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標題: | 移動式COVID-19檢測單位:醫療工作者的熱壓力及腎損傷 Mobile COVID-19 Screening Units: Heat Stress and Kidney Injury Among Health Care Workers |
作者: | 張登翔 Teng-Hsiang Chang |
指導教授: | 楊孝友 Hsiao-Yu Yang |
關鍵字: | 熱壓力,COVID-19,個人防護具,腎損傷,職業性,知識、態度、行為研究, heat stress,COVID-19,PPE,kidney injury,occupational,KAP survey, |
出版年 : | 2023 |
學位: | 碩士 |
摘要: | 序論
(1) 研究背景:在COVID-19大流行期間,熱暴露已成為醫療工作者的全新風險,然而熱壓力對醫療工作者腎損傷的影響尚不清楚。過往已有研究以「知識、態度、行為」來評估熱相關症狀和感染控制,但對腎臟傷害的影響並不清楚。 (2) 研究目的:(a) 評估穿戴個人防護設備的醫療工作者,熱壓力對腎功能的急性效應;(b) 評估有關熱壓力和使用個人防護具的「知識、態度、行為」對腎功能下降的影響。 方法 (1) 研究設計:事前及事後研究設計(before-after study或稱pre-post study)。 (2) 研究設定:於2021年9月至10月從臺北市篩檢巴士團隊總部收案。 (3) 研究對象:18歲以上且至少每個工作天在篩檢巴士工作1小時的醫療工作者。排除的條件為高血壓病史、糖尿病病史、腎結石病史、非類固醇止痛藥使用者、懷孕者。 (4) 變數:工作型態(行政護理師、篩檢護理師、駕駛);穿戴防護衣類型(P1、P3)。我們根據國際標準組織第8996號標準及英國國家標準局第7963號標準計算代謝率。我們以尿液分析中檢測到的蛋白質大於30 毫克/分升定義為蛋白尿,以全血肌酸酐上升超過0.3毫克/分升或上升超過1.5倍定義為單次腎損傷(incident kidney injury)。我們以工作前尿液比重大於等於1.020定義為工作前脫水,以工作後體重損失超過1.5%定義為工作後脫水。症狀包括頭痛、頭暈、口渴、嘔吐、過度流汗、呼吸困難、希望移動到比較舒適區域、其他不適症狀,受試者在從事這個工作之後,於工作中穿戴防護衣時至少發生一次上述症狀定義為有症狀。我們以工作期間飲用的所有液體定義為工作期間液體攝取量。我們以三個月內仍有抽菸習慣定義為抽菸。 (5) 資料收集與測量:我們以溫度及相對濕度計算熱指數(heat index),我們由臺北市氣象測站取得工作時段連續監測的溫度及相對溼度計算環境熱指數;我們以個人連續監測感測器紀錄工作時段防護衣內部的溫度及相對溼度,計算個人熱指數。我們在工作前、後以醫療級紅外線耳溫槍量測受試者的耳溫、以醫療用體重計量測受試者的體重並確保前、後測穿著同一套工作服、以血壓計量測坐姿休息5分鐘以上的受試者的血壓、收集尿液並於30分鐘之內以尿液試紙檢測尿蛋白及尿比重。我們垂直穿刺指尖採血,以手持式腎功能分析儀進行全血肌酸酐即時檢驗(point-of-care testing)。我們以結構化問卷取得性別、身高、工作期間液體攝取量、熱壓力相關的症狀、工作中的個人行為。我們以結構化問卷取得與熱壓力及穿戴防護具相關的「知識、態度、行為」。 (6) 偏誤:本研究以事前及事後研究設計平衡個體間的干擾因子,所有受試者皆作為自己的對照組,因此可控制潛在的人與人之間的干擾因子。代謝率可以透過年齡、性別、體重、心跳速率估算,我們並未直接量測受試者在工作期間的心跳速率,因此無法直接估算,僅能透過工作內容及工作期間穿著的防護具推估。我們以即時檢驗設備量測全血肌酸酐,因此使用上仰賴操作者的技巧。在紀錄水分飲用量時,我們僅經由問卷及面談,請受試者自行回報飲用量,這樣的資料取得方式會有回憶偏差,不過這樣的偏差程度在各受試者之間沒有明顯的差別。 (7) 樣本量:本研究為第一個進行移動式檢測單位的醫護人員的熱壓力及腎功能研究,未有過去的研究結果可參考估計所需的樣本量,因此我們盡可能納入最多的受試者並以護理師工作前、後腎功能差異之成對樣本t 檢定,計算本研究在現有的樣本量下具有多少的檢定力(power)。 (8) 量性變數:我們以身高及體重計算身體質量指數。我們使用慢性腎臟病流行病公式以全血肌酸酐計算腎絲球過濾率(estimated glomerular filtration rate, eGFR),以工作後的eGFR減去工作前的eGFR 定義為工作後腎功能變化(ΔeGFR),為腎功能下降的指標。我們以兩次檢驗肌酸酐的結果估計動態腎絲球過濾率(kinetic glomerular filtration rate, kGFR),評估短時間內的腎臟損傷。 (9) 數據分析方法:我們使用獨立樣本t檢定、卡方檢定或費雪精確檢定(任一欄位小於5時)進行護理師及駕駛的描述性統計,使用成對樣本t檢定分析前、後測的個人量測數值。使用獨立樣本t檢定進行症狀、風險因子及保護因子之單變數分析。我們以建構效度、聚合效度、區別效度評估「知識、態度、行為」問卷的效度;以內部一致性信度及組成信度評估「知識、態度、行為」問卷的信度。我們以結構方程模式分析(Structural Equation Modeling)描述「知識、態度、行為」構念之間的關係,以及描述「知識、態度、行為」對腎功能下降的影響。 結果 (1) 研究對象:此研究共招募50位醫療工作者,我們排除前5位受試者作為前導研究,在最後分析中納入45位受試者,其中包含39位護理師及6位駕駛。 (2) 描述性統計:工作時段環境溫度為32.4±1.3℃,熱指數為39.2±3.7℃。在個人量測中,行政護理師的熱指數為40.9±4.9℃;篩檢護理師的熱指數為46.8±6.1℃。駕駛的年齡更大、體重更重,基礎的腎功能也較護理師差。 (3) 主要結果:在單次腎損傷部分,護理師發生比例高於駕駛(23.1% versus 0%,p=0.6)。護理師較駕駛有顯著較差的動態腎絲球過濾率(18.9 versus 93.0 毫升/分鐘,p=0.001)。護理師工作後過濾率下降(104.6 versus 81.2 毫升/分鐘/1.73 公尺²,p<0.001),但在駕駛沒有此發現(79.3 versus 80.0 毫升/分鐘/1.73公尺²,p=0.9)。在蛋白尿部分,護理師發生比例高於駕駛(10.3% versus 0%,p=0.9)。護理師有工作後的核心體溫上升(36.4 versus 36.6℃,p<0.001)及體重下降(59.4 versus 59.2 公斤,p=0.05)。沒有任何一位受試者有工作後脫水的情形。 (4) 其他分析: (a) 知識、態度、行為分析 我們根據因素(factor)的所屬題項(item),辨識因素一為「穿戴防護具的負面行為」構念;因素二為「熱壓力的負面態度」構念;因素三為「熱適應的知識」構念。結構方程模式分析的測量模型中,「穿戴防護具的負面行為」與「熱壓力的負面態度」有正相關(相關係數=0.20,p=0.001)。「穿戴防護具的負面行為」與「熱適應的知識」(相關係數=0.04,p=0.7)、「熱壓力的負面態度」與「熱適應的知識」(相關係數=-0.03,p=0.7)無明顯相關。