儿童青少年肥胖干预研究需开展依从性评价

为遏制儿童青少年超重肥胖的流行，国内外学者开展各类干预研究，但是相似的干预措施效果并不一致，可能与干预研究的依从性有关。依从性评价能更好地解释干预措施和效果的关联，通过加强研究依从性能促进干预效果。然而，目前依从性评价存在研究数量少、评价指标不全面、评价方法质量低、具体实施困难等问题，需要进一步解决。未来研究中需要在干预开始前考虑能够促进依从性的措施，在实施过程中尽可能详细地进行依从性评价数据的收集，从而更好地评价干预效果。     

农村医疗机构诊治技术优化和补偿机制改革试点研究效果评价方法

目的:评价试点研究的干预措施是否产生了预期效果。方法:在准实验设计中采用干预前后带有对照组的实验设计,在中期和末期两个时间点对干预措施进行评价;通过比较干预前后医疗费用的变化,判断干预措施的临床效果;选择两个与试点医疗机构相匹配的医疗机构作为对照组,应用倍差法比较试点组与对照组干预前后治疗效果及费用变化之差。同时应用定性的方法评价、分析干预措施与干预结果的内在机理。结果:该评价方法应用到干预试点研究阶段性评估,得到世界银行和NICE专家的认可。结论:该评价设计对这种综合干预措施的评价是可行有效的。     

优质护理在急性阑尾炎中的应用

目的分析并研究对于急性阑尾炎手术患者在护理过程中应用优质护理所取得的效果,以便为临床干预工作提供可行参考。方法本研究所有研究对象均为我院收治的急性阑尾炎患者,患者入院时间为2016年4月到2017年8月,本研究选择其中的78例患者作为研究对象,并对所有患者进行随机分组,其中对照组患者39例,观察组患者39例。本研究对照组患者配合常规护理措施进行干预,观察组患者选择优质护理进行干预,对两组患者护理效果进行评价。结果评价本研究两组患者的肛门排气时间、下床活动时间和住院时间,观察组与对照组之间存在明显差异性,P<0.05,差异具有统计学意义;对两组患者并发症情况进行比较,观察组患者未出现并发症情况,对照组患者出现并发症9例,观察组患者并发症的发生率比对照组更低,两组存在差异性,P<0.05,差异具有统计学意义。结论对于急性阑尾炎患者在进行护理干预的过程中,为患者选择采用优质护理服务进行干预指导,可以有效的促进患者手术效果的提升,使患者更快的恢复,并能够降低并发症发生率,值得临床广泛应用。     

早产儿喂养不耐受的护理干预及效果评价

目的对早产儿喂养不耐受的护理干预与效果进行评价分析。方法对喂养不耐受的早产儿进行分组研究，分为干预组和对照组两组。对干预组进行综合护理干预，对照组则采取常规护理措施，对两组的研究质变进行观察。结果干预组早产儿的体重开始增加明显早于对照组，且出现腹胀、胃潴留消失、呕吐消失时间也都早于对照组。结论综合护理干预对于早产儿喂养不耐受有积极意义，可以改善其肠胃功能，促进早产儿的发育。     

老年膝骨性关节炎康复治疗期间给予中医干预的效果观察

目的探讨老年膝骨性关节炎患者康复治疗期间给予中医干预的效果。方法随机选取2016年1月—2018年1月曙光医院收治的200例老年膝骨性关节炎患者为研究对象,在征得患者同意的基础上,按照随机数字表法分为对照组和观察组,每组各100例。对照组给予常规治疗和康复训练,观察组在对照组基础上给予中医干预,比较两组的临床疗效。采用膝骨关节炎严重性指数评分表评价两组患者干预前后膝关节功能,采用视觉模拟评分法(VAS)评价两组患者干预前后疼痛程度,采用世界卫生组织生存质量测定量表评价两组患者干预前后生活质量。结果干预后,两组患者疼痛或不适、最大步行距离和日常生活能力评分较干预前显著下降,且观察组各项指标下降程度较对照组更显著(P0.05);两组患者VAS评分明显低于干预前,且观察组干预后VAS评分明显低于对照组(P0.05);两组患者生理领域和心理领域评分较干预前明显升高,且观察组升高程度较对照组显著(P0.05);观察组临床总有效率为94.0%,明显高于对照组的86.0%(P0.05)。结论老年膝骨性关节炎患者康复治疗期间给予中医干预可以有助于改善患者膝关节功能,减轻患者疼痛,提高临床疗效,从而更好地改善患者生活质量。     

