The principle aim of this HTA study is to assess feasibility of the UV based imaging innovation on both clinical and cost aspects. However, as the technology is relatively new for Indian context, hence we aim to evaluate the clinical efficacy of the innovation using global evidences. For cost-effectiveness of the innovation, HAI will be used as a surrogate marker of the impact associated with the implementation of the innovation. For the same, costing data will be retrieved from Indian studies.
The study will be conducted in three phases. In the first phase, the impact of innovation on hand hygiene compliance will be assessed through undertaking a systematic review and meta-analysis approach on effect of UV based imaging tool in improving hand hygiene compliance in ICUs. While in the phase to the translation of clinical benefits of hand hygiene in terms of reduced incidence of HAI in Indian context will be measured. The phase 3 comprises of cost-effectiveness analysis of the innovation using decision analytic modelling.
The overview of the methodology is shown in figure 2.
The methodology that will be followed to undertake the systematic review and meta-analysis is described below
We will undertake evidence synthesis using quantitative methods (meta-analysis) assessing potential of UV light based imaging technique in inducing behavioral changes in health care workers of ICU across the globe. The impact will be evaluated in terms of improvement in hand hygiene compliance rate. The overall effect will be expressed in terms of pooled odds ratio.
Criteria for considering studies for this review:
Types of studies: The articles incorporated were all kind of studies including cross-sectional, case control, randomized controlled trials (RCTs), review and meta-analysis with no restriction to language and calendar date. Reference articles of the recovered articles were also screened for potential studies.
Types of participants: Health care workers of ICU
Types of interventions: UV light based imaging technology to assess hand hygiene compliance
Types of outcome measures: Hand hygiene compliance rate
Search methods for identification of studies
We will use criteria and standard methods of Cochrane and Cochrane infectious diseases.
Electronic searches
For assessing impact of UV light based innovative technique on hand-hygiene: For retrieving articles reporting impact of UV light based fluorescent technique on hand hygiene compliance, we will be searching five databases as per following search strategy and study selection criteria: 1] PUBMED (until September 2019) 2] EMBASE (1980 to September 2019) 3] CINAHL (until September 2019) 4] Cochrane CENTRAL (until September 2019) 5] Clinical trials registries for ongoing and recently completed trials (clinicaltrials.gov; World Health Organization International Trials Registry). These databases were explored independently by the authors. The search terms to be used are: “Hand washing” OR “Hygiene” OR “Hand rubbing” OR “Compliance” AND “Ultraviolate” OR “UV” OR “Ultra-violate” OR “Fluorescent”.
Two authors will perform the final selection of studies for inclusion and exclusion by assessing full text and extracting the data independently. The third author will perform cross-verification of the extracted data. Differences of opinions will be resolved through discussion and mutual consensus.
Searching other resources
We will seek additional citations by using references in articles retrieved through searches. We will contact subject experts to identify unpublished and ongoing studies specially for retrieval of the costing data. We will contact authors of published trials to clarify or provide additional information if criteria for methodological assessment are not explicit in their publication. In cases of cluster-randomized trials, we will contact study authors for an estimate of design effect if needed. Two review authors will independently screen candidate articles to check eligibility for inclusion in the review.
Selection of studies
Two review authors will independently screen the titles and abstracts of articles identified by searches for eligibility. We will classify these studies as included, unclear, or excluded. We will retrieve full articles for studies that do not provide an abstract or that have a limited abstract and will assess them for inclusion. Two review authors will independently assess for inclusion full articles classified as ’include’ or ’unclear’ using a standardized form with explicit inclusion and exclusion criteria. These two review authors will resolve disagreements by discussion and, if required, by consultation with a third review author.
Data extraction and management
Two review authors will independently extract data from each included study using a predesigned data extraction form. We will try to contact trial authors to request incompletely reported data. We will extract the following information: general information (study ID, date of extraction, title, authors, and source of study if not published); study characteristics (study design, participants, and inclusion/exclusion criteria used in the study); details of interventions; and details necessary for ’Risk of bias’ assessment. We will resolve disagreements between review authors by discussion or by consultation with a third review author.
