Chest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated.p < 0.0001).The Spearman’s correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (The automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease. The 2019 novel coronavirus (SARS-CoV-2) disease (COVID-19) began in early December of 2019 and rapidly spread across the globe to become a pandemic . COVID-1Chest computed tomography (CT) has proven its sensitivity for early diagnosis of COVID-19 pneumonia . CT has Positive CT findings may predate positivity on real-time reverse transcription-polymerase chain reaction (RT-PCR) testing, which is the gold standard for diagnosis, and it could detect minor lung lesions at an early stage of the disease. The diagnosis and treatment protocols have been modified in the context of this pandemic to include clinically suspected patients with characteristic CT features of COVID-19 pneumonia but negative RT-PCR results .Typical CT findings in COVID-19 pneumonia include initially, bilateral ground-glass opacities (GGOs) located peripherally, followed later by consolidation, with multi-lobe affection of both lungs in most cases. Crazy paving, reticulation and reversed halo sign are encountered with progression of the disease –17. COVILung affection patterns including the number, size, and density of lesions, as well as the overall extent of diseased lung, are all indicators of lung damage and remaining lung reserve. Identifying objective prognostic parameters in CT images of COVID-19 patients may allow better management decisions that hopefully lead to improved clinical outcome [The great sensitivity of chest CT for diagnosing COVID-19 pneumonia poses a significantly increasing workload on radiologists to visually identify and evaluate the extent of COVID-19 affection from thin-thickness CT images, which becomes more challenging if follow-up CT imaging is needed , 21.Previous studies have shown a close relationship between clinical and lung CT severity in COVID-19 pneumonia , 22, 23.Quantitative CT which is already widely used for assessment of diffuse lung diseases, especially in interstitial disease, is an objective tool for detection of image features that are not visually recognizable by the radiologists , 25.The internationally adopted COVID-19 Reporting and Data System (CO-RADS), recommended by the Radiological Society of North America and other radiological societies , 27, useMost of the imaging studies since the outbreak of COVID-19 have focused on lung CT findings, with only few studies concerning the quantitative analysis of these findings .Unfortunately, this role has been reluctantly integrated into the routine radiological practice because the radiologist's semi-quantitative visual assessment is subjective, time consuming and lacking inter-observer consistency. A semi-quantitative visual severity scoring (SS) system for lung affection has been proposed  for asseThe scoring of disease severity is performed in each of the five lung lobes on a scale from 0 to 5, with 0 indicating no involvement, 1 indicating less than 5% involvement, 2 indicating 5–25% involvement, 3 indicating 26–49% involvement, 4 indicating 50–75% involvement, and 5 indicating more than 75% involvement. The sum of individual lobar scores represents the total SS that ranges from 0 (no involvement) to 25 (maximum involvement). This semi-quantitative lobar-based visual scoring system has been adopted in the assessment of the severity of COVID-19 lung affection , 32, 33.Quantitative CT analysis is superior to semi-quantitative visual SS in assessment of the severity of COVID-19 infection. Computerized quantitative segmentation methods could provide objective assessment of the percentage of the diseased part of the lung containing GGOs and consolidation to determine the disease burden , 35.The lung volumes measured by CT are well correlated with pulmonary function test results such as total lung capacities and forced vital capacities . QuantitStill there is no consensus regarding the grading of severity of lung affection in CT images of COVID-19 pneumonia. Simple subjective descriptive terms are cordially used by most radiologists for determination of the severity of lung affection such as mild, moderate, severe or critical.This study aims to explore an automated histogram-based quantitative CT method together with the conventional semi-quantitative visual severity scoring for assessment of COVID-19 pneumonia and to correlate the quantitative metrics with clinical and laboratory findings in COVID-19 pneumonia patients.This is a single-center retrospective study that included 153 patients with COVID-19 infection  who were presented to the emergency ward of Assiut University Hospitals during the period from March to July 2020. COVID-19 was diagnosed based on a positive result of RT-PCR assay on pharyngeal swab specimens. Chest CT scanning was performed within 3–6 days after the onset of symptoms. The clinical and laboratory data of these patients were collected. The patient characteristics are summarized in Table 2) ≥ 94% on room air; severe illness: individuals who have spO2 < 94% on room air, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) < 300 mm hg, respiratory frequency > 30 breaths/min, or lung infiltrates > 50% [Based on the clinical presentation, the patients were divided into three groups: mild illness: individuals who have any of the various signs and symptoms of covid-19  but who do not have shortness of breath, dyspnea, or abnormal chest imaging findings; moderate illness: individuals who show evidence of lower respiratory tract disease during clinical assessment or imaging and who have an oxygen saturation . Scanning was performed from lung apices to the diaphragm during a single breath-hold at deep inspiration, using the following parameters: tube voltage 120 kVp; automatic tube-current modulation; gantry rotation speed of 0.5 s; and beam collimation of 16 × 0.625 mm. Thin-section CT data were reconstructed at 0.625 mm thickness using standard filtered back-projection algorithm; iterative reconstruction algorithms were not used.The CT technologist and the attending nurse routinely wore personal protective equipment (PPE) while handling patients in the CT suite. The PPE included transparent face-shield, a surgical cap, a surgical mask, gloves, a fluid-resistant gown, and shoe covers. Decontamination of the CT machine was performed routinely after finishing CT scanning according to the infection control guidelines. Disposable sheets were used to cover the CT imaging table. The patients wore disposable masks and head caps before entering the CT examination room.The high-resolution computed tomography (HRCT) images were independently reviewed by two expert radiologists on a picture archiving and communication system (PACS) computer workstation at window setting for lung parenchyma . A lobar-based visual SS was independently identified by two expert radiologists, and the scores were then averaged to determine the mean total SS of COVID-19 lung affection. The scoring system considered the overall extent of parenchymal abnormalities, including the GGOs and consolidation (Co), using the definitions of the Fleischner Society glossary of terms for thoracic imaging . Any co-A commercially available computer workstation, Synapse 3D version 3.5 , was used for quantitative analysis of CT images. The digital imaging and communications in medicine (DICOM) data of the CT scans of all patients were transferred to the workstation from the scanner. Whole lung extraction was automatically performed by eliminating the thoracic wall, mediastinum, large vessels, and tracheo-bronchial airway down to tertiary bronchi. The lung extraction process (according to vendor's data) uses both Hounsfield thresholding and anatomical knowledge-based algorithms.From − 1024 to − 950 HU (red), representing emphysema .From − 949 to − 750 HU (yellow), corresponds to healthy lung tissue.From − 749 to − 300 HU (blue), it represents the lung parts which are more dense than healthy lung  and can be used to quantify ground-glass opacitiesFrom − 299 to + 40 HU (violet), this group corresponds to areas with further increase in density, including the semi-consolidation and consolidation.An additional COVID-19 analysis dataset was added to the Synapse 3D workstation for analysis of COVID-19 pneumonia in CT images. The dataset consists of 4 groups of density ranges:The volume of each lung and the total volume of both lungs (TLV) in cubic centimeter (cc)The mean density of each lung and the mean density of both lungs (MLD) in HUvolGGO and %GGO, respectively) and the volume and percentage of consolidation  in both lungs. The total volume of diseased lung was manually calculated as the sum of volGGO and volCo, and the total percentage of the diseased lung or total lesion load (TLL), representing the disease burden, was calculated as the sum of %GGO and %Co.The volume and percentage of GGOs .%GGO and MLD. p value of less than 0.05 was considered statistically significant.Statistical analysis was performed using MedCalc Statistical Software version 20 . Inter-observer agreement between the two radiologists who performed semi-quantitative analysis of CT images of patients of the study was evaluated using kappa test. Pearson correlation coefficients were calculated between the visual severity scoring and the automated quantitative measures; between the total disease burden calculated by quantitative CT analysis and the MLD; and finally between the The clinical and laboratory data of the patients included in the study are shown in Table The calculated quantitative metrics including volumes, percentages and densities are summarized in Table The inter-observer agreement of severity scoring between both radiologists who performed semi-quantitative evaluation of lung lesions in this study (Weighted Kappa) is 0.7984 ± 0.0140, with 95% confidence interval of 0.7708–0.8261. The concordance correlation coefficient between the two radiologists is 0.9615 with 95% confidence interval of 0.9474–0.9719.p < 0.0001), with confidence interval (CI) for r between 0.9092 and 0.9512    has become an important auxiliary diagnostic tool. The automated segmentation of lung lesions from volumetric 3D images allows calculation of the total burden of COVID-19 pneumonia as a percentage subvolume of the total lung volume. Quantitative methods for determination of the severity of lung affection in CT images of covid-19 patients could improve the diagnostic efficiency and mitigate the workload of radiologists, allowing more timely and appropriate treatment decisions for COVID-19 patients. Quantitative CT analysis represents a reproducible assessment that allows fast, reliable and potentially predictive tool for assessing disease progression and response to treatment in COVID-19 pneumonia .Another advantage of computer-assisted quantification methods for determination of COVID-19 disease burden is the reproducibility of the technique, allowing for more accurate comparison of data among different centers. Different patterns of lung abnormalities in COVID-19 pneumonia change the lung attenuation values and affect its histogram. The histogram-based image analysis is