Indirect COVID-19 health effects and potential mitigating interventions: Cost-effectiveness framework.

BACKGROUND: The COVID-19 pandemic led to important indirect health and social harms in addition to deaths and morbidity due to SARS-CoV-2 infection. These indirect impacts, such as increased depression and substance abuse, can have persistent effects over the life course. Estimated health and cost outcomes of such conditions and mitigation strategies may guide public health responses.
METHODS: We developed a cost-effectiveness framework to evaluate societal costs and quality-adjusted life years (QALYs) lost due to six health-related indirect effects of COVID-19 in California. Short- and long-term outcomes were evaluated for the adult population. We identified one evidence-based mitigation strategy for each condition and estimated QALYs gained, intervention costs, and savings from averted health-related harms. Model data were derived from literature review, public data, and expert opinion.
RESULTS: Pandemic-associated increases in prevalence across these six conditions were estimated to lead to over 192,000 QALYs lost and to approach $7 billion in societal costs per million population over the life course of adults. The greatest costs and QALYs lost per million adults were due to adult depression. All mitigation strategies assessed saved both QALYs and costs, with five strategies achieving savings within one year. The greatest net savings over 10 years would be achieved by addressing depression ($242 million) and excessive alcohol use ($107 million). DISCUSSION: The COVID-19 pandemic is leading to significant human suffering and societal costs due to its indirect effects. Policymakers have an opportunity to reduce societal costs and health harms by implementing mitigation strategies.

Introduction

Over the first 18 months of the SARS-CoV-2 (COVID-19) pandemic, public health efforts have focused on reducing direct health impacts of the virus by preventing and clinically managing infection. However, much evidence reveals profound indirect mental and physical health impacts. The California population has experienced a wide range of stressors, such as a prolonged period of fear, grief, and uncertainty, social isolation, and economic dislocation and hardship [1-3]. Such stressors can activate the biological stress response, leading to short- and long-term health effects [4].

Studies have shown significant increases in the prevalence of varied health harms resulting from the COVID-19 pandemic in the US and California. Depression [5,6], anxiety [5,6], family violence [7-10] and excessive alcohol and substance use [5,11,12] have increased. Poorer health outcomes, such as increased stroke mortality [13-15], higher prevalence of out-of-hospital cardiac arrests [16], and delays in cancer care and treatment [17] have also been observed [18]. As the COVID-19 pandemic evolves, government efforts will increasingly shift towards responses to indirect health harms and related high costs which could persist for many years.

The dynamic nature of the pandemic and California’s unfolding response requires decisionmakers to anticipate which indirect health impacts will prove especially costly over time and which cost-effective interventions could mitigate adverse health and financial outcomes. An effective, comprehensive policy response requires understanding the indirect impacts of the pandemic, their short- and long-term economic and health harms, and the likely effects and costs of potential mitigating interventions. To provide this information for the State of California, we formed an interdisciplinary rapid-response scientific taskforce and policy simulation laboratory. Our study, entitled RAPID (Rapid Assessment of Pandemic Indirect Impacts and Mitigating Interventions for Decision-making), aimed to systematically evaluate and analyze the pandemic’s impact on an important subset of indirect health outcomes and to estimate the effect of evidence-based mitigation strategies in ameliorating health and cost outcomes.

Methods

Overview

We developed a health and cost simulation tool employing a generic structure to portray the health and cost effects of COVID-19 pandemic-associated increases in prevalence of individual conditions. The model, called “Broad & Rapid Analysis of COVID-19 Indirect Effects” (BRACE), uses a cost-effectiveness framework that can portray short- and long-term health and cost effects and the impacts of mitigating interventions (e.g., rent support or counseling) on health and societal costs for any condition exacerbated by the pandemic (e.g., homelessness, depression). Health outcomes include deaths and quality-adjusted life years (QALYs). Costs include both direct medical and non-medical costs. The model specifies the coverage of mitigating interventions. Sensitivity analyses estimate how model input uncertainties (singly and collectively) affect results. Data were derived from literature review, public data (e.g., government reports), and expert opinion. Models were built in Excel (v16, Microsoft 365), and sensitivity analyses were conducted with @RISK (v8.2, Palisade Corporation) software.

Model structure

We modeled each indirect health condition separately. Model parameters are included in S1 File, and the functioning BRACE workbook is available on request.

Each condition’s model is split into two sections: (1) added burden due to the pandemic and (2) intervention outcomes. The added burden section incorporates condition prevalence before the pandemic, short- and long-term costs and health state utility loss and mortality per episode, and the risk ratio for the condition during the pandemic. The model estimates total and excess prevalence of the condition during the COVID-19 pandemic, and excess direct costs and QALYs lost per capita. The intervention section takes the outputs from the added burden section, along with intervention effectiveness and cost, to estimate net costs (or savings) and QALYs gained with the intervention per person and scaled up to 20% of eligibles.

Selection of health conditions and mitigating interventions

Using a systematic approach, we searched peer-reviewed literature to identify six high-priority indirect public health conditions that increased during the pandemic (Table 1): depressive symptoms, excessive alcohol use, opioid use disorder (OUD), homelessness, intimate partner violence (IPV), and stroke mortality. Selection criteria included availability of robust research as of November 2020, although many other conditions are likely to be affected by the pandemic. See S2 File for detail on search strategy and strength of evidence assessment.

Table 1

The human toll: Increased health harms due to the COVID-19 pandemic.

