Disaster Medicine and Public Health Preparedness

The International Health Regulations (IHR 2005) are binding upon Member States of the World Health Organization (WHO), requiring them to build and maintain capacities across critical domains to prevent, detect and respond to public health threats. In an analysis of 15 IHR core capacity scores reported by States Parties in the Western Pacific Region from 2021-2023, the average regional scores increased from 68% in 2021 to 72% in 2022, then declined to 66% in 2023. Seven States Parties maintained consistently strong scores ([≥]85%), whereas nine exhibited fluctuations of at least 10 percentage points. Categorization of States Parties into three groups based on geographic and economic characteristics highlighted that core capacities such as financing, food safety and zoonotic disease control were areas requiring additional support, particularly among Pacific island States Parties. Low and middle income States Parties also reported notable gaps in financing and infection prevention and control. These findings underscore the importance of strategically establishing or designating a National IHR Authority (NIA), which is a key amendment to the IHR implemented in 2024. Beyond technical improvements, a strong NIA can drive multisectoral collaboration, help mobilize resources and streamline decision making. Additionally, establishing a regional forum for health emergencies could enhance political commitment and promote joint actions, strengthening collective resilience. In turn, this fosters more resilient preparedness and response measures that address the diversity epidemiological, economic and geographical contexts in the region, thereby strengthening overall IHR implementation.

ObjectivesTo describe and understand the funding, personnel, expertise, community resources, and issues in rural settings by local public health departments post COVID-19. MethodsRural county health departments in ten states were sent a survey via web link in the Spring of 2021. 552 responses were collected with a 63% completion rate for all counties surveyed. ResultsMost counties utilized public health nurses, administrators, or community health professionals. Of these, 25% had formal education in public health and 10% had public health experience. 65% of respondents disagreed with having adequate funding, staff, and resources. 83% of counties reported working with nonprofits and 43% utilized volunteers. The top two issues in rural public health identified were mental health and substance use. ConclusionsRural county public health departments do not have the support needed to sustain or advance public health in their specific population. Policy implicationsThis report gives insight into the needs of rural health in 2021 that can be used to guide policy and funding to support rural healths specific needs.

BackgroundDuring the ongoing COVID-19 pandemic, the immediate threat of illness and mortality is not the only concern. In the United States, COVID-19 is not only causing physical suffering to patients, but also great levels of adverse sentiment (e.g., fear, panic, anxiety) among the public. Such secondary threats can be anticipated and explained through sentiment analysis of social media, such as Twitter. MethodsWe obtained a dataset of geotagged tweets on the topic of COVID-19 in the contiguous United States during the period of 11/1/2019 - 9/15/2020. We classified each tweet into "adverse" and "non-adverse" using the NRC Emotion Lexicon and tallied up the counts for each category per county per day. We utilized the space-time scan statistic to find clusters and a three-stage regression approach to identify socioeconomic and demographic correlates of adverse sentiment. ResultsWe identified substantial spatiotemporal variation in adverse sentiment in our study area/period. After an initial period of low-level adverse sentiment (11/1/2019 - 1/15/2020), we observed a steep increase and subsequent fluctuation at a higher level (1/16/2020 - 9/15/2020). The number of daily tweets was low initially (11/1/2019 - 1/22/2020), followed by spikes and subsequent decreases until the end of the study period. The space-time scan statistic identified 12 clusters of adverse sentiment of varying size, location, and strength. Clusters were generally active during the time period of late March to May/June 2020. Increased adverse sentiment was associated with decreased racial/ethnic heterogeneity, decreased rurality, higher vulnerability in terms of minority status and language, and housing type and transportation. ConclusionsWe utilized a dataset of geotagged tweets to identify the spatiotemporal patterns and the spatial correlates of adverse population sentiment during the first two waves of the COVID-19 pandemic in the United States. The characteristics of areas with high adverse sentiment may be relevant for communication of containment measures. The combination of spatial clustering and regression can be beneficial for understanding of the ramifications of COVID-19, as well as disease outbreaks in general.