我們發現「穿戴防護具的負面行為」造成ΔeGFR 惡化(標準化迴歸係數=-0.22,p=0.1),「熱壓力的負面態度」(標準化迴歸係數=0.01,p=0.9)及「熱適應的知識」(標準化迴歸係數=-0.04,p=0.9)沒有顯著影響。 (b) 症狀、風險因子及保護因子之單變數分析 有頭痛者的ΔeGFR顯著惡化(-33.5 versus -14.5 毫升/分鐘/1.73公尺²,p<0.001)。工作前避免飲水及用餐以避免上廁所(-29.6 versus -16.8 毫升/分鐘/1.73公尺²,p=0.03)使ΔeGFR更加惡化,工作中喝含糖飲料(-27.9 versus -20.6 毫升/分鐘/1.73公尺²,p=0.2)使ΔeGFR更加惡化但未達顯著,水分中加電解質(-20.2 versus -25.0 毫升/分鐘/1.73公尺²,p=0.4)對ΔeGFR有保護的作用但未達顯著。 (c) 次群組分析 行政護理師較篩檢護理師ΔeGFR下降明顯較多(-33.7 versus -19.8 毫升/分鐘/1.73公尺²,p=0.03)。 結論 (1) 關鍵結果:醫療工作者穿著個人防護具於戶外環境工作會造成腎臟急性傷害。穿戴防護具的負面行為導致工作後的腎功能下降更嚴重。 (2) 限制:以氣象測站的溫溼度推估受試者的環境熱暴露不如直接量測篩檢巴士的環境熱暴露,然而將溫溼度計放置在篩檢現場有高度的感染控制疑慮,且在儀器回收後有消毒的困難。我們估計體重時未要求受試者脫除或更換浸滿汗水的衣物再進行工作後測量,因此在估計體重的下降量時有低估的情形。以耳溫的量測推估核心體溫,準確度並不如量測肛溫,但在篩檢工作現場進行肛溫量測有困難。 (3) 判讀:我們發現單次篩檢工作就可以產生腎臟傷害,是否會有長期的影響仍需未來的研究討論。 (4) 外推性:我們將大部分臺北市篩檢巴士團隊的醫療工作者納入收案,此研究結果可反應夏秋季同樣工作類型的醫療工作者的熱危害,但是無法外推到非同樣氣候狀況或工作條件的醫療工作者。 Introduction (1) Background: During the COVID-19 pandemic, heat exposure has become a new occupational hazard for healthcare workers. However, the impact of heat stress on renal injury in healthcare workers remains unclear. Previous studies have assessed heat-related symptoms and infection control by "knowledge, attitudes, and practices" but the effect on kidney injury is not well understood. (2) Objectives: (a)To assess the acute effects of heat stress on renal function in healthcare workers wearing personal protective equipment (PPE). (b)To evaluate the impact of “knowledge, attitude, and practices” (KAP) related to heat stress and PPE on renal function decline. Method (1) Study design: before-after study (pre-post study) (2) Setting: We collected the data from the Taipei City Screening Bus Team headquarters from September to October 2021. (3) Participants: Healthcare workers aged 18 and above who worked at least 1 hour per workday on the screening bus. Exclusion criteria included a history of hypertension, diabetes, kidney stones, non-steroidal anti-inflammatory drug use, and pregnancy. (4) Variables: Job type (administrative nurse, screening nurse, driver); type of protective clothing (P1, P3). We calculate the metabolic rate based on ISO 8996 standard by the International Organization for Standardization and BS 7963 standard by the British Standards Institution. We defined proteinuria as the presence of protein greater than 30 milligrams per deciliter in urine analysis. Incident kidney injury was defined as an increase in whole blood creatinine exceeding 0.3 milligrams per deciliter or an increase of more than 1.5 times. Dehydration before work was defined as a urine specific gravity of equal to or greater than 1.020, while post-work dehydration was defined as a body weight loss exceeding 1.5%. Symptoms included headache, dizziness, thirst, vomiting, excessive sweating, difficulty breathing, desire to move to a more comfortable area, and other discomfort. Experiencing any of these symptoms at least once while wearing PPE during work was defined as having symptoms. Fluid intake during work was defined as all liquids consumed during working hours. Smoking was defined as still having a smoking habit within three months. (5) Data sources and measurement: We calculated the