Es mostren els missatges amb l'etiqueta de comentaris ehealth. Mostrar tots els missatges
Es mostren els missatges amb l'etiqueta de comentaris ehealth. Mostrar tots els missatges
possibles grups de col·laboració
Didac Gómez Salinas IMEC (Belgica) el meu grup es el grup de
“Connected Health Solutions”, que es basicament circuits i sistemes per
Biomedical applications
https://www.imec-int.com/en/wearables
https://www.imec-int.com/en/wearables
CIMTI Centre per la Integració de la Medicina i les Tecnologies innovadores
http://cimti.cat/ca/
CENTRE PER L'IMPACTE.
Accelerant el cicle d’innovació en salut mitjançant un instrument basat en el CIMIT de Boston que aconsegueix la maximització de l’impacte en la salut global a partir de la col·laboració entre experts a través del desenvolupament i la implementació de solucions per millorar el benestar dels ciutadans.
CENTRE PER L'IMPACTE.
Accelerant el cicle d’innovació en salut mitjançant un instrument basat en el CIMIT de Boston que aconsegueix la maximització de l’impacte en la salut global a partir de la col·laboració entre experts a través del desenvolupament i la implementació de solucions per millorar el benestar dels ciutadans.
Qui som
El CIMTI.cat és un projecte de país, impulsat per LEITAT, amb un conveni amb el Departament de Salut – AQuAS i amb una aliança estratègica amb el CIMIT de Boston. Més info
La missió és posar en valor la innovació del sistema sanitari social del país, per fer realitat els projectes que tinguin gran impacte en la salut amb una visió global.
CIMIT: Consortia for Improving Medicine with Innovation & Technology
CIMIT: Consortia for Improving Medicine with Innovation & Technology
DHIS2 Live Package
https://docs.dhis2.org/2.24/en/user/html/ch02s02.html
The DHIS2 Live package is the easiest way to get started with DHIS2 on your local computer. DHIS2 Live is appropriate for a stand-alone installation and demos. Simply download the application from here. Once the file is downloaded, you can simply double-click the downloaded file, and get started using DHIS2.
The DHIS2 Live package is the easiest way to get started with DHIS2 on your local computer. DHIS2 Live is appropriate for a stand-alone installation and demos. Simply download the application from here. Once the file is downloaded, you can simply double-click the downloaded file, and get started using DHIS2.
eHealth promotion
eHealth promotion: the use of the Internet for health promotion
Abstract
The use of the Internet for health promotion is explored in this edition including growth trends, general applicability, and evaluation strategies for online interventions. This article examines the range of preliminary studies of eHealth Promotion interventions and their summary results, and reviews potential evaluation tools and their use in online programming. Also assessed is their utility in population-based programming and review-selected implications for the field.
DHIS2 Mobile
https://www.dhis2.org/mobile
Browser based mobile client
In contexts where mobile data coverage is good and health workers already have phones, using the mobile browser DHIS2 interface may be an important complement to other clients. Cheap, low end mobile phone support browser-based data entry through a simple mobile interface optimized for small screen sizes. You may also consider using a more advanced user interface customized for Android smart phones. The Android smart phone interface also supports offline data entry using HTML5.
The mobile browser interfaces are also great complements for users who ordinarily use the web based data entry, but for some reason need to enter data while on the move. Because the browser is available in many existing handsets and require little extra setup, we typically recommend including basic training in how to access the system using the mobile browser when training staff at any level. Despite the large handset support for browser-based solutions, many projects still prefer limiting the handset base to a well-tested and controlled group of phones, to limit the support and training costs. The costs for the phones is often only a very small part of the rollout of the system, and spending a bit more on phones may give many advantages to future enhancements and evolution of the service.
Browser based mobile client
In contexts where mobile data coverage is good and health workers already have phones, using the mobile browser DHIS2 interface may be an important complement to other clients. Cheap, low end mobile phone support browser-based data entry through a simple mobile interface optimized for small screen sizes. You may also consider using a more advanced user interface customized for Android smart phones. The Android smart phone interface also supports offline data entry using HTML5.
The mobile browser interfaces are also great complements for users who ordinarily use the web based data entry, but for some reason need to enter data while on the move. Because the browser is available in many existing handsets and require little extra setup, we typically recommend including basic training in how to access the system using the mobile browser when training staff at any level. Despite the large handset support for browser-based solutions, many projects still prefer limiting the handset base to a well-tested and controlled group of phones, to limit the support and training costs. The costs for the phones is often only a very small part of the rollout of the system, and spending a bit more on phones may give many advantages to future enhancements and evolution of the service.
