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Assessing the impact of COVID-19 on middle-aged and older females in China: a comparative study of urban-rural disparities

BMC Public Health volume 25, Article number: 1127 (2025) Cite this article

Abstract

Background

The impact of COVID-19 has been long-lasting and severe. Middle-aged and older females, as a vulnerable group, need special attention. China is a typical urban-rural dualistic society, and the impact of the urban-rural gap on middle-aged and older females is unknown. The purpose of this study was to explore the influence of COVID-19 on middle-aged and older females in Chinese urban and rural areas.

Methods

This study used CHARLS (China Health and Retirement Longitudinal Study) database. A total of 10,063 participants were included in this study (3,680 in urban and 6,383 in rural). The chi-square test and t-test were used to analyze differences between urban and rural participants, and binary logistic regression (forward: conditional) was used to analyze differences in the factors influencing the two groups in terms of mask wearing.

Results

During the epidemic, there were significant differences between urban and rural middle-aged and older females in terms of demographic characteristics, prevention knowledge, attitude, identification, isolation, personal activities, mental health, containment measures in residential areas and individual preventive behavior. Rural middle-aged and older females had poorer preventive behavior. Age, marital status, preventive knowledge, calling and messaging, internet contact, containment measures in residential areas were the common influencing factors affecting mask wearing behavior of the participants. The mask-wearing behavior of urban participants was also influenced by the attitude towards government’s measures, COVID-19 test and fear.

Conclusions

Middle-aged and older females in Chinese urban and rural areas faced different situations during the epidemic and required targeted measures.

Peer Review reports

Introduction

In late 2019, Corona Virus Disease 2019 (COVID-19) broke out and spread rapidly, and on March 11, 2020, the World Health Organization considered that the current epidemic could be called a global pandemic [1]. On the other hand, China experiences rapidly aging, according to the latest data from China’s National Bureau of Statistics, there are approximately 280 million women over 45 years of age [2]. As people get older, they become weaker, and more attention needs to be paid to this population in a pandemic [3].

Drawing on Gender Role Theory [4,5,6,7], women are more vulnerable due to their dual roles as caregivers and income providers. The increased caregiving responsibilities may have exacerbated their psychological stress and limited their access to healthcare resources [8, 9]. This dilemma is exacerbated during a pandemic [10]. Studies in the US and Canada have found that women generally experience unemployment, increased parenting burden, domestic violence and poor mental health due to the pandemic [11, 12]. In China, reports indicated that women’s contributions had been overlooked and gender bias had intensified [13], however, many studies have concentrated on the mental health challenges, especially pregnant and professional women [14,15,16], less attention is paid to other aspects. In fact, older women are worse than younger women, as Hernández’s study noted a higher incidence of hypertension, need for invasive mechanical ventilation (IMV), and mortality among middle-aged and older females [17]. Abuse and age discrimination against older women increased worldwide during the epidemic [18], and research in China suggested that this might be related to economic instability and social isolation [19]. Therefore, attention should also be paid to middle-aged and older women.

China has a typical urban-rural dual structure [20]. A grounded theory study revealed that in China, variations in geographical and architectural planning, residents’ living habits, cultural literacy, and socio-material conditions between urban and rural areas result in disparities in the spatio-temporal distribution, management priorities, difficulty, and containment strategies of epidemic control [21]. There were differences in knowledge [22], attitudes [23], practices [24], mental health [25, 26] and difficulties encountered [27] by the urban and rural populations. However, few studies have examined the differences between urban and rural female populations.

Masks can reduce the transmission of respiratory particles that carry viral pathogens, thereby decreasing the likelihood of infection per contact [28]. Additionally, masks should be used in conjunction with handwashing and social distancing [29]. Previous studies showed that knowledge, social psychological factors, accessibility, vaccination status, and media influenced mask-wearing behavior significantly [30,31,32,33]. Few studies have specifically explored the influencing factors among women.

In summary, there is a reasonable basis to hypothesize that during the pandemic, women may face multidimensional challenges encompassing daily life, mental health, and preventive behavior, with such challenges potentially exhibiting urban-rural disparities. Investigating the middle-aged and older females during the epidemic is of great significance to fill the research gap, reveal the deep impact of gender role on health, and implement precise epidemic prevention measures based on differentiation. Therefore, the purpose of this study was to explore the current situation of middle-aged and older females in urban and rural areas during the pandemic and the factors influencing their preventive behavior.

