| Literature DB >> 33150242 |
Yosuke Suzuki1, Makoto Kosaka2, Kana Yamamoto3, Tamae Hamaki3, Eiji Kusumi3, Kenzo Takahashi4, Tetsuya Tanimoto2.
Abstract
INTRODUCTION: The cause of the syphilis resurgence in Japan is still unknown. In this study, we hypothesized that the spread of mobile dating software for use on mobile phones might have contributed to it. We investigated possible contributing factors of the syphilis resurgence in Japan.Entities:
Keywords: Japan; dating app; resurgence of syphilis; sexually transmitted diseases; social media
Year: 2020 PMID: 33150242 PMCID: PMC7590380 DOI: 10.31662/jmaj.2019-0033
Source DB: PubMed Journal: JMA J ISSN: 2433-328X
Syphilis and App Users in a Total Number of 47 Prefectures, Japan.
| Total number of 47 prefectures | |||
|---|---|---|---|
| Both sexes | Male | Female | |
| General population (×100,000) | 1,267 | 617 | 651 |
| Syphilis | 5,826 | 3,931 | 1,895 |
| HIV infection | 1,395 | 1,319 | 76 |
| CRE infection | 1,660 | 1,024 | 636 |
| ISP infection | 3,205 | 1,887 | 1,318 |
| App 1 users | 443,073 | 258,299 | 284,774 |
| App 2 users | 178,963 | 102,788 | 76,175 |
| App 3 users | 126,017 | 72,741 | 53,276 |
HIV: human immunodeficiency virus, CRE: carbapenem-resistant Enterobacteriaceae, ISP: invasive Streptococcus pneumoniae
Syphilis, App Users, and Other Explanatory Variables at the Prefectural Level, Japan.
| Median (range) | |||
|---|---|---|---|
| Both sexes | Male | Female | |
| Prefectural population (×100,000) | 16.3 (5.7–137.2) | 7.6 (2.7–67.6) | 8.6 (3.0–69.7) |
| Syphilis incidence | 2.34 (0.72–12.90) | 3.52 (1.21–18.03) | 1.52 (0.19–8.03) |
| HIV infection incidence | 0.64 (0–3.32) | 1.24 (0–6.42) | 0 (0–0.48) |
| CRE infection incidence | 1.13 (0.27–3.08) | 1.32 (0.35–4.25) | 0.91 (0.10–3.71) |
| ISP infection incidence | 2.42 (0.78–7.28) | 2.93 (0.95–8.32) | 1.71 (0–6.27) |
| App 1 penetration rate | 249 (162–744) | 230 (151–697) | 134 (76–473) |
| App 2 penetration rate | 99 (60–287) | 98 (64–338) | 59(28–173) |
| App 3 penetration rate | 54 (29–243) | 57 (36–209) | 38 (20–155) |
| Foreign national residents per 100,000 population | 1,155 (383–3,797) | ||
| International overnight guests per 100,000 population | 18,678 (1,046–166,018) | ||
| Detachment-type sex trade shops per 100,000 population | 15.1 (5.9–31.3) | ||
| Physician density per 100,000 population | 242.4 (160.1–315.9) | ||
| Smartphone penetration rate | 0.56 (0.46–0.69) | ||
HIV: human immunodeficiency virus, CRE: carbapenem-resistant Enterobacteriaceae, ISP: invasive Streptococcus pneumoniae
Figure 1.Map A indicates prefectural differences in the annual incidence of syphilis in 2017, while maps B, C, and D show penetration rates of each dating app. Prefectures are classified into three categories: the lowest 10 (white areas), the highest 10 (black areas), and intermediate (gray areas).
Figure 2.Scatter plots of syphilis incidence and dating app penetration rates.
Spearman’s Rank Correlation Analyses between Syphilis Incidence and App Penetration Rates (47 Prefectures).
