Disparities in the Care Disruption During COVID-19 and in its Impacts on the Mental and Physical Well-Being of Cancer Survivors

Abstract

Purpose

Our study explores cancer care disruption among different demographic subgroups. It also investigates these disruptions’ impacts on cancer survivors’ mental and physical well-being.

Design

Pooled cross-sectional survey data.

Setting

Health Information Trends Survey for Surveillance Epidemiology and End Results, HINTS-SEER.

Participation

n = 1234 cancer survivors participated in the study and completed the survey.

Measures

Outcome variables were treatment disruption in cancer care, mental health and physical health perceptions, age, race, education, income, and sexual orientation.

Analysis

Multiple imputations were used to address missing data. Descriptive statistics were conducted to understand the perceptions of care disruption. Partial least squares structural equation models were employed for data analysis, adjusted for socio-demographics.

Results

COVID-19 impacted cancer treatment and follow-up appointments (69.45%), routine cancer screening (60.70%), and treatment plans (73.58%), especially among elderly patients. It changed the interactions with health care providers (HCP) for 28.03% of the participants. Older adults were 2.33 times more likely to experience treatment appointment disruptions. People who thought their contact with their doctors changed during COVID-19 were more likely to be older adults (65 or more) (OR = 3.85, P = .011), white (OR >1, P = .002), and with higher income (OR = 1.81, P = .002). The changes to cancer treatment and follow-up medical appointments negatively impacted the well-being of the patients (mental: β = −.006, P = .043; physical: β = −.001, P = .006), routine screening and preventative care visits (mental: β = −.029, P = .031; physical: β = −.003, P = .008), and cancer treatment plans (mental: β = −.044, P = .024; physical: β = −.021, P = .040).

Conclusions

Our findings underscore the crucial requirement for implementing focused interventions aimed at alleviating the discrepancies in the accessibility of cancer care across diverse demographic groups, particularly during times of emergency, in order to mitigate any potential disruptions in care.

Keywords

mental health physical well-being cancer survivor COVID-19 psycho-oncology

Background & Purpose

COVID-19 has presented significant challenges to health care systems in the United States and globally.¹ Hospitals experienced extensive disruptions to their usual care protocols.¹ Moreover, the escalating number of high-acuity COVID-19 cases imposed additional burdens on doctors, who simultaneously needed to provide care for non-covid-related conditions. Consequently, this situation adversely affected the quality of patient care and increased the workload of medical staff and physicians, which made them focus all their efforts on containing the spread of the virus and limiting the growth of the case rates.^2-4 This situation limited their ability to investigate the extra support needed by vulnerable populations such as cancer patients during such a pandemic.^1,2,5

Cancer patients represent one example of a patient population whose care may have been impacted by the COVID-19 pandemic. Traditionally, cancer survivors have faced numerous difficulties throughout their journey, including physical symptoms, psychological distress, and social isolation.⁶ The compromised immune systems of cancer patients, combined with their ongoing need for specialized care, pose particular challenges during the COVID-19 pandemic.⁷ It has been observed that patients with cancer are more susceptible to severe complications and death compared to those without a cancer diagnosis if they contract COVID-19.^8,9 Consequently, these patients have expressed anxiety regarding potential exposure within health care facilities and when using public transportation.^10,11

The outbreak of the pandemic has further aggravated these challenges, leading to delays in cancer diagnosis and treatment for patients.^12-14 As a result, these delays have compounded their difficulties, potentially resulting in subsequent physical and mental implications.¹¹ Studies have indicated that in addition to the distress experienced by patients with chronic conditions such as cancer, extended treatment durations contribute to heightened levels of anxiety and depression.^15,16

Various initiatives have been undertaken to mitigate the physical and psychological distress experienced by cancer patients.¹⁷ Nevertheless, the unprecedented pandemic introduced new obstacles.¹⁷ Although numerous studies have examined the psychological challenges posed by COVID-19, limited research has specifically explored the repercussions of pandemic-related disruptions on the well-being of cancer survivors, particularly in relation to the disruptions in their care.¹⁷ Therefore, this manuscript aimed to bridge this research gap by investigating the influence of care disruptions during the pandemic on cancer survivors’ physical and mental well-being. Additionally, it sought to examine any disparities in care disruptions based on demographic factors.

Methods

Design

We utilized the HINTS-SEER data for our study. HINTS-SEER, which stands for Health Information Trends Survey for Surveillance Epidemiology and End Results, is a project initiated by the National Cancer Institute (NCI) to provide a larger sample of cancer survivors for comprehensive analysis. The NCI administered this survey to oversample cancer survivors using three cancer registries from the SEER program. Data collection occurred during COVID-19, specifically between January 11, 2021, and August 20, 2021.¹⁸ HINTS is a nationally representative survey of the US noninstitutionalized adult population. The HINTS survey was developed to investigate the American population’s needs in terms of health-related information and their access to care.^19,20

Sample

A total of 1234 respondents participated in the study and completed the survey. The sampling strategy for the HINTS survey consisted of a two-stage design. In the first stage, a stratified sample of addresses is selected from a file of residential addresses.²¹ In the second stage, one adult is selected within each sampled household. Respondents are offered the choice to respond online or through a paper survey.²¹ Both modes of the survey (paper and online) were offered in English or Spanish.²¹ All groups received a $2 pre-paid monetary incentive to encourage participation.²¹ Returned surveys were reviewed for completion and duplication (more than one questionnaire returned from the same household) to ensure they were eligible for inclusion in the final dataset.²¹ Further details on survey design, response rate, and sampling strategies are published elsewhere ²² and available online at (https://hints.cancer.gov). Only participants with a previous cancer diagnosis were included in the study.

