Out-of-Pocket Costs for Patients Diagnosed with Tuberculosis in Different Healthcare Settings in Bandung, Indonesia.

Costs related to tuberculosis (TB) can impose a significant burden on patients and their families and create barriers to diagnosis and treatment. Our study aimed to quantify out-of-pocket costs expended by TB patients in Bandung, Indonesia. This cross-sectional study recruited adults with TB from community health centers (CHCs), public and private hospitals, and private practitioners (PPs). An interview was completed at the time of diagnosis or at their return for 2- or 6-month treatment. Costs were converted to U.S. dollars (US$)-presented as median and interquartile range (IQR). Of 469 TB patients recruited, the mean age was 38 years and 57% were male. The median pretreatment direct cost per person was $37.51 (IQR 20.79-71.24). Hospitalization, diagnostic tests, and travel costs were predominant. Higher pretreatment costs were associated with no health insurance ($41.88 versus $27.41, P < 0.001), ≥ 6 visits to a healthcare provider ($39.91 versus $24.32, P < 0.001), ≥ 60 days pretreatment ($36.35 versus $26.25, P = 0.02), and presenting first to a PP ($40.71) or informal provider ($32.72) compared with private hospital ($21.26), public hospital ($19.63), or CHC ($13.52) (P = 0.01). For a subsample of 106 patients with total pre- and posttreatment costs available, the median total cost was $243.66 (IQR 128.46-550.71). For 26.5% of these patients, total costs were ≥ 20% of their annual household income. Despite having a good network of free TB diagnostic and treatment services throughout Bandung, patients experienced significant out-of-pocket costs. Increased uptake of the National Health Insurance, and systems for early recognition and diagnosis of TB, will contribute toward reducing costs.

Disease characteristics and treatment of patients with diabetes mellitus attending government health services in Indonesia, Peru, Romania and South Africa.

OBJECTIVE: To describe the characteristics and management of Diabetes mellitus (DM) patients from low- and middle-income countries (LMIC). METHODS: We systematically characterised consecutive DM patients attending public health services in urban settings in Indonesia, Peru, Romania and South Africa, collecting data on DM treatment history, complications, drug treatment, obesity, HbA1c and cardiovascular risk profile; and assessing treatment gaps against relevant national guidelines. RESULTS: Patients (median 59 years, 62.9% female) mostly had type 2 diabetes (96%), half for >5 years (48.6%). Obesity (45.5%) and central obesity (females 84.8%; males 62.7%) were common. The median HbA1c was 8.7% (72 mmol/mol), ranging from 7.7% (61 mmol/mol; Peru) to 10.4% (90 mmol/mol; South Africa). Antidiabetes treatment included metformin (62.6%), insulin (37.8%), and other oral glucose-lowering drugs (34.8%). Disease complications included eyesight problems (50.4%), EGFR <60 ml/min (18.9%), heart disease (16.5%) and proteinuria (14.7%). Many had an elevated cardiovascular risk with elevated blood pressure (36%), LDL (71.0%) and smoking (13%), but few were taking antihypertensive drugs (47.1%), statins (28.5%) and aspirin (30.0%) when indicated. Few patients on insulin (8.0%), statins (8.4%) and antihypertensives (39.5%) reached treatment targets according to national guidelines. There were large differences between countries in terms of disease profile and medication use. CONCLUSION: DM patients in government clinics in four LMIC with considerable growth of DM have insufficient glycaemic control, frequent macrovascular and other complications, and insufficient preventive measures for cardiovascular disease. These findings underline the need to identify treatment barriers and secure optimal DM care in such settings.

Disparities in model-based cost-effectiveness analyses of tuberculosis diagnosis: A systematic review.

