Active Mental Health

Behavioural, economic and social mechanisms underlying the association between chronic high temperatures and depressive symptoms among farmers and farm workers in India

In plain English

AI plain-English summary

In the dryland farming regions of Andhra Pradesh, Telangana and Karnataka, researchers will map the multiple routes by which chronic extreme heat drives depression among farmers—beyond the obvious link of lost crop income. This matters because nearly half of India’s workforce depends on agriculture, and farmers already face some of the highest rates of both heat exposure and depression. Existing studies have focused almost exclusively on the pathway from heat to reduced yields to lower income to poorer mental health. But agricultural households increasingly earn from non-cultivation sources—wage labour, livestock, migration—and heat may harm mental health through other mechanisms: disrupted social networks, increased physical exhaustion, or heightened financial insecurity from unpredictable weather. Without understanding which pathways matter most, interventions risk missing the real drivers. If the project succeeds, it will produce a heat-informed depression intervention programme, co-developed with farmers, and pilot-tested in a randomised trial. The goal is scalable solutions—practical changes to how farming communities, local health systems, and agricultural support services anticipate and respond to extreme heat, rather than treating depression only after it takes hold.

View original technical description
Farmers are one of the most vulnerable communities to both heat exposure and depression. India, where nearly half of the workforce is engaged in agriculture, is no exception. Extreme heat can reduce crop yields, lower cultivation incomes and potentially increase risk of depression. Whilst studies to date have focused almost solely on this yield-income pathway, agricultural household income is increasingly diversifying away from cultivation. We therefore need to explore other causal pathways. The overarching aim of our project is to determine the relative importance—based on both farmers’ perceptions and quantitative data—of different causal pathways between heat and depression among farmers in India, and to translate these findings into scalable solutions. The project will be based in the dryland regions of three states: Andhra Pradesh, Telangana and Karnataka. WP1 involves participatory learning activities to understand how farmers conceive of the impact of heat on depression. WP2 involves causal mediation analysis using longitudinal data from an ongoing study in Andhra Pradesh—including PHQ-9—supplemented with weather data. In WP3, we will co-develop a heat- informed depression intervention programme, targeting causal pathways identified in WPs 1 and 2. WP4 will pilot this programme in a two-arm parallel cluster randomised pilot trial.

View the original record at the funder ↗

Researchers

Angus MacBeth (EPMC Awardee)Anish V Cherian (EPMC Awardee)Lindsay Jaacks (EPMC Awardee)Nadine Seward (EPMC Awardee)Nanda Kishore Kannuri (EPMC Awardee)Poornima Prabhakaran (EPMC Awardee)Simon Tett (EPMC Awardee)Sumeet Jain (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Adaptation and mitigation strategies to cope with the effects of extreme heat in developing countries: implications for climate change.
Heat Resilience and Mental Health: Engineering Pathways to Reduce Depression and Anxiety in Schools in Nepal and Sri Lanka
Investigating the pathways linking heat exposure to mental health outcomes: mechanisms and interventions in a Ghanaian cohort (HEAT-MIND)
Living with Heat: Medical, Social and Cultural Contexts of Excess Heat in India
Mechanisms Mediating Summer Heat Effects on Mental Health: Examining Sleep, Physical Activity, and Cognitive Pathways in Young Adults with Affective Disorders

Original classification

Climate & Mental Health: Uncovering mechanisms between heat & mental health

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.