Measures

Our study used the following variables, as shown in Figure 1.

Figure 1.

Conceptual framework of the study.

The observed variables used questions from the HINTS-SEER survey that are stated in Appendix 1. Care disruption was measured with the following questions: “Has the COVID-19 pandemic affected your cancer treatment or follow-up medical appointments related to your cancer?” “Has the COVID-19 pandemic affected any of your appointments for routine cancer screening or preventative care?”, “Has your cancer treatment plan changed as a result of the COVID-19 pandemic?” and “During the COVID-19 pandemic, have you contacted a health care provider about the same or differently than you normally do?”. Each of these questions had a 2-likert scale (yes, no). For the socio-demographic factors, we considered education (< high school, High school graduate, Some college, and College graduate), Race (White, Black, Hispanic, Asian, Other), Age (18-34, 35-49, 50-64, 65 or more), sexual orientation (heterosexual, bisexual, homosexual, other), and income (low, mid, high).

Well-being outcomes were assessed through 2 variables (mental and physical well-being). For physical well-being, participants were asked about their perception of health, “How do you think your health is?” and answers were (bad or good).

For mental well-being, the Patient Health Questionnaire PHQ-4 score was used. The PHQ-4 score is a brief measure of both depression and anxiety. The measure consists of 4 questions for depression and anxiety. Depression items include the following: “Over the past two weeks, how often have you been bothered by 1) little interest or pleasure in doing things? and 2) feeling down, depressed, or hopeless?” The anxiety subscale included the following: “Over the past two weeks, how often have you been bothered by 1) Feeling nervous, anxious, or on edge? And 2) Not being able to stop or control worrying?”. The original questions used a 4-point Likert scale (nearly every day, more than half the days, several days, not at all). The total score ranges from 0 to 12. We categorized it on a 2-Likert scale (normal health if the score is (0-2), three or more low health, else)) as done in previous studies, considering that the type of analysis done requires a binary outcome.²³ The reliability of the scale was assessed using Cronbach’s Alpha, which yielded a value of .86 for the total scores.

Analysis

Missing data rates for all the questions in this study ranged between 1.28% and 4.55%, which is less than the threshold of bias of 5%.²⁴ We also conducted a missing completely at-random test (MCAR) to compare the findings with and without missing data. The results showed no significant relationships, suggesting that missingness is not dependent on observed data. Thus, to replace the missing data, we employed a multiple imputation method, which effectively handles missing data problems and accounts for imputation uncertainty.^25,26 Specifically, we employed the K-nearest-neighbor algorithm method (KNN), which replaces missing values with estimated values derived from the nearest neighbors.²⁶ KNN is good at imputing categorical and continuous variables and finds similar data in the dataset to deal with missing data without building a separate model.²⁷ A large number of samples in datasets provides a good basis for KNN imputation, and the imputation of missing data based on similar data can also provide a good basis for subsequent prediction or other research.²⁸ Therefore, KNN is an excellent method for dealing with missing data.²⁸

Descriptive statistics were conducted to explore the demographic characteristics of the cancer survivors participating in this study and their perceptions of the care systems’ disruptions during COVID-19. Wald chi-square tests identified the association between the population’s demographic characteristics and perceptions of care disruption. A P-value of less than .05 was considered significant. We used partial least squares structural equation models adjusted for the relevant demographic variables to explore the impact of care disruptions on cancer survivors’ mental and physical well-being. The PLS-SEM, the partial least squares structural equation model, is a regression-based multivariate analytical approach for testing and estimating complex causal links between variables.^29-32 It can also determine whether a proposed model is consistent with the facts gathered to support a theory.^33,34

This method allows the simultaneous estimation of multiple and interrelated dependent relationships between variables. The bootstrapping method was used with 5000 subsamples and controlled for the confounding factors. Bias-corrected and accelerated (BCA) bootstrap methods were used to estimate nonparametric confidence intervals (CIs). Statistical significance was tested at 95% CI (2-tailed). The fit of the measurement model was tested through Confirmatory Factor Analysis (CFA). Five indices were used in assessing the model fit: comparative fit index (CFI), goodness of fit index (GFI), root mean square error (RMSEA), Beetle-Browed NFI, and chi-square normalized by degrees of freedom (χ2/df). According to the literature, CFI values greater than .94 suggest a good fit between data and hypothesized models.³⁵ RMSEA values less than .09 suggest a fair or adequate approximation error, whereas less than .055 suggest a small error.³⁶ The GFI should be .90 and above to present an acceptable fit with current data [44]; χ2/df should be less than three.³⁵ The standards referenced by all indexes are generally accepted in statistics. Analyses were performed using Python software, version 3.8 (Python, New Jersey, USA), using complex survey design procedures (research, numpy, pandas, statsmodels, semopy, sklearn, etc.). In this study, the GFI, NFI, CFI, RMSEA, and χ2/df were .98 (>.9), 1.13 (>.9), 1.16 (>.94), .002 (<.09), 1.74 (<3). Thus, the goodness of fit of the structural model indicated an acceptable and satisfactory fit.