DHIS2
https://www.dhis2.org/
Data management and analytics
DHIS 2 lets you manage aggregate data with a flexible data model which has been field-tested for more than 15 years. Everything can be configured through the user interface: You can set up data elements data entry forms, validation rules, indicators and reports in order to create a fully-fledged system for data management. DHIS 2 has advanced features for data visualization, like GIS, charts, pivot tables and dashboards which lets you explore and bring meaning to your data.
Individual data records
DHIS 2 enables you to collect, manage and analyse transactional, case-based data records. It lets you store information about individuals and track these persons over time using a flexible set of identifiers. As an example, you can use DHIS 2 to collect and share essential clinical health data records across multiple health facilities. Individuals can be enrolled for longitudinal programs with several stages. You can configure SMS reminders, track missed appointments, generate visit schedules and much more.
Data management and analytics
DHIS 2 lets you manage aggregate data with a flexible data model which has been field-tested for more than 15 years. Everything can be configured through the user interface: You can set up data elements data entry forms, validation rules, indicators and reports in order to create a fully-fledged system for data management. DHIS 2 has advanced features for data visualization, like GIS, charts, pivot tables and dashboards which lets you explore and bring meaning to your data.
Individual data records
DHIS 2 enables you to collect, manage and analyse transactional, case-based data records. It lets you store information about individuals and track these persons over time using a flexible set of identifiers. As an example, you can use DHIS 2 to collect and share essential clinical health data records across multiple health facilities. Individuals can be enrolled for longitudinal programs with several stages. You can configure SMS reminders, track missed appointments, generate visit schedules and much more.
DHARMA
http://www.dharma.ai
For example:
EHR-LIGHT
Electronic health records systems can be challenging at best, especially for small facilities without existing enterprise-level systems. But no matter the location or level of connectivity, Dharma allows providers to create a HIPAA-compliant, lightweight cloud-based system that includes individual patient records, tracked over time, and requires no fancy network installation or server configuration. Healthcare workers can both enroll patients and manage their files, even from mobile devices; administrators can analyze aggregate data and view population trends. You can even create forms in one language and deploy them in another, so no matter where your hospitals are based, clinicians and administrators both have access to the data they need to make decisions.
Quick and easy setup means that a records system can be set up by anyone – no need for contractors or IT specialists.
Intuitive collection and management makes it easy for busy healthcare providers to enter accurate information (and busy administrators to track it).
Robust results dashboard provides actionable, real-time analytics for clinical teams, management, and third parties.
HEALTH FACILITY SURVEILLANCE
Today’s healthcare networks rely on information from their hospitals and clinics to ensure that they’re providing high-quality care to the people they serve. But collecting data on population demographics, treatments provided, and outcomes across a region can be a challenge – especially when different types of facilities are involved. With Dharma, it’s easy to monitor hospitals, clinics, and mobile healthcare centers, whether they’re around the corner or around the globe.
View data changes in a population over time for any question, customized by day, week, or month.
Cross-comparisons make it easy to quickly identify trends.
Staff management enables you to track healthcare providers’ ability to collect data down to the individual level.
For example:
EHR-LIGHT
Electronic health records systems can be challenging at best, especially for small facilities without existing enterprise-level systems. But no matter the location or level of connectivity, Dharma allows providers to create a HIPAA-compliant, lightweight cloud-based system that includes individual patient records, tracked over time, and requires no fancy network installation or server configuration. Healthcare workers can both enroll patients and manage their files, even from mobile devices; administrators can analyze aggregate data and view population trends. You can even create forms in one language and deploy them in another, so no matter where your hospitals are based, clinicians and administrators both have access to the data they need to make decisions.
Quick and easy setup means that a records system can be set up by anyone – no need for contractors or IT specialists.
Intuitive collection and management makes it easy for busy healthcare providers to enter accurate information (and busy administrators to track it).
Robust results dashboard provides actionable, real-time analytics for clinical teams, management, and third parties.
HEALTH FACILITY SURVEILLANCE
Today’s healthcare networks rely on information from their hospitals and clinics to ensure that they’re providing high-quality care to the people they serve. But collecting data on population demographics, treatments provided, and outcomes across a region can be a challenge – especially when different types of facilities are involved. With Dharma, it’s easy to monitor hospitals, clinics, and mobile healthcare centers, whether they’re around the corner or around the globe.
