Objectives
This is a protocol for a Cochrane Review (intervention). The objectives are as follows:
To evaluate the benefits and harms of screening for osteoporosis with bone mineral density (BMD) measurement for the prevention of osteoporotic fractures in adults with risk factors for fractures, compared with no BMD screening.
Background
Description of the condition
Osteoporosis is a disease characterised by low bone mass and structural deterioration of bone tissue, resulting in an increase in bone fragility and susceptibility to fragility fractures [1, 2]. Fragility fractures are those resulting from mechanical forces that would not ordinarily result in fracture, known as low‐level (or ‘low energy’) trauma. The World Health Organization (WHO) [3] defined these forces as equivalent to a fall from a standing height or less.
Osteoporotic fragility fractures can cause substantial pain and severe disability, often leading to a reduced quality of life, with hip and vertebral fractures associated with decreased life expectancy. Hip fracture always requires hospitalisation; is fatal in 20% of cases; and permanently disables 50%, with only 30% of people fully recovering [2]. Approximately 90% of these osteoporotic fragility fractures occur in people aged over 50 years. In most populations above 50 years, there is typically an exponential increase in the incidence of hip fractures with advancing age [4].
The estimated number of new osteoporotic fragility fractures was 9.0 million in the year 2000. Europe had the highest number of fragility osteoporotic fractures, followed by the Western Pacific region, Southeast Asia, and the Americas. Collectively, these regions accounted for over 90% of all fractures, with the Americas and Europe accounting for over 50% of the burden worldwide. Fracture sufferers were estimated at 56 million worldwide, with a female‐to‐male ratio of 1.6. The estimated burden of disease expressed as disability‐adjusted life‐years (in thousands) by WHO region was 5.8 million [5].
There are many risk factors for osteoporosis; some of these are included in the Fracture Risk Assessment Tool [6], such as country of residence, ethnicity, age, sex, body mass index (BMI), current smoking, alcohol use, previous fracture, parental history of hip fracture, use of glucocorticoids for three months or longer, rheumatoid arthritis, secondary osteoporosis, and altered femoral neck bone mineral density (BMD) [7]. Of all the risk factors listed, reduced BMD is a major risk factor for osteoporotic fragility fractures [2], followed by age and previous low‐trauma fracture [8].
Greater age is associated with lower BMD in both sexes and all races, leading to an increased risk of fractures. In women, the risk of hip fracture increases approximately four‐fold between the ages of 55 and 85 years due to the age‐related decrease in bone mass [3], and relates to over 20% of osteoporosis cases in the elderly [9].
Using the WHO definition of osteoporosis, the disease affects approximately 6% of men and over 20% of women over the age of 50 [10]. It is estimated that one in every three women and one in every five men over age 50 worldwide will experience osteoporosis fractures in their remaining lifetimes [11].
Hormonal abnormalities, as seen in hyperthyroidism, primary or secondary hyperparathyroidism, hyperprolactinaemia, and Cushing syndrome, may increase fragile osteoporotic fracture risk. Oestrogen deficiency is strongly associated with the development of osteoporotic fragility fractures in both men and women. This deficiency is commonly observed in women after menopause and also in premature ovarian failure, surgical menopause, and a short fertile period. Since testosterone is converted to oestrogen, hypogonadism in men may also be related to low oestrogen levels [3].
Individuals with a BMI of 18.5 or lower have an increased risk of any fracture, with a higher risk for osteoporotic fragile fractures. These risks decrease with increasing BMI up to the normal range. With a BMI increasing over 25, there appears to be a greater risk for hip fracture [2].
Oral glucocorticoid use is associated with an increased risk of osteoporotic fragile fractures [2, 12]. Regardless of the mechanism, epidemiological data suggest that the risk of hip, forearm, and shoulder fractures is increased approximately two‐fold in people taking glucocorticoids [3].
Description of the intervention and how it might work
Population screening is the means whereby apparently healthy individuals are selected for intervention according to a 'high risk' strategy (i.e. to identify and treat that part of the population at greatest risk of osteoporotic fragility fractures). A prerequisite for justifying a population screening programme is demonstrating that the disease constitutes a significant public health concern [3]. Osteoporosis screening aims to identify individuals at increased risk of suffering an osteoporotic fragility fracture who would benefit from intervention to minimise that risk. Osteoporosis is usually asymptomatic until a fracture occurs; preventing osteoporotic fractures is the main goal of an osteoporosis screening strategy [1]. The screening test could provide a mechanism for the effective and efficient delivery of health care to individuals at high risk and the avoidance of unnecessary treatment to others [3].