The frequency and severity of floods has increased in different regions of the world due to climate change. Although the impact of floods on human health has been extensively studied, the increase in the segments of the population that are likely to be impacted by floods in the future makes it necessary to examine how adaptation measures impact the mental health of individuals affected by these natural disasters. The goal of this scoping review is to document the existing studies on flood adaptation measures and their impact on the mental health of affected populations, in order to identify the best preventive strategies as well as limitations that deserve further exploration. This study employed the methodology of the PRISMA-ScR extension for scoping reviews to systematically search the databases Medline and Web of Science to identify studies that examined the impact of adaptation measures on the mental health of flood victims. The database queries resulted in a total of 857 records from both databases. Following two rounds of screening, 9 studies were included for full-text analysis. Most of the analyzed studies sought to identify the factors that drive resilience in flood victims, particularly in the context of social capital (6 studies), whereas the remaining studies analyzed the impact of external interventions on the mental health of flood victims, either from preventive or post-disaster measures (3 studies). There is a very limited number of studies that analyze the impact of adaptation measures on the mental health of populations and individuals affected by floods, which complicates the generalizability of their findings. There is a need for public health policies and guidelines for the development of flood adaptation measures that adequately consider a social component that can be used to support the mental health of flood victims.

BackgroundThe accelerating pace of urbanization worldwide has highlighted the improvement of disaster response in cities as a global priority. Yet, there remains a poor understanding of the emergency response to mass casualty incidents (MCI) in these environments. This study aimed to develop a conceptual framework for cities responses and potential policy levers. MethodsWe conducted a scoping review followed by in-depth interviews (IDIs), focus group discussions (FGDs), and a modified Delphi process to develop the framework for Cities Assessment of Mass Casualty Emergency Response and Action (CAMERA). ResultsCAMERA framework consists of six essential components of urban emergency response systems: 1) communication, 2) safety and security, 3) human resources, 4) policy and plans, 5) command control and coordination, and 6) care delivery. IDIs and FGDs also provided insight on assessment methodologies for evaluating response capacity. Using these components, we then developed a framework consisting of a diagnostic and management approach that city leadership can undertake in MCI management to ensure effective functioning at various levels of incident response. ConclusionThe CAMERA framework offers novel and simplified guidance to policymakers and other stakeholders in their attempt to improve MCI response systems across cities globally.

In recent years, as the population of foreign residents in Japan has rapidly increased, societal discussion about their medical coverage has intensified. Given the concerning health status and medical service utilization of foreign residents in Japan, and the critical role of medical facilities with multilingual support (MFMS)--including key facilities designated for foreign patients--this study is the first to assess the accessibility of such facilities for foreign residents at the municipal level using publicly available geographic information systems. The evaluation focused on two departments closely tied to daily life: surgery and internal medicine. The results reveal that accessibility indices for medical facilities offering multilingual services in surgery are low across most areas of Hokkaido and the Tohoku region, as well as northern Niigata and Shizuoka prefectures, central Miyazaki prefecture, and major islands with foreign resident populations. Similar patterns were observed for internal medicine. This suggests that local foreign residents may face significant challenges in accessing specialized, linguistically consistent medical services in surgery or internal medicine.

BackgroundDisaster risk reduction involves several sectors, legal, social, structural, economic, technological, educational, environmental health, with improved preparedness will be able to reduce the impact of disasters. Capacity that continues to be added with knowledge about rapid disaster response to disasters can make resilience dealing with disasters and preventing the emergence of disaster risks. PurposeConduct a literature review on articles that examine the importance of disaster education in the initial handling of emergencies in the hospital area DesignLiterature review MethodUsing databases with electronic search on ProQuest, SAGE, and Science Direct published in 2017-2021 Results100 articles were used in the review. These articles discuss the importance of disaster education in the initial handling of emergencies in the hospital area. The 15 articles reviewed are original research. ConclusionLocal communities are well placed to play a central role in hazard identification, development of preparedness plans, detection and response to emergencies, and implementation of recovery efforts. Community leaders and local health workers (e.g. family doctors, nurses, midwives, pharmacists, community health workers) can build public trust, disseminate information, and identify people at risk

