The Robust Futures Survey: Analysis of New York City Frontline COVID-19 Healthcare Providers’ Wellbeing

doi:10.21203/rs.3.rs-4953945/v1

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Research Article

The Robust Futures Survey: Analysis of New York City Frontline COVID-19 Healthcare Providers’ Wellbeing

https://doi.org/10.21203/rs.3.rs-4953945/v1

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Background

The COVID-19 pandemic uncovered vulnerabilities of health systems in the United States. Healthcare providers in New York City experienced immense strain as case numbers rose quickly and severely early in the pandemic. Healthcare provider wellbeing is still not well understood, and gaps remain in understanding how wellbeing is affected by aspects of healthcare providers’ work.

Methods

A cross-sectional survey was conducted to assess the experiences of healthcare providers in New York City during the COVID-19 pandemic. The primary outcome, wellbeing, was assessed through instruments to measure COVID-19-specific anxiety and hope, while the main exposures included exposure to frontline work, amount of infection prevention and control training received, and amount of COVID-19-specific training received. Bivariate analyses and logistic regression were used to determine the main effects between covariates and outcomes.

Results

300 healthcare providers’ survey responses contributed to the analysis between February and March 2021. Most participants reported not having COVID-19-specific anxiety and high levels of hope. Those who reported living with a partner or family member during the pandemic had lower odds of experiencing COVID-19-specific anxiety compared with those living alone. Participants reporting moderate to high levels of infection prevention and control training received had higher odds of having high levels of hope compared with those receiving little to no infection prevention and control training.

Conclusions

Our findings provide targeted areas for practical interventions to improve healthcare provider support, especially during times of extreme stress on the healthcare system. Prioritization of health workforce safety through infection prevention training will allow for better overall pandemic preparedness and response and appears to improve hopefulness amongst healthcare providers.

healthcare provider wellbeing

infection prevention and control training

COVID-19-specific training

health workforce safety

measures of hope

COVID-19-specific anxiety

The Coronavirus Disease 2019 (COVID-19) pandemic caused by the novel SARS-CoV-2 virus challenged healthcare and public health systems globally. While some countries have been able to minimize cases and deaths [1, 2], or have enacted strict social distancing measures to reduce transmission [3], the United States (US) struggled to manage and contain the virus [4]. Failure to mitigate viral spread early in the US led to more than 500,000 deaths by April, 2021 [5, 4]. Mixed messaging and politicization of the pandemic thwarted early mitigation efforts, including social distancing and mask wearing, and have contributed to the high number of infections and deaths across the US [6].

New York City (NYC), as a global transport hub for goods and people, quickly became the epicenter of the pandemic within the US and globally [7]. Described as a world or global city, NYC, like London, Paris and Tokyo has a unique power ‘to attract people, power, and enterprises’ [8]. Yet, the global economic prowess or ‘magnetism’ of global cities, like NYC, in retrospect, uncovered distinct vulnerabilities during the COVID-19 pandemic. The NYC health workforce was impacted quickly and severely with little time to adapt and respond [9].

Throughout the US, public health systems weakened by chronic underinvestment became overwhelmed and hospitals reached capacity with high numbers of individuals testing COVID-19 positive and developing severe illness [10]. The rise in NYC cases was particularly steep, with more than 203,000 laboratory confirmed cases within the first three months of the pandemic alone [7]. Frontline healthcare providers were required to work long shifts and take on new roles and responsibilities without adequate training and appropriate personal protective equipment (PPE) [11]. Risks to both the physical safety and mental health of healthcare providers responding to COVID-19 is well documented [12]. Yet, previous large-scale outbreaks, such as influenza, have taught us how we can mobilize the health workforce to prepare for outbreaks in the future and have taught us the importance in researching the effects of outbreaks on the health workforce [13].

Lack of proper infection prevention and control (IPC) measures and basic supplies led to makeshift IPC that left frontline healthcare providers uncertain of the level of protection in the work environment [14]. Anxiety and distrust in the ability of the healthcare system grew as healthcare providers became ill and died [15]. An early report from the Centers for Disease Control and Prevention (CDC) suggested that illness and deaths among healthcare providers were underreported due to the lack of proper surveillance systems [16].