Based on this information, individuals eligible for screening include those who exhibit at least one of the following risk factors outlined by [6]: age (≥ 65 years), BMI (< 20 kg/m2), prior fragility fracture, parental history of hip fracture, current tobacco smoking, long‐term oral glucocorticoid use (prednisolone 5 mg daily or more for three months or longer, current or past), rheumatoid arthritis, excessive alcohol consumption (two or more units daily), secondary osteoporosis (specifically related to type 1 diabetes mellitus, osteogenesis imperfecta in adults, untreated long‐standing hyperthyroidism, hypogonadism or premature menopause, chronic malnutrition or malabsorption, and chronic liver disease). [7]
BMD is the major criterion used for the diagnosis and monitoring of osteoporosis. This study quantitatively assesses bone mass per unit area, expressed in g/cm2. Many techniques are available to measure BMD, but the most widely used are based on X‐ray absorptiometry in bone, particularly dual‐energy X‐ray absorptiometry (DXA) [2]. DXA is most commonly tested at the hip and lumbar spine and measures BMD. Most treatment guidelines use central DXA to define osteoporosis and the threshold to start drug therapies to prevent osteoporotic fragility fracture [1]. Another available technique is quantitative computed tomography (QCT), which measures volumetric BMD (vBMD) in mg/cm3 at the spine and hip, but QCT is primarily a research tool at present [13, 14].
While BMD is not the exclusive criterion for evaluating fracture risk, it is a vital screening tool for osteoporosis. According to the American Association of Clinical Endocrinologists/American College of Endocrinology clinical practice guidelines [7]: “(...) Pharmacologic therapy is strongly recommended for people with osteopenia or low bone mass and a history of fragility fracture of the hip or spine (Grade A; BEL 1)”. This underscores the fundamental role of BMD in osteoporosis detection, irrespective of other risk factors, for starting treatment.
Low BMD is a major risk factor for fragility osteoporotic fractures. According to physiopathology and epidemiology, screening for low BMD and subsequent treatment can result in increased BMD and decrease the risk of subsequent fractures and fracture‐related morbidity and mortality [1].
The DXA usually provides additional data, like T‐score and Z‐score, that help classify BMD as normal (T‐score more than −1.5), osteopenia (T‐score between −1.0 and 2.5), and osteoporosis (T‐score ≤ −2.5) according to the WHO. This condition predicts the risk of an osteoporotic fragility fracture and the need for treatment [3].
When initial T‐scores are normal, repeat BMD testing may be considered if the results are likely to influence clinical management. Some observational and modelling studies have suggested screening intervals based on age, baseline BMD, and calculated projected time to transition to osteoporosis. However, limited evidence from two good‐quality studies found no benefit in predicting fractures from repeating bone measurement testing four to eight years after initial screening [1]
Why it is important to do this review
There is general agreement that screening for osteoporosis in women over 65 years of age has beneficial effects on improving bone quality and thus avoiding osteoporotic fragility fractures, although the grade of recommendation is variable [1, 15]. However, how and when to screen for osteoporosis in other groups of age or gender varies greatly according to clinical practice guidelines of different scientific societies, highlighting the high variability in indications for BMD screening. The added value of BMD screening for starting treatment is unclear in certain groups of people. This highlights the uncertainty in this practice's potential benefits and harms [2]. In this review we will try to bring greater understanding of when to screen BMD in people with risk factors for osteoporotic fragility fractures.
This review will be conducted according to the guidelines recommended by the Cochrane Musculoskeletal Editorial Board [16].
Objectives
To evaluate the benefits and harms of screening for osteoporosis with bone mineral density (BMD) measurement for the prevention of osteoporotic fractures in adults with risk factors for fractures, compared with no BMD screening.
Methods
Criteria for considering studies for this review
Types of studies
We will include randomised controlled trials (RCTs), including cluster‐RCTs. We will not include cross‐over RCTs since they are not relevant to the review question. We will not include non‐randomised studies of interventions.
