Suchergebnisse

BACKGROUND: Unconditional cash transfers (UCTs) are a common social protection intervention that increases income, a key social determinant of health, in disaster contexts in low‐ and middle‐income countries (LMICs). OBJECTIVES: To assess the effects of UCTs in improving health services use, health outcomes, social determinants of health, health care expenditure, and local markets and infrastructure in LMICs. We also compared the relative effectiveness of UCTs delivered in‐hand with in‐kind transfers, conditional cash transfers, and UCTs paid through other mechanisms. SEARCH METHODS: We searched 17 academic databases, including the Cochrane Public Health Group Specialised Register, the Cochrane Database of Systematic Reviews (The Cochrane Library 2014, Issue 7), MEDLINE, and EMBASE between May and July 2014 for any records published up until 4 May 2014. We also searched grey literature databases, organisational websites, reference lists of included records, and academic journals, as well as seeking expert advice. SELECTION CRITERIA: We included randomised and quasi‐randomised controlled trials (RCTs), as well as cohort, interrupted time series, and controlled before‐and‐after studies (CBAs) on UCTs in LMICs. Primary outcomes were the use of health services and health outcomes. DATA COLLECTION AND ANALYSIS: Two authors independently screened all potentially relevant records for inclusion criteria, extracted the data, and assessed the included studies' risk of bias. We requested missing information from the study authors. MAIN RESULTS: Three studies (one cluster‐RCT and two CBAs) comprising a total of 13,885 participants (9640 children and 4245 adults) as well as 1200 households in two LMICs (Nicaragua and Niger) met the inclusion criteria. They examined five UCTs between USD 145 and USD 250 (or more, depending on household characteristics) that were provided by governmental, non‐governmental or research organisations during experiments or pilot programmes in response to droughts. Two studies examined the ...

Objectives: Burden of disease estimation commonly requires estimates of the population exposed to a risk factor over a time window (yeart to yeart+n). We present a microsimulation modelling approach for producing such estimates and apply it to calculate the population exposed to long working hours for one country (Italy). Methods: We developed a three-model approach: Model 1, a multilevel model, estimates exposure to the risk factor at the first year of the time window (yeart). Model 2, a regression model, estimates transition probabilities between exposure categories during the time window (yeart to yeart+n). Model 3, a microsimulation model, estimates the exposed population over the time window, using the Monte Carlo method. The microsimulation is carried out in three steps: (a) a representative synthetic population is initiated in the first year of the time window using prevalence estimates from Model 1, (b) the exposed population is simulated over the time window using the transition probabilities from Model 2; and (c) the population is censored for deaths during the time window. Results: We estimated the population exposed to long working hours (i.e. 41-48, 49-54 and ≥55 hours/week) over a 10-year time window (2002-11) in Italy. We populated all three models with official data from Labour Force Surveys, United Nations population estimates and World Health Organization life tables. Estimates were produced of populations exposed over the time window, disaggregated by sex and 5-year age group. Conclusions: Our modelling approach for estimating the population exposed to a risk factor over a time window is simple, versatile, and flexible. It however requires longitudinal exposure data and Model 3 (the microsimulation model) is stochastic. The approach can improve accuracy and transparency in exposure and burden of disease estimations. To improve the approach, a logical next step is changing Model 3 to a deterministic microsimulation method, such as modelling of microflows.