The wellbeing of healthcare providers has been a concern throughout the COVID-19 pandemic. While the importance of caring for the caregiver is well known [17, 18], this proved difficult during the first wave of COVID-19 in NYC [12]. Institutions were overwhelmed and had to rapidly adapt to an influx of severely ill patients and lack of essential supplies. As with past epidemics [19] healthcare providers were challenged to work long hours caring for rapidly increasing numbers of severely ill patients in unsafe conditions [20]. Redeployment, often to unfamiliar settings with high levels of patient acuity, was common and with the emergence of crisis standards of care, the incidence of moral distress rose [9, 21].

Frontline healthcare providers were unable to maintain a routine, take breaks or retain a sense of control [18]. In addition, anxiety, along with other negative mental health outcomes, have been found to be associated with working on the COVID-19 frontlines [22]. Not surprisingly there has been a significant exodus of the health workforce [23]. This high turnover has led to loss of institutional memory, increased work stress on those remaining and a rapid increase in costs to attract the limited supply of healthcare providers with intensive care experience [23].

The vulnerability of global cities like NYC requires nimble and responsive systems that can rapidly adapt to evolving and complex crises. A key component of such a system is one that can quickly address the emerging threats and needs of the health workforce to stay safely at the frontlines. Without this, the quality of care is compromised, years of investment in healthcare providers put at risk and society’s ability to control a complex emergency severely compromised. As we come through the experience of a global pandemic of a novel highly infectious respiratory pathogen, it is important to study the effects it has had on healthcare providers as it could help in improving the overall response to the pandemic moving forward and epidemics in the future.

This paper explores frontline healthcare providers’ experiences, including assessment of two measures of wellbeing: anxiety and hope. Anxiety is measured given the existing knowledge surrounding the toll the pandemic has taken on mental health of individuals [24]. Hope as a construct, includes two distinct parameters- agency and pathways. Agency indicates the confidence and ability to affect change and pathways refers to knowing how to address or tackle a problem or challenges. In this study, hope is incorporated for its practicality and ability to develop targeted solutions, such as onsite counseling, decision-making, improved problem solving and ownership of what to do and how to respond, and for engaging frontline healthcare providers in building better systems of training and support [25].

The goal of this study was to explore the effect of the COVID-19 pandemic on wellbeing in healthcare providers and offer some insight into areas where future research and potential interventions could be focused. This study specifically aimed to: 1) assess wellbeing among frontline healthcare providers of patients affected by COVID-19; and 2) explore associations between hope, anxiety, and healthcare providers’ level of work exposure to COVID-19, types and amount of COVID-19-specific training, and perceptions of safety along with amounts of IPC training received.

Study Design and Participants

We conducted a cross-sectional survey to explore the experiences of frontline healthcare providers working in NYC health facilities during the COVID-19 pandemic. The survey consisted of five sections: 1) demographics; 2) time and duration caring for COVID-19 patients; 3) workplace safety response and general IPC training received during the pandemic; 4) training received specifically as response to the pandemic; and 5) validated measures of anxiety and hope to gauge wellbeing amidst the pandemic [24, 25].

The survey was distributed through Centiment, a third-party organization for distribution of survey materials to targeted audiences and was made available to participants for approximately three weeks. Individuals were eligible to take part in the study if they were a frontline healthcare provider, i.e., provided direct patient care to COVID-19 patients; were aged 18 years or older; and worked at a NYC health facility during the COVID-19 pandemic. Participants were recruited through an existing Centiment respondent pool. The goal was to include 300 participants in the study. Survey description, the estimated time for survey completion, and invitations to participate were distributed to frontline healthcare providers through email, their Centiment dashboard, and Facebook or LinkedIn advertisements. Those who chose to participate were directed to Qualtrics to complete the survey. No identifiable information was collected, and resources for mental health support were made available to participants, regardless of whether they completed the survey. Participants received approximately nine US dollars per Centiment’s compensation policy.

Outcome

The primary outcome was wellbeing, measured separately through levels of hope and COVID-19-specific anxiety. Hope was measured using the Adult Hope Scale (AHS) validated tool, titled the Future Scale when distributed to participants, which includes 12 8-point Likert-scale prompts pertaining to the measurement of agency (four items) and pathways (four items) [25]. The total score was computed by adding items pertaining to agency and pathways thinking, with higher scores associated with higher levels of agency or pathways thinking [25]. While the scale does not have a validated cutoff, we used a commonly used cutoff from previous studies and designated scores of 49 or more to indicate ‘high hope’ and scores less than 49 to indicate ‘low hope’ [25, 26].

