Exploring the Role of the Private Sector in Tuberculosis Detection and Management in Lima, Peru: A Mixed-Methods Patient Pathway Analysis.

In Latin America, little is known about the involvement of private health-care providers in tuberculosis (TB) detection and management. We sought to gain a better understanding of current and potential roles of the private sector in delivering TB services in Peru. We conducted a mixed-methods study in North Lima, Peru. The quantitative component comprised a patient pathway analysis assessing the alignment of TB services with patient care-seeking behavior. The qualitative component comprised in-depth interviews with 18 private health-care providers and 5 key informants. We estimated that 77% of patients sought care initially at a facility with TB diagnostic capacity and 59% at a facility with TB treatment capacity. Among private facilities, 43% offered smear microscopy, 13% offered radiography, and none provided TB treatment. Among public-sector facilities, 100% offered smear microscopy, 26% offered radiography, and 99% provided TB treatment. Private providers believed they offered shorter wait times and a faster diagnosis, but they struggled with a lack of referral systems and communication with the public sector. Nonrecognition of private-sector tests by the public sector led to duplicate testing of referred patients. Although expressing willingness to collaborate with public-sector programs for diagnosis and referral, private providers had limited interest in treating TB. This study highlights the role of private providers in Peru as an entry point for TB care. Public-private collaboration is necessary to harness the potential of the private sector as an ally for early diagnosis.

Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru.

Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. In the triage cohort (n = 387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n = 191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.

Biomarker-confirmed suboptimal adherence to isoniazid preventive therapy among children with HIV in western Kenya.

OBJECTIVES: The aim of this study was to assess the level and correlates of biomarker-confirmed adherence to isoniazid (INH) preventive therapy (IPT) among children with HIV (CLHIV). DESIGN: This prospective cohort study assessed adherence among CLHIV on IPT in public sector HIV clinics from 2019 through 2020. METHODS: Adherence was assessed by pill counts or caregiver or self-reports, and urine biomarkers (in-house dipstick and Isoscreen). Both urine biomarker tests detect INH metabolites within 48 h of ingestion. Consistent adherence was defined as having positive results on either biomarker at all visits. Correlates of biomarker-confirmed nonadherence at each visit were evaluated using generalized estimating equations. The in-house dipstick was validated using Isoscreen as the reference. RESULTS: Among 97 CLHIV on IPT with adherence assessments, median age was 10 years (IQR 7-13). All were on ART at IPT initiation (median duration 46 months [IQR 4-89]); 81% were virally suppressed (<1000 copies/ml). At all visits, 59% ( n  = 57) of CLHIV reported taking at least 80% of their doses, while 39% ( n  = 38) had biomarker-confirmed adherence. Viral nonsuppression (adjusted risk ratio [aRR] = 1.65; 95% confidence interval [95% CI] 1.09-2.49) and the sixth month of IPT use (aRR = 2.49; 95% CI 1.34-4.65) were independent correlates of biomarker-confirmed nonadherence at each visit. Sensitivity and specificity of the in-house dipstick were 98.1% ( 94.7 - 99.6%) and 94.7% ( 88.1 - 98.3%) , respectively, versus Isoscreen. CONCLUSION: Biomarker-confirmed adherence to IPT was sub-optimal and was associated with viral nonsuppression and duration of IPT. Urine dipstick testing may be useful in assessing adherence to IPT in clinical care.

Utility of Mycobacterium tuberculosis genome sequencing snapshots to assess transmission dynamics over time.

We explored the utility of brief Mycobacterium tuberculosis whole-genome sequencing (WGS) "snapshots" at a sentinel site within Lima, Peru for evaluating local transmission dynamics over time. Within a 17 km2 area, 15/70 (21%) isolates with WGS collected during 2011-2012 and 22/81 (27%) collected during 2020-2021 were clustered (p = 0.414), and additional isolates clustered with those from outside the area. Isolates from the later period were disproportionately related to large historic clusters in Lima from the earlier period. WGS snapshots at a sentinel site may not be useful for monitoring transmission, but monitoring the persistence of large transmission clusters might be.

Exploring the role of the private sector in tuberculosis detection and management in Lima, Peru: a mixed-methods patient pathway analysis.

