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If you don’t collect data, it’s not really research: how the primacy of “primary data” is hampering mental health science in India

Wed, 29 Apr 2026 00:00:00 +0000

Table of Contents

India is home to almost 1.5 billion people – nearly a fifth of the world’s population and more than that of Europe and North America combined ¹. An estimated 197 million people in the country live with mental illness, and this number is growing ². India also houses a substantial mental health research workforce, with a demonstrated record of leading research output across low- and middle-income countries ³. We estimate that between 2,000-4,000 articles on mental health originate in India annually ⁴, and upwards of 30,000 dissertations are generated every year in psychology alone ⁵. Yet, despite this volume, India lacks answers to fundamental questions about its population’s mental health: How is the treatment gap for common mental health disorders changing? What social and environmental factors are the strongest contributors to poor mental health? What impact do various policy initiatives have?

We propose that one significant reason is a systemic bottleneck: the overreliance of Indian mental health research on primary data collection, and the corresponding neglect of secondary data. The result is a research ecosystem dominated by large-scale clinical trials, small-sample interventional studies, and cross-sectional surveys built on convenience samples that overrepresent urban populations ⁶. Of the 47 early-career researchers (ECRs) we surveyed in a rapid poll, only 3 reported the use of any secondary data in their dissertations ⁷.

This is a particular problem because many questions India needs to answer involve variables like caste, gender, poverty, heatwaves and violence exposure that cannot be ethically or practically manipulated in randomized controlled trials. Answering them requires large, longitudinal datasets, of which India has few. The Atlas of Longitudinal Datasets (2024) ⁸ identified only 51 health-related longitudinal datasets in India, compared to 1061 in Europe – a nearly 21x differential for a region with half of India’s population. Of those 51, mental health data were absent in nearly half, and most others had large barriers to data access.

However, the availability of data is growing. In recent years, significant effort and funding have gone into generating large, longitudinal datasets and the publishing of government-owned shareable data in India ⁹. Yet, availability has not translated into uptake – even freely-available datasets, such as the Young Lives dataset and UDAYA dataset ⁸ remain under-analysed. The National Mental Health Survey (NMHS) series ¹⁰, which accesses a representative sample from all of India’s states and union territories, is one of India’s best resources. Yet, despite completing postgraduate training at the same institution it’s hosted at, we cannot recall anyone suggesting or discussing accessing its data as a possibility for our dissertations. One of us can recount distinct instances of proposals for secondary analysis of other data being shot down. Reasons for dismissal ranged from it being “too complex” or “unconventional for our field” to the concern that it would deprive students of the experience of collecting data themselves. This reflects a system where secondary analysis has not yet taken root as a research practice and we are left with datasets built at considerable public cost being chronically underused.

This orientation towards primary data is not only inefficient but also self-undermining: it generates redundancies and compounds burden on researchers and participants alike. Researchers report persistent struggles in recruiting participants ¹¹, while participants, especially those from vulnerable communities, report fatigue ¹². In a recent lecture we gave, one student shared how a sanitation worker expressed frustration at being repeatedly approached for research in ways that yielded little tangible change ¹³. While there is no doubt about the need for high-quality primary data, especially from underrepresented communities, our concern is with repetitive, high-effort studies that result in little moving of the needle on evidence-informed mental health policy. Many of these studies are too small to be conclusive and there is less synthesis of existing evidence, leaving policy and intervention reliant on Western evidence ¹⁴.

The vicious cycle of the “primacy of primary data” bottleneck

We argue that this bottleneck persists because of systemic disincentives operating at three interlocking levels: epistemic norms, training, and funding incentives. Epistemically, the norm that primary data = ‘real’ research is rarely formally documented but is enforced pervasively through curricula, departmental culture, and funding language. The skeptical reactions we described above to secondary analysis are not fringe reactions – they reflect a research ecosystem in which doing research and collecting data are deeply intertwined and almost equated. At the level of training, popular programmes that produce mental health researchers (e.g., psychology and psychiatry) introduce but rarely provide substantive preparation in epidemiology, biostatistics, and large-scale data management. Coupled with the lack of a culture of interdisciplinary collaboration, the capacity to use existing data, even when available, is severely constrained. At the level of funding, major domestic funding bodies (Indian Council for Medical Research (ICMR), Department of Biotechnology) have few dedicated calls for secondary analysis of mental health data. Funding agencies frequently emphasize ‘novelty’ and ‘innovation’, criteria that are often implicitly equated with the collection of new data. Notably, while agencies sometimes issue specific calls for secondary data analysis in health domains, mental health is not on this list.

