Is it Time to Recognize Political Anxiety as a Social Determinant of Health?

Addressing Social Determinants of Mental Health in Pediatrics During the Coronavirus Disease 2019 Pandemic.

Background: Mortality after heart failure (HF) hospitalizations is high, with 15-20% of patients dying within 90 days. Prior studies have found that individual social determinants of health (SDOH) are associated with mortality after discharge, heightening interest in SDOH as risk factors for 90-day mortality. However, little is known about how the burden of SDOH within individuals affects 90-day mortality. We examined associations between multiple within-person SDOH and 90-day mortality among adults hospitalized for HF. Methods: We used data from the REGARDS study, a large prospective cohort of 30,239 US black and white adults recruited in 2003-7, with ongoing follow-up. We studied participants 65 years of age and older who were discharged alive after an expert-adjudicated HF hospitalization, with continuous Medicare Part A for 6 months before and 90 days after hospitalization. The primary outcome was 90-day mortality. Informed by the HealthyPeople 2020 framework for SDOH, we examined 8 SDOH: 1) black race; 2) low educational attainment; 3) low annual household income; 4) social isolation; and living in a: 5) zip code with high poverty; 6) Health Professional Shortage Area (HPSA); 7) rural area; and 8) state with poor public health infrastructure. Using cox proportional hazards models, we first examined the age-adjusted association between each SDOH and 90-day mortality; those associated with p<0.20 were retained to create groups of participants with 0, 1, and 2+ SDOH. We then determined hazard ratios (HR) for SDOH groups and 90-day mortality, adjusting for demographics, medical conditions, cognition, functional status, and hospitalization characteristics. Results: Over 10 years, a total of 690 individuals were hospitalized for HF at 440 unique hospitals across the US. They had a mean age of 76 years and 44% were female. We observed 79 deaths within 90 days of hospitalization. Of 8 candidate SDOH, black race (HR: 1.56 [95% CI: 0.99-2.43]), HPSA (1.56 [1.00-2.42]), social isolation (1.71 [0.99-2.97]), and living in a rural area (1.68 [.88- 3.19]) were associated with increased risk of age-adjusted 90-day mortality. Overall, 37% had no SDOH, 40% had 1, and 23% had 2+ SDOH. Compared to those with fewer SDOH, participants with 2+ vulnerabilities were younger, female, had less education and income, worse overall health, and were more often discharged to a nursing home. In a fully adjusted model, compared to those with 0 or 1 SDOH, the HR for 90-day mortality among those with 2+ SDOH was 1.24 (95% CI: 0.72 -2.14). Discussion: Four SDOH were individually associated with increased risk of 90-day mortality, but a greater number of SDOH within individuals was not independently associated.Larger studies that can operationalize a greater number of SDOH may shed more light on the relationship between the burden of SDOH and 90-day mortality following hospitalization for HF.

Social context matters for health, healthcare processes/quality and patient outcomes. The social status and circumstances we are born into, grow up in and live under, are called social determinants of health; they drive our health, and how we access and experience care; they are the fundamental causes of disease outcomes. Such circumstances are influenced heavily by our location through neighborhood context, which relates to support networks. Geography can influence proximity to resources and is an important dimension of social determinants of health, which also encompass race/ethnicity, language, health literacy, gender identity, social capital, wealth and income. Beginning with an explanation of social determinants, we explore the use of Geospatial Analysis methods and geocoding, including the importance of collaborating with geography experts, the pitfalls of geocoding, and how geographic analysis can help us to understand patient populations within the context of Social Determinants of Health. We then explain mechanisms and methods of geospatial analysis with two examples: (1) Bayesian hierarchical regression with crossed random effects and (2) discontinuity regression i.e., change point analysis. We leveraged the local University of Utah and Yale cohorts of the Multicenter Perioperative Outcomes Group (MPOG.org), a perioperative electronic health registry; we enriched the Utah cohort with US-census tract level social determinants of health after geocoding patient addresses and extracting social determinants of health from the National Neighborhood Database (NaNDA). We explain how to investigate the impact of US-census tract level community deprivation indices and racial/ethnic composition on (1) individual clinicians’ administration of risk-adjusted perioperative antiemetic prophylaxis, (2) patients’ decisions to defer cataract surgery at the cusp of Medicare eligibility and finally (3) methods to further characterize patient populations at risk through publicly available datasets in the context of public transit access. Our examples are not rigorous analyses, and our preliminary inferences should not be taken at face value, but rather seen as illustration of geospatial analysis processes and methods. Our worked examples show the potential utility of geospatial analysis, and in particular the power of geocoding patient addresses to extract US-census level social determinants of health from publicly available databases to enrich electronic health registries for healthcare disparity research and targeted health system level countermeasures.