View data changes in a population over time for any question, customized by day, week, or month.
Cross-comparisons make it easy to quickly identify trends.
Staff management enables you to track healthcare providers’ ability to collect data down to the individual level.
Why Do Evaluations of eHealth Programs Fail?
https://www.ictworks.org/2015/12/09/why-do-evaluations-of-ehealth-programs-fail/?utm_source=ReviveOldPost&utm_medium=social&utm_campaign=ReviveOldPost
Much has been written about why electronic health (eHealth) initiatives fail. Less attention has been paid to why evaluations of such initiatives fail to deliver the insights expected of them. PLoS Medicine has published three papers offering a “robust” and “scientific” approach to eHealth evaluation.
One recommended systematically addressing each part of a “chain of reasoning”, at the centre of which was the program’s goals. Another proposed a quasi-experimental step-wedge design, in which late adopters of eHealth innovations serve as controls for early adopters. Interestingly, the authors of the empirical study flagged by these authors as an exemplary illustration of the step-wedge design subsequently abandoned it in favour of a largely qualitative case study because they found it impossible to establish anything approaching a controlled experiment in the study’s complex, dynamic, and heavily politicised context.
The approach to evaluation presented in the previous PLoS Medicine series rests on a set of assumptions that philosophers of science call “positivist”: that there is an external reality that can be objectively measured; that phenomena such as “project goals”, “outcomes”, and “formative feedback” can be precisely and unambiguously defined; that facts and values are clearly distinguishable; and that generalisable statements about the relationship between input and output variables are possible.
Alternative approaches to eHealth evaluation are based on very different philosophical assumptions. For example,
“interpretivist” approaches assume a socially constructed reality (i.e., people perceive issues in different ways and assign different values and significance to facts)—hence, reality is never objectively or unproblematically knowable—and that the identity and values of the researcher are inevitably implicated in the research process.
“critical” approaches assume that critical questioning can generate insights about power relationships and interests and that one purpose of evaluation is to ask such questions on behalf of less powerful and potentially vulnerable groups (such as patients).
ehealth-fail
10 Alternative Guiding Principles for eHealth Evaluation
Lilford et al. identify four “tricky questions” in eHealth evaluation (qualitative or quantitative?; patient or system?; formative or summative?; internal or external?) and resolve these by recommending mixed-method, patient-and-system studies in which internal evaluations (undertaken by practitioners and policymakers) are formative and external ones (undertaken by “impartial” researchers) are summative. In our view, the tricky questions are more philosophical and political than methodological and procedural.
We offer below an alternative (and at this stage, provisional) set of principles, initially developed to guide our evaluation of the SCR program, which we invite others to critique, test, and refine. These principles are deliberately presented in a somewhat abstracted and generalised way, since they will need to be applied flexibly with attention to the particularities and contingencies of different contexts and settings. Each principle will be more or less relevant to a particular project, and their relative importance will differ in different evaluations.
Think about your own role in the evaluation. Try to strike a balance between critical distance on the one hand and immersion and engagement on the other. Ask questions such as What am I investigating—and on whose behalf? How do I balance my obligations to the various institutions and individuals involved? Who owns the data I collect?
Put in place a governance process (including a broad-based advisory group with an independent chair) that formally recognises that there are multiple stakeholders and that power is unevenly distributed between them. Map out everyone’s expectations of the program and the evaluation. Be clear that simply because a sponsor pays for an evaluation it does not have special claim on its services or exemption from its focus.
Provide the interpersonal and analytic space for effective dialogue (e.g., by offering to feed back anonymised data from one group of stakeholders to another). Conversation and debate is not simply a means to an end, it can be an end in itself. Learning happens more through the processes of evaluation than from the final product of an evaluation report.
Take an emergent approach. An evaluation cannot be designed at the outset and pursued relentlessly to its conclusions; it must grow and adapt in response to findings and practical issues which arise in fieldwork. Build theory from emerging data, not the other way round (for example, instead of seeking to test a predefined “causal chain of reasoning”, explore such links by observing social practices).
Consider the dynamic macro-level context (economic, political, demographic, technological) in which the eHealth innovation is being introduced. Your stakeholder map and challenges of putting together your advisory group should form part of this dataset.