In Latin America, little is known about the involvement of private healthcare providers in TB detection and management. We sought to gain a better understanding of current and potential roles of the private sector in delivering TB services in Peru. We conducted a mixed-methods study in Lima, Peru. The quantitative component comprised a patient pathway analysis assessing the alignment of TB services with patient care-seeking behavior. The qualitative component comprised in-depth interviews with 18 private healthcare providers and 5 key informants. We estimated that 77% of patients initially sought care at a facility with TB diagnostic capacity and 59% at a facility with TB treatment capacity. The lack of TB services at initial care-seeking location was driven by the 41% of patients estimated to seek care first at a private facility. Among private facilities, 43% offered smear microscopy, 13% offered radiography, and none provided TB treatment. Among public sector facilities, 100% offered smear microscopy, 26% offered radiography, and 99% provided TB treatment. Interviews revealed that private providers believed that they offered shorter wait times and a quicker diagnosis, but they struggled with a lack of follow-up systems and communication barriers with the public sector. While expressing willingness to collaborate with public sector programs for diagnosis and referral, private providers had limited interest in treating TB. This study highlights the role of private providers in Peru as an entry point for TB care. Public-private collaboration is necessary to harness the potential of the private sector as an ally for early diagnosis.

Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru.

Introduction: Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. Methods: We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. Results: In the triage cohort (n=387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n=191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. Conclusions: qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.

Understanding barriers to tuberculosis diagnosis and treatment completion in a low-resource setting: A mixed-methods study in the Kingdom of Lesotho.

BACKGROUND: Lesotho is one of the 30 countries with the highest tuberculosis incidence rates in the world, estimated at 650 per 100,000 population. Tuberculosis case detection is extremely low, particularly with the rapid spread of COVID-19, dropping from an estimated 51% in 2020 to 33% in 2021. The aim of this study is to understand the barriers to tuberculosis diagnosis and treatment completion. METHODS: We used a convergent mixed methods study design. We collected data on the number of clients reporting symptoms upon tuberculosis screening, their sputum test results, the number of clients diagnosed, and the number of clients who started treatment from one district hospital and one health center in Berea district, Lesotho. We conducted in-depth interviews and focus group discussions with 53 health workers and patients. We used a content analysis approach to analyze qualitative data and integrated quantitative and qualitative findings in a joint display. FINDINGS: During March-August, 2019, 218 clients at the hospital and 292 clients at the health center reported tuberculosis symptoms. The full diagnostic testing process was completed for 66% of clients at the hospital and 68% at the health center. Among clients who initiated tuberculosis treatment, 68% (61/90) at the hospital and 74% (32/43) at the health center completed treatment. The main barriers to testing and treatment completion were challenges at sample collection, lack of decentralized diagnostic services, and socioeconomic factors such as food insecurity and high patient movement to search for jobs. CONCLUSIONS: Tuberculosis diagnosis could be improved through the effective decentralization of laboratory services at the health facility level, and treatment completion could be improved by providing food and other forms of social support to patients.

The Impact of COVID-19 on Tuberculosis Program Performance in the Kingdom of Lesotho.

BACKGROUND: As tuberculosis (TB) is an airborne disease requiring multi-month therapy, systems of TB detection and care were profoundly impacted by the COVID-19 pandemic. The worsening economic situation, including income, food, and housing insecurity, impacted the social conditions in which TB-already a leading killer in resource-limited settings-thrives. This study assesses the impact of COVID-19 on TB detection and treatment in Lesotho. METHODS: We used routine program data from 78 health facilities in Lesotho. We created time series models from July 2018 to March 2021 to quantify COVID-19-related disruptions to TB program indicators: outpatient visits; presumptive, diagnosed, treated, and HIV co-infected cases; and treatment outcomes including successful (cured and completed) and unsuccessful (death and treatment outcome unknown). RESULTS: We observed a significant decline in cumulative outpatient visits (-37.4%, 95% prediction interval [PI]: -40.1%, -28.7%) and new TB cases diagnosed (-38.7%, 95%PI: -47.2%, -28.4%) during the pandemic, as well as TB-HIV co-infections (-67.0%, 95%PI: -72.6%, -60.0%). However, we observed no difference in treatment success (-2.1%, 95%PI: -17.0%, 15.8%). CONCLUSIONS: TB case detection in Lesotho fell during the COVID-19 pandemic, likely related to the uptake of overall health services. However, treatment success rates did not change, indicating a strong health system and the success of local strategies to maintain treatment programs.

