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Aim: To estimate incremental costs of an implementation model for scaling up Kangaroo Mother Care (KMC) for neonates with birthweight <2000 g. Methods: Seven sites across Ethiopia and India collected data for 2018–19 to calculate incremental recurrent costs (of health worker time, supplies, and operations) and start‐up costs for KMC scale up. The costs were estimated per live newborn <2000 g eligible for KMC identified in the study population. Results: Scaling up KMC in study districts required average incremental costs of US$59 (95% CI US$ 52–67) in Ethiopia and US$72 (95% CI US$ 41–103) in India per eligible newborn in the population. Most of these costs were recurrent; the annualised start‐up costs per eligible newborn ranged from 12%–25% of total costs in Ethiopia and 9%–16% in India. The major cost driver was human resources, followed by initial and recurrent training, supplies, and communications costs. Incremental infrastructure costs were only 2%–6% of total costs in both countries. Most of the costs were for activities at the KMC implementing facility, accounting for 79%–88% of the total costs in Ethiopia and 89%–93% of those in India. Conclusion: The costs for successful scale up of KMC seem affordable but must be included in programme budgets. [ABSTRACT FROM AUTHOR]

Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods. [ABSTRACT FROM AUTHOR]

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM 2.5 and PM 10 ) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with ∼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM 2.5 (PM 10 ) concentrations were in Dhaka 185 ± 48 (220 ± 58) μg m -3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) μg m -3 . Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM 2.5 and PM 10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM 2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15 m 3 ; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Car microenvironments significantly contribute to the daily pollution exposure of commuters, yet health and socioeconomic studies focused on in-car exposure are rare. This study aims to assess the relationship between air pollution levels and socioeconomic indicators (fuel prices, city-specific GDP, road density, the value of statistical life (VSL), health burden and economic losses resulting from exposure to fine particulate matter ≤2.5 µm; PM 2.5 ) during car journeys in ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Data collected by portable laser particle counters were used to develop a proxy of car-user exposure profiles. Hotspots on all city routes displayed higher PM 2.5 concentrations and disproportionately high inhaled doses. For instance, the time spent at the hotspots in Guangzhou and Addis Ababa was 26% and 28% of total trip time, but corresponded to 54% and 56%, respectively, of the total PM 2.5 inhaled dose. With the exception of Guangzhou, all the cities showed a decrease in per cent length of hotspots with an increase in GDP and VSL. Exposure levels were independent of fuel prices in most cities. The largest health burden related to in-car PM 2.5 exposure was estimated for Dar-es-Salam (81.6 ± 39.3 μg m -3 ), Blantyre (82.9 ± 44.0) and Dhaka (62.3 ± 32.0) with deaths per 100,000 of the car commuting population per year of 2.46 (2.28-2.63), 1.11 (0.97-1.26) and 1.10 (1.05-1.15), respectively. However, the modest health burden of 0.07 (0.06-0.08), 0.10 (0.09-0.12) and 0.02 (0.02-0.03) deaths per 100,000 of the car commuting population per year were estimated for Medellin (23 ± 13.7 μg m -3 ), São Paulo (25.6 ± 11.7) and Sulaymaniyah (22.4 ± 15.0), respectively. Lower GDP was found to be associated with higher economic losses due to health burdens caused by air pollution in most cities, indicating a socioeconomic discrepancy. This assessment of health and socioeconomic parameters associated with in-car PM 2.5 exposure highlights the importance of implementing plausible solutions to make a positive impact on peoples' lives in these cities., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The 'missing women' dilemma in India has sparked great interest in investigating gender discrimination in the provision of health care in the country. No studies, however, have directly examined discrimination in health-care financing strategies in the case of severe illness of sons versus daughters. In this paper, we hypothesize that households who face tight budget constraints are more likely to spend their meager resources on hospitalization of boys rather than girls. We use the 60th round of the Indian National Sample Survey (2004) and a multinomial logit model to test this hypothesis and to throw some light on this important but overlooked issue. The results reveal that boys are much more likely to be hospitalized than girls. When it comes to financing, the gap in the usage of household income and savings is relatively small, while the gender gap in the probability of hospitalization and usage of more onerous financing strategies is very high. Ceteris paribus, the probability of boys to be hospitalized by financing from borrowing, sale of assets, help from friends, etc. is much higher than that of girls. Moreover, in line with our theoretical framework, the results indicate that the gender gap intensifies as we move from the richest to poorest households., ((c) 2009 John Wiley & Sons, Ltd.)