Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Are interventions to increase the uptake of screening for cardiovascular disease risk factors effective? A systematic review and meta-analysis

BMC Family PracticeBMC series – open, inclusive and trusted201718:4

DOI: 10.1186/s12875-016-0579-8

Received: 9 August 2016

Accepted: 26 December 2016

Published: 17 January 2017

Abstract

Background

Cardiovascular disease (CVD) is the leading cause of death globally. However, many individuals are unaware of their CVD risk factors. The objective of this systematic review is to determine the effectiveness of existing intervention strategies to increase uptake of CVD risk factors screening.

Methods

A systematic search was conducted through Pubmed, CINAHL, EMBASE and Cochrane Central Register of Controlled Trials. Additional articles were located through cross-checking of the references list and bibliography citations of the included studies and previous review papers. We included intervention studies with controlled or baseline comparison groups that were conducted in primary care practices or the community, targeted at adult populations (randomized controlled trials, non-randomized trials with controlled groups and pre- and post-intervention studies). The interventions were targeted either at individuals, communities, health care professionals or the health-care system. The main outcome of interest was the relative risk (RR) of screening uptake rates due to the intervention.

Results

We included 21 studies in the meta-analysis. The risk of bias for randomization was low to medium in the randomized controlled trials, except for one, and high in the non-randomized trials. Two analyses were performed; optimistic (using the highest effect sizes) and pessimistic (using the lowest effect sizes). Overall, interventions were shown to increase the uptake of screening for CVD risk factors (RR 1.443; 95% CI 1.264 to 1.648 for pessimistic analysis and RR 1.680; 95% CI 1.420 to 1.988 for optimistic analysis). Effective interventions that increased screening participation included: use of physician reminders (RR ranged between 1.392; 95% CI 1.192 to 1.625, and 1.471; 95% CI 1.304 to 1.660), use of dedicated personnel (RR ranged between 1.510; 95% CI 1.014 to 2.247, and 2.536; 95% CI 1.297 to 4.960) and provision of financial incentives for screening (RR 1.462; 95% CI 1.068 to 2.000). Meta-regression analysis showed that the effect of CVD risk factors screening uptake was not associated with study design, types of population nor types of interventions.

Conclusions

Interventions using physician reminders, using dedicated personnel to deliver screening, and provision of financial incentives were found to be effective in increasing CVD risk factors screening uptake.

Keywords

Systematic review Meta-analysis Intervention Cardiovascular Screening Prevention

Background

A major challenge to the control of cardiovascular disease (CVD) is the high prevalence of cardiovascular risk factors such as hypertension, diabetes and obesity [13]. A substantial proportion of the global population remain unaware of their existing cardiovascular risk factors [46]. Modifiable risk factors for CVD account for 90% of the risk of myocardial infarction, which indicates that CVD is largely preventable [7]. Effective management strategies such as lifestyle changes or pharmacotherapy are available to modify these risk factors, which have been shown to reduce CVD morbidity and mortality especially for those at high risk [812]. Two cohort studies from Korea and Japan reported that health screening for CVD was associated with lower rates of CVD, all-cause mortality, CVD events and lower healthcare utilization and costs [13, 14].

There has been considerable debate regarding the usefulness of screening for CVD risk factors [1519]. A systematic review by Krogsbøll found that general health checks did not reduce morbidity or mortality of CVD [20]. Others have argued that the results of this review cannot be generalized because it included old studies from an era when management was not as effective as current treatment [21]. As the review also focused on general health checks, the findings may differ from health checks conducted for specific conditions such as CVD and cancer [2123].

Various strategies and interventions have been used to increase individuals’ participation in CVD risk factors screening. Their effectiveness varied from study to study, ranging from no benefit to an 80% increase in the participation rate from baseline [2427]. Jepson et al. conducted a comprehensive systematic review to examine factors associated with the uptake of screening programmes and to assess the effectiveness of methods used to increase uptake [28]. However, the majority of the studies included in this review were related to cancer screening, with very limited studies on CVD risk factors screening. To the best of our knowledge, there has previously been no systematic review analyzing the effectiveness of interventions used to increase uptake rate of CVD risk factors screening amongst the general population from primary care practices and the community. Previous systematic reviews have focused on assessing the effectiveness of using community pharmacies as the site for CVD risk factors screening [29], the evaluation of behavioural components used in the intervention of screening programmes [30], and reviewing screening approaches in primary care [31]. Therefore, the objective of this study is to examine the literature to determine the effectiveness of interventions used to increase the uptake of CVD risk factors screening in adult population from primary care practices and the community.

Methods

The research question addressed in this systematic review is as follows:

What is the effectiveness of interventions that aim to increase the uptake of CVD risk factors screening?
  • Population: Adults aged 18 years and above

  • Intervention: Interventions that aim to increase participation of individuals to screen for CVD risk factors

  • Comparison: comparator groups with usual care

  • Outcome: uptake rate (participation rate by public or patients or screening rate which was represented by the tests performed by physicians)

Criteria for study selection

Types of studies

Studies on interventions that aimed to increase participation of individuals to screen for CVD risk factors were included. Study designs included randomized controlled trials (RCTs), quasi-RCTs, non-randomized trials with controlled group and studies which used baseline data as the control group (pre- and post-studies). Studies comparing different interventions were excluded if there was no controlled or baseline group.

Assessment for screening of CVD risk factors

The CVD risk factors screening included for assessment in the review were measurements of blood pressure (BP), weight, body mass index (BMI), waist circumference (WC), glucose, lipids, total cardiovascular risk score and history taking regarding smoking, physical activity, or nutritional intake. These CVD risk factors screening could have been carried out in a program specifically targeting CVD risk factors screening, or as part of a program with other preventive services such as cancer screening and vaccination.

Study population

The population for included studies involved those individuals aged 18 years and above recruited from attendees of primary care practices or the community. We included studies with mixed populations with or without known CVD and studies limited to populations without known CVD.

Studies which targeted specific populations or conditions such as safety screening for sports or exercise participation, gestational diabetes or post partum screening, familial hypercholesterolaemia and participants with a defined condition such as mental disabilities or rheumatoid arthritis, were excluded.

Types of interventions

All types of interventions or strategies to increase participation of CVD risk factors screening were included, regardless of whether they were targeted at individual, community, health-care provider or health-care system level.

Types of outcome measures

The CVD risk factors screening uptake was measured by 1) participants’ attendance rate for screening, or 2) screening rate by health-care providers. We excluded studies that only reported on the intention to participate or physicians’ compliance with the prescription. In cases where the studies had included screening for health conditions other than CVD risk factors, we chose to report only outcomes related to CVD risk factors.