We will include studies reported as full text, those published as abstract only, and unpublished data where it is possible to establish their eligibility for inclusion when data are limited. There will be no language or publication restrictions.
Types of participants
We will include adults (18 years or older) of any gender, with at least one risk factor for osteoporosis fractures, as follows [3, 6].
Age (≥ 65 years)
BMI (< 20 kg/m2)
Prior fragility fracture
Parental history of hip fracture
Current tobacco smoking
Long‐term oral glucocorticoid use (prednisolone 5 mg daily or more for three months or longer, current or past)
Rheumatoid arthritis
Excessive alcohol consumption (two or more units daily)
Secondary osteoporosis (specifically related to type 1 diabetes mellitus, osteogenesis imperfecta in adults, untreated long‐standing hyperthyroidism, hypogonadism or premature menopause, chronic malnutrition or malabsorption, and chronic liver disease) [7]
We will include studies that enroll mixed populations (e.g. both participants with and without risk factors for osteoporosis) only if data for the subgroup meeting our inclusion criteria can be extracted separately. When subgroup data are unavailable, we will contact study authors to obtain them; if this information is unobtainable, we will exclude such studies from the quantitative synthesis but may discuss them narratively.
Types of interventions
We will include trials comparing bone densitometry screening with DXA performed every four to eight years, or as reported by study authors, with no bone densitometry screening with DXA.
We will include co‐interventions, such as vitamin D supplementation, calcium supplementation, or hormone replacement therapy [1], provided they are not part of the randomised treatment and are consistent across groups.
Outcome measures
Reporting the outcomes listed here will not be an inclusion criterion for the review.
Critical outcomes
Incident hip fractures.
Incident clinical vertebral fractures (new symptomatic vertebral fractures), defined as vertebral fractures occurring during follow‐up that are accompanied by clinical signs or symptoms, or both (e.g. acute back pain, height loss, kyphosis) and confirmed by imaging (X‐ray, computed tomography (CT), or magnetic resonance imaging (MRI)) or clinical diagnosis consistent with a vertebral fracture.
Incident other (not hip or vertebral) fractures (e.g. wrist, humeral head, etc.).
Health‐related quality of life, evaluated by a validated instrument such as EQ‐5D or SF‐36 [17] or any other specific tool for quality of life assessment in osteoporosis, like Osteoporosis Quality of Life Questionnaire (OQLQ), Osteoporosis Targeted Quality of Life Questionnaire (OPTQOL), Osteoporosis Assessment Questionnaire (OPAQ), Quality of life questionnaire of the European Osteoporosis Foundation (QUALEFFO‐41), or Quality of Life Questionnaire in Osteoporosis (QUALIOST) [3]. If studies report both domain‐specific and summary scores, we will preferentially extract summary scores.
Disability associated with fractures: assessed with the Osteoporosis Functional Disability Questionnaire (OFDQ) or similar [18].
All‐cause mortality.
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Adverse events: defined as any injury related to medical management, in contrast to complications of disease [19]. We will distinguish between serious adverse events (SAEs) and any adverse events (AEs) reported in the included studies.
SAEs, defined according to the International Conference on Harmonisation (ICH) guidelines as any event that results in death, is life‐threatening, requires hospitalisation or prolongation of hospital stay, results in persistent or significant disability/incapacity, or is otherwise considered medically important by the investigator. This will include AEs leading to treatment discontinuation or withdrawal from the study.
Any AEs, defined as any untoward medical occurrence in a participant receiving the intervention, regardless of severity or causality, will be analysed separately as a minor outcome.
Important outcomes
Incident radiographic vertebral fractures (asymptomatic), identified exclusively on imaging without clinical symptoms.
Use of any pharmacological treatment: including, but not limited to, antiresorptive medication, vitamin D, calcium, parathormone analogues, calcitonin, and hormone replacement therapy [7].
Overdiagnosis: refers to the unnecessary labelling of individuals as patients, either by detecting issues that would not have caused harm or by pathologising common life experiences through the broadening of disease definitions [20]. If none of the authors estimated the rates of overdiagnosis, we will calculate the rate of overdiagnosis based on the methods described by Theriault and colleagues [21].
Timing of outcome assessments
We will consider the following time points for the extraction of outcomes:
short term: 1 to 3 years;
medium term: > 3 to 5 years;
long term: more than 5 years.