| (Both sexes) | Syphilis | HIV | CRE | IPS |
|---|---|---|---|---|
| Population | 0.50* | 0.33* | 0.02 | 0.03 |
| App 1 penetration rate | 0.59* | 0.41* | 0.02 | 0.29 |
| App 2 penetration rate | 0.57* | 0.34* | 0.02 | 0.25 |
| App 3 penetration rate | 0.56* | 0.43* | 0.02 | 0.27 |
| Foreign national residents per prefectural population | 0.46* | 0.33* | 0.01 | 0.23 |
| International overnight guests per prefectural population | 0.19 | 0.41* | 0.00 | 0.16 |
| Detachment-type sex trade shop per prefectural population | 0.24 | 0.38* | 0.19 | 0.38* |
| Physician density | 0.10 | 0.27 | 0.16 | 0.13 |
| Smartphone penetration rate | 0.54* | 0.48* | 0.14 | 0.15 |
| Population | 0.44* | 0.34* | 0.13 | 0.01 |
| App 1 penetration rate | 0.43* | 0.30* | 0.05 | 0.30* |
| App 2 penetration rate | 0.40* | 0.24 | 0.06 | 0.30* |
| App 3 penetration rate | 0.43* | 0.36* | 0.02 | 0.26 |
| Population | 0.52* | 0.33* | -0.14 | -0.02 |
| App 1 penetration rate | 0.61* | 0.44* | -0.20 | 0.11 |
| App 2 penetration rate | 0.63* | 0.41* | -0.15 | 0.03 |
| App 3 penetration rate | 0.59* | 0.47* | -0.19 | 0.12 |
HIV: human immunodeficiency virus, CRE: carbapenem-resistant Enterobacteriaceae, ISP: invasive Streptococcus pneumoniae, *P < 0.05
Spearman’s Rank Correlation Analyses between Syphilis Incidence and App Penetration Rates (36 Prefectures, Removing Prefectures Containing Cities with 1 Million or Higher Population).
| (Both sexes) | Syphilis | HIV | CRE | IPS |
|---|---|---|---|---|
| Population | 0.27 | 0.17 | -0.13 | -0.12 |
| App 1 penetration rate | 0.39* | 0.24 | -0.21 | 0.23 |
| App 2 penetration rate | 0.37 | 0.13 | -0.22 | 0.16 |
| App 3 penetration rate | 0.37 | 0.31 | -0.25 | 0.23 |
| Foreign national residents per prefectural population | 0.31 | 0.20 | -0.25 | 0.20 |
| International overnight guests per prefectural population | 0.12 | 0.32 | -0.22 | 0.07 |
| Detachment-type sex trade shop per prefectural population | 0.29 | 0.43* | -0.33 | -0.46* |
| Physician density | 0.01 | 0.20 | -0.01 | 0.03 |
| Smartphone penetration rate | 0.47* | 0.40* | -0.40* | 0.10 |
| Population | 0.25 | 0.19 | 0.03 | -0.15 |
| App 1 penetration rate | 0.22 | 0.11 | -0.11 | 0.27 |
| App 2 penetration rate | 0.13 | 0.00 | -0.13 | 0.23 |
| App 3 penetration rate | 0.23 | 0.22 | -0.16 | 0.21 |
| Population | 0.20 | 0.16 | -0.31 | -0.16 |
| App 1 penetration rate | 0.40* | 0.28 | -0.45* | -0.01 |
| App 2 penetration rate | 0.45* | 0.23 | -0.38* | -0.13 |
| App 3 penetration rate | 0.37 | 0.36 | -0.42* | 0.05 |
HIV: human immunodeficiency virus, CRE: carbapenem-resistant Enterobacteriaceae, ISP: invasive Streptococcus pneumoniae, *P < 0.05
The Optimized Models for Syphilis Incidence by Multiple Linear Regression Analyses.
| App 1 | ||||
|---|---|---|---|---|
| Standard partial regression coefficient | Standard error | T value | P value | |
| Variables | ||||
| (Intercept) | 0.94 | 0.063 | 15.0 | 0 |
| App 1 penetration rate | 0.39 | 0.064 | 6.0 | < 0.001 |
| Detachment-type sex trade shops per prefectural population | 0.18 | 0.064 | 2.8 | 0.008 |
| Adjusted R-squared value = 0.49, F-value = 22.97, P-value < 0.001 | ||||
| Variables | Standard partial regression coefficient | Standard error | T value | P value |
| (Intercept) | 0.94 | 0.065 | 14.5 | 0 |
| App 2 penetration rate | 0.37 | 0.066 | 5.6 | < 0.001 |
| Detachment-type sex trade shops per prefectural population | 0.19 | 0.066 | 2.8 | 0.007 |
| Adjusted R-squared value = 0.48, F-value = 20.5, P-value < 0.001 | ||||
| Variables | Standard partial regression coefficient | Standard error | T value | P Value |
| (Intercept) | 0.94 | 0.065 | 14.5 | 0 |
| App 3 penetration rate | 0.37 | 0.066 | 5.6 | < 0.001 |
| Detachment-type sex trade shops per prefectural population | 0.18 | 0.068 | 2.6 | 0.013 |
| Physician density | 0.10 | 0.068 | 1.4 | 0.17 |
Adjusted R-squared value = 0.46, F-value = 13.89, P-value < 0.001