NLR ≥3 remained independently associated with 28-day mortality. PESI score ≥100 and CAC score ≥6 remained independently associated with 6-month mortality. When patients with underlying cancer were excluded from the analysis, GPS≥1 remained independently associated with 6-month mortality. The role of the systemic inflammatory response (SIR) in determining treatment and prognosis requires further study. Routine reporting of CAC scores in CTPA scans for acute PE may have a role in aiding clinical decision-making regarding treatment and prognosis.

In this retrospective analysis, we aimed to analyze the epidemic characteristics of neonatal mortality due to preterm birth at 28–36 weeks gestation in different regions from 2009 to 2018. Data were obtained from China’s Under-5 Child Mortality Surveillance System (U5CMSS). The χ2 trend test, Poisson regression and the Cochran-Mantel-Haenszel method were used in this study. We found that 51.3%, 42.0% and 44.5% of neonate deaths were preterm infants, and immaturity was mainly attributed to 60.1%, 64.1% and 69.5% of these deaths, in the eastern, central and western regions, respectively. The preterm neonatal mortality rate due to immaturity dropped from 149.2, 216.5 and 339.5 in 2009 to 47.4, 83.8 and 170.1 per 100 000 live births in 2018, giving an average annual decline rate of 12.1%, 11.6% and 6.3% in the eastern, central and western regions, respectively, during the studying period. The relative risk of preterm neonatal mortality due to immaturity were 1.3 and 2.3 for the central regions and western regions in 2009–2010, ascending to 2.2 and 3.9 in 2017–2018. The proportion of preterm neonatal deaths with a gestational age

Objective The Spanish health system is made up of seventeen regional health systems. Through the official reporting systems, some inconsistencies and differences in case fatality rates between Autonomous Communities (CC.AA.) have been observed. Therefore the objective of this paper is to compare COVID-19 case fatality rates across the Spanish CC.AA. Material and methods Observational descriptive study. The COVID-19 case fatality rate (CFR) was estimated according to the official records (CFR-PCR+), the daily mortality monitory system (MoMo) record (CFR-Mo), and the seroprevalence study ENE-COVID-19 (Estudio Nacional de sero Epidemiologia Covid-19) according to sex, age group and CC.AA. between March and June 2020. The main objective is to detect whether there are any differences in CFR between Spanish Regions using two different register systems, i. e., the official register of the Ministry of Health and the MoMo. Results Overall, the CFR-Mo was higher than the CFR-PCR+, 1.59% vs 0.98%. The differences in case fatality rate between both methods were significantly higher in Castilla La Mancha, Castilla y León, Cataluña, and Madrid. The difference between both methods was higher in persons over 74 years of age (CFR-PCR+ 7.5% vs 13.0% for the CFR-Mo) but without statistical significance. There was no correlation of the estimated prevalence of infection with CFR-PCR+, but there was with CFR-Mo (R2 = 0.33). Andalucía presented a SCFR below 1 with both methods, and Asturias had a SCFR higher than 1. Cataluña and Castilla La Mancha presented a SCFR greater than 1 in any scenario of SARS-CoV-2 infection calculated with SCFR-Mo. Conclusions The PCR+ case fatality rate underestimates the case fatality rate of the SARS-CoV- 2 virus pandemic. It is therefore preferable to consider the MoMo case fatality rate. Significant differences have been observed in the information and registration systems and in the severity of the pandemic between the Spanish CC.AA. Although the infection prevalence correlates with case fatality rate, other factors such as age, comorbidities, and the policies adopted to address the pandemic can explain the differences observed between CC.AA.

Background Emergency department (ED) crowding is a frequent situation. To decrease this overload, patients without a life-threating condition are transferred to wards that offer ED support. This study aimed to evaluate if implementing a rapid response team (RRT) triggered by the modified early warning score (MEWS) in high-risk wards offering ED support is associated with decreased in-hospital mortality rate. Methods A before-and-after cross-sectional study compared in-hospital mortality rates before and after implementation of an RRT triggered by the MEWS ≥4 in two wards of a tertiary hospital that offer ED support. Results We included 6863 patients hospitalized in these wards before RRT implementation from July 2015 through June 2017 and 6944 patients hospitalized in these same wards after RRT implementation from July 2018 through June 2020. We observed a statistically significant decrease in the in-hospital mortality rate after intervention, 449 deaths/6944 hospitalizations [6.47% (95% confidence interval (CI) 5.91%– 7.07%)] compared to 534 deaths/6863 hospitalizations [7.78% (95% CI 7.17–8.44)] before intervention; with an absolute risk reduction of -1.31% (95% CI -2.20 –-0.50). Conclusion RRT trigged by the MEWS≥4 in high-risk wards that offer ED support was found to be associated with a decreased in-hospital mortality rate. A further cluster-randomized trial should evaluate the impact of this intervention in this setting.

