Area-based determinants of outreach vaccination for reaching vulnerable populations: A cross-sectional study in Pakistan.

The objective of this study is to gain a comparative understanding of spatial determinants for outreach and clinic vaccination, which is critical for operationalizing efforts and breaking down structural biases; particularly relevant in countries where resources are low, and sub-region variance is high. Leveraging a massive effort to digitize public system reporting by Lady and Community Health Workers (CHWs) with geo-located data on over 4 million public-sector vaccinations from September 2017 through 2019, understanding health service operations in relation to vulnerable spatial determinants were made feasible. Location and type of vaccinations (clinic or outreach) were compared to regional spatial attributes where they were performed. Important spatial attributes were assessed using three modeling approaches (ridge regression, gradient boosting, and a generalized additive model). Consistent predictors for outreach, clinic, and proportion of third dose pentavalent vaccinations by region were identified. Of all Penta-3 vaccination records, 86.3% were performed by outreach efforts. At the tehsil level (fourth-order administrative unit), controlling for child population, population density, proportion of population in urban areas, distance to cities, average maternal education, and other relevant factors, increased poverty was significantly associated with more in-clinic vaccinations (ß = 0.077), and lower proportion of outreach vaccinations by region (ß = -0.083). Analyses at the union council level (fifth-administrative unit) showed consistent results for the differential importance of poverty for outreach versus clinic vaccination. Relevant predictors for each type of vaccination (outreach vs. in-clinic) show how design of outreach vaccination can effectively augment vaccination efforts beyond healthcare services through clinics. As Pakistan is third among countries with the most unvaccinated and under-vaccinated children, understanding barriers and factors associated with vaccination can be demonstrative for other national and sub-national regions facing challenges and also inform guidelines on supporting CHWs in health systems.

Rampart of Health-Specific Leadership and Social Support of Colleagues to Overcome Burnout in an Emotionally Demanding Situations: The Mediating Role of Stress.

Introduction: Past studies have neglected the role of resources that enhance motivation, such as health-specific leadership (H-SL) and social support colleagues (SSC), in dealing with the prerequisites of psychological health of workers, especially the duo of stress and burnout. Objective: This empirical study aimed to identify the impact of psychosocial job demands (emotional demands) and psychosocial job resources (health-specific leadership and social support of colleagues) on the psychological health (stress, burnout) of 284 Malaysian industrial workers (who participated both times). Methods: The Hierarchical regression analysis was employed to examine all study hypotheses and a time lagged study design was used with a lag of three months between T1 and T2 for data collection. Results: The survey data found a significant impact of emotional demands on stress and burnout, while we found insignificant findings of health-specific leadership and social support from colleagues on workers' psychological health. Future Directions: Future studies should consider the different formations of psychosocial job resources and higher dimensions of health promotion leadership.

Role of Psychological Contracts in Enhancing Employee Creativity Through Knowledge Sharing: Do Boundary Conditions of Organization's Socialization and Work-Related Curiosity Matter?

COVID-19 has had a huge impact on workers and workplaces across the world while putting regular work practices into disarray. Apart from the obvious effects of COVID-19, the pandemic is anticipated to have a variety of social-psychological, health-related, and economic implications for individuals at work. Despite extensive research on psychological contracts and knowledge sharing, these domains of pedagogic endeavor have received relatively little attention in the context of employee creativity subjected to the boundary conditions of the organization's socialization and work-related curiosity. This study investigates, empirically, the role of psychological contracts in escalating employee creativity through knowledge sharing by considering the moderating role of an organization's socialization and work-related curiosity. The response received from 372 employees of the manufacturing sector has been investigated and analyzed through Smart PLS software. The results have revealed that knowledge sharing is mediating the relationship between psychological contract and employee creative performance, whereas the moderators significantly moderate the relationships between psychological contract and knowledge sharing and between knowledge sharing and employee creative performance accordingly. It has also been depicted that the moderating impact shown by both moderators is significantly high.

