Screening with urinary dipsticks for reducing morbidity and mortality

Abstract

Background

Urinary dipsticks are sometimes used for screening asymptomatic people, and for case‐finding among inpatients or outpatients who do not have genitourinary symptoms. Abnormalities identified on screening sometimes lead to additional investigations, which may identify serious disease, such as bladder cancer and chronic kidney disease (CKD). Urinary dipstick screening could improve prognoses due to earlier detection, but could also lead to unnecessary and potentially invasive follow‐up testing and unnecessary treatment.

Objectives

We aimed to quantify the benefits and harms of screening with urinary dipsticks in general populations and patients in hospitals.

Search methods

We searched the Cochrane Renal Group's Specialised Register to 8 September 2014 through contact with the Trials Search Co‐ordinator using search terms relevant to this review.

Selection criteria

Randomised controlled trials and other study types that compared urinary dipstick screening with no dipstick screening were eligible for inclusion. We searched for studies that investigated the use of urinary dipsticks for detecting haemoglobin, protein, albumin, albumin‐creatinine ratio, leukocytes, nitrite, or glucose, alone or in any combination, and in any setting. We planned to exclude studies conducted in patients with urinary disorders.

Data collection and analysis

It was planned that two authors would independently extract data from included studies and assess risk of bias using the Cochrane risk of bias tool. However, no studies met our inclusion criteria.

Main results

Literature searches to 8 September 2014 yielded 4298 records, of which 4249 were excluded following title and abstract assessment. There were 49 records (44 studies) eligible for full text assessment; of these 18 studies were not RCTs and 26 studies compared interventions or controls that were not relevant to this review. Thus, no studies were eligible for inclusion.

Authors' conclusions

We found no evidence to assess the benefits and harms of screening with urinary dipsticks, which remain unknown.

Plain language summary

Screening with urinary dipsticks for reducing morbidity and mortality

Urinary dipsticks are sometimes used for screening healthy people and patients that do not have symptoms of urinary disease. Urinary dipsticks can be used to test for several different substances, such as blood, sugar, protein, white blood cells and nitrite in the urine, which may indicate the presence of disease. Identified abnormalities sometimes lead to additional investigations, which may identify serious disease, such as bladder cancer and chronic kidney disease. Detection could improve health outcomes from finding disease at earlier stages, but could also lead to unnecessary follow‐up testing, which may be invasive, and lead to unnecessary treatment.

We searched the literature to September 2014 to identify studies that compared urinary dipstick screening with no dipstick screening. However, we found no studies that met our inclusion criteria. We were unable to determine benefits and harms associated with urinary dipstick screening.

Background

Urinary dipstick testing is widely used to screen for the presence of disease with the aim of reducing morbidity and mortality in both healthy people and patients (Grønhøj Larsen 2010; Merenstein 2006; Prochazka 2005). Dipsticks can test for either single or multiple substances in urine, and are sometimes used in general health checks.

Urinary dipstick testing is recommended for screening people with diabetes to detect a specific protein (albumin) in the urine (albuminuria) (NICE 2008b). Another potential screening population is people with high blood pressure (hypertension). At present, there is a lack of consensus on these recommendations, and most guidelines recommend use of albumin‐creatinine or protein‐creatinine ratios rather than dipsticks to detect proteinuria or albuminuria. However, dipsticks are less expensive than these tests and dipstick proteinuria is strongly related to total and cardiovascular mortality (CKDPC 2010). Screening with urine culture is recommended for pregnant women to detect bacteria in the urine (bacteriuria) (Lin 2008; NICE 2008a).

Since the 1970s, school children and employed adults in Japan have been offered urinary dipstick screening for blood and protein; from 1983, this was extended to all adults aged 40 years and over (Imai 2007). Taiwan implemented dipstick screening for children in 1990, and Korea in 1998 (Hogg 2009).

There appear to be no recommendations for population‐based screening with urinary dipsticks, and the scientific debate persists about screening for CKD with other methods (Brown 2011). Opportunistic screening is often recommended, but only for high risk groups (Krogsbøll 2014). A systematic review of screening for CKD with any method found no randomised controlled trials (RCTs) and concluded that the role of screening was uncertain (Fink 2012).

There is, however, discrepancy between recommendations and practice. Ease of use, low cost, and the perceived test safety may contribute to this discrepancy. Although screening can work (Holme 2013; Raffle 2003; Thomason 1998), it is noteworthy that experience with other screening interventions for diseases such as prostate cancer (Djulbegovic 2010), breast cancer (Gøtzsche 2013), and neuroblastoma (Schilling 2002; Woods 2002) have indicated that screening benefits can be less than expected, and that screening can cause more harm than good.

Dipstick testing is routinely used for case‐finding among people with conditions that increase the risk of kidney disease, such as diabetes and hypertension. Both have wide spectrums of severity, do not often cause symptoms, and encompass a large proportion of adults. Definitions for these conditions are derived through consensus, and have been the subject of debate as they have been broadened over time. Thus, case‐finding in such broad categories borders on screening, but RCTs are unlikely to be performed and the question must therefore be informed by detailed analysis of observational studies, which is outside the scope of this review.

