Objective: To assess the SAO production capacity of India i.e., the number of postgraduate (PG) and sub-specialty (SS) surgical training spots per 10 million population across multiple specialties and subspecialties at national and state levels. Methods: A retrospective secondary data analysis of PG and SS SAO spots across 36 states for 2018 using data from the National Health Profile (2019) was conducted. The mid-year 2018 populations were obtained from the census-based population projections. The number of PG & SS SAO spots per 10 million population were calculated and divided based on the type of program (diploma, MD/MS & DM/MCh) and type of SAO specialty to investigate SAO workforce production capacity in each state. Ratios of PG spots per 100 MBBS spots and SS spots per 100 PG spots were also calculated. Data was wrangled using Google Sheets, analyzed in JASP v0.16.0.0, and visualized using Datawrapper. Results: There are a total 13793 PG and 1350 SS SAO spots leading to densities of 104.60 and 9.90 per 10 million people, respectively. PG spot density for General Surgery (23.56), Anesthesia (24.81), and OBGYN (21.55) were comparable and were much higher than Orthopedics (14.64), Ophthalmology (11.96), and Otorhinolaryngology (8.08). SS spot densities were greater for Urology (1.90), Neurosurgery (1.86), and Cardiothoracic and Vascular Surgery (1.83), followed by Plastic Surgery (1.52), and Pediatric Surgery (1.27). The average density was significantly lower for PG SAO specialties than non-SAO specialties (p=0.001) whereas there was no significant difference in densities for SS . For 100 MBBS spots, there were only 20 PG SAO spots while for 100 PG SAO spots, there were just 9 SS SAO spots available. The distribution of spots was geographically uneven with about two-thirds of spots concentrated in 10 states/union territories. Conclusion: Annually, India can produce only about 24 General Surgeons, 15 Orthopedic Surgeons, 12 Ophthalmologists, 8 Otorhinolaryngologists, 25 Anesthetists, 22 OBGYNs, and 10 sub-specialist surgeons per 10 million population. The distribution of spots is inequitable across the states. Hence, scale up of surgical training capacity needs to be carried out with attention to reducing disparities.

Objective: To assess the SAO production capacity of India i.e., the number of postgraduate (PG) and sub-specialty (SS) surgical training spots per 10 million population across multiple specialties and subspecialties at national and state levels. Methods: A retrospective secondary data analysis of PG and SS SAO spots across 36 states for 2018 using data from the National Health Profile (2019) was conducted. The mid-year 2018 populations were obtained from the census-based population projections. The number of PG & SS SAO spots per 10 million population were calculated and divided based on the type of program (diploma, MD/MS & DM/MCh) and type of SAO specialty to investigate SAO workforce production capacity in each state. Ratios of PG spots per 100 MBBS spots and SS spots per 100 PG spots were also calculated. Data was wrangled using Google Sheets, analyzed in JASP v0.16.0.0, and visualized using Datawrapper. Results: There are a total 13793 PG and 1350 SS SAO spots leading to densities of 104.60 and 9.90 per 10 million people, respectively. PG spot density for General Surgery (23.56), Anesthesia (24.81), and OBGYN (21.55) were comparable and were much higher than Orthopedics (14.64), Ophthalmology (11.96), and Otorhinolaryngology (8.08). SS spot densities were greater for Urology (1.90), Neurosurgery (1.86), and Cardiothoracic and Vascular Surgery (1.83), followed by Plastic Surgery (1.52), and Pediatric Surgery (1.27). The average density was significantly lower for PG SAO specialties than non-SAO specialties (p=0.001) whereas there was no significant difference in densities for SS SAO and non-SAO specialties (p=0.197). For 100 MBBS spots, there were only 20 PG SAO spots while for 100 PG SAO spots, there were just 9 SS SAO spots available. The distribution of spots was geographically uneven with about two-thirds of spots concentrated in 10 states/union territories. Conclusion: Annually, India can produce only about 24 General Surgeons, 15 Orthopedic Surgeons, 12 Ophthalmologists, 8 Otorhinolaryngologists, 25 Anesthetists, 22 OBGYNs, and 10 sub-specialist surgeons per 10 million population. The distribution of spots is inequitable across the states. Hence, scale up of surgical training capacity needs to be carried out with attention to reducing disparities.