Social isolation and loneliness among older adults have garnered significant international attention, particularly as structures and services have evolved during a global pandemic. A growing body of research underscores disparities in social isolation and loneliness among intersecting social (e.g., race, ethnicity, age, gender, sexual orientation, disability) and physical (e.g., rural/urban) locations. While empirical data about these global trends has increased, conceptual and theoretical frameworks are underdeveloped about disparities in social isolation and loneliness, especially from a global perspective. This article presents a novel equitable aging framework to help contextualize, understand, and explain how power influences disparities in social isolation and loneliness among older adults. Equitable aging builds on principles in critical gerontology, public health concepts of social and political determinants of health, international human rights, and intersectionality frameworks to present a new conceptual framework for researchers, policymakers, and practitioners. Equitable aging centers five domains of power (intrapersonal, interpersonal, disciplinary, structural, and cultural) as critical components (or hub) that drive six political and social determinants of health (economic stability, education access and quality, health care access and quality, neighborhood and built environment, social and community context, and laws and politics). The sixth determinant of health (laws and policies) incorporates international human rights (economic, social, cultural, civil, political rights). When justice is infused in these domains of power, political and social determinants of health can produce equitable aging outcomes. The Equitable Aging in Health Framework presents a new tool that incorporates justice and power to help understand and explain disparities in social isolation and loneliness and ultimately how to achieve equitable opportunities for social connections for older adults. To illustrate the utility of this conceptual framework, this article presents six case studies of interventions in China, Taiwan, Spain, Sweden, Mexico, and the United States that employ this framework to address social isolation and loneliness among diverse communities of older adults. These interventions propose new services, programs, and policies that infuse different paradigms of justice and address domains of power in various ways to build social connections and support for older adults.

ImportanceWhile the association between Social Determinants of Health (SDOH) and health outcomes is well known, few studies have explored the impact of SDOH on hospitalization.ObjectiveExamine the independent association and cumulative effect of six SDOH domains on hospitalization.DesignUsing cross-sectional data from the 2016–2018 National Health Interview Surveys (NHIS), we used multivariable logistical regression models controlling for sociodemographics and comorbid conditions to assess the association of each SDOH and SDOH burden (i.e., cumulative number of SDOH) with hospitalization.SettingNational survey of community-dwelling individuals in the USParticipantsAdults ≥18 years who responded to the NHIS surveyExposureSix SDOH domains (economic instability, lack of community, educational deficits, food insecurity, social isolation, and inadequate access to medical care)MeasuresHospitalization within 1 yearResultsAmong all 55,186 respondents, most were ≤50 years old (54.2%), female (51.7%, 95% CI 51.1–52.3), non-Hispanic (83.9%, 95% CI 82.4–84.5), identified as White (77.9%, 95% CI 76.8–79.1), and had health insurance (90%, 95% CI 88.9–91.9). Hospitalized individuals (n=5506; 8.7%) were more likely to be ≥50 years old (61.2%), female (60.7%, 95% CI 58.9–62.4), non-Hispanic (87%, 95% CI 86.2–88.4), and identify as White (78.5%, 95% CI 76.7–80.3), compared to those who were not hospitalized. Hospitalized individuals described poorer overall health, reporting higher incidence of having ≥5 comorbid conditions (38.9%, 95% CI 37.1–40.1) compared to those who did not report a hospitalization (15.9%, 95% CI 15.4–16.5). Hospitalized respondents reported higher rates of economic instability (33%), lack of community (14%), educational deficits (67%), food insecurity (14%), social isolation (34%), and less access to health care (6%) compared to non-hospitalized individuals. In adjusted analysis, food insecurity (OR: 1.36, 95% CI 1.22–1.52), social isolation (OR: 1.17, 95% CI 1.08–1.26), and lower educational attainment (OR: 1.12, 95% CI 1.02–1.25) were associated with hospitalization, while a higher SDOH burden was associated with increased odds of hospitalization (3–4 SDOH [OR: 1.25, 95% CI 1.06–1.49] and ≥5 SDOH [OR: 1.72, 95% CI 1.40–2.06]) compared to those who reported no SDOH.ConclusionsAmong community-dwelling US adults, three SDOH domains: food insecurity, social isolation, and low educational attainment increase an individual’s risk of hospitalization. Additionally, risk of hospitalization increases as SDOH burden increases.

