The impact of COVID-19 policy stringency on patient satisfaction with community pharmacies in China: A cross-sectional standardized patient study

Abstract

Objectives

Community pharmacies play a crucial role in China’s primary health care system. This study aimed to assess the impact of stringent COVID-19 policy responses - such as lockdowns, travel restrictions and operational closures - on unannounced standardized patients’ (USPs) satisfaction with community pharmacy services.

Methods

A cross-sectional study was conducted from April 2021 to September 2022, using an USPs approach in community pharmacies across China. USPs’ satisfaction was measured using validated tools, with closure policies related to COVID-19 as the primary exposure variable.

Results

The study included 1076 eligible USP visits to community pharmacies. Results indicated that stricter closure policies had a significant negative impact on USPs’ satisfaction (β = −0.18, p = 0.019). This negative effect may be attributed to worsened capability of pharmaceutical service providers (β = −0.17, p = 0.002) and accessibility (β = −0.12, p = 0.019). Subgroup analyses demonstrated a negative correlation between stricter closure policies and lower satisfaction levels with regard to accessibility, capability, and communication.

Conclusions

COVID-19 closure policies in China had adverse consequences for the quality of pharmacy services. These findings highlight that governments must act with urgency when addressing abrupt infectious diseases or public health emergencies. Enhancing access to pharmacy services and capability of providers are critical strategies to ensure an effective response to sudden public health crises.

Keywords

Patient satisfaction public health policy primary health care

Introduction

The COVID-19 pandemic created unprecedented global health, social, and economic challenges, leading governments to implement a range of policies to control virus transmission and mitigate its adverse effects. Policies implemented during the COVID-19 pandemic included lockdowns, travel bans, school closures, mask mandates, testing and tracing, vaccination programs, and economic relief packages.¹ The effectiveness and timing of these policies varied depending on factors such as the epidemiological situation, health system capacity, political system, and public adherence.² To eliminate local virus transmission, China adopted a zero-tolerance policy, which was successful in suppressing COVID-19 within a few months, preventing millions of infections and deaths, However, this policy has faced challenges including social and economic costs, public opposition, and vulnerability to new virus variants.³

Community pharmacy services play a crucial role in China’s primary health care system, providing medication management, counseling, health education, and referrals.⁴ Unfortunately, the COVID-19 pandemic has significantly disrupted primary care services, with government-imposed ‘closure policies’ that restricted the operation of non-essential businesses, including pharmacies. These closure policies, which included mandatory shutdowns and restrictions on service capacity, led to supply chain disruptions, drug shortages, work stoppages, and health care worker redeployments, ultimately affecting the accessibility and quality of care.⁵ Pharmacists experienced increased demand for their services as patients turned to community pharmacies during outbreaks, potentially affecting the long-term provision of primary care in China.

The COVID-19 pandemic has significantly impacted customer experiences and pharmacy services, influenced by factors such as accessibility, supply availability, and communication between pharmacists and clients.^6–8 Research indicates that services designed to meet individual client needs enhance customer satisfaction. For instance, Parsons et al. highlight the positive effects of community pharmacy-delivered oral contraception services.⁹ Ford et al. emphasize the value of employing unannounced standardized patients (USPs) methodologies in assessing service experiences,¹⁰ a perspective further supported by Sen’s use of USPs methods to examine the relationship between client experience and technical care quality in urban India.¹¹ In our study, USPs were trained actors who posed as patients and visited community pharmacies without prior notice to the pharmacy staff. These actors were instructed to simulate specific health conditions and interactions according to predefined scenarios. The USP-based method’s reliability stems from unannounced visits, which reduce the Hawthorne effect (the phenomenon where individuals modify their behavior due to the awareness of being observed) and improve assessment accuracy.¹² Additionally, USPs minimize recall bias by completing questionnaires immediately after encounters.¹³ USPs also facilitate evaluations of patient-centered care by measuring empathy and respect demonstrated by pharmacists towards the patients (simulated by USPs) during the interactions.¹⁴ However, challenges include costs, logistical difficulties, and the risk of detection by providers, which can affect encounter authenticity. Differences in perceptions between USPs and actual patients may also arise. The application of USPs in China has been gaining momentum. There is a growing recognition of the value of USP-based research in enhancing clinical teaching and assessment within medical curricula across China.¹⁵ Indeed, Sylvia et al. conducted a survey using USPs which revealed poor quality care in China’s rural clinics.¹⁶

