基于离散选择实验的公立医院护理人员岗位选择偏好研究

目的 了解公立医院护理岗位新职工岗位选择偏好,为医院合理配置护理人员,完善岗位管理提供参考。方法 选取青岛市4家三级甲等综合医院2021年度招聘的护理新职工进行问卷调查,采用离散选择实验（Discrete Choice Experiment,DCE）的方式编制问卷,问卷涵盖调查对象的基本情况和DCE问题。采用混合Logit模型、支付意愿以及边际效应模型分析新职工对岗位选择的偏好及水平。结果 纳入的6个属性（收入水平、工作氛围、工作环境、工作压力、编制、职业发展前景）对护理新职工岗位选择偏好均有统计学意义。新职工较为关注工作氛围（OR=5.431,P<0.05）这一非经济属性;不同个体特征的新职工对工作氛围、编制、职业发展前景的支付意愿更高;工作属性的改变对岗位选择的边际效应有不同程度影响（P<0.05）。结论 护理新职工对非经济属性的偏好超过了经济属性,新职工更加关注心理状态的满足和岗位发展前景,软环境的改善对提高新职工职业归属感、心理认同感、稳定护理队伍促进医院高质量发展有积极影响。     

基于离散选择实验的互联网医院诊疗服务偏好研究

[目的]分析居民对互联网医院诊疗服务的偏好,为互联网医院的可持续发展提供参考。[方法]基于离散选择实验方法,以429名居民为调查对象,利用查阅文献、专家咨询的方法确定运营主体、医生推荐指数、就诊方式和服务费用4个属性,采用正交试验设计确定4个选择集和1个“质量控制”选择集。采用网上问卷调查的方式进行数据收集,对得到后的数据进行条件Logit回归分析。[结果]相对于I型的运营主体,居民更愿意H+I型的运营主体(β=0.379),选择H+I型是选择I型的1.461倍(P<0.01);相对于低的医生推荐指数,居民更愿意选择中(β=1.131)、高(β=0.861)医生推荐指数,选择中、高医生推荐指数分别是选择低的医生推荐指数的3.098倍、2.366倍(P<0.01);相对于以图文的问诊方式,居民更愿意选择电话的问诊方式(β=0.539),而不愿意选择视频的问诊方式(β=-0.499),选择电话是选择图文的1.715倍(P<0.01),选择视频是选择图文的0.607倍(P<0.01)。[结论]医生推荐指数是居民选择互联网医院诊疗最看重的属性。建议加强互联网医院人才队伍...     

基于离散选择实验的医生互联网诊疗服务选择偏好研究

目的：研究医生互联网医院诊疗服务的选择偏好及其影响因素,了解医生参与互联网医院诊疗服务的偏好、意愿和需求。方法：应用离散选择实验方式,抽取北京市2所综合医院及3所专科医院共119位医生进行问卷调查,应用条件Logit回归分析其选择偏好。结果：医生支付意愿从高到低为：医生对线上诊疗绩效分成比例较线下的变化、每日工作时长、响应时间。不同年龄、职称、工作年限及科室的医生选择偏好存在差异。结论：医生更偏向于在线上诊疗绩效分成比例较线下增加20%、互联网诊疗服务占年度考核的权重占比增加10%、每日工作时长为线下减少1小时同时线上增加1小时、响应时间为24小时以内的情形下选择互联网医院诊疗服务。可对医生采取经济激励和非经济激励相结合、合理分配工作量等激励机制,满足医生的偏好和需求,促进互联网医院高质量发展。     

基于离散选择实验的医学生就业偏好分析

目的 通过分析医学生就业偏好,为政策制定者和医疗卫生机构出台吸引医学生毕业后继续从事医疗卫生工作的措施提供决策支持。方法 采用分层抽样的方法,于2020年4月在江苏省南京市和连云港市各一所医科类高校开展离散选择实验(discrete choice experiment, DCE)线上问卷调查。问卷包括调查对象基本信息、DCE选择集(共包括工作地点、晋升年限、工作机构、月收入、住房条件、职业发展机会或培训机会、工作负担7个属性,每个属性又包括若干水平)。构建离散选择模型,采用Mixed Logit回归模型分析医学生的就业选择偏好。结果 共回收问卷329份,有效问卷307份,有效回收率93.3%。结果显示,医学生偏好收入高(P<0.001)、住房条件好(P<0.001)、职业发展机会多(P<0.001)、工作地点为城市(P<0.001)、工作负担轻(P<0.001)的工作。月收入是医学生最偏好的因素,医学生对各非经济因素的相对偏好程度由大至小依次为住房条件、职业发展机会或培训机会、工作地点和工作负担。结论 政策制定者与医疗卫生机构应优先关注收入与住房条件,科学...     

