Lefu la tsoekere ke lefu le atileng haholo, le amang batho ba limilione lefatšeng ka bophara.
Ho latela International Diabetes Federation, ka 2019, batho ba baholo ba ka bang limilione tse 463 ba lilemo li 20-79 ba ne ba phela le lefu la tsoekere sebakeng sa IDF Amerika Leboea le Caribbean.
Ho ata ha lefu la tsoekere ho fapana ho ea ka libaka,'me libaka tse ling li na le litekanyetso tse phahameng ho feta tse ling.
Ke habohlokoa ho hlokomela hore lipalo tsena li lula li fetoha ha ho ntse ho fumanoa bakuli ba bacha le ha baahi ba eketseha.
Ho phaella moo, ho ata ha lefu la tsoekere, kapa palo ea batho ba tšoaroang ke lefu lena selemo le selemo, le eona ke ntlha ea bohlokoa eo ho lokelang ho nahanoa ka eona.
Ho ata ha lefu la tsoekere ho 'nile ha eketseha ha nako e ntse e feta, mohlomong ka lebaka la lintho tse kang ho eketseha ha batho ba batenya haholo, ho lula fatše le ho tsofala ha baahi.
Boiteko ba bophelo bo botle ba sechaba bo lebisitsoe ho thibeleng le ho laoleng lefu la tsoekere ho fokotsa moroalo oa lona ho batho ka bomong le litsamaiso tsa tlhokomelo ea bophelo.
Appuhamy JA, Kebreab E, France J: A mathematical model for determining age-specific diabetes incidence and prevalence using body mass index. Ann Epidemiol. 2013, 23 (5): 248-54.
Blanchard JF, Ludwig S, Wajda A, Dean H, Anderson K, Kendall O, Depew N: Incidence and prevalence of diabetes in Manitoba, 1986-1991. Diabetes Care. 1996, 19 (8): 807-11.
Meza R, Barrientos-Gutierrez T, Rojas-Martinez R, Reynoso-Noverón N, Palacio-Mejia LS, Lazcano-Ponce E, Hernández-Ávila M: Burden of type 2 diabetes in Mexico: past, current and future prevalence and incidence rates. Prev Med. 2015, 81 (): 445-50.
Tönnies T, Imperatore G, Hoyer A, Saydah SH, D'Agostino RB, Divers J, Isom S, Dabelea D, Lawrence JM, Mayer-Davis EJ, Pihoker C, Dolan L, Brinks R: Estimating prevalence of type I and type II diabetes using incidence rates: the SEARCH for diabetes in youth study. Ann Epidemiol. 2019, 37 (): 37-42.
Shrestha SS, Thompson TJ, Kirtland KA, Gregg EW, Beckles GL, Luman ET, Barker LE, Geiss LS: Changes in Disparity in County-Level Diagnosed Diabetes Prevalence and Incidence in the United States, between 2004 and 2012. PLoS One. 2016, 11 (8): e0159876.
['Tlhokomeliso: tsa bongaka']
["Websaete ena e etselitsoe ho ruta le ho fana ka boitsebiso feela'me ha e fane ka keletso ea bongaka kapa litšebeletso tsa litsebi."]
["Boitsebiso bo fanoeng ha boa lokela ho sebelisoa ho hlahloba kapa ho phekola bothata ba bophelo bo botle kapa lefu,'me ba batlang keletso ea bongaka ba lokela ho buisana le ngaka e nang le lengolo la tumello."]
['Ka kōpo hlokomela hore neural net e hlahisang likarabo lipotsong, ha e nepahale haholo-holo ha ho tluoa linomorong.']
["Kamehla batla keletso ea ngaka ea hao kapa mofani e mong ea tšoanelehang oa tlhokomelo ea bophelo mabapi le boemo ba bongaka. Le ka mohla u se ke ua hlokomoloha keletso ea setsebi sa bongaka kapa ua lieha ho e batla ka lebaka la ntho eo u e balileng websaeteng ena. Haeba u nahana hore u ka 'na ua ba le boemo ba tšohanyetso ba bongaka, letsetsa 911 kapa u ee kamoreng ea tšohanyetso e haufi hang-hang. Ha ho kamano ea ngaka le mokuli e bōptjoang ke websaeteng ena kapa tšebeliso ea eona. BioMedLib kapa basebetsi ba eona, kapa motho leha e le ofe ea tlatsetsang ho websaeteng ena, ha a etse litemoso, tse hlakileng kapa tse sa hlakang, mabapi le boitsebiso bo fanoeng mona kapa tšebeliso ea eona."]