the most basic and least computation demanding segmentation technique, implemented in most PACS workstations being simple, fast and reproducible .Image thresholding is one of the most commonly used image segmentation technique that segment images depending upon the grayscale values within the image histogram. Multi-level thresholding CT densitometry techniques rely upon choosing multiple cutoff values in the analysis of the frequency distribution of the lung attenuation values in the histogram. The pixels within an image are divided into multiple classes according to multiple grayscale thresholds –47.With the rapid increase of the infected population in the COVID-19 pandemic, semi-quantitative conventional CT scoring is challenging and impractical in the overloaded Radiology service.In this study, the density-based multi-level thresholding technique was utilized for quantification of COVID-19 lung affection in high-resolution thin-cuts volumetric whole-lung CT images. The findings of this study show that the extent of COVID-19 lesions visually scored by radiologists from HRCT images does significantly correlate with the volumetric measurements obtained by the quantitative computer-assisted histogram-based automatic method.The data obtained in this study show that both semi-quantitative visual severity scoring and quantitative CT performed nearly equally, and their parameters correlated well with the clinical data of patients. Our findings indicate that the severity of COVID-19 pneumonia could be accurately stratified on the basis of objective CAD-based quantitative data of disease burden in CT images.This is consistent with the results of Li et al.  who descLanza et al.  used denBressem et al.  used denIn a recent study, Salvatore et al.  used a cIn another recent study, Romanov et al.  used hisSemi-quantitative scoring of the extent of COVID-19 pneumonia based upon visual assessment has been shown to correlate with the duration of infection , 52 as wIn ARDS, a ratio less than 40% between well-aerated lung volume and the total lung capacity was reported to be associated with a higher mortality risk .Colombi et al.  used lunPatients with COVID-19 lung affection frequently develop ARDS , 55 whicIn all cases of this study regardless its clinical or radiological severity, in presence of GGOs, the automatically extracted whole lung attenuation histograms show blunted peak that is shifted to the right compared with the normal lung, while, in presence of alveolar consolidation, the histograms show a high sharp peak that is shifted more to the right compared to ground-glass opacities and normal lung. These findings are consistent with those described by Sumikawa et al. .The results of this study show that the automated histogram-based quantification of COVID-19 disease burden is a rapid, reliable and reproducible method for objective estimation of lung affection in CT images. There is good correlation between the conventional semi-quantitative CT severity scoring and automated quantitative analysis methods. The automated quantitative methods are especially important in situations like the current COVID-19 pandemic in which radiology departments are overloaded with cases.Some limitations do exist in this study. First and foremost, the lack of longitudinal assessment of disease progression of patients enrolled in the study. The study also included a relatively small number of patients.


Previous analyses of trends in feeding indicators of children younger than 2 years have been limited to low-income and middle-income countries. We aimed to assess time trends in the consumption of different types of milk  by children younger than 2 years from 2000 to 2019 at a global level.In this time-series analysis, we combined cross-sectional data from 487 nationally representative surveys from low-income and middle-income countries and information from high-income countries to estimate seven infant and young child feeding indicators in up to 113 countries. Multilevel linear models were used in pooled analyses to estimate annual changes in feeding practices from 2000 to 2019 for country income groups and world regions.For the absolute average annual changes, we found significant gains in any breastfeeding at age 6 months in high-income countries  and at age 1 year in high-income countries  and upper-middle-income countries . We also found a small reduction in low-income countries for any breastfeeding at age 6 months  and age 1 year . Data on exclusive breastfeeding and consumption of formula and animal milk were only available for low-income and middle-income countries, where exclusive breastfeeding in the first 6 months of life increased by 0·70 PPs per year  to reach 48·6% (41·9–55·2) in 2019. Exclusive breastfeeding increased in all world regions except for the Middle East and north Africa. Formula consumption in the first 6 months of life increased in upper-middle-income countries and in east Asia and the Pacific, Latin America and the Caribbean, the Middle East and north Africa, and eastern Europe and central Asia, whereas the rates remained below 8% in sub-Saharan Africa and south Asia. Animal milk consumption by children younger than 6 months decreased significantly  in low-income and middle-income countries.We found some increases in exclusive and any breastfeeding at age 6 months in various regions and income groups, while formula consumption increased in upper-middle-income countries. To achieve the global target of 70% exclusive breastfeeding by 2030, however, rates of improvement will need to be accelerated.Bill & Melinda Gates Foundation, through WHO. WHO recognises that breastfeeding is essential for achieving optimal child growth, development, and health. The organisation recommends that children initiate breastfeeding within the first hour of life, breastfeed exclusively for the first 6 months, and thereafter receive adequate and safe complementary foods while breastfeeding continues up to age 2 years or beyond.Breastfeeding is more prevalent in low-income and middle-income countries than in high-income countries,Evidence before this studyWe searched PubMed and Google Scholar with the following combination of keywords: trends AND (“breastfeeding” OR “breast feeding”) OR (“breast milk substitutes”) AND , without any language restriction from inception until Jan 31, 2021. We also searched the UNICEF and WHO websites with similar terms, for the same time frame. We identified a few multi-country studies describing trends in feeding indicators, all restricted to low-income and middle-income countries, except for an analysis that used nationally representative surveys from countries classified as high income only. No pooled analyses including low-income, middle-income, and high-income countries were identified. Overall, increases in breastfeeding indicators were reported in most studies, particularly for exclusive breastfeeding among children younger than 6 months. In 2019, UNICEF reported that exclusive breastfeeding increased from 35% in 2005 to 42% in 2018 in 80 low-income and middle-income countries. Regarding formula consumption, a single 2021 article was identified, which reported increasing trends in four of six Latin American countries for children younger than 1 year between 1990 and 2017. We did not locate any multi-country analysis on trends in the consumption of animal milk by young children.Added value of this studyUsing 487 data points from 113 countries, we analysed global trends from 2000 to 2019 for seven feeding indicators among children younger than 2 years, using available data from low-income, middle-income, and high-income countries. Pooled global analysis showed that the proportions of children breastfed at age 6 months and 1 year were stable over time, with no significant changes at a national level. However, increases in these breastfeeding indicators were observed in high-income countries and in upper-middle-income countries, while low-income countries presented small but significant declines. Because few high-income countries had nationally representative information on exclusive breastfeeding or on consumption of formula and animal milk, these analyses were restricted to low-income and middle-income countries with standardised survey data. In these countries, exclusive breastfeeding prevalence increased at a national level and in all world regions, except in the Middle East and north Africa. The pooled estimate for 83 low-income and middle-income countries with data was 48·6% in 2019. Formula milk consumption under age 6 months and at 6–23 months remained unchanged for low-income and middle-income countries as a whole, but we found important increases in upper-middle-income countries and in some regions—the Middle East and north Africa, Latin America and the Caribbean, eastern Europe and central Asia, and especially east Asia and the Pacific. For low-income and middle-income countries, small but significant reductions were observed for the consumption of animal milk by children younger than 6 months, but not at older ages.Implications of all the available evidenceDespite positive trends in exclusive breastfeeding, the rate of improvement is insufficient for reaching the global target of at least 70% global prevalence by 2030. Findings for any breastfeeding in high-income countries and upper-middle-income countries are encouraging, but renewed efforts are needed to counteract declining trends in low-income societies where undernutrition and child mortality rates are still high, and where children can most benefit from continued breastfeeding. The rapid increase in formula milk consumption in all age ranges in upper-middle-income countries is alarming. This is the most comprehensive analysis on this topic to date, revealing the urgent need to place breastfeeding support, promotion, and protection as national priorities, and to halt the marketing and sale of infant formula by enforcing the recommendations of the International Code of Marketing of Breastmilk Substitutes.In the past two decades, global data14Despite these improvements in breastfeeding indicators, sales of commercial breastmilk substitutes  have increased globally, especially in upper-middle-income countries.Monitoring breastfeeding progress helps to understand the need for and effect of policies and programmes and to design new interventions targeted at improving the health of women and children.Aiming to address these gaps, we examined time trends and worldwide patterns in the consumption of different types of milk  by children aged under 2 years, in 113 countries from 2000 to 2019.Demographic Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and Reproductive Health Surveys (RHS). These surveys are highly comparable in terms of questionnaires, field procedures, and sampling design, which results in harmonised estimates through close collaboration with the UN's interagency groups. DHS and MICS collect data on key indicators of reproductive, maternal, newborn, and child health and nutrition, and are regularly updated.For countries with standardised surveys, most of which are low-income and middle-income countries, we analysed nationally representative surveys, namely For countries