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With this background in mind, this paper employs the Nopo [9] decomposition method to explore how much of the rural urban nutrition gap in Malawi is explained and how much is unexplained by differences in characteristics.
The objective of this paper is to explore how much of the rural-urban nutrition gap in Malawi is explained and how much is unexplained by differences in characteristics.
The results also show that ignoring the common support assumption leads to misleading conclusions about the extent to which rural urban gaps in malnutrition are driven by differences in characteristics or differences in coefficients.
The discrepancies among studies may be explained, at least in part, by differences in characteristics, smoking habits, and lifestyle of the populations examined.
Variability between general practices may be partly caused by differences in characteristics of both the patient populations and the health care providers in the practices (5, 6).
Another possibility is that the discrepancies are caused by differences in characteristics of the study population, therapy or outcome measurements (eg, duration of follow-up).
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The remaining variance is caused by differences in other characteristics such as patient characteristics, organisation characteristics, medical staff characteristics, technology characteristics, or environment.
Changes in hourly wage inequality could be explained by differences in observable characteristics, such as experience and education; different prices for different skills in the labor market; and unobservables, that is, differences in prices and skills within groups.
Such a statistical heterogeneity may be explained both by differences in clinical characteristics of study participants (clinical heterogeneity) and by different stimulation procedures (methodological heterogeneity) adopted.
However, the findings were not consistent, and owing to a lack of detailed information on characteristics of patients, disease, and study design, we could not ascertain whether inconsistencies were caused by differences in these characteristics, selection bias, or confounding factors.
Second, patient inclusion characteristics differed between countries, although logistic regression suggested that differences in outcomes were not solely explained by differences in inclusion characteristics.
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