heat index based on temperature and relative humidity. The environmental heat index was calculated using the temperature and relative humidity obtained from the Taipei City weather station. The personal heat index was calculated using continuous monitoring sensors that recorded the temperature and relative humidity inside the protective clothing during work hours. Before and after work, we measured the participants' ear temperature using a medical-grade infrared ear thermometer. We also measured their body weight using a medical-grade scale, ensuring that they wore the same set of work clothes for both measurements. Blood pressure was measured for participants who rested in a seated position for at least 5 minutes using a blood pressure monitor. Urine samples were collected and we used urine test strips to test urine protein and specific gravity within 30 minutes. Fingerstick blood samples were obtained through vertical puncture, and a handheld renal function analyzer was used for point-of-care testing of whole blood creatinine levels. Gender, height, fluid intake during work hours, heat stress-related symptoms, and personal behavior during work were obtained through a structured questionnaire. KAP related to heat stress and PPE were obtained through a structured questionnaire. (6) Bias: We use a before-after study design to control the confounding factors between individuals, with all participants serving as their own control group. Metabolic rate can be estimated through age, gender, weight, and heart rate. We did not directly measure the heart rate of the subjects during work, so we cannot estimate it directly. We can only estimate it through the nature of the work and the protective equipment worn during the work period. We relied on the operator's skills for the real-time measurement of whole blood creatinine using the point-of-care testing device. When recording fluid intake, we relied on questionnaires and interviews, where participants self-reported their intake. This may lead to recall bias; however, the extent of this bias did not show significant differences among the participants. (7) Study size: This study is the first study to investigate the thermal stress and renal function of healthcare personnel using a mobile screening unit. There are no previous research results available to estimate the required sample size. Therefore, we included as many participants as possible and conducted paired sample t-tests on the differences in renal function before and after working shifts to calculate the statistical power with the acquired sample size. (8) Quantitative variables: We calculated the body mass index (BMI) using height and weight. