Investigating inflation, living costs and mental health service utilization in post-COVID-19 England

The United Kingdom experienced a cost-of-living crisis following the COVID-19 pandemic, with price inflation significantly increasing from 1.5% to 8.9% between March 2020 and March 2023, compared to 0.7–3% in the pre-pandemic period (August 2016–February 2020). This sharp rise mirrors similar economic downturns, such as the 2008 recession, which have been linked to adverse mental health outcomes globally. Prior research established connections between increased living costs and compromised mental health due to factors like housing instability, fuel poverty, and limited access to essential goods and services. Children in poverty are also at increased risk of adverse childhood experiences, impacting their mental health. While the British government implemented financial subsidies, a lack of empirical evidence exists regarding the relationship between the rising cost of living and reported mental health cases in the post-COVID-19 context. This study aims to fill this gap by investigating which specific living costs are most strongly associated with mental health service utilization in the general population, providing crucial information for the development of effective policy interventions.

Existing literature extensively documents the detrimental effects of economic recessions on mental health. Studies from the 2008 recession in the UK and internationally showed increased rates of depression, anxiety, and suicide, particularly among vulnerable populations. Research has also linked rising living costs to poorer mental health, citing housing instability, fuel poverty, and food insecurity as key contributing factors. The impact extends to children, where poverty increases the risk of adverse childhood experiences with long-term mental health consequences. However, a comprehensive assessment of the association between specific price indices and mental health service utilization in the post-COVID-19 era remains absent. This study addresses this knowledge gap by examining the relationships between various components of the CPIH and mental health service contacts, specifically distinguishing effects across different age groups.

This ecological study used monthly data from August 2016 to February 2023, excluding data from March 2020 to March 2022 due to the confounding effects of COVID-19 restrictions. The study analyzed data from two publicly available sources: the Office for National Statistics (ONS) and NHS Digital. Price inflation was measured using the Consumer Price Index including owner occupiers' housing costs (CPIH), a comprehensive measure of household expenses. Mental health service utilization was measured as the number of people in contact with mental health services, categorized by age group (0–18 years, 19–64 years, and 65+ years). The unemployment rate served as a control variable. Linear regression models were used to analyze the association between the CPIH (and its sub-indices) and mental health service contacts, controlling for month, year, unemployment rate, and a binary variable for the pre- and post-COVID-19 periods. Interaction terms were included to assess the moderating effect of the time period. Lagged effects of price indices were also explored using the Akaike Information Criterion to determine optimal lag lengths. All analyses were conducted in R.

Compared to the pre-COVID-19 period, significant associations emerged between specific living costs and mental health service utilization in the post-COVID-19 period, particularly among adults (19–64 years) and the elderly (65+ years). A 1% increase in the 'food and non-alcoholic beverages', 'housing, water and fuels', and 'miscellaneous goods and services' indices were associated with substantial increases in the number of adults in contact with mental health services (8.89, 35.88, and 24.43 thousand, respectively). Conversely, a 1% increase in the 'alcoholic beverages, tobacco and narcotics' and 'hotels, cafes and restaurants' indices were associated with decreases in the number of adults contacting services (-41.97 and -50.55 thousand, respectively). A one-month lag effect was observed for 'hotels, cafes and restaurants'. For the elderly, increases in the CPIH, 'food and non-alcoholic beverages', and 'miscellaneous goods and services' indices were also significantly associated with increased mental health service contacts (26.12, 3.58, and 7.33 thousand, respectively).

The findings demonstrate a strong association between rising living costs and increased mental health service utilization in post-COVID-19 England, particularly for specific indices like food, housing, and miscellaneous goods and services. This supports previous research linking economic hardship to poorer mental health. The negative association with increased costs of alcohol, tobacco, and leisure activities is intriguing and may reflect changes in spending priorities or alternative coping mechanisms. The study's limitations notwithstanding, these findings provide valuable evidence for policymakers. The observed associations highlight the need for targeted policy interventions, such as financial subsidies for essential goods and services, tailored to the specific needs of different age groups. The current subsidies may be inadequate, warranting a re-evaluation of the scale and scope of financial assistance.

This study provides crucial evidence linking specific aspects of rising living costs to increased mental health service utilization in England during the post-COVID-19 period. The strong association observed for essential goods and services highlights the need for targeted, age-specific policy interventions, including financial subsidies and expanded mental health support services. Future research should investigate causal relationships and explore the role of factors like gender, race, and socioeconomic status in mediating the relationship between living costs and mental health. Regional variations and the long-term impacts of the pandemic should also be investigated.

This study's observational design prevents establishing causal relationships. The use of mental health service contacts as a proxy for mental health status might not capture the full extent of mental health issues. The lack of gender-specific data limits the analysis of gender disparities in mental health outcomes. Data granularity limitations, particularly the mismatch between regional mental health and price inflation data, prevent a full regional analysis. Unmeasured economic factors, such as personal income and debt levels, could have influenced the results. While steps were taken to mitigate the lingering effects of the pandemic, the long-term mental health consequences remain uncertain.