JMIR Public Health and Surveillance

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ISSN / EISSN : 2369-2960 / 2369-2960
Published by: JMIR Publications (10.2196)
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JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/33345

Abstract:
Background: The alcohol-attributable burden of disease is high among socially disadvantaged individuals. Interventional efforts intending to have a public health impact should also address the reduction of social inequalities due to alcohol. Objective: The aim was to test the moderating role of educational background on the efficacy of a computer-based brief intervention addressing the full spectrum of alcohol use. Methods: We recruited 1646 adults from the general population aged 18 to 64 years (920 women, 55.9%; mean age 31 years; 574 with less than 12 years of school education, 34.9%) who reported alcohol use in the past year. The participants were randomly assigned a brief alcohol intervention or to assessment only (participation rate, 66.9%, 1646/2463 eligible persons). Recruitment took place in a municipal registry office in one German city. All participants filled out a self-administered, tablet-based survey during the recruitment process and were assessed 3, 6, and 12 months later by study assistants via computer-assisted telephone interviews. The intervention consisted of 3 computer-generated and individualized feedback letters that were sent via mail at baseline, month 3, and month 6. The intervention was based on the transtheoretical model of behavior change and expert system software that generated the feedback letters automatically according to previously defined decision rules. The outcome was self-reported change in number of alcoholic drinks per week over 12 months. The moderator was school education according to highest general educational degree (less than 12 years of education vs 12 years or more). Covariates were sex, age, employment, smoking, and alcohol-related risk level. Results: Latent growth modeling revealed that the intervention effect after 12 months was moderated by educational background (incidence rate ratio 1.38, 95% CI 1.08-1.76). Individuals with less than 12 years of school education increased their weekly alcohol use to a lesser extent when they received the intervention compared to assessment only (incidence rate ratio 1.30, 95% CI 1.05-1.62; Bayes factor 3.82). No difference was found between groups (incidence rate ratio 0.95, 95% CI 0.84-1.07; Bayes factor 0.30) among those with 12 or more years of school education. Conclusions: The efficacy of an individualized brief alcohol intervention was moderated by the participants’ educational background. Alcohol users with less than 12 years of school education benefited, whereas those with 12 or more years did not. People with lower levels of education might be more receptive to the behavior change mechanisms used by brief alcohol interventions. The intervention approach may support the reduction of health inequalities in the population at large if individuals with low or medium education can be reached. Trial Registration: German Clinical Trials Register DRKS00014274; https://www.drks.de/DRKS00014274
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/35663

Abstract:
Background: Between 2014 and 2018, the penetration of smartphones in sub-Saharan Africa increased from 10% to 30%, enabling increased access to the internet, Facebook, Twitter, Pinterest, and YouTube. These platforms engage users in multidirectional communication and provide public health programs with the tools to inform and engage diverse audiences on a range of public health issues, as well as monitor opinions and behaviors on health topics. Objective: This paper details the process used by the U.S. Agency for International Development–funded Breakthrough RESEARCH to apply social media monitoring and social listening techniques in Burkina Faso, Côte d’Ivoire, Niger, and Togo for the adaptive management of the Merci Mon Héros campaign. We documented how these approaches were applied and how the lessons learned can be used to support future public health communication campaigns. Methods: The process involved 6 steps: (1) ensure there is a sufficient volume of topic-specific web-based conversation in the target countries; (2) develop measures to monitor the campaign’s social media strategy; (3) identify search terms to assess campaign and related conversations; (4) quantitatively assess campaign audience demographics, campaign reach, and engagement through social media monitoring; (5) qualitatively assess audience attitudes, opinions, and behaviors and understand conversation context through social media listening; and (6) adapt campaign content and approach based on the analysis of social media data. Results: We analyzed posts across social media platforms from November 2019 to October 2020 based on identified key search terms related to family planning, reproductive health, menstruation, sexual activity, and gender. Based on the quantitative and qualitative assessments in steps 4 and 5, there were several adaptive shifts in the campaign’s content and approach, of which the following 3 shifts are highlighted. (1) Social media monitoring identified that the Facebook campaign fans were primarily male, which prompted the campaign to target calls to action to the male audience already following the campaign and shift marketing approaches to increase the proportion of female followers. (2) Shorter videos had a higher chance of being viewed in their entirety. In response to this, the campaign shortened video lengths and created screenshot teasers to promote videos. (3) The most negative sentiment related to the campaign videos was associated with beliefs against premarital sex. In response to this finding, the campaign included videos and Facebook Live sessions with religious leaders who promoted talking openly with young people to support intergenerational discussion about reproductive health. Conclusions: Prior to launching health campaigns, programs should test the most relevant social media platforms and their limitations. Inherent biases to internet and social media access are important challenges, and ethical considerations around data privacy must continue to guide the advances in this technology’s use for research. However, social listening and social media monitoring can be powerful monitoring and evaluation tools that can be used to aid the adaptive management of health campaigns that engage populations who have a digital presence.
Yiping Li, , , Chuanteng Feng, Hong Yang, Hang Yu, Dan Yuan,
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/33429