Figure 1 shows the cycle we believe this creates. The fragmented evidence base generated reinforces the need for more data collection. No individual actor or small group is positioned to escape this system, and the cycle continues across generations of researchers.

So, what do we do now?

In our proposed solution, we focus on a training pathway for early-career researchers because, in our experience, ECRs are the “bottom of the pyramid”: forming the majority of the research workforce and the ones driving change in the system, especially as they move upwards through it. This model borrows from the experience of the open science movement, which has largely been driven by ECRs ¹⁵. Our hypothesis is that setting up a training pathway for ECRs in accessing, using and sharing data will lead to more research outputs using secondary data, more favorable attitudes towards secondary data amongst faculty and students, more skills in using secondary data, and less researcher burden.

As an experiment, we propose testing this training pathway at an Indian institution offering Master’s degrees in psychology ¹⁶. This pilot program should have three main components. First, it should teach students how to find and access relevant data, including disseminating existing sources of data, such as the ICMR data repository. Second, it should teach basic skills to work with such data, including what kinds of research questions can be answered and initial paths to answering them (e.g., introduction to causal inference methods). Third, it should teach students how to share their own data in an ethical and secure manner.

In addition to didactic teaching, this training should create spaces for coordination of data collection with other students (e.g., by setting up “speed dating” where students can discuss potential ideas with each other and form teams) as well as for collaboration with departments such as statistics and epidemiology who tend to use secondary data more. Having the buy-in of senior faculty through involving them in delivering the training, supporting them with supervision of such dissertations and addressing any concerns they have will be crucial. This training should be offered at the beginning of the academic year, so students have time to incorporate this into their dissertations.

To measure intervention efficacy, our primary outcome will be the number of student dissertations that employ secondary data in part or fully. Our secondary outcomes will be surveys and interviews measuring attitudes and skills in use of secondary data, researcher burden, as well as research outputs using secondary data (e.g., peer-reviewed publications) in the next two years from that institutional department overall.

We propose that two cohorts of students enrolled in a particular Master’s program at the same institution be compared to each other, the first (senior) cohort is the control group and the second (junior) cohort receives the training. Primary and secondary outcomes should be measured at the start and end of the academic year for both cohorts. This is a “natural experiment” design. If two consecutive cohorts are compared, we believe that possible confounders such as socio-demographic differences in the cohorts, teaching curriculum and style differences, differences in student ability and motivation and differences in institution-level secular trends are likely to be minimal. Further, key confounders can be carefully measured at baseline and adjusted for in analyses.

This is one proposed solution that would leverage the existing infrastructure at an institution and test whether integrating this training pathway as part of overall training for mental health researchers can be effective. However, another idea might be to set up this training pathway as a competitive fellowship welcoming individuals from all disciplines, which would then select individuals with high interest and motivation to use secondary data. There are also other possible interventions, which may be needed in combination with training pathways, like:

Setting up local institution-level repositories for researchers to securely and safely share and access data. Institution-level solutions are likely to have more buy-in from various stakeholders (e.g., ethics boards, faculty members) and may be more acceptable to research participants. They are also likely to be easier to govern as a local manager can be appointed to provide training and answer questions.
Indian funding agencies should put out calls specifically for secondary analysis of existing mental health data. One model may be a competition for researchers to answer a specified, urgent research question using existing data.

A call to action: for the mental health and metascience community

The urgency to address this bottleneck is considerable. Globally, mental health science is moving towards large-scale secondary data, linked administrative records, biobanks, and machine-learning-enabled analysis to inform policy and intervention. Countries that have invested in data infrastructure can generate evidence at a scale and efficiency difficult to replicate through primary data alone. India risks arriving at this table too late, not for want of researchers, but because it has not yet built a research culture that treats existing data as a serious site for discovery.

This is also a concern of epistemic equity. India’s population, diversity, and mental health burden are increasingly studied by researchers based in high-income countries with greater access to analytical tools, funding, and data ecosystems. Without capacity and incentives to analyse Indian data, we risk becoming a site of data extraction rather than knowledge production. For the first time, however, the mental health data landscape in India is shifting. The National Family Health Survey-5 included mental health modules at a national scale; the Ayushman Bharat Digital Mission is beginning to generate population-level administrative health records; and the results of the National Mental Health Survey-2 are expected soon. This is a rare convergence of need, opportunity and capacity.