公立医院不同工作年限护理人员岗位选择偏好研究——基于离散选择实验

目的：了解公立医院不同工作年限护理人员的岗位选择偏好,为稳定护理队伍提供参考。方法：运用离散选择实验的方法编制问卷,选取山东省某大型医院工作不足一年与工作十年以上护理人员为对象进行问卷调查,采用混合Logit模型、支付意愿等方法分析不同工作年限护理人员的岗位选择意愿及水平。结果：随着工作年限的变化,护理人员岗位选择偏好由非经济属性向经济属性转变;编制这一属性受到不同工作年限护理人员的关注;经济属性对稳定护理队伍具有重要意义。结论：护理岗位选择偏好随工作年限的改变而改变,需针对不同工作年限护理人员的岗位需求,制定精准的培养方案,实现护理队伍的稳定。     

终末期肾病患者血液透析服务选择偏好研究——基于离散选择实验

目的：研究终末期肾病患者对血液透析服务的选择偏好及影响因素。方法：基于标签式离散选择实验方法设计问卷,通过分层整群抽样方法开展调查,构建混合Logit模型,对408份有效样本进行服务偏好研究。结果：终末期肾病患者对社区血液透析服务具有显著的负向偏好;患者倾向于向上转诊通道流畅有效、医生固定、交通时间20分钟、提供家庭线下随访的血液透析服务,为此分别愿意多支付92.08元、74.52元、47.64元、17.63元;随着服务属性水平累积改变,患者选择社区血液透析服务的概率持续提升。结论：流畅有效的向上转诊通道是患者选择社区透析服务的关键;建议建设紧密型医联体,实现医疗同质化,保障患者安全;改善医患关系连续性,提高延续服务质量。     

基于离散选择实验的居民家庭医生签约服务偏好研究

目的：研究居民对家庭医生签约服务的偏好。方法：构建离散选择模型,采用混合Logit模型评估居民对家庭医生签约服务的偏好、每个属性的相对重要性以及不同特征居民对家庭医生签约服务的偏好异质性。结果：居民偏好选择每年签约自付费用为20元、诊疗水平高、药品配备高、提供基本服务包及中西医结合服务的家庭医生签约服务,以上属性的相对重要性分别为30.18%、20.95%、22.06%、14.55%、7.55%,不同特征居民偏好存在异质性。结论：加快推进医联体模式下的家庭医生签约服务,适当放开医保政策对签约服务的用药限制,提供“菜单式服务”,提高居民对家庭医生签约服务的认知度与参与度。     

基于离散选择实验的老年居民长期护理保险选择偏好研究

目的：分析老年居民对于长期护理保险不同属性的偏好、支付意愿以及偏好异质性,为构建符合需方偏好的长期护理保险制度提供参考。方法：利用离散选择实验方法设计问卷,以260名老年居民作为研究样本,构建混合Logit模型进行偏好分析。结果：老年居民倾向于在社区/家庭、由高级养老护理员提供“日常生活照料+康复护理+精神护理”的综合服务且报销比例为90%的长期护理保险,为此老年居民分别愿意多支付110.814元、13.959元、27.928元和31.294元。保险属性相对重要性由高到低依次为照护地点、保险报销比例、照护服务内容、照护者职业技能水平。不同性别、文化、配偶、收入的老年居民长期护理保险选择偏好具有异质性。结论：建议立足需方视角,尊重老年居民偏好,制定多样化长期护理保险方案,增强保险吸引力及发展可持续性。     

乡镇卫生院医生工作偏好——基于三省离散选择实验的分析

目的：分析乡镇卫生院医生工作偏好,为基层吸引与留住卫生人才提供政策参考。方法：应用离散选择实验设计问卷,抽取山东、安徽、陕西三省共238名乡镇卫生院医生进行问卷调查,应用条件logit回归分析医生工作偏好。结果：乡镇卫生院医生关注的工作属性依次为：收入水平、子女教育条件、编制、工作地点、培训机会和晋升年限。月收入8 000元与2 000元相比,OR值为7.0。子女教育条件和编制的货币价值分别为3 857元和3 294元。月收入提高至8 000元可将乡镇卫生院医生的离职意愿由35%降至8%左右。提供好的子女教育条件和编制均可以将医生的离职意愿降至5%左右。乡镇卫生院有编制的岗位与县城临时聘用岗位对于医生的吸引力相当。结论：收入仍然是乡镇卫生院医生最关注的工作属性,改善基层卫生机构职工的子女教育条件和提供编制,将有利于吸引与留住卫生人才。     