急性左心衰水肿采取舒适护理效果观察

目的:对急性左心衰水肿采取舒适护理效果进行分析。方法:选取2010年11月~2013年11月收治的急性左心衰水肿患者201例,随机分为两组患者,常规组患者100例,采取常规护理干预措施;舒适组患者101例,应用舒适护理干预措施,对两组患者的护理效果进行分析。结果:舒适组急性左心衰导致水肿症状完全缓解时间、平均住院治疗时间、患者护理满意度均显著优越于常规组,具有明显差异性,存在统计学意义(P0.05),两组患者均没有发生严重不良后果及死亡病例。结论:针对急性左心衰水肿患者采用舒适护理干预措施可明显缩短缓解水肿症状完全缓解时间,减少平均住院治疗时间、增加护理满意度,为左心衰水肿理想的护理方法。     

糖尿病患者社区管理效果分析

目的:探析糖尿病患者采取社区管理的效果。方法:选取100例某社区卫生服务站接受管理的糖尿病患者,随机等分法分为为常规组和实验组各50例,常规组给予基础管理措施,实验组则给予社区规范化的管理干预措施,对比其临床效果。结果:干预前,两组患者的血糖水平均无明显差异(P0.05);干预后,进行随访两组患者的血糖水平控制情况存在明显差异(P0.05)。结论:目前,对社区糖尿病患者给予规范化的管理干预,对于控制血糖水平具有一定的优势,值得在临床上推广应用。     

移动医疗App对高血压患者自我管理行为的干预研究

目的评价基于移动医疗应用软件(App)管理的干预策略对高血压患者自我管理行为的干预效果。方法采用前瞻性配对研究方法。给予对照组常规门诊就诊指导和电话随访,试验组在此基础上纳入App管理。分别于干预前后,观察时间变化下两组相关观察指标及自我管理水平评分情况,并进行两组间和各组自身前后比较。结果 6个月后,两组患者自身前后对照,治疗率、血压达标率及自我管理等评分均优于干预前水平(P0.05),两组组间对照,试验组优于对照组(P0.05);试验组体重指数干预末期组间比较,差异有统计学意义(P0.05)。结论社区高血压患者采用App管理方式与传统社区随访方式比较,治疗率更高、可以取得更好的降压治疗效果。     

社区全科医生开展高血压综合防治干预的效果分析

目的探讨社区全科医生开展高血压综合防治干预的效果,为高血压的预防和控制提供依据。方法按照随机对照法,将188例高血压患者分为对照组和观察组,每组各94例。对照组给予常规西医治疗,观察组给予综合防治措施,比较两组的干预效果。结果与干预前比较,两组患者体质指数(BMI)、吸烟情况、不良情绪、饮酒情况、膳食口味和适量运动均显著改善,且观察组改善更明显,差异有统计学意义(P0.05);与干预前比较,干预后患者血压水平显著下降,病情知晓率、治疗率和控制率显著升高,观察组改善更明显,差异有统计学意义(P0.05)。结论社区全科医生开展高血压综合防治有利于改善患者的生活习惯,提高患者的生活质量,可以更好地控制血压。     

饮食干预对急性百草枯中毒患者导泻效果的影响

目的探讨饮食干预对急性百草枯口服中毒患者的导泻效果的影响,找出最佳的饮食种类和进食时间,使病人尽快排便,增加导泻的效果。方法将急性百草枯口服中毒患者共65例分为两组,观察组35例和对照组30例。对观察组的病人实施饮食干预,观察两组病人的首次排便时间、排便次数及大便颜色,并进行统计学分析。结果观察组首次排便时间早于对照组,排便次数多于对照组,差别具有统计学意义(P0.01)。结论对急性百草枯口服中毒的患者进行饮食干预可以促进病人尽早排便,增强导泻效果,更好地排出肠道内毒物,有利于治疗。     