Priority Public Health Condition Indirectly Affected by COVID-19	Relative Risk: Change Under COVID-19 (uncertainty range)	Excess Quality-Adjusted Life Years (QALYs) Lost Due to COVID-19 per Million Total Population
		Short-Term	Long-Term
Depressive symptoms [19–21]	1.37 (1.20–2.56)	24,831	43,807
Intimate partner violence [22–24]	1.11 (1.05–1.16)	2,445	49,902
Homelessness [25]	6.67 (5.34–8.00)	12,951	3,527
Excessive alcohol use [12,20]	1.19 (1.07–1.42)	5,928	19,807
Opioid use disorder [26,27]	1.63 (1.33–1.98)	3,239	24,877
Stroke mortality [13–15,28–30]	1.53 (1.40–1.67)	1,214	--

QALYs: Quality-adjusted life years.

For each health condition, we first identified commonly-used, evidence-based interventions that were recommended by relevant health organizations (e.g., SAMHSA website, US Surgeon General reports, US Veterans Administration/Department of Defense treatment guidelines) and content experts. Next, we examined the peer-reviewed literature on each intervention, prioritizing systematic reviews and meta-analyses, to confirm that each mitigation strategy had evidence to support its effectiveness and cost-effectiveness at the population level. Our goal was to understand where gains could be achieved across important health conditions (rather than comparing cost-effectiveness of different interventions for each condition). Therefore, we extracted data on one prevention or treatment intervention per condition that was relevant for California, had higher strength of evidence, and for which effect size estimates were reported in or could be transformed into a risk ratio for dichotomous clinical outcomes (and thus were compatible with BRACE). We also considered constraints on rolling out programs under pandemic conditions, including sheltering-in-place and whether individuals would likely avail themselves of services, as our main exclusion criterion (S3 File). The final mitigation strategies included: Cognitive-behavioral therapy (CBT) and antidepressants for depressive symptoms, nurse-family partnership for IPV, rent subsidies for homelessness, screening and brief intervention for excessive alcohol use, medication-assisted treatment (MAT) for OUD, and public awareness campaign for stroke mortality.

For all mitigation strategies except the public awareness campaign for reducing stroke mortality, we assumed that the intervention’s reach was 20% of adults affected by each condition (regardless of whether the individuals are experiencing the condition due to COVID-19), scaled to a total general population of one million adults. This target was arbitrary, intended to be ambitious yet realistic. (As a point of reference, while 66% of depression is treated in the US, under 20% of those with OUD receive treatment [31,32]). For example, for a population of one million adults, if the prevalence of a condition was 2% (i.e., 20,000 affected adults), we assumed the intervention reached 20% of them (i.e., 4,000 adults). For the public awareness campaign to reduce stroke mortality, we assumed 100% potential reach among those at risk for in-hospital stroke mortality and adjusted the cost of intervention to reflect the cost per person in this population.

Health outcomes

Each model included the prevalence of the condition before the COVID-19 pandemic, derived from epidemiologic studies, and estimates of the effect of the COVID-19 pandemic on the condition, based on the risk ratio derived from the literature review. For homelessness, recent data were unavailable to assess the change during COVID-19 because the predicted homelessness had not yet occurred given eviction moratoria in California. Thus we used data from the Great Recession of the late 2000s as a proxy, given its comparable economic impact.

For each condition, we estimated health outcomes (deaths and disability) per episode using data identified in literature. Morbidity was estimated using health state utility decrements associated with each condition [33], while deaths were calculated using case-fatality ratios. Of note, we assumed that pandemic-related homelessness would have markedly lower consequences than chronic homelessness (e.g., acute, likely to stay with friends/family rather than shelter/street), so we reduced literature-derived health outcome values by 75%. For all conditions, we identified values for short- and long-term (often lifetime) outcomes. Long-term health outcomes were assumed to occur after a mean of 10 years and discounted to the present using a 3% annual discount rate. Health outcomes were standardized into QALYs, which combine morbidity (decreased health state utility for specified durations) and mortality (likelihood and associated QALY loss), to allow comparison across different diseases and interventions.

We then measured changes in short- and long-term health outcomes that may result from implementing specific mitigating strategies. This was achieved by applying reported intervention efficacy (relative reduction in the condition) to the condition prevalence during COVID-19.

Costs

Costs were considered from a societal perspective, rather than costs to specific actors (e.g., the government or insurers). The primary sources for cost data were peer-reviewed publications and grey literature, which often provided California-specific cost information. When published cost data were unavailable, we relied on payment rates from the Medi-Cal fee schedule updated monthly by the California Department of Health Care Services. Costs were adjusted for inflation and presented in 2020 US dollars. Condition-specific cost estimates included short- and long-term medical costs (e.g., ambulance, emergency room visits, medicine) and direct non-medical costs (e.g., police intervention) per episode. (Electronic supplement for costing details upon request.) Short-term costs were assumed to be incurred in the first year and not discounted. Long-term costs were assumed to be incurred after a mean of 10 years and discounted accordingly.

We estimated mitigation strategy costs following the same approach as above. The interventions we examined are typically limited in duration, with costs entirely or predominantly incurred during year one, and thus they were not discounted.

We calculated net savings over the course of an individual’s lifetime, and at one, three, and ten years following intervention implementation. We assumed that 60% of short-term costs and none of the long-term costs were incurred in year one. Over three years, 90% of the short-term costs and 20% of long-term costs were incurred. After year ten, 95% of short-term costs and 50% of long-term costs were incurred.

Cost-effectiveness

If the intervention cost exceeded savings over the lifetime, we calculated a cost-effectiveness ratio (net cost per QALY gained). If the intervention cost was less than the savings, the intervention was described as “dominant,” meaning that it both cost less than doing nothing (due to savings from averted clinical and other outcomes) and improved health outcomes. Per-person estimates were scaled up to calculate lifetime anticipated costs, savings, and QALYs gained per general adult population of one million.