without standardised surveys, a comprehensive literature search was done until Nov 30, 2019, to locate health services data on breastfeeding indicators that were nationally representative. These countries included high-income countries and a few upper-middle-income countries, according to the World Bank's income classification in the fiscal year 2018–19.We investigated seven feeding indicators using the data sources described. These indicators are defined in 17On the basis of available data, we investigated any breastfeeding at age 6 months and at 1 year. 113 countries had information on any breastfeeding, including 110 countries with information for any breastfeeding at age 6 months and 105 countries with information for age 1 year. Breastfeeding was recorded at 1 year rather than 6–23 months because of the availability of data. Using data from nationally representative surveys done in low-income and middle-income countries, we calculated exclusive breastfeeding in infants younger than 6 months,We included countries with at least two data points with an interval of 5 or more years. Initial analyses were done at a national level, followed by grouping countries by World Bank income levelDescriptive analyses were done at national, world region, and country income group levels. The proportions of countries that provided data for each region and country income group were estimated. We used multilevel linear regression to estimate average absolute annual changes in PPs for each feeding indicator, with country as the highest hierarchical level and year as the second level. Fractional polynomials were used to investigate departures from linearity. However, the polynomials did not show any improvement compared with linear regression, which was therefore used for simplicity. Available data points since the early 1990s were included in the analyses but trends are presented from 2000 to 2019, the period with the most data points. Line charts were used to illustrate changes over the 20-year period for each feeding indicator by country income groups and world regions.All analyses were done by considering the cluster sample design of the surveys using the svy command in Stata (version 16.0). Furthermore, we weighted all analyses by the national population size of children within the specific age range for each indicatorEthics approval was the responsibility of the institutions that administered the surveys and all analyses relied on anonymised databases.The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.For data points with available information on sample sizes, the median number of children per survey increased from 371 (IQR 197–517) in 1991–99 to 455 (IQR 220–827) in 2000–09 and 678 (IQR 366–1540) in 2010–19 for children aged 6 months (or 4–7 months). For children aged 1 year (or 10–13 months), the median number of children per survey increased from 379 (IQR 210–509) in the 1990s, to 402 (IQR 180–628) in the 2000s, and 582 (IQR 261–1074) in the 2010s. For the formula and animal milk consumption indicators, which were based on data restricted to children aged 6–23 months in low-income and middle-income countries, the median number of children per survey rose from 1491 (1000–1878) in the 1990s, to 1760 (1100–2774) in the 2000s, and 2356 (1402–3600) in the 2010s. The data points included and the rates of the indicators analysed are given in the Country representativeness by UNICEF world regions was 22 (88%) of 25 countries in west and central Africa, 16 (67%) of 24 in eastern and southern Africa, seven (37%) of 19 in the Middle East and north Africa, 16 (76%) of 21 in eastern Europe and central Asia, six (75%) of eight in south Asia, ten (30%) of 33 in east Asia and the Pacific, 21 (57%) of 37 in Latin America and the Caribbean, one (50%) of two in North America, and 14 (42%) of 33 in western Europe. Using the World Bank's income classification for the median year of analysis (2010), we included 20 (28%) of 71 high-income, 27 (51%) of 53 upper-middle-income, 35 (63%) of 56 lower-middle-income, and 31 (89%) of 35 low-income countries. We analysed data from 93 (65%) of 144 low-income and middle-income countries, covering more than 70% of the population of children younger than 2 years in these countries.In the pooled analysis of low-income and middle-income countries, exclusive breastfeeding in the first 6 months increased from 35·4% (95% CI 27·4–43·3) in 2000 to 48·6% (41·9–55·2) in 2019. The increase was particularly marked in upper-middle-income countries, where the rate more than doubled from less than 17% in 2000 to nearly 37% in 2019 ; table 2Across all low-income and middle-income countries, exclusive breastfeeding in the first 6 months significantly increased by 0·70 PPs per year  during the analysis period . This waThe rate of formula milk consumption indicators did not increase significantly for low-income and middle-income countries as a whole . HoweverAnnual changes in formula milk consumption for all age ranges were not significant for low-income and middle-income countries as a whole , but uppThe consumption of animal milk by children younger than 6 months decreased by more than half in the period of analysis in low-income and middle-income countries, from 17·5% (95% CI 13·6–21·5) to 9·8% (6·1–13·5), and less markedly for children aged 6–23 months, from 36·3% (26·2–46·4) to 33% (25·1–40·9). Reductions were greatest in upper-middle-income countries . All worAnnual reductions in animal milk consumption for children younger than 6 months were significant in all country income groups but only in upper-middle-income countries for children aged 6–23 months . ReductiAs far as we are aware, this is the first report on trends in various child feeding indicators including high-income countries and low-income and middle-income countries. Given the available data, comparisons among the groups of countries were only possible for the rates of any breastfeeding at age 6 months and 1 year. When all countries with data were pooled together, the rate of any breastfeeding was constant from 2000 to 2019, at just under 90% for children younger than 6 months and around 80% at age 1 year. However, we found increases in high-income and upper-middle-income countries, almost no change in lower-middle-income countries, and reductions in low-income countries. Nevertheless, in low-income countries, approximately nine in ten children aged 1 year were breastfed in 2019, compared with only approximately four in ten in high-income countries.Because the available indicators and societal contexts are different in high-income countries and low-income and middle-income countries, we discuss the findings from these two groups of countries separately. A publication on breastfeeding trends in 51 high-income countries showed that breastfeeding practices improved in most such countries.Our analysis of trends in exclusive breastfeeding for infants younger than 6 months were limited to low-income and middle-income countries with standardised surveys. An improvement was observed from rates of approximately 35% in 2000 to approximately 49% in 2019, thus meaning that the World Health Assembly's initial target of 50% by 2025 will probably be reached, as long as this trend does not reverse.Amamenta e Alimenta strategy in Brazil), legislation and policies , breastfeeding promotion, advocacy, and research, as well as changes in demographic characteristics have effectively improved breastfeeding rates in recent decades.The observed increase in exclusive breastfeeding over the past decades is consistent with efforts globally to protect, promote, and support this practice,In low-income and middle-income countries, breastfeeding promotion strategies have been focused more intensively on young infants, for whom exclusive breastfeeding confers important protection against mortality due to infectious diseases.Lancet Breastfeeding Series, the overall drop in continued breastfeeding at age 12–15 months in low-income and middle-income countries was partially due to declines among the poorest 20% of people in each country.Only low-income countries are close to 100% breastfeeding at age 12 months, which might be attributed to long-standing cultural practices rather than to breastfeeding promotion activities.Standardised survey data also allowed the study of formula and other types of milk consumption in low-income and middle-income countries. Formula milk consumption showed an alarming increase in upper-middle-income countries, particularly among children older than 6 months. Since the mid-20th century, commercial breastmilk substitutes have been available in most high-income countries. In the 1980s, the Code was developed to control the marketing of formula milk targeted at infants up to age 6 months.The paradoxical combination of increases in exclusive breastfeeding and in formula milk consumption from birth to age 23 months in upper-middle-income countries is explained by sharp declines in children who consume animal milk, a finding that was also observed to a lesser extent in lower-middle-income countries. As far as we are aware, this is the first multi-country study on trends in the consumption of animal milk. Growing affluence in middle-income countries is likely to be behind the replacement of animal milk by formula.Our analysis by world regions showed some important patterns. All regions showed increasing trends in exclusive breastfeeding, with one exception: the Middle East and north Africa. Downturns or stable trends have been described for some highly populated countries in the region, such as Egypt, Yemen, and Jordan, corroborating our findings.In 2019, UNICEF published an analysis of global trends in exclusive breastfeeding from 2005 to 2018 covering 80 low-income and middle-income countries.European Health Information Gateway from WHO Europe. More detailed analyses of factors that might affect exclusive breastfeeding, such as area of residence  or maternal reproductive or nutritional factors, were out of the scope of the present descriptive analyses.The limitations of our work include the small number of surveys for some regions of the world, such as east Asia and the Pacific and the Middle East and north Africa. The same limitation affected the 2019 UNICEF trend estimates. This gap highlights the need for more countries to routinely do standardised national surveys. Another limitation is the lack of trend data for highly populated countries, such as Mexico, Russia, and China, with China accounting for a third of world formula milk sales in 2018.Over a 20-year period, optimal breastfeeding practices have been promoted worldwide, resulting in increases in breastfeeding, particularly in exclusive breastfeeding and in breastfeeding at age 6 months and 12 months in high-income countries and upper-middle-income countries. However, declines in breastfeeding indicators in low-income countries and in the Middle East and north Africa region deserve attention. The previous global targetIndividual-level data used in these analyses are publicly available at the DHS and MICS websites. Information retrieved through literature review is freely available by accessing the links provided in the We declare no competing interests.


