Indian J Pediatr (September 2014) 81(Suppl 1):S55–S62 DOI 10.1007/s12098-014-1539-8
ORIGINAL ARTICLE
Public Health Implications of Obstructive Sleep Apnea Burden Ileana Baldi & Achal Gulati & Giulia Lorenzoni & Kiran Natarajan & Simonetta Ballali & Mohan Kameswaran & Ranjith Rajeswaran & Dario Gregori & Gulshan Sethi
Received: 25 June 2014 / Accepted: 8 July 2014 / Published online: 21 August 2014 # Dr. K C Chaudhuri Foundation 2014
Abstract Objective To assess the implications of obstructive sleep apnea (OSA) burden among Indian children. Methods MonteCarlo simulations were performed in order to estimate the number of OSA related obesity cases among Indian children (1–14 y of age) and the number of cases of stroke, coronary heart disease (CHD) and type 2 diabetes, considered as main adverse outcomes of OSA related childhood obesity, according to untreated and treated [adenotonsillectomy (AT) alone and AT associated to continuous positive airway pressure (CPAP)] pediatric OSA. Data used to perform MonteCarlo simulations were derived from a review about current literature exploring OSA related obesity. Results The analysis on the number of adverse outcomes according to treated and untreated obesity related to OSA showed that treatments reduce the number of obesity cases, resulting in a great reduction of the amount of stroke, CHD I. Baldi (*) : G. Lorenzoni : D. Gregori Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan, 18, 35121 Padova, Italy e-mail: [email protected] A. Gulati Department of Otorhinolaryngology (ENT), Maulana Azad Medical College, New Delhi, India K. Natarajan : M. Kameswaran Madras ENT Research Foundation (P) Ltd. (MERF), Chennai, India S. Ballali ZETA Research Ltd., Trieste, Italy R. Rajeswaran MERF Institute of Speech & Hearing (MERF-ISH), Chennai, India G. Sethi Department of Pediatrics, Maulana Azad Medical College, New Delhi, India
and type 2 diabetes cases. However, the cost for treating adverse outcome was higher in patients treated for obesity related to OSA compared to those not receiving any treatment. Conclusions The reduction in the number of adverse outcomes due to treatment of obesity related OSA implicates the urgent need for public health policies in providing screening for OSA among children population: an early detection and a consequently prompt reaction to pediatric OSA could improve the burden of OSA related obesity. Keywords OSA . Obesity . Children . India . MonteCarlo simulation
Introduction Obstructive Sleep Apnea (OSA) in children is a breathing disorder during sleep, that disrupts normal breathing patterns due to partial and/or complete upper airway obstruction [1]. The prevalence of pediatric OSA varies from 0.7 to 13 % [2–8]. The difference in prevalence rates is partially explained by inadequate sample sizes, a variety of methods used to assess the disease and different cut-offs used for OSA diagnosis [5]. Several studies show that disrupted sleep patterns in children and adolescents seem associated to increased obesity risk [9–11]. The association between OSA and obesity is difficult to understand because it is influenced by multiple aspects that appear to be linked in a vicious cycle. As pointed out by Narang et al. [12], the hypothesized association between OSA and obesity evolved from two types of observations: the high prevalence of OSA in obese children and the high prevalence of obese children with OSA.
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Obesity seems to contribute to the public health burden of OSA: it seems to be associated with increased morbidity that could lead to long-term complications, well into adulthood [13], especially in the context of OSA. The major co-morbidities involve cardiovascular, endocrine and metabolic systems. Regarding cardiovascular system, childhood obesity seems to be an independent risk factor for adult cardiovascular diseases (hypertension, coronary heart disease (CHD) and stroke) [14]. Regarding metabolic and endocrine systems, there is a large body of evidence that suggests an association between obesity, insulin resistance and type 2 diabetes [15]. The increased risk of cardiovascular, endocrine and metabolic diseases associated to OSA related obesity represents a big concern in developing countries where the prevalence of non-communicable diseases is dramatically increasing due to rapid changes in living conditions (sedentary lifestyle, better food habits and hygiene) [16,17]. In India, in particular, we are attending to a growing epidemic of obesity and its co-morbidities: cardiovascular disease and type 2 diabetes [18]. Moreover Indians seem to be more susceptible to these types of diseases because of the so called “Asian Indian Phenotype” which consists on high body fat, despite a low body mass index (BMI), low lean body mass, increased insulin resistance [18]. Regarding OSA, even though there are several studies [19–21] which investigated the epidemiology of the disease and its co-morbidities among Indian adults, there is a need for further studies exploring the consequences of pediatric OSA in adulthood. The aim of the present study was to estimate the burden of childhood obesity regarding pediatric OSA among Indian population by performing MonteCarlo simulations.
Material and Methods The aim of the present study was to estimate the burden of obesity cases due to pediatric OSA, on the basis of a micro simulation model. The authors estimated: & & &
the number of OSA related obesity cases among Indian pediatric population the cost of OSA’s diagnosis among Indian children and the cost of obesity treatment because of OSA among obese Indian children suffering from this disease both the number of cases and costs of stroke, CHD and type 2 diabetes (considered as adverse outcomes of OSA related childhood obesity) into adulthood, according to treated and untreated obesity related to OSA.
