Types of studies

All randomised controlled trials (RCTs), cluster‐RCTs and non‐randomised study interventions (NRSI). Eligible NRSI are non‐randomised controlled trials, prospective cohort studies, before‐and‐after studies, and interrupted‐time‐series (ITS) studies.

Due to the complex nature of the intervention and setting, we have included NRSI as we anticipate potential limitations in the availability of RCTs. NRSI evidence may highlight the need for research for high‐quality RCTs. In addition, NRSI may yield evidence on outcomes for important study processes such as referrals or longer‐term coronary outcomes which are unlikely to be available from RCTs.

Types of participants

Eligible participants of any age from the general population who access non‐specialist clinical settings and other non‐clinical community settings. Participants who are selected from specialist settings with expertise in lipid disorders will be excluded from the review. Participants who have a previous diagnosis of FH or other inherited lipid disorders will also be excluded. If the study contains both eligible and ineligible participants, the study will be included if the data on eligible participants can be extracted where at least 70% of the study contains eligible participants.

Definition of term 'non‐specialist' can be found in an appendix (Appendix 1)

Types of interventions

Interventions that aim to systematically identify people with possible or definite FH, in non‐specialist clinical settings in primary care and other community settings. Interventions which involve specialists delivering the interventions in a non‐specialist setting (e.g. FH nurse specialists performing case findings) are also appropriate for inclusion.

A systematic intervention for identifying FH is defined as:

• prospective general population screening for FH using diagnostic criteria;

• retrospective electronic and manual health records search for participants who might have FH (i.e. based on elevated cholesterol levels, relevant family history, clinical characteristics, or combination of these factors);

• proactive computer‐generated reminders for participants who might have FH (i.e. based on elevated cholesterol levels, past medical history, relevant family history, clinical characteristics, or combination of these factors);

• population based case‐finding activities (i.e. healthcare practitioner reviewing patient records and contacting individuals; pathology laboratories reporting back high cholesterol levels).

These systematic strategies to identify individuals with possible or definite FH, will be compared with usual care, where participants will continue to receive their standard or current medical care (Reeves 2016). In this situation, usual care for FH identification in non‐specialist primary care practice will be incidental and non‐systematic during routine consultation with patients. This includes noting a raised cholesterol during consultation with individuals presenting with concerns about their personal or family history. Should usual care vary between geographical settings, a description of such will be provided.

Where possible, we will compare interventions to each other if the usual care strategies are similar between or within the studies.

Types of outcome measures

Electronic searches

We will identify relevant studies from the Cystic Fibrosis and Genetic Disorders Group's Inborn Errors of Metabolism Trials Register using the term: hyperlipoproteinaemia.

The Inborn Errors of Metabolism Trials Register is compiled from electronic searches of the Cochrane Central Register of Controlled Trials (CENTRAL) (updated with each new issue of the Cochrane Library), and weekly searches of MEDLINE. Unpublished work is identified by searching the abstract books of the Society for the Study of Inborn Errors of Metabolism conference and the SHS Inborn Error Review Series. For full details of all searching activities for the register, please see the relevant section of the Cochrane Cystic Fibrosis and Genetic Disorders Group's website.

In addition to the above, we will conduct a search of the following databases combining free text terms and controlled vocabulary where applicable (Appendix 2):

• Cochrane Central Register of Controlled Trials (CENTRAL in the Cochrane Library (www.cochranelibrary.com) (all years);

• PubMed (Epub Ahead of print, In process & Other non‐Indexed Citations only) (www.ncbi.nlm.nih.gov/pubmed) (1946 to present);

• MEDLINE (OvidSP, EpubAhead of Print, In‐process & Other Non‐Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE) (1946 to present);

• Embase (OvidSP) (1974 to present);

• CINAHL (EBSCOHost) (1937 to present);

• ProQuest Dissertations & Theses Global ProQuest (www.proquest.com/) (1861 to present);

• Web of Science (CPCI‐S) (1898 to present);

• SCOPUS (Elsevier) (1823 to present).