Anxiety specific to COVID-19 was measured using the Coronavirus Anxiety Scale (CAS), which has five items on a 5-point Likert-scale (ranging 0–4) with a maximum possible score of 20 [24]. The items are related to anxiety symptoms associated with the COVID-19 pandemic, such as racing heart when listening to news about COVID-19 [24]. This instrument classifies a score of 9 or more as having symptoms of COVID-19-specific anxiety [24].

Potential covariates

Three main exposures were measured in this study. (1) Exposure to frontline work was measured by the amount of time the healthcare provider cared for COVID-19 patients during the pandemic. (2) IPC training was measured by how often the healthcare provider received general IPC training. (3) COVID-19-specific training was measured by (a) components of COVID-19 training received and (b) when the training took place. A numerical value was assigned to each of the components of the COVID-19-specific training exposure, which were summed for a total score. For example, less training components received, and training received later in the pandemic were assigned lower numerical values, given that less training and later training are viewed as generally worse for pandemic preparedness and response [27]. Each of the exposures received a final categorical label of ‘low’, ‘moderate’, or ‘high’. Time period worked and type of IPC training received were also examined, though not included in the main analysis because they were not the key covariates being assessed for this initial analysis.

Statistical Analysis

The main exposures, demographic variables, and type of IPC training received, and time period worked during the pandemic were initially analyzed through bivariate analyses using Chi-Square Test, Fisher’s Exact Test, or Wilcoxon Rank Sum Tests where appropriate. To address concerns regarding small cell-sizes, bivariate analyses using a Monte Carlo simulation of 10,000 replicates was used to obtain unbiased estimates. Variables significant at the alpha level of < 0.20, excluding the time worked and type of IPC training received, were considered for inclusion in separate multivariable binomial logistic regression models for hope and COVID-19-specific anxiety. The model with the best fit, indicated by the lowest Akaike Information Criterion (AIC) value, was used as the final regression model for each outcome. Associations with a p-value < 0.05 were considered significant within the logistic regression models. Interaction and mediation were not assessed as this is an exploratory analysis and the focus of this study is on the main effects between the covariates and outcomes. All analyses were completed using R Studio, Version 1.3.1073.

Ethical Approval

This study was reviewed and approved by the Columbia University Irving Medical Center Institutional Review Board (IRB-AAAT5204). An information sheet was provided, and consent was required prior to entering the survey.

A total of 309 individuals completed the survey between February 12 and March 13, 2021. However, only the first 300 participants were included in the analysis to abide by the protocol submitted to the IRB. Descriptive statistics were summarized for the overall totals of each covariate and outcome (Table 1). Of total participants, 58% identified as White, 68% were female, 78% worked full-time, and 79% lived with an immediate family member or partner during the pandemic. Most participants (85%) were categorized as not having COVID-19-specific anxiety and more than half (57%) reported high levels of hope.

Inclusion criteria for each individual predictor variable being in the final regression model was an unadjusted p-value of <0.20. When using the Akaike information criterion (AIC). some of the 13 variables included in the bivariate analyses were not included in the final model. Four variables; race/ethnicity, work status, the amount of COVID-19 training and receipt of competency-based training were not included in either the model for anxiety or hope. Based on the AIC, three additional variables were removed from the model for anxiety; gender, time period with COVID-19 patients, and amount of IPC training. Three additional variables were also removed from the model for hope including profession, time in profession, work status.

The crude associations between age (p=0.17), profession (p=0.08), time in profession (p=0.06), work environment (p=0.17), shared home status (p<0.001), and exposure to COVID-19 patients (p<0.001) with COVID-19-specific anxiety were considered in the logistic regression analysis for anxiety. The crude associations between age (p=0.13), gender (p=0.02), work environment (p=0.13), shared home status (p=0.04), time period worked (p=0.02), and amount of IPC training received (p<0.001) with hope were considered for inclusion in the regression model for hope (Table 2). Additional Chi-Square analysis revealed the crude association between the two outcomes, anxiety and hope, was highly significant (p<0.001; Table 3).