Description of the condition

Microscopic blood in urine (haematuria) can be caused by urological cancers of any kind, but because bladder cancer is relatively common, and haematuria a frequent sign, most research has centred on detecting blood. The prognosis for people with bladder cancer is highly dependent on the extent of invasion into the bladder wall; in contrast to muscle‐invasive lesions, non‐muscle‐invasive lesions often have a favourable prognosis following minimally invasive treatment. However, unexpected post mortem findings are less common than for other urological cancers, such as prostate and kidney cancers (Avgerinos 2001; Karwinski 1990), which suggests that bladder cancer may have a short preclinical phase, possibly rendering it a poor target for screening. Furthermore, microscopic haematuria is not a robust marker for bladder cancer because it can be associated with a plethora of benign conditions (Malmström 2003), and novel markers have not yet been tested sufficiently.

Chronic kidney disease (CKD) is a major health problem with a long preclinical phase. Staging is based on estimated glomerular filtration rate (eGFR) and evidence of kidney damage, such as proteinuria or pathological findings with ultrasound imaging (NKF 2002). When this staging formula was applied to the adult population in the United States, CKD prevalence was found to be 13%, and more than 45% among people over 70 years of age (Coresh 2007). Although most people with CKD do not go on to develop end‐stage kidney disease (ESKD), its prevalence is increasing (Hemmelgarn 2006). It has been argued that the current staging system is inappropriate because many people with CKD have low eGFR but no evidence of kidney damage (Moynihan 2013). Low eGFR could be considered normal, particularly among older people, most of whom are unlikely to develop symptomatic kidney disease (Bauer 2008).

It has been reported that proteinuria detected using urinary dipsticks was associated with subsequent ESKD (Iseki 2003) and the test can identify some people who are at risk of rapid decline in kidney function (Clark 2011). It has also been reported that asymptomatic microscopic haematuria is associated with ESKD (Iseki 2003; Vivante 2011). Both low eGFR and proteinuria are risk factors for cardiovascular and all‐cause mortality (Hillege 2001; Matsushita 2010); although they do not seem to substantially improve traditional prediction tools (Chang 2011).

Diabetes mellitus can cause glycosuria, and early detection may prevent or postpone complications such as blindness, neuropathy or cardiovascular disease through early treatment and weight loss. A study of screening for diabetes using other methods than dipsticks did not find beneficial effects (Simmons 2012a).

Asymptomatic bacteriuria may be detected with dipstick testing for leukocytes and nitrite, but detection in urine are common findings, particularly among older people, and treatment is not recommended in the absence of symptoms. On the other hand, urinary tract infections (UTI) can present with vague and uncharacteristic symptoms. Screening for asymptomatic bacteriuria is recommended only for pregnant women and before genitourinary procedures (EAU 2012; Lin 2008).

How the intervention might work

Although many variations exist, the urinary dipsticks commonly used in general health checks usually test for at least haemoglobin, protein or albumin, leukocytes, nitrite and glucose. In screening, the use of a combined urinary dipstick is in some ways comparable with a general health check, which also includes components with different potentials for benefits and harms for a range of very different diseases. Likewise, there is a wide range of relatively harmless conditions that can result in an abnormal test.

Benefits

The potential benefits from dipstick screening are well known. Many diseases screened for using urinary dipsticks are both common and serious. Early diagnosis of diabetes mellitus and appropriate interventions and lifestyle changes may reduce common comorbidities such as blindness, neuropathy, kidney disease, or cardiovascular disease. Identification of CKD may allow early therapy to reduce morbidity and mortality, although a Cochrane review concluded that the value of treating CKD stages 1 to 3 with angiotensin‐converting enzyme inhibitors remains unclear (Sharma 2011). Glomerulonephritis often responds to treatment but it has not been shown if treatment of subclinical glomerulonephritis improves prognosis. If detection of microscopic haematuria enables earlier detection of bladder cancer, morbidity, mortality and harmful effects of invasive treatments for advanced disease may be reduced.

Harms

Harms from dipstick screening mainly relate to superfluous follow‐up tests and therapeutic interventions, and not the screening itself. Harms include discomfort and anxiety related to non‐invasive follow‐up tests such as kidney ultrasound, and from concerns about possible health issues, but most importantly, the possibility of morbidity related to unnecessary invasive investigations.

Investigations for persistent microscopic haematuria often include flexible cystoscopy in local anaesthesia, computed tomography imaging (CT scan) of the urinary tract, and urine cytology. In some instances, rigid cystoscopy and biopsy under general anaesthesia may be required, which carries a risk of complications such as bladder perforation, bleeding, and infection. The initial nephrological work‐up of patients with proteinuria or microscopic haematuria may suggest the need for kidney biopsy, which carries a risk of serious complications such as haemorrhage.

