| Literature DB >> 36193127 |
Guoming Chen1,2, Chuyao Huang1, Dongqiang Luo1, Jiawei Yang1, Yuzhen Shi1, Danyun Li1, Zhuoyao Li1, Tie Song3, Hua Xu4, Fen Yang3.
Abstract
Objectives: To describe the epidemiological characteristics and medication overview of HFMD in Guangzhou and analyze the factors of length of stay (LOS) based on TCM usage. Method: From January 1, 2014, to June 30, 2019, clinical data of HFMD (ICD-10 B08.401) as the initial diagnosis, based on HIS of five medical institutions for outpatient and inpatient cases, was collected. The inpatient cases of the five hospitals in Guangzhou were utilized for hospitalization analysis. Information extracted from the warehouse was standardized. Descriptive analysis was used for baseline characteristics, medication usage, and inpatient characteristics. Potential factors were analyzed by bivariate analysis. COX regression analysis and Kaplan-Meier analysis for calculating HRs and 95% CIs were adopted to determine the predictors of LOS. Stratified COX regression was applied to analyze the relationship between predictors and LOS and to calculate interaction.Entities:
Year: 2022 PMID: 36193127 PMCID: PMC9526671 DOI: 10.1155/2022/9156186
Source DB: PubMed Journal: Evid Based Complement Alternat Med ISSN: 1741-427X Impact factor: 2.650
Baseline characteristics.
| Characteristic | Patients ( |
|---|---|
| Gender | |
| Male | 8807 |
| Female | 5365 |
| Age, year | |
| Neonate | 11 |
| Infant | 7612 |
| Preschool | 5830 |
| Child | 540 |
| Adolescent | 44 |
| Adult | 135 |
| Visit time | |
| Spring (3–5) | 3801 |
| Summer (6–8) | 4857 |
| Autumn (9–11) | 3680 |
| Winter (12–2) | 1834 |
| Type of hospital | |
| Outpatient | 10426 |
| Inpatient | 3746 |
| Visit frequency | |
| 1–3 | 14112 |
| 4–6 | 58 |
| >6 | 2 |
Chinese herb use (Top 10).
| Rank | Herbal drug | Efficacy | Frequency ( |
|---|---|---|---|
| 1 | Glycyrrhizae radix et rhizoma | Tonifying and replenishing | 1871 |
| 2 | Forsythiae Fructus | Heat-clearing, detoxicating | 1545 |
| 3 | Pogostemonis Herba | Resolving dampness | 1155 |
| 4 | Belamcandae Rhizoma | Heat-clearing, detoxicating | 1060 |
| 5 | Phragmitis Rhizoma | Heat-clearing and fire-purging | 988 |
| 6 | Lophatheri Herba | Heat-clearing and fire-purging | 984 |
| 7 | Coicis semen | Dampness-draining diuretic | 829 |
| 8 | Platycodonis Radix | Clearing and resolving heat-phlegm | 711 |
| 9 | Scutellariae Radix | Clearing heat and drying dampness | 697 |
| 10 | Menthae Haplocalycis Herba | Releasing the exterior with pungent-cool | 693 |
Chinese patent medicine use (Top 10).
| Rank | Chinese patent medicine | Usage | Frequency ( |
|---|---|---|---|
| 1 | Kangfu Xinye | External application | 3619 |
| 2 | Chushi zhiyang xiye | External application | 1944 |
| 3 | Kaihoujian penwuji | Aerosol inhalation | 1473 |
| 4 | Kouqiangyan penwuji | Aerosol inhalation | 1379 |
| 5 | Jian'er qingjie ye | Oral administration | 1306 |
| 6 | Fuganlin koufuye | Oral administration | 1080 |
| 7 | Sihuangxiaoyan xiji | External application | 1023 |
| 8 | Qingrejieduqushi keli | Oral administration | 908 |
| 9 | Fufang yuxingcao keli | Oral administration | 751 |
| 10 | Jinlian qingre Paotengpian | Oral administration | 606 |
Systemic medication use (TOP 10).