The pandemic has caused obstacles and shortages of medical resources and affected patients’ medication adherence. And it has also led to increased distress in medical encounters, affecting communication, trust, and patient satisfaction.¹⁷ However, there have been positive changes, such as increased attention to patient needs and improved satisfaction with pharmaceutical services.⁸ Assessing customer satisfaction with pharmacy visit specific characteristics is crucial in understanding how COVID-19 policy measures have impacted the quality of care delivery in pharmacies. There is a significant gap in the literature on this research area. Therefore, further investigation is needed to inform policies and interventions for improving pharmaceutical services during future pandemics.

In this study, we employed the USPs method to simulate customer interactions in community pharmacies during the COVID-19 pandemic, enabling us to evaluate service quality and accessibility under varying policy stringency levels. We aimed to explore how stringent COVID-19 policies affected customer experiences with pharmacy services, particularly regarding aspects such as accessibility, service efficiency, and communication. We hypothesized that stricter COVID-19 control measures would negatively influence USPs’ satisfaction with pharmacy services. Furthermore, we posited that the strength of this association would be contingent on the severity of COVID-19 transmission rates within the local context.

Methods

This analysis used data collected in the Primary Health Care Quality Cohort in China project, which aimed to assess the quality of primary health care services across a diverse range of public and private healthcare institutions in seven provinces.¹⁸ The project measured both the objective health care quality metrics and USP-reported quality of primary care using USPs at primary health facilities randomly pre-selected for the period April 2021 to September 2022. This period was selected to avoid the confounding effects of the abrupt reversal of China’s zero-COVID policy in December 2022 and the subsequent disruptions to healthcare services. It also addressed the issue of USPs being unable to visit pharmacies during the most severe COVID-19 outbreaks. Detailed descriptions of the USP training and safety protocols, questionnaire development and validation, sampling design, and data collection procedures have been included in the online supplement.

Exposure

To construct the key independent variable in our study, we used the daily stringency index from the Oxford COVID-19 Government Response Tracker project.² This index measures the dynamics of the overall rigor of closure policies in respective provinces in China on a daily basis. ‘Closure’ refers to the government-imposed restrictions on non-essential businesses, which varied across provinces with some provinces imposing more stringent measures than others. We calculate the average value of the daily stringency index for each province in the 30 days prior to each USP visit and categorize it as light restrictions or high restrictions according to the time-weight median values of policy stringency.

Outcomes

The primary outcome measures of this study are overall and domain-specific USPs’ satisfaction ratings with pharmacy services, assessed using a questionnaire. Importantly, these ratings reflect objective features of pharmacy services (such as accessibility, service capability and communication), rather than personal perceptions of illness or health care needs.¹⁹ The questionnaire consists of five dimensions: accessibility (six items), capability (five items), communication (11 items), continuity (two items), and overall satisfaction (one item). Each dimension is rated on a five-point Likert scale, ranging from five (strongly agree) to one (strongly disagree). For more details on the questionnaire, see Table S1 in the online supplement.

Covariates

Relevant covariates were pre-specified and determined based on a conceptual framework (see Figure S1 in the online supplement). The final covariate variables included incidence rate of COVID-19 cases, province of visit, year of visit, institution type, for-profit status, disease type, and receipt of traditional Chinese medicine. We categorized the COVID-19 incidence rates into three groups: group of no epidemic, group of intermediate epidemics (with more than zero but less than 50 new COVID-19 cases), and group of severe epidemics (with 50 or more new cases).

Statistical analysis

The significance of the distribution of categorical data was determined using Pearson χ2 testing. Pair-wise comparisons of continuous variables were evaluated using a student’s t-test or Wilcoxon rank sum test depending on the normal and skewed distributions of respective variables.