['Tlhokomeliso: litokelo tsa bangoli']
['Molao oa Digital Millennium Copyright Act oa 1998, 17 U.S.C. § 512 (DMCA) o fana ka litokelo tsa beng ba litokelo tsa bangoli ba lumelang hore thepa e hlahang Inthaneteng e hatakela litokelo tsa bona tlasa molao oa litokelo tsa bangoli ba U.S. ']
['Haeba u lumela ka tumelo e ntle hore litaba leha e le life kapa thepa e fumanehang mabapi le websaeteng kapa litšebeletso tsa rona e tlōla litokelo tsa hau tsa molao, uena (kapa moemeli oa hau) a ka re romella tsebiso e kōpang hore litaba kapa thepa e tlosoe, kapa ho fihlella ho eona ho thibeloe. ']
['Ditsebiso di tlameha ho romelwa ka lengolo ka imeile (bona karolo ya "Ho iteanya" bakeng sa aterese ya imeile). ']
["DMCA e hloka hore tsebiso ea hao ea ho tlōla ha molao ho boleloang hore e na le boitsebiso bo latelang: (1) tlhaloso ea mosebetsi o sirelelitsoeng ka molao o boleloang hore o tlōloa; (2) tlhaloso ea litaba tse boleloang hore li tlōla molao le boitsebiso bo lekaneng ho re lumella ho fumana litaba; (3) boitsebiso ba ho ikopanya le uena, ho akarelletsa aterese ea hau, nomoro ea fono le aterese ea imeile; (4) polelo ea hau ea hore u na le tumelo e ntle ea hore litaba ka tsela e belaelloang ha e lumelloe ke mong'a litokelo tsa molao, kapa moemeli oa eona, kapa ka ts'ebetso ea molao leha e le ofe; "]
['(5) polelo e saennweng ke wena, tlasa kotlo ya ho hlapanya leshano, ya hore tlhahisoleseding e tsebisong e nepahetse le hore o na le matla a ho tiisa ditokelo tsa mongodi tseo ho thweng di a tlolwa; ']
["le (6) ho saena ka letsoho kapa ka elektronike ha mong'a litokelo tsa bangoli kapa motho ea lumeletsoeng ho nka khato lebitsong la mong'a litokelo tsa bangoli. "]
['Ho hloleha ho kenyelletsa tlhahisoleseding yohle e ka hodimo ho ka baka ho dieha ha ho sebetswa tletlebo ya hao.']
['Ho Iteanya']
['Ka kōpo re romelle imeile ka potso / tlhahiso leha e le efe.']
How common is diabetes?
Diabetes is a very common disease, affecting millions of people worldwide.
According to the International Diabetes Federation, in 2019, approximately 463 million adults aged 20-79 years were living with diabetes in the IDF North America and Caribbean region.
The prevalence of diabetes varies by region, with some areas having higher rates than others.
It is important to note that these numbers are constantly changing as new cases are diagnosed and the population grows.
Additionally, the incidence of diabetes, or the number of new cases diagnosed each year, is also an important factor to consider.
The incidence of diabetes has been increasing over time, likely due to factors such as increasing obesity rates, sedentary lifestyles, and aging populations.
Public health efforts are focused on preventing and managing diabetes to reduce its burden on individuals and healthcare systems.
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Please note the neural net that generates answers to the questions, is specially inaccurate when it comes to numeric content. For example, the number of people diagnosed with a specific disease.
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['Hoo e ka bang']
["BioMedLib e sebelisa lik'homphieutha tse iketsang (machine-learning algorithms) ho hlahisa lipotso le likarabo."]
['Re qala ka lingoliloeng tse limilione tse 35 tsa biomedical tsa PubMed/Medline. Hape, maqephe a websaete a RefinedWeb.']
