UNASSIGNED: Surgical volume is known to influence failure to rescue (FTR), defined as death following a complication. Robotic lung surgery continues to expand and there is variability in outcomes among hospitals. We sought to estimate the contribution of hospital-based factors on outcomes and FTR following robotic right upper lobectomy (RRUL).
UNASSIGNED: Using the Centers for Medicare and Medicaid Services inpatient claims database, we evaluated all patients age ≥65 years with a diagnosis of lung cancer who underwent RRUL between January 2018 and December 2020. We excluded patients who had undergone segmentectomy, sublobar, wedge, or bronchoplastic resection; had metastatic or nonmalignant disease; or had a history of neoadjuvant chemotherapy. Primary outcomes included FTR rate, length of stay (LOS), readmissions, conversion to open surgery, complications, and costs. We analyzed hospitals by tertiles of volume and Medicare Mortality Index (MMI). Defined as the institutional number of deaths per number of survivors, MMI is a marker of overall hospital performance and quality. Propensity score models were adjusted for confounding using goodness of fit.
UNASSIGNED: Data for 4317 patients who underwent robotic right upper lobectomy were analyzed. Hospitals were categorized by volume of cases (low, <9; medium, 9-20; high, >20) and MMI (low, <0.04; medium, 0.04-0.13; high, >0.13). After propensity score balancing, patients from tertiles of lowest volume and highest MMI had higher costs ($34,222 vs $30,316; P = .006), as well as higher mortality (odds ratio, 7.46; 95% confidence interval, 2.67-28.2; P < .001). Compared to high-volume centers, low-volume centers had higher rates of conversion to open surgery, respiratory failure, hemorrhagic anemia, and death; longer LOS; and greater cost (P < .001 for all). The C-statistic for volume as a predictor of overall mortality was 0.6, and the FTR was 0.8. Hospitals in the highest tertile of MMI had the highest rates of conversion to open surgery (P = .01), pneumothorax (P = .02), and respiratory failure (P < .001). They also had the highest mortality and rate of readmission, longest LOS, and greatest costs (P < .001 for all) and the shortest survival (P < .001). The C-statistic for MMI as a predictor of overall mortality was 0.8, and FTR was 0.9.
UNASSIGNED: The MMI incorporates hospital-based factors in the adjudication of outcomes and is a more sensitive predictor of FTR rates than volume alone. Combining MMI and volume may provide a metric that can guide quality improvement and cost-effectiveness measures in hospitals seeking to implement robotic lung surgery programs.

BACKGROUND: Maternal mortality is an important indicator to assess the quality of services provided by the health care system. However, maternal near-misses as well as maternal mortality are also indicators of how well the health care system serves pregnant women. To improve our healthcare system in terms of investigative capacity, infrastructure, and personnel, a near-miss registry can provide important information on gaps in pregnancy facilities. This will help us to identify the requirements for referral facility improvements and the need for various health awareness programs. We, therefore, designed this study to analyze the various near-miss events in mothers and compare them with maternal mortality.
METHODS: Present study was conducted in the Department of Obstetrics and Gynecology, Lala Lajpat Rai Memorial (L.L.R.M.) Medical College associated with Sardar Vallabh Bhai Patel (S.V.B.P.) Hospital Meerut, Uttar Pradesh (UP), India for a period of one year and data were collected retrospectively from January 2022 to January 2023. All patients with life-threatening conditions such as excessive bleeding during pregnancy, hypertensive disorders of pregnancy (HDP), and septicemia that occurred during pregnancy or childbirth or within 42 days of termination of pregnancy and required ICU admissions, were included in the study. The total number of deliveries during the study period was 4,360 with 4,333 live births (LB). The total number of eligible cases was 79, out of which 52 were identified as maternal near misses and 27 were maternal mortality. Various maternal mortality and near-miss indices were analysed and statistical analysis was done using the SPSS version 21 (IBM Corp., Armonk, NY, USA).
RESULTS: Our hospital\'s maternal mortality ratio (MMR) was 623/1lakh (0.623%), which is higher than the probability due to the deficiency of appropriate medical services in the nearby areas of western UP. The number of maternal near misses per 1000 LB (maternal near-miss ratio [MNMR]) was 12/1000 LB and the severe maternal outcome rate (SMOR) was 18/1000 LB (1.82%). In our study, hemorrhage and hypertensive disorder in pregnancy were the leading cause of morbidity and mortality followed by sepsis and severe anemia. Among organ dysfunction cardiac illness followed by respiratory dysfunction was the leading cause of morbidity and mortality.
CONCLUSIONS: It is clear that there is a high burden of maternal near-miss in developing countries. There should be the establishment of well-equipped referral units at the periphery with trained manpower. The establishment of obstetrical high-dependence units (HDUs), rapid availability of blood and blood products, training of staff, and availability of multidisciplinary teams can minimize maternal mortality and morbidity.

