Health inequities in influenza transmission and surveillance

Heterogeneity of influenza infection at precise scale in Yinchuan, Northwest China, 2012-2022: evidence from Joinpoint regression and spatiotemporal analysis

Identifying high-risk regions and turning points of influenza with a precise spatiotemporal scale may provide effective prevention strategies. In this study, epidemiological characteristics and spatiotemporal clustering analysis at the township level were performed. A descriptive study and a Joinpoint regression analysis were used to explore the epidemiological characteristics and the time trend of influenza. Spatiotemporal autocorrelation and clustering analyses were carried out to explore the spatiotemporal distribution characteristics and aggregation. Furthermore, the hotspot regions were analyzed by spatiotemporal scan analysis. A total of 4025 influenza cases were reported in Yinchuan showing an overall increasing trend. The tendency of influenza in Yinchuan consisted of three stages: increased from 2012 to the first peak in 2019 (32.62/100,000) with a slight decrease in 2016; during 2019 and 2020, the trend was downwards; then it increased sharply again and reached another peak in 2022. The Joinpoint regression analysis found that there were three turning points from January 2012 to December 2022, namely January 2020, April 2020, and February 2022. The children under ten displayed an upward trend and were statistically significant. The trend surface analysis indicated that there was a shifting trend from northern to central and southern. A significant positive spatial auto-correlation was observed at the township level and four high-incidence clusters of influenza were detected. These results suggested that children under 10 years old deserve more attention and the spatiotemporal distribution of high-risk regions of influenza in Yinchuan varies every year at the township level. Thus, more monitoring and resource allocation should be prone to the four high-incidence clusters, which may benefit the public health authorities to carry out the vaccination and health promotion timely.

Mumps epidemic dynamics in the United States before vaccination (1923-1932)

Mumps is a vaccine-preventable, reemerging, and highly transmissible infectious disease. Widespread vaccination dramatically reduced cases; however, case counts have been increasing over the past 20 years. To provide a quantitative overview of historical mumps dynamics that can act as baseline information to help identify causes of mumps reemergence, we analyzed timeseries of cases reported from 1923 to 1932 in the United States. During that time, 239,230 mumps cases were reported in 70 cities. Larger cities reported annual epidemics and smaller cities reported intermittent, sporadic outbreaks. The critical community size above which transmission continuously occurred was likely between 365,583 and 781,188 individuals but could range as high as 3,376,438 individuals. Mumps cases increased as city size increased, suggesting density-dependent transmission. Using a density-dependent SEIR model, we calculated a mean effective reproductive number (Re) of 1.2. Re varied by city and over time, with periodic high values that could characterize short periods of very high transmission known as superspreading events. Case counts most often peaked in March, with higher-than-average transmission from December through April and showed a correlation with weekly births. While certain city pairs in Midwestern states had synchronous outbreaks, most outbreaks were less synchronous and not driven by distance between cities. This work demonstrates the importance of long-term infectious disease surveillance data and will inform future studies on mumps reemergence and control.

Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China

BACKGROUND

The transmission of 2019 novel coronavirus (COVID-19) has caused global panic in the past three years. Countries have learned an important lesson in the practice of responding to COVID-19 pandemic: timely and accurate diagnosis is critical. As an important technology of virus diagnosis, nucleic acid testing (NAT) is also widely used in the identification of other infectious diseases. However, geographic factors often constrain the provision of public health services such as NAT services, and the spatial nature of their resource allocation is a significant problem.

METHODS

We used OLS, OLS-SAR, GWR, GWR-SAR, MGWR, and MGWR-SAR models to identify the determinants of spatial difference and spatial heterogeneity affecting NAT institutions in China.

RESULTS

Firstly, we identify that the distribution of NAT institutions in China shows a clear spatial agglomeration, with an overall trend of increasing distribution from west to east. There is significant spatial heterogeneity in Chinese NAT institutions. Secondly, the MGWR-SAR model results show that city level, population density, number of tertiary hospitals and number of public health emergency outbreaks are important factors influencing the spatial heterogeneity of NAT institutions in China.

CONCLUSIONS

Therefore, the government should allocate health resources rationally, optimise the spatial layout of testing facilities, and improve the ability to respond to public health emergencies. Meanwhile, third-party testing facilities need to focus on their role in the public health emergency response system as a market force to alleviate the inequitable allocation of health resources between regions. By taking these measures to prepare adequately for possible future public health emergencies.