The present study examined whether subjective accelerated aging moderated the relationship between COVID-19 health worries and COVID-19 peritraumatic distress among older adults.s.d. = 6.73, range 60–92) who answered an online questionnaire during the outbreak of COVID-19 pandemic in Israel. Participants completed the measures of background characteristics, exposure to COVID-19, COVID-19 health worries, subjective accelerated aging and COVID-19-based peritraumatic distress.The sample consisted of 277 older adults (M = 69.58, Higher levels of COVID-19 health worries were correlated with higher levels of peritraumatic distress symptoms among older adults. Moreover, those reporting accelerated aging also reported a higher level of peritraumatic distress. Finally, the interaction between COVID-19 health worries and subjective accelerated aging predicted peritraumatic distress, suggesting that COVID-19 worries were associated with peritraumatic distress to a stronger degree among older adults who felt they were aging faster.These findings indicate that negative views of aging may serve as an amplifying factor for traumatic distress during the COVID-19 pandemic. Although preliminary, the findings provide insight for potential screening and interventions of older adults at risk of developing peritraumatic distress symptoms during the global pandemic. The agiet al., In a systematic review of 43 studies on the mental health consequences of the COVID-19 pandemic, the researchers found a high level of post-traumatic stress symptoms and depressive symptoms among COVID-19 patients . Participants included 277 older adults between the ages of 60 and 92, with an average age of 69.58 (s.d. = 6.73). The majority of the participants were women , married/living with a partner , and had tertiary academic education . Almost half of the participants self-rated their health as good/very good . Less than half of the participants reported having chronic diseases which are associated with increased medical complications due to COVID-19 . The data collection process began after receiving IRB approval from the Ethics Committee at Bar-Ilan University. All participants signed an electronic informed consent form before completing the questionnaire. The background characteristics of the study sample are presented in The current study used data drawn from an online survey in Israel between 16 March and 14 April 2020. On 14 April 2020, the last day of data collection, 12 046 Israeli citizens were tested positive for COVID-19, with 123 deaths, most of them older adults  and 2 (married or leaving with a partner), level of education was classified into one of six categories in line with the International Standard Classification of Educational Degrees (ISCED-97)  to 6  and health status rated from 1  to 5 (very good). In addition, participants were asked if they suffered from chronic diseases , which have been associated with increased medical complications due to COVID-19.Exposure to COVID-19 was assessed by respondents answering yes or no to six questions regarding exposure to the coronavirus . The exposure score was the sum of events the participant was exposed to.COVID-19 health worries were assessed by a four-item scale designed for this study. Participants rated their levels of health worries about self or close relatives and friends developing COVID-19, or infecting others by COVID-19  on a five-point scale from 1 (completely disagree) to 5 (completely agree). In this study, Cronbach's α for COVID-19 health worries was 0.78.Subjective accelerated aging was assessed by a single item  to 5 (my aging rate is very fast)’. Higher scores indicate higher subjective accelerated aging.Peritraumatic distress symptoms were assessed by the 13-item Peritraumatic Distress Inventory  to 4 (extremely true). The peritraumatic distress score was the sum of ratings, with higher scores indicating greater distress. Cronbach's α in a previous study that used the Hebrew translation was 0.87  to conduct a hierarchical regression analysis, where peritraumatic tress was regressed on the following variables across steps: Background characteristics were entered in the first step of the multiple hierarchical regression, COVID-19-related covariates (chronic diseases and exposure to COVID-19) in the second step, COVID-19 health worries and subjective accelerated aging in the third step, and finally the interaction between COVID-19 health worries and subjective accelerated aging was entered in the fourth step. The moderation model (Model 1) was tested, using the PROCESS 3.1 macro for SPSS Hayes, . A prelir = 0.15, p < 0.05) and with peritraumatic distress . Subjective accelerated aging was positively associated with peritraumatic distress . It was important to control for these background characteristics as they were associated with at least one of the study's variables and there has been some variability regarding participants' exposure levels to various forms of COVID-19 stressors (e.g. isolation) . In addition, those who reported higher levels of subjective accelerated aging also reported higher levels of peritraumatic distress symptoms . Finally, the interaction between COVID-19 health worries and subjective accelerated aging predicted peritraumatic distress . This interaction explained an additional 7.74% of the variance , each additional point in COVID-19 health worries score was associated with a non-significant increase of 0.72 points in peritraumatic distress symptoms . However, for older adults reporting a faster rate of aging , each additional point in COVID-19 worries score was associated with a significant increase of 4.64 points in the level of peritraumatic distress symptoms   see .Fig. 1.To the best of our knowledge, the present study is the first to examine the relationship between COVID-19 health worries and peritraumatic distress among older adults, and in particular, the moderating role of subjective accelerated aging in this relationship. As hypothesized, older adults who reported higher levels of COVID-19 health worries also reported higher levels of peritraumatic distress symptoms. Moreover, those who felt they were aging fast (i.e. higher subjective accelerated aging levels) reported a higher level of peritraumatic distress symptoms. Finally, the interaction between these measures accentuates that the relationship between COVID-19 health worries and peritraumatic distress was stronger among people who reported the feeling they were aging faster. The significance of these findings will now be discussed in detail.et al., et al., In line with the first hypothesis, high levels of COVID-19 health worries among older adults were found to be related to higher levels of peritraumatic distress. This association highlights the physical and psychological distress older adults experience during a pandemic. The present findings are in line with the association found between exposure to mass trauma events, such as natural catastrophes and adverse mental health outcomes, in particular PTSD Makwana, . In addiet al., et al., Consistent with our second hypothesis, older adults who felt they were aging faster also reported higher levels of peritraumatic distress. The findings underscore the effect of subjective accelerated aging when coping with stressful life events, such that older adults who sense their aging has accelerated are less resilient in the context of coping with COVID-19. The current findings align with the association found between subjective age and neurobiological accelerated aging , but rather only to the subjective measure of subjective accelerated aging. This finding coincides with previous studies that revealed that older chronological age is not associated with worse mental health outcomes during the COVID-19 pandemic . Fourth, COVID-19 health worries were assessed by a scale that was previously published (Bergman The present study is the first to examine the concept of subjective accelerated aging and its relationship with peritraumatic distress during the COVID-19 pandemic among older adults. The findings have theoretical and practical implications for the prevention of peritraumatic distress during the COVID-19 pandemic, as well as other natural disasters.Theoretically, the findings rejoin earlier results reviewed above and further demonstrate that similar to the biological process of accelerated aging, there appears to be a psychological process by which individuals assess the rate of their aging, which seems to be linked with psychiatric symptoms. We recommend future studies to examine the directionality of this relationship and its contribution to mental health. We also recommend that future prospective studies investigate the mechanisms connecting subjective and objective measures of accelerated aging.et al., On a practical level, the rise in health worries since the COVID-19 outbreak is understandable (Kobayashi


Background: Phenotypes have been identified within heterogeneous disease, such as acute respiratory distress syndrome and sepsis, which are associated with important prognostic and therapeutic implications. The present study sought to assess whether phenotypes can be derived from intensive care patients with coronavirus disease 2019 (COVID-19), to assess the correlation with prognosis, and to develop a parsimonious model for phenotype identification.Methods: Adult patients with COVID-19 from Tongji hospital between January 2020 and March 2020 were included. The consensus k means clustering and latent class analysis (LCA) were applied to identify phenotypes using 26 clinical variables. We then employed machine learning algorithms to select a maximum of five important classifier variables, which were further used to establish a nested logistic regression model for phenotype identification.Results: Both consensus k means clustering and LCA showed that a two-phenotype model was the best fit for the present cohort (N = 504). A total of 182 patients (36.1%) were classified as hyperactive phenotype, who exhibited a higher 28-day mortality and higher rates of organ dysfunction than did those in hypoactive phenotype. The top five variables used to assign phenotypes were neutrophil-to-lymphocyte ratio (NLR), ratio of pulse oxygen saturation to the fractional concentration of oxygen in inspired air (Spo2/Fio2) ratio, lactate dehydrogenase (LDH), tumor necrosis factor α (TNF-α), and urea nitrogen. From the nested logistic models, three-variable  and four-variable (three-variable plus TNF-α) models were adjudicated to be the best performing, with the area under the curve of 0.95 [95% confidence interval (CI) = 0.94–0.97] and 0.97 (95% CI = 0.96–0.98), respectively.Conclusion: We identified two phenotypes within COVID-19, with different host responses and outcomes. The phenotypes can be accurately identified with parsimonious classifier models using three or four variables. