Ọnọdụ ahụike

Ọdịdị ala ahụike bụ itinye ozi ala, echiche, na usoro maka ọmụmụ ahụike, ọrịa, na nlekọta ahụike. Ọdịdị ala ahụike, ngalaba nke, ma ọ bụ ngalaba nwanne nwanyị nke ọdịdị ala ahụ ike, na-elekwasị anya n'ịghọta usoro mpaghara nke ahụike na ọrịa n'ihe gbasara gburugburu ebe obibi na mmekọrịta mmadụ na ibe ya. Dị ka omenala si dị, e nwere mpaghara abụọ dị mkpa nke nyocha n'ime ala ahụike: nke mbụ na-ekwu maka nkesa oghere na ihe ndị na-ekpebi ọrịa na ọnwụ, ebe nke abụọ na-ekwu gbasara atụmatụ ahụike, omume ịchọ enyemaka, na inye ọrụ ahụike.

Nchịkọta

Ọdịdị ala

A kọwawo mpaghara mbụ nke ọmụmụ ihe n'ime ọdịdị ala ahụike dị ka ọrịa na-efe efe ma ọ bụ ọdịdị ala ọrịa ma na-elekwasị anya na usoro na usoro nke ahụike na ọrịa. Enwere ike ịmata ọdịiche dị n'ebe nyocha a site na nkuzi metụtara ya nke ọrịa na-efe efe n'ihi na ọ na-eji echiche na usoro sitere na ala, na-enye ohere maka echiche gburugburu ebe obibi na ahụike nke na-atụle etu mmekọrịta dị n'etiti ụmụ mmadụ na gburugburu ebe obibi si eme ka nsonaazụ ahụike a hụrụ. Mpaghara nke abụọ nke ọmụmụ ahụ lekwasịrị anya na atụmatụ na inye ọrụ ahụike, mgbe mgbe na-elekwasị anya na nhazi mpaghara nke usoro ahụike na nyocha nke otu ndokwa a si emetụta nnweta nlekọta.

Ọnọdụ ahụike

Nnyocha nke ọdịdị ahụike enweela mmetụta site na ịmegharị ọdịdị ahụ ike n'ime mpaghara nke ọdịdị mmekọrịta mmadụ na ibe ya n'ihi mgbanwe gaa na ihe nlereanya mmekọrịta mmadụ na nlekọta ahụike, kama ịbụ ihe nlereanya ahụike. Nke a na-akwado ka a kọwaa ahụike na nlekọta ahụike site na mgbochi na ọgwụ ọrịa naanị na otu nke ịkwalite ọdịmma n'ozuzu. N'okpuru ihe nlereanya a, ụfọdụ ọrịa ndị gara aga (dịka, ọrịa uche) ka a na-amata dị ka nsogbu omume naanị, na ụdị ọgwụ ndị ọzọ (dịka ọmụmaatụ, ọgwụgwọ na-agbakwunye ma ọ bụ ọgwụ ọzọ na ọgwụ ọdịnala) ka ndị na-eme nchọpụta na-amụ, mgbe ụfọdụ site n'enyemaka nke ndị na-ahụ maka ahụike na-enweghị agụmakwụkwọ ahụike. Mgbanwe a na-agbanwe nkọwa nke nlekọta, na-ejighi ya na oghere dịka ụlọ ọgwụ ma ọ bụ ọfịs dọkịta. Ọzọkwa, ihe nlereanya mmekọrịta mmadụ na ibe ya na-ebute ụzọ na mkpakọrịta chiri anya nke a na-eme na oghere ndị na-abụghị omenala nke ọgwụ na nlekọta ahụike yana ndị mmadụ n'otu n'otu dị ka ndị na-azụ ahịa ahụike.

Usoro ọzọ a pụtara na a na-agbasawanye Ọdịdị ala ahụike iji tinye nkà ihe ọmụma dị ka akụ na ụba ndọrọ ndọrọ ọchịchị Marxian, structuralism, mmekọrịta mmadụ na ibe ya, Humanism, feminism na echiche nke nwoke.

A black and white map John Snow's cholera map of the 1854 Broad Street cholera outbreak, with houses of infected people marked in black. — Map ọgbụgbọ na ọgbụgba nke John Snow nke ntiwapụ ọgbụba ọgbụnyụ na Broad Street na 1854.