BACKGROUND: Structural approach disparities were minimally addressed in past systematic reviews of model-based cost-effectiveness analyses addressing Tuberculosis management strategies. This review aimed to identify the structural approach disparities in model-based cost-effectiveness analysis studies addressing Tuberculosis diagnosis and describe potential hazards caused by those disparities. METHODS: A systematic search to identify studies published before October 2015 was performed in five electronic databases. After removal of duplication, studies' titles and abstracts were screened based on predetermined criteria. The full texts of potentially relevant studies were subsequently screened and excluded when they did not address active pulmonary Tuberculosis diagnosis. Quality of the studies was assessed using the "Philips' checklist." Various data regarding general information, cost-effectiveness results, and disease modeling were extracted using standardized data extraction forms. Data pertaining to models' structural approaches were compared and analyzed qualitatively for their applicability in various study settings, as well as their potential influence on main outcomes and cost-effectiveness conclusion. RESULTS: A total of 27 studies were included in the review. Most studies utilized a static model, which could underestimate the cost-effectiveness of the diagnostic tools strategies, due to the omission of indirect diagnosis effects, i.e. transmission reduction. A few structural assumption disparities were found in the dynamic models. Extensive disparities were found in the static models, consisting of varying structural assumptions regarding treatment outcomes, clinical diagnosis and empirical treatment, inpatient discharge decision, and re-diagnosis of false negative patients. CONCLUSION: In cost-effectiveness analysis studies addressing active pulmonary Tuberculosis diagnosis, models showed numerous disparities in their structural approaches. Several structural approaches could be inapplicable in certain settings. Furthermore, they could contribute to under- or overestimation of the cost-effectiveness of the diagnosis tools or strategies. They could thus lead to ambiguities and difficulties when interpreting a study result. A set of recommendations is proposed to manage issues related to these structural disparities.

Routine or targeted HIV screening of Indonesian prisoners.

PURPOSE: Routine HIV screening of prisoners is generally recommended, but rarely implemented in low-resource settings. Targeted screening can be used as an alternative. Both strategies may provide an opportunity to start HIV treatment but no formal comparisons have been done of these two strategies. The paper aims to discuss these issues. DESIGN/METHODOLOGY/APPROACH: The authors compared yield and costs of routine and targeted screening in a narcotic prison in Indonesia. Routine HIV screening was done for all incoming prisoners from August 2007-February 2009, after it was switched for budgetary reasons to targeted ("opt-out") HIV screening of inmates classified as people who inject drugs (PWIDs), and "opt-in" HIV testing for all non-PWIDs. FINDINGS: During routine screening 662 inmates were included. All 115 PWIDs and 93.2 percent of non-PWIDs agreed to be tested, 37.4 percent and 0.4 percent respectively were HIV-positive. During targeted screening (March 2009-October 2010), of 888 inmates who entered prison, 107 reported injecting drug use and were offered HIV testing, of whom 31 (29 percent) chose not to be tested and 25.0 percent of those tested were HIV-positive. Of 781 non-PWIDs, 187 (24 percent) came for testing (opt-in), and 2.1 percent were infected. During targeted screening fewer people admitted drug use (12.0 vs 17.4 percent). Routine screening yielded twice as many HIV-infected subjects (45 vs 23). The estimated cost per detected HIV infection was 338 USD for routine and 263 USD for targeted screening. ORIGINALITY/VALUE: In a resource limited setting like Indonesia, routine HIV screening in prison is feasible and more effective than targeted screening, which may be stigmatizing. HIV infections that remain unrecognized can fuel ongoing transmission in prison and lead to unnecessary disease progression and deaths.

INA-RESPOND: a multi-centre clinical research network in Indonesia.

Nationally representative observational and translational research is needed to address the public health challenges in Indonesia due to the geographic disparity, recently decentralized health system, and diverse infectious disease priorities. To accomplish this, the Indonesian Ministry of Health in collaboration with the US National Institute of Health has established INA-RESPOND (Indonesia Research Partnership on Infectious Disease) - a clinical research network comprising 9 referral hospitals, 7 medical faculties, and 2 research centres across Indonesia. The network provides a forum to conduct research at a national scale and to address scientific questions that would be difficult to address in smaller research settings. Further, it is currently conducting multi-centre research on the etiologies of fever, sepsis, and tuberculosis. There are opportunities to leverage existing network resources for other public health research needs. INA-RESPOND is an Indonesian-led network in a country with diverse population groups and public health needs which is poised to collaborate with researchers, universities, donors, and industry worldwide. This paper describes the network and its goals and values, as well as the management structure, process for collaboration, and future vision.

Management of children exposed to Mycobacterium tuberculosis: a public health evaluation in West Java, Indonesia.