Materials and methods

Data

CHARLS (China Health and Retirement Longitudinal Study) is a representative longitudinal survey of people aged 45 years and over in mainland China, designed and implemented by the National School of Development (NSD) and the Institute of Social Science Survey (ISSS) at Peking University, with the aim of building a high-quality public micro-database [34]. Its baseline sample used multistage probability sampling. In the first stage, all counties and districts in the country, except Tibet, were ranked according to urban/rural attributes and GDP (Gross Domestic Product) per capita, and then 150 counties or districts were selected with a probability proportional to the population size; in the second stage, within each sampled units, three secondary sampling units (communities in urban areas or villages in rural areas) were randomly selected with a probability proportional to the population size [35]. Therefore, CHARLS is nationally representative. After the sampling process described above, the CHARLS baseline samples were distributed among 450 villages or communities in 28 provinces covering 150 districts and counties. The CHARLS baseline survey started in 2011. Respondents are followed up every two years through face-to-face computer-assisted personal interviews (CAPI).

Round five survey carried out during July-September 2020, in order to timely record the impact of COVID-19 on the lives and health of middle-aged and elderly people in China, it added the information related to the outbreak [36]. Healthcare utilization, work status, disease prevention awareness, individual illness and isolation, personal activities and residence control were collected.

In this study, participants over 45 were defined as middle-aged and older females. Specific inclusion and exclusion standards were as follows: (1) Age ≥ 45 years old in the 2020 survey. (2) Gender is female. (3) The general demographic information such as age, marital status, urban-rural attributes, work status and medical treatment is complete. (4) No missing values in the COVID-19 module. The specific sample selection process is shown in Fig. 1.

Fig. 1
figure 1

The flowchart of the sample

Full size image

Measurements

Covariant variables

General demographic characteristics

This section includes the participant’s age, marital status, work status and healthcare utilization during the pandemic.

Independent variables

Disease prevention knowledge and attitude

This section includes knowledge of seven preventive behaviors and attitude towards government measures.

Identification and isolation

COVID-19 test, infection, quarantine are contained in this part.

Personal activities and mental health

This component is compared to the pre-outbreak period, consisting going out, physical activities (light, moderate, intense), interaction activities (visit others, square dance, calling and messaging, internet contact), and mental health (fear and anxiety).

Residence control during the pandemic

This portion is comprised of five residential control measures: no one is allowed to enter or exit, limit the number of entries and exits for residents, non-residents are not allowed to enter, non-residents are allowed to enter under specific conditions, no restrictions.

Dependent variable

Individual preventive behavior

In this study, the individual preventive behavior is mask-wearing.

Statistical analysis

This study was statistically analyzed using SPSS 26.0 (IBM Corp., Armonk, NY, US), and p value < 0.05 was considered statistically significant. Descriptive analysis (t-test, chi-square test) was used to compare the differences between urban and rural middle-aged and older females on each variable. The binary logistic regression (Forward: Conditional) was used to explore the factors influencing mask wearing behavior.

Results

General demographic characteristics is shown in Table 1. The study included 10,063 participants (Urban:3680, Rural: 6383). The mean ages of urban and rural participants were 61.12 (SD: 9.90) and 61.94 (SD: 10.10), respectively. 68.7% of urban participants were with their spouses, and this proportion was 72% in rural areas. 42.1% of urban and 70.8% of rural participants were still working during the pandemic. Only 15.8% of urban and 11.8% of rural participants reported that medical treatment was delayed. There was a significant difference in general demographic characteristics.

Table 1 General demographic characteristics about the participants
Full size table

Table 2 shows the participants’ disease prevention knowledge and attitude. In urban participants, the knowledge of hand washing, using alcohol and disinfectant, avoid handshaking, wear a mask and glove, avoid travel, avoid gathering, social distance was 86.0%, 80.3%, 73.9%, 93.9%, 80.1%, 88.0%, 81.5%, respectively, while in rural was 73.6%, 65.6%, 60.2%, 84.9%, 69.5%, 77.2%, 71.3%. As for the government’s measures, 79.3% of urban and 67.6% of rural participants considered it appropriate. There was a significant difference between urban-rural participants in disease prevention knowledge and attitude.

Table 2 Disease prevention knowledge and attitude among participants
Full size table

Table 3 indicates the identification and isolation among participants. 92.3% of urban and 96.6% of rural participants had not taken COVID-19 tests, and no one confirmed. Only 2.3% of urban and 1.4% of rural participants experienced quarantine. There was a significant difference between urban-rural participants in COVID-19 test, COVID-19 confirmed and self-isolation.

