Dr. Rishi Mediratta, MD, MSc, MA, is a Clinical Assistant Professor in Pediatrics, Pediatric Hospitalist at Lucile Packard Children’s Hospital, and Faculty Fellow at the Stanford Center for Innovation in Global Health (CIGH). He graduated from Johns Hopkins University with a BA in Public Health and went to medical school at Stanford University. Dr. Mediratta has conducted research and public health projects in Ethiopia since 2005. He founded the Ethiopian Orphan Health Foundation to provide community-based health care and education to orphans. As a British Marshall Scholar, he received a MA in Medical Anthropology from the School of Oriental and African Studies and a MSc in Public Health at the London School of Hygiene and Tropical Medicine. As a pediatrics resident at Stanford, he became passionate about decreasing neonatal mortality through hospital- and community-based education programs. As faculty, he supervises trainees and cares for hospitalized children and integrates his field experience and interdisciplinary background to create newborn and child health programs. He has worked with policymakers at the World Bank, the World Health Organization, UNICEF, and Save the Children. At Stanford University, he teaches a course to undergraduate and graduate students from all disciplines about the medical and societal implications of the COVID-19 pandemic.
Clinical Assistant Professor, Pediatrics
Boards, Advisory Committees, Professional Organizations
Faculty Fellow, Center for Innovation in Global Health (CIGH) (2021 - Present)
Residency: Stanford Health Care at Lucile Packard Children's Hospital (2018) CA
Board Certification, Pediatrics, American Board of Pediatrics (2018)
Residency, Stanford University School of Medicine, Pediatrics (2018)
Internship, Stanford University School of Medicine, Pediatrics (2016)
MD, Stanford University School of Medicine (2015)
MSc, London School of Hygiene and Tropical Medicine, Public Health (2011)
MA, School of Oriental and African Studies, Medical Anthropology (2010)
BA, Johns Hopkins University, Public Health Studies (2008)
Current Research and Scholarly Interests
I have developed a new promising neonatal mortality prediction score at the University of Gondar Neonatal Intensive Care Unit (NICU) in Gondar, Ethiopia. The score predicts approximately 84% of neonatal deaths in the NICU using clinical variables. I have a dataset over 800 NICU admissions in Gondar. I am recruiting scholars who are interested in conducting clinical and epidemiological research to validate, refine, and implement the mortality score to reduce neonatal mortality in Ethiopia. I can mentor scholars to conduct research in Ethiopia, facilitate in-country supervision at the University of Gondar, and facilitate statistical support with Stanford's Department of Statistics.
Research Methods: Diagnostic Test Characteristics.
The goal of a diagnostic test is to provide information on the probability of disease. In this article, we review the principles of diagnostic test characteristics, including sensitivity, specificity, positive and negative predictive value, receiver operating characteristics curves, likelihood ratios, and interval likelihood ratios. We illustrate how interval likelihood ratios optimize the information that can be obtained from test results that can take on >2 values, how they are reflected in the slope of the receiver operating characteristics curve, and how they can be easily calculated from published data.
View details for DOI 10.1542/hpeds.2023-007149
View details for PubMedID 37144292
Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries.
Nature human behaviour
Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from -90% to +30%, were reported in many countries following early COVID-19 pandemic response measures ('lockdowns'). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95-0.98, P value <0.0001), second (0.96, 0.92-0.99, 0.03) and third (0.97, 0.94-1.00, 0.09) months of lockdown, but not in the fourth month of lockdown (0.99, 0.96-1.01, 0.34), although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second (1.00, 0.88-1.14, 0.98), third (0.99, 0.88-1.12, 0.89) and fourth (1.01, 0.87-1.18, 0.86) months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries (1.14, 1.02-1.29, 0.02) and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second (1.09, 1.03-1.15, 0.002), third (1.10, 1.03-1.17, 0.003) and fourth (1.12, 1.05-1.19, <0.001) months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways.
View details for DOI 10.1038/s41562-023-01522-y
View details for PubMedID 36849590
View details for PubMedCentralID 8417352
Applying Kern's Six Steps to the Development of a Community-Engaged, Just-in-Time, Interdisciplinary COVID-19 Curriculum.