Suicidal behavior is a critical mental health problem in the U.S., and this is particularly true for youth with social identities that are historically minoritized and discriminated against. There is also a growing awareness of the influence of social determinants of health (SDOH) on mental health. The current study examines links between one's own thoughts of suicide and the dose of exposure to other people's suicidal thoughts, often labeled contagion, within the context of different minoritized identity groups and SDOH deficits. Project Lift Up is a national longitudinal study of youth aged 13-22 years designed to understand exposure to suicidal thoughts and behaviors in social networks. A cohort of 4,981 adolescents and young adults was recruited online via social media between June 13, 2022, and October 30, 2023. Youth who knew one person with suicidal thoughts were 1.75 times (p=0.002) more likely than those without such exposure to self-report recent thought of suicide and those who knew between 2 and 4 people were 1.81 times more likely (p<0.001). These odds increased to 3.47 (p<0.001) if the youth knew five or more people with thoughts of suicide. Youth who identified with a social identity group that experiences marginalization and systemic oppression (based on race, ethnicity, disability status, gender, and sexual identity) and exposure to suicidal thoughts had higher odds of recent thoughts of suicide compared to non-minoritized and non-exposed youth. SDOH also explained unique variance in self-reported ideation. Exposure to other people's suicidal thoughts is associated with one's own thoughts of suicide and the number of people exposed to amplifies this effect, especially for individuals also experiencing adversity burden from SDOH. Results add to the extant literature documenting the higher odds of suicidal ideation that minoritized youth face.

Social Disconnection in Late Life Mental Illness – Commentary From the National Institute of Mental Health

Social determinants of health (SDH) are individually associated with incident coronary heart disease (CHD) events. Indices reflecting social deprivation have been developed for population management, but are difficult to operationalize during clinical care. We examined whether a simple count of SDH is associated with fatal incident CHD and nonfatal myocardial infarction (MI). We used data from the prospective longitudinal REGARDS cohort study (Reasons for Geographic and Racial Differences in Stroke), a national population-based sample of community-dwelling Black and White adults age ≥45 years recruited from 2003 to 2007. Seven SDH from the 5 Healthy People 2020 domains included social context (Black race, social isolation); education (educational attainment); economic stability (annual household income); neighborhood (living in a zip code with high poverty); and health care (lacking health insurance, living in 1 of the 9 US states with the least public health infrastructure). Outcomes were expert adjudicated fatal incident CHD and nonfatal MI. Of 22 152 participants free of CHD at baseline, 58.8% were women and 42.0% were Black; 20.6% had no SDH, 30.6% had 1, 23.0% had 2, and 25.8% had ≥3. There were 463 fatal incident CHD events and 932 nonfatal MIs over a median of 10.7 years (interquartile range, 6.6 to 12.7). Fewer SDHs were associated with nonfatal MI than with fatal incident CHD. The age-adjusted incidence per 1000 person-years increased with the number of SDH for both fatal incident CHD (0 SDH, 1.30; 1 SDH, 1.44; 2 SDH, 2.05; ≥3 SDH, 2.86) and nonfatal MI (0 SDH, 3.91; 1 SDH, 4.33; ≥2 SDH, 5.44). Compared with those without SDH, crude and fully adjusted hazard ratios for fatal incident CHD among those with ≥3 SDH were 3.00 (95% CI, 2.17 to 4.15) and 1.67 (95% CI, 1.18 to 2.37), respectively; hazard ratios for nonfatal MI among those with ≥2 SDH were 1.57 (95% CI, 1.30 to 1.90) and 1.14 (95% CI, 0.93 to 1.41), respectively. A greater burden of SDH was associated with a graded increase in risk of incident CHD, with greater magnitude and independent associations for fatal incident CHD. Counting the number of SDHs may be a promising approach that could be incorporated into clinical care to identify individuals at high risk of CHD.