Our call to action is therefore twofold. To metascience, first, we appeal for greater emphasis on context in research systems. Prescriptions for “better science” cannot be imported wholesale from high-income settings where data infrastructures, funding systems, publication incentives, and institutional histories differ sharply. To the Indian mental health research ecosystem, we appeal that secondary data must no longer be treated as a lesser form of research. Training programmes, funders, ethics boards, journals, and institutions must actively support data reuse, linkage, replication, synthesis, and responsible sharing. Primary data will remain essential, but it should be collected when it is genuinely needed, designed for reuse where possible and situated within a cumulative evidence ecosystem. We invite this realignment so that we can better answer pressing questions that remain unanswered – where needs are urgent, change is rapid, and the scales are massive.

Acknowledgements: We thank Praveetha Patalay for comments on an earlier version of this essay.

AI Disclosure Statement: We used generative AI tools (Claude Sonnet 4.6, ChatGPT 5.5, Gemini 3.1 Pro) for a) copyediting of draft text, b) conducting research for funding calls in India and c) generating the initial design iteration for figure 1, based on our writeup of the proposed cycle. All final arguments and conclusions are our own, and we take responsibility for all output in this essay.

Worldometer data: population, population ↩︎
Sagar, R., Dandona, R., Gururaj, G., Dhaliwal, R. S., Singh, A., Ferrari, A., Dua, T., Ganguli, A., Varghese, M., Chakma, J. K., Kumar, G. A., Shaji, K. S., Ambekar, A., Rangaswamy, T., Vijayakumar, L., Agarwal, V., Krishnankutty, R. P., Bhatia, R., Charlson, F., … Dandona, L. (2020). The burden of mental disorders across the states of India: The Global Burden of Disease Study 1990–2017. The Lancet Psychiatry, 7(2), 148–161. ↩︎
Razzouk, D., Sharan, P., Gallo, C., Gureje, O., Lamberte, E. E., De Jesus Mari, J., Mazzotti, G., Patel, V., Swartz, L., Olifson, S., Levav, I., De Francisco, A., & Saxena, S. (2010). Scarcity and inequity of mental health research resources in low-and-middle income countries: A global survey. Health Policy, 94(3), 211–220. ↩︎
This estimate synthesizes output across clinical and social science disciplines. A baseline search of PubMed (2019-2024) for mental health and psychiatry keywords ((“Mental Disorders”[Mesh] OR “Mental Health”[Mesh] OR “Psychiatry”[Mesh] OR “Psychology”[Mesh] OR “mental health”[Title/Abstract] OR psychiatry[Title/Abstract] OR psychology[Title/Abstract]) AND (India[Affiliation] OR India[ad]) AND (2019:2024[Date - Publication])) affiliated with India yielded an average of over 1,500 clinical publications annually, representing approximately 25% of the global biomedical output for the same search parameters (same search terms, no affiliation/ad). When accounting for the vast body of psychological, sociological, and public health research published in regional and non-MEDLINE indexed social science journals, the total annual volume is conservatively estimated between 2,000 and 4,000 articles. ↩︎
This estimate is extrapolated from the All India Survey on Higher Education (AISHE) 2021-2022 data. While psychology data is not clearly delineated, we used the available categories to conservatively estimate that approximately 69,000 undergraduate, 57,000 postgraduate, and 1,300 doctoral students are enrolled in psychology programs. Assuming these student numbers are spread out across 3, 2 and 5 years respectively (as average completion times for these programs) and assuming near-universal research project mandates for final-year postgraduates and doctoral candidates, alongside 1/5th of final-year undergraduates, the annual output of student research exceeds 30,000 dissertations. ↩︎
This statement emerges from a combination of our lived experience as researchers in the Indian mental health research ecosystem, conversations with colleagues and reviewing evidence of registered studies on , where both larger clinical trials and smaller observational studies are recorded. For example, an interested reader can use “Advanced search” and search for studies with “mental health” in the “health condition/problem studied” to get one slice of ongoing mental health research in India. ↩︎
We used a convenience sample here ourselves, asking our own MPhil Clinical Psychology cohorts of 20 and 27 students respectively, whether they had used secondary data in their MPhil dissertations. ↩︎
Atlas of Longitudinal Datasets. (2024). Atlas of longitudinal datasets. Retrieved April 1, 2026 from ↩︎ ↩︎
to government-owned shareable data portal ↩︎
Gururaj, G., Varghese, M., Benegal, V., Rao, G. N., Pathak, K., Singh, L. K., Mehta, R. Y., Ram, D., Shibukumar, T. M., Kokane, A., Lenin Singh, R. K., Chavan, B. S., Sharma, P., Ramasubramanian, C., Dalal, P. K., Saha, P. K., Deuri, S. P., Giri, A. K., Kavishvar, A. B., . . . NMHS Collaborators Group. (2016). National mental health survey of India, 2015–16: Summary (NIMHANS Publication No. 128). National Institute of Mental Health and Neuro Sciences ↩︎
Leeper, T. J. (2019). Where Have the Respondents Gone? Perhaps We Ate Them All. Public Opinion Quarterly, 83(S1), 280–288. ↩︎
Ghafourifard, M. (2024). Survey Fatigue in Questionnaire Based Research: The Issues and Solutions. Journal of Caring Sciences, 13(4), 214–215. ↩︎
This point is also made in this paper: Cleary, M., Siegfried, N., Escott, P., & Walter, G. (2016). Super Research or Super-Researched?: When Enough is Enough…. Issues in Mental Health Nursing, 37(5), 380–382. ↩︎
Rajwar, E., Pundir, P., Parsekar, S. S., D S, A., D’Souza, S. R. B., Nayak, B. S., Noronha, J. A., D’Souza, P., & Oliver, S. (2023). The utilization of systematic review evidence in formulating India’s National Health Programme guidelines between 2007 and 2021. Health Policy and Planning, 38(4), 435–453. ↩︎
Toribio-Flórez, D., Anneser, L., deOliveira-Lopes, F. N., Pallandt, M., Tunn, I., Windel, H., & on behalf of Max Planck PhDnet Open Science Group. (2021). Where Do Early Career Researchers Stand on Open Science Practices? A Survey Within the Max Planck Society. Frontiers in Research Metrics and Analytics, 5, 586992. ↩︎
While we recognise that PhD students may be more likely to continue in research careers and hence may be a better target population for our training, the variable timelines of PhD degrees and typically small numbers of PhD students at a particular institution makes it difficult to conduct a controlled experiment to test our hypothesis. ↩︎