In the multivariable analysis for COVID-19-specific anxiety, those who lived with an immediate family member or partner during the pandemic had a 73% lower odds of having COVID-19-specific anxiety compared with those who lived alone or with extended family members, during the pandemic when adjusting for age, time in profession, and level of exposure to COVID-19 patients (aOR: 0.27; 95% CI: 0.10, 0.69). Those aged 31-40 years had more than two times higher odds of experiencing COVID-19-specific anxiety compared with those aged 18–30 years, adjusting for shared home status, time in profession, and level of exposure to COVID-19 patients (aOR: 2.45; 95% CI: 1.01, 5.92) (Table 4).

In the multivariable analysis for hope, participants who reported receiving a moderate amount of IPC training had more than four times greater odds of having high levels of hope compared with those who reported no receipt of IPC training, adjusting for time period worked, age, and time in profession (aOR: 4.16; 95% CI: 1.69, 10.25). Those who reported a high amount of IPC training received had more than three times greater odds of having high hope compared with those that reported no receipt of IPC training, adjusting for time-period worked, age, and time in profession (aOR: 3.68; 95% CI: 1.38, 9.83) (Table 5).

There is a significant association between anxiety and levels of hope. Therefore, to safeguard and ensure the wellbeing of the health workforce, measures to decrease anxiety and increase a sense of hope are necessary. While variables such as age, gender and shared home status can not be changed through HRH initiatives, there are many variables which can be addressed by improved HRH management and support.

We found an increase in COVID-19-specific anxiety when exposure to COVID-19 patients is moderate compared to low and a decrease in COVID-19-specific anxiety when exposure to COVID-19 patients is high compared to low, potentially indicating that over time when caring for COVID-19 patients, there is a desensitization and increased ability to adapt to dealing with COVID-19 exposure. Only the finding for increased odds of anxiety among those with moderate exposure to COVID-19 patients compared to those with low exposure is consistent with the hypothesis that those with higher exposure to COVID-19 patients would have COVID-19 related anxiety. The results for moderate exposure to COVID-19 patients are consistent with studies conducted in China and Italy earlier in the pandemic that found frontline work to be a major predictor of mental health response [28]. However, previous studies were limited in that they largely examined exposure to frontline work as whether an individual was exposed or not to frontline work.

When amount of IPC training was analyzed in association with hope, there were greater odds for higher levels of hope among those with moderate IPC training received compared to those with no IPC training. The odds were slightly lower among those with high amounts of IPC training compared to those with no training, indicating that the association between amount of IPC training received and level of hope reached a leveling point between moderate and high amounts of IPC training. These findings are not fully consistent with the hypothesis that those with higher amounts of IPC training would have higher hope. This could be due in part to smaller amounts of individuals reporting higher amounts of IPC training or another unmeasured factor confounding the association between IPC training and hope such as a saturation point at which further training does not affect hope and/or one’s sense of vulnerability to infection.

The analyses between time period working with COVID-19 patients and presence of anxiety and level of hope indicate that there is a significant relationship between the time period worked (i.e., earlier or later in the pandemic) and hope. The earliest time period worked, being February to May 2020, was given a higher value than the other time periods when coded given the lack of understanding of the pathogen and the stress on the healthcare system during the early months of the pandemic in NYC [7]. The analyses between type of IPC training received with presence of anxiety and level of hope showed insignificant relationships with both outcomes, potentially due to the opportunity to answer this question only if one reported receiving IPC training at all. The type of training received gave a higher coded value to types of training that included practical and ‘learning from doing’ components, given their applied, hands-on nature and real-world applicability [29].

Strengths and Limitations

A strength of this study is the inclusion of hope to measure wellbeing. Unlike many previous studies examining the mental health response of individuals to the COVID-19 pandemic which largely focused on negative mental health responses [30], this study incorporates an assessment of individual strength as measured through the construct of hope, examining individuals’ levels of agency and pathways thinking which hold potential for more constructive engagement and support for healthcare providers. Another strength is the examination of amount of IPC training and COVID-19-specific training and their association with anxiety and hope levels. There is a paucity of literature available regarding the effects of IPC training on the mental health response of healthcare providers currently, offering this study the potential to inform future studies examining the effects of amounts of personal protection training received on mental health outcomes.

