Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community

Spatial characteristics of non-communicable diseases and their associations to social conditions in a large urban cohort in Germany-Results from the Hamburg City Health Study

BACKGROUND

Non-communicable diseases (NCDs) are responsible for many deaths. They are associated with several modifiable and metabolic risk factors and are therefore prone to significant regional variations on different scales. However, only few intra-urban studies examined spatial variation in NCDs and its association with social circumstances, especially in Germany. Thus, the present study aimed to identify associations of personal risk factors and local social conditions with NCDs in a large German city.

METHODS

This study is based on a population-based cohort of the Hamburg City Health Study including 10,000 probands. Six NCDs were analyzed (chronic obstructive pulmonary disease [COPD], coronary heart disease [CHD], diabetes mellitus, heart failure, depression, and hypertension) in 68 city district clusters. As risk factors, we considered socio-demographic variables (age, sex, education) and risk behaviour variables (smoking, alcohol consumption). Logistic regression analyses identified associations between the district clusters and the prevalence rates for each NCD. Regional variation was detected by Gini coefficients and spatial cluster analyses. Local social condition indexes were correlated with prevalence rates of NCDs on city district level and hot-spot analyses were performed for significant high or low values.

RESULTS

The analyses included 7,308 participants with a mean age of 63.1 years (51.5% female). The prevalence of hypertension (67.6%) was the highest. Risk factor associations were identified between smoking, alcohol consumption and education and the prevalence of NCDs (hypertension, diabetes, and COPD). Significant regional variations were detected and persisted after adjusting for personal risk factors. Correlations for prevalence rates with the local social conditions were significant for hypertension (r = 0.294, p < 0.02), diabetes (r = 0.259, p = 0.03), and COPD (r = 0.360, p < 0.01).

CONCLUSIONS

The study shows that regional differences in NCD prevalence persist even after adjusting for personal risk factors. This highlights the central role of both personal socio-economic status and behaviors such as alcohol and tobacco consumption. It also highlights the importance of other potential regional factors (e.g. the environment) in shaping NCD prevalence. This knowledge helps policy- and decision-makers to develop intervention strategies.

Lessons from COVID-19 pandemic: Analysis of unequal access to food stores using the Gini coefficient

COVID-19 has dramatically altered daily life worldwide, with some urban residents resorting to panic buying at the beginning of the pandemic. Large-scale lockdowns and restaurant closures have increased the need for grocery shopping. Such shifts in consumer patterns have altered supply-demand systems. Insufficient food store availability increases the likelihood of crowding and thus increases the probability of viral infection. People who live without easy access to food stores also face high infection risks when forced to travel long distances for grocery shopping. The COVID-19 pandemic has demonstrated the importance of the number and distribution of food stores to virus transmission. Food access is also a core factor of urban resilience during the pandemic. This study used the Gini coefficient to investigate the fairness of accessibility to food stores at the city and village levels, with Taipei City chosen as the research area. Different spatial scales were considered, and we calculated the equality of food access for older (≥65 years old) and non-older populations separately to determine whether one group faces greater inequality. At the city level, both older and non-older populations in Taipei have reasonable access (Gini coefficient between 0.3 and 0.4), with mean Gini coefficients of 0.3616 and 0.3655, respectively. This city-level analysis represents the overall degree of unequal access to food stores. At the village level, eight villages (1.8 %; total N = 456) had severe access inequality (Gini coefficient > 0.6) for older adults; they are located primarily in downtown or suburban areas. For the non-older population, only two villages (0.4 %; total N = 456) in suburban areas exhibit severe access inequality. The village-level analysis identified villages with low equality of access to food stores and revealed local problems that cannot be observed at the city level.

Electronic consultation accessibility influence on patient assessments: A case-control study with user-generated tags of physician expertise

Objective

In online health communities (OHCs), patients often list their physicians' expertise by user-generated tags based on their consulted diseases. These expertise tags play an essential role in recommending the match of physicians to future patients. However, few studies have examined the impact of the accessibility of e-consults on patient assessments using marking of the physicians' expertise in OHCs. This study aims to investigate what are the patient assessments of the physicians' expertise if they have e-consult accessibility.

Methods

Through a case-control study, this article examined the effect of e-consult accessibility on patient-generated tags of physician expertise in OHCs. With data collected from the Good Doctor website, the samples consisted of 9841 physicians from 1255 different hospitals widely distributed in China. The breadth of voted expertise (BE) is measured by the number of consulted disease-related labels marked by a physician's served patients (SP). The volume of votes (VV) is measured by the number of a physician's votes given by the SP. The degree of voted diversity (DD) is measured by the information entropy of each physician's service expertise (labeled and voted by patients). The data analysis of e-consult accessibility is conducted by estimating the average treatment effect on the DD of physicians' expertise.

Results

For the BE, its mean was 7.305 for the case group of physicians with e-consults accessible (photo and text queries), while the mean was 9.465 for the control for physicians without e-consults. For the VV, its mean was 39.720 for the case group, while the mean was 84.565 for the control. For the DD, its mean on patient-generated tags was 2.103 for the case group, which is 0.413 lower than the control group.

Conclusion

The availability of e-consults increases the concentration on physician expertise in the patient-generated tags. e-Consults reinforce the increment of the already-received physician expertise (reflected in tags), reducing the tag information diversity.

Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers

Coronavirus disease 2019 (COVID-19) has triggered an enormous number of discussion topics on social media Twitter. It has an impact on the global health system and citizen responses to the pandemic. Multiple responses (replies, favorites, and retweets) reflect the followers’ attitudes and emotions towards these tweets. Twitter data such as these have inspired substantial research interest in sentiment and social trend analyses. To date, studies on Twitter data have focused on the associational relationships between variables in a population. There is a need for further discovery of causality, such as the influence of sentiment polarity of tweet response on further discussion topics. These topics often reflect the human perception of COVID-19. This study addresses this exact topic. It aims to develop a new method to unveil the causal relationships between the sentiment polarity and responses in social media data. We employed sentiment polarity, i.e., positive or negative sentiment, as the treatment variable in this quasi-experimental study. The data is the tweets posted by nine authoritative public organizations in four countries and the World Health Organization from December 1, 2019, to May 10, 2020. Employing the inverse probability weighting model, we identified the treatment effect of sentiment polarity on the multiple responses of tweets. The topics with negative sentiment polarity on COVID-19 attracted significantly more replies (69±49) and favorites (688±677) than the positive tweets. However, no significant difference in the number of retweets was found between the negative and positive tweets. This study contributes a new method for social media analysis. It generates new insight into the influence of sentiment polarity of tweets about COVID-19 on tweet responses.

Inequality indices to monitor geographic differences in incidence, mortality and fatality rates over time during the COVID-19 pandemic

BACKGROUND

It is of interest to explore the variability in how the COVID-19 pandemic evolved geographically during the first twelve months. To this end, we apply inequality indices over regions to incidences, infection related mortality, and infection fatality rates. If avoiding of inequality in health is an important political goal, a metric must be implemented to track geographical inequality over time.

METHODS

The relative and absolute Gini index as well as the Theil index are used to quantify inequality. Data are taken from international data bases. Absolute counts are transformed to rates adjusted for population size.

RESULTS

Comparing continents, the absolute Gini index shows an unfavorable development in four continents since February 2020. In contrast, the relative Gini as well as the Theil index support the interpretation of less inequality between European countries compared to other continents. Infection fatality rates within the EU as well as within the U.S. express comparable improvement towards more equality (as measured by both Gini indices).

CONCLUSIONS

The use of inequality indices to monitor changes in geographic inequality over time for key health indicators is a valuable tool to inform public health policies. The absolute and relative Gini index behave complementary and should be reported simultaneously in order to gain a meta-perspective on very complex dynamics.

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

Causal Effect of Honorary Titles on Physicians' Service Volumes in Online Health Communities: Retrospective Study

Background

An OHC online health community (OHC) is an interactive platform for virtual communication between patients and physicians. Patients can typically search, seek, and share their experience and rate physicians, who may be involved in giving advice. Some OHC providers provide incentives in form of honorary titles to encourage the web-based involvement of physicians, but it is unclear whether the award of honorary titles has an impact on their consultation volume in an OHC.

Objective

This study is designed to identify the differential treatment effect of the incentive policy on the service volumes for the subgroups of treatment and control in an OHC. This study aims to answer the following questions: Does an honorary title for physicians impact their service volumes in an OHC? During the period of discontinuity, can we identify the sharp effect of the incentive award on the outcomes of physicians’ service volumes?

Methods

We acquired the targeted samples based on treatment, namely, physicians with an honorary title or not and outcomes measured before and after the award of the 2 subgroups. A regression discontinuity design was applied to investigate the impact of the honorary titles incentive as a treatment in an OHC. There was a sharply discontinuous effect of treatment on physicians’ online health service performance. The experimental data set consisted of 346 physicians in the treatment group (with honorary titles). Applying the propensity score matching method, the same size of physicians (n=346) was matched and selected as the control group.

Results

A sharp discontinuity was found at the time of the physician receiving the honorary title. The results showed that the parametric estimates of the coefficient were significantly positively (P<.001) associated with monthly home page views. The jump in the monthly volumes of home page views was much sharper than that of the monthly consultations.

Conclusions

The changes in the volumes of monthly consultations and home page views reflect the differential treatment effect of honorary titles on physicians’ service volumes. The effect of the incentive policy with honorary titles is objectively estimated from both the perspective of online and offline medical services in an OHC. Being named with honorary titles significantly multiplied monthly home page views, yet it did not significantly impact monthly consultations. This may be because consultation capacity is limited by the physician's schedule for consultations.

The Spatial Dimension of Income Inequality: An Analysis at Municipal Level

This paper focuses on the analysis on income inequality in Italy at the municipal level of the areas defined by the National Strategy for Inner Areas. We discuss an analysis of the economic and spatial dynamics of the phenomenon through the construction of the Gini’s coefficient and the estimation of the regression model for the evaluation of the determinants of inequality. We highlight the influence of the spatial dimension on income inequality in Italy. Inequality appears to be greater in densely populated urban centers with a strong incidence of tertiary activities and young population. Conversely, in the inner areas, the distribution of income is more balanced due probably to the weakness of the social and economic structure that determines low levels of income and job opportunities mainly in the agricultural sector.