Adverse social determinants of health (SDOH) are associated with poor health.1 Cumulative social risk factors, such as food insecurity and social isolation, have a higher correlation with poor health outcomes than single social risk factors.2 New health policy incentives encourage implementation of social and behavioral risk screening and referral models into health care delivery.3 Understanding patient variation in facing adverse SDOH will allow policy makers and health care systems to prioritize screening for those most at risk, and develop targeted SDOH interventions. Women and men may have differing individual and cumulative social risk factors which is relevant when implementing social risk screening. The objective of this research was to examine gender differences in negative SDOH in adults as they age using nationally representative data.4

BACKGROUND: The WHO defines social determinants of health (SDoH) as “the conditions in which people are born, grow, live and age” which includes factors such as housing and food insecurity, employment, and social support and can account for 30-55% of health outcomes. Addressing unmet SDoH needs may reduce interruptions to cancer care caused by ED visits and hospitalizations (EDH). We aimed to determine feasibility of systematic patient-reported SDoH collection at a large academic cancer center and association of unmet SDoH needs with EDH.METHODS: We conducted a cross-sectional analysis of SDoH needs among new oncology patient (pt) consults from 5/15-9/21at Dana Farber Cancer Institute (DCFI). Pts completed an intake questionnaire including demographic, disease, as well as SDOH needs on a dichotomous or 5-point Likert scale, specifically health literacy (“how confident are you in filling out medical forms?”), health numeracy (“how confident are you in understanding medical statistics?”), financial distress (“how difficult is it for you, or your family, to meet monthly payments on your/your family’s bills?”) and social isolation (“do you currently live alone?”). We ran bivariate and multivariable models on the association between demographics, SDoH and EDH within 30 days of initial oncology visit using robust generalized estimating equations controlling for clustering by consult provider. RESULTS: 125,997new consults were seen from 05/15-09//21, of which 20,913 completed the intake questionnaire and were alive at 30 days of consult. Of those pts, most were female (60%), aged 40-64 (50%), White (90%), non-Hispanic (84%), primarily English-speaking (9%) and 7% had an EDH within 30 days of their 1st outpatient visit. The most reported SDOH need was limited health numeracy (26%). In bivariate analysis, factors associated with ED visits included: limited English proficiency lung or GU/GYN cancer, living &amp;gt; 25 mi.from DFCI, and limited health literacy and numeracy (all p&amp;lt;0.05). Demographics associated with hospitalizations included: White race and English as primary language (EPL) (both p&amp;lt;0.05). Multivariable analysis showed female gender (OR 1.53, p &amp;lt; 0.01), lung (OR 3.22*) and GU/GYN (OR 2.21*) (p &amp;lt; 0.05 for both) cancer, and living &amp;gt; 25 mi from DFCI (OR 2.50, p &amp;lt; 0.0001) were associated with increased likelihood of ED visit while EPL (OR 1.80, p&amp;lt;0.05) and GU/GYN (OR 1.65, p&amp;lt;0.01*) cancer were associated with increased likelihood of hospitalization.CONCLUSIONS: It is feasible to systematically screen for unmet SDoH which are associated with increased frequency of ED visits. Differences in characteristics associated with ED vs. hospitalization could indicate possible bias or suggest SDoH needs as a reason for avoidance of costly medical care. Further study will expand both the content and site of SDoH data collection, non-English languages used for data collection, and measure impact of resource matching to reduce disruptions to cancer care. *Compared to breast cancer Citation Format: Ashley Odai-Afotey, Ellana Haakenstad, Sunyi Zhang, Bridget A. Neville, Stuart Lipsitz, Nadine J. McCleary. Feasibility of systemic SDOH collection and associated resource utilization at a large academic cancer center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5939.