Background Lyme carditis, defined as direct infection of cardiac tissue by Borrelia bacteria, affects up to 10% of patients with Lyme disease. The most frequently reported clinical manifestation of Lyme carditis is cardiac conduction system disease. The goal of this study was to identify the incidence and predictors of permanent pacemaker implantation in patients hospitalized with Lyme disease. Methods A retrospective cohort analysis of the Nationwide Inpatient sample was performed to identify patients hospitalized with Lyme disease in the US between 2003 and 2014. Patients with Lyme carditis were defined as those hospitalized with Lyme disease who also had cardiac conduction disease, acute myocarditis, or acute pericarditis. Patients who already had pacemaker implants at the time of hospitalization (N = 310) were excluded from the Lyme carditis subgroup. The primary study outcome was permanent pacemaker implantation. Secondary outcomes included temporary cardiac pacing, permanent pacemaker implant, and in-hospital mortality. Results Of the 96,140 patients hospitalized with Lyme disease during the study period, 10,465 (11%) presented with Lyme carditis. Cardiac conduction system disease was present in 9,729 (93%) of patients with Lyme carditis. Permanent pacemaker implantation was performed in 1,033 patients (1% of all Lyme hospitalizations and 11% of patients with Lyme carditis-associated conduction system disease). Predictors of permanent pacemaker implantation included older age (OR: 1.06 per 1 year; 95% CI:1.05–1.07; P Conclusions Approximately 11% of patients hospitalized with Lyme disease present with carditis, primarily in the form of cardiac conduction system disease. In this 12-year study, 1% of all hospitalized patients and 11% of those with Lyme-associated cardiac conduction system disease underwent permanent pacemaker implantation.

Objectives To conduct a cross-sectional nationwide study examining how exclusion of nursing home COVID-19 cases influences the association between county level social distancing behavior and COVID-19 cases throughout the US during the early phase of the pandemic (February 2020-May 2020). Methods Using county-level COVID-19 data and social distancing metrics from tracked mobile devices, we investigated the impact social distancing had on a county’s total COVID-19 cases (cases/100,000 people) between when the first COVID-19 case was confirmed in a county and May 31st, 2020 when most statewide social distancing measures were lifted, representing the pandemic’s exponential growth phase. We created a mixed-effects negative binomial model to assess how implementation of social distancing measures when they were most stringent (March 2020-May 2020) influenced total COVID-19 cases while controlling for social distancing and COVID-19 related covariates in two scenarios: (1) when COVID-19 nursing home cases are not excluded from total COVID-19 cases and (2) when these cases are excluded. Model findings were compared to those from February 2020, a baseline when social distancing measures were not in place. Marginal effects at the means were generated to further isolate the influence of social distancing on COVID-19 from other factors and determine total COVID-19 cases during March 2020-May 2020 for the two scenarios. Results Regardless of whether nursing home COVID-19 cases were excluded from total COVID-19 cases, a 1% increase in average % of mobile devices leaving home was significantly associated with a 5% increase in a county’s total COVID-19 cases between March 2020-May 2020 and about a 2.5% decrease in February 2020. When the influence of social distancing was separated from other factors, the estimated total COVID-19 cases/100,000 people was comparable throughout the range of social distancing values (25%-45% of mobile phone devices leaving home between March 2020-May 2020) when nursing home COVID-19 cases were not excluded (25% of mobile phones leaving home: 163.84 cases/100,000 people (95% CI: 121.81, 205.86), 45% of mobile phones leaving home: 432.79 cases/100,000 people (95% CI: 256.91, 608.66)) and when they were excluded (25% of mobile phones leaving home: 149.58 cases/100,000 people (95% CI: 111.90, 187.26), 45% of mobile phones leaving home: 405.38 cases/100,000 people (95% CI: 243.14, 567.62)). Conclusions Exclusion of nursing home COVID-19 cases from total COVID-19 case counts has little impact when estimating the relationship between county-level social distancing and preventing COVID-19 cases with additional research needed to see whether this finding is also observed for COVID-19 growth rates and mortality.

