Diarrhea is a Major killer of Children with Severe Acute Malnutrition Admitted to Inpatient Set-up in Lusaka, Zambia

<p>Abstract</p> <p>Introduction</p> <p>Mortality of children with Severe Acute Malnutrition (SAM) in inpatient set-ups in sub-Saharan Africa still remains unacceptably high. We investigated the prevalence and effect of diarrhea and HIV infection on inpatient treatment outcome of children with complicated SAM receiving treatment in inpatient units.</p> <p>Method</p> <p>A cohort of 430 children aged 6-59 months old with complicated SAM admitted to Zambia University Teaching Hospital's stabilization centre from August to December 2009 were followed. Data on nutritional status, socio-demographic factors, and admission medical conditions were collected up on enrollment. T-test and chi-square tests were used to compare difference in mean or percentage values. Logistic regression was used to assess risk of mortality by admission characteristics.</p> <p>Results</p> <p>Majority, 55.3% (238/430) were boys. The median age of the cohort was 17 months (inter-quartile range, IQR 12-22). Among the children, 68.9% (295/428) had edema at admission. The majority of the children, 67.3% (261/388), presented with diarrhea; 38.9% (162/420) tested HIV positive; and 40.5% (174/430) of the children died. The median Length of stay of the cohort was 9 days (IQR, 5-14 days); 30.6% (53/173) of the death occurred within 48 hours of admission. Children with diarrhea on admission had two and half times higher odds of mortality than those without diarrhea; Adjusted OR = 2.5 (95% CI 1.50-4.09, P < 0.001). The odds of mortality for children with HIV infection was higher than children without HIV infection; Adjusted OR = 1.6 (95% CI 0.99-2.48 P = 0.5).</p> <p>Conclusion</p> <p>Diarrhea is a major cause of complication in children with severe acute malnutrition. Under the current standard management approach, diarrhea in children with SAM was found to increase their odds of death substantially irrespective of other factors.</p

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Bias in the eyes of resident physicians

Conclusion: The majority of resident physicians did recognize bias in their colleagues\u27 approach to patient care. Given the evidence that implicit bias can be recognized and improved upon, this study reinforces the need for implicit bias training/discussion to be included in residency programs

Bias in the Eyes of Resident Physicians

Background: The utilization of patient characteristics can allow health care providers to arrive at diagnosis or decide on treatment options; however, the subjective nature of patient characterization can negatively affect patient care. A 2003 Institute of Medicine report, called Unequal Treatment, recognized that bias or stereotyping may affect provider-patient communication or the care offered. Purpose: To investigate residents’ recognition of bias in an inpatient care setting. Methods: In order to explore the topic of bias among providers, we elected to indirectly assess its recognition among providers by asking their opinion in an anonymous manner about their fellow residents. This, we thought, would remove the issue of self-judgment and make it easier for responders to reflect on their observations. We asked residents the following two-step question: “Have you observed a colleague of yours SAY, PORTRAY, or ACT in a biased manner toward a patient while providing inpatient service?” If the answer was yes, we subsequently asked them to elaborate on the bias. Results: The survey was sent to 39 postgraduate internal medicine residents in their first to third year of training. Half of the responders (20/39) were female. The response rate was 100%, and 46% (18/39) reported observing their colleague(s) being biased toward patients. Of those who reported bias, 77.8% (14/18) reported one or more examples about the content of the perceived bias. The largest category of these, at 42.8% (9/21), regarded bias toward patients with past or current “drug/substance abuse” or “narcotic seeking” behavior; 14.3% (3/21) involved patients with repeated admissions or so-called “frequent fliers”; 9.5% (2/21) related to race/ethnicity; 14.3% (3/21) indicated providers not wanting to care for patients who were perceived to be “difficult.” Interestingly, another 9.5% (2/21) reported witnessing preferential service for “affluent/VIP” patients. Other examples included bias against obese patients, female patients and general stereotyping with no specifics given. Conclusion: The majority of resident physicians did recognize bias in their colleagues’ approach to patient care. Given the evidence that implicit bias can be recognized and improved upon, this study reinforces the need for implicit bias training/ discussion to be included in residency programs

Separate or Together? Incorporating Residents into an Established Hospital Leadership Program