The association between public policy stringency and USPs’ satisfaction with pharmaceutical services was estimated using the ordinary least squares models, with overall satisfaction modeled as a continuous (overall satisfaction rating) dependent variable. Additionally, probit and logistic regression models were employed to examine the associations between policy stringency and the outcome as a binary variable, combining responses of ‘Strongly agree’ or ‘Agree’ versus others. The probit model estimated the joint distribution of outcomes using a latent variable approach, while the logistic regression model transformed the probability into a linear expression using a logistic function. In both models, the binary variable of satisfaction (‘Satisfied’ vs ‘Not satisfied’) served as the dependent variable, and odds ratios - along with 95% confidence intervals - were reported to indicate the effects of relative changes in the public policy stringency index. To ensure robustness, the ordinary least squares, probit, and logistic regression models were compared to assess the consistency of findings across different model specifications. Statistical analyses were conducted using R version 4.1.0 (R Foundation for Statistical Computing).

Results

Out of 2128 USP visits in the study period, we excluded 1017 visits that did not utilize pharmaceutical services and 35 visits with invalid values (such as incomplete data), resulting in a final analysis sample of 1076 eligible visits for statistical analysis (Figure S2 in the online supplement).

The descriptive statistics of all main variables used in our analyses are reported in Table 1. Among the 1076 simulated physician-USPs interactions, a significant proportion of visits occurred in provinces with higher policy stringency: Hunan (33.3%) and Guangdong (46.5%). In contrast, visits in Sichuan (49.1%) were conducted under lighter restrictions. The average stringency index was significantly higher in the high restrictions group compared to the light restrictions group (54.9 [±5.9] vs. 44.5 [±3.0], p < 0.001). Similarly, the local incidence rate was elevated in the high restrictions group (14.1 [±23.4] vs. 4.3 [±17.4], p < 0.001) during the 30 days preceding the visits.

Table 1.

Sample description.

Variables	Overall	Light restrictions	High restrictions	p-value
Variables	N = 1076	N = 538	N = 538	p-value
Region, N (%)
Inner Mongolia	48 (4.5)	20 (3.7)	28 (5.2)	<0.001
Hunan	220 (20.4)	41 (7.6)	179 (33.3)
Guangdong	259 (24.1)	9 (1.7)	250 (46.5)
Sichuan	313 (29.1)	264 (49.1)	49 (9.1)
Guizhou	89 (8.3)	73 (13.6)	16 (3.0)
Shaanxi	78 (7.2)	66 (12.3)	12 (2.2)
Gansu	69 (6.4)	65 (12.1)	4 (0.7)
Year of visit, N (%)
2021	804 (74.7)	538 (100.0)	266 (49.4)	<0.001
2022	272 (25.3)	0 (0)	272 (50.6)	<0.001
Institution type, N (%)
Outpatient clinics	259 (24.1)	116 (21.6)	143 (26.6)	0.029
Village health station	418 (38.8)	229 (42.6)	189 (35.1)
Others^a	399 (37.1)	193 (35.9)	206 (38.3)
For-profit status, N (%)
Non-profit medical institutions	897 (83.4)	446 (82.9)	451 (83.8)	0.743
For-profit medical institutions	179 (16.6)	92 (17.1)	87 (16.2)	0.743
Disease type, N (%)
Acute disease^b	270 (25.1)	114 (21.2)	156 (29.0)	0.004
Chronic disease^c	806 (74.9)	424 (78.8)	382 (71.0)	0.004
Place of pharmaceutical service, N (%)
Clinics	395 (36.7)	224 (41.6)	171 (31.8)	0.001
Pharmacy	670 (62.3)	306 (56.9)	364 (67.7)
Others/Unknown	11 (1.0)	8 (1.5)	3 (0.6)
Pharmacy service provider type, N (%)
Doctors	508 (47.2)	286 (53.2)	222 (41.3)	0.001
Nurses	22 (2.0)	13 (2.4)	9 (1.7)
Pharmacists	541 (50.3)	237 (44.1)	304 (56.5)
Others/Unknown	5 (0.5)	2 (0.4)	3 (0.6)
TCM dispensing status, N (%)	667 (62.0)	338 (62.8)	329 (61.2)	0.615
COVID-19 related, mean (±SD)
Average stringency index over 30-day pre-visit period (0-100)	49.65 (6.98)	44.46 (3.02)	54.85 (5.87)	<0.001
Local incidence rate (per 1M population) over 30-day pre-visit period	9.19 (17.37)	4.32 (3.30)	14.05 (23.36)	<0.001

TCM = traditional Chinese medicine, SD - standard deviation.