Consider the different meso-level contexts (e.g., organisations, professional groups, networks), how action plays out in these settings (e.g., in terms of culture, strategic decisions, expectations of staff, incentives, rewards) and how this changes over time. Include reflections on the research process (e.g., gaining access) in this dataset.
Consider the individuals (e.g., clinicians, managers, service users) through whom the eHealth innovation(s) will be adopted, deployed, and used. Explore their backgrounds, identities and capabilities; what the technology means to them and what they think will happen if and when they use it.
Consider the eHealth technologies, the expectations and constraints inscribed in them (e.g., access controls, decision models) and how they “work” or not in particular conditions of use. Expose conflicts and ambiguities (e.g., between professional codes of practice and the behaviours expected by technologies).
Use narrative as an analytic tool and to synthesise findings. Analyse a sample of small-scale incidents in detail to unpack the complex ways in which macro- and meso-level influences impact on technology use at the front line. When writing up the case study, the story form will allow you to engage with the messiness and unpredictability of the program; make sense of complex interlocking events; treat conflicting findings (e.g., between the accounts of top management and staff) as higher-order data; and open up space for further interpretation and deliberation.
Consider critical events in relation to the evaluation itself. Document systematically stakeholders’ efforts to re-draw the boundaries of the evaluation, influence the methods, contest the findings, amend the language, modify the conclusions, and delay or suppress publication.
Adapted from Why Do Evaluations of eHealth Programs Fail? An Alternative Set of Guiding Principles by Trisha Greenhalgh and Jill Russell
Much has been written about why electronic health (eHealth) initiatives fail. Less attention has been paid to why evaluations of such initiatives fail to deliver the insights expected of them. PLoS Medicine has published three papers offering a “robust” and “scientific” approach to eHealth evaluation.
One recommended systematically addressing each part of a “chain of reasoning”, at the centre of which was the program’s goals. Another proposed a quasi-experimental step-wedge design, in which late adopters of eHealth innovations serve as controls for early adopters. Interestingly, the authors of the empirical study flagged by these authors as an exemplary illustration of the step-wedge design subsequently abandoned it in favour of a largely qualitative case study because they found it impossible to establish anything approaching a controlled experiment in the study’s complex, dynamic, and heavily politicised context.
The approach to evaluation presented in the previous PLoS Medicine series rests on a set of assumptions that philosophers of science call “positivist”: that there is an external reality that can be objectively measured; that phenomena such as “project goals”, “outcomes”, and “formative feedback” can be precisely and unambiguously defined; that facts and values are clearly distinguishable; and that generalisable statements about the relationship between input and output variables are possible.
Alternative approaches to eHealth evaluation are based on very different philosophical assumptions. For example,
“interpretivist” approaches assume a socially constructed reality (i.e., people perceive issues in different ways and assign different values and significance to facts)—hence, reality is never objectively or unproblematically knowable—and that the identity and values of the researcher are inevitably implicated in the research process.
“critical” approaches assume that critical questioning can generate insights about power relationships and interests and that one purpose of evaluation is to ask such questions on behalf of less powerful and potentially vulnerable groups (such as patients).
ehealth-fail
10 Alternative Guiding Principles for eHealth Evaluation
Lilford et al. identify four “tricky questions” in eHealth evaluation (qualitative or quantitative?; patient or system?; formative or summative?; internal or external?) and resolve these by recommending mixed-method, patient-and-system studies in which internal evaluations (undertaken by practitioners and policymakers) are formative and external ones (undertaken by “impartial” researchers) are summative. In our view, the tricky questions are more philosophical and political than methodological and procedural.
We offer below an alternative (and at this stage, provisional) set of principles, initially developed to guide our evaluation of the SCR program, which we invite others to critique, test, and refine. These principles are deliberately presented in a somewhat abstracted and generalised way, since they will need to be applied flexibly with attention to the particularities and contingencies of different contexts and settings. Each principle will be more or less relevant to a particular project, and their relative importance will differ in different evaluations.
Think about your own role in the evaluation. Try to strike a balance between critical distance on the one hand and immersion and engagement on the other. Ask questions such as What am I investigating—and on whose behalf? How do I balance my obligations to the various institutions and individuals involved? Who owns the data I collect?
Put in place a governance process (including a broad-based advisory group with an independent chair) that formally recognises that there are multiple stakeholders and that power is unevenly distributed between them. Map out everyone’s expectations of the program and the evaluation. Be clear that simply because a sponsor pays for an evaluation it does not have special claim on its services or exemption from its focus.