Interrupted time-series analysis yielded an effect estimate concordant with the cluster-randomized controlled trial result

ObjectiveWe reanalyzed the data from a cluster-randomized controlled trial (C-RCT) of a quality improvement intervention for prescribing antihypertensive medication. Our objective was to estimate the effectiveness of the intervention using both interrupted time-series (ITS) and RCT methods, and to compare the findings.Study Design and SettingWe first conducted an ITS analysis using data only from the intervention arm of the trial because our main objective was to compare the findings from an ITS analysis with the findings from the C-RCT. We used segmented regression methods to estimate changes in level or slope coincident with the intervention, controlling for baseline trend. We analyzed the C-RCT data using generalized estimating equations. Last, we estimated the intervention effect by including data from both study groups and by conducting a controlled ITS analysis of the difference between the slope and level changes in the intervention and control groups.ResultsThe estimates of absolute change resulting from the intervention were ITS analysis, 11.5% (95% confidence interval [CI]: 9.5, 13.5); C-RCT, 9.0% (95% CI: 4.9, 13.1); and the controlled ITS analysis, 14.0% (95% CI: 8.6, 19.4).ConclusionITS analysis can provide an effect estimate that is concordant with the results of a cluster-randomized trial. A broader range of comparisons from other RCTs would help to determine whether these are generalizable results.     

On the use of generalized additive models in time-series studies of air pollution and health

The widely used generalized additive models (GAM) method is a flexible and effective technique for conducting nonlinear regression analysis in time-series studies of the health effects of air pollution. When the data to which the GAM are being applied have two characteristics--1) the estimated regression coefficients are small and 2) there exist confounding factors that are modeled using at least two nonparametric smooth functions--the default settings in the gam function of the S-Plus software package (version 3.4) do not assure convergence of its iterative estimation procedure and can provide biased estimates of regression coefficients and standard errors. This phenomenon has occurred in time-series analyses of contemporary data on air pollution and mortality. To evaluate the impact of default implementation of the gam software on published analyses, the authors reanalyzed data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) using three different methods: 1) Poisson regression with parametric nonlinear adjustments for confounding factors; 2) GAM with default convergence parameters; and 3) GAM with more stringent convergence parameters than the default settings. The authors found that pooled NMMAPS estimates were very similar under the first and third methods but were biased upward under the second method.     

Who really gets strep sore throat? Confounding and effect modification of a time‐varying exposure on recurrent events

Unmeasured confounding is the fundamental obstacle to drawing causal conclusions about the impact of an intervention from observational data. Typically, covariates are measured to eliminate or ameliorate confounding, but they may be insufficient or unavailable. In the special setting where a transient intervention or exposure varies over time within each individual and confounding is time constant, a different tack is possible. The key idea is to condition on either the overall outcome or the proportion of time in the intervention. These measures can eliminate the unmeasured confounding either by conditioning or by use of a proxy covariate. We evaluate existing methods and develop new models from which causal conclusions can be drawn from such observational data even if no baseline covariates are measured. Our motivation for this work was to determine the causal effect of Streptococcus bacteria in the throat on pharyngitis (sore throat) in Indian schoolchildren. Using our models, we show that existing methods can be badly biased and that sick children who are rarely colonized have a high probability that the Streptococcus bacteria are causing their disease. Published 2016. This article is a U.S. Government work and is in the public domain in the USA     

Data analysis and sample size issues in evaluations of community-based health promotion and disease prevention programs: a mixed-model analysis of variance approach

The growing interest in community-based approaches to health promotion and disease prevention (HP/DP) has been accompanied by a growing need to evaluate the effectiveness of such programs. Special issues that arise in these evaluation studies include (1) entire communities are assigned to intervention and control groups, (2) only a small number of communities can usually be studied, (3) the time course of changes in behavior and other outcomes is often of interest, and (4) surveys to measure such changes over time can be conducted with either repeated cross-sectional samples or with longitudinal samples. This paper shows how these issues can be addressed under a mixed-model analysis of variance approach. This approach serves to unify several ideas in the literature on evaluation of community studies, including use of time-series regression and the question of whether the individual or the community should be the unit of analysis. We also describe how the method can be used to estimate sample size requirements, statistical power, or minimum detectable program effect.     

