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      Defining Catastrophic Costs and Comparing Their Importance for Adverse Tuberculosis Outcome with Multi-Drug Resistance: A Prospective Cohort Study, Peru

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          Abstract

          Tom Wingfield and colleagues investigate the relationship between catastrophic costs and tuberculosis outcomes for patients receiving free tuberculosis care in Peru.

          Please see later in the article for the Editors' Summary

          Abstract

          Background

          Even when tuberculosis (TB) treatment is free, hidden costs incurred by patients and their households (TB-affected households) may worsen poverty and health. Extreme TB-associated costs have been termed “catastrophic” but are poorly defined. We studied TB-affected households' hidden costs and their association with adverse TB outcome to create a clinically relevant definition of catastrophic costs.

          Methods and Findings

          From 26 October 2002 to 30 November 2009, TB patients ( n = 876, 11% with multi-drug-resistant [MDR] TB) and healthy controls ( n = 487) were recruited to a prospective cohort study in shantytowns in Lima, Peru. Patients were interviewed prior to and every 2–4 wk throughout treatment, recording direct (household expenses) and indirect (lost income) TB-related costs. Costs were expressed as a proportion of the household's annual income. In poorer households, costs were lower but constituted a higher proportion of the household's annual income: 27% (95% CI = 20%–43%) in the least-poor houses versus 48% (95% CI = 36%–50%) in the poorest. Adverse TB outcome was defined as death, treatment abandonment or treatment failure during therapy, or recurrence within 2 y. 23% (166/725) of patients with a defined treatment outcome had an adverse outcome. Total costs ≥20% of household annual income was defined as catastrophic because this threshold was most strongly associated with adverse TB outcome. Catastrophic costs were incurred by 345 households (39%). Having MDR TB was associated with a higher likelihood of incurring catastrophic costs (54% [95% CI = 43%–61%] versus 38% [95% CI = 34%–41%], p<0.003). Adverse outcome was independently associated with MDR TB (odds ratio [OR] = 8.4 [95% CI = 4.7–15], p<0.001), previous TB (OR = 2.1 [95% CI = 1.3–3.5], p = 0.005), days too unwell to work pre-treatment (OR = 1.01 [95% CI = 1.00–1.01], p = 0.02), and catastrophic costs (OR = 1.7 [95% CI = 1.1–2.6], p = 0.01). The adjusted population attributable fraction of adverse outcomes explained by catastrophic costs was 18% (95% CI = 6.9%–28%), similar to that of MDR TB (20% [95% CI = 14%–25%]). Sensitivity analyses demonstrated that existing catastrophic costs thresholds (≥10% or ≥15% of household annual income) were not associated with adverse outcome in our setting. Study limitations included not measuring certain “dis-saving” variables (including selling household items) and gathering only 6 mo of costs-specific follow-up data for MDR TB patients.

          Conclusions

          Despite free TB care, having TB disease was expensive for impoverished TB patients in Peru. Incurring higher relative costs was associated with adverse TB outcome. The population attributable fraction indicated that catastrophic costs and MDR TB were associated with similar proportions of adverse outcomes. Thus TB is a socioeconomic as well as infectious problem, and TB control interventions should address both the economic and clinical aspects of this disease.

          Please see later in the article for the Editors' Summary

          Editors' Summary

          Background

          Caused by the infectious microbe Mycobacterium tuberculosis, tuberculosis (or TB) is a global health problem. In 2012, an estimated 8.6 million people fell ill with TB, and 1.3 million were estimated to have died because of the disease. Poverty is widely recognized as an important risk factor for TB, and developing nations shoulder a disproportionate burden of both poverty and TB disease. For example, in Lima (the capital of Peru), the incidence of TB follows the poverty map, sparing residents living in rich areas of the city while spreading among poorer residents that live in overcrowded households.

          The Peruvian government, non-profit organizations, and the World Health Organization (WHO) have extended healthcare programs to provide free diagnosis and treatment for TB and drug-resistant strains of TB in Peru, but rates of new TB cases remain high. For example, in Ventanilla (an area of 16 shantytowns located in northern Lima), the rate of infection was higher during the study period, at 162 new cases per 100,000 people per year, than the national average. About one-third of the 277,895 residents of Ventanilla live on under US$1 per day.

          Why Was This Study Done?

          Poverty increases the risks associated with contracting TB infection, but the disease also affects the most economically productive age group, and the income of TB-affected households often decreases post-diagnosis, exacerbating poverty. A recent WHO consultation report proposed a target of eradicating catastrophic costs for TB-affected families by 2035, but hidden TB-related costs remain understudied, and there is no international consensus defining catastrophic costs incurred by patients and households affected by TB. Lost income and the cost of transport are among hidden costs associated with free treatment programs; these costs and their potential impact on patients and their households are not well defined. Here the researchers sought to clarify and characterize TB-related costs and explore whether there is a relationship between the hidden costs associated with free TB treatment programs and the likelihood of completing treatment and becoming cured of TB.

