醫療照護相關感染長期趨勢統計模式分析

Abstract

背景 對於時間數列因子(時間趨勢、季節性及自相關性)及源自醫療照護相關感染之異質性因子進行系統評估，對於醫療照護相關感染的監視扮演重要角色，特別是對於病菌或起因於抗藥性造成的大流行時及對介入方案進行評估。然而，欲對介入效益進行長期動態性時間數列預測，傳統時間數列模式往往會遭遇一連串方法學上的議題，包括非常態資料、穩定性及可轉性(自我相關階數與平均移動之交換)、階層式資料結構所導致相關性、以及異質性之時間數列因子等。 研究目的 本研究利用醫學中心時間序列之分析並以此評估長期追蹤之醫療照護相關感染(Healthcare-associated infections, HAI)資料進行感染控制實務介入成效之探討，主要目的： (1) 醫療照護相關感染發生之相關因素探討 (2) 利用傳統時間序列分析方法，探討時間序列相關因素對於醫療照護相關感染發生之影響，例如時間趨勢、季節變異及自我回歸次序等等：並進一步利用Poisson時間序列模式進行整體醫療照護相關感染、特定部位感染、特殊菌種及住院部門之醫療照護相關感染發生因素進行分析 (3) 考量上述(2)發現影響醫療照護相關感染之時間序列因素及年齡、性別對於醫療照護相關感染異質性後，進一步預測醫療照護相關感染發生趨勢演變 (4) 評估醫療照護相關感染控制介入之成效，包括整體醫療照護相關感染、特定部位感染、特殊菌種及住院部門之醫療照護相關感染控制成效評估 (5) 發展創新性廣義線性混合ARIMA模式(Autoregressive Integrated Moving Average model)應用於(3)之分析 (6) 在非隨機分派試驗設計下，本研究結合上述(5)發展之分析模式應用於醫療照護相關感染控制介入之成效評估 資料來源 本研究資料以位於台北市都市型之921床醫學中心主體，該院平均每年約有27000名住院病患。研究族群收集自1994年1月1日至2013年12月31日之住院病患為醫療照護相關感染之研究世代。 醫療照護相關感染控制介入措施 該期間醫療照護相關感染控制介入包括醫療照護之目標管理循環(PDCA)概念模式、衛生計畫、疾病管制署洗手衛生政策及台灣醫療品質策進會(台灣醫策會)對於泌尿道感染醫療照護品質提升計畫(CDC/TJCHA)及、醫療照護組合式措施(Bundle Care)等。 研究設計 第一部份利用該長期資料進行醫療照護相關感染發生率趨勢及其相關因素探討。本研究採用兩種模式進行醫療照護相關感染控制介入評估，首先以控制策略介入前與介入後之醫療照護相關感染個案數比較，以模式獲得參數後，估算事後個案數分佈並與介入後個案數進行比較，以獲得相同年數期間的個案數差異作為介入成效指標。第二種研究設計以類隨機分派試驗設計進行評估，本研究以介入前資料進行模式分析，並以其結果預測醫療照護相關感染發生數以作為對照組，即為在無介入控制時所產生的個案數(2005年之前)，該對照組與政策介入後所觀察到的醫療照護相關感染數進行比較，則可獲得該政策介入成效。 方法學特點 本論文在分析架構上，首先進行傳統時間數列模式例如時間數列分解模式以及貝氏動態線性模式，接著再逐步發展貝氏廣義線性混合自我相關平均移動模式，運用臨床醫療照護相關感染資料進行感染之長期趨勢預測及針對介入政策進行效益評估。 結果及結論 有關醫療照護相關感染發生之研究結果如下: 結果 (1) 醫療照護相關感染發生研究，本研究發現較年長男性比年輕女性有較高危險性 (2) 本研究資料發現以泌尿道感染及菌血症為最高，且隨著不同部門其變異性大 (3) 醫療照護相關感染於夏季發生率高，但於冬季則較低；依不同感染部位及特殊菌種的自我迴歸次序，醫療照護相關感染隨著時間有線性及非線性(二次方及三次方線性)下降趨勢 有關醫療照護相關感染控制策略介入評估，本研究結果及結論發現摘要如下: (1) 研究結果顯示，調整年齡、性別、時間趨勢、季節變異性及三次方自我回歸趨勢後，疾病管制署/台灣醫策會及醫療照護組合式措施於2010年介入後成效分別可降低26%及39%的醫療照護相關感染發生。然而，目標管理循環(PDCA)概念模式及衛生計畫僅能降低約10%且未達統計顯著意義。 (2) 調整年齡、性別、時間趨勢、季節變異性及三次方自我回歸趨勢後，疾病管制署/台灣醫策會及醫療照護組合式措施於2010年介入後，考慮6個月延遲效應，可有效降低36%醫療照護相關感染發生。 (3) 本研究以隨機效應模式考量醫院部門、感染部位及菌種之階層次結構後，其介入成效結果與(1)及(2)發現非常相近。 (4) 疾病管制署/台灣醫策會介入成效隨著感染部位不同而有所差異。對於菌血症及外科手術部位感染可以降低36%、泌尿道感染可降低16%、對於其他部位可以降低81%，但對於肺炎則無成效。醫療照護組合式措施介入可有效降低37%菌血症、44%外科手術部位感染、38%泌尿道感染及88%其他部位感染，而對肺炎僅能降低3%。 (5) 疾病管制署/台灣醫策會介入對於急診部成效最佳(約可降低94%)，而於小兒科最差。對於腫瘤科沒有任何成效。醫療照護組合式措施介入對於感染科成效最顯著(約可降低77%)且對於外科手術部位感染成效最低(約可降低34%)，而對於腫瘤科及小兒科不具任何成效。 (6) 疾病管制署/台灣醫策會政策或醫療照護組合式措施介入隨著菌種不同而不同。疾病管制署/台灣醫策會政策對於厭氧菌種成效最佳(約下降65%)、革蘭氏陽性 (約下降31%)及革蘭氏陰性 (約下降30%)次之，但對於黴菌成效很小(5%)，而其他菌種都無任何效果。醫療照護組合式措施對於其他菌種成效最大(約下降91%)、厭氧菌種(82%)、黴菌(52%)、革蘭氏陽性 (約下降32%)及革蘭氏陰性 (約下降31%)次之。 (7) 利用時間數列模型2005年前(介入前)後觀察HAIs數目發現，疾病管制署/台灣醫策會政策介入可以減少643位HAIs，但若以預測值(考慮估計值不確定性)則降低數目減少283位。 結論 本論文在方法學上的創新性，包括使用貝氏廣義線性混合ARIMA模式的發展，以及在非隨機試驗下對於介入效益評估之模式設計。這樣的貝氏廣義線性混合ARIMA模式結合廣泛地運用在長期追蹤研究之廣義線性混合模式及廣泛應用於經濟研究之ARIMA模式。當進行與時間數列特性之醫療照護相關感染的預測上，藉由貝氏分析估計相關參數時可以同時考量時間數列及異質性成分。在研究設計上之創新，應用時間數列模型之估計及預測，可以在非隨機試驗研究設計下仍可對介入政策進行效益評估。此模式相當彈性，不一定要進行隨機試驗設計，即可推展至任何與醫療照護相關感染介入方案之評估。

Background Systematic evaluation of time-series factors (time trend, seasonal variations, and autocorrelation) and factors responsible for heterogeneity accounting for healthcare-associated infections (HAIs) plays an important role in the surveillance of HAIs, particularly for evaluation of the efficacy of interventions and the outbreak of pathogens probably due to drug-resistance. However, forecasting for the long-term dynamic evolution and evaluation of the efficacy of interventions is often confronted with a series of methodological issues if the conventional time-series model is applied, including non-Gaussian data, stationarity and invertiblity, hierarchical data structure, and heterogeneity beyond