Social determinants of health (SDOHs) cluster together and can have deleterious impacts on health outcomes. Individually, SDOHs increase the risk of cancer mortality, but their cumulative burden is not well understood. The authors sought to determine the combined effect of SDOH on cancer mortality. Using the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, the authors studied 29,766 participants aged 45+ years and followed them 10+ years. Eight potential SDOHs were considered, and retained SDOHs that were associated with cancer mortality (P < .10) were retained to create a count (0, 1, 2, 3+). Cox proportional hazard models estimated associations between the SDOH count and cancer mortality through December 31, 2017, adjusting for confounders. Models were age-stratified (45-64 vs 65+ years). Participants were followed for a median of 10.6 years (interquartile range [IQR], 6.5, 12.7 years). Low education, low income, zip code poverty, poor public health infrastructure, lack of health insurance, and social isolation were significantly associated with cancer mortality. In adjusted models, among those <65 years, compared to no SDOHs, having 1 SDOH (adjusted hazard ratio [aHR], 1.39; 95% CI, 1.11-1.75), 2 SDOHs (aHR, 1.61; 95% CI, 1.26-2.07), and 3+ SDOHs (aHR, 2.09; 95% CI, 1.58-2.75) were associated with cancer mortality (P for trend <.0001). Among individuals 65+ years, compared to no SDOH, having 1 SDOH (aHR, 1.16; 95% CI, 1.00-1.35) and 3+ SDOHs (aHR, 1.26; 95% CI, 1.04-1.52) was associated with cancer mortality (P for trend = .032). A greater number of SDOHs were significantly associated with an increased risk of cancer mortality, which persisted after adjustment for confounders.

BACKGROUND: Socioeconomic factors can have a significant impact on a patient's health status and could be responsible for as much as 70%-80% of a patient's overall health. These factors, called the social determinants of health (SDoH), define a patient's day-to-day experiences. While the influence of such factors is well recognized, who ultimately is responsible for addressing SDoH in health care remains unclear. Physicians and other clinicians are suitably placed to assess SDoH factors that can impact clinical decision making. Understanding Medicare Advantage (MA)-contracted primary care provider (PCP) SDoH perceptions has yet to be fully explored. OBJECTIVES: To (a) understand MA-contracted PCP perceptions of SDoH and (b) investigate correlations between PCP perceptions and their CMS Part D star performances, as well as their hospital admissions and emergency room admissions. METHODS: Survey data were collected from MA-contracted PCPs serving a South Texas market during 2019. An 8-item survey consisting of short answer, ranking, and multiple-choice questions was deployed at attendance-mandatory provider meetings from August to October. Analyses were conducted to understand the providers' SDoH perceptions. PCP responses were first summarized as frequencies and percentages. Baseline descriptive characteristics of the providers were compared by Medicare star ratings using chi-square tests (for categorical variables) and t-tests (for continuous variables). Group differences in physician beliefs on how SDoH affects patients' overall health (question 1), as well as provider beliefs regarding how SDoH affects patients' medication adherence practices (question 2), were assessed using chi-square and t-tests. Associations of provider SDoH perceptions with hospital admissions and emergency room admissions were also assessed. A Fischer's chi-square test was used to examine associations between how PCPs answered the question regarding lack of consistent transportation (question 3) and emergency room admissions. The relationships between PCP perceptions of whose job it is to address SDoH (question 7) and hospital admissions were also evaluated. RESULTS: The response rate for returned surveys was 89%. Analysis revealed that the top 3 barriers were financial insecurity (24.87%), low health literacy (18.65%), and social isolation (15.03%). However, about 36% of PCPs felt they should be the primary addressor of SDoH. There was a significant association between years of practice and CMS Part D star ratings (P = 0.005). A significant association between responses in belief towards patients' overall health and CMS Part D star ratings was examined (P = 0.047). There was a statistically significant difference in mean hospital admissions with PCP perception of who should address SDOH (P = 0.03). Emergency room admissions was significantly associated with perceptions regarding lack of consistent transportation (P = 0.04). No differences with star ratings were observed. CONCLUSIONS: Previous literature recognize safety and food insecurity as key SDoH barriers. However, they were not among the top SDoH barriers in our survey. Future research should examine patient perceptions of SDoH in this population to identify ways providers can better serve their patients. DISCLOSURES: Funding for this study was provided by CareAllies, a Cigna business. Statistical analysis was completed in partnership with the University of Houston. Payne, Esse, Qian, Serna, Villarreal, and Becho-Dominguez are employees of CareAllies. Mohan and Abughosh are employed by the University of Houston College of Pharmacy. Abughosh reports grants from Valeant and Regeneron/Sanofi, unrelated to this work. Vadhariya has nothing to disclose. This research was presented virtually at the AMCP Pharmacist Virtual Learning Days event, April 2020, as well as the American College of Clinical Pharmacy Virtual Poster Symposium, May 26-27, 2020.