Data for MonteCarlo simulations were derived from a review of current literature, regarding epidemiology and treatment of obesity related to pediatric OSA (Table 1). Inclusion criteria for studies’ selection are detailed below. Population The simulated cohort represented Indian children (1–14 y of age), without a former diagnosis of OSA, who underwent an overnight, in-laboratory, polysomnography (PSG). The model started with children at 1 y of age because it is demonstrated that OSA in infants has a different pathophysiology and treatment with respect to OSA in older children [33]. Data of Indian children population by age and sex were based on 2011 Indian census (Fig. 1). Epidemiology of OSA The inclusion criteria used to select the studies to derive epidemiological data for simulations were the following: population based studies on children and adolescents from 1 to 18 y of age unselected for significant medical co-morbidities; full-night PSG as the accepted gold standard for OSA diagnosis, as recommended by the American Academy of Pediatrics [34] (it is demonstrated that Apnea-Hypopnea index (AHI) is underestimated in respiratory polygraphy, affecting management of the disease, especially in children suffering from mild or moderate OSA [35]). Given the inclusion criteria, the prevalence rate of OSA was derived from the studies of Kaditis et al. [7] and Bixler et al. [2]. OSA’s severity was classified as mild (1≤AHI85th). The authors assessed the number of obesity cases among the estimated Indian children population suffering from OSA. Diagnosis of OSA and Treatments for Childhood Obesity Related to Pediatric OSA The authors considered full-night PSG as the gold-standard for OSA’s diagnosis. Regarding treatments, as recommended by the American Academy of Pediatrics [34], adenotonsillectomy (AT) was considered as the first line treatment. The inclusion criteria
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Table 1 Literature review Prevalence of Pediatric OSA Source Anuntaseree et al. [8] Anuntaseree et al. [3] Bixler et al. [2]* Brunetti et al. [4] Kaditis et al. [7]*
Year 2001
No. of children 1,008
Country Thailand
2005 2009 2001 2004
755 5,740 895 3,680
Thailand United States Italy Greece
AHI ≥5 AHI>3 AHI≥5
China Turkey
ICSD criteria AHI>3
Country
Li et al. [5] 2010 6,447 Sogut et al. [6] 2005 1,198 Prevalence of obesity among children diagnosed with OSA Source Year No. of children
OSA criteria AHI ≥1 (the studies used the same cohort)
Kohler et al. [22] Rudnick et al. [23]*
2008 2007
190 170
Australia United States
Overweight/Obesity definition > 85th > 85th
Weinstock et al. [24]
2014
464
United States
> 95th
Year 1995
No. of children 94
Country United States, Canada, France
Efficacy rate 86 %
Country Scotland
Obesity definition ≥ 75 th
Outcome of CPAP Source Marcus et al. [25]*
Incidence rates of stroke, CHD and type 2 diabetes in adulthood Source Year No. of subjects Lawlor et al. [26]* 2005 11,106
OSA prevalence 0.69 % 1.3 % 1.2 % 1.0 % 1-6 y: 6.4 % 7-12 y: 3.7 % 4.8 % 0.9 %-1.3 % Obesity rates 52 % 2-5 y: 38 % 6-18 y: 62 % AHI 95th > 95th
Diabetes: 20 % Diabetes: 2 %
Country
Cost
India
15,500-16,340 INR
United States
No complications: $1,506 (95 % C.I. 1,492-1,519) Complications: $1,599 (95 % C.I.1,570-1,629)
2014
India
48,000-60,000 INR
2013
India
80,612 INR (95 % C.I. 72,574-88,574)
2013
India
13,135 INR (3,130-62,530)
2013
India
4,493 INR (2,640-17,095)
Cost for diagnosis, treatments and adverse outcomes Source Year PSG Maulana Azad Medical College [29]* 2014 AT Meier et al. [30]* 2014 CPAP Maulana Azad Medical College [29]* Stroke Kwatra et al. [31]* CHD Kumpatla et al. [32]* Type 2 Diabetes Kumpatla et al. [32]*
OSA Obstructive sleep apnea; AHI Apnea-hypopnea index; ICSD International criteria for sleep disorders; P-Y Persons-year; CPAP Continuous positive airway pressure; CHD Coronary heart disease; PFS Princeton follow-up study; NGHS National growth and health study; PSG Polysomnography; AT Adenotonsillectomy *Studies used to perform MonteCarlo simulations
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Fig. 1 Indian children population (1–14 y of age) by age and sex
used for the selection of studies on outcomes of AT in obese children because of OSA were: children from 1 to 18 y of age, full-night PSG performed before and after AT. Data on AT’s outcome on obese children suffering from OSA were derived from the review of Mathew et al. [36]. For treatment of obesity because of OSA, continuous positive airway pressure (CPAP) was associated to AT because guidelines recommend CPAP treatment if symptoms/signs of OSA persists after AT [34]. Data on CPAP outcomes were derived from the research of Marcus et al. [25], using the same criteria for studies selection as adopted for the previous ones. The authors referred to CPAP and AT as treatments for obesity related to OSA because they considered obesity as a consequence of OSA and estimated the potential reduction in the amount of OSA related obesity cases (and the impact of this reduction in the number of adverse outcomes), due to OSA’s treatment. Costs for full-night PSG, AT and CPAP treatment are detailed in Table 1. The cost for OSA’s diagnosis (full-night PSG) and treatment of obesity related to OSA (considering only AT and AT associated to CPAP) was assessed among estimated obese Indian pediatric population suffering from this disease. Adverse Outcomes of Childhood Obesity Because of OSA into Adulthood The aim of the present study was to estimate the burden of childhood obesity, as a consequence of OSA. The authors considered stroke, CHD and type 2 diabetes as adverse outcomes of obesity related to pediatric OSA, because of the growing epidemic of cardiovascular and metabolic disorders in Indian population and consequent need for understanding the causes for these to promote public health policies in order to face these non-communicable diseases. The authors selected studies reporting the epidemiology of the diseases among Indian adult population and cohort studies
in which anthropometric measurements were taken in children between 1 and 18 y of age and reported the incidence rates of the diseases into adulthood in participants who were overweight or obese in childhood. The incidence rates of stroke and CHD were derived from the studies of Lawlor et al. [26] and of Mohan et al. [27]. The incidence of type 2 diabetes was based on the cohort study of Morrison et al. [28]. Costs for management of stroke, CHD and type 2 diabetes are detailed in Table 1. The authors estimated both the number of cases and the cost of stroke, CHD and type 2 diabetes according to treated (with AT alone and AT associated to CPAP) and untreated obesity related to OSA. Simulations A micro-simulation approach was used as the main setting for the analysis. All quantities described above were implemented Table 2 Estimated number of cases of obesity related to OSA according to untreated and treated OSA OSA severity Mild OSA Severe OSA Total OSA severity Mild OSA Severe OSA Total OSA severity Mild OSA Severe OSA Total
No treatment, n (99 % C.I.) 29,166,339.92 (21,317,478.73; 31,905,193.56) 54,465,800.58 (52,405,604.58; 57,186,720.15) 83,632,138.49 (75,463,735.41; 87,505,367.49) Adenotonsillectomy, n (99 % C.I.) 9,105,349.95 (6,086,998.98; 9,781,713.77) 31,158,291.24 (29,469,161.36; 32,960,517.66) 40,263,641.20 (36,571,263.70; 41,701,838.96) Adenotonsillectomy and CPAP, n (99 % C.I.) 4,227,323.92 (2,771,421.24; 4,523,494.16) 14,383,227.72 (13,299,295.44; 15,331,336.93) 18,610,551.64 (16,571,446.16; 19,283,102.65)
