We systematically reviewed studies reported from LMICs to summarize delays in diagnosis of PTB patients and to identify factors contributing to high extents of delay. We thought such a specific study on delay in diagnosis of PTB patients would be more beneficial from programmatic and interventional perspectives. Meta-analysis principles were applied to combine numeric data on delays and affecting factors. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [15] as a standard guideline.

Search strategy

Various electronic databases were searched to retrieve relevant published studies using Boolean searching technique [16]. We used ‘AND’ and ‘OR’to connect key search terms to retrieve studies from Pubmed. The search terms we used include pulmonary tuberculosis, diagnosis delay, patient delay, health system delay, provider delay, doctor delay, delayed consultation, health care seeking, and health care seeking behavior (Table 1). In addition, we searched other web-based sources including Springer link, Hinari, and Google scholar, though not standard sources, to retrieve studies that are not indexed in Pubmed.

Table 1 Pudmed search strategies Full size table

We searched each source twice. The first author (FG) conducted the primary searching of studies from sources listed above and then the co-author (NA) independently and blindly conducted it again using similar searching technique, databases and terms. All the potential studies identified by both authors were set for screening and selection process. We contacted 5 authors via email for unpublished studies and raw data but none responded to our requests.

Inclusion/exclusion criteria

We included studies that were reported in English, conducted in low and middle income countries, published between 2007 and 2015 (era of Stop TB strategy), with any observational study design (cross-sectional, case-control, or cohort studies), and those which fully or partially measured diagnosis delays (patient delay/health system delay/total diagnosis delay). With respect to the study participants, we included studies on pulmonary TB patients who sought healthcare themselves and above 15 years old regardless of smear type (positive/negative/unknown) and treatment category (new/retreatment). Studies done on PTB and EPTB patients were considered when data were presented for PTB patients separately. However, we excluded studies that were on presumptive TB cases (formerly called ‘suspects’) and special groups (e.g. HIV, MDR), qualitative in design, and community based studies which applied active case-finding strategies.

Further inclusion criteria were used for Meta-analysis from eligible studies for systematic review. These included studies that categorized patient delay using 15, 21 or 30 days as cut-off points and reported cross tabulations and/or odds ratio/relative risk of explanatory variables.

Selection of studies

Eligible studies were selected using the pre-specified inclusion/exclusion criteria. Initially, the studies were searched and potential articles were identified using the title and then abstract by two reviewers (FG and NA) independently. Following identification of potential studies, FG made the final selection through thorough review of full articles including the study design, participants, outcome variable studied (delay), and publication year. Co-authors (NA and BM) closely supervised the selection process.

Quality assessment

All included studies were assessed for their scientific quality usingquality assessment tools developed by the National Collaborating Center for Environmental Health [17] and National Institute of Health (NIH) National Heart, Lung and Blood Institute [18].

The quality of each study was assessed using 4 parameters: 1) defined study population, 2) representativeness of participants and low non-response rate, 3) comparability of analysis groups and outcome ascertainment, and 4) adjusted analysis to control confounding. Each parameter was rated as 1 if ‘Yes’ and 0 if ‘No’. Then the scores were added and the quality of each studywas leveled as good (if 3 or 4 ‘yes’s), fair (2 ‘yes’s) and poor (if 0 or 1 yes). Fair and good quality studies were considered as satisfying quality (Table 2). The quality of all included studies was assessed by first author (FG), followed by immediate and independent crosschecking by two co-authors (NA and BM). Discrepancies were solved through mutual understanding.

Table 2 quality assessment result of the studies included in the systematic review and meta-analysis Full size table

Data extraction

Excel spreadsheet was used to extract available data on the name of first author, country where the study was conducted, publication year, type of health-care facility (study setting), study design, sample size, and median and/or mean delays (patient, health system and total) in days. Data from a study by Gosoniu et al. that involved 3 countries (Bangladesh, India and Malawi) was extracted separately for each country [19]. Delays reported in weeks were transformed into days by a multiple of 7 (Tables 3 and 4). The predictor variables that have statistically significant association with patient, health system and total diagnosis delay were also extracted (Table 5).