Background Childhood is considered the most important phase of human development; within it the period from birth to 5 years of age is particularly critical, given the speed at which changes occur. The context where children live can influence early childhood developmnent (ECD) by providing or limiting opportunities to learn, play and establish social interactions. This study explored the associations between characteristics of the urban environment and ECD in 2,194 children aged 36 to 59 months living in urban municipalities in Mexico Methods We obtained ECD information from the 2015 Survey of Boys, Girls, and Women (ENIM, for its Spanish acronym), measured with the Early Childhood Development Index. The urban environment was evaluated at the municipal level, considering variables from five environment domains: physical, social, service, socioeconomic, and governance. Multilevel logistic models were fitted to assess the association between urban environment characteristics and the inadequacy of ECD in general and by specific development domains: learning, socio-emotional, physical, and alpha-numeric. Results Inadequate ECD was inversely associated with the availability of libraries (OR = 0.55, 95% CI: 0.43, 0.72), and positively associated with population density (OR = 1.01, 95% CI: 1.01–1.02). For the specific ECD domains, inadequate socio-emotional development was inversely associated with the availability of libraries (OR = 0.66, 95% CI: 0.51, 0.85). Inadequate literacy-numeracy knowledge was associated inversely with the availability of daycare centers (OR = 0.56, 95% CI: 0.32, 0.97), and directly associated with the number of hospitals and clinics (OR = 1.87, 95% CI: 1.29, 2.72). Finally, the marginalization index was positively associated with inadequacy in the learning domain (OR = 1.80, 95% CI: 1.06, 3.03). Conclusions Some aspects of the urban environment associated with ECD, suggest that intervening in the urban context could improve overall child development. Investment in resources oriented to improve socio-emotional development and literacy (such as libraries and daycare), could foster ECD in Mexico.

Background The infant mortality rate (IMR) is a critical indicator of population health, but its measurement is subject to response bias in countries without complete vital registration systems who rely instead on birth histories collected via sample surveys. One of the most salient bias is the fact that child deaths in these birth histories tend to be reported with a large amount of heaping at age 12 months. Because of this issue, analysts and international agencies do not directly use IMR estimates based on surveys such as Demographic and Health Surveys (DHS); they rely instead on mortality models such as model life tables. The use of model life tables in this context, however, is arbitrary, and the extent to which this approach appropriately addresses bias in DHS-based IMR estimates remains unclear. This hinders our ability to monitor IMR levels and trends in low-and middle-income countries. The objective of this study is to evaluate age heaping bias in DHS-based IMR estimates and propose an improved method for adjusting this bias. Methods and findings Our method relies on a recently-developed log-quadratic model that can predict age-specific mortality by detailed age between 0 and 5. The model’s coefficients were derived from a newly constituted database, the Under-5 Mortality Database (U5MD), that represents the mortality experience of countries with high-quality vital registration data. We applied this model to 204 DHS surveys, and compared unadjusted IMR values to IMR values adjusted with the log-quadratic model as well as with the classic model life table approach. Results show that contrary to existing knowledge, age heaping at age 12 months rarely generates a large amount of bias in IMR estimates. In most cases, the unadjusted IMR values were not deviating by more than +/- 5% from the adjusted values. The model life table approach, by contrast, introduced an unwarranted, downward bias in adjusted IMR values. We also found that two regions, Sub-Saharan Africa and South Asia, present age patterns of under-5 mortality that strongly depart from the experience represented in the U5MD. For these countries, neither the existing model life tables nor the log-quadratic model can produce empirically-supported IMR adjustments. Conclusions Age heaping at age 12 months produces a smaller amount of bias in DHS-based IMR estimates than previously thought. If a large amount of age heaping is present in a survey, the log-quadratic model allows users to evaluate, and whenever necessary, adjust IMR estimates in a way that is more informed by the local mortality pattern than existing approaches. Future research should be devoted to understanding why Sub-Saharan African and South Asian countries have such distinct age patterns of under-five mortality.