| Rank | Treatment | Frequency ( |
|---|---|---|
| 1 | Antiviral drug | 3269 |
| 2 | Cephalosporin | 1929 |
| 3 | Adrenocortical hormones | 1732 |
| 4 | Penicillins | 1374 |
| 5 | M Receptor blocker | 1158 |
| 6 | H2 receptor blocker | 933 |
| 7 | Adrenoceptor agonists | 757 |
| 8 | Benzodiazepines | 721 |
| 9 | Mucolytic agents | 571 |
| 10 | Antituberculous drugs | 390 |
Characteristics of HFMD patient group based on the proportion of TCM use.
| Proportion of TCM use |
| |||
|---|---|---|---|---|
| TCM < 0.1 ( | TCM ≥ 0.1 ( | |||
| Age | Neonate | 2 | 2 | <0.01 |
| Infant | 1032 | 1227 | ||
| Preschool | 644 | 649 | ||
| Child | 17 | 40 | ||
| Adolescent | 1 | 1 | ||
| Adult | 2 | 0 | ||
|
| ||||
| Sex | Male | 1093 | 1253 | 0.53 |
| Female | 605 | 664 | ||
|
| ||||
| Season | Spring | 483 | 495 | <0.01 |
| Summer | 669 | 725 | ||
| Autumn | 396 | 571 | ||
| Winter | 150 | 126 | ||
|
| ||||
| Disease severity | Mild | 1512 | 1872 | <0.01 |
| Severe | 186 | 45 | ||
Univariate COX regression results.
| Coef | HR |
| 95% CI | ||
|---|---|---|---|---|---|
| Age | Adolescent | ||||
| Neonate | 2.50 | 12.15 | 0.01 | (1.70, 86.89) | |
| Infant | 1.50 | 4.47 | 0.04 | (1.11, 18.07) | |
| Preschool | 1.47 | 4.34 | 0.04 | (1.07, 17.53) | |
| Child | 1.57 | 4.82 | 0.03 | (1.17, 19.94) | |
| Adult | 0.53 | 1.70 | 0.60 | (0.24, 12.12) | |
|
| |||||
| Sex | Male | ||||
| Female | 0.03 | 1.03 | 0.40 | (0.96, 1.10) | |
|
| |||||
| Season | Autumn | ||||
| Spring | −0.15 | 0.86 | 0.00 | (0.79, 0.94) | |
| Summer | −0.14 | 0.87 | 0.00 | (0.80, 0.95) | |
| Winter | 0.12 | 0.12 | 0.09 | (0.98, 1.28) | |
|
| |||||
| Proportion of TCM use | <0.1 | ||||
| ≥0.1 | 0.64 | 0.53 | 0.00 | (0.49, 0.56) | |
|
| |||||
| Disease condition | Mild | ||||
| Severe | −0.83 | 0.44 | 0.00 | (0.38, 0.50) | |
Figure 1Kaplan–Meier for factors of hospitalization days of HFMD. (a) Comparison of gender. (b) Comparison of disease type. (c) Comparison of the proportion of TCM use).
The relationship of LOS and factors in a patient with HFMD.
| Factors | Model1 | Model2 | Model3 |
|---|---|---|---|
| TCM ≥ 0.1 | 1.90 (1.77–2.03) | 1.90 (1.78–2.04) | 1.79 (1.67–1.92) |
| Age | |||
| Neonate and infant | |||
| Preschool | 0.99 (0.92–1.06) | 0.98 (0.92–1.05) | 0.96 (0.89–1.03) |
| Child | 0.97 (0.74–1.26) | 0.96 (0.74–1.26) | 0.99 (0.76–1.28) |
| Sex (female) | 1.03 (0.96–1.10) | 1.03 (0.96–1.10) | 1.03 (0.96–1.10) |
| Season | |||
| Spring | |||
| Summer | 1.00 (0.92–1.09) | 1.02 (0.94–1.11) | |
| Autumn | 1.10 (1.00–1.20) | 1.09 (1.00–1.20) | |
| Winter | 1.31 (1.15–1.50) | 1.28 (1.12–1.47) | |
| Disease (Nonserious) | 1.93 (1.69–2.22) | ||
| Additive interaction: RERI = 1.014 (0.493–1.534), | |||
| Multiplicative interaction: Disease | |||
Model1: adjust age, sex; Model2: adjust age, sex, season; Model3: adjust age, sex, season, disease.
Figure 2The analysis between LOS and factors is based on a forest plot of stratified COX regression.