Introduction: Although social determinants of health (SDOH) were shown to be individually associated with incident stroke, few studies explored the effect of multiple SDOH within the same individuals on incident stroke. To address this limitation, we operationalized SDOH as a count and examined its association with incident stroke. This approach is practically appealing in clinical settings - it is easy to calculate and interpret. Hypothesis: We hypothesized that a greater number of SDOH within individuals would be significantly associated with a higher risk of incident stroke. Methods: REGARDS is a prospective cohort of 30,239 black and white adults aged &gt; 45 years recruited in 2003-2007. Sociodemographic, medical history, and stroke risk factors were collected at baseline. To define exposure, we followed the Healthy People 2020 framework, and initially used 10 SDOH to represent 5 domains: education, health, social, economic and neighborhood factors. We selected SDOH that were significantly associated with incident stroke: race, education, income, zip code poverty, health insurance, social isolation, and residence in states with poor public health infrastructure. We then created a primary exposure - a count of SDOH for each individual. The outcome was expert adjudicated incident strokes. Cox proportional hazards models examined associations between a count of SDOH (0, 1, 2, 3+) and incident stroke, adjusting for confounders. Results: This study included 27,813 individuals free from stroke at baseline. We observed 1470 incident strokes during the follow-up. A significant interaction between SDOH count and age resulted in stratification at 75 years. In fully adjusted Cox models, among individuals &lt;75 years, risk of stroke amplified with each increase in number of SDOH. This pattern was not observed among those 75+ years (Table 1). Conclusions: Individuals &lt;75 years with a greater number of SDOH had higher risk of stroke. Targeting individuals with multiple SDOH, especially those &lt;75 years, may help reduce risk of stroke.

Social determinants of health (SDOH) have been previously associated with incident stroke. Although SDOH often cluster within individuals, few studies have examined associations between incident stroke and multiple SDOH within the same individual. The objective was to determine the individual and cumulative effects of SDOH on incident stroke. This study included 27 813 participants from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study, a national, representative, prospective cohort of black and white adults aged ≥45 years. SDOH was the primary exposure. The main outcome was expert adjudicated incident stroke. Cox proportional hazards models examined associations between incident stroke and SDOH, individually and as a count of SDOH, adjusting for potential confounders. The mean age was 64.7 years (SD 9.4) at baseline; 55.4% were women and 40.4% were blacks. Over a median follow-up of 9.5 years (IQR, 6.0-11.5), we observed 1470 incident stroke events. Of 10 candidate SDOH, 7 were associated with stroke (P<0.10): race, education, income, zip code poverty, health insurance, social isolation, and residence in one of the 10 lowest ranked states for public health infrastructure. A significant age interaction resulted in stratification at 75 years. In fully adjusted models, among individuals <75 years, risk of stroke rose as the number of SDOH increased (hazard ratio for one SDOH, 1.26 [95% CI, 1.02-1.55]; 2 SDOH hazard ratio, 1.38 [95% CI, 1.12-1.71]; and ≥3 SDOH hazard ratio, 1.51 [95% CI, 1.21-1.89]) compared with those without any SDOH. Among those ≥75 years, none of the observed effects reached statistical significance. Incremental increases in the number of SDOH were independently associated with higher incident stroke risk in adults aged <75 years, with no statistically significant effects observed in individuals ≥75 years. Targeting individuals with multiple SDOH may help reduce risk of stroke among vulnerable populations.