Masking was the single most common non-pharmaceutical intervention in the course of the coronavirus disease 2019 (COVID-19) pandemic. Most countries have implemented recommendations or mandates regarding the use of masks in public spaces. The aim of this short study was to analyse the correlation between mask usage against morbidity and mortality rates in the 2020-2021 winter in Europe. Data from 35 European countries on morbidity, mortality, and mask usage during a six-month period were analysed and crossed. Mask usage was more homogeneous in Eastern Europe than in Western European countries. Spearman\'s correlation coefficients between mask usage and COVID-19 outcomes were either null or positive, depending on the subgroup of countries and type of outcome (cases or deaths). Positive correlations were stronger in Western than in Eastern European countries. These findings indicate that countries with high levels of mask compliance did not perform better than those with low mask usage.

A wide variety of social determinants of health have been associated with various risks and impacts on quality of life. Specifically, poverty, lack of insurance coverage, large household sizes, and social vulnerability are all factors implicated in incidence and mortality rates of infectious disease. However, no studies have examined the relationship of these factors to the COVID-19 pandemic on a state-wide level in Florida. Thereby, the objective of this study is to examine the relationship between average household size, poverty, uninsured populations, social vulnerability index (SVI), and rates of COVID-19 cases and deaths in Florida counties. The objective was accomplished by analyzing the cumulative case and death reports from state and local health departments in Florida. The data was compiled into a single dataset by the CDC COVID-19 Task Force. Using US Census Bureau data, all Florida counties were classified into tertiles of the separate categories of poverty rate, average household size, uninsured rates, and SVI (Social Vulnerability Index). The poverty level was classified as low (0-12.3%), moderate (12.3-17.3%), and high (>17.3% below the federal poverty line). The uninsured population proportion was classified as low (0-7.1%), moderate (7.1-11.4%), and high (>11.4% uninsured residents). Average county household size was classified as low (0-2.4), moderate (2.4-2.6), and high (>2.6). The Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index (SVI) used US census data on 15 social determinants of vulnerability to evaluate and assist disadvantaged communities. SVI tertiles were low (0-0.333), moderate (0.334-0.666), and high (0.667-1) on a range of 0-1, with higher numbers signifying communities with many factors of social vulnerability. The mean cumulative cases and deaths per 100,000 inhabitants were calculated in each tertile for each category. Analysis of the data revealed that case and mortality rates due to COVID-19 in the high poverty counties were markedly higher in Florida than the national average. In contrast, moderate and low poverty rates were below average. Similarly, counties with a high SVI had case and mortality rates greatly above state and national averages. Counties with a high proportion of uninsured displayed the highest case rates. However, mortality rates were the highest in counties with a low proportion of uninsured individuals. No clear correlation was observed between COVID-19 rates and household size. It was concluded that compiled CDC and US census data suggests a significant correlation between poverty, social vulnerability, lack of insurance coverage, and increased incidence and mortality from COVID-19. Future research should statistically analyze the correlations and examine the individual factors of SVI as potential COVID-19 predictors.