BackgroundFunds allocated to disaster preparedness and response in the U.S. have grown rapidly in recent decades. This analysis examines the ratio of cost per outcome of public health events classified as disasters and those not classified as disasters, e.g., smoking-related morbidity and mortality. MethodsMortality is taken as an outcome metric; the validity of this measure is assessed by examination of ratios of tangible and intangible costs of disaster and non-disaster outcomes to mortality from two conditions, using available data. The relative allocation of CDC funding to disaster and non-disaster events is assumed to conservatively represent the U.S. overall relative funding allocation. ResultsNon-disaster deaths are 2,500 more likely than disaster deaths; we allocate 370 times more funding per disaster death than we do per non-disaster death. ConclusionThe rationality of this implicit decision be reconsidered.

BackgroundEmerging in January 2020, the SARS-CoV-2 pandemic quickly exposed the limitations of traditional contact tracing and overwhelmed the contact tracing efforts of US health departments. In response, Kaiser Permanente partnered with the Public Health Institute to launch the California Contact Tracing Support Initiative. This innovative, clinically integrated program aimed to link Kaiser Permanente members diagnosed at their facilities directly with contact tracing and supportive clinical care via their network. This approach promised to address key logistical and behavioral challenges hampering traditional public health agencies. This paper evaluates the programs implementation in two California counties. MethodsWe conducted a retrospective, mixed-methods process evaluation of program activities from August 2020 to June 2021, including contact tracing implementation in Fresno and San Bernardino Counties. Our methods included scoping discussions with program stakeholders, development of an epidemiological timeline and program impact model, and document review. We also conducted semi-structured interviews with program stakeholders and staff. Interviews were conducted and audio-recorded via Zoom, transcribed, and analyzed in NVivo using inductive and deductive coding with a Framework Approach. ResultsWe reviewed 474 program documents and interviewed 47 participants. Study findings highlighted difficulties in adapting program scope due to competing partner visions of program mission and collaboration. Unforeseen data demands and complex external data sharing with public health systems further complicated and delayed program implementation. ConclusionEvaluation of this contact tracing program offers key insights into public health interventions during emergencies. While the California Contact Tracing Support Initiatives integrated design showed promise, challenges arose from data systems, inter-organizational dynamics, and planning. Findings emphasize the need for clear operational steps, real-time data monitoring, defined roles, and formalized public-private partnerships in preparedness planning. These are key lessons for future complex public health interventions, especially regarding adapting programs versus maintaining fidelity amidst evolving contexts.

BackgroundRisk communication is critical in shaping public response during infectious disease outbreaks. This study quantitatively examined whether Nigerian media coverage during the 2024 cholera outbreak reflected a proactive or reactive risk communication pattern. MethodsA Python-based systematic content analysis was conducted on 352 unique news articles published by major Nigerian media sources in 2024. K-Means was used to cluster and quantify thematic patterns, TextBlob for sentiment polarity, and time-series analysis to determine the features of media engagement. ResultsThe analysis identified a dominant reactive, crisis-driven communication pattern with media coverage surging by over 400% in June, matching the peak of reported cholera cases. Thematic analysis portrayed a severe reporting imbalance focused on Outbreak Reports and Mortality (41.5% of articles), while structural and preventive themes such as WASH and Health Education received marginal attention (less than 25% of coverage). Furthermore, communication was overwhelmingly neutral (76.4%) in sentiment, potentially limiting the perceived urgency required for public action. ConclusionsMedia reporting on the 2024 cholera outbreak in Nigeria was reactive and focused disproportionately on threat rather than solutions. These findings support the need for a strategic dual-focus communication model that shifts from crisis-driven coverage to sustained, year-round preventive messaging centered on WASH accountability and community resilience.

