Due to the workload and lack of a critical mass of trained operational researchers within their ranks, health systems and programmes may not be able to dedicate sufficient time to conducting operational research (OR). Hence, they may need the technical support of operational researchers from research/academic organisations. Additionally, there is a knowledge gap regarding implementing differentiated tuberculosis (TB) care in programme settings. In this ‘how we did it’ paper, we share our experience of implementing a differentiated TB care model along with an inbuilt OR component in Tamil Nadu, a southern state in India. This was a health system initiative through a collaboration of the State TB cell with the Indian Council of Medical Research institutes and the World Health Organisation country office in India. The learnings are in the form of eleven tips: four broad principles (OR on priority areas and make it a health system initiative, implement simple and holistic ideas, embed OR within routine programme settings, aim for long-term engagement), four related to strategic planning (big team of investigators, joint leadership, decentralised decision-making, working in advance) and three about implementation planning (conducting pilots, smart use of e-tools and operational research publications at frequent intervals). These may act as a guide for other Indian states, high TB burden countries that want to implement differentiated care, and for operational researchers in providing technical assistance for strengthening implementation and conducting OR in health systems and programmes (TB or other health programmes). Following these tips may increase the chances of i) an enriching engagement, ii) policy/practice change, and iii) sustainable implementation.

To reduce TB deaths in resource-limited settings, triaging at diagnosis can identify those with immediate need for comprehensive assessment and inpatient care. n This type of differentiated TB care model was successfully implemented in Tamil Nadu, India, without additional stress on the health system. n Half of the patients referred as a result of triaging were very severely undernourished, which implies a need for capacity-building of inpatient care facilities in clinical management of very severe undernutrition in adults.nThe identification of predictors of not being triaged and comprehensively assessed will inform ongoing and future improvements to the care model.

Background: Data on attitudes towards and utilization of health insurance in Mumbai is lacking. The aim of the study was to assess the level of awareness about health insurance, the factors influencing the decision to subscribe and the patterns of utilization of health insurance.Methods: 201 principal earning members of households belonging to the 25-45 years age group were interviewed in a community-based household survey in a conveniently selected ward in Mumbai with the help of a pretested, semi-structured interview schedule.Results: The level of awareness about health insurance was 65.3%. 33.8% of the respondents had utilized or subscribed to some form of health insurance. 27.9% had used Government health insurance while 13.4% had used private health insurance. 7.5% had access to both Government and private health insurance.Conclusions: A concerted effort is necessary to sensitize the public about health insurance with strategic use of sentiment regarding community risk pooling.

Background: Geriatric health is gaining in urgency and importance as the world is moving into the later stages of the demographic cycle. Nutritional status of the elderly is a major determinant of their overall health.Methods: The present study is a healthcare facility-based, cross-sectional study among geriatric patients attending the chronic disease clinic focusing on the morbidity profile and anthropometry. Sample size was 89. The measurements done were calf circumference (CC), mid-upper arm circumference (MUAC) and BMI.Results: More than half the study subjects had three or more chronic morbidities. The prevalence of undernourishment based on calf circumference and MUAC were 11.2% and 12.4% respectively. The correlation between BMI, CC and MUAC were all found to be strong and statistically significant.Conclusions: Both CC and MUAC can be adapted for use in primary care settings to rapidly assess nutritional status of the elderly and plan management protocols.

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