Firing on all cylinders: Configuring information technology around the constituents of corporate entrepreneurship to outperform in SME sector.

This study has examined how small and medium enterprises (SMEs) may enhance their performance under different settings of information technology (IT) capabilities and corporate entrepreneurship (CE). Established on the dynamic capability view, the researchers have analyzed the connections between IT capabilities and CE, in addition to the performance results of SMEs. The research has explored these novel relationships by utilizing partial least square-structural equation modeling (PLS-SEM) with a data sample of 447 SMEs of the manufacturing sector in Pakistan. The findings present that IT capabilities positively influence the market and financial performance of SMEs through the mediating role of CE dimensions. The study uniquely determines the mediating role of dimensional effects of corporate entrepreneurship between IT capabilities and performance outcomes of firms. Thus, the study would enable the management of SMEs to realize the potential of IT-related CE dimensions and their use to improve firms' performance.

The Psychology of Resistance to Change: The Antidotal Effect of Organizational Justice, Support and Leader-Member Exchange.

In today's business environment, the survival and sustenance of any organization depend upon its ability to introduce a successful change. However, in implementing a change, one of the biggest problems an organization faces is resistance from its employees. The current paper addresses this problem by examining the role of organizational justice dimensions in coping with the resistance to change through the intervening role of perceived organizational support (POS), leader-member exchange (LMX), and readiness for change (RFC) in a sequential framework. Data of 372 employees have been collected from the banking industry of Pakistan. The results obtained through the Partial Least Squares- Structural Equation Modeling (PLS-SEM) approach using SmartPLS suggest that distributive justice, procedural justice, and interactional justice play a critical role in lowering the resistance to change through POS, LMX, and RFC, contributing significantly to the theory and practice. Furthermore, this study also discusses recommendations for future research and limitations associated with this research work.

Neighborhood level chronic respiratory disease prevalence estimation using search query data.

Estimation of disease prevalence at sub-city neighborhood scale allows early and targeted interventions that can help save lives and reduce public health burdens. However, the cost-prohibitive nature of highly localized data collection and sparsity of representative signals, has made it challenging to identify neighborhood scale prevalence of disease. To overcome this challenge, we utilize alternative data sources, which are both less sparse and representative of localized disease prevalence: using query data from a large commercial search engine, we identify the prevalence of respiratory illness in the United States, localized to census tract geographic granularity. Focusing on asthma and Chronic Obstructive Pulmonary Disease (COPD), we construct a set of features based on searches for symptoms, medications, and disease-related information, and use these to identify illness rates in more than 23 thousand tracts in 500 cities across the United States. Out of sample model estimates from search data alone correlate with ground truth illness rate estimates from the CDC at 0.69 to 0.76, with simple additions to these models raising those correlations to as high as 0.84. We then show that in practice search query data can be added to other relevant data such as census or land cover data to boost results, with models that incorporate all data sources correlating with ground truth data at 0.91 for asthma and 0.88 for COPD.

Quantifying the localized relationship between vector containment activities and dengue incidence in a real-world setting: A spatial and time series modelling analysis based on geo-located data from Pakistan.

Increasing urbanization is having a profound effect on infectious disease risk, posing significant challenges for governments to allocate limited resources for their optimal control at a sub-city scale. With recent advances in data collection practices, empirical evidence about the efficacy of highly localized containment and intervention activities, which can lead to optimal deployment of resources, is possible. However, there are several challenges in analyzing data from such real-world observational settings. Using data on 3.9 million instances of seven dengue vector containment activities collected between 2012 and 2017, here we develop and assess two frameworks for understanding how the generation of new dengue cases changes in space and time with respect to application of different types of containment activities. Accounting for the non-random deployment of each containment activity in relation to dengue cases and other types of containment activities, as well as deployment of activities in different epidemiological contexts, results from both frameworks reinforce existing knowledge about the efficacy of containment activities aimed at the adult phase of the mosquito lifecycle. Results show a 10% (95% CI: 1-19%) and 20% reduction (95% CI: 4-34%) reduction in probability of a case occurring in 50 meters and 30 days of cases which had Indoor Residual Spraying (IRS) and fogging performed in the immediate vicinity, respectively, compared to cases of similar epidemiological context and which had no containment in their vicinity. Simultaneously, limitations due to the real-world nature of activity deployment are used to guide recommendations for future deployment of resources during outbreaks as well as data collection practices. Conclusions from this study will enable more robust and comprehensive analyses of localized containment activities in resource-scarce urban settings and lead to improved allocation of resources of government in an outbreak setting.