Abstract:
Background: Evidence on the efficacy of antiretroviral therapy (ART) regimen switches on the mortality of patients with HIV drug resistance (HIVDR) is limited. Objective: We aim to provide policy guidance for ART regimen selection and evaluate the effectiveness of ART regime switches for people living with HIV and HIV-1 drug resistance. Methods: This retrospective observational cohort study included 179 people living with HIV and HIV-1 drug resistance from 2011 to 2020. The time that participants switched treatment regimens either to protease inhibitor (PI)–based ART regimens (PIs) or nonnucleoside reverse transcriptase inhibitor (NNRTI)–based ART regimens (NNRTIs) was taken as an observation starting point and followed up every 12 months. The parametric g-formula was used to estimate the 5-year risk of mortality under the situations of (1) natural course, (2) immediate switch to NNRTIs, (3) immediate switch to PIs, and (4) if CD4(+) T cells<200 switched to PIs. Results: The follow-up time of the 179 patients ranged from 30 to 119 months. The median follow-up time was 90 months. During a follow-up of 15,606 person-months, 27 individuals died in the cohort. The estimated 5-year risk of mortality under natural course, immediate switch to NNRTIs, immediate switch to PIs, and if CD4(+), and switch to PIs if T cells<200 were 11.62% (95% CI 7.82-17.11), 31.88% (95% CI 20.79-44.94), 2.87% (95% CI 0.32-7.07), and 5.30% (95% CI 2.07-10.21), respectively. The risk ratios (RRs) of immediate switch to NNRTIs, immediate switch to PIs, and switch to PIs if CD4(+) T cells<200, compared with natural course mortality rate, were 2.74 (95% CI 2.01-3.47), 0.25 (95% CI: 0.04-0.54), and 0.46 (95% CI 0.22-0.71), respectively. The risk differences were 20.26% (95% CI 10.96-28.61), –8.76% (95% CI –13.34 to –5.09) and –6.32% (95% CI –9.75 to –3.11), respectively. Conclusions: Our study found that a PI-based ART regimen was beneficial for reducing mortality in people living with HIV and HIV-1 drug resistance. More effort should be given to find HIV-1 drug resistance earlier to ensure a timely adjustment to PI-based ART, thereby maximizing the benefit of early switch treatment for people living with HIV and HIV-1 drug resistance.
Xixi Zhao, , , Jianhua Jin, Yimeng Zeng, Jinyi Qiu, Mingrou Guo, Yuyao Zhu, Zhirui Li, Jiaxin Liu, et al.
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/35343

Abstract:
Background: COVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemic. Objective: Nonpharmaceutical interventions (NPIs) may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aim to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China. Methods: Time series data sets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public data sets from the National Health Commission of the People’s Republic of China. Several indices (peak and trough amplitudes, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak. Results: We found that the prevention and control policies for COVID-19 had a strong, significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it was significantly stronger before the COVID-19 outbreak and does not correlate with the trough amplitude. Conclusions: Our results directly demonstrate that prevention policies for COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction in outbreaks. This study may guide the implementation of nonpharmaceutical interventions to control a wider range of infectious diseases.
Liuqing Yang, Lili Ji, Qiang Wang, , Guoping Yang, Tingting Cui, Naiyang Shi, Lin Zhu, Shixin Xiu, , et al.
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/34666