On 11 March 2020, the World Health Organization announced that COVID-19 was characterised as a pandemic—a global first for coronavirus.1 Coronaviruses are a large family of viruses that cause illness such as the common cold to more severe diseases such as Severe Acute Respiratory Syndrome.2 A novel coronavirus is typically a new strain of the infectious disease that has not been previously identified in humans.2 COVID-19 is the most recent version of a novel coronavirus.2 COVID-19 has received significant public and government attention over the past weeks after it was first detected in the Wuhan province of China in December 2019, with subsequent epidemics in China, Italy, Republic of Korea and Iran.1 As of 12 March 2020, 125 000 cases were reported from 118 countries and territories globally, with predictions this will continue to rise rapidly.3 This has led to an array of public health measures being advocated by the WHO, including four critical areas for action—(a) prepare and be ready; (b) detect, protect and treat; (c) reduce transmission; and (d) innovate and learn.3 This has been complemented, to varying degrees, through concurrent action by local, state and national governments worldwide. There can be a tendency in the health promotion profession to think of infectious diseases from a biomedical viewpoint. As such, the prevention and treatment of infectious diseases is sometimes perceived to be the responsibility of the clinical realm. Yet, the reality is that both nonclinical and clinical public health responses are required—and sometimes we need to relax professional boundaries to work collaboratively for the health and wellbeing of our communities. We need to work in partnership with health surveillance teams, epidemiologists, environmental health scientists, public health physicians, infectious disease physicians, general practitioners, nurses, allied health professions, health policy-makers, health planners, health geographers and many others, to reduce the risks associated with pandemics. We also need to work across sectors to achieve the best possible outcomes. The health promotion profession plays a vital role in pandemics, and this has been abundantly evident in the responses to COVID-19. Messaging about health and hygiene, particularly hand-washing, is one example of the role that health promotion has played—ultimately drawing on our expertise in delivering health education, and implementing health-related mass media and social marketing campaigns. Over the last two decades, information technology and social media have transformed the way we can reach people during pandemics. Indeed, social media has catapulted the ability to reach large populations, while also simultaneously targeting vulnerable and at-risk populations, to deliver health messages, such as those associated with hand-washing. Over the past few weeks, there has been a steady flow of memes urging people to wash their hands, often with thoughtful use of graphics alongside a successful use of humour. JS's personal favourite, was an online post from Round Rock Texas that read: 'Texas Coronavirus Protection—wash your hands like you just got done slicing jalapenos for a batch of nachos and you need to take your contacts out (that's like 20 seconds scrubbing, y'all)'. It delivers an essential public health message in a factual, yet contextually relevant and humorous way. However, social media can also have its pitfalls. Misinformation and fake news are rampant. This has the potential to stifle health promotion efforts in times of need, such as during the current COVID-19 pandemic. Therefore, it is important to know who is saying what, why, and with what level of authority. As mentioned above, we also need to be mindful of cross-sectoral communication efforts during pandemics. As an example, JS received 12 emails from his children's schools and 14 from his current workplaces about COVID-19—a total of 26 emails from educational institutions in both Australia and the United States. Email topics ranged from: hygiene issues such as hand washing and sanitiser use; social distancing, self-isolation and self-quarantining strategies such as cancellation of school activities and fundraisers; proposed adoption of online learning options, and flexibility about attendance at school/work, including possible closures; travel restrictions imposed by schools and universities associated with concerts, plays, public events/seminars and conferences; guidance to limit travel on public transport; and advice about when to seek help and access local health services if myself or my family members experience symptoms associated with COVID-19. This bombardment of communication, albeit extremely useful, emphasises the importance of coordination in key messaging between health, education and various other sectors, when planning and implementing effective pandemic responses. In health promotion, we need new strategies to communicate important health messages in a concise and meaningful way that makes it easy and accessible for citizens to understand, navigate and take action. We also need to be careful how we convey content through electronic communication channels and consider an appropriate level of frequency of such communication to achieve optimal impact. Without doing so, there is potential to reinforce community ambivalence at one end of the spectrum and create panic at the other. The recent toilet paper saga in Australia, whereby stocks of toilet paper were rapidly depleted from grocery