Every sampled cancer survivor who completed a questionnaire for HINTS-SEER from a SEER registry received a full-sample weight and a set of 50 replicate weights.³⁷ The full-sample weight is used to calculate population and subpopulation estimates.³⁷ Replicate weights are used to compute standard errors for these estimates.³⁷ The use of sampling weights is done to ensure valid inferences from the responding sample to their respective population, correcting for nonresponse and noncoverage biases to the extent possible.³⁷ Replicate weights were calculated using the ‘delete one’ jackknife (JK1) replication method.³⁷

Results

Table 1 presents the demographic characteristics of the study participants. It reveals that a significant proportion of the participants were elderly patients, with 40.68% aged 75 years or older. In contrast, a mere .81% of the participants fall within the young patient category (18-34 years old). Regarding racial composition, 77.8% of the cancer survivors in the study identify as White, while African Americans represent only 4.6% of the participant pool. Furthermore, a substantial number of participants have attained a higher level of education, with 28.36% having some college education and 56.40% being college graduates.

Table 1.

Demographic Characteristics of the Study Sample.

Variables	N	%
Age
18-34	10	.81
35-49	36	2.92
50-64	246	19.94
65 or more	942	76.34
Race
White	960	77.80
Black	57	4.62
Hispanic	140	11.35
Asian	64	5.19
Other	13	1.05
Education
Less than high school	36	2.92
High school grad	152	12.32
Some college	350	28.36
College graduate	696	56.40
Sexual orientation
Heterosexual	1165	94.41
Homosexual	27	2.19
Bisexual	31	2.51
Other	11	.89
Income
less Than 20k	90	7.29
20k to <35k	127	10.29
35k to <50k	157	12.72
50k to <75k	278	22.53
75k or More	582	47.16

Looking at the impact of COVID-19, we found that 69.45% of the cancer survivors thought that the pandemic impacted their cancer treatment or cancer follow-up medical appointments, 60.70% of them thought that it changed their routine cancer screening or preventative care, and 73.58% of them thought that it changed their cancer treatment plans. However, only 28.03% of the patients thought the pandemic changed their interactions with their health care providers, as shown in Figure 2.

Figure 2.

Care disruption among cancer survivors during COVID-19.

Subsequently, we examined the relationship between patient demographic characteristics and perceptions of care disruptions. The findings, presented in Table 2, revealed significant correlations between care disruptions and the respondents’ age. Specifically, a higher prevalence of changes was observed among older cancer survivors. For instance, among those who reported changes in cancer treatment or follow-up medical care, 77.01% were 65 years or older. Older adults, in fact, were 2.33 times more likely to think that their cancer treatment appointments were affected by COVID-19 compared to younger adults.

Table 2.

Care Disruptions Across the Different Demographic Subgroups.