Results

We present results pertaining to overall health and economic costs for each condition, and how these could change as the result of mitigation for indirect health outcomes of the pandemic. Detailed findings for individual conditions are in S1 File.

Indirect health harms of the COVID-19 pandemic

Results suggest wide-ranging indirect health consequences of the pandemic (Table 1). While there were increases of 11% in the rate of IPV by April 2020 compared to before the shelter-in-place order, the prevalence of homelessness is expected to increase nearly sevenfold due the pandemic.

Across the six conditions assessed, we estimated that total QALYs lost were 192,530 per million population. The greatest QALYs per million population were lost to depression (68,638), followed by IPV (52,347). In-hospital stroke mortality increased 53% during COVID-19 compared to before; however, the overall reduction in QALYs (1,214) was not as pronounced as for the other conditions.

Economic consequences of the indirect health harms of the COVID-19 pandemic

When no mitigation strategies were deploted, the highest costs (both short- and long-term) were estimated to result from failing to mitigate rising rates of depressive symptoms among adults (Table 2). While the cost-per-episode of depressive symptoms from an individual patient perspective was small, the overall medical and non-medical cost rose to $2.2 billion per million total population. The magnitude of these costs was largely due to the sheer extent of the excess prevalence of depressive symptoms during the pandemic.

Table 2

Costs of doing nothing: Estimated societal costs from indirect health harms of the COVID-19 pandemic.

Priority Public Health Condition Indirectly Affected by COVID-19	Cost per Episode	Excess Societal Costs Due to COVID-19 per Million Total Population
		Short-Term	Long-Term
Depressive symptoms [34,35]	$32,599	$954M	$1,277M
Intimate partner violence [36]	$115,562	$373M	$326M
Homelessness [37,38]	$99,969	$706M	$1,448M
Excessive alcohol use [39–43]	$94,004	$138M	$952M
Opioid use disorder [44–47]	$79,551	$166M	$385M
Stroke mortality [48]	$16,773	$844,000	--

M: Million.

Other public health problems, such as homelessness, were also likely to incur large medical and non-medical costs. Even though homelessness had a lower QALY burden than other conditions, it could impose a large economic burden (nearly $2.2 billion per million total population) if not fully addressed. Increased rates of excessive drinking during the pandemic were also projected to cost almost $1.1 billion per million total population.

Effect of mitigating interventions

All intervention outcomes assumed 20% reach of the affected population, except for the public awareness campaign for stroke mortality with 100% reach. All interventions produced medical and non-medical cost-savings that increased over time, while also averting losses in QALYs (Table 3). All but one intervention, MAT for OUD, was projected to yield net cost savings within three years. By ten years, all interventions were expected to provide net cost savings.

Table 3

Mitigation strategies: Health and economic outcomes from intervention programs that reach 20%* of affected adults per million total population.

Priority Public Health Condition Indirectly Affected by COVID-19 and Associated Interventions	QALYs Gained	Intervention Costs	Cost-Effectiveness vs. Doing Nothing (lifetime)	Net Savings by Time Period
				Over 1 Year	Over 3 Years	Over 10 Years
Depressive symptoms
Cognitive-behavioral therapy +antidepressants	12,707	$44.5M	Dominant	$61.5M	$161.7M	$241.5M
Intimate partner violence
Nurse-family partnership	22,186	$164.7M	Dominant	($69.8M)	$5.3M	$54.6M
Homelessness
Rent subsidies	1,648	$47.1M	Dominant	($4.7M)	$45.4M	$92.4M
Excessive alcohol use
Screening and brief intervention	4,835	$6.6M	Dominant	$8.9M	$52.5M	$107.4M
Opioid use disorder
Medication-assisted treatment	5,674	$56.2M	Dominant	($36.0M)	($10.5M)	$14.5M
Stroke mortality
Public awareness campaign	388	$14,350	Dominant	$147,500	$228,500	$242,000

*Except for stroke, which assumes 100% coverage of those at risk for stroke. QALYs: Quality-adjusted life years, M: Million. Parentheticals indicate net costs.

The largest estimated health gains were from intervening in IPV, with over 22,000 QALYs gained per million adult population (Table 3). This was followed by treating depressive symptoms, which led to nearly 13,000 QALYs gained. Intervening to prevent stroke mortality led to smaller gains (about 400 QALYs) due to the relative rarity of the condition and the modest effects of a public awareness campaign; this was still a dominant strategy with net savings.

Greatest savings came from providing CBT and antidepressants to people experiencing depressive symptoms. Within one year, $61.5 million net savings were estimated with this intervention. Screening, brief intervention, and referral to treatment (SBIRT) for individuals experiencing increased alcohol use was estimated to save $8.9 million within one year.

Sensitivity analyses

Probabilistic sensitivity analyses indicated that interventions reliably saved both QALYs and costs across a range of uncertainties in key inputs (Fig 1). The greatest gains in both QALYs and dollars per million adult population were attained with either CBT and antidepressents for depressive symptom mitigation, which resulted in mean net savings of $383 million (95% prediction interval (PI): $130 million–$786 million) and mean QALYs gained were 13,136 (95% PI: 5,632–24,105). Alternatively, the nurse-family partnership program could save mean 21,494 QALYs (95% PI: 5,664–37,146) by mitigating the effects of IPV, but with lower overall net savings of $121 million (95% PI: -$99 million–$358 million). Among the remaining four interventions, SBIRT for excessive alcohol use could yield comparatively greater dollar savings ($199 million, 95% PI: $85 million–$367 million). MAT for OUD was associated with a mean 5,933 QALYs gained (95% PI: 2.887–9,943) and $59 million in net savings (95% PI: $10 million–$130 million).