          What Did the Researchers Do and Find?

          Over a seven-year period (2002–2009), the researchers recruited 876 study participants with TB diagnosed at health posts located in Ventanilla. To provide a comparative control group, a sample of 487 healthy individuals was also recruited to participate. Participants were interviewed prior to treatment, and households' TB-related direct expenses and indirect expenses (lost income attributed to TB) were recorded every 2–4 wk. Data were collected during scheduled household visits.

          TB patients were poorer than controls, and analysis of the data showed that accessing free TB care was expensive for TB patients, especially those with multi-drug-resistant (MDR) TB. Total expenses were similar pre-treatment compared to during treatment for TB patients, despite receiving free care (1.1 versus 1.2 times the same household's monthly income). Even though direct expenses (for example, costs of medical examinations and medicines other than anti-TB therapy) were lower in the poorest households, their total expenses (direct and indirect) made up a greater proportion of their household annual income: 48% for the poorest households compared to 27% in the least-poor households.

          The researchers defined costs that were equal to or above one-fifth (20%) of household annual income as catastrophic because this threshold marked the greatest association with adverse treatment outcomes such as death, abandoning treatment, failing to respond to treatment, or TB recurrence. By calculating the population attributable fraction—the proportional reduction in population adverse treatment outcomes that could occur if a risk factor was reduced to zero—the authors estimate that adverse TB outcomes explained by catastrophic costs and MDR TB were similar: 18% for catastrophic costs and 20% for MDR TB.

          What Do These Findings Mean?

          The findings of this study indicate a potential role for social protection as a means to improve TB disease control and health, as well as defining a novel, evidence-based threshold for catastrophic costs for TB-affected households of 20% or more of annual income. Addressing the economic impact of diagnosis and treatment in impoverished communities may increase the odds of curing TB.

          Study limitations included only six months of follow-up data being gathered on costs for each participant and not recording “dissavings,” such as selling of household items in response to financial shock. Because the study was observational, the authors aren't able to determine the direction of the association between catastrophic costs and TB outcome. Even so, the study indicates that TB is a socioeconomic as well as infectious problem, and that TB control interventions should address both the economic and clinical aspects of the disease.

          Additional Information

          Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001675.

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          Most cited references41

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          What are the economic consequences for households of illness and of paying for health care in low- and middle-income country contexts?

          This paper presents the findings of a critical review of studies carried out in low- and middle-income countries (LMICs) focusing on the economic consequences for households of illness and health care use. These include household level impacts of direct costs (medical treatment and related financial costs), indirect costs (productive time losses resulting from illness) and subsequent household responses. It highlights that health care financing strategies that place considerable emphasis on out-of-pocket payments can impoverish households. There is growing evidence of households being pushed into poverty or forced into deeper poverty when faced with substantial medical expenses, particularly when combined with a loss of household income due to ill-health. Health sector reforms in LMICs since the late 1980s have particularly focused on promoting user fees for public sector health services and increasing the role of the private for-profit sector in health care provision. This has increasingly placed the burden of paying for health care on individuals experiencing poor health. This trend seems to continue even though some countries and international organisations are considering a shift away from their previous pro-user fee agenda. Research into alternative health care financing strategies and related mechanisms for coping with the direct and indirect costs of illness is urgently required to inform the development of appropriate social policies to improve access to essential health services and break the vicious cycle between illness and poverty.
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            Financial burden for tuberculosis patients in low- and middle-income countries: a systematic review