BackgroundEarly detection of disease outbreak is among the most critical role of the sub-national authorities as mandated by the health decentralization policy. Given the continuous growth of Internet penetration and dependencies of the society on the digital ecosystem, it is essential to investigate the potential innovations to improve the existing surveillance system using digital epidemiology. Several studies, including in Indonesia, have assessed the roles of Google Trends (GT) to improve dengue surveillance systems. However, they were mostly located in specific areas or national level only. No reports are available to compare the performance of GT for early detection of dengue outbreak among high burdened provinces. AimsThis study aimed to examine the correlation between GT data on dengue-related query terms with the official dengue surveillance reports in Jakarta and Yogyakarta Province. MethodsRelative Search Volume of GT data for dengue were collected from the area of Jakarta and Yogyakarta between 2012 to 2016. Those data were compared with the official dengue reports from the Indonesian Ministry of Health using Pearsons correlation and Time-lag correlation, performed with Stata version 13. ResultsGT data are positively correlated with the routine surveillance report in Jakarta (r = 0.723, p-value= 0.000) and Yogyakarta Province (r = 0.715, p-value= 0.000). In Jakarta, search term of DBD demonstrated a very strong correlation for lag-1 (r =0.828, p-value= 0.000). This finding indicates that GT data could possibly detect the dengue outbreak a month earlier, especially in Jakarta. Hence, GT data can be used to monitor disease dynamics and improve the public awareness of a potential outbreak in near-real-time. ConclusionGT data were positively correlated with the routine surveillance report in Jakarta and Yogyakarta Province. Early warning system utilizing GT data is potentially more accurate in Jakarta than in Yogyakarta. We assume that it is related with the larger population as well as the Internet use activities that drives the higher volume of Google search on dengue in Jakarta compared to Yogyakarta. Further studies involving other digital data sources, for example, Twitter, online news, and administrative data from the national health insurance are essential to strengthen the current surveillance system with the new digital epidemiology approach.

The United States (US) public health interventions were rigorous and rapid, yet failed to arrest the spread of the Coronavirus Disease 2019 (COVID-19) pandemic as infections spread throughout the US. Many factors have contributed to the spread of COVID-19, and the success of public health interventions depends on the level of community adherence to preventative measures. Public health professionals must also understand regional demographic variation in health disparities and determinants to target interventions more effectively. In this study, a systematic evaluation of three significant interventions employed in the US, and their effectiveness in slowing the early spread of COVID-19 was conducted. Next, community-level compliance with a state-level stay at home orders was assessed to determine COVID-19 spread behavior. Finally, health disparities that may have contributed to the disproportionate acceleration of early COVID-19 spread between certain counties were characterized. The contribution of these factors for the disproportionate spread of the disease was analyzed using both univariate and multivariate statistical analyses. Results of this investigation show that delayed implementation of public health interventions, a low level of compliance with the stay at home orders, in conjunction with health disparities, significantly contributed to the early spread of the COVID-19 pandemic.

Citizen science (CS) promotes the inclusion of diverse stakeholders and offers a scientific in-depth understanding of community engagement to build trust, increase knowledge, and facilitate policymaking. Study aimed to understand concepts, practices, approaches, and sustainability issues of CS among citizens in five South and Southeast Asian countries. Qualitative study from October 2022 to March 2023 was carried in Nepal, Bangladesh, India, Philippines, and Indonesia. In each country, four focus group discussions were conducted with an overall total of 130 participants. Content analysis and coding were carried out for narrative responses of participants. Across all countries, the participants collectively comprehended the term "research" while referring to CS. Participants also related social responsibility and capacity building of citizens to CS. In terms of their contributions to pandemic response, participants stated compliance with government guidelines, helping to create awareness, and providing necessary support and assistance. Participants value personal achievement, satisfaction, happiness, and a chance to build social capital while participating in CS activities. Participants were ready to actively contribute to CS activities and share their opinions with stakeholders such as policymakers and researchers but felt that a lack of personal confidence, ineffective communication, and insufficient translation of their opinions to actions could deter them. Creation of an organization or network, provision of budget for activities, incentives to participants, and transportation assistance were considered as resources needed for the sustainability of CS. Participants expressed their readiness for CS activities considering personal and social factors, while systemic support is needed for sustained participation.