When multiple results are reported for each outcome, we will include the longest follow‐up in each category.
Minimally important differences
For quality of life, we will consider a difference of 0.074 in the EQ‐5D as the minimally important difference (MID) [22]. For other continuous outcomes where MID is not available, we will consider an MID of 0.5 standard deviations [23]. There is no reported threshold for dichotomous outcomes, therefore we will consider the clinically important difference for these outcomes as a relative risk reduction of at least 25% [24].
Search methods for identification of studies
Electronic searches
We will search the following sources from the inception of each database to the date of search. There will be no restrictions on language of publication [25]:
Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library (to Daily Update; www.cochranelibrary.com/search);
MEDLINE (Ovid MEDLINE ALL 1946 to Daily Update);
Embase (Ovid; 1974 to Daily Update);
US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov (www.clinicaltrials.gov);
World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (trialsearch.who.int).
The details of the search strategies can be found in Supplementary material 1.
Searching other resources
We will check reference lists of all primary studies and review articles for additional references. We will search relevant manufacturers' websites for trial information. We will contact experts in the field to identify additional unpublished materials. We will search for errata or retractions from included studies. We will contact the authors of the included studies to identify other unpublished studies.
Before publication, we will verify all included studies that contain any amendments, comments, or retractions.
Data collection and analysis
Selection of studies
Two review authors (of DI, GO, MB) will independently screen the titles and abstracts of studies identified by the search for potential relevance, coding them as 'retrieve' (eligible or potentially eligible/unclear) or 'do not retrieve'. We will retrieve the full‐text study reports/publication, and two review authors (of DI, GO, MB) will independently screen the full texts and identify studies for inclusion, and identify and record the reasons for exclusion of ineligible studies. We will resolve any disagreements through discussion or in consultation with a third review author (JVAF or LG) if required. We will identify and exclude duplicates and collate multiple reports of the same study under a single reference ID so that each study, rather than each report, is the unit of interest in the review. We will record the selection process in sufficient detail to complete a PRISMA flow diagram [26] and 'Characteristics of excluded studies' table.
We will use Covidence software for study selection [27].
Data extraction and management
We will use a data collection form for study characteristics and outcome data that will be piloted on at least one study in the review. One review author (of DI, GO, MB) will extract study characteristics from the included studies, and a second review author (GO or LG) will spot‐check study characteristics for accuracy against the trial report. We will extract the following study characteristics.
Methods: study design, total duration of the study, number of study centres and location, study setting, withdrawals, and date of the study.
Participants: number of participants, mean age, age range, sex, gender, severity of the condition, diagnostic criteria, risk factors (alcohol intake, corticoids therapy, BMI, etc.), [6] score; previous therapy for osteoporosis, inclusion criteria, and exclusion criteria, socio‐economic status or place of residence.
Interventions: intervention, comparison, concomitant medications, and excluded medications, frequency and duration of interventions.
Outcomes: primary and secondary outcomes specified and collected, and time points reported.
Characteristics of the design of the trial as outlined in the Risk of bias assessment in included studies section.
Notes: funding for trial, and notable declarations of interest of trial authors.
Information needed to assess GRADE (e.g. baseline risk in the control group for key outcomes).
Two review authors (of DI, GO, MB) will independently extract outcome data from the included studies. We will extract the number of events and number of participants per treatment group for dichotomous outcomes and means and standard deviations and number of participants per treatment group for continuous outcomes. We will note in the 'Characteristics of included studies' table if outcome data were not reported in a usable way and when data were transformed or estimated from a graph. We will resolve any disagreements by consensus or by involving a third review author (GO or LG). One review author (GO) will transfer data into the RevMan file [28]. We will double‐check that data are entered correctly by comparing the data presented in the systematic review with the study reports.
We will use final values for each outcome. We will base the analyses on intention‐to‐treat (ITT) and we will extract the time points according to the timing of outcomes assessments mentioned above. We will contact the authors of the included studies to obtain additional information or for clarification. We will synthesise the characteristics of all studies contributing to each comparison and present these in the 'Characteristics of included studies' table in the review. We will convert data found in studies to a format appropriate for meta‐analysis following the methods described in Chapter 6 of the Cochrane Handbook for Systematic Reviews of Interventions [29].
