Diagnostic windows in non-neoplastic diseases: a systematic review

Emma Whitfield; Becky White; Spiros Denaxas; Georgios Lyratzopoulos

doi:10.3399/BJGP.2023.0044

Abstract

Background Investigating changes in prediagnostic healthcare utilisation can help identify how much earlier conditions could be diagnosed. Such ‘diagnostic windows’ are established for cancer but remain relatively unexplored for non-neoplastic conditions.

Aim To extract evidence on the presence and length of diagnostic windows for non-neoplastic conditions.

Design and setting A systematic review of studies of prediagnostic healthcare utilisation was carried out.

Method A search strategy was developed to identify relevant studies from PubMed and Connected Papers. Data were extracted on prediagnostic healthcare use, and evidence of diagnostic window presence and length was assessed.

Results Of 4340 studies screened, 27 were included, covering 17 non-neoplastic conditions, including both chronic (for example, Parkinson’s disease) and acute conditions (for example, stroke). Prediagnostic healthcare events included primary care encounters and presentations with relevant symptoms. For 10 conditions, sufficient evidence to determine diagnostic window presence and length was available, ranging from 28 days (herpes simplex encephalitis) to 9 years (ulcerative colitis). For the remaining conditions, diagnostic windows were likely to be present, but insufficient study duration was often a barrier to robustly determining their length, meaning that diagnostic window length may exceed 10 years for coeliac disease, for example.

Conclusion Evidence of changing healthcare use before diagnosis exists for many non-neoplastic conditions, establishing that early diagnosis is possible, in principle. In particular, some conditions may be detectable many years earlier than they are currently diagnosed. Further research is required to accurately estimate diagnostic windows and to determine how much earlier diagnosis may be possible, and how this might be achieved.

INTRODUCTION

Delayed diagnosis has been associated with poorer prognosis and patient experience in different conditions, such as psoriatic arthritis, Guillain-Barré syndrome, and coeliac disease,¹^–³ making strategies to improve diagnosis an important research priority. A diagnostic window is a period during which there is a detectable increase in healthcare use before the diagnosis of an underlying condition in a population of (as-yet-undiagnosed) patients. A diagnostic window indicates the point at which diagnosis may be theoretically possible in at least some patients with the condition⁴ and can help identify conditions or patient groups that may benefit from targeted interventions to achieve earlier diagnosis.

The concept of diagnostic windows is applicable to any disease; however, most existing evidence relates to cancer.⁴^–⁷ Diagnostic windows typically range in length from a few months to 3–4 years.⁸ Diagnostic windows can be defined by any type of healthcare-use event (for example, primary care visits, symptoms, abnormal test results), with the type of event determining how the window should be interpreted. For example, when all types of healthcare encounters are considered, the diagnostic window indicates when the as-yet-undiagnosed population starts to use health care in a different way from a baseline healthy population, indicating when at least some as-yet-undiagnosed patients start to seek help and interact with the healthcare system because of their underlying condition. In contrast, for more specific healthcare events, such as ordering a test or a consultation for certain symptoms, the diagnostic window indicates when the as-yet-undiagnosed population undergo the specific test or present with that symptom at higher rates than the baseline healthy population.

By identifying published studies that contained any examination of prediagnostic healthcare utilisation, the aim of the study was to extract evidence on the presence and length of diagnostic windows in non-neoplastic conditions. A secondary aim was to describe the types of healthcare events used to define diagnostic windows.

METHOD

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.⁹ The objective of the literature search was to identify research studies that contained evidence of healthcare utilisation for patients over a period immediately before diagnosis of a non-neoplastic condition.

Improving timeliness of diagnosis is imperative across disease types. This review identified that for a range of non-neoplastic conditions healthcare use starts to increase in the time before diagnosis. For some conditions, this increase may first start to occur many years before diagnosis. Further research is needed to produce accurate estimates of how much earlier diagnosis may be possible.

How this fits in

Concept mapping and search term selection

In a preliminary phase, appropriate search terms were developed using concept mapping guided by a previous systematic review on diagnostic windows for cancer⁸ and four exemplar studies, summarised in Box 1.¹⁰^–¹³ The selected exemplar studies all present evidence on the occurrence of defined healthcare events, such as recorded symptoms or healthcare encounters, during specified periods before the diagnosis of a given condition (or before the relevant index date for controls). The reported results all allow for an examination of whether healthcare utilisation changed before diagnosis.

Study	Condition	Aims
*Schrag et al* (2015)¹⁰**	Parkinson’s disease	‘Assess the association between several prediagnostic features and a subsequent diagnosis of Parkinson’s disease, and chart the timeline of these presentations before diagnosis.’
*Blackwell et al* (2021)¹¹**	Inflammatory bowel disease (IBD)	‘Examine the prevalence of gastrointestinal symptoms attributable to undiagnosed IBD before diagnosis and … identify predictors for timely specialist review.’
*Miller et al* (2021)¹²**	Tuberculosis	‘Propose a population-based approach for estimating the incidence and duration of diagnostic delays associated with tuberculosis, and … describe the risk factors associated with patients experiencing a diagnostic delay.’
*Miller et al* (2021)¹³**	Herpes simplex encephalitis (HSE)	‘Determine the incidence of diagnostic delays for HSE … identify risk factors and outcomes associated with diagnostic delays.’

Box 1.

Summary of exemplar studies

PubMed was searched (25 March 2022) from inception; the final search term (Supplementary Box S1) consisted of four components. The first focused the search on studies that contained evidence from the most relevant areas of health care to this review: primary health care and internal medicine. This was because of the authors’ hypothesis that patients are likely to have most initial interactions within these specialties. The second selected studies that were likely to contain evidence of healthcare utilisation, and the third removed studies relating to conditions outside the scope of this review. A fourth component filtered out irrelevant study designs, such as case reports and randomised controlled trials.