            Introduction An estimated 100 million people fall below the poverty line each year because of the financial burden of disease [1]. Tuberculosis (TB), which mostly affects the poorest of the poor, is an example of a disease that can substantially contribute to the disease poverty trap [2, 3]. Most countries aim to provide TB diagnosis and treatment free of charge within public health services. Access to free TB care has expanded substantially over the past two decades through national efforts and global financial support [4]. However, many TB patients and families are still facing very high direct and indirect costs due to TB illness and care-seeking, hampering access and putting people at risk of financial ruin or further impoverishment [5, 6]. The World Health Organization (WHO) is developing a post-2015 Global TB Strategy, which highlights the need for all countries to progress towards universal health coverage to ensure “universal access to needed health services without financial hardship in paying for them,” [7] as well as social protection mechanisms for “income replacement and social support in the event of illness” [8, 9]. One of the tentative global targets for the strategy is “no TB-affected family facing catastrophic costs due to TB”, to be reached globally by 2020 [10]. This target reflects the anticipated combined financial risk protection effect of the progressive realisation of both universal health coverage and social protection. Universal health coverage has long been on the global TB control agenda, which stresses the need for universally accessible, affordable and patient-centred services [2, 11–13]. Social protection has emerged more recently as a key policy area for TB care and prevention [10, 14–17]. Social protection involves schemes to cover costs beyond direct medical costs, including compensation of lost income. Examples of social protection schemes include sickness insurance, disability grants, other conditional or unconditional cash transfers, food assistance, travel vouchers and other support packages [14]. Such schemes exist in most countries, but may not be fully implemented due to inadequate financing or insufficient capacities of the healthcare and social welfare systems [18]. Furthermore, they may not include TB patients among those eligible [10, 14, 17]. In order to inform the development of appropriate strategies for improved access and financial risk protection for people with TB, we have undertaken a systematic literature review on medical costs, non-medical costs, as well as income loss for TB patients and affected households in different settings, as well as the main drivers of those costs. Methods Eligibility criteria This review includes studies written in English, conducted in low- and middle-income countries and published from inception to March 31, 2013, reporting data on medical costs, non-medical costs and/or income loss incurred by TB patients during the process of seeking and receiving care for TB, as well as coping strategies. We excluded studies in which only total cost was reported without any disaggregation into direct and indirect costs and studies using secondary data derived from other published articles. Information sources and search strategies We searched the following electronic databases: PubMed; Global Information Full Text; Index Medicus for Africa, South-East Asia, Eastern Mediterranean region, and Western Pacific region; and Literatura Latinoamericana y del Caribe en Ciencias de la Salud. Furthermore, we checked reference lists of reviewed studies [19–22] and of documents and meeting reports from the World Bank and WHO websites. The search terms were “tuberculosis” (tuberculosis, TB, or tuberculosis as a MeSH Term in PubMed) and “cost” (cost(s), expense(s), economic, expenditure(s), payment(s), out-of-pocket, financial, impoverishment, or catastrophic). Data extraction We extracted the following background information: country, location, urban/rural, year of the publication and data collection, setting characteristics, and method of data collection and calculation of costs and income loss. We stratified, to the extent data allowed, into the following cost components: direct medical costs (consultations, tests, medicines and hospitalisation, etc.), direct non-medical cost (transport and food during healthcare visits, etc.) and indirect costs (lost income). If possible, cost was stratified by socioeconomic status, hospitalisation/ambulatory treatment, drug-resistant TB or drug-susceptible TB, and sex. The cost components were extracted separately for the pre- and post-TB diagnosis period, if available. Pre-TB treatment costs are those incurred between the onset of symptoms and the initiation of treatment for TB. In all studies, this data was collected retrospectively at a point in time after diagnosis. Post-diagnostic costs are those incurred from TB diagnosis to completion of treatment. Costs during treatment were either collected prospectively through repeat surveys of patients in treatment or retrospectively. If retrospectively collected at some point during treatment, the cost was then extrapolated to the planned treatment duration in most studies. We also extracted data on costs as a percentage of reported individual and/or household income, if available. For all studies done in countries for which both “gross average nominal monthly wage” in the International Labour Organization's global wage database [23] and “income share held by lowest 20%” in the World Bank's online data [24] were available, we also computed total costs as percentage of average annual income and percentage of annual income in the lowest quintile for each respective country. The latter was done under the assumption that TB mostly affects the poorest quintile in any given setting. We used the available data for the nearest year to a year of the data collection. Where available, we extracted information about mechanisms for coping with financial burden, such as taking a loan or selling property. Summary measures and synthesis of results The focus of the analysis was on the distribution of the magnitude and components of costs across settings. We also report descriptive analyses of the central tendencies of the data. For each variable we provide the range of reported means across studies, unweighted average of means (with standard deviation), and the median and interquartile range of means. When a mean value for all study subjects in a given study was not available, we re-calculated an unweighted mean across subgroup within the study. We also report the range and unweighted average of percentage distributions of different cost components. Under the assumption of large heterogeneity, we decided a priori to focus the analysis on the variations across studies, while providing summary estimates for some variables as an indication of central tendencies across studies. We opted not to calculate confidence intervals for the unweighted average of means, in order to avoid a false impression of precision for the measures of central tendency. If