Risk of bias assessment in included studies
We will assess the risk of bias in each study using the Cochrane RoB 2 tool, following the guidance in Chapter 8 of the Cochrane Handbook for Systematic Reviews of Interventions [30].
Two review authors (of DI, GO, MB) will independently assess risk of bias based on the following five domains for the critical outcomes.
Bias arising from the randomisation process
Bias due to deviations from the intended interventions
Bias due to missing outcome data
Bias in measurement of the outcome
Bias in selection of the reported result
Answers to signalling questions and supporting information will collectively lead to a domain‐level judgement in the form of 'low risk of bias', 'some concerns', or 'high risk of bias'. These domain‐level judgments will inform an overall risk of bias judgement for the outcome. These judgements will be presented visually next to each analysis. Any discrepancies between the two review authors will be resolved by discussion to reach a consensus. If necessary, a third review author (LG or JVAF) will be consulted to reach a decision. We will assess the risk of bias for the critical outcomes. For each of these outcomes, we will assess risk of bias at the longest follow‐up per study.
We will provide a quote from the study report and justification for our judgement in the risk of bias table. We will summarise the risk of bias judgements across studies for each of the domains listed. When judging 'bias due to deviations from the intended interventions', we will focus the analyses on the effect of assignment to intervention [31]. Where information on risk of bias relates to unpublished data or correspondence with a study author, we will note this in the risk of bias table.
We will use the RoB 2 Excel tool to manage the data supporting the answers to the signalling questions and risk of bias judgements (available at www.riskofbias.info/). These data will be publicly available in the Open Science Framework platform (www.osf.io).
When considering treatment effects, we will take into account the risk of bias for the studies that contribute to the outcome, as part of the GRADE methodology.
We will present the figures generated by the risk of bias tool to provide summary assessments of the risk of bias.
Assessment of bias in conducting the systematic review
We will conduct the review according to this published protocol and report any deviations from it.
Assessment of risk of bias in cluster‐randomised trials
For cluster‐RCTs, we will use the RoB 2 tool and add a specific domain for cluster‐RCTs from the archived version of the tool (Domain 1b ‐ 'Bias arising from the timing of identification and recruitment of participants'; www.riskofbias.info/) with its corresponding signalling questions, following the guidance in Section 23.1.2 and Table 23.1.a of the Cochrane Handbook for Systematic Reviews of Interventions [31].
Measures of treatment effect
We will enter the outcome data for each study into the data tables in RevMan software to calculate the treatment effects [28].
We will analyse dichotomous data as risk ratios (RRs) or Peto odds ratios (ORs) when the outcome is a rare event (approximately less than 10%), and use 95% confidence intervals (CIs). We will analyse continuous data as mean difference (MD) or standardised mean difference (SMD) and 95% CIs, depending on whether the same scale is used to measure the outcome. We will enter data presented as a scale with a consistent direction of effect across studies.
When different scales are used to measure the same conceptual outcome (e.g. disability), we will calculate SMDs instead, with corresponding 95% CIs. SMDs will be back‐translated to a typical scale (e.g. 0 to 10 for pain) by multiplying the SMD by a typical among‐person standard deviation (e.g. the standard deviation of the control group at baseline from the most representative trial) [29].
Unit of analysis issues
Where multiple trial arms are reported in a single trial, we will include only the relevant arms. If two comparisons (e.g. drug A versus placebo and drug B versus placebo) are combined in the same meta‐analysis, we will halve the control group to avoid double‐counting.
For cluster‐RCTs, we will consider the cluster as the unit of analysis, not the individual participant, to avoid unit of analysis errors, as stated in Section 23.1.1 of the Cochrane Handbook for Systematic Reviews of Interventions [31]. If the effect measure for the cluster is not determined by appropriate methods in the included studies, we will multiply the standard error of the effect estimate (from an analysis ignoring clustering) by the square root of the design effect, calculated using an intracluster (or intraclass) correlation coefficient (ICC) of 0.02, following the guidance in Sections 23.1.4 and 23.1.5 of the Cochrane Handbook for Systematic Reviews of Interventions [31].
When outcomes are measured at multiple time points, we will analyse results according to prespecified time intervals. If more than one measurement falls within the same interval, we will use the longest follow‐up time point.