Variables	COVIDCA_CAT Affected				COVIDROUTINE_CAT Affected				COVIDCHANGECATREATMENT Affected				COVIDLIFE_CONTACTEDHCP Affected
Variables	N	%	OR [CI]	P	N	%		P	N	%		P	N	%	OR	P
Age
18-34	5	.58		.040	5	.67		.041	10	1.10		.01	6	1.73		.011
35-49	27	3.15	3.01 [.71-12.81]		21	2.80	1.40 [.34-5.71]		24	2.64	.11 [.05-1.72]		9	2.60	4.51 [1.03-19.63]
50-64	165	19.25	2.06 [.58-7.32]		141	18.83	1.35 [.38-4.81]		188	20.70	.15 [.01-2.70]		69	19.94	3.82 [1.04-13.97]
65 or more	660	77.01	2.33 [.67-8.11]		582	77.70	1.61 [.46-5.61]		686	75.55	.12 [.01-2.17]		264	76.30	3.85 [1.08-13.78]
Race
White	655	76.43		.095	560	74.77		.067	685	75.44		.183	238	68.39		.002
Black	52	6.07	1.37 [.78 -2.41]		48	6.41	.91 [.57-1.43]		55	6.06	.71 [.43-1.14]		21	6.03	.912 [.60-1.69]
Hispanic	98	11.44	1.07 [.73-1.59]		93	12.42	1.26 [.87-1.82]		110	12.11	1.24 [.81-1.88]		57	16.38	.51 [.35-.73]
Asian	44	5.13	.98 [.57-1.70]		41	5.47	1.18 [.69-2.01]		50	5.51	1.28 [.69-2.37]		27	7.76	.47 [.28-.78]
Other	8	.93	.71 [.23-2.21]		7	.93	.77 [.25-2.32]		8	.88	.57 [.18-1.78]		5	1.44	.54 [.17-1.69]
Education
< High school	26	3.03		.250	25	3.34		.058	30	3.30		.051	16	4.62		.005
High school graduate	96	11.20	.65 [.29-1.44]		90	12.02	.61 [.28-1.32]		112	12.33	.52 [.20-1.33]		56	16.18	1.41 [.67-2.94]
Some college	244	28.47	.89 [.41-1.91]		206	27.50	.64 [.31-1.36]		253	27.86	.54 [.21-1.35]		104	30.06	1.86 [.92-3.73]
College graduate	491	57.29	.92 [.43-1.94]		428	57.14	.70 [.33-1.44]		513	56.50	.56 [.23-1.36]		172	49.71	2.44 [1.23-4.81]
Sexual orientation
Heterosexual	801	93.47		.084	680	90.79		.061	835	91.96		.127	314	90.75		.016
Homosexual	19	2.22	1.41 [.56-3.58]		15	2.00	.98 [.44-2.21]		20	2.20	1.40 [.52-3.77]		7	2.02	.99 [.41-2.39]
Bisexual	6	.70	.89 [.22-3.60]		7	.93	2.31 [.47-11.15]		5	.55	.43 [.11-1.64]		1	.29	3.08 [.38-7.24]
Other	31	3.62	1.54 [.73-3.27]		47	6.28	1.24 [.75-2.04]		48	5.29	.70 [.42-1.16]		26	7.51	.68 [.41-1.12]
Income
Low	147	17.15		.07	133	17.76		.072	150	16.52		.037	82	23.70		.002
Mid	114	13.30	1.12 [.71-1.75]		75	10.01	1.05 [.66-1.67]		88	9.69	1.23 [.74-2.03]		38	10.98	1.31 [.81-2.11]
High	596	69.54	1.05 [.76-1.45]		541	72.23	.92 [.68-1.26]		670	73.79	1.26 [.91-1.75]		228	65.90	1.81 [1.32-2.48]

COVIDCA_CAT affected: Has the COVID-19 pandemic affected your cancer treatment or follow-up medical appointments related to your cancer?.

COVIDROUTINE_CAT affected: Has the COVID-19 pandemic affected any of your appointments for routine cancer screening or preventative care?.

COVIDCHANGECATREATMENT affected: Has your cancer treatment plan changed as a result of the COVID-19 pandemic?.

COVIDLIFE_CONTACTEDHCP affected: Has the COVID-19 pandemic affected your contact with your health care providers?.

Similarly, 77.70% of individuals who experienced alterations in routine cancer screening or preventative care, 75.55% whose cancer treatment plans were modified, and 76.30% whose interactions with health care providers (HCP) were affected were also aged 65 years or older. Moreover, the interactions with health care providers showed significant correlations with various demographic characteristics. For instance, people who thought their contact with their doctors changed during COVID-19 were more likely to be older adults (65 or more) were more likely (OR = 3.85, P = .011), white (OR > 1, P = .002) and with higher income (OR = 1.81, P = .002).

We also found that, respectively, the mental and the physical well-being of the patients were negatively impacted by the changes to cancer treatment and follow-up medical appointments (mental: β = −.006, P = .043; physical: β = −.001, P = .006), routine screening and preventative care visits (mental: β = −.029, P = .031; physical: β = −.003, P = .008), and cancer treatment plans (mental: β = −.044, P = .024; physical: β = −.021, P = .040). The changes in the interactions with HCPs have not impacted the physical and mental well-being of the cancer survivors, as shown in Table 3.

Table 3.

Results of the Structural Equation Models.

Variable	Mental Well-Being			Physical Well-Being
Variable	Estimate β	SD	P-Value	Estimate β	SD	P-Value
COVIDCA_CAT	−.006	.011	.043	−.001	.04	.006
COVIDROUTINE_CAT	−.029	.010	.031	−.003	.03	.008
COVIDCHANGECATREATMENT	−.044	.024	.011	−.021	.08	.040
COVIDLIFE_CONTACTEDHCP	.003	.026	.087	.012	.09	.19

COVIDCA_CAT affected: Has the COVID-19 pandemic affected your cancer treatment or follow-up medical appointments related to your cancer?.

COVIDROUTINE_CAT affected: Has the COVID-19 pandemic affected any of your appointments for routine cancer screening or preventative care?.

COVIDCHANGECATREATMENT affected: Has your cancer treatment plan changed as a result of the COVID-19 pandemic?.

COVIDLIFE_CONTACTEDHCP affected: Has the COVID-19 pandemic affected your contact with your health care providers?.