Relationships between place and health have long been recognized throughout human history, predating modern health delivery systems and providing insights into the transmission of infectious agents, well before the germ theory paradigm shift in the late 1800s. Throughout history, there have been many examples of place and location playing major roles in shaping perceptions of health and risk. The associations between geographical characteristics and health outcomes, which essentially form the foundation of modern medical geography, were recognized more than 2,000 years ago by Hippocrates in his treatise ’’On Airs, Waters, and Places’’ (c. 400 BC). The Industrial Revolution in the 1700s brought with it a plethora of novel public health issues stemming from rapid urban development and poor sanitation, conditions which fueled the development of disease mapping, or medical cartography. A precursor to medical geography, medical cartography arose from the need to communicate spatial discrepancies in risk for diseases of unknown cause, particularly urban outbreaks of cholera and yellow fever. One of the most prominent figures in both epidemiology and medical geography is John Snow, the physician who correctly identified the source of exposure during the 1854 Broad Street cholera outbreak. Snow's famous 1854 map of the cholera outbreak graphically demonstrates that cases were clustered around the Broad Street pump, the source of contaminated water that fueled the epidemic. This map led Snow to identify the contaminated pump and conclude that cholera was a waterborne illness, a remarkable feat given that bacteria were unknown to science at the time. While Snow's contributions to medical geography and epidemiology are irrefutable, the role of the map in this particular investigation is somewhat overstated. Dot maps of cases produced during the industrial period were powerful tools in communicating the findings of traditional epidemiological measures of association, but their role as analytic tools were restricted due to technological limitations.

Ọdịdị ahụike nke oge a bilitere na United States n'afọ ndị 1950 site na ọrụ ọsụ ụzọ nke Jacques May, onye rụrụ ọrụ dị ka dọkịta na-awa ahụ na Thailand na Vietnam ma chọpụta ọdịiche dị n'etiti ahụmịhe ahụike nke ndị ọrịa ya n'ebe ndị a na Europe. Ọ bụ ezie na a ghọtara echiche ahụ na gburugburu ebe obibi nwere ike imetụta ahụike mmadụ kemgbe Hippocrates, ọdịdị ala ahụike dị ka May chere na ọ dabeere n'echiche a, na-akọwa ọdịdị ala ọgwụ dị ka ịrụ ọrụ iji ghọta ọdịdị mmekọrịta dị n'etiti nnyefe pathogen na ihe ndị metụtara ala. May malitere n'oge na-adịghị anya ịkọwapụta nkesa zuru ụwa ọnụ nke ọrịa na ịchọpụta ihe ọdịbendị na gburugburu ebe obibi nke metụtara nkesa ndị a. ^[1]^[2]

A na-ewere ọdịdị ahụike dị ka nkewa n'ime ihe abụọ dị iche iche. Nke mbụ n'ime ha lekwasịrị anya na mpaghara ọrịa na ahụike na-adịghị mma, na-agụnye nyocha nkọwa na-akọwa ọnụọgụ ọrịa na nkesa, yana nyocha nyocha na-emetụta ịchọta ihe njirimara na-eme ka onye ọ bụla ma ọ bụ ndị mmadụ nwee ike ibute ọrịa. Nke a chọrọ nghọta nke ọrịa na-efe efe.^[3] Akụkụ nke abụọ nke ọdịdị ahụike bụ ọdịdị nke nlekọta ahụike, ọkachasị ebe ụlọ ọrụ, nnweta, na ojiji. Nke a chọrọ iji nyocha nke oghere ma na-agbaziri mgbe niile site na akụ na ụba omume.^[4]

Ndị na-ahụ maka ahụike na-echegbu onwe ha banyere mgbasa nke ọrịa dị iche iche n'akụkụ dịgasị iche iche site na mpaghara ruo n'echiche zuru ụwa ọnụ, ma na-enyocha ụwa okike, na mgbagwoju anya ya niile, maka njikọ dị n'etiti ọrịa na ebe. Nke a na-etinye ọdịdị ahụike n'akụkụ ngalaba ndị ọzọ na-ahụ maka mmekọrịta mmadụ na gburugburu ebe obibi. Ndị na-ahụ maka ahụike na-eji ngwá ọrụ nyocha nke oge a iji mee map nke mgbasa nke ahụike, ^[5] gụnyere ọrịa dị iche iche, ka ndị mmadụ na-agbasa ha n'etiti onwe ha, na gafee oghere sara mbara ka ha na-akwaga. Ndị na-ahụ maka ahụike na-elekwa ụdị oghere niile anya dị ka ihe ize ndụ ahụike, site na Ọdachi ndị na-emere onwe ha, ime ihe ike, nrụgide, na ihe ize ndụ ndị ọzọ.

Ọ bụ ezie na nlekọta ahụike bụ ihe bara uru ọha na eze, ọ bụghị onye ọ bụla na-enweta ya. Mkpa maka ọrụ ọha na eze na-arịwanye elu. Ndị mmadụ chọrọ ihe ọmụma na-aga n'ihu na teknụzụ amụma kachasị ọhụrụ, nke ahụike na-enye. Ihe atụ kachasị ọhụrụ nke teknụzụ dị otú ahụ bụ Telemedicine. Ọtụtụ ndị mmadụ na United States enweghị ike ịnweta nlekọta ahụike kwesịrị ekwesị n'ihi enweghị nhata na mkpuchi ahụike na ụzọ isi nweta nlekọta ahụike.^[6]