Fig 1

Comparison of health benefits and economic savings of alternative mitigation strategies.

QALY: Quality-adjusted life year. 20% intervention coverage for all interventions except stroke prevention public awareness campaign, which assumes 100% coverage.

Due to high uncertainty in the increase in prevalence of homelessness, we conducted a one-way deterministic sensitivity analysis on this parameter. If the relative risk for homelessness during the pandemic was one-fourth of the base case value (1.67 versus 6.67), QALYs gained were reduced to 369, and net savings to $14 million. Rent subsidies remained dominant, although savings did not occur until after three years.

Discussion

Policymakers need scientific guidance to craft interventions that address the long-term indirect effects of the COVID-19 pandemic. The BRACE model provides a flexible tool for assessing the health and economic impacts of these indirect effects. We found that adverse conditions increased at widely vaying rates during the pandemic, ranging from an 11% increase in IPV to a 63% increase in OUD. The rate of in-hospital stroke mortality increased over 50%, likely due to delayed presentation to care settings due to fear of contracting SARS-CoV-2 or delays in receiving definitive treatment once in the hospital due to additional precautionary measures [13-15,30]. While increases in homelessness were not actually observed, given the temporary nature of eviction moratoria (which were issued for a few months at a time with no certainty of extension), we nevertheless included this condition as the 700% potential rise in homelessness was worrisome. The health harms and economic tolls of all these changes were substantial, with an estimated 192,000 QALYs lost and nearly $7 billion net cost per million population, primarily due to increased depressive symptoms. Mitigating these harms now will achieve significant cost-savings within one-to-three years. Across the six interventions considered, an estimated 47,000 QALYs were gained and potential net savings reached $5 billion, for a population of one million adults.

Some health and cost burdens have already been incurred. However, the sooner mitigation strategies are implemented, the more suffering and long-term costs can be averted by arresting increases in the severity of indirect health conditions and limiting downstream impacts. While these conditions may eventually return to pre-pandemic levels, interventions described in this study would still have health benefits and cost-savings, since they improve health for both pandemic-related and pre-existing cases. The magnitude of benefits will depend on the extent that indirect health impacts continue. Additionally, intervention benefits may vary for different populations within California. Due to the novelty of the pandemic, data on indirect effects for different populations (e.g., by geographic location, race/ethnicity, economic status) were not available at the time of analysis, thus we did not attempt to evaluate differences in intervention outcomes across groups. However, we believe results would be similar even in populations for which interventions may be less effective, given our consistently strong favorable findings.

The findings of this analysis were more favorable than published studies for the individual conditions [49-51]. Prior studies often include only short-term costs and are generally limited to direct medical costs, leading to smaller estimates of savings from mitigation. We were also more likely than others to find dominance because our comparator was ‘no intervention’; we did not evaluate the incremental benefit of a more expensive intervention over a less expensive one. We also did not incorporate productivity losses or the spillover effect of the evaluated conditions on children, which may lead to additional savings.

We described a menu of economically attractive alternatives for mitigation following the first year of the COVID-19 pandemic. Other considerations apply when implementing these interventions, including community acceptability and barriers to implementation. For example, interventions that require in-person contact can be challenging if specific populations are not confident about seeking healthcare services. Decisionmakers must be attentive to these potential barriers and identify locally-appropriate responses. Moreover, the changing pandemic context requires agility in the face of evolving impacts to prevent human suffering and financial costs. Specifically, the effects of the pandemic on children are likely to be substantial and long-lasting [52,53].

Governments have access to a variety of potential interventions that may ameliorate the adverse health events resulting from the COVID-19 pandemic. These interventions include financing the expansion of existing clinical interventions (e.g., counseling and medications for depression), leveraging macro-level economic policies (e.g., financial stimulus packages; interest rate reductions; rent, eviction, and mortgage moratoria; relaxed conditions of debt payments and income taxes), and using organization-level measures (e.g., school enhancements, workplace safety protections) and community public health interventions (e.g., information campaigns). When considering results from the BRACE model, which estimated the cost and effect of each condition separately, one might consider leveraging upstream mitigation strategies that may generate benefits across multiple conditions. Such strategies include economic tools (e.g., earned income tax credit, enhanced unemployment benefits) that have unique advantages in the current context; they tend to reach a broad swath of the population, can mitigate multiple negative health outcomes, and may have lower administration burdens. These strategies are particularly valuable for building population resistance to stressors on health that unfold over time (e.g., exposure to family violence, depression). Insights from the BRACE model should be incorporated and synchronized with existing programs and systems of care, to leverage existing infrastructure, reduce redundancy, and potentially lead to large health impacts and cost savings if streamlined and coordinated.

In addition to ongoing planning and research that builds from BRACE, we recommend establishing a tracking and evaluation system with built-in quality improvement strategies to ascertain how interventions are being implemented. These data could help refine and improve interventions to assure that those needing support do indeed receive it. As always, it is important to demonstrate concrete returns on investment for preventive health services, which, while typically quite large, can easily become taken-for-granted. Process and outcome studies could also be conducted, using qualitative and quantitative methods and community-engaged research approaches, to measure short- and longer-term outcomes, best practices, adaptations, and anticipated and unanticipated outcomes. An equally important action is compilation of cost data, including information on who pays for the intervention and who reaps benefits.