For vertebral fractures (clinical and radiographic), the unit of analysis will be the participant. We will count the number of people with at least one incident vertebral fracture during follow‐up. When trials report the number of fractures (events) rather than the number of participants affected, we will preferentially extract the number of participants with one or more fractures. If only counts of fractures are available, we will contact the study authors for person‐based data; if this information is unobtainable, we will analyse counts as rates using appropriate methods (e.g. rate ratios with person‐time as denominator) in sensitivity analyses, separate from the primary meta‐analysis. If both clinical and radiographic vertebral outcomes are reported, we will extract and analyse clinical vertebral fractures as primary, and radiographic‐only fractures separately. We will apply the same unit of analysis approach to other incident fracture outcomes, where studies may also report fracture counts rather than number of participants affected.
Dealing with missing data
We will contact investigators or study sponsors in order to verify key study characteristics and obtain missing numerical outcome data where possible (e.g. when a study is identified as abstract only, or when data are not available for all participants). Where this is not possible, and the missing data are thought to introduce serious bias, we will explore the impact of including such studies in the overall assessment of results by a sensitivity analysis. We will clearly describe any assumptions and imputations to handle missing data and explore the effect of imputation by sensitivity analyses.
For dichotomous outcomes (e.g. number of withdrawals due to adverse events), we will calculate the withdrawal rate using the number of participants randomised in the group as the denominator.
For continuous outcomes (e.g. mean change in pain score), we will calculate the MD or SMD based on the number of participants analysed at that time point. If the number of participants analysed is not presented for each time point, we will use the number of randomised participants in each group at baseline.
Where possible, we will calculate missing standard deviations from other statistics, such as standard errors, CIs, or P values, according to the methods recommended in Chapter 6 of the Cochrane Handbook for Systematic Reviews of Interventions [29]. If standard deviations cannot be calculated, they will be imputed (e.g. from other studies in the meta‐analysis).
Reporting bias assessment
If more than 10 trials can be pooled, we will create and examine a funnel plot to explore possible small‐study biases in a meta‐analysis. In interpreting funnel plots, we will examine the possible reasons for funnel plot asymmetry as outlined in Chapter 13 of the Cochrane Handbook for Systematic Reviews of Interventions and relate this to the results of the review [32].
To assess outcome reporting bias, we will check trial protocols against published reports. For studies published after 1 July 2005, we will screen ClinicalTrials.gov and WHO ICTRP trial registers for the a priori trial protocol. We will evaluate whether selective reporting of outcomes is present.
Synthesis methods
Main planned comparisons
Our primary comparison will be BMD screening with DXA performed every four to eight years versus no BMD screening with DXA.
Meta‐analysis of numerical data
We will perform the meta‐analysis using RevMan [28]. We will undertake meta‐analyses only when this is meaningful, that is if the treatments, participants, and the underlying clinical question are sufficiently similar for pooling to make sense. If more than one study provides usable data in any single comparison, we will perform a meta‐analysis. We will use a random‐effects model, as this is usually a more conservative approach. We will use the Restricted Maximum Likelihood (REML) estimator to estimate between‐trial variance (Tau2). We will use the Hartung‐Knapp‐Sidik‐Jonkman method to calculate the CI for the pooled effect estimate when there are at least three studies and the estimate of heterogeneity is greater than zero. In other situations (i.e. pooled analyses including only two studies, or when the estimate of heterogeneity is equal to zero), we will apply the Wald‐type method. We will present results as pooled effect estimates with 95% CIs, and will assess heterogeneity using the I2 statistic.
For dichotomous outcomes, we will use the Mantel‐Haenszel method; for continuous outcomes, we will use the inverse‐variance method; for time‐to‐event outcomes, we will use the generic inverse‐variance method to combine final scores or change score of continuous outcomes.
We will include all studies in the primary analysis, and will explore the effect of bias in a sensitivity analysis (see Sensitivity analysis).
When available, we will report the absolute number of events for each outcome and the corresponding statistics (P values). If sufficient data are available, we will report RRs or hazard ratios (HRs) and their 95% CIs or risk differences (RDs) when absolute risks in both groups are < 1%. We will summarise the results using vote‐counting based on the direction of effect, prioritising the findings from larger studies and, when available, studies at low risk of bias. In this scenario, we will assess heterogeneity qualitatively, and follow GRADE methods for assessing the overall certainty of evidence and presenting results in summary of findings tables.