Sensitivity of Various Case Detection Algorithms for Community-based Tuberculosis Screening.

Using data from 388 people diagnosed with tuberculosis through a community-based screening program in Lima, Peru, we estimated that cough screening followed by sputum smear microscopy would have detected only 23% of cases found using an algorithm of radiographic screening followed by rapid nucleic acid amplification testing and clinical evaluation.

Tuberculosis care models for children and adolescents: a scoping review.

Objective: To map which tuberculosis care models are best suited for children and adolescents. Methods: We conducted a scoping review to assess the impact of decentralized, integrated and family-centred care on child and adolescent tuberculosis-related outcomes, describe approaches for these care models and identify key knowledge gaps. We searched seven literature databases on 5 February 2021 (updated 16 February 2022), searched the references of 18 published reviews and requested data from ongoing studies. We included studies from countries with a high tuberculosis burden that used a care model of interest and reported tuberculosis diagnostic, treatment or prevention outcomes for an age group < 20 years old. Findings: We identified 28 studies with a comparator group for the impact assessment and added 19 non-comparative studies to a qualitative analysis of care delivery approaches. Approaches included strengthening capacity in primary-level facilities, providing services in communities, screening for tuberculosis in other health services, co-locating tuberculosis and human immunodeficiency virus treatment, offering a choice of treatment location and providing social or economic support. Strengthening both decentralized diagnostic services and community linkages led to one-to-sevenfold increases in case detection across nine studies and improved prevention outcomes. We identified only five comparative studies on integrated or family-centred care, but 11 non-comparative studies reported successful treatment outcomes for at least 71% of children and adolescents. Conclusion: Strengthening decentralized services in facilities and communities can improve tuberculosis outcomes for children and adolescents. Further research is needed to identify optimal integrated and family-centred care approaches.

The impact of the COVID-19 pandemic on patients' experiences obtaining a tuberculosis diagnosis in Peru: a mixed-methods study.

BACKGROUND: The COVID-19 pandemic disrupted TB services worldwide, leading to diagnostic delays. There have been few published reports describing how the pandemic affected people's pathway to diagnosis from their own perspectives. We sought to evaluate the impact on the pandemic on people's experiences obtaining a TB diagnosis. METHODS: We performed a mixed-methods study, enrolling newly diagnosed TB patients from 12 health centers in Lima, Peru. We used structured surveys to quantify diagnostic delay, defined as the time between symptom onset and diagnosis, and in-depth interviews to understand the ways in which the pandemic affected the pathway to care. We compared diagnostic delay between patients enrolled during the first year of the pandemic to those diagnosed after using a Wilcoxon rank-sum test. We used an inductive content analysis approach to analyze interview content related to the pandemic. RESULTS: We enrolled 51 patients during November 2020-April 2021 (during the first year of the pandemic) and 49 patients during October 2021-February 2022. Median diagnostic delay was longer for patients diagnosed during the first year of the pandemic (median 15 [IQR 5-26] weeks compared to 6 [IQR 3-18] weeks, p = 0.027). Qualitative analysis of 26 interviews revealed that the pandemic affected participants' care-seeking behavior and their ability to access to TB diagnostic services, particularly for those diagnosed in the first year of the pandemic. Many participants initially had their symptoms attributed to COVID-19, resulting in delayed TB evaluation and additional costs for COVID-19 treatment. CONCLUSIONS: The COVID-19 pandemic impacted multiple steps in the pathway to care for TB patients in Lima, causing delays in TB diagnosis. These findings demonstrate how the shifting of health care resources to prioritize COVID-19 can lead to collateral damage for people with TB and other conditions.

Geospatial analysis of reported activity locations to identify sites for tuberculosis screening.

Mobile screening units can help close tuberculosis case detection gaps. Placing screening units where people at high risk for undiagnosed tuberculosis preferentially spend time could make screening more resource-effective. We conducted a case-control study in Lima, Peru to identify locations where people with tuberculosis were more likely to spend time than community controls. We surveyed participants about activity locations over the past 6 months. We used density-based clustering to assess how patient and control activity locations differed, and logistic regression to compare location-based exposures. We included 109 tuberculosis patients and 79 controls. In density-based clustering analysis, the two groups had similar patterns of living locations, but their work locations clustered in distinct areas. Both groups were similarly likely to use public transit, but patients predominantly used buses and were less likely to use rapid transit (adjusted odds ratio [aOR] 0.31, 95% confidence interval [CI] 0.10-0.96) or taxis (aOR 0.42, 95% CI 0.21-0.85). Patients were more likely to have spent time in prison (aOR 11.55, 95% CI 1.48-90.13). Placing mobile screening units at bus terminals serving locations where tuberculosis patients have worked and within and around prisons could help reach people with undiagnosed tuberculosis.