Mbugharị na nlekota ọrịa: Site na ọbịbịa nke teknụzụ mkpanaka na mgbasa ya, ọ ga-ekwe omume ugbu a iji nyochaa mmegharị nke onye ọ bụla. Site n'ịkọwa mmegharị nke ndị mmadụ n'otu n'otu site na nyochaa ngwaọrụ site na iji ụlọ elu ịnweta ma ọ bụ usoro nsochi ndị ọzọ, ọ ga-ekwe omume ugbu a ikpebi na ọbụna ịchịkwa mgbasa ọrịa. Ọ bụ ezie na iwu nzuzo na-ajụ maka izi ezi nke nsuso ndị mmadụ n'otu n'otu, ndị na-enye ọrụ mkpanaka azụmahịa na-eji usoro nzuzo ma ọ bụ na-enweta nhapụ gọọmentị iji nye ikike soro ndị mmadụ. ^{[citation needed]}

Ụzọ e si eme ya

A na-eji usoro ozi ala (GIS) eme ihe nke ukwuu na ọdịdị ala ahụike iji hụ ma nyochaa data metụtara ahụike. Ihe omuma ihe omuma ndi a nwere ike ịbụ vector (isi, akara, ma obu polygon) ma obu raster (grid na-aga n'ihu) ma na-egosipụtakarị na map isiokwu. A na-ejikarị nsonaazụ ọrịa na njirimara mmekọrịta mmadụ na ibe ya anakọtara site na usoro nlekota na ọnụ ọgụgụ mmadụ eme ihe dị ka isi iyi data na ọmụmụ ala ahụike. N'ọmụmụ ihe gbasara ọrịa, data ihu igwe interpolated, nyocha ala, na ihe oyiyi dịpụrụ adịpụ bụ ihe atụ nke data eji amata njirimara gburugburu Ebe obibi nke usoro ọrịa. A na-etinye ọnụ ọgụgụ ma ọ bụ nyocha nke oghere iji nwalee echiche gbasara usoro ma ọ bụ mmekọrịta dị n'ime data ndị a, dị ka ihe onwunwe nke ịdabere na oghere (ihe ndị dị nso na oghere yiri ma ọ bụ metụtara karịa ihe ndị dị anya na oghere) ma ọ bụ ọdịiche dị iche iche (ebe dị iche iche na ebe ndị ọzọ). Ihe atụ ụfọdụ nke nyocha nke oghere eji eme ihe na ọdịdị ala na-agụnye nyocha nke akara, ule maka njikọ onwe onye nke oghere, ndaghachi azụ nke ala (GWR), modeling nke gburugburu ebe obibi, ọnụ ọgụgụ nyocha oghere, na nyocha netwọk.

Ndị na-ahụ maka ahụike

Ndị ọkà mmụta ahụike a ma ama gụnyere:

Sarah Curtis
William C. Gorgas
Kelvyn Jones
John Snow
Mei-Po Kwan
Nadine Schuurman
Kirsty Duncan

Hụkwa

Ìgwè (epidemiology)
Ihe nlereanya mmekọrịta nke nkwarụ
Ọrịa na-efe efe
Iwu mbụ nke ala nke Tobler
Iwu nke abụọ nke Tobler banyere ala

Ihe odide

↑ Kpọpụta njehie: Invalid <ref> tag; no text was provided for refs named Monmonier 1976
↑ Kpọpụta njehie: Invalid <ref> tag; no text was provided for refs named Meade and Emch 2010
↑ Ocaña-Riola (2010). "Common errors in disease mapping". Geospatial Health 4 (2): 139–54. DOI:10.4081/gh.2010.196. PMID 20503184.
↑ Litva (1995). "Coming out: exposing social theory in medical geography". Health 1 (1): 5–14. DOI:10.1016/1353-8292(95)00002-4.
↑ Murray (2022). "Measuring the health of people in places: A scoping review of OECD member countries" (in en). Health & Place 73. DOI:10.1016/j.healthplace.2021.102731. PMID 34929525.
↑ "The costs of inequality: Money = quality health care = longer life", Harvard Gazette. Retrieved on 2017-02-19. (in en)

Njikọ mpụga

Enweghị nhata na mmekọrịta mmadụ na ibe ya
GeoHealth Laboratory E debere ya na Wayback Machine

Templeeti:Navboxes

[Monmonier_1976-1] Kpọpụta njehie: Invalid <ref> tag; no text was provided for refs named Monmonier 1976

[Meade_and_Emch_2010-2] Kpọpụta njehie: Invalid <ref> tag; no text was provided for refs named Meade and Emch 2010

[3] Ocaña-Riola (2010). "Common errors in disease mapping". Geospatial Health 4 (2): 139–54. DOI:10.4081/gh.2010.196. PMID 20503184.

[4] Litva (1995). "Coming out: exposing social theory in medical geography". Health 1 (1): 5–14. DOI:10.1016/1353-8292(95)00002-4.

[5] Murray (2022). "Measuring the health of people in places: A scoping review of OECD member countries" (in en). Health & Place 73. DOI:10.1016/j.healthplace.2021.102731. PMID 34929525.

[6] "The costs of inequality: Money = quality health care = longer life", Harvard Gazette. Retrieved on 2017-02-19. (in en)

[1]

[2]

[3]

[4]

[5]

[6]