Synthesis using other methods
If meta‐analysis is not possible due to incompletely reported outcome data or clinical and methodological diversity, we will perform a narrative synthesis of the available quantitative data following the guidance in the Cochrane Handbook for Systematic Reviews of Interventions [33] and the latest guidance on Synthesis Without Meta‐analysis (SWiM) [34].
We will assess clinical and methodological diversity in terms of participants, interventions, outcomes, and study characteristics (e.g. study design, outcome measurement tools, etc.) for the included studies, by observing the relevant data in the 'Characteristics of included studies' table, to determine whether a meta‐analysis is appropriate. We will assess statistical heterogeneity by visual inspection of the forest plot to assess the direction and magnitude of effects and the degree of overlap between CI.
We will use the I2 statistic to quantify inconsistency among the trials in each analysis. We will also consider the P value from the Chi2 test. If we identify substantial heterogeneity, we will report it and explore possible causes by prespecified subgroup analysis. We will use caution in applying the thresholds below to interpret statistical heterogeneity when there are few studies.
We will use this approximate guide to interpret the I2 value [35]:
0% to 40%: might 'not be important';
30% to 60%: may represent 'moderate' heterogeneity;
50% to 90%: may represent 'substantial' heterogeneity;
75% to 100%: represents 'considerable' heterogeneity.
We will keep in mind that the observed value of I2 depends on: (i) magnitude and direction of effects and (ii) strength of evidence for heterogeneity (e.g. P value from the Chi2 test, or a CI for the I2 statistic: uncertainty in the value of the I2 statistic is substantial when the number of studies is small).
We will interpret the Chi2 test where a P value of ≤ 0.10 indicates evidence of statistical heterogeneity.
If we identify substantial heterogeneity, we will report it and investigate possible causes by following the recommendations in Section 10.10 of the Cochrane Handbook for Systematic Reviews of Interventions [35].
Investigation of heterogeneity and subgroup analysis
To address all possible reasons for heterogeneity in the research question and to consider known reported risk factors for fractures, we plan to carry out the following subgroup analyses, addressing groups with different approaches that could imply a different indication for screening.
Older versus younger than 65 years
Menopause
Gender
BMI
Corticosteroid use
We will conduct subgroup analyses for the following outcomes.
Incident hip fractures
Incident clinical vertebral fractures
Incident other (not hip or vertebral) fractures (e.g. wrist, humeral head, etc.)
We will analyse subgroups only at the trial level (when there are more than 10 trials per comparison) or those reported by the individual clinical trials. We will use the formal test for subgroup interactions in RevMan [28]. We will use caution in interpreting subgroup analyses, as advised in Chapter 10 of the Cochrane Handbook for Systematic Reviews of Interventions [35]. We will compare the magnitude of the effects between the subgroups by means of assessing the overlap of the CIs of the summary estimate. Non‐overlap of the CIs indicates statistical significance.
Equity‐related assessment
We will explore potential subgroup differences by age, gender, hormonal status, BMI, and corticosteroid use, as these factors may influence both fracture risk and indications for screening. In addition, we will describe the distribution of participant characteristics according to the PROGRESS‐Plus framework (place of residence, race/ethnicity, occupation, gender, religion, education, socioeconomic status, social capital, and other context‐specific factors), when such data are available. We will interpret these analyses cautiously and without conducting a formal equity assessment, in accordance with the Cochrane Handbook for Systematic Reviews of Interventions guidance [31].
Sensitivity analysis
We will conduct sensitivity analysis by excluding the studies with an overall high risk of bias. We will consider excluding studies with outlier clinical characteristics (listed in Investigation of heterogeneity and subgroup analysis).
Certainty of the evidence assessment
We will follow the guidelines in Chapters 14 and 15 of the Cochrane Handbook for Systematic Reviews of Interventions [36, 37] for interpreting results, and will be aware of distinguishing a lack of evidence of effect from a lack of effect. We will base our conclusions only on findings from the quantitative or narrative synthesis of studies included in the review. We will avoid making recommendations for practice, and our implications for research will suggest priorities for future research and outline what the remaining uncertainties are in the area.