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation, based on the serum creatinine levels in whole blood. The change in renal function after work (ΔeGFR) was defined as the difference between post-shift eGFR and pre-shift eGFR, serving as an indicator of decline in renal function. The kinetic glomerular filtration rate (kGFR), which estimates the glomerular filtration rate over a short period of time, was assessed by measuring creatinine levels twice, allowing us to evaluate short-term kidney injury. (9) Statistical methods: Descriptive statistics for nurses and drivers were analyzed using independent samples t-test, chi-square test, or Fisher's exact test (when any cell count was less than 5). Paired samples t-test was used to analyze the individual measurement values before and after the intervention. Univariate analysis of symptoms, risk factors, and protective factors was conducted using independent samples t-test. The validity of the KAP questionnaire was evaluated using construct validity, convergent validity, and discriminant validity. The reliability of the KAP questionnaire was assessed using internal consistency reliability and composite reliability. Structural equation modeling was employed to describe the relationships between the constructs of KAP and to examine the impact of KAP on the decline in renal function. Result (1) Participants: A total of 50 healthcare workers were recruited for this study. The first 5 participants were excluded as a pilot study. In the final analysis, 45 participants were included, including 39 nurses and 6 drivers. (2) Descriptive data: The environmental temperature during work hours was 32.4±1.3°C, with a heat index of 39.2±3.7°C. In terms of individual measurements, the heat index for administrative nurses was 40.9±4.9°C, while it was 46.8±6.1°C for screening nurses. Drivers were older, having higher BMI, and had poorer baseline kidney function compared to nurses. (3) Main results: In terms of incident kidney injury, nurses had a higher occurrence rate compared to drivers (23.1% versus 0%, p=0.6). Nurses also had significantly lower kinetic glomerular filtration rate (kGFR) compared to drivers (18.9 versus 93.0 mL/minute, p=0.001). The nurses showed a decline in glomerular filtration rate (eGFR) after work (104.6 versus 81.2 mL/min/1.73m², p<0.001), while no significant change was observed in drivers (79.3 versus 80.0 mL/min/1.73m², p=0.9). Regarding proteinuria, nurses had a higher occurrence rate compared to drivers (10.3% versus 0%, p=0.9). Nurses experienced an increase in core body temperature after work (36.4 versus 36.6℃, p<0.001) and a decrease in body weight (59.4 versus 59.2 kg, p=0.05). None of the participants showed signs of dehydration after work. (4) Other analyses: (a)"knowledge, attitude, and practices" (KAP) questionnaire analysis According to the items associated with each factor, Factor 1 was identified as the construct of "Negative Behavior in Wearing Protective Equipment," Factor 2 as the construct of "Negative Attitude towards Heat Stress," and Factor 3 as the construct of "Knowledge of Heat Adaptation." In the measurement model of the structural equation analysis, there was a positive correlation between "Negative Behavior in Wearing Protective Equipment" and "Negative Attitude towards Heat Stress" (correlation coefficient = 0.20, p = 0.001). There was no significant correlation between "Negative Behavior in Wearing Protective Equipment" and "Knowledge of Heat Adaptation" (correlation coefficient = 0.04, p = 0.7), as well as between "Negative Attitude towards