Provide the interpersonal and analytic space for effective dialogue (e.g., by offering to feed back anonymised data from one group of stakeholders to another). Conversation and debate is not simply a means to an end, it can be an end in itself. Learning happens more through the processes of evaluation than from the final product of an evaluation report.
Take an emergent approach. An evaluation cannot be designed at the outset and pursued relentlessly to its conclusions; it must grow and adapt in response to findings and practical issues which arise in fieldwork. Build theory from emerging data, not the other way round (for example, instead of seeking to test a predefined “causal chain of reasoning”, explore such links by observing social practices).
Consider the dynamic macro-level context (economic, political, demographic, technological) in which the eHealth innovation is being introduced. Your stakeholder map and challenges of putting together your advisory group should form part of this dataset.
Consider the different meso-level contexts (e.g., organisations, professional groups, networks), how action plays out in these settings (e.g., in terms of culture, strategic decisions, expectations of staff, incentives, rewards) and how this changes over time. Include reflections on the research process (e.g., gaining access) in this dataset.
Consider the individuals (e.g., clinicians, managers, service users) through whom the eHealth innovation(s) will be adopted, deployed, and used. Explore their backgrounds, identities and capabilities; what the technology means to them and what they think will happen if and when they use it.
Consider the eHealth technologies, the expectations and constraints inscribed in them (e.g., access controls, decision models) and how they “work” or not in particular conditions of use. Expose conflicts and ambiguities (e.g., between professional codes of practice and the behaviours expected by technologies).
Use narrative as an analytic tool and to synthesise findings. Analyse a sample of small-scale incidents in detail to unpack the complex ways in which macro- and meso-level influences impact on technology use at the front line. When writing up the case study, the story form will allow you to engage with the messiness and unpredictability of the program; make sense of complex interlocking events; treat conflicting findings (e.g., between the accounts of top management and staff) as higher-order data; and open up space for further interpretation and deliberation.
Consider critical events in relation to the evaluation itself. Document systematically stakeholders’ efforts to re-draw the boundaries of the evaluation, influence the methods, contest the findings, amend the language, modify the conclusions, and delay or suppress publication.
Adapted from Why Do Evaluations of eHealth Programs Fail? An Alternative Set of Guiding Principles by Trisha Greenhalgh and Jill Russell
Blood Pressure Under Pressure
Learning to Predict Blood Pressure with Deep Bidirectional LSTM Network. PAPER
Around 3 in 10 deaths globally are caused by cardiovascular diseases (CVD) - diseases of the
heart and blood vessels that can cause heart attacks and stroke. 2 As the leading risk factor
of CVD (Lim et al., 2013), high blood pressure (BP) has been commonly used as the critical
criteria for diagnosing and preventing CVD. High BP, which is also known as hypertension,
normally develops without obvious symptoms at early stage, making it a “silent killer”.
Therefore, accurate and continuous BP monitoring during people’s daily live is extremely
imperative for CVD prevention and diagnosis. In addition, blood pressure variability (BPV)
reflects how a cardiovascular system regularize itself and response to external stimulus, and
is another critical cardiovascular indicator that can only be obtained through continuous
and long-duration BP monitoring.
Current BP measurement devices, e.g., Omron products, are cuff-based and therefore
bulky, discomfort to wear, and only suitable for snapshot measurements. These disadvantages
restrict the use of the cuff-based devices for continuous and frequent BP measurement,
which are essential for nighttime monitoring and precise diagnosis of different CVD symptoms.
Recent advancements in sensing technologies provide a wearable sensor network
solution that can achieve cuffless and continuous BP monitoring (Chan et al., 2007; Zheng
et al., 2014). These new emerged sensing technologies can detect several human physiological
signs through contacting corresponding sensors to the human body. For example,
electrocardiography (ECG) sensor can detect tiny skin impedance variations that arise from
the hearts electrophysiologic pattern during each heart beat; photoplethysmogram (PPG)
sensor can probe blood volume variation inside arteries, and etc. While all these physiological
sensing signals contain enormous information of the functionality and health status of
our cardiovascular system, the data is difficult to mine effectively due to noisy observation,
missing value and varying length. Extensive research efforts have been made to develop
effective models to predict or estimate BP from the sensor output; examples include the
well established physiological modeling method - Pulse transit time model, and the recently
proposed machine learning approach - regression model such as support vector machine,
decision tree and etc
10 facts on the state of global health WHO
from:
A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
El nuevo protagonismo de las enfermedades crónicas en los ODS
El nuevo protagonismo de las enfermedades crónicas en los ODS
La
agenda de los Objetivos de Desarrollo Sostenible reconoce la
importancia de luchar contra dolencias no infecciosas para luchar contra
la pobreza y favorecer el desarrollo
¿Por qué las enfermedades no infecciosas o crónicas (en inglés, non communicable diseases) han pasado de ser indiferentes en los Objetivos del Milenio (ODM) a un protagonismo principal en los nuevos Objetivos de Desarrollo Sostenible (ODS) que deberán guiar la acción internacional hasta 2030?
