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

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## Abstract

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## 1. Introduction

#### 1.1. Research Issues

#### 1.2. Literature Review

## 2. Materials and Methods

#### 2.1. Research Models

^{2}), health technical personnel, beds in health care institutions and health investment, while Beijing occupied the second place [25]. Second, those two cities are often formally treated as special cases, compared to any other cities in China. One study [26] revealed that Shanghai with the highest level of economic development had more advanced computed tomography and magnetic resonance imaging machines, and higher government subsidies on these two types of equipment.

#### 2.2. Materials

#### 2.3. Measures

#### 2.3.1. Gini Coefficient: Quantifying the Distribution of Service Inequality

#### 2.3.2. Measures of Doctors’ Endorsement

#### 2.3.3. Propensity Score: Measure of the Likelihood Being Treated

#### 2.4. Statistical Analyses

## 3. Results

#### 3.1. Overlap of the Confounding Variables

#### 3.2. Lorenz Curve of the Inequality Service

#### 3.3. Causal Effects of City-level on Services Inequality

## 4. Discussion

#### 4.1. Principal Results

#### 4.2. Limitations

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## List of Abbreviations

ATE | Average treatment effect |

OHC | online health community |

$\mathrm{SCGini}$ | the specialty category’s Gini coefficient |

O2O | online-to-offline |

SP | served patients |

OR | online reviews |

$NDA$ | the mean of the number of Doctors’ articles |

$BVS$ | the breadth of the voted specialties |

$DRR$ | the ratings in user reviews of the doctors |

$DOC$ | the contribution score for the doctors |

CTM | Chinese traditional medicine |

PSM | propensity score matching |

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**Figure 1.**Unequal geograpghical distribution of medical resources in the investigated online health community. Beijing and Shanghai are the cites (city level = 1) with richer healthcare resources (including a larger population of doctors) and patients than those of the other cities (city level = 0). The size of circles indicates the number of patients, and the darkness of the color in the circles indicates the number of doctors. Data were collected from the online health service platform www.haodf.com on 26 June 2017.

**Figure 3.**Distribution of Propensity Scores with the Experimental Data. (

**a**) Diagnostics Graphical Plot, (

**b**) Absolute Standard Difference Means.

**Figure 4.**Lorenz Curve of the Empirical Experimental Data on Patient and Views Before Matching and After Matching. The Horizontal Axis Represents the Rank Percentile $p$ of Severed Patients.

Variables | Definitions | Measurements |
---|---|---|

Dependent Variables | ||

$SCGin{i}_{j}$(SP) | Specialty category’s Gini coefficient of serviced patients | Gini coefficient of doctors’ service delivery (serviced patients) for the doctors clustered in specialty category j |

$SCGin{i}_{j}$(OR) | Specialty category’s Gini coefficient of online views | Gini coefficient of doctors’ online views for the doctors clustered in specialty category j |

Covariates | ||

$NDAMe{a}_{j}$ | Average number of articles | Average number of articles of the doctors clustered in specialty category j, and $ND{A}_{i}$ is the number of articles of the doctor i |

$BVSMe{a}_{j}$ | Average breadth of service diversity | Average breadth of the voted specialties (from patient votes) of all the doctors clustered in specialty category j, and $BV{S}_{i}$ is the breadth of the voted specialties (from patient votes) of the doctor i |

$DRRMe{a}_{j}$ | Average doctor review rating | Mean of the overall ratings in user reviews of the doctors clustered in the specialty category $j$ (scoring from 1–5, already excluding 0), and $DR{R}_{i}$ is the number of the overall ratings in user reviews of the doctor i |

$DOCMe{a}_{j}$ | Average doctor online contribution | Mean of doctors’ online contribution across the category’s doctors clustered in specialty category $j$, and $DO{C}_{i}$ is the number of doctors’ online contribution of the doctor i |

Treatment variables: Divide the Samples Separately | ||

$CIT{Y}_{i}$ | City level | A dummy variable ${T}_{i}\text{}$ with two levels: level-1 indicates doctors from resource-rich cities (Beijing and Shanghai); level-0 indicates doctors from other cities |

Variables | Focus Cases (n = 2603) | Matched Controls (n = 2603) | 95% CI * in Difference After Matching | p-Value After Matching |
---|---|---|---|---|

$NDA\left(i\right)$ | 31.235 | 31.581 | (−6.521; 7.215) | 0.921 |

$BVS\left(i\right)$ | 9.244 | 9.376 | (−0.067; 0.330) | 0.194 |

$DRR\left(i\right)$ | 2.818 | 2.809 | (−0.048; 0.029) | 0.628 |

$DOC\left(i\right)$ | 34065.2 | 32516.6 | (−4903.7; 1806.7) | 0.366 |

Mean of Focus Cases | Mean of Matched Controls | 95% CI * in Difference | p-Value | |
---|---|---|---|---|

Patients before matching | 1698 | 2680 | (−1158; −805) | <0.001 |

Patients after matching | 2465 | 2680 | (−436; 6) | 0.056 |

Views before matching | 1,065,312 | 2,191,087 | (−1340802; −910749) | <0.001 |

Views after matching | 1,771,188 | 2,191,087 | (−695284; −144514) | 0.003 |

Gini of Focus Cases | Gini of Controls After Matching | Gini of Controls Before Matching | Gini of All the Cases after Matching | Gini of All the Cases Before Matching | |
---|---|---|---|---|---|

SP | 0.635 | 0.629 | 0.604 | 0.632 | 0.622 |

OR | 0.758 | 0.789 | 0.780 | 0.774 | 0.783 |

Difference | 0.123 | 0.16 | 0.176 | 0.142 | 0.161 |

n | 2603 | 2603 | 7041 | 5206 | 9644 |

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**MDPI and ACS Style**

Yu, H.-Y.; Chen, J.-J.; Wang, J.-N.; Chiu, Y.-L.; Qiu, H.; Wang, L.-Y.
Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community. *Int. J. Environ. Res. Public Health* **2019**, *16*, 2314.
https://doi.org/10.3390/ijerph16132314

**AMA Style**

Yu H-Y, Chen J-J, Wang J-N, Chiu Y-L, Qiu H, Wang L-Y.
Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community. *International Journal of Environmental Research and Public Health*. 2019; 16(13):2314.
https://doi.org/10.3390/ijerph16132314

**Chicago/Turabian Style**

Yu, Hai-Yan, Jing-Jing Chen, Jying-Nan Wang, Ya-Ling Chiu, Hang Qiu, and Li-Ya Wang.
2019. "Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community" *International Journal of Environmental Research and Public Health* 16, no. 13: 2314.
https://doi.org/10.3390/ijerph16132314