Imaging of the abdomen may reveal unexpected abnormalities, which can lead to further investigations (Furtado 2005). A study of CT colonography reported that the prevalence of incidental abnormalities was very high, around 40%, which led to additional investigations in 14% of cases (Xiong 2005). Even when serious abnormalities such as cancer are found incidentally, there is no guarantee that this improves prognosis (Welch 2004).

Common to all conditions that may be detected using urinary dipstick testing is a risk that the identified condition would never have caused symptoms in the person's remaining lifetime (over‐diagnosis), and that the diagnosis therefore will not improve prognosis, but instead lead to unnecessary worry and over treatment with inherent harms. Over‐diagnosis and over‐treatment are documented in screening for breast cancer (Gøtzsche 2013; Jørgensen 2009), prostate cancer (Djulbegovic 2010), lung cancer, melanoma, and thyroid cancer (Welch 2010). Although the concepts of over‐diagnosis and over‐treatment are most familiar in cancer screening, they also apply to screening for other conditions such as hypertension, hypercholesterolaemia, diabetes (Welch 2011), and CKD (Moynihan 2013).

The possibility of adverse psychological effects associated with diagnostic tests and treatment must also be considered as a potential harm, as well as the impact of negative screening results on providing a false sense of security, with the possibility of some people ignoring important symptoms.

Why it is important to do this review

The possible benefits of urinary dipstick screening must be weighed against possible harms. The main question is whether screening reduces morbidity and mortality and if the harms are acceptable.

Our review planned to investigate the use of urinary dipsticks to screen healthy people and hospital in‐ or outpatients for the presence of disease. Our main interest was the effects of combined dipsticks use, but we anticipated that the existing literature would be scant and therefore also planned to assess RCTs of screening for individual components of dipsticks, such as blood or protein.

This review focused on clinical outcomes that are relevant to people, such as mortality and ESKD.

Objectives

We aimed to quantify the benefits and harms of screening with urinary dipsticks in general populations and in patients at hospitals.

Methods

Criteria for considering studies for this review

Types of studies

All RCTs and other studies in which allocation to screening with urinary dipsticks or no screening was obtained using alternation (e.g. alternate medical records), date of birth or similar methods were eligible for inclusion.

Types of participants

Inclusion criteria

We did not impose age limitations and included studies from both general and patient populations. We included studies of screening using urinary dipsticks performed as part of a health check, such as in general practice or at the community level, as well as studies of screening hospital in‐ or outpatients, and patients in non‐hospital specialist clinics.

Exclusion criteria

We excluded studies where dipstick testing was done on indication, e.g. in people with suspected UTI, as well as studies conducted exclusively in populations of patients with urinary diseases because the pretest probability of disease would be high. Dipstick testing in these situations could also be viewed more properly as diagnostic testing rather than screening.

Types of interventions

We included studies of single or repeat use of urinary dipstick screening that tested for one or more of the following: haemoglobin, protein, albumin, albumin‐creatinine ratio, glucose, leukocytes and nitrite. We included studies regardless of who performed the test, such as healthcare professionals or study participants (following instruction).

Types of outcome measures

Primary outcomes

• All‐cause mortality

• Cardiovascular mortality

• Cancer mortality

• ESKD (patients requiring renal replacement therapy, i.e. dialysis or kidney transplantation).

Secondary outcomes

• Admission to hospital

• Drug therapy

• Surgery

• New diagnoses (cancers, including cancer stages, urolithiasis, CKD (stages 1 to 3), CKD (stages 4 and 5), diabetes mellitus, bacteriuria)

• Follow‐up investigations resulting from a positive test

• Complications to follow‐up investigations

• Self‐reported health

• Quality of life

• Disability.

Search methods for identification of studies

Electronic searches

We searched the Cochrane Renal Group's Specialised Register to 8 September 2014 through contact with the Trials Search Co‐ordinator using search terms relevant to this review. The Cochrane Renal Group’s Specialised Register contains studies identified from sources.

1. Quarterly searches of the Cochrane Central Register of Controlled Trials CENTRAL

2. Weekly searches of MEDLINE OVID SP

3. Handsearching of renal‐related journals and the proceedings of major renal conferences

4. Searching of the current year of EMBASE OVID SP

5. Weekly current awareness alerts for selected renal journals

6. Searches of the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov.

Studies contained in the Specialised Register are identified through search strategies for CENTRAL, MEDLINE, and EMBASE based on the scope of the Cochrane Renal Group. Details of these strategies as well as a list of handsearched journals, conference proceedings and current awareness alerts are available in the Specialised Register section of information about the Cochrane Renal Group.

Searching other resources

Reference lists of review articles, relevant studies and clinical practice guidelines.

Data collection and analysis

Selection of studies

Titles and abstracts were screened independently by two authors who discarded studies that were clearly not eligible and assessed the full text of potentially eligible studies to determine which satisfied inclusion criteria. Disagreements were to be resolved through discussion, with the third author as arbiter when necessary.