Estimating county-level flu vaccination in the United States

In the United States, influenza vaccines are an important part of public health efforts to blunt the effects of seasonal influenza epidemics. This in turn emphasizes the importance of understanding the spatial distribution of influenza vaccination coverage. Despite this, high quality data at a fine spatial scale and spanning a multitude of recent flu seasons are not readily available. To address this gap, we develop county-level counts of vaccination across five recent, consecutive flu seasons and fit a series of regression models to these data that account for bias. We find that the spatial distribution of our bias-corrected vaccination coverage estimates is generally consistent from season to season, with the highest coverage in the Northeast and Midwest but is spatially heterogeneous within states. We also observe a negative relationship between a county's vaccination coverage and social vulnerability. Our findings stress the importance of quantifying flu vaccination coverage at a fine spatial scale, as relying on state or region-level estimates misses key heterogeneities.

Attitudes towards booster, testing and isolation, and their impact on COVID-19 response in winter 2022/2023 in France, Belgium, and Italy: a cross-sectional survey and modelling study

Background

European countries are focusing on testing, isolation, and boosting strategies to counter the 2022/2023 winter surge due to SARS-CoV-2 Omicron subvariants. However, widespread pandemic fatigue and limited compliance potentially undermine mitigation efforts.

Methods

To establish a baseline for interventions, we ran a multicountry survey to assess respondents’ willingness to receive booster vaccination and comply with testing and isolation mandates. Integrating survey and estimated immunity data in a branching process epidemic spreading model, we evaluated the effectiveness and costs of current protocols in France, Belgium, and Italy to manage the winter wave.

Findings

The vast majority of survey participants (N = 4594) was willing to adhere to testing (>91%) and rapid isolation (>88%) across the three countries. Pronounced differences emerged in the declared senior adherence to booster vaccination (73% in France, 94% in Belgium, 86% in Italy). Epidemic model results estimate that testing and isolation protocols would confer significant benefit in reducing transmission (17–24% reduction, from R = 1.6 to R = 1.3 in France and Belgium, to R = 1.2 in Italy) with declared adherence. Achieving a mitigating level similar to the French protocol, the Belgian protocol would require 35% fewer tests (from 1 test to 0.65 test per infected person) and avoid the long isolation periods of the Italian protocol (average of 6 days vs. 11). A cost barrier to test would significantly decrease adherence in France and Belgium, undermining protocols’ effectiveness.

Interpretation

Simpler mandates for isolation may increase awareness and actual compliance, reducing testing costs, without compromising mitigation. High booster vaccination uptake remains key for the control of the winter wave.

Funding

The 10.13039/501100000780European Commission, ANRS–Maladies Infectieuses Émergentes, the Agence Nationale de la Recherche, the Chaires Blaise Pascal Program of the Île-de-France region.

Social and demographic patterns of influenza vaccination coverage in Norway, influenza seasons 2014/15 to 2020/21

AIMS

To examine influenza vaccination coverage among risk groups (RG) and health care workers (HCW), and study social and demographic patterns of vaccination coverage over time.

METHODS

Vaccination coverage was estimated by self-report in a nationally representative telephone survey among 14919 individuals aged 18-79 years over seven influenza seasons from 2014/15 to 2020/21. We explored whether belonging to an influenza RG (being >=65 years of age and/or having >=1 medical risk factor), being a HCW or educational attainment was associated with vaccination status using logistic regression.

RESULTS

Vaccination coverage increased from 27 % to 66 % among individuals 65-79 years, from 13 % to 33 % among individuals 18-64 years with >=1 risk factor, and from 9 % to 51 % among HCWs during the study period. Being older, having a risk factor or being a HCW were significantly associated with higher coverage in all multivariable logistic regression analyses. Higher education was also consistently associated with higher coverage, but the difference did not reach significance in all influenza seasons. Educational attainment was not significantly associated with coverage while coverage was at its lowest (2014/15-2017/18), but as coverage increased, so did the differences. Individuals with intermediate or lower education were less likely to report vaccination than those with higher education in season 2018/19, OR = 0.61 (95 % CI 0.46-0.80) and OR = 0.58 (95 % CI 0.41-0.83), respectively, and in season 2019/20, OR = 0.69 (95 % CI 0.55-0.88) and OR = 0.71 (95 % CI 0.53-0.95), respectively. When the vaccine was funded in the COVID-19 pandemic winter of 2020/21, educational differences diminished again and were no longer significant.