ObjectiveIndoor mass gatherings in counties with high COVID-19 incidence have been linked to infections. We examined if outdoor mass gatherings in counties with low COVID-19 incidence are also followed by infections. MethodsWe retrospectively examined COVID-19 incidence in 20 counties that held mass gathering rallies (19 outdoor and 1 indoor) in the United States in August-September 2020. They were compared to the rest of the United States counties. We utilized a 7-day moving average and compared the change on the gathering date and 15 days later, based on the 95% confidence interval. For control counties we used the median of the gathering dates. SettingThe United States Population8.4 million in the counties holding mass gatherings, and 324 Million in the rest of the counties in the United States. Main Outcome MeasureChange in COVID-19 incidence rate per 100,000 capita during the two weeks following mass gatherings. ResultsIn the two weeks following the gatherings, the COVID-19 incidence increased significantly in 14 of 20 counties. The county with the highest incidence increase (3.8-fold) had the 2nd lowest incidence before the gathering. The county with the highest decrease (0.4-fold) had the 3rd highest incidence before the gathering. At the gathering date, the average incidence of counties with gatherings was lower than the rest of the United States, and after the gathering, it increased 1.5-fold, while the rest of the United States increased 1.02-fold. ConclusionThese results suggest that even outdoor gatherings in areas with low COVID-19 incidence are followed by increased infections, and that further precautions should be taken at such gatherings. What is already known on the topicMass gatherings have been linked to COVID-19 infections, but it is less clear how much it happens outdoors, and in areas with low incidence. What this study addsCOVID-19 infections increased significantly in 14 of 20 counties that held mass gathering rallies in the United States, 19 of which were outdoors. The county with the highest incidence increase (3.8-fold) was outdoors and had a low incidence before the gathering. The average incidence of all 20 counties with gatherings was lower at the gathering day compared with the rest of the United State, and it increased 1.5-fold following the gatherings. Our findings suggest a need for precautions in mass gatherings, even when outdoors and in areas with a low incidence of COVID-19.

IntroductionThe Philippines is plagued with natural disasters and resulting precipitating factors for disease outbreaks. The developing country has a strong disease surveillance program during and post-disaster phases; however, latent disease contracted during these emergency situations emerges once the Filipinos return to their homes. Coined the social media capital of the world, the Philippines provides an opportunity to evaluate the potential of social media use in disease surveillance during the post-recovery period. By developing and defining a non-traditional method for enhancing detection of infectious diseases post-natural disaster recovery in the Philippines, this research aims to increase the resilience of affected developing countries through advanced passive disease surveillance with minimal cost and high impact. MethodsWe collected 50 million geo-tagged tweets, weekly case counts for six diseases, and all natural disasters from the Philippines between 2012 and 2013. We compared the predictive capability of various disease lexicon-based time series models (e.g., Twitters BreakoutDetection, Autoregressive Integrated Moving Average with Explanatory Variable [ARIMAX], Multilinear regression, and Logistic regression) and document embeddings (Gensims Doc2Vec). ResultsThe analyses show that the use of only tweets to predict disease outbreaks in the Philippines has varying results depending on which technique is applied, the disease type, and location. Overall, the most consistent predictive results were from the ARIMAX model which showed the significance in tweet value for prediction and a role of disaster in specific instances. DiscussionOverall, the use of disease/sick lexicon-filtered tweets as a predictor of disease in the Philippines appears promising. Due to the consistent and large increase use of Twitter within the country, it would be informative to repeat analysis on more recent years to confirm the top method for prediction. In addition, we suggest that a combination disease-specific model would produce the best results. The model would be one where the case counts of a disease are updated periodically along with the continuous monitoring of lexicon-based tweets plus or minus the time from disaster.