Data extraction and management

Data extraction was to be carried out independently by two authors using standard data extraction forms. Studies reported in non‐English language journals were to be translated before assessment. Where more than one publication of one study existed, reports were to be grouped together and the publication with the most complete data used in the analyses. Where relevant outcomes were only published in earlier versions these data were to be used. Any discrepancies between published versions were to be highlighted.

Assessment of risk of bias in included studies

We planned to assess risk of bias using the Cochrane risk of bias assessment tool (Higgins 2011) (Appendix 2).

• Was there adequate sequence generation (selection bias)?

• Was allocation adequately concealed (selection bias)?

• Was knowledge of the allocated interventions adequately prevented during the study (detection bias)?

  • Participants and personnel

  • Outcome assessors

• Were incomplete outcome data adequately addressed (attrition bias)?

• Are reports of the study free of suggestion of selective outcome reporting (reporting bias)?

• Was the study apparently free of other problems that could put it at a risk of bias?

Measures of treatment effect

For dichotomous outcomes (mortality, ESKD), we planned to use the risk ratio (RR) with 95% confidence intervals (CI). For measurement scale outcomes (self‐reported health, quality of life, disability), we planned to use the mean difference (MD), or the standardised mean difference (SMD) if different scales were used. Some outcomes may have been reported in various ways (admission to hospital, drug therapy, surgery, new diagnoses, follow‐up investigations, complications to follow‐up investigations), such as rates, continuous, or dichotomous outcomes. We planned to choose the format that would have informed the best synthesis of available results.

For measurement scale outcomes, we planned to extract both change from baseline and final means when available. Missing standard deviations were planned to have been estimated from similar studies, when possible. Time‐to‐event data were planned to be treated as dichotomous data, because the relevant outcomes (mortality, ESKD) were likely to have been ascertained for all participants.

Unit of analysis issues

We planned to include cluster RCTs, and when possible, extract effect measures and standard error rates from an analysis that takes clustering into account. If that was not possible, we planned to extract the number of clusters and estimate the intra‐cluster correlation coefficient to inform a reliable analysis. If this was not possible, we planned to disregard the clustering and investigate the effect of this in a sensitivity analysis.

Dealing with missing data

We planned to extract data for intention‐to‐treat analyses (ITT) and contact authors if required information was missing. Where ITT analysis was not possible, we planned to extract data from an available case analysis and assess the risk of bias from attrition.

Assessment of heterogeneity

We planned to analyse heterogeneity using a Chi² test on N‐1 degrees of freedom, with an alpha of 0.05 used for statistical significance, and the I² statistic (Higgins 2003).

Assessment of reporting biases

We did not expect that a sufficient number of studies would be identified to create a useful funnel plot. Assessing reporting bias is difficult, but we planned to note whether outcomes that we considered important were reported. We planned to contact authors about possible unpublished outcomes.

Data synthesis

We planned to use a random‐effects model and to express the results as both relative risks and number‐needed‐to‐screen to achieve the relevant outcomes, both beneficial and harmful.

Subgroup analysis and investigation of heterogeneity

We planned to perform the following subgroup analyses.

• Risk of bias

• Substances tested for (e.g. haemoglobin, protein/albumin or albumin‐creatinine ratio, glycosuria, leukocytes/nitrite, or combinations of substances)

• Population type (general populations, pregnant women, patients)

• Age of participants.

Sensitivity analysis

If possible, we planned to perform sensitivity analyses to explore the influence of the following factors on effect size.

• Excluding cluster RCTs

• Repeating the analysis excluding unpublished studies

• Repeating the analysis excluding any very long or large studies to establish how much they dominate the results.

Results

Description of studies

Results of the search

Searches yielded 4298 records, of which 4249 were excluded based on title and abstract (Figure 1). We identified 49 records (44 studies) for possible inclusion and full‐text assessment. These were either not RCTs (18 studies) or compared interventions or controls that were not relevant to this review (26 studies) (Characteristics of excluded studies). Thus, no studies could be included in this review.

1.

Study flow diagram

Risk of bias in included studies

Risk of bias assessment could not be conducted.

Effects of interventions

No studies met our inclusion criteria.

Discussion

Summary of main results

We found no studies that compared screening with urinary dipsticks with no screening. Screening with urinary dipsticks for haemoglobin, protein, albumin, albumin‐creatinine ratio, leukocytes, nitrite, or glucose, alone or in any combination, has unknown benefits and harms.

Agreements and disagreements with other studies or reviews

General populations

The older observational literature is mainly concerned with assessing the diagnostic yield, or exploring the feasibility and cost of screening programs, tacitly implying that any discovery of asymptomatic illness is beneficial. Given knowledge about over‐diagnosis and over‐treatment associated with several types of screening tests (Black 2010; Independent UK Panel 2012; Welch 2011) and their sometimes disappointing benefits (Djulbegovic 2010; Gøtzsche 2013; Krogsbøll 2012a; Schilling 2002; Simmons 2012; Woods 2002) such an assumption is not warranted.