Search methods

A systematic search was conducted using four electronic databases: PubMed (12 June 2014), CINAHL (3 July 2014), EMBASE (10 July 2014), and Cochrane Central Register of Controlled Trials (3 July 2014). Updates of this search strategy were obtained from PubMed weekly until August 2015 and no new study was identified that fitted our inclusion criteria.

A mixture of medical subject headings (MeSH terms) and free text was used for the concept of “cardiovascular”, “uptake” and “screening”. The search strategy for PubMed is shown in Table 1. These search terms and limits were modified accordingly for each individual database, in order to meet its specification. Limits applied were English language and adult population. There was no limit applied for the year of publication. Additional articles were located through cross-checking of reference lists and bibliography citations of the included studies. This reference list and bibliography citations included review papers. For these review papers, we checked the reference lists to retrieve relevant studies.
Table 1

Search strategy in PubMed

#1 Search (((((((("Hyperlipidemias"[Mesh]) OR "Cardiovascular Diseases"[Mesh]) OR "Hypertension"[Mesh]) OR "Diabetes Mellitus"[Mesh])) OR ((((((((((cardiovascular[Text Word]) OR coronary[Text Word]) OR stroke[Text Word]) OR heart[Text Word]) OR family history[Text Word]) OR early cardiovascular death[Text Word]) OR hyperlipidemias[Text Word]) OR diabetes mellitus) OR hypertension)))

#2 Search ((((((((general practice[Text Word]) OR preventive health service[Text Word]) OR health check*[Text Word]) OR mass screening[Text Word]) OR opportunistic screening[Text Word])) OR (((((health check*) OR "General Practice"[Mesh]) OR "Preventive Health Services"[Mesh]) OR "Mass Screening"[Mesh])))) OR screening[Text Word]

#3 Search ((((((((("Patient Acceptance of Health Care"[Mesh]) OR "Patient Participation"[Mesh]) OR "Consumer Participation"[Mesh]) OR "Refusal to Participate"[Mesh]) OR uptak*))) OR (((((patient participation[Text Word]) OR consumer participation[Text Word]) OR uptak*[Text Word]) OR patient acceptance of health care[Text Word]) OR refusal to participate[Text Word]))) OR participat*

#4 Search #1AND #2 AND #3

Data collection and analysis

Study selection and data extraction

Two reviewers (ATC and NFMZ) screened the titles and abstracts of the articles and conference proceedings. Full papers were then retrieved for potentially eligible articles and reviewed for relevance by ATC and NFMZ. An article was included when there was agreement on the fulfilment of the inclusion criteria. In circumstances where there was a discrepancy, discussions with other team members (SML and EMK) were held to reach a consensus. When further details of the paper were required, corresponding authors were contacted via email; for example when full details of the numerator and denominator of the screening rate were not available, or when there was uncertainty about the same data being presented in more than one publication that described a single study, to avoid problems of double counting of subjects in the meta-analysis [27, 3240].

Data were then extracted independently by two reviewers (ATC and NFMZ) from the included studies. Relevant information extracted included author(s), year, country of study, title, type of setting, type of screening assessment, study design, study population characteristics, type of intervention and the proportion of participation (numerators and denominators) in the intervention and the controlled arm, which represented the CVD risk factors screening uptake rate. Information on study design, study population characteristics, and types of intervention were extracted to allow for meta-regression.

Assessment of quality

Appraisal of the quality of methodology of the included studies was conducted using The Cochrane Collaboration’s “Risk of bias” tool [41]. Each study was assessed based on the features of selection bias, performance bias, detection bias, attrition bias, reporting bias and other potential sources of bias for quality of methodology [41]. The quality of descriptions of interventions in publications was assessed using the Template for Intervention Description and Replication (TIDieR) checklist and guide [42]. The TIDier checklist has 12 items to assess the reproducibility of the intervention based on the description, which included items such as brief name, why, what (materials), what (procedure), who provided, how, where, when and how much, tailoring, modifications, how well (planned), how well (actual) [42].

Data synthesis and analysis

In this review, two analyses were conducted. First, an analysis was carried out on the overall effectiveness of screening uptake of the interventions compared with its control. Second, subgroup analysis was done measuring the effectiveness of screening uptake by study design and type of intervention. Relative risk (RR) with 95% confidence interval (95% CI) was performed for all sets of comparisons. Data from relevant studies were pooled using a random-effects model with OpenMetaAnalyst software [43, 44]. A test of heterogeneity (Cochran’s Q statistic) with reported p-value was performed and the degree of inconsistency across studies was quantified using I 2 [45].

Meta-regression is a method used to explore heterogeneity seen in meta-analysis by examining differences between studies by effect modifiers [46]. In this study, meta-regression was performed in order to explore whether the differences in study designs (RCT, Controlled trial, pre- and post-studies), types of population (no known CVD, mixed population of known and unknown CVD) and types of intervention (physician reminder, patient invitation, using financial incentives, using dedicated personnel and multifaceted approach) could explain the heterogeneity. Random-effects meta-regression was performed using OpenMetaAnalyst software [43].

Some studies compared more than one type of intervention with usual care [27, 4749]. Each of these intervention groups was analyzed independently and compared with the group with usual care. For studies with separate screening uptake rates for the different risk factors, the outcome could be represented by any one of these rates [27, 33, 5057]. For example, the study by Harari et al. reported the uptake rate for BP, cholesterol and blood glucose separately [52]. In order to provide a range of the effectiveness of such interventions, two meta-analyses were performed; one pooling the highest effect sizes of the uptake rate (hereon referred to as optimistic) and the other pooling the lowest effect sizes of the uptake rate (hereon referred to as pessimistic). In studies that reported results of screening uptakes using different time periods, we used the longest duration of timeline in the analysis [48, 56].

Results

Literature retrieval process

The search strategy identified 21,307 citations from four databases after removing duplicates. After screening the titles and abstracts, 167 full papers were retrieved for assessment for eligibility. Of these, 158 papers were excluded as they did not fulfill the inclusion criteria. The reasons for exclusion included the age of the study population, the absence of a controlled group, or the outcomes were not related to screening uptake. One study published three papers from data obtained at different periods [3234], and the most recent paper was included [33]. A total of 9 studies fulfilled the inclusion criteria and were included [27, 33, 35, 36, 47, 54, 55, 58, 59]. Forward and backward searches of the reference lists and bibliography citations of the 9 studies yielded an additional 16 studies [37, 39, 4853, 56, 57, 6065] and resulted in a total of 25 studies for qualitative synthesis. We contacted authors of six studies which did not have full details of the numerators and denominators of the screening uptake rates required for meta-analysis. Two authors provided the requested information [27, 37] while authors of the other four studies were either not contactable or stated that they no longer had access to the data. These four studies [35, 36, 39, 65] were excluded, and the final number of studies included in the meta-analysis was 21. Figure 1 illustrates the process of study search and selection.
Fig. 1