La
respuesta a esta pregunta no puede ser contestada con un único
argumento, sino que responde a múltiples explicaciones que pueden ser
valoradas de forma diferente según la subjetividad de cada analista. La
afirmación implícita en la pregunta, puede no ser compartida por igual
por todos los lectores, pero pretende provocar intelectualmente para
favorecer diferentes opiniones que en su conjunto puedan aportar más luz
a la pregunta propuesta.
Entendemos
por enfermedades crónicas las dolencias cardiovasculares, accidentes
vasculares cerebrales e hipertensión arterial; así como el sobrepeso,
obesidad y diabetes, el cáncer y enfermedades crónicas pulmonares. Todas
ellas estaban implícitamente incluidas en el ODM 6 como
"otras enfermedades". La misma redacción otorgaba a las enfermedades
crónicas un protagonismo muy poco relevante respecto a los otros
objetivos de salud como la lucha contra la malaria, tuberculosis y el
sida. Esta indiferencia podía ser una de las explicaciones de la
asimetría en la asignación de recursos económicos que se dedicaron a
combatir las enfermedades crónicas respecto a las otras mencionadas.
A modo de ejemplo, en una publicación en The Lancet del
2014, se afirmaba que por cada dólar invertido en mejorar la calidad de
vida de las personas con discapacidad, se invertían 770 dólares en VIH,
180 en malaria o seis en salud maternal y recién nacidos.
Esta discriminación entrañaba una contradicción con los análisis realizados por la propia Organización Mundial de la Salud (OMS). En
el 2008, el organismo publicaba que las enfermedades no infecciosas
eran la principal causa porcentual de muerte en el mundo respecto al
conjunto de las demás (infecciosas, maternal, perinatal y condiciones
nutricionales). Asimismo, acontecían en mayor proporción en personas
jóvenes (menores de 60 años) en los países de media y baja renta.
Esta paradoja ha sido posteriormente confirmada con creces. Basta atender a los datos: las enfermedades no infecciosas causan
más muertes que todas las demás combinadas. Se calcula que las
defunciones aumentarán de los 38 millones en 2012 (el 68% del total de
muertes al año) a 52 millones para 2030. Y la carga de enfermedad de las
dolencias crónicas aumentará en los próximos 10 años un promedio del 17
% en el mundo. El continente más afectado será África con un aumento
del 27%. Es importante destacar que de los 38 millones de muertes en
2012, más del 40% (16 millones) fueron prematuras (personas menores de
70 años) y en su mayoría (82%) se produjeron en países de renta media-baja.
El
crecimiento económico ha venido acompañado de una rápida urbanización,
un descenso de la actividad física, un aumento de hábitos alimentarios
no saludables y mayor consumo de alcohol y tabaco
Esta
constatación de los resultados fue la que justificó la celebración de
la segunda reunión de la Asamblea General de Naciones Unidas dedicada a
un tema monográfico de salud, y la posterior declaración en 2011 sobre
la prevención y control de las enfermedades no infecciosas.
Como
expone la pregunta inicial del artículo, las dolencias crónicas han
pasado a tener un protagonismo principal en los nuevos ODS relacionados
con la salud global. En una primera lectura, puede llamar la atención
que solo el objetivo 3 esté dedicado a salud. Pero en su desarrollo, ha
incorporado nueve metas principales y cuatro secundarias que incluyen la
continuidad de los retos que se fijaban en los ODM, pero que incorporan
además los accidentes de tráfico, las enfermedades mentales y, de forma
muy importante, la universalización de la salud. De estas 13 metas de
salud quisiera destacar la que llama a reducir a un tercio la mortalidad
prematura por las enfermedades crónicas mediante la prevención y el
tratamiento, así como promover la salud mental y el bienestar.