Study selection

Titles and abstracts were screened to remove papers that did not contain evidence of healthcare utilisation. For example, studies of proposed new tests or algorithms and evaluations of educational interventions to improve diagnosis were excluded. Subsequently, the full texts of remaining papers were assessed for eligibility (Supplementary Table S1). Initially 100 randomly selected titles were screened to develop consensus rules for exclusion and inclusion. The remaining titles, abstracts, and full articles were then screened, with any uncertain cases resolved by discussion with a second reviewer.

Other study identification methods

Finally, a graph of similar papers was produced for each included study, using the Connected Papers website (17 May 2022)¹⁴ — an automated approach to citation tracking. The titles, abstracts, and full texts of all yielded ‘connected papers’ were screened to identify other relevant studies, following the same procedure described above.

Data extraction and synthesis

Details of the study design, aims, setting, sample size, type of prediagnostic healthcare events examined, and the methods used to measure and evaluate prediagnostic healthcare utilisation were extracted from each study. Risk of bias was assessed by a single reviewer using the JBI critical appraisal checklist for case-control studies.¹⁵

Evidence of a difference or change in prediagnostic healthcare utilisation in cases from a measured baseline was used to determine the presence of a diagnostic window. Baseline healthcare utilisation could be measured in controls in a case–control study, or from historic healthcare use in cases in a case-only study. The earliest point relative to diagnosis at which a difference or change from the measured baseline was observed and sustained was extracted. Figure 1 shows how this was used to determine diagnostic window presence and length.

Figure 1.

Flow diagram for determining diagnostic window presence and length.

Where estimates of the timing of differences or changes reported in reviewed studies differed between the text and figures or tables of the same publication, evidence reported in the main text was prioritised. Where studies reported evidence on types of healthcare-use events or in different patient groups, evidence of all possible diagnostic windows is reported in the current review.

A narrative synthesis described results across studies, and estimates of diagnostic window length were visualised. As diagnostic windows will differ across conditions, healthcare systems, and the different types of healthcare-use event considered, numerical synthesis was not deemed appropriate.

RESULTS

A PRISMA diagram outlines the study search and inclusion process (Figure 2). The PubMed search identified 3738 papers; 3659 papers were excluded in title and abstract screening. Of the remaining 79 papers, 61 were excluded in full-text screening — almost exclusively because they considered the entire prediagnostic time period en bloc. By considering the ‘connected papers’ of the 18 included papers (four of which are the exemplar papers noted previously) a further nine papers were identified for inclusion.

Figure 2.

PRISMA flowchart for literature review of diagnostic windows in non-neoplastic conditions.⁹

Selected studies were published between 2007 and 2022. Total sample size ranged from 568 patients (319 cases)¹⁶ to 459 774 patients (21 894 cases)¹⁷ in case–control studies, and from 2667 cases¹³ to 367 768 cases¹⁸ for case-only studies; sample sizes varied with data source and disease incidence. Most of the included studies were of moderate quality (Supplementary Table S2). The most common issue was the lack of identification of confounding factors or strategies to deal with them (n = 16).

Countries and data sources

Of the 27 included studies, 13 were conducted in the UK,¹⁰^,¹¹^,¹⁶^,¹⁹^–²⁸ of which all but one used longitudinal primary care electronic health record datasets, such as the Clinical Practice Research Datalink and The Health Improvement Network database. In Europe, three studies conducted in Denmark used national registries¹⁷^,²⁹^,³⁰ and one in Germany used statutory insurance claims data.³¹ Six studies conducted in Canada used centralised administrative and clinical databases of hospital visits, physician billing claims, and prescriptions, with data availability determined at province level.³²^–³⁷ Three papers from the US and one paper from Singapore used insurance claims data.¹²^,¹³^,¹⁸^,³⁸

Conditions

Studies measured prediagnostic healthcare utilisation for 17 different conditions: acute myocardial infarction, childhood type 1 diabetes mellitus, chronic obstructive pulmonary disease, clinically isolated syndrome, coeliac disease, common variable immunodeficiency, dementia, granulomatosis with polyangiitis, herpes simplex encephalitis, inflammatory bowel disease, multiple sclerosis, Parkinson’s disease, psychosis, rheumatoid arthritis, schizophrenia, stroke, and tuberculosis.

Measures of healthcare utilisation

The main type of healthcare-use event considered was healthcare encounters, although several subtypes were noted: all-cause healthcare encounters versus encounters for specific symptoms, signs, comorbidities, diagnoses, or International Classification of Diseases-10 chapter; encounters in primary care, secondary care, or specific clinical specialties; and encounters via any medium versus face-to-face only. A small number of studies considered other aspects of healthcare use, such as healthcare costs or prescriptions dispensed. Half of the studies considered multiple types of healthcare-use events, for example, daytime GP contacts, out-of-hours GP contacts, and secondary care contacts;¹⁷ or primary care contacts with individual symptoms.¹⁰^,²³

Several papers estimated rates or rate ratios for healthcare-use events, typically using either Poisson or negative binomial regression. Others estimated the prevalence of healthcare-use events in different time intervals (typically by year pre-diagnosis). Three studies fitted logistic regression models to measure the odds of having a healthcare-use event in specific time intervals in cases compared with controls. Few papers explicitly attempted estimation of diagnostic window length, with the exception of the series of papers from Miller et al using change-point detection methods.¹²^,¹³^,¹⁸

Diagnostic windows

All included studies contained evidence of a sustained difference in prediagnostic healthcare utilisation compared with a baseline ‘healthy’ utilisation for at least one type of healthcare-use event. A summary of study results is provided in Supplementary Table S3. For 15 studies this difference existed for the entire lookback period (study duration), ranging in length from 1 to 10 years, meaning diagnostic windows for these conditions may be longer than their respective study duration(s) and it was not possible to estimate the full length of the diagnostic window (Figure 1). Card et al observed an excess of new diagnoses of irritable bowel syndrome in patients subsequently diagnosed with coeliac disease compared with controls from the start of the 10-year study duration.²⁷ For two additional studies, ranges were identified that likely contained inflection points, for example, there was evidence that balance problems first become detectable 5–10 years before the diagnosis of Parkinson’s disease.¹⁰

In the remaining 10 studies at least one diagnostic window was identified. In five studies, a diagnostic window was identified for at least one type of healthcare-use event or patient group, but for other healthcare events or patient groups differences extended for the entire study duration and it was not possible to make any further conclusions on diagnostic window length. For example, Yusuf et al showed a change in physician visits for anaemia 2 years before diagnosis of multiple sclerosis and that differences in physician visit rates for fatigue, sleep disorders, and pain existed for 5 years before diagnosis; however, the study duration was 5 years.³³

For the remaining five studies inflection points were identified. In total, 25 diagnostic windows were identified for 10 different conditions over 10 studies, ranging in length from 28 days to 9 years (Figure 3 and Figure 4, Supplementary Table S3).