Flow chart of the study search and selection

Study characteristics of the studies included

Among the 21 studies, ten were randomized or cluster randomized controlled trials [27, 37, 4752, 58, 60], six were non-randomized trials with controlled group [5355, 59, 61, 62] and five were pre and post- studies [33, 56, 57, 63, 64]. Eleven studies analyzed CVD risk factors screening [33, 37, 47, 49, 53, 5863] and 10 studies focused on multiple preventive services including screening for cancer, vitamin B12, vaccination promotion, and others [27, 48, 5052, 5457, 64] (refer to Table 2). The follow-up period of these studies ranged from 2 months to 3 years. Out of the 21 studies, eight were conducted in Europe (five in the United Kingdom [50, 52, 53, 60, 61], one each in the Netherlands [49], Denmark [59] and Sweden [54]), seven in North America (five in the United States of America [48, 51, 57, 62, 64], two in Canada [27, 47]), three in Australia [55, 56, 58], two in New Zealand [37, 63] and one in Singapore [33].
Table 2

Overview of the studies included in the review

Study (Author, year, country)

Population

Type of screening

Setting

Intervention

Related CVR Outcome measures for uptake rate

Randomized/cluster randomized controlled trials

 McDowell, 1989

Canada [47]

Adults aged ≥18 yearsa

BP

Primary care practice

3 intervention groups:

1. Physician reminder (computer-based reminder)

2. Patient reminder (telephone)

3. Patient reminder (letter)

Control: usual care

BP

 Robson, 1989

UK [50]

Patients aged 30–64 yearsa

Multiple screening

Primary care practice

Dedicated personnel: health promotion nurse

Control: doctor worked alone

BP, smoking history, cholesterol, family history of heart attack

 Ornstein, 1991

USA [48]

Patients aged ≥18 yearsa

Multiple screening

Primary care practice

3 intervention groups:

1. Physician reminder (paper based)

2. patient reminder (letter)

3. physician and patient reminder (multifaceted approach)

Control: usual care

Cholesterol

 Apkon, 2005

USA [51]

Patients aged ≥18 yearsa

Multiple screening

Primary care practice

Physician reminders (computer based) vs. usual care

Lipid, smoking screening

 Kenealy, 2005

New Zealand [37]

Adults aged 50 years or oldera

Diabetes screening

Primary care practice

3 intervention arms:

1. Physician reminder (flash alert in computer screen)

2. Physician reminder (patient giving the completed diabetes risk self-assessment form to the doctor)

3. Both 1 & 2 (multifaceted approach)

Control: usual care

Glucoseb

 Harari, 2008

UK [52]

Adults aged ≥65 yearsa

Multiple screening

Primary care practice

Health Risk Appraisal via mailed questionnaire and feedback to participants and general practitioners (multifaceted approach) vs. usual care

BP, cholesterol, blood glucose

 van Wyk, 2008

Netherland [49]

Men aged 18 to 70 years and women aged 18 to 75 yearsa

cholesterol

Primary care practice

2 intervention for physician reminder (computer based) vs. usual care:

1. Auto-alert

2. On demand alert

Cholesterol

 Holt, 2010

UK [60]

patients aged 50–74 years identified as probable high-risk

CVRs

Primary care practice

Physicians reminder (computer based screen alerts vs. usual care

Overall CVRs

 Stocks, 2012

Australia [58]

Patients aged 40–74 years

CVRs

Primary care practice

Financial incentives (added voucher incentives) vs. usual care (free test)

Overall CVRs

 Grunfeld, 2013

Canada [27]

Adults aged 40–65 years

Multiple screening

Primary care practice

3 intervention arms:

1. Dedicated personnel (practice facilitator at practice level)

2. Dedicated personnel (prevention practitioner at patient level)

3. Multifaceted approach (both 1&2)

control: usual care

FBS, BP, Framingham risk calculated, BMI, waist circumference, smoking, physical activity, nutritionb

Non-randomized trials with controlled group

 Fullard, 1987

UK [53]

Patients aged 35–64 yearsa

CVRs

Primary care practice

Multifaceted approach (practice facilitator with a practice prevention nurse) vs. usual care

Weight, BP, and smoking history

 Franks, 1991

USA [62]

Patients aged ≥18 yearsa

cholesterol

Primary care practice

Financial incentives:

Free vs. usual care (paid)

Cholesterol

 Christensen, 1995

Denmark [59]

Men aged 40-49

CVRs

Primary care practice

Financial incentives:

Free vs. paid

Overall CVRS

 Toth-Pal, 2004

Sweden [54]

Adults aged ≥70 years

Multiple screening

Primary care practice

Physician reminder (computer-based) vs. usual care

BP, diabetes

 Frank, 2004

Australia [55]

Eligible adults fulfilled screeninga

Multiple screening

Primary care practice

Physician reminder (computer-based) vs. usual care

Weight, smoking status, BP, diabetes, lipid

 Marshall, 2008

UK [61]

patients aged 35–74 years identified as probable high-risk

CVRs

Primary care practice

Dedicated personnel (project nurse) vs. usual care

Overall CVRs

Pre- and Post- studies

 Vincent, 1995

USA [64]

Adult populationa

Multiple screening

Primary care practice

multifaceted approach: computer generated worksheet with a reminder on health maintenance procedure, periodic physician performance report, patients reminder (letter invitation)

Cholesterol

 Bailie, 2003

Australia [56]

≥50 years (majority indigenous)

Multiple screening

Primary care practice

multifaceted approach: clinical guidelines, computerised reminder systems, audit and feedback

Weight, BP, waistcircumference, BMI, glucose

 Sinclair, 2006

New Zealand [63]

Adults eligible for cardiovascular risk screeninga

CVRs

Primary care practice

Multifaceted approach:

1) practice management software enhancement (alert to identify eligible patients for screening, electronic cardiovascular risk assessment tool)

2) CME for the clinical champion who oversee the project

3) Provision of relevant reporting and feedback

4) Eligible patients: letter invitation

Completed cardiovascular risk screen (5-year absolute cardiovascular risk)

 wee, 2013

Singapore [33]

Adults aged ≥40 years

CVRs

community

Multifaceted approach: free screening and convenient screening at housing estate

BP, fasting blood glucose and lipid

 Butala, 2013

USA [57]

adultsa

Multiple screening

Primary care practice

Physician reminders (paper-based notes for recommended preventive services)

Lipid and glucose

BP blood pressure, CVRS uptake for cardiovascular risk factors as a whole

amixed population which consisted of those with known and unknown CVD

bresults provided by the corresponding author

The interventions for CVD risk factors screening were classified into five types based on their characteristics, adapted from the classification by Jepson et al. [28] whenever possible. These were: (1) physician reminder (paper-based and computer-based reminder), (2) patient invitation (letter and telephone invitation), (3) financial incentives, (4) using dedicated personnel such as project nurse and practice facilitator to help organize and/or carry out screening, and (5) using a multifaceted approach targeting both physicians and patients for screening or using more than one measure to target a population for screening. Physician reminder, patient invitation and financial incentives for screening were interventions that could influence provider or patient behaviour directly or indirectly while using dedicated personnel targeted at provider and organizational level. Multifaceted approaches were targeted at either behavioural or organizational level or both. Table 2 represents an overview of the characteristics of the included studies. Details of the interventions, screening uptake rates (for different risk factors and periods) and the types of assessment are provided in Additional file 1.