Discussion

This study utilized nationally representative data to examine the variations in care disruptions among different demographic subgroups of cancer survivors during the COVID-19 pandemic and their impact on mental and physical well-being. The results indicated that many cancer survivors perceived the pandemic as impacting their cancer treatment, follow-up appointments, routine screening or preventative care, and cancer treatment plans. These findings align with Patt et al.’s study, which utilized a clearinghouse database and demonstrated decreases and delays in cancer identification and treatment delivery within the US health care system.³⁸ Similarly, Richards et al. found disruptions in the entire spectrum of cancer care, including delays in diagnoses, treatments, and the suspension of clinical trials, which may explain our results.³⁹ However, it is worth noting that only 28.03% of participants in our study reported changes in their interactions with health care providers. This finding suggests that health care providers communicated effectively with their patients despite the challenging circumstances. This finding is consistent with a study by Aguirre et al., which explored the doctor-patient relationship during the COVID-19 pandemic and revealed that many patients expressed appreciation and understanding towards health care providers in emergencies.⁴⁰ Furthermore, our findings indicated that older cancer survivors were more vulnerable to disruptions caused by the pandemic, highlighting the need for targeted support and interventions for this demographic group.

Additionally, we found that the interactions with health care providers demonstrated significantly correlated with demographic characteristics, underscoring the importance of tailored approaches to address disparities. These findings are consistent with other studies showing older adults experience more pronounced disruptions in care.^41,42 This result can be attributed to age-related decline, higher likelihood of comorbidities, and compromised immune status.^41,42 These results emphasize the need for health care providers to recognize the vulnerability of elderly populations during crises and provide customized support to address their specific care needs.

To mitigate the disparities in care delivery among cancer survivors, it is crucial to consider demographic characteristics such as age and ethnicity when designing interventions and care strategies. Policymakers and health care organizations should prioritize addressing these disparities to ensure equitable access to cancer treatment and follow-up care for all cancer survivors. Strategies may include implementing targeted outreach programs, developing culturally sensitive communication materials, and enhancing access to health care resources in underserved communities. By adopting these measures, health care systems can work towards reducing disparities and promoting equitable health care access and outcomes among cancer survivors.

The study further examined the impact of care disruptions on cancer survivors’ mental and physical well-being. Regression analysis, adjusting for demographic factors, revealed significant adverse effects on physical and mental well-being associated with changes in cancer treatment and follow-up appointments, routine screening or preventative care, and cancer treatment plans. However, changes in interactions with health care providers did not significantly impact the mental and physical well-being of cancer survivors.

These findings emphasize the importance of maintaining regular cancer care and implementing strategies to minimize disruptions during crises such as the COVID-19 pandemic. Access to continuous cancer care is crucial as interruptions in care can increase anxiety levels and negatively impact the mental well-being of patients, who are already dealing with the stress of their health conditions and the social isolation measures imposed to control the spread of the virus.^43,44 Health care providers should consider implementing interventions that provide alternative patient support, including psychological support services, virtual support groups, and home-based exercise programs. The involvement of multidisciplinary care teams can also play a vital role in identifying and addressing the holistic needs of cancer survivors during care disruptions.

While changes in interactions with health care providers were reported by a smaller proportion of cancer survivors in our study, we did not find significant associations between these changes and the mental and physical well-being of the participants. This suggests that alternative health care delivery and communication modes, such as telemedicine, have effectively maintained continuity of care and mitigated the negative impacts on patient well-being, as supported by previous studies.⁴⁵ The finding highlights the potential effectiveness of remote care and alternative communication channels in ensuring continuity of care, reducing disruptions, and promoting the well-being of patients, even during health care system disruptions. Health care systems should continue investing in and improving telemedicine infrastructure to ensure that cancer survivors can access remote consultations, monitoring, and support services.

Overall, our findings underscore the vulnerability of cancer care systems during the COVID-19 pandemic. To prevent such disruptions in future pandemics and crises, policymakers and health care organizations should incorporate the lessons learned from the COVID-19 pandemic to enhance preparedness. Measures include developing contingency plans to maintain the continuity of essential care services, ensuring the availability of necessary resources and infrastructure, and improving coordination between health care providers and patients.

Clinical Implications

The study at hand has significant clinical implications for the improvement of cancer care delivery during public health emergencies like the COVID-19 pandemic. First, the findings highlight the need for health care providers to recognize and address the unique challenges faced by different demographic groups, particularly older cancer survivors. This vulnerable population is highly susceptible to care disruptions due to factors such as age-related decline, comorbidities, and compromised immune status. Therefore, clinicians must implement customized support strategies for these patients, taking into account their specific needs and vulnerabilities. Furthermore, this study emphasizes the crucial role of regular cancer care maintenance in preventing adverse effects on the mental and physical well-being of survivors. Health care providers should strive to minimize disruptions in cancer treatment, follow-up appointments, routine screening, and preventative care. This can be achieved by adopting innovative approaches, such as telemedicine, which has shown effectiveness in maintaining continuity of care and mitigating the negative impacts on patient well-being.^46,47

Additionally, the integration of psychological support services, virtual support groups, and home-based exercise programs should be considered to address the holistic needs of cancer survivors. Moreover, the study sheds light on the importance of effective communication between health care providers and patients, particularly during challenging circumstances, to maintain the mental and physical well-being of cancer survivors. This underscores the need for health care providers to develop robust communication channels and alternative health care delivery methods, such as telehealth services, to ensure continuous patient care and support.