^aOther institution type including outpatient department, community health service center/station, health clinics in towns and townships, and clinics.

^bAcute disease including postpartum depression, infantile diarrhea, and common cold as tracer conditions.

^cChronic disease including gastritis, angina pectoris, asthma, back pain, diabetes, hypertension, migraine, and stress urinary incontinence as tracer conditions.

Figure 1 illustrates the temporal variations in the stringency index and the daily new COVID-19 cases in China during our study period. The dotted line represents the logarithm of new cases, which fluctuated markedly over time, showing an overall upward trend. The solid line indicates the policy stringency index, which remained consistently high throughout the study period, ranging from 60 to 80 on the scale. As Figure S3 in the online supplement indicates, there were strong positive correlations between the policy stringency level and the incidences of COVID-19 cases in all provinces included in the study.

Figure 1.

COVID-19 incidence and stringency of closure policies in China (2021-2022).

We conducted exploratory analyses to examine the bivariate association between the stringency of closure policies and USPs’ satisfaction (Table 2). Stricter control policies in COVID-19 were associated with a higher likelihood of ‘agree’ responses (338 [62.9%] vs 273 [50.8%], p < 0.001), but the lower likelihood for ‘Strongly agree’ responses (23 [4.3%] vs 30 [5.6%], p < 0.001). The individual satisfaction dimensions showed no significant differences under different degrees of policy stringency. USP satisfaction on individual questions about pharmacy services is reported in Table S1 in the online supplement.

Table 2.

Patient satisfaction with pharmacy service stratified by stringency of control measures.

Variables	Overall	Light restrictions	High restrictions	Statistical value	p Value
Variables	N = 1076	N = 538	N = 538	Statistical value	p Value
Overall satisfaction, N (%)				χ2 value
Agree & strongly agree^a	611 (56.8)	273 (50.8)	338 (62.9)	15.51	<0.001
Strongly agree	53 (4.9)	30 (5.6)	23 (4.3)	24.22	<0.001
Agree	558 (51.9)	243 (45.2)	315 (58.6)
Neutral	339 (31.5)	203 (37.7)	136 (25.3)
Disagree	104 (9.7)	53 (9.9)	51 (9.5)
Strongly disagree	22 (2.0)	9 (1.7)	13 (2.4)
Overall/individual satisfaction dimensions^b, mean (±SD)				t Value
Overall satisfaction	3.5 (0.8)	3.4 (0.8)	3.5 (0.8)	102.18	<0.001
Accessibility	3.6 (0.5)	3.6 (0.5)	3.6 (0.5)	138.86	<0.001
Capability	3.6 (0.6)	3.5 (0.6)	3.6 (0.6)	127.88	<0.001
Communication	3.3 (0.7)	3.3 (0.7)	3.3 (0.7)	110.55	<0.001
Continuity	3.1 (0.8)	3.0 (0.8)	3.1 (0.8)	89.01	<0.001

SD = standard deviation.

^aAgree & Strongly agree is the sum of Agree and Strongly agree.

^bIndividual satisfaction dimensions included accessibility (six questions), capability (five questions), communication (11 questions), and continuity (two questions), in which all responses were measured on a five-point Likert scale, ranking from five (Strongly agree) to one (Strongly disagree). Individual satisfaction dimensions were calculated by the mean value of responses to each problem as continuous variables in each dimension.