Limitations

Our simulation tool enables rapid assessments of health conditions and evidence-based interventions. Favoring the ability to consider multiple health conditions simultaneously with adequate precision for policy decisions required omitting the usual disease-specific nuance used to portray clinical progression and multiple interventions. Detailed estimation of costs and health outcomes was also beyond the scope of this analysis. Further, we considered each intervention individually, disregarding any potential cost-sharing. Greater savings could be achieved by streamlining interventions and leveraging synergistic effects. More sophisticated modeling could be linked with the model as urgency, resources, and priority allow. We suspect that the broad conclusions would remain unchanged.

We were limited by availability of robust data at the time of analysis and by the model requirement of dichotomous efficacy data, which restricted the selection of health conditions and interventions. While we sought guidance from content area experts, the timeline and breadth of the project inhibited more intensive formal approaches (e.g., Delphi elicitation) that might have been ideal. Precise efficacy of interventions (especially within the context of the pandemic) remained uncertain; different strategies may lead to greater health gains and/or cost savings. Our assumption of 20% intervention coverage may be overestimating the pool of eligible individuals, thereby overstating costs. Incremental coverage is largely a public health policy decision, although constrained by available capacity and resources. Smaller efforts would result in smaller gains, but identical patterns of net savings, thus not affecting cost-effectiveness ratios. Regardless, this assumption may need to be adjusted depending on available human resources and scale-up processes.

Conclusions

The COVID-19 pandemic has severely damaged public health around the world. There is an opportunity to ameliorate the indirect health and cost impacts of the pandemic. Evidence-based research and modeling are important tools to inform solutions to these widespread and severe effects. Implementing evidence-based interventions should be a policy priority, particularly as the pandemic has exacerbated inequality in a wide range of socio-economic conditions. We identified and evaluated the impact of mitigation strategies for health conditions that worsened under the COVID-19 pandemic and present a menu of options for improving the health of communities and generate cost-savings. Implementing any of these strategies may lead to significant dollar savings for the State of California and to substantial improvements in the health of millions of state residents, for years to come.

Model inputs and results.

(DOCX)

Click here for additional data file.

Strength of evidence.

(DOCX)

Click here for additional data file.

Selection of conditions and interventions.

(DOCX)

Click here for additional data file.

30 May 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Sebsibe Tadesse, PhD

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf  and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This interesting paper presents data on the cost-effectiveness of key evidence-based mitigation strategies that address six priority health conditions that have been indirectly affected by the COVID-19 pandemic in California. It is well written and results are presented clearly and conclusions are drawn from data presented.

As COVID-19 vaccine population coverage continues to increase in many countries, many governments are keen to ease both health and border restrictions to enable economic rebound and communities to recover under ‘COVID-normal’ policies. However, as experts predicted the COVID-19 restrictions and acute focus on managing the COVID-19 pandemic meant that many other health conditions have been deprioritized in the process and over the next 1-5 years, we are likely to see a second wave of health issues that have resulted from this deprioritization.

The authors undertook a systematic review of the recent literature to identify key health conditions that have been exacerbated during COVID pandemic - and findings of the review and selection of the six conditions are presented in supplementary files.

I only have a few minor suggestions:

- Table 1. The Human Toll: Increased Health Harms due to the COVID-19 Pandemic and Table 2. Cost of Doing Nothing: Estimated Societal Costs from Indirect Health Harms of the 211 COVID-19 Pandemic - should include confidence intervals for estimates, as included in S1.

- The description of the ‘Selection of health condition to be included in BRACE model’ in the main paper is quite brief - with further details provided in S3. However, it is unclear as to why other public health conditions with more available evidence in Table C1 (ex. Heart disease, Suicide/self-harm, Diabetes mellitus) were not included, while housing insecurity was selected. Please provide further details regarding the decision process.

- In Table B1 in Supplementary File 2 - given the strength of evidence was much stronger for the ‘Social determinants of health’ factors related to Food insecurity, compared to housing insecurity and Job Loss, why did the authors select Homelessness as the included Public Health Condition?

- The reach of public awareness campaigns to reduce stroke mortality was assumed at 100% - why? This seems unrealistic. Suggest reducing this based on previous public awareness campaigns.

https://pubmed.ncbi.nlm.nih.gov/23013373/

https://pubmed.ncbi.nlm.nih.gov/33875043/

https://pubmed.ncbi.nlm.nih.gov/34844126/

https://pubmed.ncbi.nlm.nih.gov/9565010/

- Costs for a public awareness campaign ($14,350) appear low if the assumption is 100% coverage of those at risk of stroke. Where did these costs come from? Consider revising based on large public health campaigns.

- It would be helpful to include more detail in Suppl S1 - inputs and results regarding what constitutes the non-medical direct costs.

- In-hospital stroke mortality increased 53% during COVID-19 compared 200 to before - yet, it was unclear as to what was contributing to such a stark increase. Please provide some additional discussion as to the likely causes for such increase

- Given recent data on homelessness was not available due to the eviction moratoria currently in place in California, which is an effective intervention itself, the authors should consider adjusting down the estimate derived from the Great Recession from late 2000s to ensure they do not overestimate the impact of COVID on homelessness.

Reviewer #2: Comments to authors

Thank you for the opportunity to review this manuscript. In this study, the authors estimate the cost-effectiveness of addressing various phenomena which were exacerbated as result of the COVID-19 pandemic in California (depressive symptoms, intimate partner violence, homelessness, excessive alcohol use, opioid use disorder, stroke mortality). The authors find mitigation strategies for each of the six phenomena to be cost saving after a year. I have a few concerns with the study implementation which I detail below.