Lastly, the study calls for health care systems to learn from the experiences of the COVID-19 pandemic to enhance their preparedness for future crises. This involves developing contingency plans for maintaining essential care services, ensuring the availability of necessary resources and infrastructure, and improving coordination between health care providers and patients. By doing so, health care systems can better protect vulnerable populations, such as cancer survivors, from the impacts of health care system disruptions in future pandemics and crises. Overall, these findings underscore the need for a proactive, patient-centered approach in cancer care, particularly during times of crisis, to ensure equitable access to treatment and care and to support the well-being of cancer survivors.

Study Limitations

While our study offers valuable insights into the impact of the pandemic on the well-being of cancer survivors, it is important to acknowledge its limitations. Firstly, the data collection process relied on oversampling cancer survivors from only three cancer registries within the SEER program. This sampling approach introduces the possibility of selection bias and limits the generalizability of the findings to a broader population of cancer survivors. Therefore, caution should be exercised when applying these findings to other populations. Secondly, the data collected is based on self-reporting and is susceptible to recall bias, social desirability bias, and individual interpretations. The subjective nature of the data may introduce measurement errors that could affect the accuracy of the findings.

Additionally, the study’s cross-sectional design restricts our ability to establish causal relationships. Longitudinal studies and continued research are necessary to assess the lasting effects of the pandemic on cancer survivors’ well-being and to inform future interventions. Furthermore, although we adjusted our analysis for relevant demographic variables, other confounding variables, such as cancer type, stage, and comorbidities, may not be accounted for in our study. Considering these variables could provide further insight and enhance the significance of the findings. Recognizing these limitations and encouraging future research to address them is important. By doing so, we can gain a more comprehensive understanding of the impact of the pandemic on the well-being of cancer survivors and develop more effective interventions to support this population.

Conclusions

The present study sheds light on the multifaceted impacts of the COVID-19 pandemic on cancer care, revealing significant disruptions that have adversely affected the physical and mental well-being of cancer survivors. The study highlights disparities in care disruptions across various demographic groups, particularly among older adults, which underscores the necessity for targeted interventions that address the specific needs of vulnerable populations. The study’s findings suggest that despite the challenges posed by the pandemic, effective communication between health care providers and patients has remained relatively intact, which has been crucial in mitigating some of the adverse effects. Furthermore, the study illuminates the importance of maintaining regular cancer care and employing innovative strategies to minimize disruptions, which are essential for protecting the mental health of survivors.

In light of these findings, lessons learned from the COVID-19 pandemic must be integrated into future health care policies and practices to enhance preparedness for similar crises. Developing contingency plans and improving infrastructure and coordination in health care are critical steps toward ensuring that cancer survivors receive the necessary care without interruption. The study’s findings not only highlight the vulnerabilities of cancer care systems during public health emergencies but also point to the critical role of personalized, patient-centered care approaches. By focusing on the unique challenges faced by different demographic groups and enhancing the capabilities of telemedicine, health care providers can better support the well-being of cancer survivors during times of crisis. This proactive approach is crucial for safeguarding equitable access to treatment and care, ultimately improving the outcomes for this vulnerable population.

So What? (Implications for Health Promotion Practitioners and Researchers)

What is Already Known on This Topic?

Due to COVID-19, health care systems have been disrupted worldwide. These changes covered cancer care as well. Previous studies have highlighted the adverse effects of the pandemic on the delivery of cancer care, including delays in diagnosis and treatment, which can exacerbate the physical and mental well-being of this vulnerable population.

What Does This Article Add?

We provided, through this study, empirical evidence on the extent of care disruptions among cancer survivors during the COVID-19 pandemic and their impact on survivors’ physical and mental well-being. We identified elderly cancer survivors as particularly vulnerable to these disruptions. Our findings revealed significant disparities in the experience of cancer care disruptions based on demographic factors such as age, race, and income level. Additionally, the study underlines the resilience of the patient-healthcare provider relationship during the pandemic, suggesting that maintained communication might mitigate some negative effects on patient well-being.

What are the Implications for Health Promotion Practices or Research?

The findings underscore the importance of maintaining regular cancer care and implementing strategies to minimize disruptions in order to ensure survivors’ mental and physical well-being. Additionally, the study suggests that maintaining communication between patients and their health care providers may have played an important role in mitigating the disruptions to patient well-being. Future research should continue exploring interventions and policies to address the challenges cancer survivors face during pandemics and other crises. The study also emphasizes the need for targeted interventions to address disparities in access to cancer treatment and follow-up care among different demographic groups. For researchers, this study highlights the need for further investigation into effective communication strategies between health care providers and cancer survivors, as well as the development of resilient health care delivery models that can withstand future crises.

Footnotes

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: The project was supported by funds from the National Heart, Lung and Blood Institute under award number (1U54CA280808-01, Dr. Matthews). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

ORCID iD

Safa Elkefi

Appendix 1. Questions Used from the HINTS SEER Survey.