Drop-offs in the isoniazid preventive therapy cascade among children living with HIV in western Kenya, 2015-2019.

INTRODUCTION: Isoniazid preventive therapy (IPT) can reduce the risk of tuberculosis (TB) in children living with HIV (CLHIV), but data on the outcomes of the IPT cascade in CLHIV are limited. METHODS: We evaluated the IPT cascade among CLHIV aged <15 years and newly enrolled in HIV care in eight HIV clinics in western Kenya. Medical record data were abstracted from September 2015 through July 2019. We assessed the proportion of CLHIV completing TB symptom screening, IPT eligibility assessment, IPT initiation and completion. TB incidence rate was calculated stratified by IPT initiation and completion status. Risk factors for IPT non-initiation and non-completion were assessed using Poisson regression with generalized linear models. RESULTS: Overall, 856 CLHIV were newly enrolled in HIV care, of whom 98% ([95% CI 97-99]; n = 841) underwent screening for TB symptoms and IPT eligibility. Of these, 13 (2%; 95% CI 1-3) were ineligible due to active TB and 828 (98%; 95% CI 97-99) were eligible. Five hundred and fifty-nine (68%; 95% CI 64-71) of eligible CLHIV initiated IPT; median time to IPT initiation was 3.6 months (interquartile range [IQR] 0.5-10.2). Overall, 434 (78%; 95% CI 74-81) IPT initiators completed. Attending high-volume HIV clinics (aRR = 2.82; 95% CI 1.20-6.62) was independently associated with IPT non-initiation. IPT non-initiation had a trend of being higher among those enrolled in the period 2017-2019 versus 2015-2016 (aRR = 1.91; 0.98-3.73) and those who were HIV virally non-suppressed (aRR = 1.90; 95% CI 0.98-3.71). Being enrolled in 2017-2019 versus 2015-2016 (aRR = 1.40; 1.01-1.96) was independently associated with IPT non-completion. By 24 months after IPT screening, TB incidence was four-fold higher among eligible CLHIV who never initiated (8.1 per 1000 person years [PY]) compared to CLHIV who completed IPT (2.1 per 1000 PY; rate ratio [RR] = 3.85; 95% CI 1.08-17.15), with a similar trend among CLHIV who initiated but did not complete IPT (8.2/1000 PY; RR = 4.39; 95% CI 0.82-23.56). CONCLUSIONS: Despite high screening for eligibility, timely IPT initiation and completion were suboptimal among eligible CLHIV in this programmatic cohort. Targeted programmatic interventions are needed to address these drop-offs from the IPT cascade by ensuring timely IPT initiation after ruling out active TB and enhancing completion of the 6-month course to reduce TB in CLHIV.

Geographic accessibility to health facilities predicts uptake of community-based tuberculosis screening in an urban setting.

OBJECTIVES: Annually, more than 30% of individuals with tuberculosis (TB) remain undiagnosed. We aimed to assess whether geographic accessibility measures can identify neighborhoods that would benefit from TB screening services targeted toward closing the diagnosis gap. METHODS: We used data from a community-based mobile TB screening program in Carabayllo district, Lima, Peru. We constructed four accessibility measures from the geographic center of neighborhoods to health facilities. We used logistic regression to assess the association between these measures and screening uptake in one's residential neighborhood versus elsewhere, with quasi-information criterion values to assess the association. RESULTS: We analyzed the screening locations for 25,000 Carabayllo residents from 49 neighborhoods. Pedestrian walk time was preferable to Euclidean distance or vehicular time in our models. For each additional 12 minutes walking time between the neighborhood and the health facility, the odds of residents using TB screening units located in their neighborhoods increased by 50% (95% CI: 26%-78%). Females had 9% (95% CI: 3%-16%) increased odds versus males of using a screening unit in their own neighborhood. CONCLUSION: Placing mobile TB screening units in neighborhoods with longer pedestrian time to access health facilities could benefit individuals who face more acute access barriers to health care.