CONCLUSIONS

We observed widening educational differences in influenza vaccination coverage as coverage increased from 2014/15 to 2019/20. When influenza vaccination was funded in 2020/21, differences in coverage by educational attainment diminished. These findings indicate that economic barriers influence influenza vaccination decisions among risk groups in Norway.

Spatiotemporal Bayesian estimation of the number of under-reported COVID-19 cases in Victoria Australia

Having an estimate of the number of under-reported cases is crucial in determining the true burden of a disease. In the COVID-19 pandemic, there is a great need to quantify the true disease burden by capturing the true incidence rate to establish appropriate measures and strategies to combat the disease. This study investigates the under-reporting of COVID-19 cases in Victoria, Australia, during the third wave of the pandemic as a result of variation in geographic area and time. It is aimed to determine potential under-reported areas and generate the true picture of the disease in terms of the number of cases. A two-tiered Bayesian hierarchical model approach is employed to estimate the true incidence and detection rates through Bayesian model averaging. The proposed model goes beyond testing inequality across areas by looking into other covariates such as weather, vaccination rates, and access to vaccination and testing centres, including interactions and variations between space and time. This model aims for parsimony yet allows a broader range of scope to capture the underlying dynamic of the reported COVID-19 cases. Moreover, it is a data-driven, flexible, and generalisable model to a global context such as cross-country estimation and across time points under strict pandemic conditions.

Bayesian estimation of the effect of health inequality in disease detection

BACKGROUND

Measuring health inequality is essential to ensure that everyone has equal accessibility to health care. Studies in the past have continuously presented and showed areas or groups of people affected by various inequality in accessing the health resources and services to help improve this matter. Alongside, disease prevention is as important to minimise the disease burden and improve health and quality of life. These aspects are interlinked and greatly contributes to one's health.

METHOD

In this study, the Gini coefficient and Lorenz curve are used to give an indication of the overall health inequality. The impact of this inequality in granular level is demonstrated using Bayesian estimation for disease detection. The Bayesian estimation used a two-component modelling approach that separates the case detection process and incidence rate using a mixed Poisson distribution while capturing underlying spatio-temporal characteristics. Bayesian model averaging is used in conjunction with the two-component modelling approach to improve the accuracy of estimates by incorporating many candidate models into the analysis instead of using fixed component models. This method is applied to an infectious disease, influenza, in Victoria, Australia between 2013 and 2016 and the corresponding primary health care of the state.

RESULT

There is a relatively equal distribution of health resources and services pertaining to general practitioners (GP) and GP clinics in Victoria, Australia. Roughly 80 percent of the population shares 70 percent of the number of GPs and GP clinics. The Bayesian estimation with model averaging revealed that access difficulty to health services impacts both case detection probability and incidence rate. Minimal differences are recorded in the observed and estimated incidence of influenza cases considering social deprivation factors. In most years, areas in Victoria's southwest and eastern parts have potential under-reported cases consistent with their relatively lower number of GP or GP clinics.

CONCLUSION

The Bayesian model estimated a slight discrepancy between the estimated incidence and the observed cases of influenza in Victoria, Australia in 2013-2016 period. This is consistent with the relatively equal health resources and services in the state. This finding is beneficial in determining areas with potential under-reported cases and under-served health care. The proposed approach in this study provides insight into the impact of health inequality in disease detection without requiring costly and time-extensive surveys and relying mainly on the data at hand. Furthermore, the application of Bayesian model averaging provided a flexible modelling framework that allows covariates to move between case detection and incidence models.

Non-pharmaceutical Interventions and Social Distancing as Intersubjective Care and Collective Protection

The paper discusses non-pharmaceutical interventions (NPIs) as a collective form of protection that, in terms of health justice, benefits groups at risk, allowing them to engage in social life and activities during health crises. More specifically, the paper asserts that NPIs that realize social distancing are justifiable insofar as they are constitutive of a type of social protection that allows everyone, especially social disadvantaged agents, to access the public health sphere and other fundamental social spheres, such as the family and civil society.

Addressing the socioeconomic divide in computational modeling for infectious diseases

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

Large university with high COVID-19 incidence is not associated with excess cases in non-student population

Large US colleges and universities that re-opened campuses in the fall of 2020 and the spring of 2021 experienced high per capita rates of COVID-19. Returns to campus were controversial because they posed a potential risk to surrounding communities. A large university in Pennsylvania that returned to in-person instruction for Fall 2020 and Spring 2021 semesters reported high incidence of COVID-19 among students. However, the co-located non-student resident population in the county experienced fewer COVID-19 cases per capita than reported in neighboring counties. Activity patterns from mobile devices indicate that the non-student resident population near the university restricted their movements during the pandemic more than residents of neighboring counties. Respiratory virus prevention and management in student and non-student populations requires different, specifically targeted strategies.