Figure 3.

Identified diagnostic windows and corresponding study duration (under 1 year). Solid lines indicate a diagnostic window; dotted lines indicate the total study duration. The type of healthcare-use event and study design used are given on the right, with different colours denoting different conditions. AMI = acute myocardial infarction. HSE = herpes simplex encephalitis. SSD = symptomatically similar diagnoses.¹² TB = tuberculosis. T1DM = childhood diabetes mellitus type 1.

Figure 4.

Identified diagnostic windows and the corresponding study duration (over 1 year). Solid lines indicate a diagnostic window; dotted lines indicate the total study duration. The type of healthcare-use event and study design used are given on the right, with different colours denoting different conditions. CD = Crohn’s disease. IBD = inflammatory bowel disease. MS = multiple sclerosis. UC = ulcerative colitis.

DISCUSSION

Summary

Evidence from health records indicates that diagnostic windows are likely to exist for other conditions and are not unique to cancer.

For acute conditions these windows are likely to be relatively short (<1 year),¹²^,¹³^,¹⁸ but for chronic conditions — in particular autoimmune conditions — windows may span several years.¹⁹^,³³

Some conditions, such as Parkinson’s disease and coeliac disease, may be detectable in some patients many years earlier than they are currently diagnosed.¹⁰^,²⁷ Further research is needed to produce exact estimates of diagnostic windows for non-neoplastic conditions.

Comparison with existing literature

Literature explicitly examining diagnostic windows for non-neoplastic conditions is limited, and the authors of the current study are not aware of any previous reviews of this evidence. However, some relevant research has been published since this literature search was carried out, including some formal analyses of diagnostic windows for non-neoplastic conditions such as rheumatoid arthritis and necrotising soft-tissue infections of the genitalia.³⁹^,⁴⁰ Some of these studies found different diagnostic windows for conditions also included in this review. For example, Bohlken et al⁴¹ found slightly longer diagnostic windows for Parkinson’s disease for many symptoms included in Schrag et al.¹⁰ This could be because of differences in healthcare settings, data recording practices, or sample size.

A previous review of diagnostic windows for neoplastic conditions found changes in healthcare use at least 6 months before diagnosis for many common cancer sites, with longer windows present for colorectal cancer, brain tumours, and multiple myeloma.⁸ Considered alongside the current review, this supports the hypothesis that the concept of diagnostic windows is applicable to all disease types.

Strengths and limitations

To the authors’ knowledge, this is the first evaluation of evidence on the presence and length of diagnostic windows outside cancer. The presented evidence of prediagnostic healthcare utilisation from 27 studies spanning 17 different conditions shows that diagnostic windows likely exist and that some may be several years long.

As the concept of ‘diagnostic windows’ has not been widely considered outside cancer, most studies (n = 24) did not use statistical methods to formally estimate diagnostic window length, instead relying on visual identification, which may be suboptimal.⁴² A JBI critical appraisal tool was used to assess study quality. Some studies did not report sufficient statistics for the current review’s purpose, and it was therefore necessary to rely on text narrative information or exhibits, which sometimes lacked detail.²⁶^,²⁹

Constructing a search term that captured all available evidence of prediagnostic healthcare use, without including large numbers of false positives, was challenging. The search focused on studies with a primary care setting — where patients typically first present — but only one database (PubMed) was searched, and most studies were excluded through title screening. Some additional studies that were not captured in this search were identified from Connected Papers; however, it is likely that some relevant papers were not captured by either of these approaches. In future, the identification of relevant papers could be facilitated by wider use of the term ‘diagnostic window’, where appropriate, and by expanding searches to cover multiple databases. A further limitation is that risk of bias was only assessed by a single reviewer. As such, risk of bias assessment was not used as an exclusion criterion for study selection and is only provided as additional context for the reader.

Studies considered a wide range of healthcare-use events that can provide useful insights in combination. By first considering all-cause healthcare utilisation (in primary and/or secondary care) the earliest timepoint before diagnosis when the condition becomes detectable in some patients can be estimated. Then considering more specific types of healthcare utilisation related to the condition can elucidate how and when the condition may manifest in different ways. However, because of the large variety of healthcare systems, data sources, phenotypes, and definitions of healthcare-use events, comparing individual diagnostic window estimates between studies would be generally inappropriate.

Finally, most studies evaluated healthcare utilisation at yearly intervals. When the study duration was relatively long (5–10 years), this was sufficient. However, for studies with shorter durations (≤3 years) a difference between cases and controls was sometimes evident, but it was not always possible to discern when a change first occurred.²⁹^,³⁸ Additionally, most studies (n = 20) did not justify their chosen study duration.

Implications for research and practice

Estimating diagnostic windows can help identify conditions for which future diagnostic quality and safety research efforts should be pursued. However, it should not be assumed that a condition with a longer diagnostic window necessarily represents a more promising target for diagnostic improvement. Every condition has a unique diagnostic process and, when identifying research priorities, the diagnostic window length should be considered alongside the potential consequences of a delayed diagnosis (disease progression, treatment options, and prognosis) and the nature of the condition (chronic versus acute, rare versus common).

Questions remain as to how diagnostic windows of different lengths should be interpreted. As noted in White et al, diagnostic window length can vary by patient factors (age, sex, socioeconomic status, comorbidities), healthcare factors (location, size, and type of healthcare setting), disease factors (subtypes, typical disease course), and study era.⁸ Further research is needed to, first, produce accurate estimates of diagnostic window presence and length for non-neoplastic conditions, and, second, examine how these windows are associated with relevant factors, to help identify targets for diagnostic improvement efforts.