one study reported data from several different country surveys, each survey was analysed as a separate observation. Data availability for variables of interest varied across studies. Summary statistics are therefore based on different number of studies. Mean cost values were available from 44 studies (reporting 47 surveys) of the 49 studies (reporting 52 surveys). Only median values were reported in five studies. We therefore did not use median values for summarising the key variables across studies. However, where applicable, median values were used for comparison of different subgroups within studies. Costs in international dollars ($) were calculated by multiplying raw cost data in US dollars, the exchange rate with the local currency for the year of data collection and the cumulative inflation rate [25] from the year of data collection to 2010 (latest year of data availability), and divided it by the purchasing power parities conversion factor [26]. The exchange rates reported in reviewed articles were preferentially used for the calculation and, in the absence of them, we used the exchange rates from the “National Accounts Main Aggregates Database” of the United Nations Statistics Division [27] and the exchange rate of Sudan from UN data [28] as the data of Sudan in a studied year is missing in the former source. Results 49 studies fulfilled the inclusion criteria (fig. 1). One study without cost data was included since it provided data on coping strategies [29]. Details about included studies are provided in table 1. Figure 1– Flow chart of literature search. Table 1– Type of costs Study Mean/ median/both Phase coverage# Components of Breakdown of Disaggregation by Costs as percentage of annual income Coping mechanism Direct costs Direct med. costs Direct non-med. costs Hosp. cost Lost income Direct med. costs Direct non-med. costs Lost income Before/ during treatment¶ Hosp./amb. MDR/ non-MDR SES Sex Individ. House. LQ Muniyandi (India, 2000) [30] Both Both √ √ D&I √ √ √ √ √ Rajeswari (India, 1995+) [31] Both Both √ √ √ √ √ √ Mauch (Ghana, 2009+) [5] Both Both √ √ √ √ √ √ √ All √ √ √ √ Mauch (Vietnam, 2009+) [5] Both Both √ √ √ √ √ D&I √ √ √ √ Mauch (Dominican Republic, 2009+) [5] Both Both √ √ √ √ √ D&I √ √ √ √ Karki (Nepal, 2002) [32] Both Both √ √ √ √ √ √ √ √ √ √ Xu (China, 2002) [33] Both Both √ √ √ D √§ √§ √§ Kemp (Malawi, 2001) [34] Both Before √ √ √ √ √ √ √ √ Needham (Zambia, 1995) [35] Both Before √ √ √ √ √ √ √ √ √ Mesfin (Ethiopia, 2005) [36] Both Before √ √ √ √ √ √ √ √ √ √ Jacquet (Haiti, 2003) [37] Mean Both √ √ D&I √ Lönnroth (Myanmar, 2004) [38] Mean Both √ √ √ √ √ √ All √ √ √ Gibson (Sierra Leone, 1994) [39] Mean Both √ D Kamolratanakul (Thailand, 1996/97) [40] Mean Both √ √§ √§ √ √ √ D √ √ √ √ Wyss (Tanzania, 1996) [41] Mean Both √ √ √ √ √ √ Saunderson (Uganda, 1992) [42] Mean Both √ √ √ √ √ Sinanovic (South Africa, 1998) [43] Mean Both √ √ √ √ √ Jackson (China, 2002–2005) [44] Mean Both √ √ √ √ √ √ √ √ √ Pantoja (India, 2005) [45] Mean Both √ √ √ √ √ √ √ √ All √ √ √ √ Ananthakrishnan (India, 2007) [46] Mean Both √ √ √ √ All √ √ √ √ Othman (Yemen, 2008/09) [47] Mean Both √ √ √ √ √ √ Pichenda (Cambodia, 2008) [48] Mean Both √ √ √ √ √ √ All √ √ √ Ayé (Tajikistan, 2006/07) [49] Mean Both √ √ √ √ √ √ D&I √ √ √ √ Steffen (Brazil, 2007/08) [50] Mean Both √ √ √ √ √ √ √ √ All √ √ √ √ Rouzier (Ecuador, 2007) [51] Mean Both √ √§ √§ √ √ √ √ √ √ √ John (India, 2007) [52] Mean Both √ √ √ √ √ √ √ √ All √ √ √ √ √ Muniyandi (India, 2000) [53] Mean Both √ √ √ Elamin (Malaysia, 2002) [54] Mean Both √ √ √ √ √ √ Mahendradhata (Indonesia, 2004/05) [55] Mean Both √ √ √ √ √ √ Sinanovic (South Africa, 2002) [56] Mean Both √ √ √ √ √ Vassall (Ethiopia, 2005) [57] Mean Both √ √ √ √ √ All √ √ Costa (Brazil, 2000) [58] Mean Both √ √ √ √ √ √ √ √ √ √ El Sony (Sudan, 1998/99) [59] Mean Both √ √ √ Khan (Pakistan, 1997/98) [60] Mean Both √ √ √ Umar (Nigeria, 2008) [61] Mean Both √ √ √ √§ Vassall (Syria, 1999) [62] Mean Both √ √ √ Vassall (Egypt, 1999) [62] Mean Both √ √ √ Meng (China, 2000) [63] Mean Both √ √ √§ Zhan (China, 2000/01) [64] Mean Bothƒ √ √ √ Dƒ √§ √§ Ray (India, 2003) [65] Mean Before √ √ √ √ Datiko (Ethiopia, 2007) [66] Mean Before √ √ √ √ √ √ Croft (Bangladesh, 1996) [67] Mean Before## √ √ √ √ √ √ √ Okello (Uganda, 1998) [68] Mean During √ √ √ √ √ √ √ Wandwalo (Tanzania, 2002) [69] Mean During √ √ √ √ Prado (Brazil, 2005/06) [70] Mean During √ √ √ √ √ √ Mirzoev (Nepal, 2001/02) [71] Mean During √ √ √ √ Jacobs (Russia, 1997) [72] Mean During √ √ √ √ Total number of surveys 47 (44 studies) 44 31 29 9 42 18 16 14 17 6 3 11 9 25 13 36 10 Mauch (Kenya, 2008) [73] Median Both √ √ √ √ Laokri (Burkina Faso, 2007/08) [74] Median Both √ Umar (Nigeria, 2008) [75] Median Both √ √ √ √ √ √ √ Aspler (Zambia, 2006) [76] Median Both √ √ √ √ √ √ D √ √ √ Liu (China, 2004) [77] Median Both √ √ √ D Total number of surveys 5 (5 studies) 5 3 3 0 2 2 2 0 2 3 0 1 2 0 0 0 1 The years in which the majority of data collection took place are provided for each study. Hosp.: hospitalisation; amb.: ambulatory; SES: socioeconomic status; individ.: individual annual income; house.: household annual income; LQ: lowest quintile. #: before treatment, during treatment, or before and during treatment (both). ¶: only direct costs (D); direct and indirect costs without medical and non-medical subcomponents (D&I); or all costs including medical and non-medical subcomponents (all). +: estimated year of data collection using the average gap of 4 years calculated from other articles. §: data are only for part of the costs and were excluded from the calculation of the average and figure 3. ƒ: costs of diagnosis are included in post-diagnosis. ##: data is before reaching facilities of national tuberculosis programme. Mean total costs ranged from $55 to $8198 across 40 surveys for which mean costs and conversion values were available, with an unweighted average of $847, and a median of $379. The proportion of direct medical costs out of total cost ranged from 0–62% (unweighted average 20%) across the 25 surveys that provided disaggregated data on direct medical, direct non-medical, and indirect costs. Direct non-medical costs ranged from 0–84% (unweighted average 20%) and indirect costs (income loss) from 16–94% (unweighted average 60%) of total cost (table 2). Table 2– Patient costs and distribution of costs from 25 surveys with disaggregated medical direct costs, non-medical direct costs and income loss Cost category Direct costs Indirect costs Total costs Medical costs Non-medical costs Unweighted average of mean costs $ (sd) (range) 296.8 (376.0) 450.8 (553.4) 738.1 (821.3) (21.9–1316.4) (29.8–2184.0) (54.6–3500.4) 144.9 (206.8) 152.0 (275.9) (0–801.7) (0–1271.4) Median (IQR) of mean costs $ 136.2 (58.0–304.9) 206.9 (109.0–486.3) 397.1 (155.4–1097.2) 50.0 (14.2–140.0) 32.1 (22.8–120.7) Unweighted average contribution % (range) 39.8 (6.2–83.7) 60.2 (16.3–93.8) 100 20.1 (0–62.4) 19.8 (0–83.7) IQR: interquartile range. Costs are quoted in international dollars. Eight studies fully disaggregated direct and indirect costs