Changing socio-economic and ethnic disparities in influenza/A/H1N1 infection early in the 2009 UK epidemic: a descriptive analysis

Background

Higher incidence of and risk of hospitalisation and death from Influenza A(H1N1)pdm09 during the 2009 pandemic was reported in ethnic minority groups in many high-income settings including in the United Kingdom (UK). Many of these studies rely on geographical and temporal aggregation of cases and can be difficult to interpret due to the spatial and temporal factors in outbreak spread. Further, it can be challenging to distinguish between disparities in health outcomes caused by variation in transmission risk or disease severity.

Methods

We used anonymised laboratory confirmed and suspected case data, classified by ethnicity and deprivation status, to evaluate how disparities in risk between socio-economic and ethnic groups vary over the early stages of the 2009 Influenza A(H1N1)pdm09 epidemic in Birmingham and London, two key cities in the emergence of the UK epidemic. We evaluated the relative risk of infection in key ethnic minority groups and by national and city level deprivation rank.

Results

We calculated higher incidence in more deprived areas and in people of South Asian ethnicity in both Birmingham and London, although the magnitude of these disparities reduced with time. The clearest disparities existed in school-aged children in Birmingham, where the most deprived fifth of the population was 2.8 times more likely to be infected than the most affluent fifth of the population.

Conclusions

Our analysis shows that although disparities in reported cases were present in the early phase of the Influenza A(H1N1)pdm09 outbreak in both Birmingham and London, they vary substantially depending on the period over which they are measured. Further, the development of disparities suggest that clustering of social groups play a key part as the outbreak appears to move from one ethnic and socio-demographic group to another. Finally, high incidence and large disparities between children indicate that they may hold an important role in driving inequalities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06936-5.

Networks, cultures, and institutions: Toward a social immunology

This paper calls for increased attention to the ways in which immune function – including its behavioral aspects – are responsive to social contexts at multiple levels. Psychoneuroimmunology has demonstrated that the quantity and quality of social connections can affect immune responses, while newer research is finding that sickness temporarily affects these same social networks and that some aspects of culture can potentially “get under the skin” to affect inflammatory responses. Social immunology, the research framework proposed here, unifies these findings and also considers the effects of structural factors – that is, a society's economic, political, and environmental landscape – on exposure to pathogens and subsequent immune responses. As the COVID-19 pandemic has highlighted, a holistic understanding of the effects of social contexts on the patterning of morbidity and mortality is critically important. Social immunology provides such a framework and can highlight important risk factors related to impaired immune function.

The missing season: The impacts of the COVID-19 pandemic on influenza

Throughout the COVID-19 pandemic, many have worried that the additional burden of seasonal influenza would create a devastating scenario, resulting in overwhelmed healthcare capacities and further loss of life. However, many were pleasantly surprised: the 2020 Southern Hemisphere and 2020-2021 Northern Hemisphere influenza seasons were entirely suppressed. The potential causes and impacts of this drastic public health shift are highly uncertain, but provide lessons about future control of respiratory diseases, especially for the upcoming influenza season.

Improvement of Contact Tracing with Citizen's Distributed Risk Maps

The rapid spread of COVID-19 has demonstrated the need for accurate information to contain its diffusion. Technological solutions are a complement that can help citizens to be informed about the risk in their environment. Although measures such as contact traceability have been successful in some countries, their use raises society’s resistance. This paper proposes a variation of the consensus processes in directed networks to create a risk map of a determined area. The process shares information with trusted contacts: people we would notify in the case of being infected. When the process converges, each participant would have obtained the risk map for the selected zone. The results are compared with the pilot project’s impact testing of the Spanish contact tracing app (RadarCOVID). The paper also depicts the results combining both strategies: contact tracing to detect potential infections and risk maps to avoid movements into conflictive areas. Although some works affirm that contact tracing apps need 60% of users to control the propagation, our results indicate that a 40% could be enough. On the other hand, the elaboration of risk maps could work with only 20% of active installations, but the effect is to delay the propagation instead of reducing the contagion. With both active strategies, this methodology is able to significantly reduce infected people with fewer participants.