BackgroundThere have been outbreaks of SARS-CoV-2 in long term care facilities and recent reports of disproportionate death rates among the vulnerable population. The goal of this study was to better understand the impact of SARS-CoV-2 infection on the non-institutionalized disabled population in the United States using data from the most affected states as of April 9th, 2020. MethodsThis was an ecological study of county-level factors associated with the infection and mortality rate of SARS-CoV-2 in the non-institutionalized disabled population. We analyzed data from 369 counties from the most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts) in the United States using data available by April 9th, 2020. The variables include changes in mobility reported by Google, race/ethnicity, median income, education level, health insurance, and disability information from the United States Census Bureau. Bivariate regression analysis adjusted for state and median income was used to analyze the association between death rate and infection rate. ResultsThe independent sample t-test of two groups (group 1: Death rate[&ge;]3.4% [median] and group 2: Death rate < 3.4%) indicates that counties with a higher total population, a lower percentage of Black males and females, higher median income, higher education, and lower percentage of disabled population have a lower rate (< 3.4%) of SARS-CoV-2 related mortality (all p-values<4.3E-02). The results of the bivariate regression when controlled for median income and state show counties with a higher White disabled population (est: 0.19, 95% CI: 0.01-0.37; p-value:3.7E-02), and higher population with independent living difficulty (est: 0.15, 95% CI: -0.01-0.30; p-value: 6.0E-02) have a higher rate of SARS-CoV-2 related mortality. Also, the regression analysis indicates that counties with higher White disabled population (est: - 0.22, 95% CI: -0.43-(-0.02); p-value: 3.3E-02), higher population with hearing disability (est: -0.26, 95% CI: - 0.42- (-0.11); p-value:1.2E-03), and higher population with disability in the 18-34 years age group (est: -0.25, 95% CI: -0.41-(-0.09); p-value:2.4E-03) show a lower rate of SARS-CoV-2 infection. ConclusionOur results indicate that while counties with a higher percentage of non-institutionalized disabled population, especially White disabled population, show a lower infection rate, they have a higher rate of SARS-CoV-2 related mortality.

BackgroundWisconsin (WI) held a primary election in the midst of the COVID-19 pandemic. Live voting at polls was allowed despite concern over increasing the spread of COVID-19. In addition to 1.1 million absentee ballots cast, 453,222 persons voted live. The purpose of our study was to determine if an increase in COVID-19 activity was associated with the election. MethodsUsing the voting age population for the United States (US), WI, and its 3 largest counties, and daily new COVID-19 case reports from various COVID-19 web-based dashboards, daily new case rates were calculated. With election day April 7, the incubation period included April 12-21. The new case activity in the rest of the US was compared with the Wisconsin activity during the incubation period. ResultsWI daily new case rates were lower than those of the rest of the US for the 10-day period before the election and remained lower during the post exposure incubation period. The ratio of Wisconsin new case rates to US new case rates was 0.34 WI: 1 US for the 10 days leading up to the election and declined to 0.28 WI: 1 US for the 10-day post-incubation period after the election. Similar analysis for Milwaukee county showed a pre-election ratio of 1.02 Milwaukee: 1 US and after the election the ratio was 0.63 Milwaukee: 1 US. Dane county had a pre-election ratio of 0.21 Dane: 1 US case, and it fell to 0.13 Dane: 1 US after the election. Waukesha county had a pre-election ratio of 0.27 Waukesha: 1 US case and that fell to 0.19 Waukesha: 1 US after the election. ConclusionsThere was no increase in COVID-19 new case daily rates observed for Wisconsin or its 3 largest counties following the election on April 7, 2020, as compared to the US, during the post-incubation interval period.