Introduction/Background Success in today’s complex and evolving health care settings requires that leaders be able to guide problem finding and solving teams. Lack of leadership skills – particularly in the areas of systems-based practice, professionalism, and communication – has been linked to patient harm. The need for leadership training is recognized across health care as programs are offered through graduate medical education (GME) offices and residency programs, hospitals, and health care systems. GME sponsored leadership programs may cross medical specialties but typically they do not professions. In contrast, leadership programs at the hospital/system level are often interprofessional in nature (e.g., physicians, RNs, quality directors, financial leaders, pharmacists, allied health professions) mirroring the multi-disciplinary nature of health care teams but do not include residents. While integrating residents into these established interprofessional leadership development programs would be a win-win for GME and our sponsoring organizations a review of the GME-related leadership literature yielded no models for this approach. Hypothesis/Aim Statement To integrate residents into an established interprofessional hospital leadership program. Methods Aurora St. Luke’s Medical Center (ASLMC) has a 12-month interprofessional leadership development program with topics common to resident focused leadership programs: teamwork/teambuilding, conflict and change management, leadership and communication essentials, process improvement and finance/budgeting. ASLMC’s program format includes 18 hours of interactive face-to-face (F2F) sessions, required readings, and leadership of a quality improvement project presented at the conclusion of the program. The hospital’s chief medical officer leads the program and as a member of the CLER synergy leadership group, exceeded an invitation to GME to include residents. Results National Initiative (NI) resident leaders were invited to participate during the pilot year with their NI disparity project meeting the program’s requirement. Four residents expressed interest with two residents (and their program director) able to flex training program schedules and met program requirements as a longitudinal elective. Residents are periodically debriefed about their participation reporting that they strongly value the leadership program’s quality, the opportunity to participate as a physician (not viewed as resident) in an interprofessional program, and the ability to apply key concepts/principles from the program to NI work. Limitations include inability to attend F2F sessions due to duty hours, synching calendar year with academic year, and limited project time (true for all NI participants independent of leadership program participation). Conclusions Utilizing an established hospital based interprofessional leadership programs is a strong ROI: residents engage with other health care professionals learning as peers gaining valued skills to support NI project, hospital leaders gain from residents’ perspectives, GME’s saves costs associated with sponsoring a separate leadership program

Anthropometric predictors of mortality in undernourished adults in the Ajiep Feeding Programme in Southern Sudan.

BACKGROUND: Various nutritional assessment tools are available to assess adult undernutrition, but few are practical in poorly served areas of low-income countries. OBJECTIVE: The objective was to assess the relation between midupper arm circumference (MUAC), weight, body mass index (BMI), and clinical assessment for edema in predicting mortality in adults with severe acute undernutrition. DESIGN: Demographic and anthropometric data that were collected in an observational study of 197 adults were analyzed. Participants were aged 18-59 y and were admitted to a therapeutic feeding center in Ajiep, Southern Sudan, during the height of the 1998 famine. Receiver operating curves were calculated and compared. RESULTS: The mean (±SD) age of the participants was 40.1 ±10.8 y, and the mean (±SD) MUAC, weight, and BMI (in kg/m(2)) were 16.4 ± 1.3 cm, 35.1 ± 5.2 kg, and 12.6 ± 1.5, respectively. The area under the receiver operating curve for MUAC (0.71) was higher (P = 0.01) than those of BMI (0.57) and weight (0.51). Mean age, weight, and BMI on admission did not differ between survivors and nonsurvivors (P > 0.17). MUAC and edema were independently associated with mortality. For every 1-cm increase in admission MUAC, the odds of subsequent mortality decreased by 58% (adjusted OR: 0.42; 95% CI: 0.28, 0.63; P < 0.001). CONCLUSIONS: In this study, which was conducted at the height of a major famine among adults with extremely severe grades of undernutrition, MUAC and edema were better indicators of short-term prognosis than was BMI. Further studies are needed to define a critical MUAC threshold for the diagnosis of acute adult undernutrition

Three Residency Programs’ Lessons Learned about Disparities from a Deep Dive into Our Population Data

Introduction/Background To deliver person-centric, best-in class health care we must transition to value-based care. As part of managing this transition, we must identify at risk populations – those with disparities in clinical measures - by leveraging our existing data sets to provide actionable data to inform how we manage these populations. Currently our health care system provides clinical quality metrics to support providers’ ability to engage in continuous improvement. This data is complimented by provider’s knowledge of the literature, which consistently identifies certain populations, often using the REAL-G categories, as at risk. For example, hypertension has well established risk factors including age, gender, and race: HTN increases through early middle age; women are more likely to develop HTN \u3e 65; HTN is more common among blacks. However, our current clinical quality data does not normally provide detailed clinical/service level population specific metrics (e.g., REAL-G specific data) limiting providers’ ability to understand the clinical quality disparities in their patient populations. Hypothesis/Aim Statement To identify actionable disparity gaps for quality improvement through detailed analysis of selected clinic level quality metrics by REAL-G Categories (Race, Ethnicity, Age, Language, Gender). Methods Three residency programs participating in the Alliance of Independent Academic Medical Center’s National Initiative V (AIAMC-NIV) identified a current system-wide quality metric that was not at/above system goal: Family Medicine - colorectal cancer (CRC) screening; Internal Medicine – diabetes; and Ob/Gyn - postpartum readmission for hypertension. Through a partnership between Graduate Medical Education (GME) and Service Quality leaders, a retrospective analysis of selected quality metrics was undertaken to determine if there were disparities using REAL-G categories over a 12-month period (12.2014-11.2015). Each residency team then reviewed the data to identify the largest disparities by REAL-G category for quality improvement. Results The largest disparities in our clinics/service areas were sometimes consistent with the literature (e.g., 65% of African American DM Patients \u3e HbA1cs compared to 70% of White-Hispanic and 71% White-Non Hispanic) but not always! For example the largest CRC screening disparity was not race, ethnicity or gender ( Conclusions Diving into our clinical quality metrics using REAL-G categories, provided the actionable data needed in each of our three residency programs to plan disparity targeted improvement cycles