Background Chronic pulmonary aspergillosis (CPA) has a wide spectrum of illnesses depending on the progression of the disease and comorbid conditions. However, there is an inadequacy of investigations regarding clinical, laboratory, risk factor and prognostic data on CPA. The current study is aimed to consider the clinical manifestations, risk factors and outcomes of CPA. Methodology Retrospective records of all patients with a confirmed diagnosis of CPA who sought treatment at Gulab Devi Chest Hospital Lahore, Pakistan from January 2017 to December 2019 were evaluated. Data regarding demographics, clinical manifestations, comorbidities, radiographic and microbiological findings, length of hospital stay (LOS) and intensive care unit (ICU) admission was collected and analyzed to identify the factors associated with mortality. The independent factors associated with mortality were also identified by appropriate analyses. Results A total of 218 CPA patients were included in this study. The mean age was 45.75 ± 6.26 years. Of these, 160 (73.4%) were male, and 65 (29.8%) had diabetes. The mean LOS was 18.5 ± 10.9 days. The most common type of CPA was simple aspergilloma (56%) followed by chronic cavitary pulmonary aspergillosis (CCPA) (31.2%). About one half of the patients had a history of pulmonary tuberculosis (TB) and treatment response rates were low in patients with active TB. The overall mortality rate was 27.1%. ICU admission was required for 78 (35.8%) patients. Diabetes mellitus (DM), hematological malignancies and chronic kidney disease (CKD) were the common underlying conditions predicting a poor outcome. Mean LOS, hematological malignancies, consolidation and ICU admission were identified as the independent factors leading to mortality. Conclusions CPA had a significant association with TB in the majority of cases. Treatment response rates in cases with active TB were comparatively low. Cases with high mean LOS, hematological malignancies, consolidation, ICU admission, CKD and DM experienced poor outcomes. High mean LOS, hematological malignancies, consolidation and ICU stay were identified as independent risk factors for mortality. Future large prospective studies, involving aspergillus specific immunoglobulin G (IgG) antibody testing, are required for a better understanding of CPA in Pakistan.

The mortality rates of COVID-19 vary widely across countries, but the underlying mechanisms remain unelucidated. We aimed at the elucidation of relationship between gut microbiota and the mortality rates of COVID-19 across countries. Raw sequencing data of 16S rRNA V3-V5 regions of gut microbiota in 953 healthy subjects in ten countries were obtained from the public database. We made a generalized linear model (GLM) to predict the COVID-19 mortality rates using gut microbiota. GLM revealed that low genus Collinsella predicted high COVID-19 mortality rates with a markedly low p-value. Unsupervised clustering of gut microbiota in 953 subjects yielded five enterotypes. The mortality rates were increased from enterotypes 1 to 5, whereas the abundances of Collinsella were decreased from enterotypes 1 to 5 except for enterotype 2. Collinsella produces ursodeoxycholate. Ursodeoxycholate was previously reported to inhibit binding of SARS-CoV-2 to angiotensin-converting enzyme 2; suppress pro-inflammatory cytokines like TNF-α, IL-1β, IL-2, IL-4, and IL-6; have antioxidant and anti-apoptotic effects; and increase alveolar fluid clearance in acute respiratory distress syndrome. Ursodeoxycholate produced by Collinsella may prevent COVID-19 infection and ameliorate acute respiratory distress syndrome in COVID-19 by suppressing cytokine storm syndrome.

Non-Pharmaceutical Public Health Interventions (NPHIs) have been used by different countries to control the spread of the COVID-19. Despite available evidence regarding the effectiveness of NPHSs, there is still no consensus about how policymakers can trust these results. Studies on the effectiveness of NPHSs are single studies conducted in specific communities. Therefore, they cannot individually prove if these interventions have been effective in reducing the spread of the infection and its adverse health outcomes. In this systematic review, we aimed to examine the effects of NPHIs on the COVID-19 case growth rate, death growth rate, Intensive Care Unit (ICU) admission, and reproduction number in countries, where NPHIs have been implemented. We searched relevant electronic databases, including Medline (via PubMed), Scopus, CINAHL, Web of Science, etc. from late December 2019 to February 1, 2021. The key terms were primarily drawn from Medical Subject Heading (MeSh and Emtree), literature review, and opinions of experts. Peer-reviewed quasi-experimental studies were included in the review. The PROSPERO registration number is CRD42020186855. Interventions were NPHIs categorized as lockdown, stay-at-home orders, social distancing, and other interventions (mask-wearing, contact tracing, and school closure). We used PRISMA 2020 guidance for abstracting the data and used Cochrane Effective Practice and Organization of Practice (EPOC) Risk of Bias Tool for quality appraisal of the studies. Hartung-Knapp-Sidik-Jonkman random-effects model was performed. Main outcomes included COVID-19 case growth rate (percentage daily changes), COVID-19 mortality growth rate (percentage daily changes), COVID-19 ICU admission (percentage daily changes), and COVID-19 reproduction number changes. Our search strategies in major databases yielded 12,523 results, which decreased to 7,540 articles after eliminating duplicates. Finally, 35 articles qualified to be included in the systematic review among which 23 studies were included in the meta-analysis. Although studies were from both low-income and high-income countries, the majority of them were from the United States (13 studies) and China (five studies). Results of the meta-analysis showed that adoption of NPHIs has resulted in a 4.68% (95% CI, -6.94 to -2.78) decrease in daily case growth rates, 4.8% (95 CI, -8.34 to -1.40) decrease in daily death growth rates, 1.90 (95% CI, -2.23 to -1.58) decrease in the COVID-19 reproduction number, and 16.5% (95% CI, -19.68 to -13.32) decrease in COVID-19 daily ICU admission. A few studies showed that, early enforcement of lockdown, when the incidence rate is not high, contributed to a shorter duration of lockdown and a lower increase of the case growth rate in the post-lockdown era. The majority of NPHIs had positive effects on restraining the COVID-19 spread. With the problems that remain regarding universal access to vaccines and their effectiveness and considering the drastic impact of the nationwide lockdown and other harsh restrictions on the economy and people’s life, such interventions should be mitigated by adopting other NPHIs such as mass mask-wearing, patient/suspected case isolation strategies, and contact tracing. Studies need to address the impact of NPHIs on the population’s other health problems than COVID-19.

Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs’ visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents’ responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.

Background Preterm birth is a leading cause of death among children under five years. Previous estimates indicated global preterm birth rate of 10.6% (14.8 million neonates) in 2014. We aim to update preterm birth estimates at global, regional, and national levels for the period 2010 to 2019. Methods Preterm birth is defined as a live birth occurring before 37 completed gestational weeks, or Discussion Accurate measurement of preterm birth is challenging in many countries given incomplete or unavailable data from national administrative sources, compounded by limited gestational age assessment during pregnancy to define preterm birth. Up-to-date modelled estimates will be an important resource to measure the global burden of preterm birth and to inform policies and programs especially in settings with a high burden of neonatal mortality. Trial registration PROSPERO registration: CRD42021237861.

The housefly, Musca domestica (Diptera: Muscidae), is capable of transmitting many pathogens that cause severe diseases in humans and animals. Mostly the management tactics rely on synthetic chemicals, but these chemicals creates lethal effects on biological ecosystem. For natural and safe options, bio-control strategy is one of the choice. The present study was a part of such effort to use this strategy and validate the biological performance of the potential pupal parasitoid Dirhinus giffardii (Silvestri) against house fly and provide alternative and safe control of filthy flies. This is the first report on parasitism potential and preference of D. giffardii against house fly. The D. giffardii, early reported as an effective pupal parasitoid of tephritid flies, here in the case presented, showed overall 70% reduction in the house fly population by parasitizing pupae. The parasitism efficiency and longevity of hymenopteran parasitoid was remarkably noted two-fold higher and one-fold more female production on house fly pupae as compared to primary hosts (Tephritids). Furthermore, sex ratio of the resultant progeny was also confirmed the dominancy of female by 74% as compared to males. Based on the novel findings we therefore conclude that D. giffardii is the best bio-control agent for controlling house flies.

Background How effective have lockdowns been at reducing the covid-19 infection and mortality rates? Lockdowns influence contact among persons within or between populations including restricting travel, closing schools, prohibiting public gatherings, requiring workplace closures, all designed to slow the contagion of the virus. The purpose of the present study was to assess the impact of lockdown measures on the spread of covid-19 and test a theoretical model of the covid-19 pandemic employing structural equation modelling. Methods Lockdown variables, population demographics, mortality rates, infection rates, and health were obtained for eight countries: Austria, Belgium, France, Germany, Italy, Netherlands, Spain, and the United Kingdom. The dataset, owid-covid-data.csv, was downloaded on 06/01/2020 from: https://github.com/owid/covid-19-data/tree/master/public/data. Infection spread and mortality data were depicted as logistic growth and analyzed with stepwise multiple regression. The overall structure of the covid-19 data was explored through factor analyses leading to a theoretical model that was tested using latent variable path analysis. Results Multiple regression indicated that the time from lockdown had a small but significant effect (β = 0.112, p< 0.01) on reducing the number of cases per million. The stringency index produced the most important effect for mortality and infection rates (β = 0.588,β = 0.702, β = 0.518, β = 0.681; p< 0.01). Exploratory and confirmatory analyses resulted in meaningful and cohesive latent variables: 1) Mortality, 2) Infection Spread, 3) Pop Health Risk, and 4) Health Vulnerability (Comparative Fit Index = 0.91; Standardized Root Mean Square Residual = 0.08). Discussion The stringency index had a large impact on the growth of covid-19 infection and mortality rates as did percentage of population aged over 65, median age, per capita GDP, diabetes prevalence, cardiovascular death rates, and ICU hospital beds per 100K. The overall Latent Variable Path Analysis is theoretically meaningful and coherent with acceptable fit indices as a model of the covid-19 pandemic.