It has already been established that health is not merely freedom from any specific disease; instead, it is a state of complete physical, mental, and social well-being. In other words, to become healthy, we also need to be mentally fit. However, mental health has emerged as a critical health problem in Nepal. For instance, depression has become widespread, affecting individuals and society. It has, in fact, become a common health problem. Unfortunately, depression has not been well understood by everyone and has also not received a prominent space within the health system. In this blog, I will present some facts on mental health, particularly depression, and my thoughts on how we can address the problem of depression through data-driven decision-making approaches.
Key facts on mental health
The National Mental Health Survey (NMHS) was conducted in Nepal from January 2019 to January 2020 in all seven provinces of the country. Among the surveyed adults, 10 percent reported any lifetime mental disorder and 4.3 percent responded that they had a mental disorder during the time of the survey. The burden of mental health including depression is further exacerbated by factors such as poverty, social stigma, limited mental health infrastructure, and the impact of disasters like the 2015 earthquake and pandemic like COVID-19.
Between 1990 and 2017, the highest incidence and Disability-adjusted life years (DALYs) were found for depressive disorder in females. On the other hand, in males, the incidence of anxiety disorder remained stable after 2005 while the DALYs showed an increasing trend.
The following table shows Age-standardized incidence (A) and DALYs (B) by sex from 1990-2017.
Similarly, a study conducted by the World Health Organization (WHO) based on data collected online from April 23, 2020 to May 3, 2020 tracked the psychosocial status of Nepalese people during the pandemic. This study showed that half of the respondents experienced at least one psychological symptom, with a relatively high prevalence of mental health disorders, including depression. The prevalence of depression was 4.0 percent in both sexes. However, it was higher among females than males.
In addition to this evidence, during my professional life as an Emergency Department Medical Officer in Nepal, I’ve encountered a relatively larger number of individuals dealing with mental health issues, including cases of attempted suicide. As a medical professional, I provided them with counseling and other medical services to help meet their specific needs, following evidence-based treatment guidelines.
The power of data
The data is very important in informing intervention design to address these mental health issues. By strengthening data-driven decision-making in mental health including depression across the ecosystem of healthcare delivery in the federalized context of Nepal, there is high potential for organized coordination mechanisms to address depression. This can result in improved access to and utilization of mental health services. To effectively promote mental health services to prevent depression, it’s crucial to understand the data landscape surrounding mental health in Nepal. Gathering accurate and comprehensive data about the prevalence of depression, risk factors, and access to mental health services is the first step to inform evidence-based decision-making. On the other hand, the uptake of already-available data is equally important. We need to foster a culture of analyzing and using data for decision-making in the landscape of the health system in all three tiers of government. So how exactly does data support decision-making?
Identify risk factors: Data can help identify common risk factors contributing to depression, such as socioeconomic status, education levels, employment opportunities, and exposure to traumatic events like natural
Understand the gap: Analyzing data on the availability and accessibility of mental health services across different regions can highlight gaps in care, particularly in remote or underserved areas.
Make early detection: Data analytics can help develop algorithms to identify early signs of depression by analyzing patterns in behavior, social media usage, or medical records. Early detection enables timely intervention and support.
Locate vulnerable populations: Population-level disaggregated data by geographical areas can help locate vulnerable groups and communities and deliver targeted interventions with appropriate resource allocation. Also, it enables health systems at various levels to monitor changes in their health outcomes.
Design-Targeted Interventions: Tailoring interventions to specific risk factors can be more effective. For instance, if data shows that unemployment is a significant risk factor, targeted job training programs and support systems can be designed.
Utilize digital platforms: In a country like Nepal, digital platforms can be utilized for mental health outreach. Mobile apps and websites can provide resources, and self-assessment tools, and connect users to mental health professionals.
Apply telemedicine: Data on connectivity and technology usage can inform the development of telemedicine services, allowing individuals to access mental health support remotely.
Conduct public awareness campaigns: Analyzing data on media consumption and communication preferences can aid in crafting effective public awareness campaigns that destigmatize depression and encourage seeking help.
Data-driven approaches have the power to transform the landscape of mental health in Nepal. By utilizing data to identify risk factors, design targeted interventions, and improve accessibility to support services, Nepal can take significant strides in preventing depression and promoting overall mental well-being. As technology continues to evolve, so does the potential to create a more proactive, responsive, and compassionate mental health support system for people in the country.
The author is a medical practitioner and researcher who is passionate about making contributions to the healthcare field in Nepal and beyond. She has been closely collaborating with HERD International, expanding her expertise, especially in the realm of data-driven decision-making within the healthcare system.)