Although the existence of diagnostic windows for a given condition does not prove that any individual patient could have been diagnosed earlier, it provides ‘proof-of-concept’ that earlier diagnosis is possible for some patients, while pointing to possible avenues for improvement. Further research is required to identify missed diagnostic opportunities for specific conditions.

Evidence exists of changing healthcare utilisation before the diagnosis of several non-neoplastic conditions — including acute, autoimmune, and mental health conditions — documenting the potential for earlier diagnosis.

The ‘diagnostic windows’ of some of these conditions may span several years and further research is needed to accurately estimate their presence and length. These realisations lay the groundwork for future diagnostic quality and safety research aimed at identifying targets for interventions to improve diagnosis.

Notes

Funding

Emma Whitfield acknowledges the receipt of a studentship award from Health Data Research UK — the Alan Turing Institute Wellcome PhD Programme in Health Data Science (Grant Ref: 218529/Z/19/Z). Georgios Lyratzopoulos and Becky White were supported by the Cancer Research UK Advanced Clinician Scientist Fellowship Award (C18081/A18180, to Georgios Lyratzopoulos). Spiros Denaxas is funded from: (a) Health Data Research (HDR) UK, which receives its funding from HDR UK Ltd (HDR-9006) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF), and the Wellcome Trust; (b) the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under Grant Agreement No. 116074; (c) the National Institute for Health and Care Research (NIHR) University College London Hospitals Biomedical Research Centre; (d) an Alan Turing Fellowship (EP/N510129/1); (e) the Longitudinal Health and Wellbeing COVID-19 National Core Study, which was established by the UK Chief Scientific Officer in October 2020 and funded by UK Research and Innovation (UKRI) (Grant References MC_PC_20030 and MC_PC_20059), by the Data and Connectivity National Core Study, led by HDR UK in partnership with the Office for National Statistics and funded by UKRI (Grant reference MC_PC_20058), and by the CONVALESCENCE study of long COVID, which is funded by the NIHR/UKRI; and (f) the British Heart Foundation Data Science Centre (Grant No. SP/19/3/34678, awarded to HDR UK).

Ethical approval

Not applicable

Data

All data included in this review paper are accessible through the literature sources cited.

Provenance

Freely submitted; externally peer reviewed.

Competing interests

The authors have declared no competing interests.

Discuss this article

Contribute and read comments about this article: bjgp.org/letters

Received January 25, 2023.
Revision requested February 28, 2023.
Accepted April 4, 2023.

http://creativecommons.org/licenses/by/4.0/

This article is Open Access: CC BY 4.0 licence (http://creativecommons.org/licences/by/4.0/).