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia is a newly recognized infectious disease first reported in Wuhan, China, and expeditiously spread to hundreds of countries with massive mortality rate –4. The cVarious studies have proposed different phenotypes of COVID-19. According to 85 consecutive ICU COVID-19 patients, Azoulay et al. identified three clinical and biological phenotypes at ICU admission using hierarchical clustering. ICU mortality rates were 8, 18, and 39% in clusters 1, 2, and 3, respectively . GattinoUnsupervised machine learning approaches, such as consensus k means clustering  and lateThe first goal of the study was to identify novel clinical phenotypes in ICU COVID-19 patients, using consensus k means clustering and LCA. The second goal was to develop parsimonious models that could ultimately be used prospectively to identify COVID-19 phenotypes.This single-center, retrospective, observational study was performed at Tongji Hospital, which was designated to admit patients with SARS-CoV-2 infection in Wuhan. Adult patients (≥18 years) with laboratory-confirmed SARS-CoV-2 infection and admitted to ICUs between January 2020 and March 2020 were included in the present study. According to the World Health Organization guidance , laboratThis study was approved by the Research Ethics Commission of Tongji Hospital. Written informed consent was waived by the Ethics Commission because of the emergency circumstance. Patient-level informed consent was not required. Part of present patients have been described previously by Chen et al.  and WangO2), we adopted pulse oxygen saturation (Spo2) instead of PaO2, as well as the fraction of inspired oxygen (Fio2). Sequential Organ Failure Assessment (SOFA) scores were calculated to determine the severity of illness using data from the first 24 h of ICU admission. All patients were closely followed until 28 days after ICU admission. Data were collected using a case record form modified from the standardized International Severe Acute Respiratory and Emerging Infection Consort.All data were drawn from electronic health record data at Tongji hospital (Tongji cohort). Demographic data, chronic comorbidities, vital signs, and laboratory results within the first 24 h after ICU admission were collected, as well as treatments and outcomes. Because of incomplete measurement and recording of arterial oxygen partial pressure , d-dimer, fibrinogen, albumin, and glucose. All variables were collected within 24 h of ICU admission, and we recorded the most abnormal value if a variable was recorded more than once.We selected 26 candidate clinical variables based on their association with severity or outcome of COVID-19, including age, vital signs , markers of inflammation , markers of organ dysfunction [hypersensitive troponin I (hs-TnI), international normalized ratio (INR), platelet (PLT) count, total bilirubin, creatinine, urea nitrogen, lactate dehydrogenase (LDH), and Spr > 0.5). Additional details of consensus k means clustering are presented in Consensus k means clustering was conducted to 26 variables using a partitioning approach. We first assessed the candidate variable distributions, missingness, and correlation. Multiple imputations with chained equations , Bayesian information criteria, entropy, class size , and the Vuong–Lo–Mendell–Rubin (VLMR) likelihood ratio test (which compares fit of model k classes to k-1 classes) were used to determine the optimal number of classes. Once determined, each individual was assigned a class according to model-generated probabilities. More details of LCA are presented in the Based on previous research, we attempted to construct a parsimonious model (three-variable or four-variable model) to predict phenotypes. First, machine learning algorithms, including classification tree with bootstrapped aggregating (bagging), extreme gradient boosting (XGBoost), and gradient boosted model (GBM), were used to identify the most important classifier variables. To select the most important variables, variable importance was used for the bagging model and XGBoost. Relative influence factor of variable was used for GBM. More details of machine learning algorithms are presented in the 2 test or Fisher exact test for categorical variables and Student t-test or Mann–Whitney U-test for continuous variables as appropriate. A p < 0.05 was used to determine statistical significance for all tests. LCA was conducted using Mplus software (version 8.3). All other analyses were done using R (version 3.6.0).Values are presented as the mean (standard deviation) or median (interquartile range) for continuous variables as appropriate and as the total number (percentage) for categorical variables. Comparisons between groups were made using the χDuring the study period, a total of 504 patients with COVID-19 were included in the Tongji cohort. The schematic of study is shown in In Tongji cohort, based on gap statistics, consensus matrix plots, and consensus values , the cond-dimer, higher heart rate, higher respiratory rate, and extreme laboratory values regarding the organ dysfunction . Additionally, in comparison with the hypoactive phenotype, the hyperactive phenotype had significantly higher SOFA score on ICU admission and higher comorbidity rates (The characteristics of phenotypes in the two-class model are shown in ty rates .p < 0.001), high-flow nasal cannula oxygen therapy , non-invasive mechanical ventilation , invasive mechanical ventilation , and renal replacement therapy  during their ICU stay, compared to those with hypoactive phenotype (p < 0.001) and higher rates of organ dysfunction during their ICU stay compared to those assigned to hypoactive phenotype , and no further improvement in model fit was observed when the three-class (p = 0.058), four-class (p = 0.41), or five-class model (p = 0.40) was involved. Good class separation was observed in the two-class model (entropy > 0.80), indicating strong separation between the classes  and four-variable models  were both the best in terms of balancing classifying accuracy and model simplicity.The most important classifier variables from the bagging, XGBoost, and GBM are presented . The top model 4 . ConsideMultivariable analyses showed that three variables or four variables in the model were all predictors of the phenotypes . The AUCThe novel findings of our analyses can be summarized as follows. We identified two distinct COVID-19 phenotypes with different clinical and biological characteristics, mortality, and other clinical outcomes. We also developed a parsimonious model to predict phenotypes of COVID-19 using machine learning algorithms. These findings have important implications for early detection of patients who are likely to develop critical illness, as well as future researches in COVID-19.Clinical and biological heterogeneity of critical illness  is thought to be dead ends for pharmacotherapy trials. Not a single clinical or biological variable was sufficient to identify phenotype . To put io2 and higher positive end-expiratory pressure (PEEP), and prone positioning may differ in type L and type H  and four-variable model  for identifying the hyperactive phenotype of COVID-19. Unlike traditional forward stepwise modeling, we used three machine algorithms to identify the most important classifier variables. The ability to identify phenotypes using a small set of variables is a crucial step toward their clinical application. On the one hand, to predict the occurrence of critical illness in COVID-19: according to 1,590 COVID-19 patients, Wenhua Liang et al. , but COVID-19 patients in Tongji cohort are more heterogeneous with respect to pulmonary variables .Interestingly, different from the ARDS phenotypes , 27, we The first strength of our study is the identification of two class phenotypes for intensive care patients with COVID-19 and development of the first parsimonious model for predicting hyperactive phenotype. The observational nature of the present study is another strength as it included all consecutive patients with COVID-19 during 3 months, and the results are therefore more likely to represent the population as encountered in the ICU in clinical practice.This study also has several limitations. First, our study is a single-center, retrospective, observational study, and we lack the external validation of the phenotypes and the parsimonious model. Testing for COVID-19 phenotypes in more heterogeneous samples is an important direction in future researches. Second, the 26 candidate clinical variables did not fully reflect the host response to SARS-CoV-2 infection; we cannot exclude that adding other markers would provide different phenotypes. Third, whether these phenotypes are dynamic and change over time, resulting in distinct COVID-19 trajectories, is unknown. Finally, although a three- or four-variable model has a good accuracy in predicting the phenotypes, when phenotypes are defined by the parsimonious model rather than the clustering analysis or LCA, we may no longer detect the statistically significant differences in outcomes and treatment responses.In summary, this analysis confirmed the existence of two distinct phenotypes for intensive care patients with COVID-19. We also provide evidence for accurate parsimonious classifier models of COVID-19 phenotypes. Promisingly, these simple models may aid clinicians in predicting which COVID-19 patients are likely to develop critical illness, delivering timely treatments, and improving patient selection in clinical trials, which in turn could significantly impact patient outcomes.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Tongji Hospital Ethics Committee. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.YL and HC conceptualized the research aims, design the study, take responsibility for the integrity of the data and the accuracy of the data analysis. HC did the statistical analysis. HC and ZZ wrote the first draft of the manuscript. All authors contributed to acquisition of data, provided comments and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Secondary school closures aimed at limiting the number of infections and deaths due to COVID-19 may have amplified the negative sexual and reproductive health (SRH) and schooling outcomes of vulnerable adolescent girls. This study aimed to measure pandemic-related effects on adolescent pregnancy and school dropout among school-going girls in Kenya.We report longitudinal findings of 910 girls in their last 2 years of secondary school. The study took place in 12 secondary day schools in rural western Kenya between 2018 and 2021. Using a causal-comparative design, we compared SRH and schooling outcomes among 403 girls who graduated after completion of their final school examinations in November 2019 pre-pandemic with 507 girls who experienced disrupted schooling due to COVID-19 and sat examinations in March 2021. Unadjusted and adjusted generalised linear mixed models were used to investigate the effect of COVID-19-related school closures and restrictions on all outcomes of interest and on incident pregnancy.At study initiation, the mean age of participants was 17.2 (IQR: 16.4–17.9) for girls in the pre-COVID-19 cohort and 17.5 (IQR: 16.5–18.4) for girls in the COVID-19 cohort. Girls experiencing COVID-19 containment measures had twice the risk of falling pregnant prior to completing secondary school after adjustment for age, household wealth and orphanhood status (adjusted risk ratio (aRR)=2.11; 95% CI:1.13 to 3.95, p=0.019); three times the risk of school dropout  and 3.4 times the risk of school transfer prior to examinations  relative to pre-COVID-19 learners. Girls in the COVID-19 cohort were more likely to be sexually active  and less likely to report their first sex as desired . These girls reported increased hours of non-school-related work . In the COVID-19 cohort, 80.5% reported worsening household economic status and COVID-19-related stress was common.The COVID-19 pandemic deleteriously affected the SRH of girls and amplified school transfer and dropout. Appropriate programmes and interventions that help buffer the effects of population-level emergencies on school-going adolescents are warranted.NCT03051789. Experiences from past outbreaks show that emergency response policies have differential effects on girls and women, limiting their economic opportunities in the long term.Adolescent pregnancy constitutes a public health crisis in western Kenya, where over one in five girls enter motherhood during adolescence.In Kenya, adolescent secondary schoolgirls who remained out of school for 6 months due to the COVID-19 lockdown had twice the risk of becoming pregnant and three times the risk of dropping out of school when compared with similar girls graduating just prior to the outbreak.The sexual and reproductive health and schooling outcomes of these girls were affected in multiple ways, with girls reporting heightened sexual debut, sexual coercion and school transfer relative to girls who completed secondary school in the prior year.COVID-19 containment measures negatively affected vulnerable adolescent girls.Gender sensitive policy responses and interventions are needed to buffer the effects of health emergencies on individuals and communities.The COVID-19 pandemic reached Kenya in March 2020, and with the first case came nationwide curfews, lockdowns and restrictions of movement. As part of the containment measures, the Kenyan Government shuttered all schools from March 2020 until January 2021 countrywide,2 3In Kenya, media reports state that adolescent pregnancies have spiked due to COVID-19-related containment measures, raising concerns about the sexual and reproductive health (SRH) and longer-term schooling outcomes of this vulnerable population.COVID-19-related school closures may have inadvertently magnified girls’ barriers to education and SRH vulnerabilities. Evidence suggests that staying in school de facto protects girls against early marriage, pregnancy and sexual and reproductive tract infections such as HIV.This study is nested in the CCG Trial which evaluated the effect of conditional cash transfer and/or menstrual cups on a composite of deleterious outcomes, detailed elsewhere.In this analysis, we use a causal-comparative design to compare pregnancy and schooling outcomes between girls who experienced school closures  and girls who graduated the year prior . Study questionnaires were administered annually to study participants. For comparability, the survey at the initiation of girls’ penultimate year of secondary school, Form 3, is used for both groups to assess baseline study conditions. While schools did not reopen until January 2021 for the majority of learners, students in their last academic year (Form 4) were allowed back to school in October 2020 to prepare for their final examinations. End of study surveys were conducted at school during participants’ last term of study prior to examinations: between May and July 2019 for the pre-COVID-19 cohort and between October and December 2020 for girls in the COVID-19 cohort. If COVID-19 containment measures prevented study staff from entering a school, a location near to the school, such as a church or community hall, was used for follow-up visits with school attending participants informed via the school and all others informed via phone.Female day scholars who were resident of the area were attending 1 of the 12 selected schools and in the designated class years at enrolment, had reached menarche, were not visibly or declared pregnant at enrolment, had no disability precluding participation and had informed parent–guardian consent and gave individual assent to participate were eligible for the cRCT.Girls reporting a prior or current pregnancy and girls who were visibly pregnant or reported pregnant by their schoolteacher were followed up by a study counsellor for documentation of the pregnancy outcome and date of delivery. School dropout and school migration were monitored via school register data, collected termly from all schools to check student attendance and confirmed against teacher report and school visits. For those no longer attending school, home visits were conducted to establish whether girls had discontinued schooling or had transferred to a new school. This follow-up was also done after the completion of the final year examinations to capture if any girls dropped out in their final term. Reason for dropout was also collected during these home visits, including due to pregnancy.Participation in the trial was entirely voluntary; girls could withdraw from the study at any time.The study took place in Rarieda subcounty, Siaya County, western Kenya. The area borders the eastern shores of Lake Victoria and is situated 400 km northwest of the capital Nairobi. The population are mainly subsistence farmers, livestock producers and fisherfolk and are predominantly ethnic Luo.13The study questionnaire included indicators on household conditions, individual characteristics and sexual activity and behaviours. For COVID-19 group girls who experienced school closures, the follow-up questionnaire contained questions on COVID-19-related changes to household income, perception of violence and crime in the community and individual stress.Primary response variables included incident pregnancy: girls who became pregnant during Forms 3–4 contributed to this outcome (projected delivery date between 1 October 2018 and 31 August 2020 in the pre-COVID-19 cohort and delivered or had a pregnancy detected after 1 October 2019 in the COVID-19 cohort). For comparability girls who delivered prior to 1 October, dates were coded as having been pregnant prior to the start of the present study and did not contribute to the endpoint. Schooling outcomes were measured with a binary response: (1) school dropout—girls who discontinued their education prior to the completion of their final exam and (2) school migration—girls who transferred schools prior to the completion of their final exam.Secondary outcomes explored included sexual activity and sexual debut, condom and hormonal contraceptive use, engaging in work for pay and prior-day non-school-related workload measured in hours and sexual violence.To measure if both groups were comparable at baseline in girls’ third year of secondary school, characteristics were selected due to their established importance in understanding girls’ vulnerability to adolescent pregnancy and school dropout. Covariates explored included individual characteristics , family characteristics , household characteristics (socioeconomic status (SES)) and financial characteristics  and transactional sex).Certain characteristics were dichotomised: (1) marital status—‘married, cohabiting, divorced’ versus ‘single, other’; (2) having a baby at home to care for; (3) being harassed for sex in or out of school; (4) having ever been indecently touched; (5) performing work for pay; (6) performing sex for goods or favours and (7) having ever consumed alcohol. Hours of non-school-related tasks or work on the previous day were measured as a continuous variable  and restricted to weekdays when girls would also have school. Certain sexual activity measures were dichotomised and measured cumulatively over different survey rounds: (1) self-reported sexual activity, (2) early sexual initiation—before age 15 years, (3) whether first sex was desired and (4) having had a previous pregnancy. Practices measured only at end of study included: (1) incident sexual debut, (2) experiencing intimate partner sexual violence in the past 6 months, (3) condom use in the past 6 months and (4) current use of hormonal contraceptives. Age of sexual debut was maintained as continuous variable .et al.Participant age was calculated as a girl’s age at the start of the Form 3 academic year, dated 2 January. SES and girls’ orphanhood status were measured only once at study enrolment. Orphanhood status was dichotomised into having no living parent—‘orphan’ versus ‘one or both parents alive’. An absolute index for SES was constructed following the methodology of Kabudula Descriptive statistics were used to summarise sample characteristics. We assessed comparability of baseline covariates via multilevel mixed effects generalised linear models (GLMMs) with a random effect for school. GLMMs were fitted with a Gaussian distribution and identity link function and assessed at a level of significance of 5% for continuous variables; binary endpoints were modelled using a Poisson distribution with a log link function and a robust SE adjustment. To investigate if COVID-19-related school closures and restrictions were associated with the key binary outcomes of interest, GLMMs were constructed with the same model fitting to estimate risk ratios (RRs), adjusted RRs (aRRs) and their corresponding 95% CIs. We calculated unadjusted RRs by fitting the GLMMs to individual follow-up data with the COVID-19 exposure status as the only study variable. We calculated aRRs by adding participant age, household SES and having a living parent to the unadjusted GLMMs; these factors