both before and during treatment. On average, costs incurred before TB treatment was initiated represented 50% of the total cost (fig. 2). While indirect costs dominated both before and during treatment, direct costs were relatively more important before than during treatment. Direct costs were driven mostly by medical costs before treatment and by non-medical costs during treatment. Figure 2– Breakdown of direct and indirect costs before and during treatment (eight studies). Percentages are proportion of respective sub-component cost out of the total cost. Across 18 studies that further disaggregated direct medical costs, the proportion of drug costs out of direct medical costs ranged from 0% to 86% (unweighted average of 34%), while the contribution from diagnostic and follow-up test costs ranged from 0% to 94% (unweighted average of 27%,) and hospitalisation costs from 0% to 71% (unweighted average of 24%). Transport costs (range 11–96%, unweighted average 50%), and food costs (range 0–89%, unweighted average 37%,) were the largest contributors to direct non-medical costs in 16 studies that disaggregated the direct non-medical costs. There was a large variation across studies in the mean total cost as percentage of income, with skewed distributions due to a few studies reporting very high costs (table 3 and fig. 3). Total cost as percentage of reported annual individual income ranged from 5% to 306% (unweighted average 58%, median 44%), while the total cost as percentage of reported household income ranged from 4% to 148% (unweighted average 39%, median 23%). Total cost as percentage of the average annual income in the lowest income quintile of the country of study ranged from 3% to 578% (unweighted average 89%, median 21%). Table 3– Costs as percentage of annual income Surveys n Direct costs % Lost income % Total costs % Range of total costs % Individual  Reported income 22 Average of mean (SD) 21 (27) 37 (43) 58 (64) 5–306 Median of mean (IQR) 10 (5–23) 24 (12–37) 24 (12–37)  Annual wage 35 Average of mean (SD) 9 (14) 21 (29) 30 (42) 0–211 Median of mean (IQR) 3 (2–12) 3 (2–12) 7 (4–41)  Wage of lowest 20% 34 Average of mean (SD) 25 (42) 25 (42) 89 (139) 3–578 Median of mean (IQR) 8 (4–29) 14 (6–88) 21 (10–101) Reported household income 7 Average of mean (SD) 16 (17) 22 (29) 39 (46) 4–148 Median of mean (IQR) 11 (9–15) 14 (4–20) 23 (14–36) IQR: interquartile range. Figure 3– Costs as percentage of a) reported annual individual income, b) reported annual household income and c) annual wage of the lowest quintile. The far right bars are truncated and percentages are shown above. avg.: average across subgroups for which separate means were reported in the original study. MDR: multidrug resistant; TB: tuberculosis. In 12 studies that disaggregated data by socioeconomic status group, there was no consistent tendency of difference in the absolute total cost incurred. However, the five studies that reported the cost as percentage of the reported income specific to each group found that the cost was considerably higher among the lower socioeconomic status groups [30, 34, 38, 40, 46]. Among the three studies that disaggregated the total cost for patients with multidrug-resistant (MDR)-TB versus drug-susceptible TB, the cost was considerably higher for MDR-TB patients (fig. 3). The difference in indirect costs was larger than that of the direct costs in two studies [48, 51]. The total costs as percentage of reported individual income for MDR-TB patients and drug-susceptible TB patients in two of the three studies were 223% ($14 388) versus 31% ($2008) in Ecuador [51] and 76% ($2953) versus 24% ($923) in Cambodia [48]. For the third study, from Brazil, that calculated income loss based on reported income after TB diagnosis, the cost burden was similar for MDR-TB and drug-susceptible patients (34% versus 27% of reported annual income) [58]. In 11 studies that disaggregated the total costs between males and females there was no consistent tendency of difference in absolute total costs. However, in two studies in Nigeria and Zambia that also reported individual income by sex, the costs for females as percentage of reported income were significantly larger [75, 76]. Commonly reported coping mechanisms included taking a loan, selling household items, using savings, and transfers from relatives (table 4). The amounts were not reported. Table 4– Percentage of patients pursuing specific coping strategies Country, area, year of data collection Taking loan % Selling household items % Using own savings % Transfers from relatives % Ghana, urban and rural, 2009 [5] 47 37 Vietnam, urban and rural, 2009 [5] 17 5 Dominican Republic, urban and rural, 2009 [5] 45 19 Tajikistan, urban and rural, 2006/2007 [29] 30 49 30 India, rural, 2000 [30] 71 India, urban and rural, 1995 [31]  Governmental hospitals 76  NGO-run hospitals 58  Private health facilities 68 Myanmar, urban, 2004 [38]  Higher socioeconomic status 27  Lower socioeconomic status 55 Thailand, nationwide, 1996/97 [40]  Income below poverty line 12 16 22 23  Income below average 9 7 21 21  Income above average 8 8 14 17 China, rural, 2002-05 [44] 8 45 66 Bangladesh, 1996 [67] 14 38 Kenya, 2008 [73] 57 NGO: nongovernment organisation. Discussion This review demonstrates that the economic burden of seeking TB care is often very high for patients and affected households. Clearly, accessing TB care and continuing treatment comes with a high risk of financial ruin or further impoverishment for many people. In most settings, income loss is a dominating reason for the high costs. However, the financial burden varies considerably both between individuals in the same setting and between settings. This should be expected as the burden is determined by a range of factors, such as socioeconomic status, clinical needs, health system structure, TB service delivery model, distance to health services, insurance coverage, capacity to work, existence of any social protection scheme, and effectiveness of informal social networks supporting patients and families. This review shows that, while costs are catastrophic for many patients, they are minimal for others. It is crucial to identify the factors that contribute to costs incurred and to financial ruin. Unfortunately, few studies provided sufficient details about the models and context of care to allow us to quantify the relative importance of the different factors. However, the available data hint at some key explanations and intervention entry points. Cost of medicines and diagnostic tests were important drivers of direct medical costs, despite TB medicines and basic TB-specific tests being free of charge in services linked to the national TB programme in most countries. Detailed accounts of which medicines and tests were accessed were not available from any of the studies, but authors of some studies speculated about several possible reasons for cost incurred: patients may not have been offered free medicines for drug-resistant TB; some patients pay for services outside national TB programme facilities, e.g. in