General comments

For an economic evaluation, this study approach is atypical. I understand including several heterogenous phenomena in one study requires a generalized approach. The authors sufficiently describe this approach in the manuscript. However, I feel a key limitation of this approach is the sacrifice of depth for breadth. Due to data limitations, many strong assumptions are necessary for the authors to implement this model. I detail these concerns below which also include recommendations on improving the methodology.

Specific comments

1. Title: the title/abstract should mention California as this is the perspective of the study.

2. Page 4 lines 84-85: These data sources should be mentioned in the abstract as well.

3. Page 5 lines 88-89: As this is the primary focus of the publication, the BRACE workbook should be uploaded and shared as supplemental information.

4. Page 5 lines 90-97: The model results hinge on assuming all changes in the prevalence of specific phenomena are attributable to COVID. This is a strong assumption and should be reconsidered.

5. Page 5 line 97: Why 20%? This number should be further justified.

6. Table 1: Please add a source column.

7. Page 7 lines 128-136: The assumptions of 20% reach or 100% reach (for stroke) are arbitrary and should be replaced with justifiable estimates supported by the medical literature.

8. Page 8 lines 142-143: The use of the Great Recession as a proxy for homelessness is not appropriate. The economic impacts of the Great Recession and the COVID-19 pandemic are markedly different in that the Great Recession was primarily attributable to the U.S. housing market whereas many jurisdictions implemented eviction moratoria during the COVID-19 pandemic. Alternative data should be used.

9. Page 8 line 149: The reduction in literature-derived health outcomes by 75% is arbitrary and should be replaced with estimates from the literature.

10. Page 9 lines 168-169: These costing details should be provided as a supplement.

11. Page 9 line 176: This 60% figure is arbitrary and should be justified or replaced.

12. Table 2: The estimated societal cost for stroke mortality does not have face validity ($16,773). For example, Wang et al. estimates the cost of hospitalizations for stroke at $20,396 (in 2008 USD), which is $29,422.40 when inflated to 2021 USD using the medical component of the consumer price index. In addition, Wang’s is likely an underestimate as it does not include costs to society.

Reference: Wang et al. J Stroke Cerebrovasc Dis. 2014 ; 23(5): 861–868. doi:10.1016/j.jstrokecerebrovasdis.2013.07.017.

13. Table 3. Why is each intervention compared to do-nothing in a pairwise manner? A proper methodological approach would be to conduct one economic evaluation with all comparators in addition to do-nothing evaluated together. This would involve evaluating dominance, extended dominance, etc. with resulting interventions remaining on the cost-effectiveness frontier assigned ICERs.

14. Page 14. Lines 242-252: The authors provide 95% confidence intervals for the findings from the study. However, generating confidence intervals from probabilistic sensitivity analysis results, i.e., Monte Carlo simulations, is not straightforward. How were these intervals constructed – was bootstrapping or another statistical method used? The methods to generate these bounds should be clearly described in the main text and briefly mentioned in the abstract. Are these bounds simply the 2.5th and 97.5th percentiles of the PSA outputs? If so, these are not valid confidence intervals.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

23 Jun 2022

We thank the reviewers for their thorough review of our manuscript and the detailed comments they have provided. Below, we have responded to each point that was raised.

Reviewer #1: This interesting paper presents data on the cost-effectiveness of key evidence-based mitigation strategies that address six priority health conditions that have been indirectly affected by the COVID-19 pandemic in California. It is well written and results are presented clearly and conclusions are drawn from data presented.

As COVID-19 vaccine population coverage continues to increase in many countries, many governments are keen to ease both health and border restrictions to enable economic rebound and communities to recover under ‘COVID-normal’ policies. However, as experts predicted the COVID-19 restrictions and acute focus on managing the COVID-19 pandemic meant that many other health conditions have been deprioritized in the process and over the next 1-5 years, we are likely to see a second wave of health issues that have resulted from this deprioritization.

The authors undertook a systematic review of the recent literature to identify key health conditions that have been exacerbated during COVID pandemic - and findings of the review and selection of the six conditions are presented in supplementary files.

I only have a few minor suggestions:

- Table 1. The Human Toll: Increased Health Harms due to the COVID-19 Pandemic and Table 2. Cost of Doing Nothing: Estimated Societal Costs from Indirect Health Harms of the 211 COVID-19 Pandemic - should include confidence intervals for estimates, as included in S1.

RESPONSE: Thank you. We now added the uncertainty range for the relative risk values.

- The description of the ‘Selection of health condition to be included in BRACE model’ in the main paper is quite brief - with further details provided in S3. However, it is unclear as to why other public health conditions with more available evidence in Table C1 (ex. Heart disease, Suicide/self-harm, Diabetes mellitus) were not included, while housing insecurity was selected. Please provide further details regarding the decision process.

RESPONSE: Health conditions were selected primarily based on availability of robust data at the time on impact of COVID on the prevalence of the condition. Potential for cascading effects (i.e., ACEs impacts) were also considered. This study was part of a report prepared for the Office of the California Surgeon General, and their priorities were taken into account to narrow down our selection.

- In Table B1 in Supplementary File 2 - given the strength of evidence was much stronger for the ‘Social determinants of health’ factors related to Food insecurity, compared to housing insecurity and Job Loss, why did the authors select Homelessness as the included Public Health Condition?

RESPONSE: Our selection of health conditions was informed by the factors described above. Additionally, the selected conditions needed to be specific enough so that outcomes could be modeled; social determinants of health was too broad. Furthermore, homelessness was selected less for actual observed increase in prevalence than a concern that without eviction moratoria there would be a big jump.

