Factors associated with patient, and diagnostic delays in Chinese TB patients: a systematic review and meta-analysis

According to the Chinese Ministry of Health (MOHC) [24], “patient delay” is defined as a time interval of more than two weeks between the onset of TB symptoms and the patient’s first presentation to a health facility. The Ministry also defines “diagnostic delay” as a time interval of more than two weeks between a patient’s first visit to a health facility and the receipt of TB diagnosis.

The review was prepared following standard procedures of the Cochrane Collaboration [25] and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and checklist [26] (see Additional file 1). To identify eligible studies on factors associated with TB patient and diagnostic delays published up to November 2012, three major databases, Medline/PubMed, EMBASE and CNKI (China National Knowledge Infrastructure), were searched. We used a mixture of free text and index terms to maximize retrieval of potentially relevant studies. The following terms were used for the PubMed search: “pulmonary tuberculosis and diagnostic delay in China”, “pulmonary tuberculosis and health system delay in China”, “pulmonary tuberculosis and detection delay in China”, “pulmonary tuberculosis and patient delay in China”, “pulmonary tuberculosis and health provider delay in China”, “pulmonary tuberculosis and doctor delay in China”, “pulmonary tuberculosis and identification delay in China”. Medline subject search terms included: “tuberculosis, pulmonary”[Mesh]) AND “diagnosis”[Mesh]) AND “China”[Mesh] or “tuberculosis, pulmonary”[Mesh]) AND “patient acceptance of health care”[Mesh]) AND “China”[Mesh]. Searches in Medline/Pubmed were limited to full-text, humans, and English. The search terms used in EMBASE were ‘lung tuberculosis’/exp AND ‘early diagnosis’/exp AND ‘risk factor’/exp, ‘lung tuberculosis’/exp AND ‘patient’/exp AND ‘delayed diagnosis’/exp, ‘lung tuberculosis’/exp AND ‘delayed diagnosis’/exp AND ‘risk factor’/exp, ‘lung tuberculosis’/exp AND ‘patient’/exp AND ‘patient referral’/exp AND ‘risk factor’/exp, ‘lung tuberculosis’/exp AND ‘health care facility’/exp AND ‘patient referral’/exp AND ‘risk factor’/exp. CNKI is a key national e-publishing project that can be used for searching articles published in 8,200 Chinese journals. It was utilized to search for articles in Chinese. The terms and concepts searched included “patient delay”, “diagnostic delay”, “risk factors for patient delay” and “risk factors for diagnostic delay”. We sought unpublished data from the gray literature through Google and Google Scholar searches. We also hand-searched reference lists of identified articles. Two reviewers (JE and YL) conducted the literature searches.

When there was evidence of multiple publications of the same study over time, only the article with a full report was included. Reports on TB suspects, extra-pulmonary TB, HIV infected TB patients, qualitative studies (data collected by qualitative research methods) and studies that focus on PTB patients in Hong Kong, Macao and Taiwan were excluded.

Application of inclusion and exclusion criteria to identified studies was done by two reviewers (DL and YB). Both reviewers independently screened titles and abstracts of identified studies to assess their eligibility for inclusion in the review, using an eligibility form based on the inclusion criteria. Where there were disagreements regarding eligibility of studies, all reviewers participated in the decision about inclusion by discussion and consensus.

We assessed the quality of case-control and cohort studies using the Newcastle-Ottawa Scale [27]. For case-control studies, we assessed the adequacy of case and control definition, representativeness of the cases, whether controls were derived from the same population as cases, comparability of cases and controls on the basis of design and analyses, ascertainment of exposure and non-response rates. For cohort studies, we assessed representativeness of the exposed cohort in the study setting, selection of non-exposed cohort, ascertainment of exposure, evidence that participants do not have outcome of interest at recruitment into the study, comparability of cohorts on the basis of design and analyses, outcome assessment and adequacy of follow-up [27]. After assessing the quality of each included study on the basis of these criteria, we assigned a composite quality score ranging from 0 (low) to 8 (high).

For cross-sectional studies, we used the guideline for critical appraisal of cross-sectional studies developed by the National Collaborating Center for Environmental Health [28], which was adapted from a combination of items from the Newcastle-Ottawa Scale [27], the Critical Appraisal Skills Program [29], Critical Appraisals by Elwood [30], and Aschengrau and Seage III [31]. Specifically, we assessed representativeness of the study participants, methods for ascertaining exposure; comparability of exposure groups (including unexposed) in terms of age, sex, socioeconomic status and response bias; determination and validation of outcomes; internal validity; and how confounding factors were assessed and addressed. After reviewing the quality of each included study on the basis of these criteria, we assigned a composite quality score that ranged from 0 (low) to 4 (high). Two reviewers (YL and JE) assessed study quality and reached a consensus grade for each included study.

Data from eligible studies were independently abstracted by two reviewers (BY and KL). Differences were resolved by consensus among all reviewers. Data extracted from each study included name of first author, year of publication, type of study design, place of study, type of participants (newly diagnosed/retreatment and smear positive/negative pulmonary TB patients), residence of participants (rural/urban), sampling size, outcome (patient delay/diagnostic delay), information used for comparing analyses (numbers of participants with/without exposure variables) and the main results were extracted for all included studies.