the private sector; and costs of adjuvant medicines may have contributed. Hospitalisation was another key driver of direct costs. In some settings, patients are routinely hospitalised, especially if MDR-TB is diagnosed. The necessity of some medical procedures and routine hospitalisation is not substantiated. Ensuring use of evidence-based cost-effective diagnostic and treatment routines can reduce direct medical costs [49, 52]. The costs of appropriate services, within national programmes as well as outside, should be fully subsidised given the public health implications of failure to ensure access and use of quality TB care, the known low socioeconomic status of most TB patients, and recommended prioritisation of coverage of priority health interventions like for TB under universal health coverage objectives [78]. Ensuring provision of free-of-charge TB diagnosis and treatment also in private facilities have been shown to reduce the direct costs for patients [45, 79]. Transport and food costs accounted for a major part of direct non-medical costs for patients. Provision of transport vouchers, reimbursement schemes and food assistance could be used to reduce or compensate for such costs. Furthermore, decentralisation of patient supervision (including directly observed therapy), e.g. through community-based [43, 66] or workplace-based treatment [43], can reduce transport costs as well as income loss for patients. Minimising costs during treatment does not guarantee financial risk protection since a large part of the cost is often incurred before treatment starts. In addition, costs during the first 2 months of treatment tend to dominate the costs incurred during treatment [29, 57, 74]. Peaking costs around the time of diagnosis and treatment initiation may constitute one of the most powerful barriers for people ill with TB to complete the diagnostic search, to start treatment once diagnosed, and to adhering to treatment to cure. Therefore, effective intervention at the time of diagnosis and treatment initiation may have significant impact. Affordable health services, as well as social protection schemes, are needed to enable access, reduce delays and to compensate for direct and indirect costs. Social protection schemes cover general categories of vulnerable persons, such as those with disabilities or sickness or other causes of limited or reduced income. TB patients may in some settings meet criteria for such support. In other settings, TB-specific targeting may be in place for provision of specific packages of social support such as food stuffs or cash transfers, with or without means testing. This review identified two groups of TB patients that require special attention: people with MDR-TB and people in the lowest income brackets. For the first group, the debilitating nature of the disease, its long-term care, and associated income loss may put them at special risk for catastrophic costs. For the second group, low-income means that the relative costs of direct medical care and non-medical costs, as well as income loss due to precarious informal employment in many cases, may exacerbate already serious economic vulnerability and catastrophic costs may carry relatively greater impact. This study has several limitations. First, there may be both publication and selection bias that could limit the representativeness of the findings. All studies included only people who have been diagnosed with TB. Costs for those ill with TB who seek care but never get diagnosed may be very different, and could for example be dominated by progressing income loss due to untreated illness. Furthermore, most of the studies only included persons diagnosed and started on treatment within national TB programmes. Many people are treated in the private sector. Direct costs are often higher in the private sector than in facilities linked to the national programme [31, 55]. There is thus a bias towards surveys of public sector patients. Furthermore, there is inclusion bias with regards to some publication languages. Finally, the search strategy was not optimal for the inclusion of studies that only reported on copying mechanism. Secondly, there were large variations in how data were collected analysed and reported. In particular, the methods for calculating the income loss varied considerably. To accurately measure income loss is more difficult than to measure direct costs [80]. We could not find any clear patterns of methods used which affected cost estimations, except that the indirect costs in studies using reported income after diagnosis was lower than in other studies [58, 73]. Additional research is needed to validate different measurement approaches. Thirdly, the studies provided limited information about the health system context. This review provides a cross-sectional snapshot of the financial burden of TB across very different settings. The relevant drivers of costs and interventions to minimise costs will have to be determined locally, based on further local operational research. There is a “TB patient-cost toolkit” available to guide the design of local surveys [6]. Fourthly, while studies reported mean values (and median to a lesser extent), no study reported the full distribution of costs, the costs as a percentage of income, or the percentage of patients that had faced “catastrophic costs”. However, several possible definitions of “catastrophic costs” were discussed in the reviewed papers, including “>10% of monthly household income” [52], “>10% of annual household income” [61, 74]; “>40% of non-subsistence household income” [5, 44]; or “using non-reversible coping strategies” [29]. The WHO has proposed that “catastrophic health expenditure” be defined as direct healthcare expenditures corresponding to >40% of annual discretionary income (income after basic needs, such as food and housing) [7]. The World Bank has proposed a similar definition but has not specified a cut-off value [81]. Indirect costs of care and income loss are not included in these measures. The WHO's Global TB Programme is considering development of TB-specific indicators and target for reduction in catastrophic costs due to TB for patients and their families [10]. Here, all care-related expenditures, as well as income loss, are being considered as relevant elements of overall catastrophic costs. A threshold for TB-related “catastrophic costs” needs to be defined. One possible option would be to adopt the definition of “total costs corresponding to >10% of annual household income”, which has been proposed by Ranson [82] as appropriate for measuring catastrophic total costs. Incidence of impoverishment may also be considered. Another option is to use generic or locally defined irreversible coping strategies as proxy indicators for catastrophic costs. Further work is needed to assess the correlation between high total cost in relation to income and seemingly irreversible coping strategies.
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              The burden of non communicable diseases in developing countries

