Chapter-174 The MOGE(S) Classification of Cardiomyopathy: Is it Relevant in India and Other Low and Middle-income Countries?

Objectives: To overcome the inherent limitations of the medical and social models of disability, the World Health Organization developed the first international conceptualization of disability: the International Classification of Functioning, Disability, and Health (ICF). Despite the ICF’s robustness, it is still underutilized in research and practice in Low- and Middle-Income Countries (LMICs). This article discusses the applications, strengths, limitations, and unique considerations when using the ICF to explore disabilities in LMICs like Nigeria. Methods: A literature search was conducted in Medline, CINAHL, Web of Science, AMED, and Google Scholar. Articles were selected if they reported on any of the development, structure, applications, strengths, and limitations of the ICF. The review draws from the selected articles using a narrative discussion. Results: The literature search yielded 22 articles that met the eligibility criteria. The ICF is a classification of components of health and functioning, which views disability as an outcome of a dynamic interaction between a person’s health condition and contextual factors. The ICF was developed cross-culturally; thus, it is applicable in various socio-environmental contexts. However, despite its comprehensiveness, the ICF is criticized for lack of clear theoretical underpinnings, overlapping and redundant components, and absence of systematized personal factors. Discussion: The ICF has brought a significant paradigm shift in the measurement of disability by explicitly recognizing the role of contextual factors in the incapacitation process and placing all health conditions on an equal footing. Hence, stakeholders in disability research and practice in LMICs need to prioritize the ICF over other disablement models.

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Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs)

Social Sciences ◽

10.3390/socsci9050080 ◽

2020 ◽

Vol 9 (5) ◽

pp. 80 ◽

Cited By ~ 5

Author(s):

Dana R. Thomson ◽

Monika Kuffer ◽

Gianluca Boo ◽

Beatrice Hati ◽

Tais Grippa ◽

...

Keyword(s):

Urban Areas ◽

Informal Settlement ◽

Social Characteristics ◽

Visual Interpretation ◽

Middle Income ◽

Household Level ◽

Middle Income Countries ◽

The People ◽

Low And Middle Income

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low- and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas.

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