- The reach of public awareness campaigns to reduce stroke mortality was assumed at 100% - why? This seems unrealistic. Suggest reducing this based on previous public awareness campaigns.

https://pubmed.ncbi.nlm.nih.gov/23013373/

https://pubmed.ncbi.nlm.nih.gov/33875043/

https://pubmed.ncbi.nlm.nih.gov/34844126/

https://pubmed.ncbi.nlm.nih.gov/9565010/

RESPONSE: The eligible population to receive this intervention was people at risk for in-hospital stroke mortality (i.e., not everyone who is at risk for stroke) and we assumed 100% potential reach. We now clarify this within the text. We agree that this may not be feasible, and we now added a one-way sensitivity analysis on this parameter in Suppl S1. Briefly, when campaign coverage was halved, all health and cost outcomes were also reduced at scale, and the intervention remained dominant.

- Costs for a public awareness campaign ($14,350) appear low if the assumption is 100% coverage of those at risk of stroke. Where did these costs come from? Consider revising based on large public health campaigns.

RESPONSE: This cost per person was derived from peer-reviewed literature, adjusted for the California population and proportion at risk for in-hospital stroke mortality (i.e., those at risk for delayed care for stroke), as described above.

- It would be helpful to include more detail in Suppl S1 - inputs and results regarding what constitutes the non-medical direct costs.

RESPONSE: The exact composition of non-medical direct costs differs for each condition, partly because each condition has a unique set of associated costs, and also because these data came from different sources. Each study used a different set of cost items, and we selected the data points that were the most comprehensive. We now added to Suppl S1 brief summaries of what each source included when calculating costs.

- In-hospital stroke mortality increased 53% during COVID-19 compared to before - yet, it was unclear as to what was contributing to such a stark increase. Please provide some additional discussion as to the likely causes for such increase

RESPONSE: Thank you, we now briefly discuss why stroke mortality may have increased (lines 283-286).

- Given recent data on homelessness was not available due to the eviction moratoria currently in place in California, which is an effective intervention itself, the authors should consider adjusting down the estimate derived from the Great Recession from late 2000s to ensure they do not overestimate the impact of COVID on homelessness.

RESPONSE: We agree that an eviction moratorium was an effective intervention. However, eviction moratoria in California were issued temporarily, for defined periods of time, and there was uncertainty about whether they would be extended. The large impact on livelihoods suggested that homelessness might have increased once the moratoria were lifted. Figures A5 and A6 in Suppl S1 include 1-way sensitivity analyses on this relative risk: net savings are still achieved with lower relative risks and substantial QALYs are gained. We now added to Suppl S1 an additional scenario where the relative risk is halved.

Reviewer #2: Comments to authors

Thank you for the opportunity to review this manuscript. In this study, the authors estimate the cost-effectiveness of addressing various phenomena which were exacerbated as result of the COVID-19 pandemic in California (depressive symptoms, intimate partner violence, homelessness, excessive alcohol use, opioid use disorder, stroke mortality). The authors find mitigation strategies for each of the six phenomena to be cost saving after a year. I have a few concerns with the study implementation which I detail below.

General comments

For an economic evaluation, this study approach is atypical. I understand including several heterogenous phenomena in one study requires a generalized approach. The authors sufficiently describe this approach in the manuscript. However, I feel a key limitation of this approach is the sacrifice of depth for breadth. Due to data limitations, many strong assumptions are necessary for the authors to implement this model. I detail these concerns below which also include recommendations on improving the methodology.

Specific comments

1. Title: the title/abstract should mention California as this is the perspective of the study.

RESPONSE: Thank you. Our abstract already mentions in the methods paragraph that we modeled outcomes for California (line 28-30).

2. Page 4 lines 84-85: These data sources should be mentioned in the abstract as well.

RESPONSE: Thank you. We have added this to the abstract.

3. Page 5 lines 88-89: As this is the primary focus of the publication, the BRACE workbook should be uploaded and shared as supplemental information.

RESPONSE: We provide abundant detail in the supplements about out methods, including all data sources, assumptions, and calculations. We can provide the interactive workbook to readers upon request.

4. Page 5 lines 90-97: The model results hinge on assuming all changes in the prevalence of specific phenomena are attributable to COVID. This is a strong assumption and should be reconsidered.

RESPONSE: Our “change during COVID” data come from peer-reviewed articles that were recently published at the time of the analysis and showed statistically significant changes in the conditions during and immediately before COVID (with the exception of homelessness). These changes can be due to COVID itself, because of public health responses to COVID, or due to unrelated reasons. We do not aim to distinguish between the cause of increased prevalence. It is obvious and (now) very well established that COVID and the public health response has led to significant disruptions in people’s lives, so it is plausible (and very likely) that the pandemic and the response may be the underlying reasons for these changes. While it is true that certain individuals might have experienced these conditions unrelated to COVID-19, the pandemic remains the most plausible explanation for these large population-level changes in prevalence and severity of these downstream health conditions.

Additionally, as we explain in lines 133-135, we assume that interventions would be implemented in eligible populations (i.e., those with the condition) regardless of whether the condition is caused by the COVID pandemic. For example, people with pre-existing or non-COVID-related recent onset depression are also eligible to receive CBT and SSRI. Indeed, it may be impossible to distinguish. The magnitude of cost-savings and health benefits are determined based on the amount of change that occurred in the prevalence of the depression, regardless of why that changed occurred.

5. Page 5 line 97: Why 20%? This number should be further justified.

RESPONSE: It is an assumption intended to be ambitious but realistic. Greater or lesser coverage would increase or decrease the magnitude of the findings to scale, but this would not alter the nature and interpretation of findings.

6. Table 1: Please add a source column.

RESPONSE: We now added references to Table 1 and Table 2.