REFERENCES

1.↵
1. Haroon M,
2. Gallagher P,
3. FitzGerald O
(2015) Diagnostic delay of more than 6 months contributes to poor radiographic and functional outcome in psoriatic arthritis. Ann Rheum Dis 74, 1045–1050.
OpenUrl Abstract/FREE Full Text
2.
1. Dubey D,
2. Kapotic M,
3. Freeman M,
4. et al.
(2016) Factors contributing to delay in diagnosis of Guillain-Barré syndrome and impact on clinical outcome. Muscle Nerve 53, 384–387.
OpenUrl
3.↵
1. Fuchs V,
2. Kurppa K,
3. Huhtala H,
4. et al.
(2018) Delayed celiac disease diagnosis predisposes to reduced quality of life and incremental use of health care services and medicines: a prospective nationwide study. United European Gastroenterol J 6, 567–575.
OpenUrl PubMed
4.↵
1. Hansen PL,
2. Hjertholm P,
3. Vedsted P
(2015) Increased diagnostic activity in general practice during the year preceding colorectal cancer diagnosis. Int J Cancer 137, 615–624.
OpenUrl CrossRef PubMed
5.
1. Christensen KG,
2. Fenger-Grøn M,
3. Flarup KR,
4. Vedsted P
(2012) Use of general practice, diagnostic investigations and hospital services before and after cancer diagnosis — a population-based nationwide registry study of 127,000 incident adult cancer patients. BMC Health Serv Res 12, 224.
OpenUrl CrossRef PubMed
6.
1. Zhou Y,
2. Abel GA,
3. Hamilton W,
4. et al.
(2020) Imaging activity possibly signalling missed diagnostic opportunities in bladder and kidney cancer: a longitudinal data-linkage study using primary care electronic health records. Cancer Epidemiol 66, 101703.
OpenUrl CrossRef PubMed
7.↵
1. Renzi C,
2. Lyratzopoulos G,
3. Card T,
4. et al.
(2016) Do colorectal cancer patients diagnosed as an emergency differ from non-emergency patients in their consultation patterns and symptoms? A longitudinal data-linkage study in England. Br J Cancer 115, 866–875.
OpenUrl CrossRef PubMed
8.↵
1. White B,
2. Renzi C,
3. Rafiq M,
4. et al.
(2022) Does changing healthcare use signal opportunities for earlier detection of cancer? A review of studies using information from electronic patient records. Cancer Epidemiol 76, 102072.
OpenUrl CrossRef PubMed
9.↵
1. Page MJ,
2. McKenzie JE,
3. Bossuyt PM,
4. et al.
(2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71.
OpenUrl FREE Full Text
10.↵
1. Schrag A,
2. Horsfall L,
3. Walters K,
4. et al.
(2015) Prediagnostic presentations of Parkinson’s disease in primary care: a case-control study. Lancet Neurol 14, 57–64.
OpenUrl CrossRef PubMed
11.↵
1. Blackwell J,
2. Saxena S,
3. Jayasooriya N,
4. et al.
(2021) Prevalence and duration of gastrointestinal symptoms before diagnosis of inflammatory bowel disease and predictors of timely specialist review: a population-based study. J Crohns Colitis 15, 203–211.
OpenUrl CrossRef
12.↵
1. Miller AC,
2. Arakkal AT,
3. Koeneman S,
4. et al.
(2021) Incidence, duration and risk factors associated with delayed and missed diagnostic opportunities related to tuberculosis: a population-based longitudinal study. BMJ Open 11, e045605.
OpenUrl Abstract/FREE Full Text
13.↵
1. Miller AC,
2. Koeneman SH,
3. Arakkal AT,
4. et al.
(2021) Incidence, duration, and risk factors associated with missed opportunities to diagnose herpes simplex encephalitis: a population-based longitudinal study. Open Forum Infect Dis 8, ofab400.
OpenUrl
14.↵
1. Connected Papers
Find and explore academic papers, . No date. https://www.connectedpapers.com/about (accessed 19 Apr 2023).
15.↵
1. Aromataris E,
2. Munn Z
1. Moola S,
2. Munn Z,
3. Tufanaru C,
4. et al.
(2020) Chapter 7: Systematic reviews of etiology and risk. in JBI Manual for Evidence Synthesis, eds Aromataris E, Munn Z (JBI, Australia), 217–269.
16.↵
1. Bamford C,
2. Eccles M,
3. Steen N,
4. Robinson L
(2007) Can primary care record review facilitate earlier diagnosis of dementia? Fam Pract 24, 108–116.
OpenUrl CrossRef PubMed
17.↵
1. Nørgaard HCB,
2. Pedersen HS,
3. Fenger-Grøn M,
4. et al.
(2016) Increased use of primary care during 6 years of prodromal schizophrenia. Acta Psychiatr Scand 134, 225–233.
OpenUrl
18.↵
1. Miller AC,
2. Cavanaugh JE,
3. Arakkal AT,
4. et al.
(2023) A comprehensive framework to estimate the frequency, duration and risk factors for diagnostic delays using simulation-based methods. BMC Med Inform Decis Mak 23, 68.
OpenUrl
19.↵
1. Blackwell J,
2. Saxena S,
3. Petersen I,
4. et al.
(2021) Depression in individuals who subsequently develop inflammatory bowel disease: a population-based nested case–control study. Gut 70, 9, 1642–1648.
OpenUrl Abstract/FREE Full Text
20.
1. Townson J,
2. Cannings-John R,
3. Francis N,
4. et al.
(2019) Presentation to primary care during the prodrome of type 1 diabetes in childhood: a case-control study using record data linkage. Pediatr Diabetes 20, 330–338.
OpenUrl CrossRef PubMed
21.
1. Sullivan SA,
2. Hamilton W,
3. Tilling K,
4. et al.
(2018) Association of primary care consultation patterns with early signs and symptoms of psychosis. JAMA Netw Open 1, e185174.
OpenUrl
22.
1. Muller S,
2. Hider S,
3. Machin A,
4. et al.
(2019) Searching for a prodrome for rheumatoid arthritis in the primary care record: a case-control study in the clinical practice research datalink. Semin Arthritis Rheum 48, 815–820.
OpenUrl
23.↵
1. Disanto G,
2. Zecca C,
3. MacLachlan S,
4. et al.
(2018) Prodromal symptoms of multiple sclerosis in primary care. Ann Neurol 83, 1162–1173.
OpenUrl CrossRef
24.
1. Lee JJ,
2. Thompson MJ,
3. Usher-Smith JA,
4. et al.
(2018) Opportunities for earlier diagnosis of type 1 diabetes in children: a case-control study using routinely collected primary care records. Prim Care Diabetes 12, 254–264.
OpenUrl
25.
1. Pearce FA,
2. Lanyon PC,
3. Watts RA,
4. et al.
(2018) Novel insights into the aetiology of granulomatosis with polyangiitis — a case–control study using the Clinical Practice Research Datalink. Rheumatology 57, 1002–1010.
OpenUrl
26.↵
1. Pearce FA,
2. Hubbard RB,
3. Grainge MJ,
4. et al.
(2018) Can granulomatosis with polyangiitis be diagnosed earlier in primary care? A case–control study. QJM 111, 39–45.
OpenUrl CrossRef PubMed
27.↵
1. Card TR,
2. Siffledeen J,
3. West J,
4. Fleming KM
(2013) An excess of prior irritable bowel syndrome diagnoses or treatments in celiac disease: evidence of diagnostic delay. Scand J Gastroenterol 48, 801–807.
OpenUrl CrossRef PubMed
28.↵
1. Card TR,
2. Siffledeen J,
3. Fleming KM
(2014) Are IBD patients more likely to have a prior diagnosis of irritable bowel syndrome? Report of a case-control study in the General Practice Research Database. United European Gastroenterol J 2, 505–512.
OpenUrl CrossRef PubMed
29.↵
1. Ilkjær F,
2. Rasmussen LD,
3. Martin-Iguacel R,
4. et al.
(2019) How to identify common variable immunodeficiency patients earlier: general practice patterns. J Clin Immunol 39, 641–652.
OpenUrl
30.↵
1. Hastrup LH,
2. Simonsen E,
3. Ibsen R,
4. et al.
(2020) Societal costs of schizophrenia in Denmark: a nationwide matched controlled study of patients and spouses before and after initial diagnosis. Schizophr Bull 46, 68–77.
OpenUrl PubMed
31.↵
1. Gasperi C,
2. Hapfelmeier A,
3. Daltrozzo T,
4. et al.
(2021) Systematic assessment of medical diagnoses preceding the first diagnosis of multiple sclerosis. Neurology 96, e2977–e2988.
OpenUrl CrossRef
32.↵
1. Johnson KM,
2. Khakban A,
3. Bryan S,
4. et al.
(2020) Healthcare system encounters before COPD diagnosis: a registry-based longitudinal cohort study. Thorax 75, 108–115.
OpenUrl Abstract/FREE Full Text
33.↵
1. Yusuf FLA,
2. Wijnands JMA,
3. Kingwell E,
4. et al.
(2021) Fatigue, sleep disorders, anaemia and pain in the multiple sclerosis prodrome. Mult Scler J 27, 290–302.
OpenUrl
34.
1. Wijnands JM,
2. Zhu F,
3. Kingwell E,
4. et al.
(2019) Five years before multiple sclerosis onset: phenotyping the prodrome. Mult Scler 25, 1092–1101.
OpenUrl
35.
1. Wijnands JMA,
2. Kingwell E,
3. Zhu F,
4. et al.
(2017) Health-care use before a first demyelinating event suggestive of a multiple sclerosis prodrome: a matched cohort study. Lancet Neurol 16, 445–451.
OpenUrl CrossRef
36.
1. Marrie RA,
2. Wijnands JMA,
3. Kingwell E,
4. et al.
(2019) Higher health care use before a clinically isolated syndrome with or without subsequent MS. Mult Scler Relat Disord 35, 42–49.
OpenUrl
37.↵
1. Marrie RA,
2. Yu N,
3. Wei Y,
4. et al.
(2013) High rates of physician services utilization at least five years before multiple sclerosis diagnosis. Mult Scler 19, 1113–1119.
OpenUrl CrossRef PubMed
38.↵
1. Soh EML,
2. Neo S,
3. Saffari SE,
4. et al.
(2022) Longitudinal healthcare utilization and costs in Parkinson’s disease: pre-diagnosis to 9 years after. J Parkinsons Dis 12, 957–966.
OpenUrl
39.↵
1. de Thurah A,
2. Jensen H,
3. Maribo T,
4. et al.
(2023) Use of primary health care and radiological imaging preceding a diagnosis of rheumatoid arthritis: a nationwide cohort study in Denmark. Rheumatology Oxford 62, 2, 555–564.
OpenUrl
40.↵
1. Erickson BA,
2. Miller AC,
3. Warner HL,
4. et al.
(2022) Understanding the prodrome of necrotizing soft tissue infections of the genitalia (Fournier’s Gangrene) and the incidence, duration, and risk factors associated with potential missed opportunities for an earlier diagnosis: a population-based longitudinal study. J Urol 208, 1259–1267.
OpenUrl
41.↵
1. Bohlken J,
2. Schrag A,
3. Riedel-Heller S,
4. Kostev K
(2022) Identification of prodromal presentations of Parkinson’s disease among primary care outpatients in Germany. Neuroepidemiology 56, 41–49.
OpenUrl
42.↵
1. Price S,
2. Wiering B,
3. Mounce LTA,
4. et al.
(2023) Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: an exemplar applied to oesophagogastric cancer. Cancer Epidemiol 82, 102310.
OpenUrl

