| Literature DB >> 35757475 |
Dong Yang1,2,3,4, Nengfu Bin5, Ziyan Zhou1,3,4, Zhiru Li1,3,4, Mingjun Shen1,3,4, Chaolin Yang1,3,4, Yating Qin1,3,4, Rensheng Wang1,3,4, Wei Lv5, Bo Wei6, Lifang Zhou7,8, Min Kang1,3,4.
Abstract
Objective: Nasopharyngeal carcinoma is particularly prevalent in Guangdong and Guangxi (southern China); the economic burden of nasopharyngeal cancer patients is heavy in China. This study is aimed at retrospectively analyzing the basic features and economic burden of newly diagnosed nasopharyngeal carcinoma patients admitted to the First Affiliated Hospital of Guangxi Medical University and at providing a scientific basis for nasopharyngeal carcinoma prevention and control strategies.Entities:
Mesh:
Year: 2022 PMID: 35757475 PMCID: PMC9217537 DOI: 10.1155/2022/6958806
Source DB: PubMed Journal: Biomed Res Int Impact factor: 3.246
Figure 1CONSORT diagram of the patient selection process.
Gender comparison of nasopharyngeal carcinoma patients in different years.
| Year | Male | Female | Total | Men-women ratio | |||
|---|---|---|---|---|---|---|---|
| Cases | Constituent ratio (%) | Cases | Constituent ratio (%) | Cases | Constituent ratio (%) | ||
| 2012 | 146 | 72.64 | 55 | 27.36 | 201 | 5.39 | 2.65 |
| 2013 | 181 | 72.98 | 67 | 27.02 | 248 | 6.65 | 2.70 |
| 2014 | 357 | 75.64 | 115 | 24.36 | 472 | 12.67 | 3.10 |
| 2015 | 358 | 77.32 | 105 | 22.68 | 463 | 12.42 | 3.41 |
| 2016 | 356 | 75.91 | 113 | 24.09 | 469 | 12.58 | 3.15 |
| 2017 | 366 | 72.62 | 138 | 27.38 | 504 | 13.52 | 2.65 |
| 2018 | 314 | 72.85 | 117 | 27.15 | 431 | 11.57 | 2..68 |
| 2019 | 351 | 73.43 | 127 | 26.57 | 478 | 12.83 | 2.76 |
| 2020 | 336 | 72.89 | 125 | 27.11 | 461 | 12.37 | 2.69 |
| Total | 2765 | 74.03 | 962 | 25.97 | 3727 | 100 | 2.87 |
Age distribution of patients.
| Age group (years) | Male | Female | Total | Men-women ratio | Constituent ratio (%) |
|---|---|---|---|---|---|
| <20 | 24 | 12 | 36 | 2 | 0.97 |
| 20~ | 148 | 57 | 205 | 2.60 | 5.50 |
| 30~ | 526 | 217 | 743 | 2.42 | 19.93 |
| 40~ | 970 | 304 | 1274 | 3.19 | 34.18 |
| 50~ | 731 | 251 | 982 | 2.91 | 26.35 |
| 60~ | 309 | 108 | 417 | 2.86 | 11.19 |
| 70~ | 55 | 12 | 67 | 4.58 | 1.80 |
| 80~ | 2 | 1 | 3 | 2 | 0.08 |
| Total | 2765 | 962 | 3727 | 2.87 | 100 |
Figure 2Ethnic distribution of patients.
Variation trend of average length of stay.
| Year | Average length of stay (days) | Annual increment (days) | Link relative ratio (%) |
|---|---|---|---|
| 2012 | 46.32 ± 20.55 | — | — |
| 2013 | 43.84 ± 20.11 | -2.48 | -5.35 |
| 2014 | 39.23 ± 23.76 | -4.61 | -10.52 |
| 2015 | 37.17 ± 24.86 | -2.06 | -5.25 |
| 2016 | 37.75 ± 23.25 | 0.58 | 1.56 |
| 2017 | 32.67 ± 22.97 | -5.08 | -13.46 |
| 2018 | 31.47 ± 24.63 | -1.2 | -3.67 |
| 2019 | 40.99 ± 15.34 | 9.52 | 30.25 |
| 2020 | 35.42 ± 16.75 | -5.57 | -13.59 |
| Average value | 38.32 ± 4.61 | −1.36 | -2.50 |
Variation trend of hospitalization expenses.
| Year | Hospitalization cost per case (yuan) | Annual increment (yuan) | Link relative ratio (%) |
|---|---|---|---|
| 2012 | 54312.95 | — | — |
| 2013 | 63901.44 | 9588.49 | 17.65 |
| 2014 | 56635.44 | -7266 | -11.37 |
| 2015 | 55187.16 | -1448.28 | -2.55 |
| 2016 | 59009.9 | 3822.74 | 6.92 |
| 2017 | 59600.7 | 590.8 | 1.00 |
| 2018 | 57750.38 | -1850.32 | -3.10 |
| 2019 | 90443.44 | 32693.06 | 56.61 |
| 2020 | 85988.87 | -4454.57 | -4.93 |
| Average value | 64758.92 | 3959.49 | 7.53 |
Analysis of partial factors influencing total hospitalization costs.
| Factors |
| Hospitalization cost (yuan) |
|
|---|---|---|---|
| Gender | |||
| Female | 962 | 76470.07 ± 42281.67 | 0.807 |
| Male | 2765 | 77305.90 ± 42954.43 | |
| Ethnicity | |||
| Han | 2153 | 68800.63 ± 43354.23 | ≤0.001 |
| Zhuang | 1460 | 60624.60 ± 41663.67 | |
| Others | 114 | 68787.48 ± 40027.48 | |
| Rural/urban | |||
| Rural | 2223 | 61454.37 ± 41559.61 | ≤0.001 |
| Urban | 1504 | 71720.99 ± 43815.87 |
Figure 3Scatter plot of the relationship between total hospitalization cost and age (a) and hospitalization days (b). P values were calculated with the Spearman rank correlation calculation.
Multiple linear regression analysis of hospitalization expenses.
| Variable |
| SE |
|
|
|
|---|---|---|---|---|---|
| Constant | 3.992 | 0.011 | 373.311 | ≤0.001 | |
| Ethnic group | -0.019 | 0.008 | -0.022 | -2.204 | 0.028 |
| Rural/urban | 0.027 | 0.010 | 0.027 | 2.780 | 0.005 |
| Length of stay | 0.017 | 0.000 | 0.800 | 81.675 | ≤0.001 |
Impact of comparison between rural and urban patients on total hospitalization costs.
| Variable |
| SE |
|
|
|
|---|---|---|---|---|---|
| Constant | 4.698 | 0.012 | 381.648 | ≤0.001 | |
| Rural | -0.093 | 0.016 | -0.095 | -5.825 | ≤0.001 |
| Urban | 0 |