Inadequate access to pain management has consequences for perioperative care. Access to perioperative care globally is critical for equitable surgical, obstetric, trauma, and anesthesia (SOTA) care. Adequacy of prescription opioid consumption (AOC) can fill this gap. This study aimed to use the AOC index to assess global adequacy using recent data across 214 countries and territories, World Health Organization (WHO) regions, and World Bank Income Groups (WBIGs). We conducted a cross-sectional retrospective analysis using data on prescription opioid consumption for 214 countries and territories using previously published data for 2017. Country-wise data on the mean annual consumption of all prescription opioids were obtained in milligrams per capita. For adequacy, AOC normative threshold was calculated as the arithmetic mean of prescription opioid consumption of twenty countries with the topmost HDI values as of 2017. Country-wise AOC index was calculated as the mean annual prescription opioid consumption of a country divided by the threshold and the fraction multiplied by a hundred. The adequacy levels were classified as adequate (AOC >100), moderate (<100 and >30), low (<30 and >10), very low (<10 and >3), and extremely (<3). AOC was also calculated for WBIGs (as of 2017) and WHO regions. AOC values across WBIGs and WHO regions were compared using Welch Analysis of Variance (ANOVA) tests. Post-hoc pairwise comparisons were conducted using the Games-Howell test with Holm Bonferroni correction for multiple comparisons. A conventional significance level of 5% was used for all tests. Analysis was conducted in Google Sheets, JAMOVI, and RStudio. The average prescription opioid consumption of the top twenty HDI countries was found to be 172.8 mg/capita annually. Across countries, AOC index values ranged from 279.23 for Germany to 0.0012 for Angola. Merely 4.21% of the 214 countries and territories had an adequate level of consumption of prescription opioids. 6.54% of countries had moderate, 10.75% had low, 18.69% had very low, and a staggering 59.81% of countries had extremely low AOC index depicting massive differences in adequacy. 190 countries could be assigned to WHO regions and WBIGs. AOC values differed significantly across WHO regions (p<0.001). AOC values differed significantly across country income groups (p=0.001). In this systematic global analysis, we find that most low- and middle-income countries (LMICs) situated in the Global South lack access to prescription opioids. A majority of LMICs fall severely short, with their AOC index <1%. Low AOC can be seen as an indicator of poor access to pain management and thereby anesthesia care. This calls for the integration of AOC in the broader SOTA care indicators.

Delay in discharge of the patient reduces bed availability in any healthcare organization. In spite of advancement in medical systems and hospital management, delay in patient discharges still exist. Hence, there was a need to evaluate factors contributing to the delay in an exclusive paediatric hospital. A study of patient discharges from a private and a general ward was undertaken at a tertiary care paediatric hospital in Mumbai, between September-October 2021. A total of 60 discharges, 30 from each ward were analysed. The time from discharge order given by the consultant to that of the patient leaving the hospital and the reasons for delay were recorded by the investigators. The hospital followed a policy of 180 min for a discharge. The mean duration of discharge in the general ward was 153.18±34.60 min and whereas it was 165.41±62.29 min in the private ward. However, 28% discharges in general ward and 43% discharges in private ward were delayed with a mean of 5.23±10.04 and 20.66±26.88 min respectively. Time taken for billing and making of discharge sheet contributed to the maximum delay in both wards. Though the mean discharge time in both wards was found to be within the standards, a significant proportion were still delayed. We suggest a multidisciplinary team consisting of administrators, nurses, resident doctors and consultants to help ease the overall burden on the healthcare system.

There has been an increasing trend of research studies bearing the phrase “medical students” in their title or keywords. In the haste of publication, the relevance of study population is being forgotten and an emerging phenomenon has been making its presence, that we would like to call the “guinea pig effect”- wherein the choice of study population invariably ends up being members of the medical fraternity- whether or not it is relevant to the research question at hand. To give a rough estimate, a search via Google Scholar with key-words (Medical Student India -Education -Teaching) yields 4,43,000 results while a PubMed search brings 145 indexed papers. Most of these studies are either one-time KAP studies or Mental Health surveys. During the COVID-19 pandemic such studies have been on the rise, which has reduced the scientific quality of evidence. Convenience, lack of resources, time constraints, and easy access to a vulner-able population are the key factors driving such studies. These studies have an inherent selection bias, poor generalizability, and ethical concerns like over-researching a vulnerable population. As a consequence, they contribute to survey fatigue among students leading to poor response rates and quality of collected data. To tackle this, we advocate for the use of appropriate population selection and partici-pant recruitment strategies. To conclude, researchers need not shy away from recruiting medical students as the study population where required, but they must make it a point to re-evaluate the applicability and reach of their study topic in all the populations.