OBJECTIVE: This study seeks to examine the prevalence of maternal morbidities and deaths in Ayder Comprehensive Specialized Hospital from 1 July 2018 to 30 June 2019.
METHODS: This was a cross-sectional study. Total purposive sampling method was employed to collect data prospectively using modified World Health Organization criteria for baseline assessment of maternal near-miss and mortality. Pregnant women or those who are within 42 days postpartum/any form of pregnancy termination that satisfy the inclusion criteria were enrolled.
RESULTS: A total of 691 mothers were recorded as having severe maternal complications. Out of these, 170 women developed severe maternal outcome, ending with 146 maternal near-miss cases and 24 maternal deaths. The maternal near-miss ratio and maternal mortality ratio were 28.5 per 1000 live births and 469.1 per 100,000 live births, respectively. The overall mortality index was 14%. The top underlying causes of severe maternal complications were the infamous triads of preeclampsia (n = 303, 43.8%), obstetric hemorrhage (n = 166, 24.0%) and sepsis (n = 130, 18.8%). About 62.5% of mothers who died were not admitted to intensive care unit.
CONCLUSIONS: This study found that the infamous triads of preeclampsia, obstetric hemorrhage and sepsis persist as the commonest causes of severe maternal complications in the study area. A significant number of women with severe maternal outcome were not admitted to intensive care unit. It also highlights that the severe maternal complications, severe maternal outcome, maternal near-miss ratio and mortality index in the study area are disproportionately higher than the global average. These staggering numbers call for a system re-thinking at multiple junctures.

Background and objective Database research has shaped policies, identified trends, and informed healthcare guidelines for numerous disease conditions. However, despite their abundant uses and vast potential, administrative databases have several limitations. Adjusting outcomes for comorbidities is often needed during database analysis as a means of overcoming non-randomization. We sought to obtain a model for comorbidity adjustment based on Clinical Classifications Software Refined (CCSR) variables and compare this with current models. Our aim was to provide a simplified, adaptable, and accurate measure for comorbidities in the Agency for Healthcare Research and Quality (AHRQ) databases, in order to strengthen the validity of outcomes.  Methods The Nationwide Inpatient Sample (NIS) database for 2018 was the data source. We obtained the mortality rate among all included hospitalizations in the dataset. A model based on CCSR categories was mapped from disease groups in Sundararajan\'s adaptation of the modified Deyo\'s Charlson Comorbidity Index (CCI). We employed logistic regression analysis to obtain the final model using CCSR variables as binary variables. We tested the final model on the 10 most common reasons for hospitalizations. Results The model had a higher area under the curve (AUC) compared to the three modalities of the CCI studied in all the categories. Also, the model had a higher AUC compared to the Elixhauser model in 8/10 categories. However, the model did not have a higher AUC compared to a model made from stepwise backward regression analysis of the original 21-variable model. Conclusion We developed a 15-CCSR-variable model that showed good discrimination for inpatient mortality compared to prior models.

UNASSIGNED: The major causes of maternal near miss (MNM) and maternal death (MD) are similar, so review of MNM cases is likely to yield valuable information regarding severe morbidity, which, if untreated may lead to maternal mortality.
UNASSIGNED: The objective is to determine frequency of near miss cases and identify the risk factors associated with MNM.
UNASSIGNED: A cross-sectional study was done from June 2015 to October 2017 in three hospitals in Manipur and Nagaland. All cases of MNM, which occurred during this period, were included and were reviewed using their records. Family members and health care providers of 9 recent cases were interviewed. Data collected were coded and relevant themes were identified.
UNASSIGNED: There were 32,110 deliveries, 147 near miss cases and 12 MDs, resulting in maternal mortality ratio of 38/100,000 live birth (LB), severe maternal outcome ratio of 5/1000 LB and MNM ratio of 4.6/1000 LB. MNM to mortality ratio was 12.2:1 and mortality index was 7.5%. 83% of the cases of MNM were pregnancy related while 15.6% were related to preexisting disorders. The three delays remain the decisive factors in maternal mortality.
UNASSIGNED: Most of the near miss cases experienced delay in decision to seek health care, which resulted from underestimating the severity of various pregnancy-related conditions. Poor knowledge of the risk of warning signs of pregnancy plays a major part in the delay of management.