Care disruptions
COVIDCA	Has the COVID-19 pandemic affected your cancer treatment or follow-up medical appointments related to your cancer?	Affected/not affected
COVIDROUTINE	Has the COVID-19 pandemic affected any of your appointments for routine cancer screening or preventative care?	Affected/not affected
COVIDCHANGECATREATMENT	Has your cancer treatment plan changed as a result of the COVID-19 pandemic?	Affected/not affected
COVIDLIFE_CONTACTEDHCP	During the COVID-19 pandemic, have you contacted a health care provider about the same or differently than you normally do?	Changed/same
Demographics
Education	What is your highest level of education?	< High school, high school graduate, some college, college graduate
Race	Which race do you identify as?	White, black, Hispanic, Asian, and other
Age	What is your age?	18-34, 35-49, 50-64, 65 or more
Sexual orientation	How do you identify?	Heterosexual, bisexual, homosexual, other
Income	What is your household income?	Low, mid, high
Well-being
Mental well-being	PHQ-4; derived composite from (LittleInterest, hopeless, nervous, and worrying)	Low (score under 3), high (3 or more)
Physical well-being	How do you think your health is?	Bad, good

References

Huggins

Husaini

Wang

, et al. Care disruption during covid-19: a national survey of hospital leaders. J Gen Intern Med. 2023;38:1232-1238.

Buxbaum

Rak

. Equity and the uneven distribution of federal COVID-19 relief funds to US hospitals: study examines the distribution of federal COVID-19 relief funds to US hospitals. Health Aff. 2021;40(9):1473-1482.

Kakani

Chandra

Mullainathan

Obermeyer

. Allocation of COVID-19 relief funding to disproportionately Black counties. JAMA. 2020;324(10):1000-1003.

Kelly

Parker

Pollack

. Low-income COVID-19 patients die needlessly because they are stuck in the wrong hospitals—while the right hospitals too often shut them out. Health Affairs Forefront 2021; (accessed date on June 11th 2024). https://www.healthaffairs.org/content/forefront/low-income-covid-19-patients-die-needlessly-because-they-stuck-wrong-hospitals-while#:~:text=HealthEquity-,Low%2DIncomeCOVID%2D19PatientsDieNeedlesslyBecauseTheyAre,TooOftenShutThemOut&text=Aheart%2DwrenchingFebruary8,Angeles’slatestCOVID%2D19surge

Kumar

. COVID-19 pandemic and healthcare disruptions: count the most vulnerable. Lancet Global Health. 2021;9(6):e722-e723.

Reville

Foxwell

. The global state of palliative care—progress and challenges in cancer care. Ann Palliat Med. 2014;3(3):129-138.

Riera

Bagattini

ÂM

Pacheco

Pachito

Roitberg

Ilbawi

. Delays and disruptions in cancer health care due to COVID-19 pandemic: systematic review. JCO Global Oncology. 2021;7(1):311-323.

Liang

Guan

Chen

, et al. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. Lancet Oncol. 2020;21(3):335-337.

Kuderer

Choueiri

Shah

, et al. Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study. Lancet. 2020;395(10241):1907-1918.

10.

Al‐Shamsi

Alhazzani

Alhuraiji

, et al. A practical approach to the management of cancer patients during the novel coronavirus disease 2019 (COVID‐19) pandemic: an international collaborative group. Oncol. 2020;25(6):e936-e945.

11.

Poortmans

Guarneri

Cardoso

M-J

. Cancer and COVID-19: what do we really know? Lancet. 2020;395(10241):1884-1885.

12.

Kutikov

Weinberg

Edelman

Horwitz

Uzzo

Fisher

. A War on Two Fronts: Cancer Care in the Time of COVID-19. Philadelphia, PA: American College of Physicians; 2020:756-758.

13.

Ouyang

Chua

Xie

. SARS-CoV-2 transmission in patients with cancer at a tertiary care hospital in Wuhan, China. JAMA Oncol. 2020;6(7):1108-1110.

14.

Alom

Chiu

Jha

Lai

SHD

Yau

THL

Harky

. The effects of COVID-19 on cancer care provision: a systematic review. Cancer Control. 2021;28:1073274821997425.

15.

Pitman

Suleman

Hyde

Hodgkiss

. Depression and anxiety in patients with cancer. BMJ. 2018;361:k1415.

16.

Tsaras

Papathanasiou

Mitsi

, et al. Assessment of depression and anxiety in breast cancer patients: prevalence and associated factors. Asian Pac J Cancer Prev APJCP: Asian Pac J Cancer Prev APJCP. 2018;19(6):1661.

17.

Momenimovahed

Salehiniya

Hadavandsiri

Allahqoli

Günther

Alkatout

. Psychological distress among cancer patients during COVID-19 pandemic in the world: a systematic review. Front Psychol. 2021;12:682154.

18.

Westat . Health Information National Trends Survey 5 (HINTS 5): Cycle 4 Methodology Report. Health Information National Trends Survey. Bethesda, Ma: National Cancer Institute; 2020. https://hints.cancer.gov/docs/methodologyreports/HINTS5_Cycle4_MethodologyReport.pdf

19.

Elkefi

. Access and Usage of mobile health (mHealth) for communication, health monitoring, and decision-making among patients with multiple chronic diseases (comorbidities). In: IISE Transactions on Healthcare Systems Engineering. Oxfordshire: Taylor and Francis; 2023:1-22.