OBJECTIVE: To investigate qualitatively and quantitatively the performance of a programme for managing the child contacts of adult tuberculosis patients in Indonesia. METHODS: A public health evaluation framework was used to assess gaps in a child contact management programme at a lung clinic. Targets for programme performance indicators were derived from established programme indicator targets, the scientific literature and expert opinion. Compliance with tuberculosis screening, the initiation of isoniazid preventive therapy in children younger than 5 years, the accuracy of tuberculosis diagnosis and adherence to preventive therapy were assessed in 755 child contacts in two cohorts. In addition, 22 primary caregivers and 34 clinic staff were interviewed to evaluate knowledge and acceptance of child contact management. The cost to caregivers was recorded. Gaps between observed and target indicator values were quantified. FINDINGS: THE GAPS BETWEEN OBSERVED AND TARGET PERFORMANCE INDICATORS WERE: 82% for screening compliance; 64 to 100% for diagnostic accuracy, 50% for the initiation of preventive therapy, 54% for adherence to therapy and 50% for costs. Many staff did not have adequate knowledge of, or an appropriate attitude towards, child contact management, especially regarding isoniazid preventive therapy. Caregivers had good knowledge of screening but not of preventive therapy and had difficulty travelling to the clinic and paying costs. CONCLUSION: The study identified widespread gaps in the performance of a child contact management system in Indonesia, all of which appear amenable to intervention. The public health evaluation framework used could be applied in other settings where child contact management is failing.

Implementation of LED fluorescence microscopy for diagnosis of pulmonary and HIV-associated tuberculosis in a hospital setting in Indonesia.

BACKGROUND: Fluorescence microscopy (FM) has not been implemented widely in TB endemic settings and little evaluation has been done in HIV-infected patients. We evaluated diagnostic performance, time and costs of FM with light-emitting diodes technology (LED-FM), compared with conventional (Zieh-Neelsen) microscopy in a hospital in Indonesia which acts as referral centre for HIV-infected patients. METHOD: We included pulmonary tuberculosis suspects from the outpatient and HIV clinic. Direct and concentrated sputum smears were examined using LED-FM and ZN microscopy by two technicians who were blinded for the HIV-status and the result of the comparative test. Mean reading time per slide was recorded and cost of each slide was calculated. Mycobacteria culture served as the reference standard. RESULTS: Among 404 tuberculosis suspects from the outpatient clinic and 256 from the HIV clinic, mycobacteria culture was positive in 12.6% and 27%, respectively. The optimal sensitivity of LED-FM was achieved by using a threshold of ≥2 AFB/length. LED-FM had a higher sensitivity (75.5% vs. 54.9%, P<0.01) but lower specificity (90.0% vs 96.6%, P<0.01) compared to ZN microscopy. HIV was associated with a lower sensitivity but similar specificity. The average reading time using LED-FM was significantly shorter (2.23±0.78 vs 5.82±1.60 minutes, P<0.01), while costs per slide were similar. CONCLUSION: High sensitivity of LED-FM combined with shorter reading time of sputum smear slides make this method a potential alternative to ZN microscopy. Additional data on specificity are needed for effective implementation of this technique in high burden TB laboratories.

Total lymphocyte count is a good marker for HIV-related mortality and can be used as a tool for starting HIV treatment in a resource-limited setting.

OBJECTIVES: Total lymphocyte counts (TLC) may be used as an alternative for CD4 cell counts to monitor HIV infection in resource-limited settings, where CD4 cell counts are too expensive or not available. METHODS: We used prospectively collected patient data from an urban HIV clinic in Indonesia. Predictors of mortality were identified via Cox regression, and the relation between TLC and CD4 cell counts was calculated by linear regression. Receiver operating characteristics (ROC) curves were used to choose the cut-off values of TLC corresponding with CD4 cell counts <200 and ≤350 cells/µl. Based on these analyses, we designed TLC-based treatment algorithms. RESULTS: Of 889 antiretroviral treatment (ART)-naïve subjects included, 66% had CD4 cell counts <200 and 81% had 350 ≤ cells/µl at baseline. TLC and CD4 cell count were equally strong predictors of mortality in our population, where ART was started based on CD4 cell count criteria. The correlation coefficient (R) between TLC and √CD4 was 0.70. Optimal cut-off values for TLC to identify patients with CD4 cell counts <200 and ≤350 cells/µl were 1500 and 1700 cells/µl, respectively. Treatment algorithms based on a combination of TLC, gender, oral thrush, anaemia and body mass index performed better in terms of predictive value than WHO staging or TLC alone. In our cohort, such an algorithm would on average have saved $14.05 per patient. CONCLUSION: Total lymphocyte counts is a good marker for HIV-associated mortality. Simple algorithms including TLC can prioritize patients for HIV treatment in a resource-limited setting, until affordable CD4 cell counts will be universally available.