Some studies avoided this assumption, but used methods that did not enable reliable conclusions to be made about benefits and harms. Japanese urine screening programs for children and adults that used dipstick testing for haemoglobin and protein were implemented in the 1970s. An analysis of incidence rates of ESKD in Japan, using data from a nationwide dialysis registry from 1983 to 2000, found steadily increasing incidence rates during the entire period (Wakai 2004). This rise was mainly due to diabetic kidney disease, nephrosclerosis, and unknown causes, while ESKD due to glomerulonephritis rose until the mid‐1990s, where it started to decline. This observation is compatible with the hypothesis that screening caused the decline, given the expected latency of effect, but other explanations are also possible, such as a decrease in incidence of glomerulonephritis or the implementation of possibly useful treatments for glomerulonephritis (Reid 2011; Samuels 2003). We have not found studies that compared the incidence of glomerulonephritis before and after the introduction of the Japanese screening programs in the 1970s, and an increase in incidence caused by detection of asymptomatic cases is also possible, as glomerulonephritis, particularly immunoglobulin A (IgA) nephropathy, can remain subclinical.

Comparisons between countries are difficult to interpret because of variations in biopsy policies, and a systematic review of glomerulonephritis incidence found very large variations (McGrogan 2011). For example, five studies reported the incidence of IgA nephropathy in children: four non‐screening studies and one screening study. In the non‐screening studies, the incidence ranged between 0.03/100,000/year and 0.57/100,000/year and the screening study found 4.5/100,000/year.

The prognosis of children with screening‐detected glomerular disease appears to be good (Ito 1990), and better than for symptomatic cases (Takebayashi 1992). This could be due to effective treatments arresting or slowing the disease at an early stage, but it could also reflect over diagnosis of subclinical cases, that is, cases that would not have become symptomatic if not discovered by screening, or length bias (screening preferentially detects less aggressive disease as there is more time to detect these cases).

Messing 2006 compared long‐term outcomes of bladder cancer detected through screening with outcomes of clinically detected bladder cancer and found dramatic differences in mortality between men with screen‐detected cancers and men with cancers not detected by screening. However, the populations were probably not comparable because the risk of death from other causes than bladder cancer was smaller among the men with screen detected cancers, which suggests selection bias. Furthermore, the possibility of over‐diagnosis of less aggressive tumours has not been ruled out, and this would also confer a spurious survival advantage to the screened group.

The observations that proteinuria and eGFR are clearly and consistently associated with the risk of ESKD, myocardial infarction, acute kidney injury, and death suggest that screening could be beneficial (Hemmelgarn 2010; James 2010). However, such observations do not resolve the classic screening‐related questions, e.g. whether the efficacy of treating screening‐detected disease is similar to what is observed in studies of disease not detected in screening, whether the compliance with both screening and preventive treatments is adequate in asymptomatic persons, how much over‐diagnosis the screening causes, and whether the benefits outweigh the harms. A simulation study of screening for proteinuria found that it was cost‐effective when targeted to people with hypertension, those aged over 60 years, or when conducted at the infrequent interval of 10 years (Boulware 2003). However, many assumptions are needed for simulation studies, and they cannot constitute proof.

A review of general health checks (Krogsbøll 2012a) included five studies (19,813 participants) that contained screening with a urinary dipstick as part of the intervention (Engberg 2002; Friedman 1986; Lannerstad 1977; Olsen 1976; Tibblin 1982). These studies did not find beneficial effects on morbidity or mortality. Friedman 1986 (which included 10,674 participants and 16 years of follow‐up) reported cause‐specific mortality in detail and did not find effects on deaths from genitourinary disease, or in other cancers, which included cancers of the bladder, kidney and ureter. The studies were likely underpowered to detect small beneficial effects of dipstick screening.

Hospitalised patients and outpatients

A cohort study of dipstick testing in medical outpatients without relevant symptoms, found that 17% had an abnormal result, but that management was changed as a result of this finding in only 0.7% of cases. (Rüttimann 1994) Three older cohorts assessed routine urine microscopy and similarly found many abnormal test results but few consequences for management (Boland 1995; Boland 1996; Kroenke 1986).

High risk patients

A special issue is case‐finding in people with known conditions that are strongly associated with kidney disease where screening borders on monitoring the development of a disease. Although we found no RCT evidence for such populations, this practice may be justified when the association is very strong, such as with diabetes mellitus. Also, this practice is so ubiquitous that randomised trials are unlikely to be conducted. Exactly what level of risk justifies case‐finding in high‐risk groups is not clear and is not the topic of this review.

Authors' conclusions

Implications for practice.

We found no trials that investigated dipstick screening versus no dipstick screening, and therefore the benefits and harms remain unknown. Because there are potential harms related to dipstick screening, and since any screening program entails financial and opportunity costs, the findings of our review justify the use of dipstick screening in non‐pregnant persons only in the context of a study setting. This conclusion does not address dipstick testing done on clinical indications such as fever, or in high risk patients such as people with diabetes.