We will create a summary of findings table for the primary comparison of BMD screening with DXA performed every four to eight years versus no BMD screening with DXA using the following outcomes.
Incident hip fractures
Incident clinical vertebral fractures
Incident other (not hip or vertebral) fractures (e.g. wrist, humeral head, etc.)
Health‐related quality of life
Disability associated with fractures
All‐cause mortality
Adverse events (SAE/AEs)
Each outcome will be presented once in the summary of findings table. We will use a single time point and measurement method selected corresponding to the longest follow‐up available, with prioritisation of measurement methods based on validated instruments recommended in the literature and the most commonly reported measures across studies.
Two review authors (of DI, GO, MB) will independently assess the certainty of the evidence, with any disagreements resolved by discussion or by involving a third review author (LG or JVAF). We will use the five GRADE considerations (study limitations (overall risk of bias), consistency of effect, imprecision, indirectness, and publication bias) to assess the certainty of a body of evidence as it relates to the studies which contribute data to the analyses for the prespecified outcomes, and report the certainty of evidence as high, moderate, low, or very low. We will justify, document, and incorporate judgements into the reporting of results for each outcome.
We will use GRADEpro GDT software integrated into RevMan to prepare the summary of findings tables [38]. We will justify all decisions to downgrade the certainty of evidence for each outcome using footnotes, and will make comments to aid the reader's understanding of the review where necessary.
Consumer involvement
Consumers or members of the public were not involved in the design of this protocol and will not be directly involved in conducting the review.
Supporting Information
Supplementary materials are available with the online version of this article: 10.1002/14651858.CD015847.
Supplementary materials are published alongside the article and contain additional data and information that support or enhance the article. Supplementary materials may not be subject to the same editorial scrutiny as the content of the article and Cochrane has not copyedited, typeset or proofread these materials. The material in these sections has been supplied by the author(s) for publication under a Licence for Publication and the author(s) are solely responsible for the material. Cochrane accordingly gives no representations or warranties of any kind in relation to, and accepts no liability for any reliance on or use of, such material.
Supplementary material 1 Search strategies
New
Additional information
Acknowledgements
The Methods section is based on the standard Cochrane Musculoskeletal protocol template and the Cochrane Methods Support protocol template.
Editorial and peer‐reviewer contributions
The following people conducted the editorial process for this article:
Sign‐off Editor (final editorial decision): the Sign‐off Editor chose not to be publicly acknowledged;
Managing Editor (selected peer reviewers, provided editorial guidance to authors, edited the article): Sam Hinsley, Cochrane Central Editorial Service;
Editorial Assistant (conducted editorial policy checks, selected peer reviewers, collated peer‐reviewer comments, and supported the editorial team): Cynthia Stafford, Cochrane Central Editorial Service;
Copy Editor (copy editing and production): Lisa Winer, Cochrane Central Production Service;
Peer reviewers (provided comments and recommended an editorial decision): Nuala Livingstone, Cochrane Evidence Production and Methods Directorate (methods review), Jo Platt, Central Editorial Information Specialist (search review).
Contributions of authors
GO: conceptualisation, methodology, project administration, writing – original draft.
MB: validation, methodology, writing – original draft.
DI: validation, methodology, writing – original draft.
CMEL: conceptualisation, methodology, data curation (search strategy), writing – original draft.
JF: conceptualisation, methodology, supervision, writing – original draft
LG: conceptualisation, methodology, supervision, writing – original draft.
Declarations of interest
GO: has declared that they have no conflict of interest.
MB: has declared that they have no conflict of interest.
DI: has declared that they have no conflict of interest.
CMEL: has declared that they have no conflict of interest.
JF: has declared that they have no conflict of interest. The author is a Cochrane editor but was not involved in the editorial process.
LG: has declared that they have no conflict of interest.
Sources of support
Internal sources
-
Instituto Universitario Hospital Italiano de Buenos Aires, Argentina
Provides salary for GO, MB, DI, CMEL, and LG
External sources
-
None, Other
No external sources of support for this project
Registration and protocol
Cochrane approved the proposal for this review in February 2023.
Data, code and other materials
Data sharing not applicable to this article as it is a protocol, so no datasets were generated or analysed.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material 1 Search strategies
Data Availability Statement
Data sharing not applicable to this article as it is a protocol, so no datasets were generated or analysed.