              Background By the dawn of the third millennium, non communicable diseases are sweeping the entire globe, with an increasing trend in developing countries where, the transition imposes more constraints to deal with the double burden of infective and non-infective diseases in a poor environment characterised by ill-health systems. By 2020, it is predicted that these diseases will be causing seven out of every 10 deaths in developing countries. Many of the non communicable diseases can be prevented by tackling associated risk factors. Methods Data from national registries and international organisms are collected, compared and analyzed. The focus is made on the growing burden of non communicable diseases in developing countries. Results Among non communicable diseases, special attention is devoted to cardiovascular diseases, diabetes, cancer and chronic pulmonary diseases. Their burden is affecting countries worldwide but with a growing trend in developing countries. Preventive strategies must take into account the growing trend of risk factors correlated to these diseases. Conclusion Non communicable diseases are more and more prevalent in developing countries where they double the burden of infective diseases. If the present trend is maintained, the health systems in low-and middle-income countries will be unable to support the burden of disease. Prominent causes for heart disease, diabetes, cancer and pulmonary diseases can be prevented but urgent (preventive) actions are needed and efficient strategies should deal seriously with risk factors like smoking, alcohol, physical inactivity and western diet.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                PLoS
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                1549-1277
                1549-1676
                July 2014
                15 July 2014
                : 11
                : 7
                : e1001675
                Affiliations
                [1 ]Innovación Por la Salud Y Desarrollo (IPSYD), Asociación Benéfica PRISMA, Lima, Perú
                [2 ]Innovation For Health And Development (IFHAD), London, United Kingdom
                [3 ]Infectious Diseases & Immunity, Imperial College London, and Wellcome Trust Imperial College Centre for Global Health Research, London, United Kingdom
                [4 ]The Monsall Infectious Diseases Unit, North Manchester General Hospital, Manchester, United Kingdom
                [5 ]Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [6 ]Laboratorio de Investigación y Desarrollo, Universidad Peruana Cayetano Heredia, Lima, Perú
                [7 ]Policy Strategy and Innovations, Stop TB Department, World Health Organization, Geneva, Switzerland
                Perelman School of Medicine at the University of Pennsylvania, United States of America
                Author notes