We will search the following trial databases and resources:

• ISRCTN registry (www.isrctn.com/) (all years);

• ClinicalTrials.gov (www.clinicaltrials.gov) (all years);

• WHO International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp.en) (all years)

Searching other resources

Selection of studies

We will select studies according to chapter 7 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011b). We will save the results from the searches in Endnote reference managing software (Endnote X5). Two authors (JT, MD) will independently screen the titles and abstracts of the identified studies to check for eligibility. We will then retrieve the full texts (were available) of the potentially eligible studies and two authors (JT, MD) will independently screen these. We will resolve any disagreements through discussion, or where necessary, with the assistance of a third author (JLB).

To guard against potential duplicate publication, we will link multiple reports using the same study participants. If any uncertainties arise concerning study eligibility for the review, we will correspond with the investigators of the study in question. If it is not possible to resolve any disagreements on whether to include a study without obtaining further information, we will categorize the study in the review as 'awaiting assessment' until the additional information is obtained from the study authors.

We will record details of studies excluded at the full text stage together with reasons for exclusion based on the inclusion criteria for the review.

Data extraction and management

Two authors will independently extract and record data from included studies following guidance in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a). We will use standard data extraction forms, based on the checklist from the Cystic Fibrosis and Genetics Disorders Group, which we will modify to allow relevant data to be captured from all the study designs which are eligible for inclusion in this review. Where possible we will pre‐pilot the modified version of the data extraction form with a minimum of three studies with different study designs.

The data extraction form will include study characteristics such as study methodology, participant characteristics (including ethnic or cultural characteristics, geographical location), sample size, strategies and characteristics (including process and duration of intervention), primary and secondary outcome measures, FH diagnostic criteria and definitions of other outcomes, and analysis performed in the original trials.

For NRSI, we will extract additional data on confounding factors, the comparability of groups on confounding factors, methods used to control for confounding and on multiple effect estimates (both unadjusted and adjusted estimates) (Reeves 2011). Confounding factors need to be associated with both the intervention and the outcomes, thus are unlikely to be participant‐level factors but population‐ or service‐level factors. These may include, but are not limited to:

• type of health professional(s) delivering the intervention;

• size of the population being assessed;

• workload of the health professional(s)

We plan to resolve any discrepancies in data extraction through discussion, or where necessary, through arbitration with a third author (JLB). One author (JT) will enter data into the Review Manager Software and a second author (SW) will verify the data (RevMan 2014).

Assessment of risk of bias in included studies

For all studies, two review authors (NQ and SW) will independently assess the risk of bias for each study; and if there is a disagreement, a third author (JLB) will check each assessment and discuss the outcome until consensus is achieved.

For RCTs, we will assess the risk of bias using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions according to the following domains: random sequence generation; allocation concealment; blinding of participants and personnel; blinding of outcome assessment; incomplete outcome data; selective outcome reporting; and other sources of bias (Higgins 2011c).

For NRSI, we will assess risk of bias for each outcome of interest in the study using the ROBINS‐I tool developed by the Cochrane Non‐Randomised Studies Methods Group (Sterne 2016). The tool considers seven domains: two domains of bias pre‐intervention (bias due to confounding and bias in selection of participants into the study); one domain of bias at intervention (bias in the measurement of interventions); and four domains of bias post‐intervention (bias due to departures from intended interventions, bias due to missing data, bias in measurement of outcomes and bias in selection of the reported result).

The authors will grade the methods used in each included study as either a high, low or unclear risk of bias. We will record all judgements in the 'Risk of bias' tables, together with the characteristics of each included study and we will prepare a 'Risk of bias' summary figure. We will contact relevant authors of studies published within the past 10 years for missing information or when further clarification is needed. We will document the outcomes of the correspondence in the review.

Measures of treatment effect

Where possible, we will report dichotomous outcomes using risk ratios (RR) together with 95% confidence intervals (95% CIs). For studies which have used randomisation, we will extract 2x2 data and estimate crude RRs; however, for studies without randomisation, we will extract RRs which have been adjusted for baseline differences or the ratio of the RR post‐intervention compared to the RR for pre‐intervention. Where studies report other adjusted measures of effect, e.g. odds ratios, we will extract these and report them separately.