OSA Obstructive sleep apnea; CHD Coronary heart disease; CPAP Continuous positive airway pressure Figures are estimated numbers (99 % Credibility Interval)
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Table 3 Estimated number of cases of stroke, CHD and type 2 diabetes in adulthood according to untreated and treated obesity related to OSA in childhood No treatment, n (99 % C.I.) OSA severity Stroke Mild 2,037,514 (1,116,878; 2,116,391) Severe 3,191,010 (2,407,682; 3,933,653) Total 5,228,524 (3,921,398; 5,821,578) Adenotonsillectomy, n (99 % C.I.) OSA severity Stroke Mild 495,165 (321,509; 644,176) Severe 1,776,219 (1,375,033; 2,252,345) Total 2,271,384 (1,835,425; 2,792,898) Adenotonsillectomy and CPAP, n (99 % C.I.) OSA severity Mild Severe Total
Stroke 248,414 (145,624; 295,843) 944,903 (624,202; 1,043,173) 1,193,317 (837,180; 1,286,514)
CHD 3,164,950 (2,143,971; 3,963,178) 5,158,676 (4,802,073; 7,433,444) 8,323,626 (7,472,232; 10,889,230)
Type 2 diabetes 10,783,343 (6,326,516; 14,867,926) 21,073,747 (13,817,374; 28,213,965) 31,857,090 (22,597,744; 40,714,646)
CHD 1,174,781 (613,453; 1,194,057) 3,728,326 (2,721,373; 4,248,524) 4,903,107 (3,525,243; 5,230,944)
Type 2 diabetes 4,061,683 (1,831,341; 4,487,192) 14,120,447 (7,848,785; 16,144,648) 18,182,130 (10,624,361; 19,592,401)
CHD 542,786 (279,880; 556,926) 1,564,530 (1,236,087; 1,964,493) 2,107,316 (1,601,738; 2,405,843)
Type 2 diabetes 1,036,783 (841,511; 2,063,613) 7,067,725 (3,577,895; 7,448,528) 8,104,508 (4,857,774; 9,012,500)
OSA Obstructive sleep apnea; CHD Coronary heart disease; CPAP Continuous positive airway pressure Figures are estimated numbers (99 % Credibility Interval)
in the stochastic simulation model as expected values of suitable probability functions. More in detail, for discrete random variables (e.g., number of people incident for OSA), a binomial model was used (e.g., sampling from the Indian population with probability equal to the age-specific incidence rate). For continuous random variables (e.g., weight of patients for drug dosage administration), including costs, a Kumaraswamy distribution, which is a very flexible both symmetric and asymmetric two-parameters distribution, was used [37]. Thus, 10,000 MonteCarlo runs were performed, deriving the empirical distributions of the target quantities of interest (e.g., cost of care), for which selected summary measures were computed (e.g., mean, 1th and 99th percentile, to be used as 99 % credibility intervals for inferential purposes). All estimated quantities are reported along with 99 % credibility intervals. Software used for simulations was the VOSE Model Risk analyzer [38].
Results Estimated number of obese children in Indian pediatric population diagnosed with OSA is shown in Table 2. Most of the obese children were suffering from severe OSA; this is because the epidemiological studies on obesity related to OSA show that children with severe OSA are more likely to be obese. Performing AT decreases the number of cases of obesity on both the categories of mild and severe OSA. Additionally performing both AT and CPAP treatments corresponds to an estimate reduction of 65,021,586.85 total cases of obesity compared to no treatment. Table 3 shows the estimated number of cases of stroke, CHD and type 2 diabetes in adulthood (considered as adverse outcomes of childhood obesity because of pediatric OSA) according to treated and untreated OSA. The simulations showed a decrease in the number of adverse outcomes
Table 4 Estimated cost for OSA’s diagnosis and treatments Cost of diagnosis and treatments, n (99 % C.I.) OSA severity Mild OSA
PSG 43,504.15 (32,838.12; 50,267.63)
Adenotonsillectomy 4,694.44 (3,388.70; 5,106.64)
CPAP 165,891.56 (121,066.90; 184,780.06) Severe OSA 85,192.25 (76,669.26; 91,325.70) 8,627.77 (8,268.32; 9,184.34) 318,929.47 (282,827.95; 335,016.42) Total 128,696.40 (114,868.70; 138,137.85) 13,322.21 (12,008.40; 14,001.88) 484,821.03 (423,469.06; 507,375.78)
OSA Obstructive sleep apnea; PSG Polysomnography; CPAP Continuous positive airway pressure Costs are expressed in crores
Total 214,090.15 (157,733.76; 239,448.17) 412,749.49 (374,173.19; 432,512.10) 626,839.65 (553,636.43; 656.321,01)
Type 2 diabetes 33,581.15 (21,910.22; 36,524.25) 116,254.37 (103,107.94; 125,362.09) 149,835.52 (129,749.93; 156,583.25)
CHD 35,215.65 (21,930.70; 36,290.47) 113,939.87 (102,716.66; 124,449.06) 149,155.53 (129,474.84; 155,953.75)
Type 2 diabetes 72,229.50 (48,187.80; 78,527.49) 255,031.32 (227,538.96; 270,319.66) 327,260.83 (285,820.90; 339,055.68)
CHD 73,101.80 (48,208.81; 78,364.32) 257,670.57 (226,982.15; 268,432.03) 330,772.38 (284,921.18; 337,191.30)
Type 2 diabetes 18,405.42 (6,904.11; 24,895.76) 34,416.05 (15,124.43; 48,050.43) 52,821.47 (27,799.02; 65,998.24)
CHD 18,675.93 (6,743.43; 21,962.11) 30,992.26 (13,999.32; 42,466.01) 49,668.19 (59,033.09; 26,988.22)
Costs are expressed in crore
OSA Obstructive sleep apnea; CHD Coronary heart disease; CPAP Continuous positive airway pressure
Mild OSA 71,228.23 (48,117.92; 77,895.13) Severe OSA 250,430.32 (227,977.88; 266,629.70) Total 321,658.56 (284,891.85; 335,097.37) Adenotonsillectomy and CPAP, n (99 % C.I.) OSA severity Stroke Mild OSA 33,978.42 (21,848.87; 36,163.51) Severe OSA 113,037.18 (103,000.31; 123,575.18) Total 147,015.60 (129,702.48; 154,854.49)
No treatment, n (99 % C.I.) OSA severity Stroke Mild OSA 17,891.76 (9,442.88; 18,072.84) Severe OSA 27,812.09 (20,417.73; 33,844.98) Total 45,703.85 (32,967.40; 49,683.24) Adenotonsillectomy, n (99 % C.I.) OSA severity Stroke
Table 5 Estimated costs of stroke, CHD and type 2 diabetes according to untreated and treated obesity related to OSA
Total 134,640.94 (86,311.97; 142,740.35) 448,044.85 (405,981.91; 487,656.79) 582,685.79 (510,978.12; 611,364.52)
283,538.95 (189,572.16;307,590.17) 998,321.65 (898,050.85; 1,051,472.68) 1,281,860.61 (1,123,968.05; 1,321,104.83)
Total
Total 54,973.11 (29,962.47; 59,153.56) 93,220.40 (65,200.00; 112,576.90) 148,193.52 (104,806.06; 161,226.23)
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because of the reduction of obesity cases due to both AT alone and AT associated to CPAP. Furthermore, simulations showed a reduction of 4,035,207 cases of stroke, 6,216,310 cases of CHD and 23,752,582 cases of type 2 diabetes when considering AT associated to CPAP, compared to those without treatment. Simulated costs of PSG, AT and CPAP on Indian pediatric population suffering from OSA are summarized in Table 4. Diagnosis and treatments are more expensive for children with severe OSA, being the most represented category. Regarding treatments, the most expensive is CPAP. The estimated cost for stroke, CHD and type 2 diabetes according to treated and untreated obesity related to OSA are given in Table 5. Costs for adverse outcomes in patients treated only with AT are higher compared to patients treated with both AT and CPAP: even if CPAP results as more expensive than AT, the two treatments adopted together promote a greater reduction of obese subjects (as it is shown in Table 2) and a consequently higher reduction of adverse outcomes (as is shown in Table 4), resulting in an overall reduction of costs compared to AT alone.