A robust interrupted time series model for analyzing complex health care intervention data

Current health policy calls for greater use of evidence‐based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be “interrupted” by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi‐experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a prespecified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel robust interrupted time series (robust‐ITS) model that overcomes these omissions and limitations. The robust‐ITS model formally performs inference on (1) identifying the change point; (2) differences in preintervention and postintervention correlation; (3) differences in the outcome variance preintervention and postintervention; and (4) differences in the mean preintervention and postintervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The robust‐ITS model is implemented in an R Shiny toolbox, which is freely available to the community.     

On the bias of estimates of influenza vaccine effectiveness from test–negative studies

Estimates of the effectiveness of influenza vaccines are commonly obtained from a test-negative design (TND) study, where cases and controls are patients seeking care for an acute respiratory illness who test positive and negative, respectively, for influenza infection. Vaccine effectiveness (VE) estimates from TND studies are usually interpreted as vaccine effectiveness against medically-attended influenza (MAI). However, it is also important to estimate VE against any influenza illness (symptomatic influenza (SI)) as individuals with SI are still a public health burden even if they do not seek medical care. We present a numerical method to evaluate the bias of TND-based estimates of influenza VE with respect to MAI and SI. We consider two sources of bias: (a) confounding bias due to a (possibly unobserved) covariate that is associated with both vaccination and the probability of the outcome of interest and (b) bias resulting from the effect of vaccination on the probability of seeking care. Our results indicate that (a) VE estimates may suffer from substantial confounding bias when a confounder has a different effect on the probabilities of influenza and non-influenza ARI, and (b) when vaccination reduces the probability of seeking care against influenza ARI, then estimates of VE against MAI may be unbiased while estimates of VE against SI may be have a substantial positive bias.     

Roaming through the methodology. XX. Randomization as a means of avoiding confounding through indication

Two methodological issues are central in discussions about the value of randomised studies for evaluation of the effectiveness of medical interventions: confounding by indication and generalizability. Confounding occurs when a factor with bearing on the outcome of disease, has a different distribution among intervention groups. Confounding by indication is a special case and results from the fact that in medical practice the choice between intervention alternatives is dependent on the perceived prognosis of the patient for whom these interventions are considered. As the precise arguments for this choice in general are not explicit and hence not measurable, correction for confounding by indication is impossible, and randomisation is applied. Surprisingly, systematic comparison of the results of randomised and non-randomised studies for the same interventions shows remarkable resemblance. This suggests that non-randomised studies may have value in specific situations, but unfortunately so far we do not know for which situations this may be the case.     

Early Parenting Program as Intervention Strategy for Emotional Distress in First-Time Mothers: A Propensity Score Analysis

The purpose of this study is to evaluate the effectiveness of a single session intervention designed to reduce emotional distress in first-time mothers. We held a parenting class for first-time mothers who had given birth at a university hospital in Tokyo, Japan. The program of the class consists of lectures on infant care and group discussion, which is a common form of intervention in Japan. The effectiveness of intervention is assessed according to differences in emotional distress experienced by class participants and nonparticipants, and analyzed by the use of a propensity score method to avoid self-selection bias. In order to be more confident about our results, we employ several variations of this method. Results from statistical analysis show that although the effectiveness of the intervention was limited, it was able to alleviate subjects’ loss of self-confidence as mothers. Because this outcome shows a good degree of consistency across methods, it can be considered robust. Moreover, it is roughly consistent with previous studies. Effectiveness can probably be increased by developing a program that improves upon the intervention.