离散选择实验模型在卫生人力资源研究中的应用

目的整合目前国内外离散选择实验模型（DCE）在卫生人力资源研究中的运用情况,以及DCE在人力资源研究中的优缺点和初步效果。方法通过对目前国内外一些研究的回顾,在概念、运用方法和步骤等方面进行了总结。结果研究发现,DCE可运用于测量医疗卫生工作者的工作意向,研究某一项工作特征的改变对选择工作的影响程度和某一项工作中某个属性特征水平的货币价值。建议在卫生人力资源研究中,可以利用DCE帮助进行相关的卫生政策的制定和实施。     

Discrete choice experiments in health economics

One method that is increasingly being used in health economics to elicit stated preferences concerning health matters is the discrete choice experiment (DCE). This editorial explores four sets of issues facing researchers who wish to employ DCE techniques: (a) normative issues about how data from DCE studies might be used to inform policy, (b) psychological issues concerning the meaningfulness of the data generated, (d) technical issues relating to how the data are generated and (d) issues relating to the generalisability of the data from DCE studies. Given current uncertainties surrounding these issues, it is our view that more caution and greater circumspection towards DCE is appropriate at this stage.     

Discrete choice experiments in health economics

Bryan and Dolan have offered a critique of the use of discrete choice experiments in health economics. Their call for more open debate on the relative strengths and limitations of the DCE method, particularly when applied in health settings is warranted. However, their paper has only added to part of this debate in that it focuses on the application of choice experiments in the health sector but says little on the strengths and limitations of the DCE method in general. We argue that while the criticisms posed by Bryan and Dolan rightly challenge the manner in which DCEs have been applied in health economics, such criticism does not challenge the theoretical/methodological basis of DCEs per se.     

Effects coding in discrete choice experiments

This paper discusses the inherent problems associated with applying dummy coding when including a fixed comparator in a discrete choice experiment, and seeks to illustrate the misinterpretations that may arise if the analyst is not aware of the problem. This note provides two examples of possible misinterpretations with dummy coding and how it is solved with the use of effects coding.     

Best-worst scaling vs. discrete choice experiments: an empirical comparison using social care data

This paper presents empirical findings from the comparison between two principal preference elicitation techniques: discrete choice experiments and profile-based best-worst scaling. Best-worst scaling involves less cognitive burden for respondents and provides more information than traditional "pick-one" tasks asked in discrete choice experiments. However, there is lack of empirical evidence on how best-worst scaling compares to discrete choice experiments. This empirical comparison between discrete choice experiments and best-worst scaling was undertaken as part of the Outcomes of Social Care for Adults project, England, which aims to develop a weighted measure of social care outcomes. The findings show that preference weights from best-worst scaling and discrete choice experiments do reveal similar patterns in preferences and in the majority of cases preference weights--when normalised/rescaled--are not significantly different.     

Empirical investigation of experimental design properties of discrete choice experiments in health care

Experimental design is critical to valid inference from the results of discrete choice experiments (DCEs). In health economics, DCEs have placed limited emphasis on experimental design, typically employing relatively small fractional factorial designs, which allow only strictly linear additive utility functions to be estimated. The extensive literature on optimal experimental design outside health economics has proposed potentially desirable design properties, such as orthogonality, utility balance and level balance. However, there are trade-offs between these properties and emphasis on some properties may increase the random variability in responses, potentially biasing parameter estimates.This study investigates empirically the design properties of DCEs, in particular, the optimal method of combining alternatives in the choice set. The study involves a forced choice between two alternatives (treatment and non-treatment for a hypothetical health care condition), each with three, four-level, alternative-specific attributes. Three experimental design approaches are investigated: a standard six-attribute, orthogonal main effects design; a design that combines alternatives to achieve utility balance, ensuring no alternatives are dominated; and a design that combines alternatives randomly. The different experimental designs did not impact on the underlying parameter estimates, but imposing utility balance increases the random variability of responses.     

Deriving distributional weights for QALYs through discrete choice experiments

This paper presents the first attempt to use a discrete choice experiment to derive distributional weights for quality adjusted life years (QALYs), based on characteristics (age and severity) of the beneficiaries. A novel approach using the Hicksian compensating variation is applied. Advantages include derivation of weights for QALYs, not just for life or life years saved, and investigation of the impact of the size of the health gain by allowing the gain to be traded against other characteristics. Results suggest one would generally not weight QALYs, except in a small number of specific cases and in those cases the weights are relatively small. Methodological challenges are highlighted as is a future research agenda.     