Assessment of risk of bias in included studies
We will use the ‘Risk of bias’ assessment tool and criteria set out in the Cochrane Handbook for Systematic Reviews of Interventions to assess risk of bias for included studies [23]. Two review authors will independently assess risk of bias in the included studies by assessing randomization sequence generation; allocation concealment; blinding of participants, personnel, and outcome assessors; incomplete outcome data; selective outcome reporting; and other sources of bias. We will resolve disagreements through discussion or by consultation with a third review author. We will evaluate and report the following results in ’Risk of bias’ tables.
- Selection bias (random sequence generation and allocation concealment): For each included study, we will categorize risk of selection bias as:
◦ low risk - adequate (any truly random process, e.g. random number table; computer random number generator; coin tossing; shuffling of cards; throwing of dice; drawing of lots; minimization);
◦ high risk - inadequate (any non-random process, e.g. odd or even date of birth; hospital or clinic record number); or
◦ unclear risk - insufficient information about the sequence generation process to permit judgement of ‘low risk’ or ‘high risk’.
- Allocation concealment: For each included study, we will categorize risk of bias regarding allocation concealment as:
◦ low risk - adequate (e.g. telephone or central randomization; consecutively numbered, sealed opaque envelopes);
◦ high risk - inadequate (open random allocation; unsealed or non-opaque envelope alternation; date of birth); or
◦ unclear risk - no or unclear information provided.
- Performance bias: For each included study, we will categorize methods used to blind study personnel from knowledge of which intervention a participant received. As our study population will consist of neonates, all will be blinded to the study intervention. We will categorize risk of performance bias as:
◦ low risk - adequate for personnel;
◦ high risk - inadequate for personnel (aware of group assignment); or
◦ unclear risk - no or unclear information provided.
- Detection bias: For each included study, we will categorize methods used to blind outcome assessors from knowledge of which intervention a participant received. We will categorize methods used with regards to detection bias as:
◦ low risk - adequate (follow-up was performed with assessors blinded to group assignment);
◦ high risk - inadequate (assessors at follow-up were aware of group assignment); or
◦ unclear risk - no or unclear information provided.
- Attrition bias: For each included study and for each outcome, we will describe completeness of data including attrition and exclusions from the analysis. We will note whether attrition and exclusions were reported, numbers included in the analysis at each stage (compared with total randomized participants), reasons for attrition or exclusion when reported, and whether missing data were balanced across groups or were related to outcomes. When trial authors reported or supplied sufficient information, we will re-include missing data in the analyses. We will categorize methods with respect to risk of attrition bias as:
◦ low risk - adequate (< 10% missing data);
◦ high risk - inadequate (> 10% missing data); or
◦ unclear risk - no or unclear information provided.
- Reporting bias: For each included study, we will describe how we investigated risk of selective outcome reporting bias and what we found. We will assess methods as:
◦ low risk - adequate (when it is clear that all of the study’s pre-specified outcomes and all expected outcomes of interest to the review have been reported);
◦ high risk - inadequate (when not all of the study’s pre-specified outcomes have been reported; when ≥ 1 reported primary outcomes were not pre-specified; when outcomes of interest are reported incompletely and so cannot be used; when study fails to include results of a key outcome that will be expected to have been reported); or
◦ unclear risk - no or unclear information provided (study protocol was not available).
- Other bias: For each included study, we will describe any important concerns that we had about other possible sources of bias (e.g. whether a potential source of bias was related to the specific study design, whether the trial was stopped early owing to some data-dependent process). We will assess whether each study was free of other problems that could put it at risk of bias, as:
◦ low risk - no concerns of other bias raised;
◦ high risk - concerns raised about multiple looks at data, with results made known to investigators, difference in numbers of participants enrolled in abstract and in final publications of the paper; or
◦ unclear risk - concerns raised about potential sources of bias that could not be verified by contacting trial authors. We plan to explore the impact of level of bias by undertaking sensitivity analyses.