Así,
las diferencias más significativas entre los ODM y los ODS respecto a
la salud se resumen en tres. Los primeros fueron redactados por un
comité de expertos nombrados por Naciones Unidas a diferencia de los
ODS, que son el resultado de un extenso y largo proceso consultivo que
ha involucrado a muchos grupos de trabajo abiertos, organizaciones de la
sociedad civil, consultas por temas y países, participación popular,
reuniones presenciales, encuestas... Los ODS se asientan sobre las cinco P (personas, planeta, prosperidad, paz y partenariados).
En
segundo lugar, los ODM tenían ocho objetivos, 21 metas y 61
indicadores. Los nuevos ODS tienen 17 objetivos con 169 metas y más de
200 indicadores. Los primeros estaban centrados en los países en
desarrollo, mientras sus sucesores son universales, pues todos los
países están llamados a cumplirlos. Los ODS incluyen una visión de
asociación con el sector privado y, muy importante, los ODM no estaban
específicamente dirigidos a los principales asesinos de la salud
En
mi opinión, la planificación de los ODM quedó sobrepasada por la
realidad de los acontecimientos A ello habría que sumar que la capacidad
de respuesta de los países ha sido muy rudimentaria, con resultados (si
es que los hay) tardíos o muy lentos ante los nuevos retos que se van
presentando.
Así,
se hace necesario exponer —aunque sea de una manera muy breve— cuáles
son esos cambios que están sucediendo y que no fueron incorporados en la
propia planificación de los ODM. Uno es la transición demográfica: en
el transcurso del siglo XX hemos doblado la esperanza de vida y
cuadriplicado la población mundial pasando de 2.500 millones en 1950 a
proyectarse que en 2050 habrá 9.500. El porcentaje de personas mayores
(más de 60) respecto a la población mundial era de un 7,5% en 1950 y se
calcula que se incrementará hasta el 22 % en 2050. Al inicio de la
revolución industrial, la esperanza de vida en el mundo era de 35 años,
de 46 años en la década de los 50, para incrementarse hasta los 70 en
los 70. Es decir, se había doblado desde la revolución industrial. Esta
tendencia se da en todos los países del mundo, pero con una diferencia
de 20 años (menos) entre los países de media-baja respecto a los ricos
Cabe
destacar también la transición epidemiológica. El crecimiento económico
ha venido acompañado de una rápida urbanización, un descenso de la
actividad física individual, un aumento de los hábitos alimentarios no
saludables, un mayor consumo de alcohol y tabaco... Todo ello está
provocando que las principales causas de mortalidad se hayan desplazado
de las infecciosas a las crónicas.
Aunque
esta transición ha sido completada en muchos países, pero hay que
destacar que en los de renta media y baja sufren una doble carga: sin
haber finalizado la lucha contra las enfermedades infecciosas, la
desnutrición o la mortalidad infantil y maternal, se les pide que
combatan, como marca la agenda internacional, las crónicas.
Las
enfermedades no infecciosas son reconocidas por los Gobiernos como uno
de los mayores retos que tienen los países de media y baja renta para su
desarrollo. La pobreza potencia los factores de riesgo y, en un círculo
vicioso, padecerlas favorece la pobreza crónica.
Hay
que romper este círculo asignando a la prevención y tratamiento de
estas dolencias los recursos humanos y económicos necesarios para poder
dar respuesta a una realidad cambiante. cambiantes de la nueva realidad.
Xavier de las Cuevas es el responsable de cooperación del Colegio de Médicos de Barcelona.
http://weberpafrica.blogspot.co.uk/2013/11/announcing-project-mtuha.html
a plan to produce an application based on merging +KwaMoja with Care2x
a plan to produce an application based on merging +KwaMoja with Care2x
ICT4D: The tale of the West African Health Messager – the pitfalls, the triumphs and the lessons etched in stone
http://www.smartmonkeytv.com/channel/newsletters/ict4d_the_tale_of_the_west_african_health_messager_the_pitfalls_the_triumph
Firstly, data collection was far from accurate largely because it was a low priority in places that are extremely busy. Furthermore, getting data accurate will need large challenges to be overcome as things like some form of unique user ID is required.
Firstly, data collection was far from accurate largely because it was a low priority in places that are extremely busy. Furthermore, getting data accurate will need large challenges to be overcome as things like some form of unique user ID is required.
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