time-series factors. Aims By using a longitudinal follow-up time-series data on HAIs from a medical center, my thesis aimed to, from the practical aspect of HAIs control, (1) identify the risk factors responsible for HAIs incidence; (2) elucidate how time-series factors such as time trend, seasonal variation, and autoregressive order made contribution to incident HAIs using the conventional time series model and the extended Poisson time-series model for the overall HAIs, site-specific, pathogen-specific and department-specific HAIs; (3) to forecast the evolution of HAIs making allowance for time-series components as identified in (2) and heterogeneity contributed from other covariates such as age and gender with 95% confidence interval; (4) to evaluate the efficacy of interventions related to HAIs control in the site-specific, pathogen-specific, and department-specific reduction in HAIs. My thesis also aimed to, form the aspect of methodology, (5) to develop a novel generalized linear mixed ARIMA model to achieve the objective (3); (6) devise a time-series model-based design together with the proposed model in (5) to evaluate the efficacy of interventions associated with HAIs in the absence of randomized controlled trial as mentioned in (4) . Data Sources: A cohort of healthcare-associated infections was followed during the period of January 1, 1994 and December 31, 2013 in an urban tertiary medical center in northern Taipei with 921-bed and approximately 27,000 inpatient admission annually. Intervention programs indicators: Intervention of PDCA, Hygiene programs, Taiwan Centers for Disease Control (CDC) National Hand Hygiene Campaign and the urinary tract infection quality improvement program of Taiwan Joint Commission on Hospital Accreditation (TJCHA) called CDC/TJCHA, and Bundle care program. Study Design: The first part of study design was in the light of an incident follow-up cohort over time to identify the HAI episode. There are two study designs proposed for evaluation of efficacy of these intervention programs. The first is based on before and after comparison of counts of HAIs. The estimated HAIs counts that were computed on the basis of the posterior distribution with the same length of period conducted with the intervention program were compared with the observed HAIs after the intervention program. The second study design was based on a pseudo randomized controlled trial design. The HAIs counts in the observed were compared with the control group created by predicting rather than estimating the HAIs counts based on the predictive distribution formed by the posterior distribution estimated from the time series data before interventions (i.e. the year before 2005). Model Specification The analysis framework began with the conventional time-series model including decomposition method and Bayesian dynamic linear model and then step-by-step developed the proposed Bayesian linear mixed autoregressive moving average model, combining with for forecasting the long-term time trend of HAIs based on the empirical data presented here and also for evaluation of the efficacy of intervention programs. Results and Conclusions As far as factors affecting the occurrence of HAIs are concerned, the summary of results and conclusions consists of the following points: (1) The elderly males are more likely to be susceptible to HAIs than the young female by using demographic features. (2) The most frequent infection sites are UTI and