Factors associated with self-reported ill health among older Ugandans: A cross sectional study

Wandera, Stephen Ojiambo; Golaz, Valérie; Kwagala, Betty; Ntozi, James

doi:10.1016/j.archger.2015.05.006

Cited by 31 publications

(34 citation statements)

References 45 publications

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“…Other studies highlight that women over-report worse health status than men [41][42][43][44]. Various authors have argued that women are more sensitive, interface with the healthcare system as caregivers of their children and easily report health conditions than men [43,44]. In our study, we found that the large gender disparities are confined to rural areas and absent in urban areas.…”

Section: Discussionsupporting

confidence: 51%

“…Several studies argue that poorer health among women is due to biological as well as behavioural factors and cannot be explained by differences in socio-economic and demographic factors only [4,[40][41][42]. Other studies highlight that women over-report worse health status than men [41][42][43][44]. Various authors have argued that women are more sensitive, interface with the healthcare system as caregivers of their children and easily report health conditions than men [43,44].…”

Section: Discussionmentioning

confidence: 99%

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Decomposing gender inequalities in self-assessed health status in Liberia

Murendo¹,

Murenje²

2018

Global Health Action

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Background: Understanding the magnitude of inequalities and drivers for reducing genderrelated health inequalities is crucial in developing countries. This is particularly the case for Liberia with its very high level of gender-related inequalities in health and health outcomes. Objective: This paper assesses the magnitude of gender health inequalities and the relative contribution of different factors to health inequality in Liberia. Methods: Data came from the Liberian Household Income Expenditure Survey 2014. A two stage sampling methodology was used and 4,104 households were randomly selected and interviewed. The main variable of interest is dichotomised, good versus poor self-assessed health. Gender-related health inequality is assessed using the Oaxaca-Blinder decomposition for non-linear models. The decomposition reveals the magnitude of inequality and contributions of different factors. Results: We found large gender disparities (0.054, p < 0.01) characterised by women disadvantages in health status. In addition, the gender health disparities are mostly pronounced in rural areas. About 54% of the gender inequalities in health status were explained by the differences in endowments. Equalizing access to information, wealth and utilization of mosquito nets would reduce the gender gaps by 44, 5 and 4%, respectively. Conclusions: Addressing gender health inequalities inter alia requires access to health information (i.e. electronic and print media), gender responsive interventions that improve wealth in key sectors (i.e. education, employment, social protection, housing, and other appropriate infrastructure). In addition, the government, private sector and civil society should ensure that the health sector provides access to quality mosquito nets and improved health services including preventive care in order to reduce disease burden.

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Section: Discussionsupporting

confidence: 51%

Section: Discussionmentioning

confidence: 99%

Decomposing gender inequalities in self-assessed health status in Liberia

Murendo¹,

Murenje²

2018

Global Health Action

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“…Featured research topics in Figure 5 were based on the top cited publications from each income group in each year: high–income countries [2,13,23–36], upper–middle income countries [8,37–45], lower-middle income countries [7,9,46–50], and low–income countries [51–53]. …”

Section: Resultsmentioning

confidence: 99%

Mapping the global research landscape and knowledge gaps on multimorbidity: a bibliometric study

Mishra

Jones

2017

Journal of Global Health

104

120

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BackgroundTo summarize global research trends and activities on multimorbidity; then to assess the knowledge gaps and to identify implications for knowledge exchange between high income countries (HICs) and low– and middle– income countries (LMICs).MethodsA comprehensive search was conducted to identify research publications on multimorbidity in the Web of ScienceTM, as well as diabetes, depression, hypertension, and Chronic Obstructive Pulmonary Disease (COPD). The time frame for the search was from 1900 to June, 2016. Information (such as publication date, subject category, author, country of origin, title, abstract, and keywords) were extracted and the full texts were obtained for the co–citation analysis. Data were linked with the life expectancy at birth (years) and Gross National Income (GNI). Co–citation and hierarchal clustering analysis was used to map the trends and research networks with CiteSpace II (JAVA freeware, copyright Chaomei Chen, http://cluster.cis.drexel.edu/~cchen/citespace/).FindingsWe identified 2864 relevant publications as at June 2016, with the first paper on this topic indexed in 1974 from Germany, but 80% were published after 2010. Further analysis yielded two knowledge gaps: (1) compared with single conditions (diabetes, hypertension, depression, and COPD), there is a mismatch between the high prevalence of multimorbidity and its research outputs (ratio of articles on multimorbidity vs other four single conditions is 1:13–150); (2) although a total of 76 countries have contributed to this research area, only 5% of research originated from LMICs where 73% of non–communicable disease (NCD) related deaths had occurred. Additional analysis showed the median year of first publication occurred 15 years later in the LMICs compared with HICs (2010 vs 1995); and longer life expectancy was associated with exponentially higher publication outputs (Pearson correlation coefficient r = 0.95) at the global level. The life expectancy at the median year (1994) of first publication was 66.1, with the gap between LMICs and HICs 7.9 (68.4 vs 76.3).ConclusionsThis study confirms substantial knowledge gaps in the research agenda on multimorbidity, with input urgently needed to move us forward worldwide, especially for and in LMICs. There is the possibility that LMICs can learn from and collaborate with HICs in this area.

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“…Different vulnerability indicators were tested on the 1991 and 2002 population census data (Antoine and Golaz 2011; Golaz and Rutaremwa 2011). Disabilities and ill health among older people were obtained using Uganda National Household Survey (UNHS) data (Wandera et al 2014, 2015). Perceptions related to old age (Najjumba-Mulindwa 2003) and the feeling of loneliness among older people (Nzabona, Ntozi and Rutaremwa 2015) were analysed through specific quantitative surveys in different parts of the country.…”

Section: Vulnerability and Resilience In The Ugandan Contextmentioning

confidence: 99%