Journal of medical education and curricular development
2022; 9: 23821205221096370
Universities and medical schools often work towards operationalizing their shared mission of facilitating community-engaged work independently. Based on their experience teaching the COVID-19 Elective course at Stanford University School of Medicine, the authors proposed a novel solution for universities and medical schools to achieve an interdisciplinary collaboration within a diverse student population by creating targeted, project-based, and community-engaged courses for addressing emergent health needs. In this article, the authors discuss their curriculum, which was created using Kern's six-step approach for curriculum development, to address emergent health needs related to the novel coronavirus pandemic. The curriculum provides an opportunity for universities and medical schools to advance community health, educate students across the medical and non-medical education continuum, and foster interdisciplinary cooperation.
View details for DOI 10.1177/23821205221096370
View details for PubMedID 35509682
View details for PubMedCentralID PMC9058336
Overnight admissions to a neonatal intensive care unit in Ethiopia are not associated with increased mortality.
2022; 17 (3): e0264926
In 2019, 2.4 million neonates died globally, with most deaths occurring in low-resource settings. Despite the introduction of neonatal intensive care units (NICUs) in these settings, neonatal mortality remains high, and caring for sick neonates around the clock can be challenging due to limited staff and resources.To evaluate whether neonatal intensive care admissions during daytime and overnight hours affects in-hospital neonatal mortality.A retrospective case-control study was conducted using 2016 chart data at a University hospital in Ethiopia. Cases were defined as neonates who died in the NICU, and controls were defined as neonates who survived. Overnight hours were defined as 17:00 to 07:59, and day hours were defined as 08:00 to 16:59. Univariate and multivariate logistic regressions were used to investigate the relationship between time of admission and mortality, along with perinatal characteristics.A total of 812 neonates, 207 cases and 605 controls, met inclusion criteria. There were 342 admissions during the day and 470 overnight. Neonatal mortality (aOR 1.02, 95% CI [0.64-1.62], p = 0.93) was not associated with overnight admissions after controlling for maternal age, parity, C-section, birthweight, and gestational age, respiratory distress, and admission level of consciousness. Admission heart rate >160 (aOR 0.52, 95% CI [0.30-0.91], p = 0.02) was the only variable significantly associated with overnight admissions.Being admitted overnight to the NICU in Gondar, Ethiopia was not associated with increased mortality, consistent with a constant level of care, regardless of the time of admission. Further qualitative and implementation research are needed to understand contextual factors that have affected these data.
View details for DOI 10.1371/journal.pone.0264926
View details for PubMedID 35324936
View details for PubMedCentralID PMC8947129
The Burden of Critical Illness in Hospitalized Children in Low- and Middle-Income Countries: Protocol for a Systematic Review and Meta-Analysis.
Frontiers in pediatrics
2022; 10: 756643
Background: The majority of childhood deaths occur in low- and middle-income countries (LMICs). Many of these deaths are avoidable with basic critical care interventions. Quantifying the burden of pediatric critical illness in LMICs is essential for targeting interventions to reduce childhood mortality.Objective: To determine the burden of hospitalization and mortality associated with acute pediatric critical illness in LMICs through a systematic review and meta-analysis of the literature.Data Sources and Search Strategy: We will identify eligible studies by searching MEDLINE, EMBASE, CINAHL, and LILACS using MeSH terms and keywords. Results will be limited to infants or children (ages >28 days to 12 years) hospitalized in LMICs and publications in English, Spanish, or French. Publications with non-original data (e.g., comments, editorials, letters, notes, conference materials) will be excluded.Study Selection: We will include observational studies published since January 1, 2005, that meet all eligibility criteria and for which a full text can be located.Data Extraction: Data extraction will include information related to study characteristics, hospital characteristics, underlying population characteristics, patient population characteristics, and outcomes.Data Synthesis: We will extract and report data on study, hospital, and patient characteristics; outcomes; and risk of bias. We will report the causes of admission and mortality by region, country income level, and age. We will report or calculate the case fatality rate (CFR) for each diagnosis when data allow.Conclusions: By understanding the burden of pediatric critical illness in LMICs, we can advocate for resources and inform resource allocation and investment decisions to improve the management and outcomes of children with acute pediatric critical illness in LMICs.
View details for DOI 10.3389/fped.2022.756643
View details for PubMedID 35372149
- Challenges and Opportunities in Deploying COVID-19 Cough AI Systems. Journal of voice : official journal of the Voice Foundation 2021
Derivation and validation of a prognostic score for neonatal mortality in Ethiopia: a case-control study.