Fine-grained dengue forecasting using telephone triage services.

Thousands of lives are lost every year in developing countries for failing to detect epidemics early because of the lack of real-time disease surveillance data. We present results from a large-scale deployment of a telephone triage service as a basis for dengue forecasting in Pakistan. Our system uses statistical analysis of dengue-related phone calls to accurately forecast suspected dengue cases 2 to 3 weeks ahead of time at a subcity level (correlation of up to 0.93). Our system has been operational at scale in Pakistan for the past 3 years and has received more than 300,000 phone calls. The predictions from our system are widely disseminated to public health officials and form a critical part of active government strategies for dengue containment. Our work is the first to demonstrate, with significant empirical evidence, that an accurate, location-specific disease forecasting system can be built using analysis of call volume data from a public health hotline.

FluBreaks: early epidemic detection from Google flu trends.

BACKGROUND: The Google Flu Trends service was launched in 2008 to track changes in the volume of online search queries related to flu-like symptoms. Over the last few years, the trend data produced by this service has shown a consistent relationship with the actual number of flu reports collected by the US Centers for Disease Control and Prevention (CDC), often identifying increases in flu cases weeks in advance of CDC records. However, contrary to popular belief, Google Flu Trends is not an early epidemic detection system. Instead, it is designed as a baseline indicator of the trend, or changes, in the number of disease cases. OBJECTIVE: To evaluate whether these trends can be used as a basis for an early warning system for epidemics. METHODS: We present the first detailed algorithmic analysis of how Google Flu Trends can be used as a basis for building a fully automated system for early warning of epidemics in advance of methods used by the CDC. Based on our work, we present a novel early epidemic detection system, called FluBreaks (dritte.org/flubreaks), based on Google Flu Trends data. We compared the accuracy and practicality of three types of algorithms: normal distribution algorithms, Poisson distribution algorithms, and negative binomial distribution algorithms. We explored the relative merits of these methods, and related our findings to changes in Internet penetration and population size for the regions in Google Flu Trends providing data. RESULTS: Across our performance metrics of percentage true-positives (RTP), percentage false-positives (RFP), percentage overlap (OT), and percentage early alarms (EA), Poisson- and negative binomial-based algorithms performed better in all except RFP. Poisson-based algorithms had average values of 99%, 28%, 71%, and 76% for RTP, RFP, OT, and EA, respectively, whereas negative binomial-based algorithms had average values of 97.8%, 17.8%, 60%, and 55% for RTP, RFP, OT, and EA, respectively. Moreover, the EA was also affected by the region's population size. Regions with larger populations (regions 4 and 6) had higher values of EA than region 10 (which had the smallest population) for negative binomial- and Poisson-based algorithms. The difference was 12.5% and 13.5% on average in negative binomial- and Poisson-based algorithms, respectively. CONCLUSIONS: We present the first detailed comparative analysis of popular early epidemic detection algorithms on Google Flu Trends data. We note that realizing this opportunity requires moving beyond the cumulative sum and historical limits method-based normal distribution approaches, traditionally employed by the CDC, to negative binomial- and Poisson-based algorithms to deal with potentially noisy search query data from regions with varying population and Internet penetrations. Based on our work, we have developed FluBreaks, an early warning system for flu epidemics using Google Flu Trends.