In this issue

Download PDF

Download PowerPoint

Email Article

Citation Tools

Keywords

More in this TOC Section

Show more Research

Cited By...

Intended for Healthcare Professionals

[1] 1.↵
Haroon M,
Gallagher P,
FitzGerald O
(2015) Diagnostic delay of more than 6 months contributes to poor radiographic and functional outcome in psoriatic arthritis. Ann Rheum Dis 74, 1045–1050.
OpenUrl Abstract/FREE Full Text

[2] Haroon M,

[3] Gallagher P,

[4] FitzGerald O

[5] 2.
Dubey D,
Kapotic M,
Freeman M,
et al.
(2016) Factors contributing to delay in diagnosis of Guillain-Barré syndrome and impact on clinical outcome. Muscle Nerve 53, 384–387.
OpenUrl

[6] Dubey D,

[7] Kapotic M,

[8] Freeman M,

[9] et al.

[10] 3.↵
Fuchs V,
Kurppa K,
Huhtala H,
et al.
(2018) Delayed celiac disease diagnosis predisposes to reduced quality of life and incremental use of health care services and medicines: a prospective nationwide study. United European Gastroenterol J 6, 567–575.
OpenUrl PubMed

[11] Fuchs V,

[12] Kurppa K,

[13] Huhtala H,

[14] et al.

[15] 4.↵
Hansen PL,
Hjertholm P,
Vedsted P
(2015) Increased diagnostic activity in general practice during the year preceding colorectal cancer diagnosis. Int J Cancer 137, 615–624.
OpenUrl CrossRef PubMed

[16] Hansen PL,

[17] Hjertholm P,

[18] Vedsted P

[19] 5.
Christensen KG,
Fenger-Grøn M,
Flarup KR,
Vedsted P
(2012) Use of general practice, diagnostic investigations and hospital services before and after cancer diagnosis — a population-based nationwide registry study of 127,000 incident adult cancer patients. BMC Health Serv Res 12, 224.
OpenUrl CrossRef PubMed

[20] Christensen KG,

[21] Fenger-Grøn M,

[22] Flarup KR,

[23] Vedsted P

[24] 6.
Zhou Y,
Abel GA,
Hamilton W,
et al.
(2020) Imaging activity possibly signalling missed diagnostic opportunities in bladder and kidney cancer: a longitudinal data-linkage study using primary care electronic health records. Cancer Epidemiol 66, 101703.
OpenUrl CrossRef PubMed

[25] Zhou Y,

[26] Abel GA,

[27] Hamilton W,

[28] et al.

[29] 7.↵
Renzi C,
Lyratzopoulos G,
Card T,
et al.
(2016) Do colorectal cancer patients diagnosed as an emergency differ from non-emergency patients in their consultation patterns and symptoms? A longitudinal data-linkage study in England. Br J Cancer 115, 866–875.
OpenUrl CrossRef PubMed

[30] Renzi C,

[31] Lyratzopoulos G,

[32] Card T,

[33] et al.

[34] 8.↵
White B,
Renzi C,
Rafiq M,
et al.
(2022) Does changing healthcare use signal opportunities for earlier detection of cancer? A review of studies using information from electronic patient records. Cancer Epidemiol 76, 102072.
OpenUrl CrossRef PubMed

[35] White B,

[36] Renzi C,

[37] Rafiq M,

[38] et al.

[39] 9.↵
Page MJ,
McKenzie JE,
Bossuyt PM,
et al.
(2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71.
OpenUrl FREE Full Text

[40] Page MJ,

[41] McKenzie JE,

[42] Bossuyt PM,

[43] et al.