Discussion The aim of the present study was to assess the burden of childhood obesity related to pediatric OSA among Indian population. The authors considered childhood obesity as a consequence of pediatric OSA. Disrupted sleep patterns might promote induction of fatigue, along with a progressive reduction of physical activity. Improving sleep attitudes, patients may feel more comfortable when performing physical activity, interrupting the circular chain of poor sleep enhancing perception of fatigue, which co-causes reduced levels of motivation, provoking finally, lack of physical activity and consequently, weight gain [39]. In addition, irregular sleeping rhythms are strictly associated with modifications in leptin and ghrelin secretion, increasing appetite and consequently, obesity risk [29]. Adverse Outcomes of Obesity in OSA Children The authors considered stroke, CHD and type 2 diabetes as main adverse outcomes of OSA related obesity, this because of the concerning growing epidemic of non-communicable disease among Indian population [16] and the consequent need for public health policies facing these diseases. The authors found out that the treatment of obesity related to OSA results in a reduction of the number of obesity cases and consequently, decrease of the adverse outcomes related to obesity. The reduction of adverse outcomes depending on the decrease of obesity cases is consistent with current literature, demonstrating that weight loss reduces the risk of type 2 diabetes and cardiovascular diseases [40].
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Foreseeable Burden to the Health Care System of Obesity in OSA Children Analyzing the estimated cost for adverse outcomes according to treated and untreated obesity related to OSA, it has been demonstrated that adverse outcomes in patients untreated for obesity related to OSA are less expensive than those in patients treated with only AT or both AT and CPAP. Despite the increased cost of adverse outcomes due to treatments, one has to take into account that after treatments (AT alone and AT and CPAP together) a great reduction in the number of stroke, CHD and type 2 diabetes was observed. Study Limitations The present study, more than a real survey based in direct observation of clinical trials, constitutes just a simulation in order to estimate potential prevalence of OSA related obesity cases. Further research, therefore, should be specifically focused on representative Indian pediatric population. Moreover this study just considered obesity as a consequence of OSA, without taking into account the inverse relationship: obesity as a co-causing factor for OSA. Indeed, it is still less clear which factor affects the other. Thus, future research should investigate contextual variables, influencing rise of such diseases.
Conclusions The potential reduction in the number of adverse outcomes is relevant for public health, providing directions for planning health policies facing non-communicable diseases epidemics, to which obesity related to OSA contributes consistently. Since it has been shown that treatments for obesity related to OSA potentially provide a great reduction in the number of adverse outcomes, governments should provide public health polices which promote the screening for OSA among children: an early detection and a consequently prompt reaction to pediatric OSA could improve the burden of obesity related to this breathing disease.
Contributions IB, AG, DG Designed the study; IB, GL Wrote the manuscript; DG, GL Performed the statistical analysis; KN, SB, RR, GS critically revised the manuscript and contributed to the discussion. All authors contributed to results interpretation, read and approved the final manuscript. IB will act as guarantor for this paper. Conflict of Interest None. Source of Funding This work is partially supported by an unrestricted grant from the Italian Ministry of Foreign Affairs, Directorate General for Country Promotion, and from Prochild ONLUS (Italy).
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Indian J Pediatr (September 2014) 81(Suppl 1):S55–S62 21. Udwadia ZF, Doshi AV, Lonkar SG, Singh CI. Prevalence of sleepdisordered breathing and sleep apnea in middle-aged urban indian men. Am J Respir Crit Care Med. 2004;169:168–73. 22. Kohler M, Lushington K, Couper R, Martin J, van den Heuvel C, Pamula Y, et al. Obesity and risk of sleep related upper airway obstruction in caucasian children. J Clin SleepMed. 2008;4:129–36. 23. Rudnick EF, Walsh JS, Hampton MC, Mitchell RB. Prevalence and ethnicity of sleep-disordered breathing and obesity in children. Otolaryngol Head Neck Surg. 2007;137:878–82. 24. Weinstock TG, Rosen CL, Marcus CL, Garetz S, Mitchell RB, Amin R, et al. Predictors of obstructive sleep apnea severity in adenotonsillectomy candidates. Sleep. 2014;37:261–9. 25. Marcus CL, Ward SL, Mallory GB, Rosen CL, Beckerman RC, Weese-Mayer DE, et al. Use of nasal continuous positive airway pressure as treatment of childhood obstructive sleep apnea. J Pediatr. 1995;127:88–94. 26. Lawlor DA, Leon DA. Association of body mass index and obesity measured in early childhood with risk of coronary heart disease and stroke in middle age: findings from the aberdeen children of the 1950s prospective cohort study. Circulation. 2005;111:1891–6. 27. Mohan V, Venkatraman JV, Pradeepa R. Epidemiology of cardiovascular disease in type 2 diabetes: the indian scenario. J Diabetes Sci Technol. 2010;4:158–70. 28. Morrison JA, Glueck CJ, Horn PS, Wang P. Childhood predictors of adult type 2 diabetes at 9- and 26-year follow-ups. Arch Pediatr Adolesc Med. 2010;164:53–60. 29. Cappuccio FP, Taggart FM, Kandala NB, Currie A, Peile E, Stranges S, et al. Meta-analysis of short sleep duration and obesity in children and adults. Sleep. 2008;31:619–26. 30. Meier JD, Duval M, Wilkes J, Andrews S, Korgenski EK, Park AH, et al. Surgeon dependent variation in adenotonsillectomy costs in children. Otolaryngol Head Neck Surg. 2014;150:887–92. 31. Kwatra G, Kaur P, Toor G, Badyal DK, Kaur R, Singh Y, et al. Cost of stroke from a tertiary center in northwest India. Neurol India. 2013;61:627–32. 32. Kumpatla S, Kothandan H, Tharkar S, Viswanathan V. The costs of treating long-term diabetic complications in a developing country: a study from India. J Assoc Physicians India. 2013;61:102–9. 33. Katz ES, Mitchell RB, D’Ambrosio CM. Obstructive sleep apnea in infants. Am J Respir Crit Care Med. 2012;185:805–16. 