Quality assessment of the studies included

Quality of methodology

The risk of bias for randomization was unclear and low in 9 out of 10 of the randomized controlled trials, except for one which was allocated high risk as the randomization used odd or even numbers of the last digit of the registration number – a process that was not true randomization [60]. The risk of bias for randomization was high for all non-randomized trials. A description of allocation concealment was presented in five (24%) studies [37, 49, 52, 59, 60]. Blinding of participants and personnel was lacking in all studies due to the nature of the interventions, which involved the participants or health-care professionals directly. The risk of bias for blinding of outcome assessment was low in 13 (62%) studies as the outcome measured in most studies was generated from electronic record systems. One study was found to have a high risk of bias for risk of incomplete outcome data: data from two practices could not be extracted and analyzed [49]. For reporting bias, low risk of bias was found in all studies except the study by Frank et al. where it was unclear [55]. For other biases, the validity of one of the studies needed to be interpreted with caution as one of the authors was related to the company that programmed the trial software [60]. The proportion of studies with low, unclear and high risk of bias is presented in Fig. 2. The summary of risk of bias for individual studies is provided in Additional file 2.
Fig. 2

Proportion of studies with low, unclear and high risk of bias

Quality of intervention: description and replication

Most studies were clear in the descriptions of the interventions. Two studies lacked descriptions of the education and training materials [33, 53]. Another two studies were unclear on whether training and feedback were provided to the groups [27, 56]. Details of the descriptions of the interventions are provided in Additional file 3.

Overall effect of interventions compared with controls

Based on the pooled estimate of the effects of interventions, in both optimistic and pessimistic analyses, the CVD risk factors screening uptake rate was higher in the intervention groups compared with the controls. The RR was 1.443 (95% CI 1.264 to 1.648) using the pessimistic estimate and 1.680 (95% CI 1.420 to 1.988) using the optimistic estimate (refer to Fig. 3a & b).
Fig. 3

a Effect of interventions vs. controls (using lowest effect size as outcome measure). b Effect of interventions vs. controls (using highest effect size as outcome measure)

Subgroup analyses

Effects of study designs

The quality of studies differed with different study designs. Therefore, we pooled data according to study designs i.e. RCT, non-randomized with controlled group and pre- and post-studies (refer to Fig. 4a & b).
Fig. 4

a Effect of interventions vs. controls according to study designs (using lowest effect size as outcome measure). b Effect of interventions vs. controls according to study designs (using highest effect size as outcome measure)

Of the ten studies in the randomized/cluster randomized controlled trials, four studies had three arms of interventions [27, 37, 47, 48], one study had two arms of interventions [49] and others had one arm of intervention. The majority of the studies showed positive effects from the interventions. Two studies, Grunfeld et al. and Apkon et al., reported more than one effect size, and the effects varied from negative to positive effect when pessimistic and optimistic analyses were performed respectively [27, 51]. However, the overall pooled estimate using both pessimistic and optimistic analyses were positive with a RR of 1.383 (95% CI 1.240 to 1.543) and 1.448 (95% 1.271 to 1.649), respectively (refer to Fig. 4a & b, subgroup 1).

All of the non-randomized trials with controlled groups and the pre- and post-studies were significantly in favour of the intervention, except for one study each from both groups when the pessimistic analysis was performed [55, 56]. One study from the pre- and post-study remained ineffective even when an optimistic analysis was performed [56]. The pooled estimate of the effect for non-randomized trials with controlled groups in both pessimistic (RR 1.347; 95% CI 1.197 to 1.517) and optimistic analyses (RR 1.647; 95% CI 1.301 to 2.087) were significantly in favour of the interventions. For the pre- and post-studies the pooled estimate of the effect was not significant in both pessimistic (RR 1.875; 95% CI 0.677 to 5.194) and optimistic analyses (RR 2.428; 95% CI 0.971 to 6.074) (refer to Fig. 4a & b, subgroup 2 and 3).

There was significant heterogeneity between the studies in all three groups of study designs. The I2 for all were more than 90%.

Effects of types of interventions

Using physician reminders (RR 1.392; 95% CI 1.192 to 1.625 in pessimistic analysis and RR 1.471; 95% CI 1.304, 1.660 in optimistic analysis), providing financial incentives (RR 1.462; 95% CI 1.068 to 2.000) and using dedicated personnel (RR 1.510; 95% CI 1.014 to 2.247 in pessimistic analysis and 2.536; 95% CI 1.297 to 4.960 in optimistic analysis) for screening significantly increased the uptake of CVD risk factors screening, compared to the controlled groups. Interventions using multifaceted approaches were effective when optimistic analysis was performed (RR 2.268; 95% CI 1.401, 3.672) but not when pessimistic analysis was performed (RR 1.549; 95% CI 0.978, 2.453). Patient invitations were not effective in increasing the uptake of CVD risk factors screening (RR1.285; 95% CI 0.980, 1.686) (refer to Fig. 5a & b).
Fig. 5

a Effect of types of interventions vs. controls (using lowest effect size as outcome measure). b Effect of types of interventions vs. controls (using highest effect size as outcome measure)

In the intervention using physician reminders, six studies used computer-based screen alert system reminders [47, 49, 51, 54, 55, 60], and two studies used paper-based reminders [48, 57]. One study used two approaches, one arm used computer-based screen alert system reminders while in the other arm, physicians were reminded when patients handed the completed diabetes risk self-assessment forms to them [37]. All studies were significantly in favour of the intervention [37, 4749, 54, 57, 60] except for two [51, 55]. A study by Frank et al. showed a significant positive effect in the optimistic analysis but not in the pessimistic analysis [55]. A study by Apkon et al. showed effects were not significant in both optimistic and pessimistic analyses [51]. (refer to Fig. 5a & b, Subgroup 1).

In the interventions using financial incentives for screening, all studies [59, 62] were significantly in favour of the intervention except for the study by Stocks et al. [58]. Stocks et al. used shopping vouchers as rewards for screening while the other two studies offered free or subsidized screening as the intervention (refer to Fig. 5a & b, Subgroup 3).