Implications for research.

Conduct of RCTs are feasible for routine screening of healthy people, hospital inpatients and outpatients because the intervention is widespread but not standard of care. Careful consideration to ensure that studies are adequately powered is needed for future studies.

Feedback

Comment, 10 March 2015

Summary

Thank you for this recent Cochrane review regarding screening with urinary dipsticks. I was puzzled by the assertion that "Urinary dipstick testing is recommended for screening pregnant women to detect bacteria in the urine (bacteriuria) (Lin 2008; NICE 2008a)".

Certainly the NICE guideline states that "1.8.1.1 New Women should be offered routine screening for asymptomatic bacteriuria by midstream urine culture early in pregnancy. Identification and treatment of asymptomatic bacteriuria reduces the risk of pyelonephritis." Of note, it says that screening should be by midstream urine culture, not by dipstick.

The NICE guideline recommends the use of dipstick for protein at each antenatal visit to screen for pre‐eclampsia (rather than bacteriuria). If there is an alternative reference in the NICE guideline that you found as evidence of a recommendation to screen for asymptomatic bacteriuria with dipstick, please could you indicate where it is?

I agree with the conflict of interest statement below:

I certify that I have no affiliations with or involvement in any organization or entity with a financial interest in the subject matter of my feedback.

Jane Currie

Reply

Thank you for pointing out this error. Both the NICE guideline and the 2008 USPSTF statement recommends screening pregnant women with urine culture, not dipstick. We have now corrected this.

Lasse Krogsbøll

Karsten Juhl Jørgensen

Peter C Gøtzsche

Contributors

Comment: Jane Currie

What's new

Date	Event	Description
14 April 2015	Amended	Feedback incorporated ‐ correction of background
14 April 2015	Feedback has been incorporated	Responded to feedback and incorporated minor edit to background

Appendices

Appendix 1. Electronic search strategies

Database	Search terms
CENTRAL	reagent next strip*:ti,ab,kw urinalysis:ti,ab,kw MeSH descriptor Urine, this term only with qualifier: AN test next strip*:ti,ab,kw dipstick*:ti,ab,kw (urin* near/2 (strip or strips or stick or sticks)):ti,ab,kw urine next test*:ti,ab,kw stick next test*:ti,ab,kw multistix:ti,ab,kw (#1 OR #2 OR #3 OR #4 OR #5 OR #6 OR #7 OR #8 OR #9) ((leukocyte* or esterase* or nitrite* or haemoglobin or hemoglobin or protein or glucose or ketones) near/3 (screen or screened or screening or test or tests or tested or testing)):ti,ab,kw (leukocyte* or esterase* or nitrite* or haemoglobin or hemoglobin or protein or glucose or ketones):ti,ab and screening:kw (#10 OR #11 OR #12) asymptomatic:ti,ab,kw healthy:ti,ab,kw (patients or patient or inpatient* or outpatient*):ti,ab,kw ((general next practic*) or (family next practic*) or (family next physician* or general physician*)):ti,ab,kw ((community next health) or (community next nurs*)):ti,ab,kw (#14 OR #15 OR #16 OR #17 OR #18) (#13 AND #19)
MEDLINE	Reagent Strips/ Urinalysis/ Urine/an dipstick*.tw. (urin* adj2 (strip or strips or stick or sticks)).tw. urinalysis.tw. urine test*.tw. stick test*.tw. multistix.tw. or/1‐9 ((leukocyte* or esterase* or nitrite* or haemoglobin or hemoglobin or protein or glucose or ketones) adj5 (screen or screened or screening or test or tests or tested or testing)).tw. (leukocyte* or esterase* or nitrite* or haemoglobin or hemoglobin or protein or glucose or ketones).tw. and Mass Screening/ or/10‐12 Asymptomatic Diseases/ Asymptomatic Infections/ (asymptomatic or healthy).tw. Patients/ Inpatients/ Outpatients/ (patients or patient or inpatient* or outpatient*).tw. exp General Practice/ Community Health Services/ Community Health Nursing/ (general practic* or family practice* or family physician* or general physician*).tw. (community health or community nurs*).tw. or/14‐25 and/13,26 volunteers.tw. 28 not 29
EMBASE	test strip/ urinalysis/ dipstick*.tw. (urin* adj2 (strip or strips or stick or sticks)).tw. urinalysis.tw. urine test*.tw. stick test*.tw. multistix.tw. or/1‐8 ((leukocyte* or esterase* or nitrite* or haemoglobin or hemoglobin or protein or glucose or ketones) adj5 (screen or screened or screening or test or tests or tested or testing)).tw. (leukocyte* or esterase* or nitrite* or haemoglobin or hemoglobin or protein or glucose or ketones).tw. and (screening/ or screening test/ or mass screening/) or/9‐11 asymptomatic disease/ asymptomatic infection/ (asymptomatic or healthy).tw. patient/ outpatient/ hospital patient/ aged hospital patient/ (patients or patient or inpatient* or outpatient*).tw. general practice/ general practitioner/ health center/ community health nursing/ (general practic* or family practice* or family physician* or general physician*).tw. (community health or community nurs*).tw. or/13‐26 and/12,27 limit 28 to human volunteers.tw. 29 not 30