Table 3 displays the results of our three regression models that examined the effect of COVID-19 closure policies on USPs’ satisfaction under different measurements. The government stringency index had a negative and significant effect on USPs’ satisfaction in all models. The β value for the government stringency index is −0.183 (95% CI [-0.34, −0.03], p = 0.019). This coefficient indicates that when the policy index changed from lighter restriction to lockdown, overall satisfaction with pharmaceutical services decreased by 0.18 units. The OR for the government stringency index on USPs’ satisfaction was 0.793 (p = 0.065) and 0.683 (p = 0.063). The coefficients of the control variables are reported in Table S2 in the online supplement.

Table 3.

Association of patient satisfaction with COVID-19 related public policy stringency.

	Ordinary least squares model		Probit model		Logistic regression model
	Overall satisfaction		Satisfied (Y/N)
Reported coefficients	β (SE)		Odds ratios (SE)
Stringency index	−0.183** (0.078)	−0.183** (0.078)	0.791* (0.126)	0.793* (0.126)	0.681* (0.205)	0.683* (0.205)
Ln incidence rate per million+1		−0.007 (0.117)		1.069 (0.191)		1.110 (0.314)
Reported coefficients	Average marginal effects
Stringency index	−0.183** (0.078)	−0.183** (0.078)	−0.083* (0.043)	−0.082* (0.043)	−0.083* (0.043)	−0.082* (0.043)
Controls & fixed effects	YES	YES	YES	YES	YES	YES
Number of observations	1076	1076	1076	1076	1076	1076
R²/Pseudo-R²	0.055	0.055	0.069	0.070	0.069	0.069

SE = standard error.

Note: **p < 0.05, *p < 0.1. Control variables include province of visit (Guangdong, Gansu, Hunan, Shaanxi, Inner Mongolia, Sichuan, and Guizhou), year of visit (2021, 2022), institution type (outpatient clinics, village health station, and others), for-profit status (non-profit vs for-profit medical institutions), disease type (acute vs chronic disease), and whether received traditional Chinese medicine.

Figure 2 indicates the effects of COVID-19’s control policies on the various dimensions of USPs’ satisfaction in pharmacy service. The results demonstrate that the negative and significant effect of the policy stringency index on pharmacy service satisfaction was mainly reflected in worsened capability (β = −0.17, 95% CI [-0.28, −0.06], p = 0.002) of the pharmacy service provider and accessibility (β = −0.12, 95% CI [-0.21, −0.02], p = 0.019) of pharmacy services.

Figure 2.

Association between overall/domain-specific patient satisfaction and COVID-19 related public policy stringency.

To explore the effects of COVID-19 outbreaks on pharmacy service satisfaction, we conducted stratified analyses to assess how varying levels of COVID-19 control policies influenced satisfaction across different epidemic scenarios. As Figure S4 in the online supplement shows, in the subgroup with no new cases, stricter control policies were associated with lower levels of USPs’ satisfaction in terms of accessibility (β = −0.38, 95% CI [-0.74, −0.02], p = 0.036), capability (β = −0.37, 95% CI [-0.80, 0.06], p = 0.089), and communication (β = −0.34, 95% CI [-0.74, 0.06], p = 0.095). In the subgroup restricted to visits with higher case numbers over the 30-day pre-visit period, stricter control policies were associated with lower levels of USPs’ satisfaction in terms of accessibility (β = −0.24, 95% CI [-0.47, −0.01], p = 0.040). No statistically significant associations between the policy stringency index and dimension-specific USPs’ satisfaction were observed in the group of intermediate epidemics.

As shown in Table S3 in the online supplement, heterogeneity analyses indicated that the policy stringency index had relatively consistent impacts on USPs’ satisfaction in all provinces except Gansu province, where the policy stringency index had a significant negative effect on overall (β = −1.45, 95% CI [-2.26, −0.66], p < 0.001) and all dimensions of USPs’ satisfaction (Figure S5 in the online supplement). We categorized the average of the daily provincial stringency indices for the 30 days prior to USPs visit as either mildly restricted or lockdown based on the median of the time weights for policy stringency for each province during the study period. The results of this analysis, based on the provincial stringency index, are consistent with the findings from the national-level analysis.