All three studies that used dedicated personnel as the intervention to increase screening uptake were conducted at clinics [27, 50, 61]. Two studies used dedicated personnel (project nurse and health promotion nurse) to deliver the screening [50, 61]. Their tasks were to invite, follow-up and deliver the preventive care service. One study used two different approaches; one used dedicated personnel to deliver the screening while the other used a practice facilitator to help the organization improve the system and implement changes for better care [27]. The first two studies that used dedicated personnel to deliver screening showed positive effects in both optimistic and pessimistic analysis [50, 61]. The third study that used dedicated personnel to deliver screening was effective in the optimistic analysis; however, using dedicated personnel targeted at the organizational level was not [27] (refer to Fig. 5a & b, Subgroup 4).

The multifaceted approach was shown to be effective in increasing the screening uptake in the optimistic analysis (RR 2.268; 95% CI 1.401 to 3.672) but not in the pessimistic analysis (RR 1.549; 95% CI 0.978 to 2.453) (refer to Fig. 5a & b, Subgroup 5). There were only two studies that used patient invitations to increase uptake of CVD risk factors screening. The trend was towards a positive effect but it was not significant (RR 1.285; 95% CI 0.980 to 1.686) (refer to Fig. 5a & b, Subgroup 2).

All 21 studies were conducted in primary care settings except for one study from Singapore that was conducted in a housing estate. We have included this community study in the analysis as the objective of this review was to determine the effectiveness of intervention strategies carried out in primary care settings or the community. Removal of this study from the analysis did not alter the effect size nor the statistical significance, although there were magnitude changes in the estimates and CI (refer to Additional file 4 for comparison of the effect size by including and excluding this study in the analysis).

Meta-regression analysis showed that the effect size of CVD risk factors screening uptake was not associated with study designs, types of population and types of interventions in both optimistic and pessimistic analyses.

Discussion

Summary of principal findings

We set out to determine the effectiveness of interventions aimed to increase uptake of CVD risk factors screening. Overall, our results showed that the interventions were effective in increasing CVD risk factors screening uptake. This effect size of the uptake was not associated with the study design, types of population nor types of interventions. Effective interventions that increased screening participation included: using physician reminders, use of dedicated personnel to deliver screening and providing financial incentives for screening. Multifaceted approaches were effective when optimistic analysis was performed.

Interpretation of the findings and comparison with previous findings

We obtained 21 articles for analysis of which 9 were the results from the initial search and 16 were from the forward and backward bibliography and citation checks of the search. The cross checking of bibliographies and citations led to increased yield. This was especially true when an article was cited in a review paper; by going through the studies included in the review paper, we found more papers relevant to our study. Thus, the back and forth citation search could be a better way of tracking relevant articles.

Our results showed that studies with lower quality (pre- and post-studies) had larger effect size (RR ranged from 1.875 to 2.428) but lower precision compared with studies with higher quality such as the non-randomized trials with controlled groups (RR ranged from 1.347 to 1.647) and randomized controlled trials (RR ranged from 1.383 to 1.448). This is expected as studies with better methodology had lower effect size but higher precision, which was consistent with the literature and suggested quality assessment of papers was useful [66].

The heterogeneity observed was significant and high for all the meta-analyses performed. This was expected given the diverse population, settings, study designs, interventions and risk factors measured [66, 67]. Despite this, the results for study effects were robust in one direction (refer to Figs. 3a & b, 4a & b, and 5a & b). This implied that the results could possibly be generalized to various populations [66].

The meta-regression was performed to explore whether the differences in study designs, type of populations and types of intervention could explain the heterogeneity seen in the meta-analysis. Meta-regression is used to explore associations between study-level features and the outcome. For example, the quality of study design can result in artefactual variation. There may also be true differences in effects arising from associations with differences in study population (for instance variation in disease severity) or intervention. Such effect modification may help identify participants for whom the intervention is likely to produce benefit [46]. In this meta-regression analysis, it was found that study designs, types of population and types of intervention did not influence the effect size.

The risk factors targeted for screening were heterogenous and ranged from single to multiple CVD risk factors. There were also interventions that involved other preventive services such as vaccination. By performing two sets of meta-analysis on the highest and lowest uptake rate for each intervention, a range of effect size was provided to clearly demonstrate the effectiveness of interventions across various risk factors.

We found using physician reminders, dedicated personnel or financial incentives for screening were effective interventions. Our study extends the evidence from the previous systematic review by Jepson et al., where the types of interventions previously found to be effective in cancer screening seem to have similar effects in CVD risk factors screening [28]. This suggests that people’s health behaviour towards interventions to improve screening was similar regardless of the condition they had.

Although using physician reminders can increase CVD risk factors screening uptake rate, its effect is limited to patients attending the clinic for other reasons. For interventions using financial incentives to improve screening uptake rates, our result was consistent with the results of Jepson’s review [28]. Both reviews showed that interventions using reduced cost or free screening increased uptake but not those providing incentives such as shopping vouchers, gifts or transportation incentives. The effect of free or subsidized screening is likely to be different depending on the way in which health services are funded. When free screening is provided by existing health-care systems, added rewards do not provide a further effect on the uptake of screening [58].

Dedicated personnel can be used to deliver the screening or to facilitate screening uptake at organizational level. This review found that using dedicated personnel to deliver the screening was effective in increasing CVD risk factors screening uptake; the effect was uncertain for using dedicated personnel targeted at the organizational level as there was only one study researching this intervention. The use of dedicated personnel (non-physician providers) to increase preventive activities has been shown to be effective in previous literature for adult immunization and cancer screening [68, 69]. This intervention requires system resources and support such as organisational change in staffing and clinical procedures. Although using dedicated personnel at organizational level such as practice facilitator has been shown to be effective in improving the use of evidence-based guideline in primary care and preventive care performance [70, 71], the intervention did not consistently show positive results in other studies [27, 72]. This highlights the challenge in the implementation of this intervention which may vary from practice to practice.

For intervention using a multifaceted approach, our results shown inconsistent results in the effectiveness of this intervention. The intervention was effective in optimistic analysis but not when a pessimistic model was used. A systematic review by Jepson et al. reported some evidence in the effectiveness of multiple interventions aimed at individuals or physicians and interventions aimed at both physicians and individuals in increasing screening uptake [28]. Further studies are needed to confirm the effectiveness of multifaceted approaches in increasing the uptake of CVD risk factors screening.

In contrast with other reviews for breast, cervical and colorectal cancer screening [7375], an invitation to patients either by telephone or letter did not show a significant effect in increasing CVD risk factors screening uptake, although a positive trend was observed. Due to the small samples (two studies and three comparisons), it is difficult to conclude the effectiveness of this intervention for CVD risks factor screening. With the global use of information technology with mobile phones, telephone invitation could be used as a mode for such invitations. There was an increased use of mobile text messages that have been shown to be effective in delivering reminders for adherence to treatment and appointments in health-care services [76]. Thus, mobile text messages might be useful as a mode of invitation for screening. However, we did not find any study using this mode as an intervention for CVD risk factors screening.