bacteremia and there is much variation of HAIs across departments. (3) There was much preponderance in summer but less in winter seasons, a decreasing time trends with linear and non-linear (quadratic and cubic) pattern, the consideration of autoregressive orders depending on the site of infection and pathogens. Regarding the efficacy of intervention, the summarized findings and conclusions were as follows. (1) Around 26% and 39% reduction resulting from CDC/TJCHA and Bundle care program, respectively, after 2010 were estimated with adjustment for age, gender, time trend, seasonal variation, and third-order of autoregressive order. However, there was a 10% non-significant reduction for hygiene program and lacking of significant benefit for PCDA. (2) The 36% reduction resulting from time lag (6 months) of either CDC/TJCHA or Bundle care program after 2010 was estimated with adjustment for age, gender, time trend, seasonal variation, and autoregressive order. (3) The similar findings on (1) were found when random-effects considering the hierarchical structure of department, infection site, and pathogen were allowed. (4) The results of efficacy of CDC/TJCHA and Bundle care varied with site of infection. CDC/TJCHA was conducive to 36% reduction in HAIs for bacteremia and SSI, 16% for UTI, 81% for others but there was lacking of any benefit for pneumonia. Bundle care was conducive to 37% reduction in HAIs for bacteremia, 44% for SSI, 38% for UTI, 88% for others but only 3%for pneumonia. (5) The reduction in HAIs for CDC/TJCHA was the greatest in emergency department (almost 94%) and the least in pediatrics (7%). There was lacking any benefit for oncology. The reduction in HAIs for Bundle care was the greatest in infection department (almost 77%) and the least in surgical (34%). There was lacking any benefit for oncology and pediatric department. (6) The results of efficacy of CDC/TJCHA and Bundle care largely varied with pathogen. The reduction in HAIs with CDC/TJCHA was the greatest for anaerobic pathogen (65%), followed by Gram-positive (31%) and Gram-negative (30%), but smallest for Fungi pathogen (5%). There was lacking of any benefit for other pathogens. The reduction in HAIs with Bundle care was the greatest for others (91%), followed by anaerobic pathogen (82%), by Fungi (52%), Gram-positive (34%), and Gram-negative (31%). Regarding the novelty of methodology, there are two parts pertaining to the novelty of methodology presented in this thesis, the development of a Bayesian generalized linear mixed ARIMA model and the model-based design for evaluation of the efficacy of intervention dispensing with the randomized controlled trial. Specifically, this thesis developed a generalized linear mixed effect ARIMA model by combining the generalized linear mixed model widely used in longitudinal follow-up study and ARIMA model widely used in economic studies. It can be useful for monitoring the episodes of HAIs by projecting time-series-featuring HAIs with the relevant parameters estimated by Bayesian approach making allowance for both properties of heterogeneity and time series components. The thesis has devised a time-series model-based design to evaluate the efficacy of intervention associated with HAIs in the absence of randomized controlled trial. Such a time-series model-based design is very flexible in the evaluation of any kind of evaluation of intervention in association with HAIs without needing a randomized controlled trial design.