Measurement of treatment effect
We will perform statistical analysis according to statistical guidelines referenced in the Cochrane Handbook for Systematic Reviews of Interventions [23] For dichotomous outcomes, we will express measures of effects as typical odds ratios (ORs) and typical odds differences (ODs) with 95% confidence intervals (CIs). If the RD is statistically significant, we will calculate the number needed to treat for an additional beneficial or harmful outcome. For continuous outcomes, we will express measures of effect as weighted mean differences (MDs) with 95% CIs. In the event that continuous data are reported on different continuous scales, we will standardize outcomes, when possible, to calculate the standardized Mean Difference.
Unit of analysis issues
Analyses will consider the level at which randomization was done - individual or cluster. In the event that cluster-randomized studies are included, we will appropriately adjust for clustering; and we will multiply the standard error derived from the confidence interval of the effect estimate by the square root of the design effect. We will use the generic inverse variance method in Review Manager 5 to perform meta-analysis using inflated variances [24].
Dealing with missing data
We will contact trial authors to request missing data and will use imputation methods when necessary. We will clearly label analyses by including imputed data.
Assessment of heterogeneity
When data from included trials can be pooled, we will assess statistical heterogeneity via visual inspection of forest plots of included trials, using the Chi2 test and the I2 statistic. We will examine trial characteristics (participants, design, interventions, outcomes, and risk of bias) to identify the source of any observed heterogeneity. We would use cut-offs recommended by Cochrane Infectious diseases for results of the I2 test: < 25% none, 25% to 49% low, 50% to 74% moderate, and 75%+ high heterogeneity.
Assessment of reporting biases
We will assess reporting biases by trying to identify whether the study was included in a trial registry, whether a protocol is available, and whether the methods section provides a list of outcomes. We will compare the list of outcomes from those sources versus outcomes reported in the published paper. We will create an inverted funnel plot to check for possible publication bias if a sufficient number of studies are available for specific outcomes.
Data synthesis
If studies are found to be similar, we will use Review Manager 5 software to combine in the meta-analysis data for outcomes from studies that meet the inclusion criteria [24]. We will
perform statistical analyses according to statistical guidelines of the Cochrane Handbook for Systematic Reviews of Interventions and Cochrane Infectious Diseases [23]. We will conduct a fixed-effect meta-analysis if appropriate. We will not conduct meta-analysis when a high level of heterogeneity is evident. We will provide only a narrative summary of trial findings if data cannot be combined in a meta-analysis. In the event that continuous data are reported on different continuous scales, we will standardize outcomes, when possible, to calculate the standardized MD.
Quality of evidences
We will use the GRADE approach, as outlined in the GRADE Handbook, to assess the quality of evidence for the following (clinically relevant) outcomes: percent improvement in hand hygiene compliance, incidence of HAI [25]. Two review authors will independently assess the quality of evidence for each of the outcomes above. We will consider evidence from RCTs as high quality but will downgrade the evidence one level for serious (or two levels for very serious) limitations on the basis of the following: design (risk of bias), consistency across studies, directness of evidence, precision of estimates, and presence of publication bias. We will use GRADEproGDT to create a ‘Summary of findings’ table to report the quality of the evidence. The GRADE approach yields an assessment of the quality of a body of evidence according to one of four grades.
- High: We are very confident that the true effect lies close to that of the estimate of the effect.
- Moderate: We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.
- Low: Our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect.
- Very low: We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect.
Sensitivity analysis
We will conduct sensitivity analyses to assess the impact of high risk of bias on the outcome of meta-analyses by adding studies with high risk of bias to pooled studies with low risk of bias.