It is interesting to note that when using the AIC, race/ethnicity, work status, the amount of COVID-19 training and receipt of competency-based training did not improve the model. The possible greater value of general IPC training versus pathogen specific training is worthy of further study. With the continued rise of H5N1 and limited human resources for health, focusing training on what yields the greatest benefit vis à vis anxiety and hope should be encouraged.

A limitation of this study is its cross-sectional nature, which limits our ability to draw causal conclusions. Similarly, mediators or interaction between variables could have skewed the results. Another limitation is the small sample size, with small cell counts present in some of the analyses performed, which may have contributed to some inconsistent conclusions.

This exploratory cross-sectional study provides further evidence of the relationship between variables associated with the care of COVID-19 patients and anxiety. In addition, it adds to the literature by suggesting what can be done to improve health workforce experience before and amidst crises. Through the addition of Snyder’s Hope scale to this survey, we found that the amount of IPC training received correlates with hope thus providing a tangible and practical intervention for workforce support. Pre-service, in-service and just-in-time training that focuses on infection prevention and health workforce safety needs to be prioritized and seen as an essential component of pandemic preparedness and health system resilience.

Further analysis on potential mediation, confounding, and modification of the relationships between these exposures and outcomes is encouraged, as is the further study of hope in healthcare providers and the effects of amounts of IPC training received during times of extreme stress on the healthcare system. Using a measure such as hope encourages an asset-based versus deficit-based approach to workforce strengthening which could be seen as less stigmatizing and more proactive to both employers and staff. Implementation science could prove beneficial to test new models of care for frontline healthcare providers against measures of hope and anxiety amidst future disease outbreaks and extreme stress on the healthcare system. With climate change, increased migration and encroachment on wildlife habitats, safeguarding our workforce should be seen as a health security priority.

AHS

Adult Hope Scale

AIC

Akaike Information Criterion

aOR

Adjusted Odds Ratio

CAS

Coronavirus Anxiety Scale

CDC

Centers for Disease Control and Prevention

Confidence Interval

COVID-19

Coronavirus Disease 2019

IPC

Infection Prevention and Control

IRB

Institutional Review Board

NYC

New York City

PPE

Personal Protective Equipment

United States

Acknowledgements

The authors wish to thank all study respondents for their time and their commitment to caring for patients throughout the COVID-19 pandemic and beyond.

Authors Contributions

SM-S oversaw the development and distribution of the survey and contributed to the background, conclusion, and editing of the manuscript. RH developed the survey, completed the analysis, and contributed to the literature review, methods, results, discussion, and overall organization of the manuscript. YH-M contributed to the methods, results, discussion, and editing of the manuscript. All authors read and approved the manuscript.

Funding

No external funding was used for this study.

Availability of Data and Materials

Data collected and used for this study are available from the corresponding author upon reasonable request.

Ethics Approval to Consent and to Participate

The Columbia University Irving Medical Center’s IRB reviewed this proposed research study and gave it a “non-research” determination (IRB-AAAT5204). An information sheet was provided, and consent was required prior to entering the survey.

Consent for Publication

The views expressed in this article are those of the authors and should not be construed to represent the positions of the organizations with which the authors are affiliated.

Competing Interests

The authors declare that they have no competing interests.

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Table 1. Participant demographics for covariates and outcomes

	Total (N=300)
	N	%
Participant Demographics
Age
Between 18-30 years	103	34.3
31-40 years	107	35.7
41-50 years	49	16.3
51 years or older	41	13.7
Race
African American	47	15.7
Asian	22	7.3
White	173	57.7
Latino or Hispanic	21	7.0
Other (Native American, Native Hawaiian/Pacific Islander, Other)	18	6.0
Multiple	19	6.3
Gender
Female	204	68.0
Male	91	30.3
Other (Transgender Female, Transgender Male, Gender variant/Non-conforming, Other)	5	1.7
Profession
Medical Doctor	56	18.7
Nurse	90	30.0
Physical Therapist	20	6.7
Physician's Assistant and Nurse Practitioner/Certified Nurse Midwife	22	7.3
Other (Pharmacist, Pharmacy Technician, Laboratorian, Lab Technician, Respiratory Therapist, Nutritionist, Other)	112	37.3
Time in Profession
1-5 years	144	48.0
More than 5 years	156	52.0
Work Status
Full time	234	78.0
Part time	66	22.0
Work Environment
Emergency Department	44	14.7
In-patient acute care	81	27.0
Long-term care	25	8.3
Outpatient setting	128	42.7
Rehabilitation center	22	7.3
Shared Home Status
Live alone	30	10.0
Non-relatives	11	3.7
Extended family	23	7.7
Immediate family/partner	236	78.7
COVID-19 Exposure
Time Period Worked
February-May 2020	57	19.0
June-September 2020	38	12.7
October 2020-March 2021	74	24.7
Multiple Periods*	131	43.7
Exposure to COVID-19 Patients
Low	48	16.0
Moderate	73	24.3
High	179	59.7
Training
Amount of IPC Training
No training	29	9.7
Low	82	27.3
Moderate	128	42.7
High	61	20.3
Amount of COVID-19 Training
No training	64	21.3
Low	50	16.7
Moderate	158	52.7
High	28	9.3
Receipt of Competency-Based Training
Yes	74	24.7
No	226	75.3
Outcomes
COVID-19-Specific Anxiety Outcome
Anxiety	45	15.0
No anxiety	255	85.0
Hope Outcome
Low Hope	130	43.3
High Hope	170	56.7