[44] 10.↵
Schrag A,
Horsfall L,
Walters K,
et al.
(2015) Prediagnostic presentations of Parkinson’s disease in primary care: a case-control study. Lancet Neurol 14, 57–64.
OpenUrl CrossRef PubMed

[45] Schrag A,

[46] Horsfall L,

[47] Walters K,

[48] et al.

[49] 11.↵
Blackwell J,
Saxena S,
Jayasooriya N,
et al.
(2021) Prevalence and duration of gastrointestinal symptoms before diagnosis of inflammatory bowel disease and predictors of timely specialist review: a population-based study. J Crohns Colitis 15, 203–211.
OpenUrl CrossRef

[50] Blackwell J,

[51] Saxena S,

[52] Jayasooriya N,

[53] et al.

[54] 12.↵
Miller AC,
Arakkal AT,
Koeneman S,
et al.
(2021) Incidence, duration and risk factors associated with delayed and missed diagnostic opportunities related to tuberculosis: a population-based longitudinal study. BMJ Open 11, e045605.
OpenUrl Abstract/FREE Full Text

[55] Miller AC,

[56] Arakkal AT,

[57] Koeneman S,

[58] et al.

[59] 13.↵
Miller AC,
Koeneman SH,
Arakkal AT,
et al.
(2021) Incidence, duration, and risk factors associated with missed opportunities to diagnose herpes simplex encephalitis: a population-based longitudinal study. Open Forum Infect Dis 8, ofab400.
OpenUrl

[60] Miller AC,

[61] Koeneman SH,

[62] Arakkal AT,

[63] et al.

[64] 14.↵
Connected Papers
Find and explore academic papers, . No date. https://www.connectedpapers.com/about (accessed 19 Apr 2023).

[65] Connected Papers

[66] 15.↵
Aromataris E,
Munn Z
Moola S,
Munn Z,
Tufanaru C,
et al.
(2020) Chapter 7: Systematic reviews of etiology and risk. in JBI Manual for Evidence Synthesis, eds Aromataris E, Munn Z (JBI, Australia), 217–269.

[67] Aromataris E,

[68] Munn Z

[69] Moola S,

[70] Munn Z,

[71] Tufanaru C,

[72] et al.

[73] 16.↵
Bamford C,
Eccles M,
Steen N,
Robinson L
(2007) Can primary care record review facilitate earlier diagnosis of dementia? Fam Pract 24, 108–116.
OpenUrl CrossRef PubMed

[74] Bamford C,

[75] Eccles M,

[76] Steen N,

[77] Robinson L

[78] 17.↵
Nørgaard HCB,
Pedersen HS,
Fenger-Grøn M,
et al.
(2016) Increased use of primary care during 6 years of prodromal schizophrenia. Acta Psychiatr Scand 134, 225–233.
OpenUrl

[79] Nørgaard HCB,

[80] Pedersen HS,

[81] Fenger-Grøn M,

[82] et al.

[83] 18.↵
Miller AC,
Cavanaugh JE,
Arakkal AT,
et al.
(2023) A comprehensive framework to estimate the frequency, duration and risk factors for diagnostic delays using simulation-based methods. BMC Med Inform Decis Mak 23, 68.
OpenUrl

[84] Miller AC,

[85] Cavanaugh JE,

[86] Arakkal AT,

[87] et al.

[88] 19.↵
Blackwell J,
Saxena S,
Petersen I,
et al.
(2021) Depression in individuals who subsequently develop inflammatory bowel disease: a population-based nested case–control study. Gut 70, 9, 1642–1648.
OpenUrl Abstract/FREE Full Text

[89] Blackwell J,

[90] Saxena S,

[91] Petersen I,

[92] et al.

[93] 20.
Townson J,
Cannings-John R,
Francis N,
et al.
(2019) Presentation to primary care during the prodrome of type 1 diabetes in childhood: a case-control study using record data linkage. Pediatr Diabetes 20, 330–338.
OpenUrl CrossRef PubMed

[94] Townson J,

[95] Cannings-John R,

[96] Francis N,

[97] et al.

[98] 21.
Sullivan SA,
Hamilton W,
Tilling K,
et al.
(2018) Association of primary care consultation patterns with early signs and symptoms of psychosis. JAMA Netw Open 1, e185174.
OpenUrl

[99] Sullivan SA,

[100] Hamilton W,

[101] Tilling K,

[102] et al.

[103] 22.
Muller S,
Hider S,
Machin A,
et al.
(2019) Searching for a prodrome for rheumatoid arthritis in the primary care record: a case-control study in the clinical practice research datalink. Semin Arthritis Rheum 48, 815–820.
OpenUrl

[104] Muller S,

[105] Hider S,

[106] Machin A,

[107] et al.

[108] 23.↵
Disanto G,
Zecca C,
MacLachlan S,
et al.
(2018) Prodromal symptoms of multiple sclerosis in primary care. Ann Neurol 83, 1162–1173.
OpenUrl CrossRef

[109] Disanto G,

[110] Zecca C,

[111] MacLachlan S,

[112] et al.

[113] 24.
Lee JJ,
Thompson MJ,
Usher-Smith JA,
et al.
(2018) Opportunities for earlier diagnosis of type 1 diabetes in children: a case-control study using routinely collected primary care records. Prim Care Diabetes 12, 254–264.
OpenUrl

[114] Lee JJ,

[115] Thompson MJ,

[116] Usher-Smith JA,

[117] et al.

[118] 25.
Pearce FA,
Lanyon PC,
Watts RA,
et al.
(2018) Novel insights into the aetiology of granulomatosis with polyangiitis — a case–control study using the Clinical Practice Research Datalink. Rheumatology 57, 1002–1010.
OpenUrl

[119] Pearce FA,

[120] Lanyon PC,

[121] Watts RA,

[122] et al.

[123] 26.↵
Pearce FA,
Hubbard RB,
Grainge MJ,
et al.
(2018) Can granulomatosis with polyangiitis be diagnosed earlier in primary care? A case–control study. QJM 111, 39–45.
OpenUrl CrossRef PubMed

[124] Pearce FA,

[125] Hubbard RB,

[126] Grainge MJ,

[127] et al.