COVID-19 closure policies differed in 2022, as all USP encounters were categorized under higher control measures, resulting in a shift to stricter policies during that period. To better understand the impact of these changes, we conducted another heterogeneity analysis restricted to all visits that occurred in calendar year 2021. This analysis yields similar results compared to the main models (Table S4 in the online supplement). A shift to higher policy control measures was associated with a decrease of 0.13 units in overall satisfaction, albeit not statistically significant due to limited sample size and statistical power.

Discussion

The impact of COVID-19 lockdown policies on population health has received significant attention from the academic community. Our study fills a gap in the existing literature base by focusing on the impacts of closure policies on client satisfaction with community pharmaceutical services.

This empirical study is, to our knowledge, the first to establish a negative association between stricter COVID-19 closure policies and satisfaction ratings based on USPs in primary care settings in China. Limited access to pharmacy services and USPs’ perceptions of the capability of providers were key factors contributing to USPs dissatisfaction.

Our results align with Herzik et al., who explored the impact of COVID-19 on pharmacy transitions of care services.⁸ Both studies highlight the disruptions to the care continuity due to pandemic-induced travel restrictions, resulting in decreased patient satisfaction. In contrast, our research diverges in focus and context from Traiki et al., which examined patient satisfaction and surgical outcomes in hospital settings.²⁰ While their study emphasized the direct impact of the pandemic on surgical services, our research centres on community pharmacies, illustrating the distinct roles and challenges faced by different sectors of health care during the pandemic. Furthermore, unlike Sylvia et al., whose findings indicated poor quality care in rural clinics,¹⁶ our study provides a broader examination of how policy stringency affects client satisfaction across various types of pharmacy services. We found that strict public health measures, while crucial for controlling the pandemic, significantly influenced the clients-pharmacy relationship, affecting both care quality and client perceptions.

The COVID-19 pandemic has negatively impacted the accessibility of pharmacy services. The associations may be explained by two complementary explanations. Firstly, the implementation of COVID-19 control measures has disrupted the delivery of essential medical services, resulting in appointment postponements or cancellations and affecting client experiences during visits.²¹ Secondly, production and supply chain issues for prescription drugs and over-the-counter medical products have led to potential shortages.²²

Pharmaceutical service providers had been significantly impacted by the COVID-19 pandemic, leading to a decline in USPs’ satisfaction with their capability. Frontline workers have experienced profound physical and mental health effects, including emotional distress and burnout,²³ which can adversely affect patient safety and care quality. The pandemic has also contributed to the general public’s distrust towards scientific communities, governmental institutions, and non-governmental organizations, potentially leading to harmful health behaviors.²⁴ Clients’ distrust of community pharmaceutical service providers may further exacerbate client dissatisfaction. Furthermore, changes in medical care delivery models during the pandemic have resulted in reduced work efficiency initially,²⁵ affecting client satisfaction with pharmaceutical care.

The COVID-19 pandemic has had diverse effects on client satisfaction with pharmacy services that vary based on the severity of the epidemic situation. Both policy stringency and pandemic intensity are associated with worsened mental health status.²³ When there are no or few new cases, restrictive health measures may cause dissatisfaction among clients due to perceived limitations in accessing services. Conversely, during severe epidemics, clients perceive higher risks of contagion and thus value the implementation of restrictive health measures that are intended to safeguard personal health.²⁶ This dual perspective underscores the need for a nuanced understanding of client satisfaction. In addition, some medical institutions have undergone significant service reconfigurations to provide care safely and robustly during the pandemic’s peak, resulting in higher client satisfaction.²⁷ Furthermore, clients have reported receiving care beyond their expectations during severe epidemics.²⁰ Therefore, while policymakers should prioritize measures that reduce virus transmission, they must also be cautious when imposing restrictive policies that could adversely impact clients’ daily experiences and access to services.

