Reduction in healthcare services during the COVID-19 pandemic in China

Abstract

Introduction: The COVID-19 pandemic caused a healthcare crisis in China and continues to wreak havoc across the world. This paper evaluated COVID-19's impact on national and regional healthcare service utilisation and expenditure in China.

Methods: Using a big data approach, we collected data from 300 million bank card transactions to measure individual healthcare expenditure and utilisation in mainland China. Since the outbreak coincided with the 2020 Chinese Spring Festival holiday, a difference-in-difference (DID) method was employed to compare changes in healthcare utilisation before, during and after the Spring Festival in 2020 and 2019. We also tracked healthcare utilisation before, during and after the outbreak.

Results: Healthcare utilisation declined overall, especially during the post-festival period in 2020. Total healthcare expenditure and utilisation declined by 37.8% and 40.8%, respectively, while per capita expenditure increased by 3.3%. In a subgroup analysis, we found that the outbreak had a greater impact on healthcare utilisation in cities at higher risk of COVID-19, with stricter lockdown measures and those located in the western region. The DID results suggest that, compared with low-risk cities, the pandemic induced a 14.8%, 26.4% and 27.5% reduction in total healthcare expenditure in medium-risk and high-risk cities, and in cities located in Hubei province during the post-festival period in 2020 relative to 2019, an 8.6%, 15.9% and 24.4% reduction in utilisation services; and a 7.3% and 18.4% reduction in per capita expenditure in medium-risk and high-risk cities, respectively. By the last week of April 2020, as the outbreak came under control, healthcare utilisation gradually recovered, but only to 79.9%-89.3% of its pre-outbreak levels.

Conclusion: The COVID-19 pandemic had a significantly negative effect on healthcare utilisation in China, evident by a dramatic decline in healthcare expenditure. While the utilisation level has gradually increased post-outbreak, it has yet to return to normal levels.

Keywords: diseases; disorders; health economics; health services research; infections; injuries; other study design; public health.

Figure 1

The percentage change of healthcare service utilisation of 2020 compared with 2019 in Eastern/Central/Western of China (%). Notes: This figure present the geographic distributions of province with percentage change of healthcare services utilisation, which was defined as the percentage of decrease or increase in 2020 compared with same period in 2019. (A, B and C): Percentage change of total healthcare expenditure during pre-festival, Spring Festival and post-festival periods in 2020, respectively; (D, E and F): Percentage change of utilisation frequency during pre-festival, Spring Festival and post-festival periods in 2020, respectively; (G, H and I): Percentage change of per capita expenditure during pre-festival, Spring Festival and post-festival period in 2020, respectively.

Figure 2

The dynamic impact of the COVID-19 on healthcare service utilisation in panels A/B/C. Notes: The figures plot the impact of COVID-19 on healthcare service utilisation in panels A/B/C. We included 2019 and 2020 dummies in the regressions, the left side of dotted line is 2019 and the right side is 2020. The estimated coefficients and their 95% CIs were plotted. A, B and C were the parallel trend tests of average weekly total healthcare expenditure, utilisation frequency and per capita expenditure in panel A, respectively. D, E and F were the parallel trend tests of average weekly total healthcare expenditure, utilisation frequency and per capita expenditure in panel B, respectively. G, H and I were the parallel trend tests of average weekly total healthcare expenditure, utilisation frequency and per capita expenditure in panel C, respectively. Specifically, we report estimated coefficients from the following regression: Y_{i, t}=β₀+β₁Treat_i*Week_t¹+β₂Treat_i*Week_t²+…+β₁₇Treat_i*Week_t¹⁷+d_i+d_t+ε_{i, t} where i (i=1,…, 363) denotes the city, t (t=-9,…,−1, 0, 1,…, 8) denotes the week, the added variables Week_t^j (j=1,…,17) are the dummy variables of week j that equals 1 when week t=j and equals 0 otherwise, and the omitted benchmark week is the first week in 2019 (ie, t=−9). Treat_i is the treatment variable of city i that equals 1 when i is medium-risk, high-risk city and city in Hubei and equals 0 otherwise, Y_{i, t} is the healthcare service utilisation in city i during week t. The city-specific fixed effects d_i control for unobserved city-specific heterogeneities. The week-specific fixed effects d_t control for unobserved time-specific factors, including travel related to the Spring Festival (a time with high rates of travel in China).

Figure 3

Trends for the utilisation of healthcare services during November 2019 to April 2020 in 365 mainland cities in China. Notes: The Spring Festival week is 25 January 2020 to 1 February 2020. Weeks 1–12 is pre-outbreak period (1 November 2019 to 22 January 2020), weeks 13–16 is during outbreak period (23 January 2020 to 20 February 2020), and weeks 17–26 is post-outbreak period (21 February 2020 to 30 April, 2020). (A) Trend for the total healthcare expenditure during November 2019 to April 2020 in China; (B) Trend for utilisation frequency during November 2019 to April 2020 in China; (C) Trend for per capita expenditure during November 2019 to April 2020 in China.