stores in response to the perceived likelihood of home quarantining measures, is one such example (albeit somewhat humorous and embarrassing). Panic buying like this reinforces the powerful ramifications of communication gone wrong. Health literacy research that embraces new and emerging technologies will be particularly important to guide online health promotion efforts of this nature in the future. To emphasise the importance of getting health communication right, the Australian Medical Association were particularly critical of the mixed-messaging of public health directives between the Australian, State and Territory Governments concerning COVID-19.4 There was concern about how this mixed-messaging was being interpreted by the Australian public, but also how it was likely to impact health professionals and the use of Australia's hospitals and health care system more broadly. The Australian Government has since committed a $2.4 billion health package to protect all Australians from COVID-19, including vulnerable groups such as the elderly, those with chronic conditions and Indigenous communities.5 The US Government pledged $50 billion on the same day. Importantly, the Australian health package includes $30 million for implementing an information campaign to provide people with practical advice on how they can play their part in containing the virus and staying healthy.5 We trust health promotion professionals with expertise in health literacy, health communication, and social marketing will be consulted throughout its development. We also trust that health promoters will be involved in the multi-million dollar primary care and research responses outlined by the Australian Prime Minister. At this juncture, it is worth reflecting on who is most vulnerable in pandemics. While COVID-19 has the potential to impact everyone in society, these impacts will be felt differentially. That is, the way we prepare, protect, treat, reduce transmission and innovate, needs to be viewed from a health equity lens. It is essential to recognise that pandemics—and the respective Government and corporate decisions that emanate—both influence and are influenced by social, economic and political determinants of health. As the WHO Director-General has recently stated—'all countries must strike a fine balance between protecting health, preventing economic and social disruption, and respecting human rights'.3 However, knowing what this 'fine balance' constitutes can be difficult. As such, it helps to reflect on what we know. While we do not know much about COVID-19, we do now how pandemics can impact vulnerable populations. We know that many developing countries do not have the surveillance systems, health resources and health infrastructure to respond in a manner that can slow the harms of COVID-19 in the way we would like.6-8 We know that there are vulnerable populations, such as: the elderly, those with disabilities, people in prison, Aboriginal and Torres Strait Islander communities, people with chronic conditions, and people from Culturally and Linguistically Diverse (CALD) backgrounds, that will be impacted disproportinately by COVID-19, particularly if assertive health promotion action is absent.9-13 We know that people from low socio-economic backgrounds, those who work in casual employment, and many racial and ethnic minorities, are unlikely to have the necessary financial resources to make self-distancing and self-isolation a viable option within the context of their daily livelihoods.12-14 We know that access to health services in some countries, including basic primary health care, is contingent upon insurance and user-pays systems that already make them inaccessible to the people most at-risk.15, 16 We know that the elderly and people with disabilities rely on public transport to access essential services, including food shopping and health services that are required during pandemics.17, 18 We know that vulnerable populations may not have the necessary language and literacy skills to understand and appropriately respond to pandemic messaging.19 We know that mental health concerns among the most vulnerable within our communities will be exacerbated by expectations to self-isolate if not approached sensitively.20, 21 We know that governments have trouble implementing strategies focused on reducing health inequities through action on social determinants of health.22 We know all these things, but what do we do about them? Most of the evidence-based discussion presented above demonstrates the power of privilege in a pandemic. It indicates that those most vulnerable will be the hardest hit. The health promotion community must ensure that considerations of health equity and social justice principles remain at the forefront of pandemic responses.12, 14 This will not be easy at a time when neoliberal forces pitch population health against national economic stability. While hand-washing is a significant health promotion intervention, it can also act as a useful façade for advancing actions that enhance equitable social and economic outcomes for those most vulnerable during pandemics. The WHO has encouraged us to think innovatively.1, 3 The health promotion profession can lead this charge and advocate for a national public health social media campaign and other pragmatic measures that reach people most in need. This will help support them to get accurate and timely information to prepare and reduce the risk to themselves, their families, friends and their community.