34. Marcus CL, Brooks LJ, Draper KA, Gozal D, Halbower AC, Jones J, et al. Diagnosis and management of childhood obstructive sleep apnea syndrome. Pediatrics. 2012;130:576–84. 35. Tan HL, Gozal D, Ramirez HM, Bandla HP, Kheirandish-Gozal L. Overnight polysomnography versus respiratory polygraphy in the diagnosis of pediatric obstructive sleep apnea. Sleep. 2014;37:255– 60. 36. Mathew JL, Narang I. Sleeping too close together: obesity and obstructive sleep apnea in childhood and adolescence. Paediatr Respir Rev. 2013. doi:10.1016/j.prrv.2013.09.001. 37. Kumaraswamy P. A generalized probability density function for double-bounded random processes. J Hydrol. 1980;46:79–88. 38. Van Hauwermeiren M, Vose D, Vanden Bossche S. A Compendium of distributions. In: Software V, ed. Belgium: Ghent; 2012. 39. Seetho IW, Wilding JP. Screening for obstructive sleep apnoea in obesity and diabetes–potential for future approaches. Eur J Clin Invest. 2013;43:640–55. 40. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX, et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss: an update of the 1997 american heart association scientific statement on obesity and heart disease from the obesity committee of the council on nutrition, physical activity, and metabolism. Circulation. 2006;113:898–918.
ORIGINAL ARTICLE
Public Health Implications of Obstructive Sleep Apnea Burden Ileana Baldi & Achal Gulati & Giulia Lorenzoni & Kiran Natarajan & Simonetta Ballali & Mohan Kameswaran & Ranjith Rajeswaran & Dario Gregori & Gulshan Sethi
Received: 25 June 2014 / Accepted: 8 July 2014 / Published online: 21 August 2014 # Dr. K C Chaudhuri Foundation 2014
Abstract Objective To assess the implications of obstructive sleep apnea (OSA) burden among Indian children. Methods MonteCarlo simulations were performed in order to estimate the number of OSA related obesity cases among Indian children (1–14 y of age) and the number of cases of stroke, coronary heart disease (CHD) and type 2 diabetes, considered as main adverse outcomes of OSA related childhood obesity, according to untreated and treated [adenotonsillectomy (AT) alone and AT associated to continuous positive airway pressure (CPAP)] pediatric OSA. Data used to perform MonteCarlo simulations were derived from a review about current literature exploring OSA related obesity. Results The analysis on the number of adverse outcomes according to treated and untreated obesity related to OSA showed that treatments reduce the number of obesity cases, resulting in a great reduction of the amount of stroke, CHD I. Baldi (*) : G. Lorenzoni : D. Gregori Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan, 18, 35121 Padova, Italy e-mail: [email protected] A. Gulati Department of Otorhinolaryngology (ENT), Maulana Azad Medical College, New Delhi, India K. Natarajan : M. Kameswaran Madras ENT Research Foundation (P) Ltd. (MERF), Chennai, India S. Ballali ZETA Research Ltd., Trieste, Italy R. Rajeswaran MERF Institute of Speech & Hearing (MERF-ISH), Chennai, India G. Sethi Department of Pediatrics, Maulana Azad Medical College, New Delhi, India
and type 2 diabetes cases. However, the cost for treating adverse outcome was higher in patients treated for obesity related to OSA compared to those not receiving any treatment. Conclusions The reduction in the number of adverse outcomes due to treatment of obesity related OSA implicates the urgent need for public health policies in providing screening for OSA among children population: an early detection and a consequently prompt reaction to pediatric OSA could improve the burden of OSA related obesity. Keywords OSA . Obesity . Children . India . MonteCarlo simulation
Introduction Obstructive Sleep Apnea (OSA) in children is a breathing disorder during sleep, that disrupts normal breathing patterns due to partial and/or complete upper airway obstruction [1]. The prevalence of pediatric OSA varies from 0.7 to 13 % [2–8]. The difference in prevalence rates is partially explained by inadequate sample sizes, a variety of methods used to assess the disease and different cut-offs used for OSA diagnosis [5]. Several studies show that disrupted sleep patterns in children and adolescents seem associated to increased obesity risk [9–11]. The association between OSA and obesity is difficult to understand because it is influenced by multiple aspects that appear to be linked in a vicious cycle. As pointed out by Narang et al. [12], the hypothesized association between OSA and obesity evolved from two types of observations: the high prevalence of OSA in obese children and the high prevalence of obese children with OSA.
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Obesity seems to contribute to the public health burden of OSA: it seems to be associated with increased morbidity that could lead to long-term complications, well into adulthood [13], especially in the context of OSA. The major co-morbidities involve cardiovascular, endocrine and metabolic systems. Regarding cardiovascular system, childhood obesity seems to be an independent risk factor for adult cardiovascular diseases (hypertension, coronary heart disease (CHD) and stroke) [14]. Regarding metabolic and endocrine systems, there is a large body of evidence that suggests an association between obesity, insulin resistance and type 2 diabetes [15]. The increased risk of cardiovascular, endocrine and metabolic diseases associated to OSA related obesity represents a big concern in developing countries where the prevalence of non-communicable diseases is dramatically increasing due to rapid changes in living conditions (sedentary lifestyle, better food habits and hygiene) [16,17]. In India, in particular, we are attending to a growing epidemic of obesity and its co-morbidities: cardiovascular disease and type 2 diabetes [18]. Moreover Indians seem to be more susceptible to these types of diseases because of the so called “Asian Indian Phenotype” which consists on high body fat, despite a low body mass index (BMI), low lean body mass, increased insulin resistance [18]. Regarding OSA, even though there are several studies [19–21] which investigated the epidemiology of the disease and its co-morbidities among Indian adults, there is a need for further studies exploring the consequences of pediatric OSA in adulthood. The aim of the present study was to estimate the burden of childhood obesity regarding pediatric OSA among Indian population by performing MonteCarlo simulations.