Abstract:
Background: Promoting vaccination and eliminating vaccine hesitancy are key measures for controlling vaccine-preventable diseases. Objective: We aimed to understand the beliefs surrounding and drivers of vaccination behavior, and their relationships with and influence on vaccination intention and practices. Methods: We conducted a web-based survey in 31 provinces in mainland China from May 24, 2021 to June 15, 2021, with questions pertaining to vaccination in 5 dimensions: attitude, subjective norms, perceived behavioral control, intention, and behavior. We performed hierarchical regression analysis and structural equation modeling based on the theory of planned behavior—in which, the variables attitude, subjective norms, and intention each affect the variable intention; the variable intention mediates the relationships of attitude and subjective norms with behavior, and the variable perceived behavioral control moderates the strength of this mediation—to test the validity of the theoretical framework. Results: A total of 9924 participants, aged 18 to 59 years, were included in this study. Vaccination intention mediated the relationships of attitude and subjective norms with vaccination behavior. The indirect effect of attitude on vaccination behavior was 0.164 and that of subjective norms was 0.255, and the difference was statistically significant (P<.001). The moderated mediation analysis further indicated that perceived behavioral control would affect the mediation when used as moderator, and the interaction terms for attitude (β=–0.052, P<.001) and subjective norms (β=–0.028, P=.006) with perceived behavioral control were significant. Conclusions: Subjective norms have stronger positive influences on vaccination practices than attitudes. Perceived behavioral control, as a moderator, has a substitution relationship with attitudes and subjective norms and weakens their positive effects on vaccination behavior.
, Max Su, , Marcia Testa, Beth Goldberg, , , Vanessa Maturo, Elena Savoia
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/34615

Abstract:
Background: Over the course of the COVID-19 pandemic, a variety of COVID-19-related misinformation has spread and been amplified online. The spread of misinformation can influence COVID-19 beliefs and protective actions, including vaccine hesitancy. Belief in vaccine misinformation is associated with lower vaccination rates and higher vaccine resistance. Attitudinal inoculation is a preventative approach to combating misinformation and disinformation, which leverages the power of narrative, rhetoric, values, and emotion. Objective: This study seeks to test inoculation messages in the form of short video messages to promote resistance against persuasion by COVID-19 vaccine misinformation. Methods: We designed a series of 30-second inoculation videos and conducted a quasi-experimental study to test the use of attitudinal inoculation in a population of individuals who were unvaccinated (N=1991). The 3 intervention videos were distinguished by their script design, with intervention video 1 focusing on narrative/rhetorical (“Narrative”) presentation of information, intervention video 2 focusing on delivering a fact-based information (“Fact”), and intervention video 3 using a hybrid design (“Hybrid”). Analysis of covariance (ANCOVA) models were used to compare the main effect of the intervention on the 3 outcome variables: ability to recognize misinformation tactics (“Recognize”), willingness to share misinformation (“Share”), and willingness to take the COVID-19 vaccine (“Willingness”). Results: There were significant effects across all 3 outcome variables comparing inoculation intervention groups to controls. For the Recognize outcome, the ability to recognize rhetorical strategies, there was a significant intervention group effect (P<.001). For the Share outcome, support for sharing the mis- and disinformation, the intervention group main effect was statistically significant (P=.02). For the Willingness outcome, there was a significant intervention group effect; intervention groups were more willing to get the COVID-19 vaccine compared to controls (P=.01). Conclusions: Across all intervention groups, inoculated individuals showed greater resistance to misinformation than their noninoculated counterparts. Relative to those who were not inoculated, inoculated participants showed significantly greater ability to recognize and identify rhetorical strategies used in misinformation, were less likely to share false information, and had greater willingness to get the COVID-19 vaccine. Attitudinal inoculation delivered through short video messages should be tested in public health messaging campaigns to counter mis- and disinformation.
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/32718

Abstract:
Background: Youth and young adults continue to experience high rates of HIV and are also frequent users of social media. Social media platforms such as Twitter can bolster efforts to promote HIV prevention for these individuals, and while HIV-related messages exist on Twitter, little is known about the impact or reach of these messages for this population. Objective: This study aims to address this gap in the literature by identifying user and message characteristics that are associated with tweet endorsement (favorited) and engagement (retweeted) among youth and young men (aged 13-24 years). Methods: In a secondary analysis of data from a study of HIV-related messages posted by young men on Twitter, we used model selection techniques to examine user and tweet-level factors associated with tweet endorsement and engagement. Results: Tweets from personal user accounts garnered greater endorsement and engagement than tweets from institutional users (aOR 3.27, 95% CI 2.75-3.89; P<.001). High follower count was associated with increased endorsement and engagement (aOR 1.05, 95% CI 1.04-1.06; P<.001); tweets that discussed STIs garnered lower endorsement and engagement (aOR 0.59, 95% CI 0.47-1.74; P<.001). Conclusions: Findings suggest practitioners should partner with youth to design and disseminate HIV prevention messages on social media, incorporate content that resonates with youth audiences, and work to challenge stigma and foster social norms conducive to open conversation about sex, sexuality, and health.
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/33099