Implications for policy and practice

Our results show that active recruitment targeted at any level, either individual, health-care professional, or health-care system were effective in increasing CVD risk factors screening uptake. However, the intervention one chooses would depend on the practice resources and support and the target of coverage within a set time frame. For example, physician reminders would not be applicable to individuals who did not attend the clinic, and they could potentially be the most at risk group. Provision of free screening can be effective in health-care systems where participants have to pay for screening. Using dedicated personnel to deliver screening was effective, but the cost and human resources demand would be high.

Strengths and weaknesses of this review

Our review has included both randomized and non-randomized controlled trials as well as pre-and post-studies interventions to provide more comprehensive views of various interventions aimed to increase the uptake of CVD risk factors screening. We believe that each of these studies can contribute useful information to the review. Although the non-randomized and non-controlled trials could inflate the effect size of the interventions, the meta-regression we performed did not show any significant association between study designs and the effect size of the CVD risk factors screening uptake.

There are several limitations in this review. At study level, there was a high risk of bias for blinding of participants and personnel in all the studies. However, this was unavoidable due to the nature of the interventions. At review level, we have employed an extensive search strategy. However, we limited it to publications in the English language, due to limited resources. Hence, the analysis should be treated with caution. In addition, we did not identify any unpublished trials; thus publication bias could not be examined. Other interventions such as providing incentives to health practices might be useful, but we could not find any of such studies.

We did not include cost-effectiveness of interventions in this review which is an important area to look into when choosing an intervention. This will be a useful area to explore in future reviews.

Conclusion

Physician reminders and providing financial incentives were effective in influencing the provider’s and patient’s behaviour to increase CVD risk factors screening uptake. At organizational level, using dedicated personnel to deliver the screening was found to be effective.

Abbreviations

95% CI: 

95% confidence interval

BMI: 

Body mass index

BP: 

Blood pressure

CVD: 

Cardiovascular diseases

MeSH: 

Medical subject headings

RCTs: 

Randomized controlled trials

RR: 

Relative risk

TIDieR: 

Template for Intervention Description and Replication

WC: 

Waist circumference

Declarations

Acknowledgements

Not applicable.

Funding

Financial support for this study was provided by University of Malaya Postgraduate Research Fund (PG049-2014A). The funder had no role in study design, analysis and interpretation of this study.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its additional files].

Authors’ contributions

ATC, SML, EMK, NFMZ and KC developed the study design and participated in analysis. ATC and NFHZ managed data collection. ATC drafted the initial manuscript. All authors have contributed to subsequent revisions and critically revised the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Primary Care Medicine, University of Malaya Primary Care Research Group (UMPCRG), Faculty of Medicine, University of Malaya
(2)
Department of Family Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia
(3)
Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya

References

  1. Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet. 2005;365:217–23.View ArticlePubMedGoogle Scholar
  2. Kelly T, Yang W, Chen C-S, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond). 2008;32:1431–7.View ArticleGoogle Scholar
  3. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87:4–14.View ArticlePubMedGoogle Scholar
  4. Ikeda N, Sapienza D, Guerrero R, Aekplakorn W, Naghavi M, Mokdad AH, et al. Control of hypertension with medication: a comparative analysis of national surveys in 20 countries. Bull World Health Organ. 2014;92:10–19C.View ArticlePubMedGoogle Scholar
  5. Gakidou E, Mallinger L, Abbott-Klafter J, Guerrero R, Villalpando S, Ridaura RL, et al. Management of diabetes and associated cardiovascular risk factors in seven countries: a comparison of data from national health examination surveys. Bull World Health Organ. 2011;89:172–83.View ArticlePubMedGoogle Scholar
  6. Roth GA, Fihn SD, Mokdad AH, Aekplakorn W, Hasegawa T, Lim SS. High total serum cholesterol, medication coverage and therapeutic control: an analysis of national health examination survey data from eight countries. Bull World Health Organ. 2011;89:92–101.View ArticlePubMedGoogle Scholar
  7. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case–control study. Lancet. 2004;364:937–52.View ArticlePubMedGoogle Scholar
  8. Björck L, Rosengren A, Bennett K, Lappas G, Capewell S. Modelling the decreasing coronary heart disease mortality in Sweden between 1986 and 2002. Eur Heart J. 2009;30:1046–56.View ArticlePubMedGoogle Scholar
  9. Capewell S, Beaglehole R, Seddon M, McMurray J. Explanation for the decline in coronary heart disease mortality rates in Auckland, New Zealand, between 1982 and 1993. Circulation. 2000;102:1511–6.View ArticlePubMedGoogle Scholar
  10. Ford ES, Ajani UA, Croft JB, Critchley JA, Labarthe DR, Kottke TE, et al. Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. N Engl J Med. 2007;356:2388–98.View ArticlePubMedGoogle Scholar
  11. Unal B, Critchley JA, Capewell S. Explaining the decline in coronary heart disease mortality in England and Wales between 1981 and 2000. Circulation. 2004;109:1101–7.View ArticlePubMedGoogle Scholar
  12. Vartiainen E, Laatikainen T, Peltonen M, Juolevi A, Männistö S, Sundvall J, et al. Thirty-five-year trends in cardiovascular risk factors in Finland. Int J Epidemiol. 2010;39:504–18.View ArticlePubMedGoogle Scholar
  13. Lee H, Cho J, Shin DW, Lee S-P, Hwang S-S, Oh J, et al. Association of cardiovascular health screening with mortality, clinical outcomes, and health care cost: a nationwide cohort study. Prev Med. 2015;70:19–25.View ArticlePubMedGoogle Scholar
  14. Hozawa A, Kuriyama S, Watanabe I, Kakizaki M, Ohmori-Matsuda K, Sone T, et al. Participation in health check-ups and mortality using propensity score matched cohort analyses. Prev Med. 2010;51:397–402.View ArticlePubMedGoogle Scholar
  15. MacAuley D. The value of conducting periodic health checks. BMJ. 2012;345:e7775.View ArticlePubMedGoogle Scholar
  16. Goodyear-Smith F. Government’s plans for universal health checks for people aged 40–75. BMJ. 2013;347:f4788.View ArticlePubMedGoogle Scholar
  17. Kmietowicz Z. Drive to boost health checks in England is criticised. BMJ. 2013;347:f4675.View ArticlePubMedGoogle Scholar
  18. Krogsbøll LT, Jørgensen KJ, Gøtzsche PC. Universal health checks should be abandoned. BMJ. 2013;347:f5227.View ArticlePubMedGoogle Scholar
  19. Wookey SL, Pryke M, KrogsbØll LT, Lipman HM, Sanchez-Delgado EJ, Connolly CK, et al. Rapid responses. Government’s plans for universal health checks for people aged 40–75. BMJ. 2013;347:f4788.Google Scholar
  20. Krogsbøll LT, Jørgensen KJ, Grønhøj Larsen C, Gøtzsche PC. General health checks in adults for reducing morbidity and mortality from disease: Cochrane systematic review and meta-analysis. BMJ. 2012;345:e7191.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Prochazka AV, Caverly T. General health checks in adults for reducing morbidity and mortality from disease: summary review of primary findings and conclusions. JAMA Intern Med. 2013;173:371–2.View ArticlePubMedGoogle Scholar
  22. Gidlow C, Kumar J, Iqbal Z, Chambers R, Mawby Y. Re: The value of conducting periodic health checks. BMJ [Internet]. Available from: http://www.bmj.com/content/345/bmj.e7775/rr/619308. [Accessed 29 Mar 2016].
  23. NHS Health Check: our approach to the evidence [Internet]. Available from: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/224537/NHS_Health_Check_our_approach_to_the_evidence_v2.pdf. [Accessed 29 Mar 2016].
  24. Marteau TM, Mann E, Prevost AT, Vasconcelos JC, Kellar I, Sanderson S, et al. Impact of an informed choice invitation on uptake of screening for diabetes in primary care (DICISION): randomised trial. BMJ. 2010;340:c2138.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Hutchison B, Birch S, Evans CE, Goldsmith LJ, Markham BA, Frank J, et al. Screening for hypercholesterolaemia in primary care: randomised controlled trial of postal questionnaire appraising risk of coronary heart disease. BMJ. 1998;316:1208–13.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Van der Meer V, Nielen MMJ, Drenthen AJM, Van Vliet M, Assendelft WJJ, Schellevis FG. Cardiometabolic prevention consultation in the Netherlands: screening uptake and detection of cardiometabolic risk factors and diseases--a pilot study. BMC Fam Pract. 2013;14:29.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Grunfeld E, Manca D, Moineddin R, Thorpe KE, Hoch JS, Campbell-Scherer D, et al. Improving chronic disease prevention and screening in primary care: results of the BETTER pragmatic cluster randomized controlled trial. BMC Fam Pract. 2013;14:175.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Jepson R, Clegg A, Forbes C, Lewis R, Sowden A, Kleijnen J. The determinants of screening uptake and interventions for increasing uptake: a systematic review. Health Technol Assess. 2000;4:i–vii, 1–133.PubMedGoogle Scholar
  29. Willis A, Rivers P, Gray LJ, Davies M, Khunti K. The effectiveness of screening for diabetes and cardiovascular disease risk factors in a community pharmacy setting. PLoS One. 2014;9:e91157.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Holland C, Cooper Y, Shaw R, Pattison H, Cooke R. Effectiveness and uptake of screening programmes for coronary heart disease and diabetes: a realist review of design components used in interventions. BMJ Open. 2013;3:e003428.View ArticlePubMedPubMed CentralGoogle Scholar
  31. den Engelsen C, Koekkoek PS, Godefrooij MB, Spigt MG, Rutten GE. Screening for increased cardiometabolic risk in primary care: a systematic review. Br J Gen Pract. 2014;64:e616–26.View ArticlePubMedGoogle Scholar
  32. Wee LE, Koh GC-H. The effect of neighborhood, socioeconomic status and a community-based program on multi-disease health screening in an Asian population: a controlled intervention study. Prev Med. 2011;53:64–9.View ArticlePubMedGoogle Scholar
  33. Wee LE, Koh GC-H, Yeo WX, Chin RT, Wong J, Seow B. Screening for cardiovascular disease risk factors in an urban low-income setting at baseline and post intervention: a prospective intervention study. Eur J Prev Cardiol. 2013;20:176–88.View ArticlePubMedGoogle Scholar
  34. Wee LE, Koh GCH, Toh ZJ. Multi-disease health screening in an urban low-income setting: a community-based study. Ann Acad Med Singapore. 2010;39:750–7.PubMedGoogle Scholar
  35. McMenamin J, Nicholson R, Leech K. Patient Dashboard: the use of a colour-coded computerised clinical reminder in Whanganui regional general practices. J Prim Health Care. 2011;3:307–10.PubMedGoogle Scholar
  36. Putnam W, Mann K, Lindsay E, Davis D. Identification of individuals with elevated cholesterol in family physicians’ offices. J Contin Educ Health Prof. 1998;18:100–6.View ArticleGoogle Scholar
  37. Kenealy T, Arroll B, Petrie KJ. Patients and computers as reminders to screen for diabetes in family practice. Randomized-controlled trial. J Gen Intern Med. 2005;20:916–21.View ArticlePubMedPubMed CentralGoogle Scholar