Material and Methods The aim of the present study was to estimate the burden of obesity cases due to pediatric OSA, on the basis of a micro simulation model. The authors estimated: & & &
the number of OSA related obesity cases among Indian pediatric population the cost of OSA’s diagnosis among Indian children and the cost of obesity treatment because of OSA among obese Indian children suffering from this disease both the number of cases and costs of stroke, CHD and type 2 diabetes (considered as adverse outcomes of OSA related childhood obesity) into adulthood, according to treated and untreated obesity related to OSA.
Data for MonteCarlo simulations were derived from a review of current literature, regarding epidemiology and treatment of obesity related to pediatric OSA (Table 1). Inclusion criteria for studies’ selection are detailed below. Population The simulated cohort represented Indian children (1–14 y of age), without a former diagnosis of OSA, who underwent an overnight, in-laboratory, polysomnography (PSG). The model started with children at 1 y of age because it is demonstrated that OSA in infants has a different pathophysiology and treatment with respect to OSA in older children [33]. Data of Indian children population by age and sex were based on 2011 Indian census (Fig. 1). Epidemiology of OSA The inclusion criteria used to select the studies to derive epidemiological data for simulations were the following: population based studies on children and adolescents from 1 to 18 y of age unselected for significant medical co-morbidities; full-night PSG as the accepted gold standard for OSA diagnosis, as recommended by the American Academy of Pediatrics [34] (it is demonstrated that Apnea-Hypopnea index (AHI) is underestimated in respiratory polygraphy, affecting management of the disease, especially in children suffering from mild or moderate OSA [35]). Given the inclusion criteria, the prevalence rate of OSA was derived from the studies of Kaditis et al. [7] and Bixler et al. [2]. OSA’s severity was classified as mild (1≤AHI85th). The authors assessed the number of obesity cases among the estimated Indian children population suffering from OSA. Diagnosis of OSA and Treatments for Childhood Obesity Related to Pediatric OSA The authors considered full-night PSG as the gold-standard for OSA’s diagnosis. Regarding treatments, as recommended by the American Academy of Pediatrics [34], adenotonsillectomy (AT) was considered as the first line treatment. The inclusion criteria
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Table 1 Literature review Prevalence of Pediatric OSA Source Anuntaseree et al. [8] Anuntaseree et al. [3] Bixler et al. [2]* Brunetti et al. [4] Kaditis et al. [7]*
Year 2001
No. of children 1,008
Country Thailand
2005 2009 2001 2004
755 5,740 895 3,680
Thailand United States Italy Greece
AHI ≥5 AHI>3 AHI≥5
China Turkey
ICSD criteria AHI>3
Country
Li et al. [5] 2010 6,447 Sogut et al. [6] 2005 1,198 Prevalence of obesity among children diagnosed with OSA Source Year No. of children
OSA criteria AHI ≥1 (the studies used the same cohort)
Kohler et al. [22] Rudnick et al. [23]*
2008 2007
190 170
Australia United States
Overweight/Obesity definition > 85th > 85th
Weinstock et al. [24]
2014
464
United States
> 95th
Year 1995
No. of children 94
Country United States, Canada, France
Efficacy rate 86 %
Country Scotland
Obesity definition ≥ 75 th
Outcome of CPAP Source Marcus et al. [25]*
Incidence rates of stroke, CHD and type 2 diabetes in adulthood Source Year No. of subjects Lawlor et al. [26]* 2005 11,106
OSA prevalence 0.69 % 1.3 % 1.2 % 1.0 % 1-6 y: 6.4 % 7-12 y: 3.7 % 4.8 % 0.9 %-1.3 % Obesity rates 52 % 2-5 y: 38 % 6-18 y: 62 % AHI 95th > 95th
Diabetes: 20 % Diabetes: 2 %
Country
Cost
India
15,500-16,340 INR
United States
No complications: $1,506 (95 % C.I. 1,492-1,519) Complications: $1,599 (95 % C.I.1,570-1,629)
2014
India
48,000-60,000 INR
2013
India
80,612 INR (95 % C.I. 72,574-88,574)
2013
India
13,135 INR (3,130-62,530)
2013
India
4,493 INR (2,640-17,095)
Cost for diagnosis, treatments and adverse outcomes Source Year PSG Maulana Azad Medical College [29]* 2014 AT Meier et al. [30]* 2014 CPAP Maulana Azad Medical College [29]* Stroke Kwatra et al. [31]* CHD Kumpatla et al. [32]* Type 2 Diabetes Kumpatla et al. [32]*
OSA Obstructive sleep apnea; AHI Apnea-hypopnea index; ICSD International criteria for sleep disorders; P-Y Persons-year; CPAP Continuous positive airway pressure; CHD Coronary heart disease; PFS Princeton follow-up study; NGHS National growth and health study; PSG Polysomnography; AT Adenotonsillectomy *Studies used to perform MonteCarlo simulations
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Fig. 1 Indian children population (1–14 y of age) by age and sex