2020; 20 (1): 238
BACKGROUND: Early warning scores for neonatal mortality have not been designed for low income countries. We developed and validated a score to predict mortality upon admission to a NICU in Ethiopia.METHODS: We conducted a retrospective case-control study at the University of Gondar Hospital, Gondar, Ethiopia. Neonates hospitalized in the NICU between January 1, 2016 to June 31, 2017. Cases were neonates who died and controls were neonates who survived.RESULTS: Univariate logistic regression identified variables associated with mortality. The final model was developed with stepwise logistic regression. We created the Neonatal Mortality Score, which ranged from 0 to 52, from the model's coefficients. Bootstrap analysis internally validated the model. The discrimination and calibration were calculated. In the derivation dataset, there were 207 cases and 605 controls. Variables associated with mortality were admission level of consciousness, admission respiratory distress, gestational age, and birthweight. The AUC for neonatal mortality using these variables in aggregate was 0.88 (95% CI 0.85-0.91). The model achieved excellent discrimination (bias-corrected AUC) under internal validation. Using a cut-off of 12, the sensitivity and specificity of the Neonatal Mortality Score was 81 and 80%, respectively. The AUC for the Neonatal Mortality Score was 0.88 (95% CI 0.85-0.91), with similar bias-corrected AUC. In the validation dataset, there were 124 cases and 122 controls, the final model and the Neonatal Mortality Score had similar discrimination and calibration.CONCLUSIONS: We developed, internally validated, and externally validated a score that predicts neonatal mortality upon NICU admission with excellent discrimination and calibration.
View details for DOI 10.1186/s12887-020-02107-8
View details for PubMedID 32434513
Liver Failure and Rash in a 6-week-old Girl
PEDIATRICS IN REVIEW
2018; 39 (6): 315–U22
View details for PubMedID 29858298
Galvanizing medical students in the administration of influenza vaccines: the Stanford Flu Crew.
Advances in medical education and practice
2015; 6: 471-477
Many national organizations call for medical students to receive more public health education in medical school. Nonetheless, limited evidence exists about successful servicelearning programs that administer preventive health services in nonclinical settings. The Flu Crew program, started in 2001 at the Stanford University School of Medicine, provides preclinical medical students with opportunities to administer influenza immunizations in the local community. Medical students consider Flu Crew to be an important part of their medical education that cannot be learned in the classroom. Through delivering vaccines to where people live, eat, work, and pray, Flu Crew teaches medical students about patient care, preventive medicine, and population health needs. Additionally, Flu Crew allows students to work with several partners in the community in order to understand how various stakeholders improve the delivery of population health services. Flu Crew teaches students how to address common vaccination myths and provides insights into implementing public health interventions. This article describes the Stanford Flu Crew curriculum, outlines the planning needed to organize immunization events, shares findings from medical students' attitudes about population health, highlights the program's outcomes, and summarizes the lessons learned. This article suggests that Flu Crew is an example of one viable service-learning modality that supports influenza vaccinations in nonclinical settings while simultaneously benefiting future clinicians.
View details for DOI 10.2147/AMEP.S70294
View details for PubMedID 26170731
View details for PubMedCentralID PMC4492543
Risk Factors and Case Management of Acute Diarrhoea in North Gondar Zone, Ethiopia
JOURNAL OF HEALTH POPULATION AND NUTRITION
2010; 28 (3): 253-263
In Ethiopia, evidence is lacking about maternal care-taking and environmental risk factors that contribute to acute diarrhoea and the case management of diarrhoea. The aim of this study was to identify the risk factors and to understand the management of acute diarrhoea. A pretested structured questionnaire was used for interviewing mothers of 440 children in a prospective, matched, case-control study at the University of Gondar Referral and Teaching Hospital in Gondar, Ethiopia. Results of multivariate analysis demonstrated that children who were breastfed and not completely weaned and mothers who were farmers were protective factors; risk factors for diarrhoea included sharing drinking-water and introducing supplemental foods. Children presented with acute diarrhoea for 3.9 days with 4.3 stools per day. Mothers usually did not increase breastmilk and other fluids during diarrhoea episodes and generally did not take children with diarrhoea to traditional healers. Incorporating messages about the prevention and treatment of acute diarrhoea into child-health interventions will help reduce morbidity and mortality associated with this disease.
View details for Web of Science ID 000279758900007
View details for PubMedID 20635636