  38. McBride P, Underbakke G, Plane MB, Massoth K, Brown RL, Solberg LI, et al. Improving prevention systems in primary care practices: the Health Education and Research Trial (HEART). J Fam Pract. 2000;49:115–25.PubMedGoogle Scholar
  39. Melnikow J, Kohatsu ND, Chan BK. Put prevention into practice: a controlled evaluation. Am J Public Health. 2000;90:1622–5.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Cheney C, Ramsdell JW. Effect of medical records’ checklists on implementation of periodic health measures. Am J Med. 1987;83:129–36.View ArticlePubMedGoogle Scholar
  41. Higgins JPT, Altman DG, Sternne JAC. Chapter 8: Assessing risk of bias in included studies. Handbook for Systematic Reviews of Interventions [Internet]. Version 5.1.0. The Cochrane Collaboration; 2011. Available from: http://handbook.cochrane.org/chapter_8/8_assessing_risk_of_bias_in_included_studies.htm. [Accessed 17 Sept 2015].
  42. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687.View ArticlePubMedGoogle Scholar
  43. OpenMeta[Analyst] -- CEBM @ Brown [Internet]. [cited 2015 Sep 17]. Available from: http://www.cebm.brown.edu/openmeta/. Accessed 9 Jan 2017.
  44. Wallace BC, Schmid CH, Lau J, Trikalinos TA. Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data. BMC Med Res Methodol. 2009;9:80.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–60.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Glasziou PP, Sanders SL. Investigating causes of heterogeneity in systematic reviews. Stat Med. 2002;21:1503–11.View ArticlePubMedGoogle Scholar
  47. McDowell I, Newell C, Rosser W. A randomized trial of computerized reminders for blood pressure screening in primary care. Med Care. 1989;27:297–305.View ArticlePubMedGoogle Scholar
  48. Ornstein SM, Garr DR, Jenkins RG, Rust PF, Arnon A. Computer-generated physician and patient reminders. Tools to improve population adherence to selected preventive services. J Fam Pract. 1991;32:82–90.PubMedGoogle Scholar
  49. van Wyk JT, van Wijk MAM, Sturkenboom MCJM, Mosseveld M, Moorman PW, van der Lei J. Electronic alerts versus on-demand decision support to improve dyslipidemia treatment: a cluster randomized controlled trial. Circulation. 2008;117:371–8.View ArticlePubMedGoogle Scholar
  50. Robson J, Boomla K, Fitzpatrick S, Jewell AJ, Taylor J, Self J, et al. Using nurses for preventive activities with computer assisted follow up: a randomised controlled trial. BMJ. 1989;298:433–6.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Apkon M, Mattera JA, Lin Z, Herrin J, Bradley EH, Carbone M, et al. A randomized outpatient trial of a decision-support information technology tool. Arch Intern Med. 2005;165:2388–94.View ArticlePubMedGoogle Scholar
  52. Harari D, Iliffe S, Kharicha K, Egger M, Gillmann G, von Renteln-Kruse W, et al. Promotion of health in older people: a randomised controlled trial of health risk appraisal in British general practice. Age Ageing. 2008;37:565–71.View ArticlePubMedGoogle Scholar
  53. Fullard E, Fowler G, Gray M. Promoting prevention in primary care: controlled trial of low technology, low cost approach. Br Med J (Clin Res Ed). 1987;294:1080–2.View ArticleGoogle Scholar
  54. Toth-Pal E, Nilsson GH, Furhoff A-K. Clinical effect of computer generated physician reminders in health screening in primary health care--a controlled clinical trial of preventive services among the elderly. Int J Med Inform. 2004;73:695–703.View ArticlePubMedGoogle Scholar
  55. Frank O, Litt J, Beilby J. Opportunistic electronic reminders. Improving performance of preventive care in general practice. Aust Fam Physician. 2004;33:87–90.PubMedGoogle Scholar
  56. Bailie RS, Togni SJ, Si D, Robinson G, d’Abbs PHN. Preventive medical care in remote Aboriginal communities in the Northern Territory: a follow-up study of the impact of clinical guidelines, computerised recall and reminder systems, and audit and feedback. BMC Health Serv Res. 2003;3:15.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Butala NM, Chang H, Horwitz LI, Bartlett M, Ellis P. Improving quality of preventive care at a student-run free clinic. PLoS One. 2013;8:e81441.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Stocks N, Allan J, Frank O, Williams S, Ryan P. Improving attendance for cardiovascular risk assessment in Australian general practice: an RCT of a monetary incentive for patients. BMC Fam Pract. 2012;13:54.View ArticlePubMedPubMed CentralGoogle Scholar
  59. Christensen B. Payment and attendance at general practice preventive health examinations. Fam Med. 1995;27:531–4.PubMedGoogle Scholar
  60. Holt TA, Thorogood M, Griffiths F, Munday S, Friede T, Stables D. Automated electronic reminders to facilitate primary cardiovascular disease prevention: randomised controlled trial. Br J Gen Pract. 2010;60:e137–43.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Marshall T, Westerby P, Chen J, Fairfield M, Harding J, Westerby R, et al. The Sandwell Project: a controlled evaluation of a programme of targeted screening for prevention of cardiovascular disease in primary care. BMC Public Health. 2008;8:73.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Franks P, Engerman J. The impact of office cholesterol testing. J Fam Pract. 1991;32:493–6.PubMedGoogle Scholar
  63. Sinclair G, Kerr A. The Bold Promise Project: a system change in primary care to support cardiovascular risk screening. N Z Med J. 2006;119:U2312.PubMedGoogle Scholar
  64. Vincent EC, Hardin PA, Norman LA, Lester EA, Stinton SH. The effects of a computer-assisted reminder system on patient compliance with recommended health maintenance procedures. Proc Annu Symp Comput Appl Med Care. 1995;656–60.Google Scholar
  65. Lemelin J, Hogg W, Baskerville N. Evidence to action: a tailored multifaceted approach to changing family physician practice patterns and improving preventive care. CMAJ. 2001;164:757–63.PubMedPubMed CentralGoogle Scholar
  66. Dickersin K, Berlin JA. Meta-analysis: state-of-the-science. Epidemiol Rev. 1992;14:154–76.PubMedGoogle Scholar
  67. Lau J, Ioannidis JP, Schmid CH. Quantitative synthesis in systematic reviews. Ann Intern Med. 1997;127:820–6.View ArticlePubMedGoogle Scholar
  68. Stone EG, Morton SC, Hulscher ME, Maglione MA, Roth EA, Grimshaw JM, et al. Interventions that increase use of adult immunization and cancer screening services: a meta-analysis. Ann Intern Med. 2002;136:641–51.View ArticlePubMedGoogle Scholar
  69. Winston CA, Mims AD, Leatherwood KA. Increasing pneumococcal vaccination in managed care through telephone outreach. Am J Manag Care. 2007;13:581–8.PubMedGoogle Scholar
  70. Hogg W, Lemelin J, Moroz I, Soto E, Russell G. Improving prevention in primary care. Can Fam Physician. 2008;54:712–20.PubMedPubMed CentralGoogle Scholar
  71. Baskerville NB, Liddy C, Hogg W. Systematic review and meta-analysis of practice facilitation within primary care settings. Ann Fam Med. 2012;10:63–74.View ArticlePubMedPubMed CentralGoogle Scholar
  72. Liddy C, Hogg W, Singh J, Taljaard M, Russell G, Deri Armstrong C, et al. A real-world stepped wedge cluster randomized trial of practice facilitation to improve cardiovascular care. Implement Sci. 2015;10:150. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4625868/. Accessed 31 Jan 2016.
  73. Bonfill X, Marzo M, Pladevall M, Martí J, Emparanza JI. Strategies for increasing women participation in community breast cancer screening. Cochrane Database Syst Rev. 2001;1:CD002943.Google Scholar
  74. Everett T, Bryant A, Griffin MF, Martin-Hirsch PP, Forbes CA, Jepson RG. Interventions targeted at women to encourage the uptake of cervical screening. Cochrane Database Syst Rev. 2011;5:CD002834.Google Scholar
  75. Ferroni E, Camilloni L, Jimenez B, Furnari G, Borgia P, Guasticchi G, et al. How to increase uptake in oncologic screening: a systematic review of studies comparing population-based screening programs and spontaneous access. Prev Med. 2012;55:587–96.View ArticlePubMedGoogle Scholar
  76. Kannisto KA, Koivunen MH, Välimäki MA. Use of mobile phone text message reminders in health care services: a narrative literature review. J Med Internet Res. 2014;16:e222.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2017