Appendix 2. Risk of bias assessment tool

Potential source of bias	Assessment criteria
Random sequence generation Selection bias (biased allocation to interventions) due to inadequate generation of a randomised sequence	Low risk of bias: Random number table; computer random number generator; coin tossing; shuffling cards or envelopes; throwing dice; drawing of lots; minimization (minimization may be implemented without a random element, and this is considered to be equivalent to being random).
	High risk of bias: Sequence generated by odd or even date of birth; date (or day) of admission; sequence generated by hospital or clinic record number; allocation by judgement of the clinician; by preference of the participant; based on the results of a laboratory test or a series of tests; by availability of the intervention.
	Unclear: Insufficient information about the sequence generation process to permit judgement.
Allocation concealment Selection bias (biased allocation to interventions) due to inadequate concealment of allocations prior to assignment	Low risk of bias: Randomisation method described that would not allow investigator/participant to know or influence intervention group before eligible participant entered in the study (e.g. central allocation, including telephone, web‐based, and pharmacy‐controlled, randomisation; sequentially numbered drug containers of identical appearance; sequentially numbered, opaque, sealed envelopes).
	High risk of bias: Using an open random allocation schedule (e.g. a list of random numbers); assignment envelopes were used without appropriate safeguards (e.g. if envelopes were unsealed or non‐opaque or not sequentially numbered); alternation or rotation; date of birth; case record number; any other explicitly unconcealed procedure.
	Unclear: Randomisation stated but no information on method used is available.
Blinding of participants and personnel Performance bias due to knowledge of the allocated interventions by participants and personnel during the study	Low risk of bias: No blinding or incomplete blinding, but the review authors judge that the outcome is not likely to be influenced by lack of blinding; blinding of participants and key study personnel ensured, and unlikely that the blinding could have been broken.
	High risk of bias: No blinding or incomplete blinding, and the outcome is likely to be influenced by lack of blinding; blinding of key study participants and personnel attempted, but likely that the blinding could have been broken, and the outcome is likely to be influenced by lack of blinding.
	Unclear: Insufficient information to permit judgement
Blinding of outcome assessment Detection bias due to knowledge of the allocated interventions by outcome assessors.	Low risk of bias: No blinding of outcome assessment, but the review authors judge that the outcome measurement is not likely to be influenced by lack of blinding; blinding of outcome assessment ensured, and unlikely that the blinding could have been broken.
	High risk of bias: No blinding of outcome assessment, and the outcome measurement is likely to be influenced by lack of blinding; blinding of outcome assessment, but likely that the blinding could have been broken, and the outcome measurement is likely to be influenced by lack of blinding.
	Unclear: Insufficient information to permit judgement
Incomplete outcome data Attrition bias due to amount, nature or handling of incomplete outcome data.	Low risk of bias: No missing outcome data; reasons for missing outcome data unlikely to be related to true outcome (for survival data, censoring unlikely to be introducing bias); missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk not enough to have a clinically relevant impact on the intervention effect estimate; for continuous outcome data, plausible effect size (difference in means or standardized difference in means) among missing outcomes not enough to have a clinically relevant impact on observed effect size; missing data have been imputed using appropriate methods.
	High risk of bias: Reason for missing outcome data likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk enough to induce clinically relevant bias in intervention effect estimate; for continuous outcome data, plausible effect size (difference in means or standardized difference in means) among missing outcomes enough to induce clinically relevant bias in observed effect size; ‘as‐treated’ analysis done with substantial departure of the intervention received from that assigned at randomisation; potentially inappropriate application of simple imputation.
	Unclear: Insufficient information to permit judgement
Selective reporting Reporting bias due to selective outcome reporting	Low risk of bias: The study protocol is available and all of the study’s pre‐specified (primary and secondary) outcomes that are of interest in the review have been reported in the pre‐specified way; the study protocol is not available but it is clear that the published reports include all expected outcomes, including those that were pre‐specified (convincing text of this nature may be uncommon).
	High risk of bias: Not all of the study’s pre‐specified primary outcomes have been reported; one or more primary outcomes is reported using measurements, analysis methods or subsets of the data (e.g. subscales) that were not pre‐specified; one or more reported primary outcomes were not pre‐specified (unless clear justification for their reporting is provided, such as an unexpected adverse effect); one or more outcomes of interest in the review are reported incompletely so that they cannot be entered in a meta‐analysis; the study report fails to include results for a key outcome that would be expected to have been reported for such a study.
	Unclear: Insufficient information to permit judgement
Other bias Bias due to problems not covered elsewhere in the table	Low risk of bias: The study appears to be free of other sources of bias.
	High risk of bias: Had a potential source of bias related to the specific study design used; stopped early due to some data‐dependent process (including a formal‐stopping rule); had extreme baseline imbalance; has been claimed to have been fraudulent; had some other problem.
	Unclear: Insufficient information to assess whether an important risk of bias exists; insufficient rationale or evidence that an identified problem will introduce bias.