were selected a priori due to their established association with adolescent pregnancy.Overall, 910 participants were included in this study, 507 in the COVID-19 cohort and 403 in the pre-COVID-19 cohort. Complete Form 3 survey data were collected for 434 girls in the COVID-19 cohort (85.6%) and 401 in the pre-COVID-19 cohort (99.5%) , leading10.1136/bmjgh-2021-007666.supp1Supplementary dataAt the start of Form 3, the mean age of participants was 17.2 years (IQR: 16.4–17.9) for girls in the pre-COVID-19 cohort and 17.5 years (IQR: 16.5–18.4) for girls in the COVID-19 cohort, suggesting girls in the COVID-19 cohort were slightly older. Groups were mostly balanced at baseline, with both groups having comparable individual and household characteristics , with thAmong girls experiencing COVID-19 school closures and restrictions, the incidence of dropout was 9.4% versus 3.2% in the pre-COVID-19 cohort . After aSelf-reported sexual activity by Form 4 follow-up was reported by 66.1% of the COVID-19 cohort versus 50.7% of their pre-COVID-19 peers . Incident sexual debut among girls not sexually active at the start of Form 3 was reported by 47.4% of girls in the COVID-19 cohort versus 25.5% of girls in the pre-COVID-19 cohort . Among girls who were sexually active, 36.0% of girls in the pre-COVID-19 cohort reported their first sex as having been desired, while only 17.8% of girls in the COVID-19 cohort reported the same . No differences were seen between groups for girls reporting condom use in the past 6 months or current use of hormonal contraceptives .Girls in the COVID-19 cohort reported similar rates of performing work for pay as their pre-COVID-19 cohort peers; however, the number of hours worked in the prior school day was significantly higher (3.32 hours vs 2.63 hours), indicating a heightened non-school-related workload at home . Participants’ reported exposure to indecent touching and sexual violence was comparable between the two groups.At end of study, among the 443 girls who experienced the COVID-19 containment measures, 442 completed a COVID-19-related questionnaire which captured information on pandemic-related changes in economic conditions, violence and individual stress. Of these girls, 80.5% reported their household income decreased during the pandemic, with 41.7% of previously employed girls reporting losing their source of income. The majority of girls who experienced COVID-19-related restrictions voiced worrying about money (59.7%) and running out of food (68.1%). Girls reported elevated stress due to COVID-19, with most girls worried about contracting or transmitting the disease .A sizeable percentage of girls reported an increase in violence (36.2%) and crime (43.4%) in their communities after COVID-19; 36.2% of girls in the COVID-19 cohort reported feeling less safe at home.The COVID-19 school closures and containment measures notably impacted the daily life of vulnerable adolescent schoolgirls in western Kenya. This study follows girls from the start of their third year of secondary school through to the conclusion of their final school examinations to measure COVID-19’s impact on their sexual health and schooling. Girls, whose schooling was disrupted due to COVID-19, experienced a threefold risk of dropping out of school and 3.4 times the risk of changing schools relative to their peers. Girls who experienced school closures also had twice the risk of falling pregnant prior to completing school, with 1 in 10 COVID-19 cohort girls becoming pregnant prior to sitting their examinations. This group also saw increased sexual activity, with nearly one in two COVID-19 cohort girls becoming sexually active during follow-up. These girls were twice as likely to report their first sex was not desired when compared with girls who did not experience COVID-19 during secondary school. Girls in the COVID-19 cohort also reported increased work hours and four out of every five girls in this group reported their household income decreased during the pandemic.Our results show that adolescent girls in this study area, which typifies many rural areas of sub-Saharan Africa, are particularly vulnerable to sexual and reproductive harms during emergencies. These findings are in line with other studies that have shown heightened rates of adolescent pregnancies after population-level crises. For example, during the west-African Ebola outbreak, in Sierra Leone teenage pregnancies increased by 25% and were hypothesised to represent underlying vulnerabilities to transactional sex and sexual exploitation.Our study highlighted the detrimental and potentially long-term effects COVID-19 containment measures had on girls schooling. Girls enrolled in secondary school during COVID-19 were 3.4 times more likely to change schools relative to their non-COVID-19 peers. In addition to being disruptive to education, among adolescents, domestic migration has been associated with unstable living arrangements and early engagement in high-risk sexual activity and informal work.Schools act as a social vaccine in preventing adolescent SRH harms, including pregnancy.In Kenya, increased domestic abuse within households has been reported, partly due to pandemic-related stress and income loss.43–46Some limitations of this study should be considered. First, girls in the COVID-19 cohort were followed up for an additional four months due to their delayed KCSE examination. This unequal follow-up may have given them additional time to fall pregnant relative to their pre-COVID-19 peers. In response we employed person–time analysis to adjust for this unequal follow-up. To note, because this study was completed immediately after the COVID-19 cohort girls sat their final examinations, girls who were in the early months of their pregnancy and sat their examination may have been missed. Consequently, the estimated pregnancy prevalence in the COVID-19 cohort may be an underestimate of all girls who became pregnant while still enrolled in secondary school. Because the trial was still active in the study area for a year after pre-COVID-19 girls completed their school, all pregnancy outcomes occurring during school were documented in full. Second, behavioural data were self-reported and certain sensitive indicators, such as sexual activity and sexual behaviours, may be under-reported. We also resensitised girls at every survey round on the importance of truthful reporting which may have improved response bias but led to differential reporting between survey rounds. In response, we took a cumulative measure of girls reporting sexual activity at any survey. Third, this study only measured effects in a secondary school-going population and may not reflect vulnerability to harms of other populations such as out-of-school girls. Lastly, this study was embedded within a cRCT that was testing different interventions to improve girls schooling and SRH harms, with girls in the COVID-19 cohort experiencing interventions for an additional year relative to their pre-COVID-19 comparators. We note that any beneficial impact from being in the trial longer would push the measure of effect in the COVID-19 cohort towards the null, suggesting the results presented here may be conservative estimates of the pandemic’s effects on girls’ health and schooling relative to non-trial schoolgirl populations.Our study provides important insights into the SRH and schooling consequences of the COVID-19 pandemic on a vulnerable population of originally school-going adolescent girls in rural Kenya. We found that the COVID-19 pandemic and related school closures had significant harmful effects on schooling and girls’ sexual activity, including increased pregnancy incidence and possible sexual coercion. These harms, occurring during adolescence, could have lasting consequences well after the pandemic subsides. Country reliance on school closures as a primary mechanism to abate infectious disease outbreaks must take into consideration the resulting side effects that occur. Appropriate programmes and interventions that help buffer the effects of population-level emergencies on vulnerable adolescents are warranted.


Large cell neuroendocrine carcinoma of the lung (LCNEC) is a rare and highly aggressive type of lung cancer, with a complex biology that shares similarities with both small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). The prognosis of LCNEC is poor, with a median overall survival of 8–12 months. The diagnosis of LCNEC requires the identification of neuroendocrine morphology and the expression of at least one of the neuroendocrine markers . In the last few years, the introduction of next-generation sequencing allowed the identification of molecular subtypes of LCNEC, with prognostic and potential therapeutic implications: one subtype is similar to SCLC (SCLC-like), while the other is similar to NSCLC (NSCLC-like). Because of LCNEC rarity, most evidence comes from small retrospective studies and treatment strategies that are extrapolated from those adopted in patients with SCLC and NSCLC. Nevertheless, limited but promising data about targeted therapies and immune checkpoint inhibitors in patients with LCNEC are emerging. LCNEC clinical management is still controversial and standardized treatment strategies are currently lacking. The aim of this manuscript is to review clinical and molecular data about LCNEC to better understand the optimal management and the potential prognostic and therapeutic implications of molecular subtypes. Lung neuroendocrine tumors account for approximately 20% of all lung cancers . PulmonaTypically, LCNEC and SCLC are characterized by higher mitotic rates and more extensive necrosis compared to lower grade neuroendocrine tumors, namely TC and AC. Both LCNEC and SCLC are highly aggressive tumors, with 5-year overall survival (OS) rates at advanced stage of approximately 15–25% and 5%, respectively ,10. ReceConsidering LCNEC rarity and clinicopathological similarities with both SCLC and NSCLC of lung cancer, treatment approaches of LCNEC are extrapolated from those of SCLC and NSCLC, since studies specifically addressing patients with LCNEC are small in size and retrospective in design ,13. MoreFurthermore, genomic and transcriptomic analyses have shown the existence of different molecular subtypes of LCNEC, which could potentially allow patients to receive more personalized treatments ,14,15,16Clinical management of LCNEC is currently controversial and treatment strategies are not standardized due to the lack of definitive evidence supporting a specific approach, so that there is an emerging need to define the optimal management for this aggressive disease. The aim of