Research into the appropriate governmental response to pandemics has found that policymakers should adopt a balanced approach that prioritizes reducing virus transmission while maintaining accessible pharmacy services. For example, implementing designated ‘safe access hours’ for vulnerable populations can enhance accessibility while minimizing health risks.²⁸ Moreover, training programs for pharmacy staff should emphasize both technical competencies and interpersonal skills, fostering improved clients interactions and enhancing the quality of care.²³ This dual focus is essential for rebuilding trust in pharmaceutical services in the post-pandemic landscape. Additionally, policymakers should invest in strategies to strengthen the pharmaceutical supply chain, mitigating disruptions during crises and ensuring consistent access to essential medications.²⁹ Community engagement initiatives that educate the public about the critical role pharmacies play in public health can help alleviate fears and build trust, particularly during health crises.³⁰

Future research should concentrate on understanding the long-term impacts of pandemic policies on client satisfaction, especially within primary care settings. By systematically incorporating client feedback into policy development, health care authorities can enhance service delivery and more effectively address areas of concern. Longitudinal studies are particularly valuable in assessing how varying levels of policy stringency and epidemic intensity influence client satisfaction over time, offering critical insights for future public health responses.

Limitations

Three main limitations should be considered when interpreting our findings. First, the inability to conduct USPs visits during the most severe COVID-19 outbreaks may have introduced selection bias, potentially underestimating the relationship between control policies and USPs’ satisfaction.

Second, the provincial incidence rate of COVID-19 outbreaks may introduce endogenous effects on policy stringency in our causal inference, as higher case numbers could have led to stricter policies in certain provinces. This could potentially affect the reliability of our results, as the observed relationship between policy stringency and USPs’ satisfaction might be influenced by regional outbreaks. We sought to address this issue by using multivariable adjustment and subgroup analyses. These yielded similar results.

Third, although the experiences of USPs are designed to reflect real patients’ encounters, they may not capture the same emotional or health-related urgency as actual patients. Our results are informed by structured, scenario-driven USP visits, which offer insight into accessibility and provider capability, but future work should validate these findings from actual patients’ perspectives.

Conclusion

This study offers empirical insights that can inform government decision-making during public health crises. Our findings indicate that stricter COVID-19 control policies are linked to reduced client satisfaction with pharmaceutical services, highlighting the delicate balance between public health measures and client care. To enhance client satisfaction during the future health emergencies, it is crucial to improve the accessibility of services and the capabilities of pharmacy providers. This involves not only ensuring that pharmacies remain operational during restrictions but also equipping staff with the necessary skills to address clients’ needs effectively. By prioritizing these improvements, policymakers can better support both public health objectives and the quality of client care.

Supplemental Material

Supplemental Material - The impact of COVID-19 policy stringency on patient satisfaction with community pharmacies in China: A cross-sectional standardized patient study

Supplemental Material for The impact of COVID-19 policy stringency on patient satisfaction with community pharmacies in China: A cross-sectional standardized patient study by Ruijian Huang , Dong Roman Xu , Jay Pan , Xiaohui Wang , Yingsong Chen , Qingyuan Xue , Jiamei Liu , Jingyun Xu , Yue Xiao , Feng Jiang , Yanfei Chen , Siyu Ding , Di Wang and Jifang Zhou in Journal of Health Services Research & Policy

Footnotes

Acknowledgments

This study is part of the ACACIA project, led by the Acacia Lab. for Implementation Science at Southern Medical University, with contributions from, in alphabetical order, Central South University, China Pharmaceutical University, Guizhou Medical University, Inner Mongolia Medical University, Lanzhou University, Macquarie University, Sun Yat-sen University, Xi’an Jiaotong University, and Sichuan University. We express our heartfelt gratitude to the researchers, students and volunteers from these institutions for their tremendous contributions and valuable efforts in the design, execution and analysis of the project. Additionally, we extend our sincere appreciation to the Swiss Agency for Development and Cooperation for their generous support, which has made this project possible.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Swiss Agency for Development and Cooperation (Contract no. 81067392), China National Natural Science Foundation (Contract no. 71974211), China Medical Board (Contract no. 16-260), China Medical Board (Contract no. 18-301), China Medical Board (Contract no. 18-300), China Medical Board (Contract no. 19-310), China Medical Board (Contract no. 20-368), and National Natural Science Foundation of China (Contract no. 72064032). The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.

Ethical statement

ORCID iDs

Jay Pan

Jifang Zhou

Supplemental Material

Supplemental material for this article is available online.

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