Heat Stress" and "Knowledge of Heat Adaptation" (correlation coefficient = -0.03, p = 0.7). We found that "Negative Behavior in Wearing Protective Equipment" had a negative effect on the change in glomerular filtration rate (ΔeGFR) after work (standardized path coefficient = -0.22, p = 0.1), while "Negative Attitude towards Heat Stress" (standardized regression coefficient = 0.01, p = 0.9) and "Knowledge of Heat Adaptation" (standardized regression coefficient = -0.04, p = 0.9) did not significantly affect it. (b) Univariate analysis of symptoms, risk factors, and protective factors Participants with headache showed a significant greater decrease in ΔeGFR (-33.5 versus -14.5 mL/min/1.73m², p < 0.001). Avoiding drinking and eating before work to prevent going to the restroom (-29.6 versus -16.8 mL/min/1.73m², p = 0.03) showed a significant greater decrease of ΔeGFR. Consuming sugary beverages during work (-27.9 versus -20.6 mL/min/1.73m², p = 0.2) also showed a greater decrease of ΔeGFR but did not reach statistical significance. Adding electrolytes to fluids (-20.2 versus -25.0 mL/min/1.73m², p = 0.4) had a protective effect on ΔeGFR but did not reach statistical significance. (c) Subgroup analysis Administrative nurses showed a significantly greater decrease in ΔeGFR compared to screening nurses (-33.7 versus -19.8 mL/min/1.73m², p = 0.03). Conclusion (1) Key results: Healthcare workers wearing PPE in outdoor environments experienced acute kidney injury. Negative behaviors related to wearing PPE were associated with a more severe decline in kidney function after work. (2) Limitations: Estimating participants' environmental heat exposure using temperature and humidity measurements from a weather station is not as accurate as directly measuring the environmental heat exposure in the screening bus. However, placing temperature and humidity sensors at the screening site raises concerns regarding infection control, and it is difficult to disinfect the instruments after retrieval. Additionally, when estimating body weight, we did not require participants to remove or change clothing soaked in sweat before conducting post-work measurements. As a result, there may be an underestimation of the decrease in body weight during the estimation process. Estimating core body temperature using ear temperature measurements may be less accurate than rectal temperature. (3) Interpretation: We found that even a single screening shift can result in kidney injury, but the long-term effects require further investigation and discussion in future studies. (4) Generalisability: We included most healthcare workers from the screening bus teams in Taipei City. The findings of this study can reflect heat hazards for healthcare workers with similar job types during the summer and autumn seasons. However, the result cannot be generalized to healthcare workers in different climate conditions or work settings. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89644 |
DOI: | 10.6342/NTU202304177 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 環境與職業健康科學研究所 |
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