[128] 27.↵
Card TR,
Siffledeen J,
West J,
Fleming KM
(2013) An excess of prior irritable bowel syndrome diagnoses or treatments in celiac disease: evidence of diagnostic delay. Scand J Gastroenterol 48, 801–807.
OpenUrl CrossRef PubMed

[129] Card TR,

[130] Siffledeen J,

[131] West J,

[132] Fleming KM

[133] 28.↵
Card TR,
Siffledeen J,
Fleming KM
(2014) Are IBD patients more likely to have a prior diagnosis of irritable bowel syndrome? Report of a case-control study in the General Practice Research Database. United European Gastroenterol J 2, 505–512.
OpenUrl CrossRef PubMed

[134] Card TR,

[135] Siffledeen J,

[136] Fleming KM

[137] 29.↵
Ilkjær F,
Rasmussen LD,
Martin-Iguacel R,
et al.
(2019) How to identify common variable immunodeficiency patients earlier: general practice patterns. J Clin Immunol 39, 641–652.
OpenUrl

[138] Ilkjær F,

[139] Rasmussen LD,

[140] Martin-Iguacel R,

[141] et al.

[142] 30.↵
Hastrup LH,
Simonsen E,
Ibsen R,
et al.
(2020) Societal costs of schizophrenia in Denmark: a nationwide matched controlled study of patients and spouses before and after initial diagnosis. Schizophr Bull 46, 68–77.
OpenUrl PubMed

[143] Hastrup LH,

[144] Simonsen E,

[145] Ibsen R,

[146] et al.

[147] 31.↵
Gasperi C,
Hapfelmeier A,
Daltrozzo T,
et al.
(2021) Systematic assessment of medical diagnoses preceding the first diagnosis of multiple sclerosis. Neurology 96, e2977–e2988.
OpenUrl CrossRef

[148] Gasperi C,

[149] Hapfelmeier A,

[150] Daltrozzo T,

[151] et al.

[152] 32.↵
Johnson KM,
Khakban A,
Bryan S,
et al.
(2020) Healthcare system encounters before COPD diagnosis: a registry-based longitudinal cohort study. Thorax 75, 108–115.
OpenUrl Abstract/FREE Full Text

[153] Johnson KM,

[154] Khakban A,

[155] Bryan S,

[156] et al.

[157] 33.↵
Yusuf FLA,
Wijnands JMA,
Kingwell E,
et al.
(2021) Fatigue, sleep disorders, anaemia and pain in the multiple sclerosis prodrome. Mult Scler J 27, 290–302.
OpenUrl

[158] Yusuf FLA,

[159] Wijnands JMA,

[160] Kingwell E,

[161] et al.

[162] 34.
Wijnands JM,
Zhu F,
Kingwell E,
et al.
(2019) Five years before multiple sclerosis onset: phenotyping the prodrome. Mult Scler 25, 1092–1101.
OpenUrl

[163] Wijnands JM,

[164] Zhu F,

[165] Kingwell E,

[166] et al.

[167] 35.
Wijnands JMA,
Kingwell E,
Zhu F,
et al.
(2017) Health-care use before a first demyelinating event suggestive of a multiple sclerosis prodrome: a matched cohort study. Lancet Neurol 16, 445–451.
OpenUrl CrossRef

[168] Wijnands JMA,

[169] Kingwell E,

[170] Zhu F,

[171] et al.

[172] 36.
Marrie RA,
Wijnands JMA,
Kingwell E,
et al.
(2019) Higher health care use before a clinically isolated syndrome with or without subsequent MS. Mult Scler Relat Disord 35, 42–49.
OpenUrl

[173] Marrie RA,

[174] Wijnands JMA,

[175] Kingwell E,

[176] et al.

[177] 37.↵
Marrie RA,
Yu N,
Wei Y,
et al.
(2013) High rates of physician services utilization at least five years before multiple sclerosis diagnosis. Mult Scler 19, 1113–1119.
OpenUrl CrossRef PubMed

[178] Marrie RA,

[179] Yu N,

[180] Wei Y,

[181] et al.

[182] 38.↵
Soh EML,
Neo S,
Saffari SE,
et al.
(2022) Longitudinal healthcare utilization and costs in Parkinson’s disease: pre-diagnosis to 9 years after. J Parkinsons Dis 12, 957–966.
OpenUrl

[183] Soh EML,

[184] Neo S,

[185] Saffari SE,

[186] et al.

[187] 39.↵
de Thurah A,
Jensen H,
Maribo T,
et al.
(2023) Use of primary health care and radiological imaging preceding a diagnosis of rheumatoid arthritis: a nationwide cohort study in Denmark. Rheumatology Oxford 62, 2, 555–564.
OpenUrl

[188] de Thurah A,

[189] Jensen H,

[190] Maribo T,

[191] et al.

[192] 40.↵
Erickson BA,
Miller AC,
Warner HL,
et al.
(2022) Understanding the prodrome of necrotizing soft tissue infections of the genitalia (Fournier’s Gangrene) and the incidence, duration, and risk factors associated with potential missed opportunities for an earlier diagnosis: a population-based longitudinal study. J Urol 208, 1259–1267.
OpenUrl

[193] Erickson BA,

[194] Miller AC,

[195] Warner HL,

[196] et al.

[197] 41.↵
Bohlken J,
Schrag A,
Riedel-Heller S,
Kostev K
(2022) Identification of prodromal presentations of Parkinson’s disease among primary care outpatients in Germany. Neuroepidemiology 56, 41–49.
OpenUrl

[198] Bohlken J,

[199] Schrag A,

[200] Riedel-Heller S,

[201] Kostev K

[202] 42.↵
Price S,
Wiering B,
Mounce LTA,
et al.
(2023) Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: an exemplar applied to oesophagogastric cancer. Cancer Epidemiol 82, 102310.
OpenUrl

[203] Price S,

[204] Wiering B,

[205] Mounce LTA,

[206] et al.

Main menu

User menu

Search