10 considerations for applying to UK PhD programs

Sat, 03 Jan 2026 00:00:00 +0000

Acknowledgement: Thank you to Niranjanraj Ramasundaram for providing comments on and editing an earlier version of this blog.

Table of Contents

Overview

I sometimes get questions about how to search for and apply for doctoral (PhD) programs. I thought I would create a short post describing my experience of searching for and applying for programs in the UK in 2022-2023, particularly as an international student.

There are many great blog posts and other resources on the Internet that talk about applying for PhDs in general. I will mention two of them: wonderful post which I keep recommending to everyone. It’s the best summary I have found of the differences between the grad school application system across UK, Netherlands, USA and Canada. Another recommended blog is detailed one about applying for PhDs. I also recently gave a talk to Masters students on applying for PhDs in the UK, slides for which are available . These slides have a more detailed overview about the process of selecting and applying for PhDs. The current post is focused on my personal experience as an international student, with the context that I was based in India and had some years of work ex in India when I decided to apply for PhD.

How did I decide where to apply: country?

When I decided I wanted to do a PhD outside of my home country, India, I initially considered several different English-speaking countries, such as USA, UK and Australia. An extremely valuable piece of advice that I got from someone at this point was to focus on applying to one country. After some thought, I chose the UK, because I had already done a Masters here and so I felt some degree of familiarity with the academic system here as well as with the idea of living here. Focusing on one country made my job easier in terms of understanding the system in depth and being able to write competitive applications.

How did I decide where to apply: program and university?

My preferred way of finding things out is to use the Internet. Once I’d decided I wanted to apply to the UK, I spent many days first just reading various blog posts, university websites and other resources for information and made notes to help myself understand the types of programs available. This helped me to formulate specific questions I was still unclear about that I could then ask people I contacted.
One super important aspect to understand was funding availability for international students, which is often different and harder to find than for home students. I wanted to find a program that would cover, at the very least, full tuition fees and stipend. In 2017, I had got accepted to a PhD program in the UK, but as this was pre-Brexit, there was very little full funding for international students and this program made me an offer that included funding of “home tuition fees” and a stipend. This meant I would have to cover the difference between the tuition fees for a home student and an international student. This difference was substantial (up to £20000 per year) and was the reason I rejected the offer.

My experience looking for programs in 2022 was different. Many programs, such as DTPs (Doctoral Training Partnerships) or the Wellcome PhD programs, often made clear whether they had funding for international students, what they funded and how many places they funded. However, I found that advertised PhDs (where the project already has funding and the PI is recruiting a PhD student) were sometimes less clear. Sometimes, programs would invite applications from “home and overseas students” but state that the funding was for tuition fee at home rates, leaving me to intuit that I would have to find some other way of covering the difference. So I narrowed down my list of where to apply by isolating the programs that had at least, my minimum levels of funding for international students: full international tuition fee + stipend. It was also important to figure this out before contacting potential supervisors, as it is usually expected that you will mention to a potential supervisor how you are planning to fund your PhD.