Abstract:
Background: Observational data enables large-scale vaccine safety surveillance but requires careful evaluation of the potential sources of bias. One potential source of bias is the index date selection procedure for the unvaccinated cohort or unvaccinated comparison time (“anchoring”). Objective: Here, we evaluated the different index date selection procedures for 2 vaccinations: COVID-19 and influenza. Methods: For each vaccine, we extracted patient baseline characteristics on the index date and up to 450 days prior and then compared them to the characteristics of the unvaccinated patients indexed on (1) an arbitrary date or (2) a date of a visit. Additionally, we compared vaccinated patients indexed on the date of vaccination and the same patients indexed on a prior date or visit. Results: COVID-19 vaccination and influenza vaccination differ drastically from each other in terms of the populations vaccinated and their status on the day of vaccination. When compared to indexing on a visit in the unvaccinated population, influenza vaccination had markedly higher covariate proportions, and COVID-19 vaccination had lower proportions of most covariates on the index date. In contrast, COVID-19 vaccination had similar covariate proportions when compared to an arbitrary date. These effects attenuated, but were still present, with a longer lookback period. The effect of day 0 was present even when the patients served as their own controls. Conclusions: Patient baseline characteristics are sensitive to the choice of the index date. In vaccine safety studies, unexposed index event should represent vaccination settings. Study designs previously used to assess influenza vaccination must be reassessed for COVID-19 to account for a potentially healthier population and lack of medical activity on the day of vaccination.
Jingwei Li, , , , Tailai Wu, Qingnan Wang
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/35266

Abstract:
Background: The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. Objective: The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. Methods: We first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis. Results: Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=–8.722, P<.001; model 2, t198=–5.000, P<.001, model 3, t198=–1.882, P=.06; model 4, t198=–4.644, P<.001; model 5, t198=–4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks. Conclusions: Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.
Gabriel Urbanin, Wagner Meira, Alexandre Serpa, , , , , Anísio Mendes Lacerda, , , et al.
JMIR Public Health and Surveillance, Volume 8; https://doi.org/10.2196/34020

Abstract:
Background: Human behavior is crucial in health outcomes. Particularly, individual behavior is a determinant of the success of measures to overcome critical conditions, such as a pandemic. In addition to intrinsic public health challenges associated with COVID-19, in many countries, some individuals decided not to get vaccinated, streets were crowded, parties were happening, and businesses struggling to survive were partially open, despite lockdown or stay-at-home instructions. These behaviors contrast with the instructions for potential benefits associated with social distancing, use of masks, and vaccination to manage collective and individual risks. Objective: Considering that human behavior is a result of individuals' social and economic conditions, we investigated the social and working characteristics associated with reports of appropriate protective behavior in Brazil. Methods: We analyzed data from a large web survey of individuals reporting their behavior during the pandemic. We selected 3 common self-care measures: use of protective masks, distancing by at least 1 m when out of the house, and handwashing or use of alcohol, combined with assessment of the social context of respondents. We measured the frequency of the use of these self-protective measures. Using a frequent pattern–mining perspective, we generated association rules from a set of answers to questions that co-occur with at least a given frequency, identifying the pattern of characteristics of the groups divided according to protective behavior reports. Results: The rationale was to identify a pool of working and social characteristics that might have better adhesion to behaviors and self-care measures, showing these are more socially determined than previously thought. We identified common patterns of socioeconomic and working determinants of compliance with protective self-care measures. Data mining showed that social determinants might be important to shape behavior in different stages of the pandemic. Conclusions: Identification of context determinants might be helpful to identify unexpected facilitators and constraints to fully follow public policies. The context of diseases contributes to psychological and physical health outcomes, and context understanding might change the approach to a disease. Hidden social determinants might change protective behavior, and social determinants of protective behavior related to COVID-19 are related to work and economic conditions. Trial Registration: Not applicable.
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