Comparing welfare estimates from payment card contingent valuation and discrete choice experiments

This study presents the first comparison of willingness to pay estimates derived from the payment card (PC) contingent valuation and discrete choice experiment (DCE) methods. A within-sample experiment was used to elicit women's preferences for Chlamydia screening. The willingness to pay estimate derived from the DCE was larger than that derived from the PC. To investigate why the willingness to pay estimates were different, a range of validity tests were conducted. Both methods produced theoretically valid results, and there was no difference in the reported difficulty of completing the tasks. Evidence of a prominence effect was found in the PC responses. Responses to the DCE satisfied tests of non-satiation. Responses to both methods were compared with revealed preference data. There were significant differences between stated screening intention in both methods and actual screening uptake. Future work should address the external validity of stated preference methods.     

Developing attributes and levels for discrete choice experiments using qualitative methods

OBJECTIVES: The rigour with which the first two stages of discrete choice experiments (attribute development and the choice of levels of these attributes) are generally conducted is questionable. This paper provides a case study describing how attributes and their levels were developed for a study of access to dermatology specialist services for non-urgent skin conditions. METHODS: Semi-structured interviews were conducted with 19 dermatology patients with non-urgent skin conditions. Informants were purposively sampled for maximum variation and interviews continued until all attributes were fully and clearly defined. An iterative approach was used with data collection and analysis proceeding concurrently. RESULTS: The interviews and parallel analysis generated three iterations. The first iteration comprised early exploratory work with expertise and waiting time emerging as important to informants. The second iteration continued to emphasize these attributes, but individualized care and convenience were added. By the end of the third iteration all attributes were fully elaborated. CONCLUSIONS: Qualitative methods enabled attributes to be defined. There was clear tension between the aim in qualitative work to explore and describe, and the reductiveness needed to encapsulate the different aspects of the service within a minimum number of attributes for use in the discrete choice modelling. Improved reporting of the methods of attribute development in all discrete choice experiments is required.     

Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters

Health services researchers are increasingly using discrete choice experiments (DCEs) to model a latent variable, be it health, health-related quality of life or utility. Unfortunately it is not widely recognised that failure to model variance heterogeneity correctly leads to bias in the point estimates. This paper compares variance heterogeneity latent class models with traditional multinomial logistic (MNL) regression models. Using the ICECAP-O quality of life instrument which was designed to provide a set of preference-based general quality of life tariffs for the UK population aged 65+, it demonstrates that there is both mean and variance heterogeneity in preferences for quality of life, which covariate-adjusted MNL is incapable of separating. Two policy-relevant mean groups were found: one group that particularly disliked impairments to independence was dominated by females living alone (typically widows). Males who live alone (often widowers) did not display a preference for independence, but instead showed a strong aversion to social isolation, as did older people (of either sex) who lived with a spouse. Approximately 6–10% of respondents can be classified into a third group that often misunderstood the task. Having a qualification of any type and higher quality of life was associated with smaller random component variances. This illustrates how better understanding of random utility theory enables richer inferences to be drawn from discrete choice experiments. The methods have relevance for all health studies using discrete choice tasks to make inferences about a latent scale, particular QALY valuation exercises that use DCEs, best-worst scaling and ranking tasks.     

Parental preferences for rotavirus vaccination in young children: A discrete choice experiment

Objective

This study aimed to identify characteristics that affect parental decisions about rotavirus vaccination, to determine the relative importance of those characteristics and subsequently to estimate vaccination coverage for different implementation strategies.

Methods

A Discrete choice experiment (DCE) questionnaire was sent to the parents of 1250 newborns aged 6 weeks (response rate 37.3%). Mixed-logit models were used to estimate the relative importance of the five included rotavirus vaccine and implementation characteristics; vaccine effectiveness, frequency of severe side effects, protection duration, the healthcare facility that administrates vaccination and out-of-pocket costs. Based on the utility functions of the mixed-logit model, the potential vaccination coverage was estimated for different vaccine scenarios and implementation strategies.

Results

All characteristics, except for healthcare facility that administrates vaccination, influenced parental willingness to vaccinate their newborn against rotavirus. Parents were willing to trade 20.2 percentage points vaccine effectiveness for the lowest frequency of severe side effects (i.e., 1 in 1,000,000) or 20.8 percentage points for a higher protection duration. Potential vaccination coverage ranged between 22.7 and 86.2%, depending on vaccine scenario (i.e., vaccine effectiveness and protection duration) and implementation strategy (i.e., out-of-pocket costs and healthcare facility that administrates vaccination).

Conclusions

When deciding about vaccination against rotavirus, parents are mostly driven by the out-of-pocket costs, vaccine effectiveness, protection duration, and frequency of severe side effects. The highest vaccination coverage is expected for a vaccine with high effectiveness and protection duration that is implemented within the current National Immunization Program context. Implementation of the same rotavirus vaccine in the free market will result in lowest coverage.