We carefully studied the article titled \"A practical laboratory index to predict institutionalization and mortality - an 18-year population-based follow-up study\" written by Heikkilä et al. and published in BMC Geriatrics on 25 February 2021 with great interest. We would like to make some comments regarding this article and tool. Laboratory Index (LI) has been executed with the data of 728 patients who had followed-up in our center, however the LI score was not able to predict the 10-year and 18-year mortality. Therefore, a question mark has been aroused in our minds at some points. Neither frailty nor comorbidities were considered in this index. For a geriatric patient, it would be inadequate to evaluate laboratory results regardless of the clinical status. Similarly, it would not be appropriate to predict mortality only on the basis of laboratory results without considering the clinical status of the patient.We think that although the recent study has a great impact, it can be improved by incorporating data on the comorbidities and frailty status of the patients into the analysis.

UNASSIGNED: Women who survive life-threatening complications related to pregnancy and delivery have many common aspects with those who die of such complications. This similarity brought forward the near miss concept in maternal health. Analysis of the similarities, differences, and the relationship between these two groups of women provide a complete assessment of quality of maternal health care.
UNASSIGNED: The aim of this study is to assess the baseline indices of maternal near miss (MNM) and analyze the quality of care at a tertiary care center in Andaman and Nicobar Islands.
UNASSIGNED: Facility-based, cross-sectional study.
UNASSIGNED: The study was conducted for a period of 18 months from January 1, 2015, to August 31, 2016. Cases, who met the World Health Organization (WHO) criteria of severe obstetric morbidity, were included and followed up during their hospital stay and till their discharge or death. Quality of maternal health care was assessed through the WHO near-miss criteria and criterion-based clinical audit methodology.
UNASSIGNED: Descriptive statistics using mean and percentages and Student\'s t-test were used.
UNASSIGNED: Among 4720 women who delivered in our hospital, there were 4677 live births, 52 patients were near miss, and there were 9 maternal deaths. The MNM incidence ratio was 11.11%, the MNM mortality ratio was 5.77, and the mortality index 14.75%. The most common cause of maternal morbidity was hemorrhage followed by hypertensive disorders.
UNASSIGNED: Improving referral systems, effective use of critical care, and evidence-based interventions can potentially reduce severe maternal outcomes.

OBJECTIVE: This study aimed to estimate the incidence of maternal near-miss (MNM) morbidity in a tertiary hospital setting in Turkey.
METHODS: In this retrospective study, we concluded 125 MNM patients who delivered between January 2017 and December 2017 and fulfilled the WHO management-based criteria and severe pre-eclamptic and HELLP patients which is the top three highest mortality rates due to pregnancy. Two maternal death cases were also included. The indicators to monitor the quality of obstetric care using MNM patients and maternal deaths were calculated. Demographic characteristics of the patients, the primary diagnoses causing MNM and maternal deaths, clinical and surgical interventions in MNM patients, shock index (SI) value of the patients with obstetric hemorrhage and maternal death cases were evaluated.
RESULTS: The MNM ratio was 5.06 patients per 1000 live births. Maternal mortality (MM) ratio was 8.1 maternal deaths per 100 000 live births. SMOR was 5.14 per 1000 live births. The MI was 1.57%, and the MNM/maternal death ratio was 62.4:1. The SI of MNM patients with obstetric hemorrhage was 1.36 ± 0.43, and the SI of the patient who died due to PPH was 1.74.
CONCLUSIONS: The MNM rates and MM rates in our hospital were higher than high-income countries but were lower than in low- and middle-income countries. Hypertensive disorders and obstetric hemorrhage were the leading conditions related to MNM and MM. However, the MIs for these causes were low, reflecting the good quality of maternal care and well-resourced units. Adopting the MNM concept into the health system and use as an indicator for evaluating maternal health facilities is crucial to prevent MM.