7. Page 7 lines 128-136: The assumptions of 20% reach or 100% reach (for stroke) are arbitrary and should be replaced with justifiable estimates supported by the medical literature.

RESPONSE: We now note the treatment coverage levels for several of the studied conditions (lines 136-137). This serves as a useful point of reference, but the incremental coverage is largely a public health policy decision, although constrained by available capacity and resources. We now emphasize that smaller efforts would result in smaller gains, but identical patterns of net savings (lines 371-373).

8. Page 8 lines 142-143: The use of the Great Recession as a proxy for homelessness is not appropriate. The economic impacts of the Great Recession and the COVID-19 pandemic are markedly different in that the Great Recession was primarily attributable to the U.S. housing market whereas many jurisdictions implemented eviction moratoria during the COVID-19 pandemic. Alternative data should be used.

RESPONSE: We agree that the two events have led to different outcomes. This is why we adjusted the health and cost effects associated with homelessness, assuming 75% lower health consequences. We used similar rates of potential homelessness because of the very steep jump in unemployment but as noted above, a smaller scale would affect the estimated magnitude but not the nature of the net economic benefits. We now report a wide sensitivity analysis (lines 261-265).

9. Page 8 line 149: The reduction in literature-derived health outcomes by 75% is arbitrary and should be replaced with estimates from the literature.

RESPONSE: Given eviction moratoria and the recency of the pandemic, these expected health outcomes have not yet occurred. As such, there is no direct evidence on the mortality associated with COVID-19-related homelessness, which is why we used a very conservative assumption.

10. Page 9 lines 168-169: These costing details should be provided as a supplement.

RESPONSE: Thank you. All our data sources are listed in Suppl 1. We now also note in Suppl 1 tables what types of costs each cost input included.

11. Page 9 line 176: This 60% figure is arbitrary and should be justified or replaced.

RESPONSE: We agree that this was an estimate. We now include sensitivity analyses in Suppl 1.

12. Table 2: The estimated societal cost for stroke mortality does not have face validity ($16,773). For example, Wang et al. estimates the cost of hospitalizations for stroke at $20,396 (in 2008 USD), which is $29,422.40 when inflated to 2021 USD using the medical component of the consumer price index. In addition, Wang’s is likely an underestimate as it does not include costs to society.

Reference: Wang et al. J Stroke Cerebrovasc Dis. 2014 ; 23(5): 861–868. doi:10.1016/j.jstrokecerebrovasdis.2013.07.017.

RESPONSE: Thank you. The value $16,773 is the mean of one-time costs for three different types of strokes, which represent the medical direct cost, obtained from the Healthcare Cost and Utilization Project (HCUP). HCUP includes public and private insurances.

Wang et al "excluded patients with capitated plans because their costs of hospitalization would not reflect all the medical services provided to them." They also excluded all patients above age 64. The inclusion of only employer-sponsored insurance, exclusion of Medicare and capitation plans likely led to Wang et al.’s higher estimates. Additionally, while Wang et al. aimed to capture an estimated cost that reflects all medical services provided to patients, the cost they use is the payment amount to providers and only payment amount from private insurances -- which may be skewed to the higher end. We believe our estimate of $16,773 is more representative of the population.

Regardless, greater cost would lead to more potential savings by preventing stroke mortality; the intervention would remain dominant and would likely be even more desirable to policymakers given greater net savings.

13. Table 3. Why is each intervention compared to do-nothing in a pairwise manner? A proper methodological approach would be to conduct one economic evaluation with all comparators in addition to do-nothing evaluated together. This would involve evaluating dominance, extended dominance, etc. with resulting interventions remaining on the cost-effectiveness frontier assigned ICERs.

RESPONSE: We ordinarily agree with the proposed approach, but we did not envision this as an integrated decision problem for the state policy makers. Instead, a series of independent decisions.

We did not intend to investigate which health condition would be most cost-effective to intervene in. Our goal was to demonstrate the large health harms and costs associated with the rise in each condition, and that there is a possibility to avert these outcomes. We attempted to provide a menu of reasonable interventions that could alleviate suffering and costs that would provide an evidence-base for policy makers in California, but we do not believe it would be appropriate to compare such vastly different health conditions to one another.

We note that with net savings for all the interventions over time, the economically optimal choice for the state would probably be to implement all of them (i.e., there are no ICERs over several years).

14. Page 14. Lines 242-252: The authors provide 95% confidence intervals for the findings from the study. However, generating confidence intervals from probabilistic sensitivity analysis results, i.e., Monte Carlo simulations, is not straightforward. How were these intervals constructed – was bootstrapping or another statistical method used? The methods to generate these bounds should be clearly described in the main text and briefly mentioned in the abstract. Are these bounds simply the 2.5th and 97.5th percentiles of the PSA outputs? If so, these are not valid confidence intervals.

RESPONSE: Yes, they are 2.5 and 97.5 percentiles of PSA outputs, which represent the range of possible outcomes given simultaneous variation of all inputs, given specified confidence bounds and assumption of independence between inputs. Because the term CI invokes empirical statistics, we now revised our manuscript to use the term “prediction interval (PI)”.

Submitted filename: BRACE model PLOS One response to reviewers.docx

Click here for additional data file.

5 Jul 2022

Indirect COVID-19 Health Effects and Potential Mitigating Interventions: Cost-effectiveness framework

PONE-D-22-09397R1

Dear Dr. Sigal Maya,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Sebsibe Tadesse, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

8 Jul 2022

PONE-D-22-09397R1

Indirect COVID-19 Health Effects and Potential Mitigating Interventions: Cost-effectiveness framework

Dear Dr. Maya:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Sebsibe Tadesse

Academic Editor

PLOS ONE