Table 3 Identification and isolation among participants
Full size table

The personal activities and mental health are included in Table 4. For urban participants, 75.9% reduced going out, while in rural areas it was 57.9%. The majority of participants in both urban and rural areas showed no change in intense and moderate activities. 59.3% of urban participants and 40.5% of rural participants reported a decrease in light activities. 56.5% of urban participants and 49.0% of rural participants reported a reduction in visits. 76.4% of urban participants and 85.3% of rural participants reported never engaging in dancing. Nearly half of the participants reported no change in calling and messaging. 34.4% of urban participants reported no online communication, compared to 53.9% in rural areas. Nearly half of urban and rural respondents reported no fear or anxiety. There was a significant difference between urban-rural participants except intense activities.

Table 4 Personal activities and mental health during the pandemic
Full size table

Table 5 presents the containment measures in residential areas. In urban areas, 43.0% of participants reported no one is allowed to enter or exit (rural areas: 59.1%); 59.7% of participants were limited the number of entries and exits (rural areas: 43.8%); 57.7% reported non-residents were not allowed to enter (rural areas: 56.4%); 50.5% indicated non-residents were allowed to enter under specific conditions (rural areas: 37.0%); only 6.7% experienced no restrictions on entry and exit (rural areas: 7.9%). There was a significant difference between participants in this module except non-residents are not allowed to enter.

Table 5 Containment measures in residential areas during the pandemic
Full size table

As shown in Tables 6 and 88.6% of urban participants reported wearing masks, compared to 66.7% of rural participants.

Table 6 Individual preventive behavior of the participants
Full size table

Binary logistic regression (Forward: Conditional) was used to analyze the influences affecting mask-wearing behavior in both groups. P-values < 0.05 were regarded as statistically significant. All variables in Tables 1, 2, 3, 4, 5 and 6 were included in the regression analysis. Table 7 shows the results of the regression analysis. Age, marital status, wear a mask and glove, calling and messaging, internet contact, limit the number of entries and exits for residents were statistically significantly related to both groups’ mask-wearing behavior. Moreover, urban participants’ mask-wearing behavior was also statistically significantly related to using alcohol and disinfectant and no one is allowed to enter or exit, rural participants’ mask-wearing behavior was statistically significantly related to government’s measures, COVID-19 test and fear.

Table 7 Binary logistic regression (Forward: Conditional) of factors affecting mask wearing among participants
Full size table

Discussion

This study used the CHARLS database to explore the impact of the epidemic on urban and rural middle-aged and older females in China, and found that they were different in knowledge, attitude, identification, isolation, personal activities, mental health, containment measures in residential areas and preventive behavior. The factors affecting their preventive behavior also varied.

This study found most participants continued to work during the epidemic, especially in rural areas, this finding confirms that Chinese women play a crucial role as contributors to their family [37]. The knowledge of COVID-19 prevention is worse in rural areas, this is consistent with the findings of chen et al. in Chinese older adults in the early pandemic [38]. On the contrary, this study found that the proportion of participants received the COVID-19 test was extremely low, which was inconsistent with previous studies in China [39]. Previous studies reported that the test rate among the Chinese population was approximately 50% [40, 41], while in this study, it was less than 10%. This might be related to the sampling population and sampling areas, and further verification is needed.

This study reveals that over half of rural participants aged 45 and older do not use the Internet. This finding is inconsistent with the rapid advancement of China’s digital infrastructure [42]. Furthermore, the challenge of verifying online information has introduced new complexities [43, 44]. This study found comparable levels of pandemic-related fear and anxiety to those in other Chinese population studies [45]. Rural participants demonstrated more pronounced psychological distress, consistent with existing domestic research [46, 47]. These findings underscore the need to prioritize mental health interventions in rural areas [48].