Characteristics of studies

Characteristics of excluded studies [ordered by study ID]

Study	Reason for exclusion
Allen 1991	Wrong intervention. Compared urinary glucose monitoring in diabetes with blood glucose monitoring
Apoola 2009	Wrong intervention. Compared partner notification with urethral swab and urine antigen testing
Balogun 2011	Not RCT
Battelino 2011	Wrong intervention. Compared blood glucose meter with continuous blood glucose monitoring for children with type 1 diabetes
Beatty 1994	Not RCT
Bubner 2009	Wrong intervention. Compared point‐of‐care testing with laboratory testing in general practice
Calderon‐Margalit 2005	Not RCT
Calero 2011	Not RCT
Charles 2009	Wrong intervention. Compared intensified treatment of people with screen‐detected diabetes with usual care
Dallosso 2012	Wrong intervention. Compared monitoring with blood glucose or urine testing for people with type 2 diabetes
Davies 1991	Wrong intervention. No unscreened control group. Compared two different methods of screening for glycosuria
Davies 1993	Not RCT
Davies 1999	Not RCT
Diercks 2002	Wrong intervention. Factorial design that compared fosinopril, pravastatin and placebo in people with elevated urinary albumin excretion
Dolan 1987	Wrong intervention. Compared urinary glucose monitoring by dipstick with urine glucose monitoring by tablet system
Downing 2012	Not RCT
DPPRG 2005	Wrong intervention. Compared lifestyle intervention, metformin, and placebo for prevention of diabetes in people with elevated fasting glucose and impaired glucose tolerance
Falguera 2010	Wrong intervention. Compared empirical treatment of pneumonia with targeted treatment based on urine antigen testing
Fulcher 1991	Not RCT
Gallichan 1994	Wrong intervention. Compared blood glucose monitoring with urine dipstick monitoring in patients with type 2 diabetes
Goldby 2011	Not RCT
Grimm 1997	Wrong comparison. Both groups had dipstick testing
Jolic 2011	Not RCT
Jou 1998	Not RCT
Kazemier 2012	Wrong intervention. Compared antibiotic treatment with no treatment in pregnant women with asymptomatic bacteriuria
Kenealy 2005	Wrong intervention. Compared patient reminders, computer reminders, both reminders, and usual care, for screening for diabetes
Koschinsky 1984	Wrong intervention. Compared urinary and blood glucose testing in diabetics. Both groups tested for urinary glucose for 4 weeks and then for blood glucose for 4 weeks
Lauritzen 1994	Wrong intervention. Described variation in albumin‐creatinine ratio in the RCT screened arms
Lauritzen 2008	Wrong intervention. Compared health checks, health checks and lifestyle conversations, and usual care
Lenz 2002	Wrong comparison. Compared nurse practitioner and physician treatment of diabetes
Little 2009	Wrong intervention. Compared five different management strategies for suspected UTI
McEwan 1990	Wrong intervention. Dipstick screening was part of a complex intervention
McGhee 1997	Not RCT
Messing 1995	Not RCT
Morris 2012	Not RCT. Systematic review of diagnostic studies comparing spot protein‐creatinine ratio with 24 hour protein‐creatinine ratio for screening pregnant women
Naimark 2001	Wrong intervention. Tested education directed towards physicians to increase their microalbuminuria testing pattern among people with type 2 diabetes.
Neumann 2008	Wrong intervention. Compared urine microscopy with a malaria dipstick
Nevedomskaya 2011	Not RCT
Ochoa 2007	Not RCT
Reyburn 2007	Wrong intervention. Compared urine microscopy and a rapid diagnostic test for malaria
Schwab 1992	Not RCT
Simmons 2012	Wrong intervention. Compared screening for diabetes with no screening for diabetes (no dipsticks)
Tissot 2001	Not RCT
Worth 1982	Wrong intervention. Compared dipstick testing for glycosuria with dipstick testing for glycosuria combined with one of two methods for blood glucose measurements. Used cross‐over design. Included people with known diabetes

RCT ‐ randomised controlled trial; UTI ‐ urinary tract infection

Contributions of authors

1. Drafting the protocol: LTK, KJ, PCG

2. Study selection: LTK, KJ

3. Extract data from studies: LTK, KJ

4. Enter data into RevMan: LTK

5. Carry out the analysis: not performed

6. Interpret the analysis: not performed

7. Draft the final review: LTK, KJ, PCG

8. Disagreement resolution: not performed

9. Update the review: LTK

Declarations of interest

• Lasse T Krogsbøll: none known

• Karsten Juhl Jørgensen: none known

• Peter C Gøtzsche: none known.

Edited (no change to conclusions), comment added to review