Mapping local hot spots with routine tuberculosis data: A pragmatic approach to identify spatial variability.

OBJECTIVE: To use routinely collected data, with the addition of geographic information and census data, to identify local hot spots of rates of reported tuberculosis cases. DESIGN: Residential locations of tuberculosis cases identified from eight public health facilities in Lima, Peru (2013-2018) were linked to census data to calculate neighborhood-level annual case rates. Heat maps of tuberculosis case rates by neighborhood were created. Local indicators of spatial autocorrelation, Moran's I, were used to identify where in the study area spatial clusters and outliers of tuberculosis case rates were occurring. Age- and sex-stratified case rates were also assessed. RESULTS: We identified reports of 1,295 TB cases across 74 neighborhoods during the five-year study period, for an average annual rate of 124.2 reported TB cases per 100,000 population. In evaluating case rates by individual neighborhood, we identified a median rate of reported cases of 123.6 and a range from 0 to 800 cases per 100,000 population. Individuals aged 15-44 years old and men had higher case rates than other age groups and women. Locations of both hot and cold spots overlapped across age- and gender-specific maps. CONCLUSIONS: There is significant geographic heterogeneity in rates of reported TB cases and evident hot and cold spots within the study area. Characterization of the spatial distribution of these rates and local hot spots may be one practical tool to inform the work of local coalitions to target TB interventions in their zones.

Reaching 95-95-95 targets: The role of private sector health facilities in closing the HIV detection gap-Kisumu Kenya, 2018.

BACKGROUND: HIV testing efficiency could be improved by focusing on high yield populations and identifying types of health facilities where people with undiagnosed HIV infection are more likely to attend. METHODS: A retrospective cohort analysis of data collected during an integrated TB/HIV active case-finding intervention in Western Kenya. Data were analyzed from health facilities' registers on individuals who reported TB-suggestive symptoms between 1 July and 31 December 2018 and who had an HIV test result within one month following symptom screening. We used logistic regression with general estimating equations adjusting for sub-county level data to identify health facility-level predictors of new HIV diagnoses. RESULTS: Of 11,376 adults with presumptive TB identified in 143 health facilities, 1038 (9%) tested HIV positive. The median HIV positivity per health facility was 6% (IQR = 2-15%). Patients with TB symptoms were over three times as likely to have a new HIV diagnosis in private not-for-profit facilities compared to those in government facilities (adjusted odds ratio (aOR) 3.40; 95% CI = 1.96-5.90). Patients tested in hospitals were over two times as likely to have a new HIV diagnosis as those tested in smaller facilities (i.e., health centers and dispensaries) (aOR 2.26; 95% CI = 1.60-3.21). CONCLUSION: Individuals with presumptive TB who attended larger health facilities and private not-for-profit facilities had a higher likelihood of being newly diagnosed with HIV. Strengthening HIV services at these facilities and outreach to populations that use them could help to close the HIV diagnosis gap.

A role for community-level socioeconomic indicators in targeting tuberculosis screening interventions.

Tuberculosis screening programs commonly target areas with high case notification rates. However, this may exacerbate disparities by excluding areas that already face barriers to accessing diagnostic services. We compared historic case notification rates, demographic, and socioeconomic indicators as predictors of neighborhood-level tuberculosis screening yield during a mobile screening program in 74 neighborhoods in Lima, Peru. We used logistic regression and Classification and Regression Tree (CART) analysis to identify predictors of screening yield. During February 7, 2019-February 6, 2020, the program screened 29,619 people and diagnosed 147 tuberculosis cases. Historic case notification rate was not associated with screening yield in any analysis. In regression analysis, screening yield decreased as the percent of vehicle ownership increased (odds ratio [OR]: 0.76 per 10% increase in vehicle ownership; 95% confidence interval [CI]: 0.58-0.99). CART analysis identified the percent of blender ownership (≤ 83.1% vs > 83.1%; OR: 1.7; 95% CI: 1.2-2.6) and the percent of TB patients with a prior tuberculosis episode (> 10.6% vs ≤ 10.6%; OR: 3.6; 95% CI: 1.0-12.7) as optimal predictors of screening yield. Overall, socioeconomic indicators were better predictors of tuberculosis screening yield than historic case notification rates. Considering community-level socioeconomic characteristics could help identify high-yield locations for screening interventions.