                CAE is a member of the Editorial Board of PLOS Medicine. All other authors have declared that no competing interests exist.

                Conceived and designed the experiments: CAE AG KZ RM. Performed the experiments: CAE AG KZ MT RM. Analyzed the data: TW AG CAE KZ MT DB. Contributed reagents/materials/analysis tools: CAE RM. Wrote the first draft of the manuscript: TW CAE AG DB. Contributed to the writing of the manuscript: TW CAE KZ AG MT DB KL. ICMJE criteria for authorship read and met: TW AG KZ MT DB KL CAE RM. Agree with manuscript results and conclusions: TW AG KZ MT DB KL CAE RM. Enrolled patients: CAE MT KZ RM.

                Article
                PMEDICINE-D-13-03175
                10.1371/journal.pmed.1001675
                4098993
                25025331
                aa304108-1fbd-4e07-8f85-2044655fbd9b
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 October 2013
                : 5 June 2014
                Page count
                Pages: 17
                Funding
                The Wellcome Trust, IFHAD, and the Joint Global Health Trials Consortium. TW was also supported by the British Infection Association with a research project primer grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Epidemiology
                Clinical Epidemiology
                Infectious Disease Epidemiology
                Social Epidemiology
                Health Care
                Health Economics
                Infectious Diseases
                Infectious Disease Control
                Tropical Diseases
                Science Policy
                Science Policy and Economics
                Social Sciences
                Political Science
                Public Policy
                Poverty Reduction

                Medicine
                Medicine

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