Notably, the participants’ mask wearing behavior in this study was good, which is consistent with other studies [49, 50]. However, Rural participants demonstrated poorer adherence to mask wearing behavior compared to urban. This aligns with Cao’s research, he pointed out that the proportion of rural residents taking preventive measures is lower than that of urban residents, women and rural populations failed to take protective measures [22]. Therefore, these groups may benefit from health education and policies. Age, marital status, knowledge, network connections and place of residence control are the factors influencing all participants to wear masks in this study, which is similar to the results of many studies conducted during the epidemic [49, 51, 52]. A nationwide study spanning 31 Chinese provinces revealed that married individuals, respondents with health literacy, and residents in communities implementing containment protocols demonstrated higher compliance with preventive behaviors [38]. Our study did not identify any additional valuable influencing factors for the mask-wearing behavior of the urban participants. Apart from the above-mentioned influencing factors, this study surprisingly found that the mask-wearing behavior of rural participants is also affected by their attitudes towards government measures, COVID-19 testing and fear. Those participants who are satisfied with the measures implemented by the government exhibit better preventive behavior. This is similar to the result of an international study, which confirms the significant role of national identity in guiding group behavior and indicates that the stronger the national identity, the higher the support and compliance with public health measures [53]. Therefore, in the context of the epidemic, national identity may have certain predictive value for people’s compliance with preventive behavior.

Implications

This study fills the research gap on the current situation and influencing factors of preventive behaviors among middle-aged and older females in China during the epidemic. Our findings emphasize the importance of paying attention to the health of middle-aged and older females in urban and rural areas and provide an empirical basis for the government to formulate targeted policies and measures for different groups. In response to the pandemic, this study recommends that the Chinese government should strengthen economic support, implement preventive health education programs, facilitate interactive communication, and enforce management of residential areas for middle-aged and older females. What deserves special attention is, for rural middle-aged and elderly females, the government should strengthen the publicity and explanation of prevention knowledge, expand the coverage of COVID-19 tests, and improve the conditions of internet, the measures should be appropriate and satisfactory.

Study strengths and limitations

This study conducted research on middle-aged and older females in China using nationally representative data. It explored the real situations of people during the epidemic and emphasized the significance of paying attention to different groups and different regions (especially rural) when facing public health emergencies.

Despite the advantages, this study has several limitations: Firstly, it was a cross-sectional study, which means that it could not identify the causal relationship between the variables; secondly, the differences in the level of the investigator and memory bias of the respondents may cause bias of the results; thirdly, this survey only asked the mask wearing behavior, further research should expand to other preventive measures; fourthly, the relationships among variables are merely assumed based on experience, lacking causal chains driven by theory, future research should deepen theoretical exploration; fifthly, caution is warranted in generalizing these findings beyond the studied context.

Conclusions

Based on data from the CHARLS survey, this study examined middle-aged and older females in Chinese urban and rural areas during the pandemic. The results indicated that both groups showed differences in demographic characteristics, prevention knowledge, attitude, identification, isolation, personal activities, mental health, containment measures in residential areas, individual preventive behavior and influences affecting preventive behavior. Prevention behavior was poorer among rural middle-aged and older females. More attention should be paid to the knowledge and network conditions of rural middle-aged and older females. During the pandemic, the government should pay attention to the physical and mental health of middle-aged and older females and implement targeted policy between urban and rural areas.

Data availability

The China Health and Retirement Longitudinal Study Database used in this study are available to the public under a Creative Commons license at: https://charls.charlsdata.com/pages/data/111/zh-cn.html.

Abbreviations

CHARLS:

China health and retirement longitudinal study

COVID-19:

Corona virus disease 2019

IMV:

Invasive mechanical ventilation

NSD:

National school of development at peking university

ISSS:

The institute of social science survey at peking university

GDP:

Gross domestic product

CAPI:

Face-to-face computer-assisted personal interviews

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Acknowledgements

This study expresses its gratitude to the CHARLS research team, the people who organized and participated in the fieldwork, each of the interviewees and all those who provided support for the survey. Furthermore, we thank the National School of Development (NSD) and the Institute of Social Science Survey (ISSS) at Peking University for providing the CHARLS data.

Funding

This study has no funded support.

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Authors and Affiliations

  1. Maternal and Child Health Development Research Center, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, China

    Xiaoxu Jiang & Jie Gao

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Conceptualization, validation and supervision—JG, writing, methodology, software, formal analysis and original draft preparation—XJ. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Jie Gao.

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Ethics approval for the study was granted by the Ethical Review Committee of Peking University. The IRB approval number is IRB00001052-11015. Informed consent was obtained at the time of participation. All methods of this study were performed in accordance with the relevant guidelines and regulations. All experimental protocols were approved by Institutional Review Board at Peking University.

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The authors declare no competing interests.

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Jiang, X., Gao, J. Assessing the impact of COVID-19 on middle-aged and older females in China: a comparative study of urban-rural disparities. BMC Public Health 25, 1127 (2025). https://doi.org/10.1186/s12889-025-22355-6

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BMC Public Health

ISSN: 1471-2458

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