20.

Elkefi

Asan

. The impact of patient-centered care on cancer patients’ QOC, self-efficacy, and trust towards doctors: analysis of a national survey. Journal of Patient Experience. 2023;10:23743735231151533.

21.

HINTS Methodology D.Reports. https://hints.cancer.gov/data/methodology-reports.aspx (accessed June 11th 2024).

22.

Finney Rutten

Davis

Beckjord

Blake

Moser

Hesse

. Picking up the pace: changes in method and frame for the health information national trends survey (2011–2014). J Health Commun. 2012;17(8):979-989.

23.

Guidance OP . PHQ-4: The Four-Item Patient Health Questionnaire for Anxiety and Depression. https://www.oregonpainguidance.org/app/content/uploads/2016/05/PHQ-4.pdf

24.

Schafer

. Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3-15.

25.

Faisal

Tutz

. Multiple imputation using nearest neighbor methods. Inf Sci. 2021;570:500-516.

26.

Mohammed

Abdelkader

Pławiak

Hammad

. A novel model to optimize multiple imputation algorithm for missing data using evolution methods. Biomed Signal Process Control. 2022;76:103661. DOI: 10.1016/j.bspc.2022.103661.

27.

Pujianto

Wibawa

Akbar

. K-Nearest Neighbor (k-NN) Based Missing Data Imputation. Piscataway, NJ: IEEE; 2019:83-88.

28.

Guo

, et al. Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets. BMC Med Res Methodol. 2024;24(1):41.

29.

Liu

Lai

Shi

Zhong

. The influencing factors of HIV-preventive behavior based on health belief model among hiv-negative msms in Western China: a structural equation modeling analysis. Int J Environ Res Publ Health. 2022;19(16):10185.

30.

Orji

Vassileva

Mandryk

. Towards an effective health interventions design: an extension of the health belief model. Online Journal of Public Health Informatics. 2012;4(3):ojphi.v4i3.4321.

31.

Ahadzadeh

Sharif

Ong

Khong

. Integrating health belief model and technology acceptance model: an investigation of health-related internet use. J Med Internet Res. 2015;17(2):e3564.

32.

Deshpande

Basil

. Factors influencing healthy eating habits among college students: an application of the health belief model. Health Market Q. 2009;26(2):145-164.

33.

Kline

. Principles and Practice of Structural Equation Modeling. New York City: Guilford Publications; 2023.

34.

Hair

Sarstedt

Hopkins

Kuppelwieser

. Partial least squares 4 structural equation modeling (PLS-SEM): an emerging tool in business research. Eur Bus Rev. 2014;26:106-121.

35.

Bentler

. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model: A Multidisciplinary Journal. 1999;6(1):1-55.

36.

Cudeck

Browne

. Cross-validation of covariance structures. Multivariate Behav Res. 1983;18(2):147-167.

37.

HINTS . Health information national trends survey 5 (HINTS 5), HINTS-SEER methodology report. https://hints.cancer.gov/data/methodology-reports.aspx

38.

Patt

Gordan

Diaz

, et al. Impact of COVID-19 on cancer care: how the pandemic is delaying cancer diagnosis and treatment for American seniors. JCO clinical cancer informatics. 2020;4:1059-1071.

39.

Richards

Anderson

Carter

Ebert

Mossialos

. The impact of the COVID-19 pandemic on cancer care. Nat Can (Ott). 2020;1(6):565-567.

40.

Aguirre

Jogerst

Ginsberg

, et al. COVID-19 impact on the doctor-patient relationship: patient perspectives on emergency physician empathy and communication. Bulletin of Emergency & Trauma. 2021;9(3):125.

41.

Haase

Kain

Merchant

, et al. Older survivors of cancer in the COVID-19 pandemic: reflections and recommendations for future care. Journal of Geriatric Oncology. 2021;12(3):461-466.

42.

Verma

Kilgour

Haase

. The psychosocial impact of COVID-19 on older adults with cancer: a rapid review. Curr Oncol. 2022;29(2):589-601.

43.

Alradhawi

Shubber

Sheppard

Ali

. Effects of the COVID-19 pandemic on mental well-being amongst individuals in society-A letter to the editor on “The socio-economic implications of the coronavirus and COVID-19 pandemic: a review. Int J Surg. 2020;78:147.

44.

Nienhuis

Lesser

. The impact of COVID-19 on women’s physical activity behavior and mental well-being. Int J Environ Res Publ Health. 2020;17(23):9036.

45.

Elkefi

Layeb

. Telemedicine’s future in the post-Covid-19 era, benefits, and challenges: a mixed-method cross-sectional study. Behav Inf Technol. 2022;42:1-15.

46.

Elkefi

Layeb

. Telemedicine’s future in the post-Covid-19 era, benefits, and challenges: a mixed-method cross-sectional study. Behav Inf Technol. 2023;42(15):2639-2653.

47.

Sirintrapun

Lopez

. Telemedicine in cancer care. American Society of Clinical Oncology Educational Book. 2018;38:540-545.