Table 2. Unadjusted bivariate analyses between covariates and outcomes

	COVID-19-Specific Anxiety					Hope
	Anxiety (N=45)		No Anxiety (N=255)		P-Value¹	Low Hope (N=130)		High Hope (N=170)		P-Value¹
	N	%	n	%		n	%	N	%
Participant Demographics
Age					0.1693*					0.1335*
Between 18-30 years	14	31.1	89	34.9		48	36.9	55	32.4
31-40 years	20	44.4	87	34.1		39	30.0	68	40.0
41-50 years	9	20.0	40	15.7		27	20.8	22	12.9
51 years or older	2	4.4	39	15.3		16	12.3	25	14.7
Race					0.6393					0.4605
African American	10	22.2	37	14.5		24	18.5	23	13.5
Asian	4	8.9	18	7.1		10	7.7	12	7.1
White	22	48.9	151	59.2		67	51.5	106	62.4
Latino or Hispanic	4	8.9	17	6.7		9	6.9	12	7.1
Other (Native American, Native Hawaiian/Pacific Islander, Other)	3	6.7	15	5.9		9	6.9	9	5.3
Multiple	2	4.4	17	6.7		11	8.5	8	4.7
Gender					0.4450					0.0145*
Female	28	62.2	176	69.0		96	73.8	108	63.5
Male	16	35.6	75	29.4		30	23.1	61	35.9
Other (Transgender Female, Transgender Male, Gender variant/Non-conforming, Other)	1	2.2	4	1.6		4	3.1	1	0.6
Profession					0.0795*					0.4532
Medical Doctor	6	13.3	50	19.6		21	16.2	35	20.6
Nurse	19	42.2	71	27.8		41	31.5	49	28.8
Physical Therapist	1	2.2	19	7.5		12	9.2	8	4.7
Physician's Assistant and Nurse Practitioner/Certified Nurse Midwife	6	13.3	16	6.3		8	6.2	14	8.2
Other (Pharmacist, Pharmacy Technician, Laboratorian, Lab Technician, Respiratory Therapist, Nutritionist, Other)	13	28.9	99	38.8		48	36.9	64	37.6
Time in Profession					0.0562*					0.2343
1-5 years	28	62.2	116	45.5		68	52.3	76	44.7
More than 5 years	17	37.8	139	54.5		62	47.7	94	55.3
Work Status					0.3102					0.9776
Full time	32	71.1	202	79.2		102	78.5	132	77.6
Part time	13	28.9	53	20.8		28	21.5	38	22.4
Work Environment					0.1693*					0.1335*
Emergency Department	7	15.6	37	14.5		14	10.8	30	17.6
In-patient acute care	10	22.2	71	27.8		40	30.8	41	24.1
Long-term care	7	15.6	18	7.1		13	10.0	12	7.1
Outpatient setting	18	40.0	110	43.1		54	41.5	74	43.5
Rehabilitation center	3	6.7	19	7.5		9	6.9	13	7.6
Shared Home Status					0.0023*					0.0394*
Live alone	9	20.0	21	8.2		15	11.5	15	8.8
Non-relatives	3	6.7	8	3.1		5	3.8	6	3.5
Extended family	7	15.6	16	6.3		16	12.3	7	4.1
Immediate family/partner	26	57.8	210	82.4		94	72.3	142	83.5
COVID-19 Exposure
Time Period Worked					0.2620					0.0228*
February-May 2020	12	26.7	45	17.6		35	26.9	22	12.9
June-September 2020	8	17.8	30	11.8		16	12.3	22	12.9
October 2020-March 2021	9	20.0	65	25.5		28	21.5	46	27.1
Multiple Periods	16	35.6	115	45.1		51	39.2	80	47.1
Exposure to COVID-19 Patients					0.0014*					0.3718
Low	8	17.8	40	15.7		25	19.2	23	13.5
Moderate	20	44.4	53	20.8		32	24.6	41	24.1
High	17	37.8	162	63.5		73	56.2	106	62.4
Training
Amount of IPC Training					0.5750					0.0003*
No training	5	11.1	24	9.4		20	15.4	9	5.3
Low	12	26.7	70	27.5		45	34.6	37	21.8
Moderate	16	35.6	112	43.9		42	32.3	86	50.6
High	12	26.7	49	19.2		23	17.7	38	22.4
Amount of COVID-19 Training					0.7766					0.3595
No training	11	24.4	53	20.8		29	22.3	35	20.6
Low	9	20.0	41	16.1		26	20.0	24	14.1
Moderate	22	48.9	136	53.3		66	50.8	92	54.1
High	3	6.7	25	9.8		9	6.9	19	11.2
Receipt of Competency-Based Training					0.8807					0.6985
Yes	12	26.7	62	24.3		34	26.2	40	23.5
No	33	73.3	193	75.7		96	73.8	130	76.5