used for the selection of studies on outcomes of AT in obese children because of OSA were: children from 1 to 18 y of age, full-night PSG performed before and after AT. Data on AT’s outcome on obese children suffering from OSA were derived from the review of Mathew et al. [36]. For treatment of obesity because of OSA, continuous positive airway pressure (CPAP) was associated to AT because guidelines recommend CPAP treatment if symptoms/signs of OSA persists after AT [34]. Data on CPAP outcomes were derived from the research of Marcus et al. [25], using the same criteria for studies selection as adopted for the previous ones. The authors referred to CPAP and AT as treatments for obesity related to OSA because they considered obesity as a consequence of OSA and estimated the potential reduction in the amount of OSA related obesity cases (and the impact of this reduction in the number of adverse outcomes), due to OSA’s treatment. Costs for full-night PSG, AT and CPAP treatment are detailed in Table 1. The cost for OSA’s diagnosis (full-night PSG) and treatment of obesity related to OSA (considering only AT and AT associated to CPAP) was assessed among estimated obese Indian pediatric population suffering from this disease. Adverse Outcomes of Childhood Obesity Because of OSA into Adulthood The aim of the present study was to estimate the burden of childhood obesity, as a consequence of OSA. The authors considered stroke, CHD and type 2 diabetes as adverse outcomes of obesity related to pediatric OSA, because of the growing epidemic of cardiovascular and metabolic disorders in Indian population and consequent need for understanding the causes for these to promote public health policies in order to face these non-communicable diseases. The authors selected studies reporting the epidemiology of the diseases among Indian adult population and cohort studies
in which anthropometric measurements were taken in children between 1 and 18 y of age and reported the incidence rates of the diseases into adulthood in participants who were overweight or obese in childhood. The incidence rates of stroke and CHD were derived from the studies of Lawlor et al. [26] and of Mohan et al. [27]. The incidence of type 2 diabetes was based on the cohort study of Morrison et al. [28]. Costs for management of stroke, CHD and type 2 diabetes are detailed in Table 1. The authors estimated both the number of cases and the cost of stroke, CHD and type 2 diabetes according to treated (with AT alone and AT associated to CPAP) and untreated obesity related to OSA. Simulations A micro-simulation approach was used as the main setting for the analysis. All quantities described above were implemented Table 2 Estimated number of cases of obesity related to OSA according to untreated and treated OSA OSA severity Mild OSA Severe OSA Total OSA severity Mild OSA Severe OSA Total OSA severity Mild OSA Severe OSA Total
No treatment, n (99 % C.I.) 29,166,339.92 (21,317,478.73; 31,905,193.56) 54,465,800.58 (52,405,604.58; 57,186,720.15) 83,632,138.49 (75,463,735.41; 87,505,367.49) Adenotonsillectomy, n (99 % C.I.) 9,105,349.95 (6,086,998.98; 9,781,713.77) 31,158,291.24 (29,469,161.36; 32,960,517.66) 40,263,641.20 (36,571,263.70; 41,701,838.96) Adenotonsillectomy and CPAP, n (99 % C.I.) 4,227,323.92 (2,771,421.24; 4,523,494.16) 14,383,227.72 (13,299,295.44; 15,331,336.93) 18,610,551.64 (16,571,446.16; 19,283,102.65)
OSA Obstructive sleep apnea; CHD Coronary heart disease; CPAP Continuous positive airway pressure Figures are estimated numbers (99 % Credibility Interval)
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Table 3 Estimated number of cases of stroke, CHD and type 2 diabetes in adulthood according to untreated and treated obesity related to OSA in childhood No treatment, n (99 % C.I.) OSA severity Stroke Mild 2,037,514 (1,116,878; 2,116,391) Severe 3,191,010 (2,407,682; 3,933,653) Total 5,228,524 (3,921,398; 5,821,578) Adenotonsillectomy, n (99 % C.I.) OSA severity Stroke Mild 495,165 (321,509; 644,176) Severe 1,776,219 (1,375,033; 2,252,345) Total 2,271,384 (1,835,425; 2,792,898) Adenotonsillectomy and CPAP, n (99 % C.I.) OSA severity Mild Severe Total
Stroke 248,414 (145,624; 295,843) 944,903 (624,202; 1,043,173) 1,193,317 (837,180; 1,286,514)
CHD 3,164,950 (2,143,971; 3,963,178) 5,158,676 (4,802,073; 7,433,444) 8,323,626 (7,472,232; 10,889,230)
Type 2 diabetes 10,783,343 (6,326,516; 14,867,926) 21,073,747 (13,817,374; 28,213,965) 31,857,090 (22,597,744; 40,714,646)
CHD 1,174,781 (613,453; 1,194,057) 3,728,326 (2,721,373; 4,248,524) 4,903,107 (3,525,243; 5,230,944)
Type 2 diabetes 4,061,683 (1,831,341; 4,487,192) 14,120,447 (7,848,785; 16,144,648) 18,182,130 (10,624,361; 19,592,401)
CHD 542,786 (279,880; 556,926) 1,564,530 (1,236,087; 1,964,493) 2,107,316 (1,601,738; 2,405,843)
Type 2 diabetes 1,036,783 (841,511; 2,063,613) 7,067,725 (3,577,895; 7,448,528) 8,104,508 (4,857,774; 9,012,500)
OSA Obstructive sleep apnea; CHD Coronary heart disease; CPAP Continuous positive airway pressure Figures are estimated numbers (99 % Credibility Interval)
in the stochastic simulation model as expected values of suitable probability functions. More in detail, for discrete random variables (e.g., number of people incident for OSA), a binomial model was used (e.g., sampling from the Indian population with probability equal to the age-specific incidence rate). For continuous random variables (e.g., weight of patients for drug dosage administration), including costs, a Kumaraswamy distribution, which is a very flexible both symmetric and asymmetric two-parameters distribution, was used [37]. Thus, 10,000 MonteCarlo runs were performed, deriving the empirical distributions of the target quantities of interest (e.g., cost of care), for which selected summary measures were computed (e.g., mean, 1th and 99th percentile, to be used as 99 % credibility intervals for inferential purposes). All estimated quantities are reported along with 99 % credibility intervals. Software used for simulations was the VOSE Model Risk analyzer [38].