this review is to collect current clinical and emerging molecular data regarding LCNEC, and to highlight new potential treatment strategies, also based on new molecular subtypes.Macroscopically, most LCNECs appear as peripherally located nodular and necrotic masses of the lung. Histological/Cytological diagnosis of LCNEC is complex because small biopsies are usually insufficient due to crushing artifacts, whereas a surgical lung biopsy is usually required .2 (average 60–80 mitoses per 2 mm2) .,28.2, anLCNEC shows a high gene mutation transversion:transition ratio which is associated with smoking and a high tumor mutational burden (8.5 to 10.5 mutations per megabase) ,15. MoreTP53) and RB1, and an NSCLC-like subset (56%), lacking RB1 and TP53 co-alteration and characterized by NSCLC typical mutations such as serine/threonine kinase 11 (STK11), Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations (characteristic of LUAD), and kelch-like ECH associated protein 1 (KEAP1) mutations  -FDG) avid, thus making [18F]-FDG positron emission tomography ([18F]-FDG-PET) a useful staging tool with high sensitivity and specificity and consequently a valid imaging assessment to select patients with early-stage disease for radical treatment . Biopsy The prognosis of patients with LNCEC is dismal overall, with a five-year survival rate as low as 8% in the advanced stage . Age, seThe newly identified molecular subtypes of LCNEC currently lack a clear prognostic correlate: indeed, SCLC-like LCNEC was associated with a not significant trend toward shorter RFS compared to the NSCLC-like one, with no difference in terms of OS .These data suggest that lymphatic invasion and the presence of neuroendocrine IHC markers might be potentially considered as prognostic factors in an integrated LCNEC-specific staging system ,49.LCNEC is diagnosed at an early stage  in about 25% of cases and, despite the lack of strong evidence, surgery is considered the cornerstone of treatment in patients with early-stage LCNEC in those fit for surgery ,50,51. AHowever, retrospective and population-based studies have reported that surgery yields a significant survival improvement in patients with early stage LCNEC and that it was independently associated with better OS ,55,56. IConsidering the high recurrence rate after surgery  , periopeThe rarity of LCNEC makes it difficult to perform prospective studies and thus to properly evaluate the real value of adjuvant chemotherapy, but several retrospective studies showed a probable benefit of cisplatin-based regimens in this setting ,54,59,60A small single-arm prospective trial of adjuvant chemotherapy with cisplatin-etoposide in 15 resected LCNEC patients showed a significant survival improvement in these patients when compared to historical data  .With respect to the type of adjuvant chemotherapy, a phase III trial enrolling patients with high-grade NENs of the lung (both LCNEC and SCLC) failed to demonstrate the superiority of cisplatin-irinotecan compared to cisplatin-etoposide as an adjuvant regimen . Indeed,The role of radiotherapy (RT) in the treatment of pulmonary LCNEC is still unclear, but some authors suggest its use in the setting of non-metastatic disease . A largeFurthermore, the role of post-operative RT was evaluated in two large retrospective trials among patients with resected early-stage LCNEC, demonstrating that RT was not associated with survival benefit ,64. On tTo sum up, dedicated guidelines to support the management of patients with early-stage LCNEC are lacking. Nevertheless, available data suggest that surgery is currently the mainstay of treatment in patients with resectable LCNEC, whereas RT should be preferentially reserved for patients not eligible for surgery, particularly in the context of a multimodal approach that includes chemotherapy. The reviewed studies are reported in Locally-advanced LCNEC, namely stage IIIB-C, is diagnosed in approximately 20% of cases ,73. CurrA large retrospective study of 5797 patients with locally-advanced LCNEC compared the use of definitive chemoradiotherapy to chemotherapy alone, showing an mOS of 16.1 months in the chemoradiotherapy group compared to 11.9 months in the chemotherapy group . SimilarConsidering the limited data and the absence of currently available large prospective clinical trials, chemoradiotherapy with platinum-etoposide for four cycles could represent an effective treatment for patients with unresectable stage III LCNEC.p = 0.09) and mOS  [Prophylactic cranial irradiation (PCI) in limited-stage SCLC with complete remission after chemoradiotherapy is associated with a lower risk of symptomatic brain metastases and improved OS . Similar = 0.05) , and canMost of the patients with LCNEC (40–50%) are diagnosed with metastatic disease ,72. FirsThe only prospective evidence comes from a phase II multicenter study which evaluated the efficacy of cisplatin-etoposide among 42 patients with untreated advanced LCNEC . The medEfficacy of different chemotherapy drugs may vary depending on the genomic profile of the tumor and cfDNA analysis can be a useful and convenient way to subgroup LCNEC and drive treatment.RB1 and TP53) and NSCLC-like subtypes [RB1-wild-type advanced LCNEC had a significantly longer OS when treated with NSCLC-based chemotherapy  compared with platinum-etoposide chemotherapy , while no difference was observed in RB1 mutated patients treated with different chemotherapy regimens [In a retrospective study, 63 patients with LCNEC were divided according to cfDNA and tumor DNA profile in SCLC-like  might be active in LCNEC, as shown by a retrospective study in patients with unresectable LCNEC that have been compared to a cohort of patients with SCLC. The study reported similar outcomes in the two groups of patients with ORR of 50% for patients with LCNEC and 53% in patients with SCLC and mOS of 10 and 12.3 months, respectively . FurtherEverolimus is a tyrosine-kinase inhibitor (TKI) of mTOR. A multicenter phase II trial evaluated the efficacy of everolimus in combination with paclitaxel and carboplatin for four cycles followed by maintenance everolimus until progression in 49 chemotherapy-naïve patients with stage IV LCNEC . The ORRTP53 and RB1 status. Reviewed studies are reported in First-line treatment of metastatic LCNEC is still controversial, since most of the available studies are small and retrospective and some also showed conflicting results. However, platinum-etoposide chemotherapy seems to be more active than other regimens commonly used in NSCLC, but larger and prospective studies are needed to define the optimal treatment in this setting and the predictive value of molecular alterations, such as pure LCNEC, targetable driver mutations can occur in mixed forms of LCNEC-adenocarcinoma, especially EGFR mutations [EGFR mutation treated with a EGFR-targeting TKI was reported about 10 years ago: a 66-year-old, never-smoker female was diagnosed with stage IV LCNEC carrying an EGFR exon 19 deletion and received gefitinib, a first-generation EGFR inhibitor, showing a dramatic response after two months of treatment, with a time to progression longer than six months [EGFR exon 19 deletion and treated with icotinib, another first-generation EGFR inhibitor, exhibited a long-lasting response for eight months [ALK)-translocation, which is another driver alteration commonly found in young, non-smoker patients with NSCLC and treated with selective TKI were reported, with controversial results [While they are seldom detected in utations . Moleculutations . The firx months . Anothert months . A few c results ,93.PI3KCA (3%), PTEN (4%), AKT2 (4%) RICTOR (5%), mTOR (1%) and alterations in EGFR (1%), ERBB2 (4%) and FGFR1 (5%) in the LCNEC group, with similar frequency to what was observed in the SCLC group. Apart from the aforementioned study of everolimus, an mTOR inhibitor [Alterations of the PI3K/AKT/mTOR pathway are commonly encountered in SCLC and LCNEC . A Japannhibitor , no drugnhibitor .Because prospective studies are difficult to carry on in such a rare and controversial disease, international efforts would be needed, hopefully in modern-designed trials, such as umbrella trials, to investigate the activity and efficacy of targeted therapies in patients with LCNEC. To date, considering the limited data on the efficacy of targeted therapy in LCNEC, NGS is not routinely recommended but could be useful after progression to first-line treatment or in non-smoker patients.Recently, the combination of immunotherapy with platinum-etoposide chemotherapy has been established as the standard first-line treatment in advanced SCLC based on the results of two randomized large prospective trials demonstrating a significative OS benefit compared to chemotherapy alone ,96. HoweA retrospective analysis including 661 stage IV LCNEC patients from the NCDB evaluated the impact of ICIs on the OS of 37 patients. The use of ICIs was associated with improved OS, whereas 12 and 18-month survival rates were 34.0% and 29.1%, respectively, compared to 24.1% and 15.0% in the non-ICI group . FurtherAs recently observed, the non-neuroendocrine SCLC inflamed phenotype (SCLC-I) showed increased susceptibility to the addition of anti-programmed death ligand 1 (PD-L1) inhibition to chemotherapy , but to LCNEC is a rare and aggressive lung tumor with a dismal prognosis and a challenging management. Because of its rarity and the peculiar biology, which is still largely not defined, patients with LCNEC should be addressed to tertiary centers with specific knowledge about rare neuroendocrine lung disease for a careful evaluation and treatment. Treatment strategies in both early-stage and advanced disease are not yet established considering the lack of prospective evidence. However, in current clinical practice, patients with advanced LCNEC receive chemotherapy regimens commonly used for SCLC patients, with similar or worse results in terms of response and survival. Recent findings about LCNEC molecular subtypes suggest that the treatment approach could be driven by molecular features, but further and larger studies are needed. Moreover, limited but promising data were reported regarding the efficacy of ICIs and, to a lesser extent, targeted therapies in these patients. Therefore, molecular testing and the use of genetic signatures could represent important tools to better understand the nature of LCNEC and to select the best treatment approach in this rare disease. Finally, the inclusion of LCNEC patients in clinical trials is strongly recommended in order to validate new treatment options and potentially correlate genomic profiles with response to different treatment strategies.