Table 3 The median delay (days) in diagnosis of pulmonary tuberculosis in Sub-Saharan Africa, 2007 to 2015 (n = 19) Full size table

Table 4 The median delay (days) in diagnosis of pulmonary tuberculosis in low and middle income countries other than Sub-Saharan Africa, 2007 to 2015 (n = 22) Full size table

Table 5 Summary of risk factors for delay in diagnosis in low and middle income countries, 2007 to 2015 Full size table

Further data were extracted on the event (delay) among exposure categories and/or odds ratio for meta-analysis. Health system and total delays were not considered for meta-analysis because of lack of clear cutoff values. The studies we reviewed did not use standard cutoff value to categorize patient delays. Therefore, we used the WHO recommended cutoff points (15 and 21 days) [20] and the commonly used 30 days by numerous studies. There was also considerable variability in the way studies categorized explanatory variables, thus variables categorized in the same way were considered for meta-analysis. The final explanatory variables we came up with, therefore, were sex, residence, educational status, first care seeking from informal healthcare provider, and HIV status.

Definition of terms

The definitions of terms in included studies were critically reviewed. Different studies used different terms to describe patient, health system, and diagnosis delays. For instance, some studies described patient delay as TB test delay or TB test seeking delay [21] and patients’ application interval [22]. Health system delay is also referred to as doctor delay or provider delay [22,23,24]. Most studies used the term ‘total delay’ as the time until the diagnosis while some until treatment. In such a case, we took the delay until diagnosis [25,26,27,28,29]. Accordingly, delay terms have been defined as follows:

Patient delay

The time interval between the onsets of patient recognized PTB symptom(s) recognize and the patient’s first consultation to a healthcare provider.

Health system delay

The time interval between the patient’s first consultation with a health care provider and the date of diagnosis.

Diagnosis/Total delay

The time interval between the onset of PTB symptom(s) and the date of diagnosis. Alternatively, the sum of patient delay and health system delay (Fig. 1).

Fig. 1 Illustration of patient, health system and total delay Full size image

Data analysis

Systematic review

The data organized on excel spreadsheet were exported to SPSS version 21 for analysis. The median delay (in days) was summarized using median, box plots, inter quartile ranges (25th and 75th percentile), and 95% confidence interval and ranges. Since the distribution of median delays was skewed, the pooled median delay was taken rather than the mean to compare delays between Sub-Saharan Africa and other LMICs. The median differences were estimated using the non-parametric Mann-Whitney test which was used to test the statistical difference of pooled median delays between Sub-Saharan Africa and other LMICs. In addition, the 95% confidence interval was calculated using bootstrap method.

Meta-analysis

The data for each of the six predictor variables were entered into the Comprehensive Meta-analysis software version 2. The effect size measurement computed were odds ratio (pooled and individual) and prevalence of patient delay at 30 days cutoff point. Forest plots were drawn to visualize effect size (odds ratio with 95%CI). Both fixed and random effect models were used for pooled analysis based on the heterogeneity level of studies included. Heterogeneity was evaluated using Cochrane Q and I2 tests as well as Q/df (degree of freedom) ratio. Cochrane Q test (p = 0.1), Q/df = 1and I2 = 50% were considered as cutoff points to mark heterogeneity and to select the effect model for combined analysis.

As initial test of pooled analysis, we used fixed effect model to combine individual effect sizes. If there was no heterogeneity observed during initial test using fixed effect model (i.e. P > 0.1 and I2 ≤ 50% and Q/df ≤ 1or p ≤ 0.1 but I2 ≤ 50%), we used fixed model as final model to estimate combined effect sizes. However, if there was significant heterogeneity observed (i.e. p ≤ 0.1 and I2 > 50%, or p > 0.1 but Q/df > 1), we used random effect model. The sources of heterogeneity were assessed by subgroup analysis based on clinical heterogeneity of the studies mainly using smear status of participants. Funnel plot and Egger’s regression test were used for checking graphic and statistical publication biases, respectively. Sensitivity test was done to check the effect of each study on combined effect size.