Results Estimated number of obese children in Indian pediatric population diagnosed with OSA is shown in Table 2. Most of the obese children were suffering from severe OSA; this is because the epidemiological studies on obesity related to OSA show that children with severe OSA are more likely to be obese. Performing AT decreases the number of cases of obesity on both the categories of mild and severe OSA. Additionally performing both AT and CPAP treatments corresponds to an estimate reduction of 65,021,586.85 total cases of obesity compared to no treatment. Table 3 shows the estimated number of cases of stroke, CHD and type 2 diabetes in adulthood (considered as adverse outcomes of childhood obesity because of pediatric OSA) according to treated and untreated OSA. The simulations showed a decrease in the number of adverse outcomes
Table 4 Estimated cost for OSA’s diagnosis and treatments Cost of diagnosis and treatments, n (99 % C.I.) OSA severity Mild OSA
PSG 43,504.15 (32,838.12; 50,267.63)
Adenotonsillectomy 4,694.44 (3,388.70; 5,106.64)
CPAP 165,891.56 (121,066.90; 184,780.06) Severe OSA 85,192.25 (76,669.26; 91,325.70) 8,627.77 (8,268.32; 9,184.34) 318,929.47 (282,827.95; 335,016.42) Total 128,696.40 (114,868.70; 138,137.85) 13,322.21 (12,008.40; 14,001.88) 484,821.03 (423,469.06; 507,375.78)
OSA Obstructive sleep apnea; PSG Polysomnography; CPAP Continuous positive airway pressure Costs are expressed in crores
Total 214,090.15 (157,733.76; 239,448.17) 412,749.49 (374,173.19; 432,512.10) 626,839.65 (553,636.43; 656.321,01)
Type 2 diabetes 33,581.15 (21,910.22; 36,524.25) 116,254.37 (103,107.94; 125,362.09) 149,835.52 (129,749.93; 156,583.25)
CHD 35,215.65 (21,930.70; 36,290.47) 113,939.87 (102,716.66; 124,449.06) 149,155.53 (129,474.84; 155,953.75)
Type 2 diabetes 72,229.50 (48,187.80; 78,527.49) 255,031.32 (227,538.96; 270,319.66) 327,260.83 (285,820.90; 339,055.68)
CHD 73,101.80 (48,208.81; 78,364.32) 257,670.57 (226,982.15; 268,432.03) 330,772.38 (284,921.18; 337,191.30)
Type 2 diabetes 18,405.42 (6,904.11; 24,895.76) 34,416.05 (15,124.43; 48,050.43) 52,821.47 (27,799.02; 65,998.24)
CHD 18,675.93 (6,743.43; 21,962.11) 30,992.26 (13,999.32; 42,466.01) 49,668.19 (59,033.09; 26,988.22)
Costs are expressed in crore
OSA Obstructive sleep apnea; CHD Coronary heart disease; CPAP Continuous positive airway pressure
Mild OSA 71,228.23 (48,117.92; 77,895.13) Severe OSA 250,430.32 (227,977.88; 266,629.70) Total 321,658.56 (284,891.85; 335,097.37) Adenotonsillectomy and CPAP, n (99 % C.I.) OSA severity Stroke Mild OSA 33,978.42 (21,848.87; 36,163.51) Severe OSA 113,037.18 (103,000.31; 123,575.18) Total 147,015.60 (129,702.48; 154,854.49)
No treatment, n (99 % C.I.) OSA severity Stroke Mild OSA 17,891.76 (9,442.88; 18,072.84) Severe OSA 27,812.09 (20,417.73; 33,844.98) Total 45,703.85 (32,967.40; 49,683.24) Adenotonsillectomy, n (99 % C.I.) OSA severity Stroke
Table 5 Estimated costs of stroke, CHD and type 2 diabetes according to untreated and treated obesity related to OSA
Total 134,640.94 (86,311.97; 142,740.35) 448,044.85 (405,981.91; 487,656.79) 582,685.79 (510,978.12; 611,364.52)
283,538.95 (189,572.16;307,590.17) 998,321.65 (898,050.85; 1,051,472.68) 1,281,860.61 (1,123,968.05; 1,321,104.83)
Total
Total 54,973.11 (29,962.47; 59,153.56) 93,220.40 (65,200.00; 112,576.90) 148,193.52 (104,806.06; 161,226.23)
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because of the reduction of obesity cases due to both AT alone and AT associated to CPAP. Furthermore, simulations showed a reduction of 4,035,207 cases of stroke, 6,216,310 cases of CHD and 23,752,582 cases of type 2 diabetes when considering AT associated to CPAP, compared to those without treatment. Simulated costs of PSG, AT and CPAP on Indian pediatric population suffering from OSA are summarized in Table 4. Diagnosis and treatments are more expensive for children with severe OSA, being the most represented category. Regarding treatments, the most expensive is CPAP. The estimated cost for stroke, CHD and type 2 diabetes according to treated and untreated obesity related to OSA are given in Table 5. Costs for adverse outcomes in patients treated only with AT are higher compared to patients treated with both AT and CPAP: even if CPAP results as more expensive than AT, the two treatments adopted together promote a greater reduction of obese subjects (as it is shown in Table 2) and a consequently higher reduction of adverse outcomes (as is shown in Table 4), resulting in an overall reduction of costs compared to AT alone.
Discussion The aim of the present study was to assess the burden of childhood obesity related to pediatric OSA among Indian population. The authors considered childhood obesity as a consequence of pediatric OSA. Disrupted sleep patterns might promote induction of fatigue, along with a progressive reduction of physical activity. Improving sleep attitudes, patients may feel more comfortable when performing physical activity, interrupting the circular chain of poor sleep enhancing perception of fatigue, which co-causes reduced levels of motivation, provoking finally, lack of physical activity and consequently, weight gain [39]. In addition, irregular sleeping rhythms are strictly associated with modifications in leptin and ghrelin secretion, increasing appetite and consequently, obesity risk [29]. Adverse Outcomes of Obesity in OSA Children The authors considered stroke, CHD and type 2 diabetes as main adverse outcomes of OSA related obesity, this because of the concerning growing epidemic of non-communicable disease among Indian population [16] and the consequent need for public health policies facing these diseases. The authors found out that the treatment of obesity related to OSA results in a reduction of the number of obesity cases and consequently, decrease of the adverse outcomes related to obesity. The reduction of adverse outcomes depending on the decrease of obesity cases is consistent with current literature, demonstrating that weight loss reduces the risk of type 2 diabetes and cardiovascular diseases [40].
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Foreseeable Burden to the Health Care System of Obesity in OSA Children Analyzing the estimated cost for adverse outcomes according to treated and untreated obesity related to OSA, it has been demonstrated that adverse outcomes in patients untreated for obesity related to OSA are less expensive than those in patients treated with only AT or both AT and CPAP. Despite the increased cost of adverse outcomes due to treatments, one has to take into account that after treatments (AT alone and AT and CPAP together) a great reduction in the number of stroke, CHD and type 2 diabetes was observed. Study Limitations The present study, more than a real survey based in direct observation of clinical trials, constitutes just a simulation in order to estimate potential prevalence of OSA related obesity cases. Further research, therefore, should be specifically focused on representative Indian pediatric population. Moreover this study just considered obesity as a consequence of OSA, without taking into account the inverse relationship: obesity as a co-causing factor for OSA. Indeed, it is still less clear which factor affects the other. Thus, future research should investigate contextual variables, influencing rise of such diseases.
Conclusions The potential reduction in the number of adverse outcomes is relevant for public health, providing directions for planning health policies facing non-communicable diseases epidemics, to which obesity related to OSA contributes consistently. Since it has been shown that treatments for obesity related to OSA potentially provide a great reduction in the number of adverse outcomes, governments should provide public health polices which promote the screening for OSA among children: an early detection and a consequently prompt reaction to pediatric OSA could improve the burden of obesity related to this breathing disease.
Contributions IB, AG, DG Designed the study; IB, GL Wrote the manuscript; DG, GL Performed the statistical analysis; KN, SB, RR, GS critically revised the manuscript and contributed to the discussion. All authors contributed to results interpretation, read and approved the final manuscript. IB will act as guarantor for this paper. Conflict of Interest None. Source of Funding This work is partially supported by an unrestricted grant from the Italian Ministry of Foreign Affairs, Directorate General for Country Promotion, and from Prochild ONLUS (Italy).
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