Introduction Louisiana ranks last in the United States of America in terms of maternal health outcomes. Previous works have highlighted the impact of some social determinants of health on the incidence of adverse birth outcomes. These works have subjectively selected specific social determinants of health from larger datasets. Here, we attempt to replicate their results with objective variable selection techniques. Methods By deriving principal components from the Agency of Healthcare Research and Quality's parish-level social determinants of health dataset, we were able to objectively find social determinants of health associations instead of the conventional subjective variable selection approach. Then, we applied Bayesian linear mixed-effects models to calculate more conservative parameter estimates about the effects of social determinants of health on adverse birth outcome incidence. Then, we used local Moran's I to identify clusters of spatially autocorrelated parishes. Finally, we combined the results of these two methods and inspected the relationship between important predictors and clusters of spatial autocorrelation. Results We identified several significant effects on the incidence of adverse birth outcomes, including populational composition and economic attainment, and several clusters of high and low incidences of adverse birth outcomes in Louisiana. There was also a concordant relationship between important predictors from our predictive models and the cluster assignments of Local Moran's I. Conclusion Our results validate previous works in the subject area and hold implications for precision development of maternal health interventions in Louisiana.

BackgroundThe COVID-19 pandemic continues to demonstrate the risks and profound health impacts that result from infectious disease emergencies. Emergency preparedness has been defined as the knowledge, capacity and organizational systems that governments, response and recovery organizations, communities and individuals develop to anticipate, respond to, or recover from emergencies. This scoping review explored recent literature on priority areas and indicators for public health emergency preparedness (PHEP) with a focus on infectious disease emergencies. MethodsUsing scoping review methodology, a comprehensive search was conducted for indexed and grey literature with a focus on records published from 2017 and 2020 onward, respectively. Records were included if they: a) described PHEP, b) focused on an infectious emergency, and c) were published in an Organization for Economic Co-operation and Development country. An evidence-based all-hazards Resilience Framework for PHEP consisting of 11 elements was used as a reference point to identify additional areas of preparedness that have emerged in recent publications. The findings were summarized thematically. ResultsThe included publications largely aligned with the all-hazards Resilience Framework for PHEP. In particular, the elements related to collaborative networks, community engagement, risk analysis and communication were frequently observed across the publications included in this review. Emergent themes were identified that expand on the Resilience Framework for PHEP. These were related to mitigating inequities, public health capacities (vaccination, laboratory system capacity, infection prevention and control capacity, financial investment in infrastructure, public health legislation, phases of preparedness), scientific capacities (research and evidence-informed decision making, climate and environmental health), and considerations for health system capacity. ConclusionsThe themes from this review contribute to the evolving understanding of critical public health preparedness actions; however, there was a paucity of recent evidence on PHEP indicators. The themes can expand on the 11 elements outlined in the Resilience Framework for PHEP, specifically relevant to infectious disease emergencies and risks. Further research will be important to validate these findings, and expand understanding of how refinements to PHEP frameworks and indicators can support public health practice.

ObjectivesExamine the relationship between various demographic characteristics and meso variables measuring social vulnerability, religiosity, political partisanship, and the built environment on the probability of testing positive for COVID-19 and dying after testing positive for the virus. MethodsThe individual-level variables are from the data collected by the Louisiana Department of Health and Hospitals. Meso variables were sourced from various platforms. The data are analyzed using a spatial bivariate probit model with copulas in a generalized additive model framework. ResultsThe main results suggest a strong and positive association between individual-level covariates and testing positive for the virus and dying from it. The effects of social vulnerability, religiosity, political partisanship, and the built environment varied non-linearly; their effects were within a given critical range. ConclusionsTo mitigate the impact of future pandemics like COVID-19, public health policies should focus on addressing existing health disparities, fostering meaningful engagement with community institutions and diverse leaders, and applying proven and scientific public health considerations, while minimizing the influence of political ideologies and culture.