Table 3. Crude association between hope and COVID-19-specific anxiety outcomes with corresponding Chi-Square results

	Low Hope	High Hope
Anxiety	32	13
No Anxiety	98	157
Chi-Square	15.331
P-Value	0.0001

Table 4. Adjusted multivariable logistic regression analysis between covariates and COVID-19-specific anxiety outcome

Term	Adjusted Odds Ratio (aOR)	95% Confidence Interval (CI)	P-Value
Shared Home Status
Live alone	-	-	-
Non-relatives	0.65	0.12-3.38	0.607
Extended family	1.05	0.29-3.80	0.936
Immediate family/partner	0.27	0.10-0.69	0.007
Age
18-30 years	-	-	-
31-40 years	2.45	1.01-5.92	0.047
41-50 years	2.99	0.96-9.26	0.058
51 years or older	0.91	0.16-5.11	0.911
Time in Profession
1-5 years	-	-	-
More than 5 years	0.57	0.24-1.33	0.192
Exposure to Patients
Low	-	-	-
Moderate	1.88	0.69-5.11	0.219
High	0.63	0.24-1.66	0.347

Table 5. Adjusted multivariable logistic regression analysis between covariates and hope outcome

Term	Adjusted Odds Ratio (aOR)	95% Confidence Interval (CI)	P-Value
Time Period Worked
February-May 2020	0.42	0.17-1.02	0.056
June-September 2020	-	-	-
October 2020-March 2021	1.14	0.49-2.64	0.761
Multiple Periods	1.0	0.45-2.20	0.996
IPC Training
No training	-	-	-
Low	1.56	0.62-3.95	0.344
Moderate	4.16	1.69-10.25	0.002
High	3.68	1.38-9.83	0.009
Age
18-30 years	-	-	-
31-40 years	1.22	0.63-2.37	0.547
41-50 years	0.67	0.28-1.60	0.368
51 years or older	1.57	0.62-4.02	0.344
Time in Profession
1-5 years	-	-	-
More than 5 years	1.12	0.59-2.14	0.731

No competing interests reported.

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Reviews received at journal
14 Oct, 2024
Reviewers agreed at journal
29 Sep, 2024
Reviewers invited by journal
27 Sep, 2024
Editor assigned by journal
21 Aug, 2024
Submission checks completed at journal
21 Aug, 2024
First submitted to journal
21 Aug, 2024

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The Robust Futures Survey: Analysis of New York City Frontline COVID-19 Healthcare Providers’ Wellbeing

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Abstract

Background

Methods

Results

Conclusions

Background

Methods

Study Design and Participants

Outcome

Potential covariates

Statistical Analysis

Results

Discussion

Strengths and Limitations

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Status:

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