Where possible, we will report continuous outcomes using mean differences (MD) together with 95% CIs. For studies which have used randomisation, we will extract raw data and estimate crude MDs; however, for studies without randomisation, we will extract MDs for the absolute change or the relative change, which have been adjusted for baseline differences.

For studies with a non‐randomised study design, we will consider additional analysis on adjusting for baseline group differences, with appropriate regression analysis based on the form of the outcome variables (continuous or binary).

For studies which have used an ITS design, we will extract quantitative results from either a regression analysis with time trends before and after the intervention, adjusted for autocorrelation and any periodic changes, or from an ARIMA analysis. We will present the results for the outcomes as the change in level (immediate effect of the intervention) and the change in slope (longer‐term effect of the intervention). If the results from a study using an ITS design are only reported as data points in a scanable graph or in a table, we will attempt to re‐analyse the data by contacting study authors for permission to use their raw study data (Ramsay 2003).

Unit of analysis issues

We will consider whether any unit of analysis errors are made in the reported analysis for each study. For cluster‐randomised studies, where the analysis may not adjust for the effect of clustering, if we identify a unit‐of‐analysis issue, we will attempt to correct the analysis by re‐analysing the presented data. Or if there is insufficient information presented, we will contact the authors to obtain the necessary information or raw data. If we are not able to re‐analyse the data taking into account clustering, we will report the uncorrected estimates without a measure of uncertainty (e.g. 95% CI).

For studies that have accounted for repeated measures we will report their study measures.

Dealing with missing data

Where important data are missing (e.g. standard deviations), we will contact the authors of studies published less than 10 years ago to request additional data. Where we are unable to retrieve missing data, we will conduct a sensitivity analysis to compare study outcomes grouped by how much data are missing: large amount (more than 30%); moderate amount (10% to 30%); low amount (less than 10%). We will then discuss the potential impact of the missing data on the findings of the review.

Assessment of heterogeneity

Assessment of reporting biases

Where the meta‐analyses contains more than 10 studies, funnel plots will be used to assess the potential effects of reporting biases. Where funnel plot asymmetry is apparent, the review authors will consider possible sources of asymmetry other than publication bias, for example based on domains of methodological quality, and conduct appropriate sensitivity analyses.

Data synthesis

We will initially summarise all included studies using narrative synthesis methods. This will involve the use of narrative text and tables to summarise data based on the type of intervention and according to setting, consider outcomes in the light of differences in study designs and address potential sources of bias, potential confounding factors and any further methodological limitations for each of the studies being reviewed, including how these may have impacted on the study findings. We will then provide a synthesised summary of the studies, including the range and size of any reported associations and important study characteristics.

Meta‐analysis for RCTs and NRSI will be conducted separately. We will use random‐effects models to conduct meta‐analyses due to anticipated differences in the effectiveness of the intervention by type of intervention, setting, and populations.

We will be perform meta‐analysis using the Review Manager (RevMan) software (RevMan 2014) or Stata version 14 (STATA 2015).

Subgroup analysis and investigation of heterogeneity

In each meta‐analysis including at least five studies, the authors will perform subgroup analyses based on study‐level variables, and will report a P value relating to the statistical test for differences between subgroups, where appropriate. We will consider the following characteristics:

• mode of diagnosis for FH (genetic or clinical);

• age of participant (under 10 years of age or 10 years of age and above);

• type of systematic intervention (prospective population screen, retrospective computer search, proactive computer reminders, population‐based case finding);

• type of setting (primary medical care, employer‐based clinics, community pharmacists).

We will undertake subgroup analyses using RevMan (RevMan 2014) or Stata version 14 (STATA 2015). We anticipate that due to the complexity of the intervention that most studies will include complex interventions and therefore we will group studies primarily based on similarities between outcome measures.

Sensitivity analysis

We will assess the impact of methodological quality on the results of the meta‐analyses by re‐analysing only those studies with a overall low risk of bias. We will compare the pooled effect size from this secondary analyses to the pooled effect size from the original analyses. We will report sensitivity analyses in table format within this review.