Heterogeneity was evaluated using the Q test [32] and the I-squared statistic (I² = 100% × (Q-df)/Q) [33]. For the Q test, a P-value of 0.10 or less was considered statistically significant, indicating marked heterogeneity among studies. Where the P-value was ≤0.10, we calculated I², and studies with heterogeneity levels of ≤50% were deemed acceptable. Where heterogeneity was more than 50%, we conducted subgroup analysis to explore possible reasons for the heterogeneity. For subgroup analyses, the heterogeneity within groups was also tested, using the same statistical methods.

The first step of the data analysis involved a qualitative synthesis aimed at summarizing, comparing and contrasting the extracted data. Meta-analyses were then conducted to assess the association among patient delay, diagnostic delay, and gender, age, education, residence, healthcare seeking behaviors (TB health facility for first consultation or seeking care from Traditional Chinese Medicine (TMC) providers) and lifestyle factors (smoking and alcohol use). Only studies that adopted the definitions of patient and diagnostic delays proposed by the Office of TB Control in the MOHC [24] were included in the quantitative meta-analysis. We calculated pooled odds ratio (OR) separately for each factor, using RevMan 5.2 [25]. As noted earlier, we used the fixed effect model where the level of heterogeneity was acceptable (that is, P = >0.10, or P = ≤0.10, but I² = ≤50%), and the random effects model was used where there was significant heterogeneity (that is, P = ≤0.10, but I² = >50%). We assessed sources of heterogeneity by conducting sub-group analyses.

Figure 1 presents an illustration of the search output. Altogether, the initial search yielded 434 potentially relevant articles. Only 29 studies [14, 20‐23, 34‐57] met the criteria for inclusion in the review which involved 38,947 pulmonary TB patients from 17 provinces (Table 1). The included studies comprised 27 cross-sectional studies, 1 cohort, and 1 case-control study. A majority of the studies (16 out of 29) included newly diagnosed smear-positive PTB patients. The studies applied different definitions of patient and diagnostic delays (Figure 2). However, a majority (23 of 29 studies) adopted the definitions of MOHC (Figure 2A). All 29 studies were included in qualitative data synthesis. However, only 13 cross-sectional studies [14, 20‐23, 36, 38‐42, 44] with 23,917 TB patients had sufficient data to qualify for inclusion in the meta-analysis.

The quality assessment of studies indicated that eight cross-sectional studies [14, 34, 35, 46, 48‐51] had a high quality score of 4 (out of 4). Eighteen cross-sectional studies had a score of 3, mainly because they did not control for confounding factors (Table 2). The cohort study [52] and case-control study [53] had high quality scores of 7 and 8, respectively, (out of 8) (Table 3).

This review did not identify randomized controlled trials, which was expected since experiments that expose people of diverse backgrounds to TB in order to assess how they seek diagnosis and treatment would be unethical. Consequently, the review included mostly cross-sectional, one case-control and one cohort study. These studies have a number of inherent limitations that have the potential to introduce bias in the results of this review. For example, the included cross-sectional studies had low comparability and were fraught with numerous confounders. Although the studies controlled for these confounders using logistic regressions, their residual effect might have introduced some degree of bias to the review.

Most of the available studies had different definitions of patient and diagnostic delays, as well as multiple classification of socio-demographic variables, including age, income, education and type of health facility consulted by TB patients. This limits comparability of the studies, and makes pooled analyses of results difficult. The meta-analysis was restricted to studies that adopted the definition of patient delay proposed by the Office of TB Control in the MOHC (that is, a time interval of more than two weeks between the onset of TB symptoms and the patient’s first presentation to a health facility). We acknowledge the limitation in accurately determining actual onset of TB symptoms by patients, given the generally low knowledge of TB among the public [2].

As findings of this review show, rural residence is an important determinant of delay in seeking diagnosis by TB patients in China. At a time when there is global emphasis on the use of community health workers for health promotion and disease prevention at the community level, the need to resurrect the use of health workers at village and township levels to fight TB in rural China cannot be over-emphasized [67]. They are well-placed to provide individualized and community-based education and promotion to dispel ignorance and to challenge TB stigma. They can also be effective in referring suspected cases to appropriate sources of diagnosis and treatment, and can serve as key liaisons between communities and the national TB control program.

To address the negative impact of high out-of-pocket payments, it is important to integrate TB control efforts into the overall health system, especially health insurance schemes. The National TB Program (NTP) in China has recently started to work with the Ministry of Civil Affairs that is responsible for medical financing assistance for the poor to ensure that TB patients, particularly MDR-TB patients, can be diagnosed in a timely fashion and treated successfully, regardless of their ability to pay [64]. It is also important for the China CDC to develop measures to ensure that clinical guidelines for TB diagnosis and case management are effectively implemented not only in TB dispensaries and TB-designated hospitals, but also in the general hospitals of China. Training of health workers in effective implementation of established guidelines for TB diagnosis and treatment should also be seen as an important component of the arsenal available for addressing the high burden of TB in China, as is the development of monitoring and evaluation systems to assess the performance of these service providers.