In terms of deciding how many programs to apply to, I eventually ended up applying to 4 programs. I wanted to apply to more than one because I wanted to maximize my chances of getting in to something. However, I didn’t want to apply to too many as that would simply take up too much energy and time and dilute the quality of my applications. I only applied to programs where I thought I had a good chance of getting in. The way I determined this was fit with the program and response from supervisors (if the program required contacting the supervisor beforehand). Once I’d identified that the program orientation and potential research topic were of interest to me, I only applied to a program if the supervisor had responded and shown interest (if required by the program; note that there are programs that expressly say not to contact potential supervisors).

How did I establish contact and build relationships?

A big part of applying for a PhD involves contacting potential supervisors and the subtle art of building relationships, often through cold emails. I managed to speak to someone who’d already done their PhD in the UK (I was able to reach out to them through another friend) and to get a sense of the culture around contacting supervisors. I then started sending cold but careful emails to potential supervisors. A draft of one of my emails is available in the presentation I’ve linked above.

I will be the first to admit that my previous experience of the UK was a privilege that certainly gave me a leg up in the system (in terms of being perceived as credible, familiar, knowing the language that academics in the UK speak, the interpersonal culture), but do not let not having experience deter you in any way. I generally found that supervisors were open to me and welcoming, even if they didn’t know me. One of the very best supervisory experiences I had during my PhD application process was with a supervisor who didn’t know me at all from before and whom I had cold-emailed; we developed a great working relationship and they supported me immensely to develop a research proposal and funding application.

How did I prepare my written applications?

Most PhD program applications in the UK involve first submitting a written application (usually involving a CV, personal statement, other specific requirements) and then appearing for an interview (usually done online) if you are shortlisted. I was preparing my applications along with full-time work. This meant that much of the application work had to be done in the evenings/nights and cut into the time with my family. Some things that I would recommend during the application prep:

Being organized: This is crucial, especially if you are applying to more than one program, like I did. There are several deadlines to keep track of, including crucially, enough time to contact potential supervisors and referees and get a reply. I would really recommend having a table or a spreadsheet or some document that notes everything in one place, whatever format works for you.
Being authentic about limitations: I knew that my recent experience in research was limited and not the type of experience that students already in the UK would have. For example, I had much more limited statistical training and was only used to running basic correlations and t-tests or doing manual coding for qualitative studies, rather than more sophisticated forms of analyses. So, I acknowledged these things in my applications by mentioning the reason for them as well as mentioning what I hoped to learn and improve upon. After all, a PhD is a training program and I think the self-awareness is usually appreciated by those who read applications, having been on that side as well.
Taking time and writing well in the application: I do think the personal statement and research proposal in an application act as demonstrations of your writing skills. Editing and re-editing the personal statement and research proposal, to make it as clear and engaging as you can, is important. It’s also important to “show rather than tell”, that classic writing advice, meaning that rather than just saying “I have experience in quantitative research”, it is useful to describe the specifics of what experience you have. More examples are provided in the slides I link above.

How did I prepare for interviews?

I’m not the best at immediately responding to questions – I usually do better with having some time to think and formulate my answer – so I tend to always over-prepare for interviews and try to anticipate questions. Some things that helped me included:

Familiarizing myself with the work of the interview committee: I sought to familiarize myself with the work of the potential panellists on the interview panel. This helped me to anticipate the kinds of areas they might ask questions in, the opinions they might have about certain things and to defend my own ideas and previous research. I also familiarized myself with the research themes, phrases and language used and methodological paradigms they mentioned in their research; this helped me align to this language and also explain why my research was different. For example, I wasn’t really used to epidemiology in the context of mental health research from my experience in India and reading some of this work helped me understand where some interviewers might be coming from.
Mentalizing the interviewers: This is similar to the above point but is more about questions which elicit a more contextual response, for example, being asked about previous experience about a conflict in an interpersonal relationships. By mentalizing, I mean thinking of the experience and perspective the interviewers have and what they may require more information to understand. I actually think I didn’t do this as well as I could have. For example, when answering questions about interpersonal conflicts, the nature of conflicts I was discussing was quite unique to the Indian context, and I now think that providing more background to this would have helped, otherwise I could see some interviewers seeming a bit flummoxed as to what I was talking about.

I hope these 10 points have been helpful for you if you are thinking about applying to the UK for a PhD! One last thing I would say to sum up is that the application process itself is a learning process. I got rejected pre-interview from the very first program I applied to and that made me go back to my personal statement/application and think of what I could do better. At the same time, I recognized that some factors are out of my control and it’s important to not take rejection personally and keep going. All you need is one acceptance - good luck!

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