Nutrition plays an important role in bone health. The aim of our study was to update the evidence regarding dairy intake, osteoporotic fracture (OF) risk, and prospective bone mass density (BMD) evolution assessed by dual-energy X-ray absorptiometry in Europeans and non-Hispanic whites from North America. A systematic search was conducted in MEDLINE, EMBASE, and Scopus for papers published from 1 January, 2000 to 30 April, 2018. The eligibility criteria were as follows: healthy adults; measurable dairy exposure; hip, vertebral, wrist or OF as outcomes; and cohort or case-control studies. Two independent investigators conducted the search and the data extraction. A pooled analysis was conducted with random-effects models. Publication bias and meta-regression were considered. Ten cohort studies relating to OF risk were selected for meta-analysis. Three papers reporting BMD changes associated with dairy intake could not be aggregated in the meta-analysis. The pooled HRs of the highest compared with the lowest levels of dairy intake were 0.95 (95% CI: 0.87, 1.03; I 2 = 82.9%; P-heterogeneity < 0.001) for OF at any site; 0.87 (95% CI: 0.75, 1.01; I 2 = 86.7%; P-heterogeneity < 0.001) for hip fractures; and 0.82 (95% CI: 0.68, 0.99; I 2 = 0.0%; P-heterogeneity = 0.512) for vertebral fractures. Concerning BMD, the selected studies described a 1.7-3% lower hip BMD in young and postmenopausal women with poor intake of milk in their youth, a positive relationship between baseline milk ingestion and the percentage of trochanter BMD change in elderly people, and a positive correlation between milk consumption and BMD change at the radius in women aged >65 y. In conclusion, in the studied population, the highest consumption of dairy products did not show a clear association with the total OF or hip fracture risks; however, a diminished risk of vertebral fracture could be described. The results regarding BMD change were heterogeneous and did not allow for a definitive conclusion. Adv Nutr 2019;10:S120-S143.

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Background: The COVID-19 pandemic has had global effects; cases have been counted in the tens of millions, and there have been over two million deaths throughout the world. Health systems have been stressed in trying to provide a response to the increasing demand for hospital beds during the different waves. This paper analyzes the dynamic response of the hospitals of the Community of Madrid (CoM) during the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in the period between 18 March and 31 May 2020. The aim was to model the response of the CoM’s health system in terms of the number of available beds. Methods: A research design based on a case study of the CoM was developed. To model this response, we use two concepts: “bed margin” (available beds minus occupied beds, expressed as a percentage) and “flexibility” (which describes the ability to adapt to the growing demand for beds). The Linear Hinges Model allowed a robust estimation of the key performance indicators for capturing the flexibility of the available beds in hospitals. Three new flexibility indicators were defined: the Average Ramp Rate Until the Peak (ARRUP), the Ramp Duration Until the Peak (RDUP), and the Ramp Growth Until the Peak (RGUP). Results: The public and private hospitals of the CoM were able to increase the number of available beds from 18,692 on 18 March 2020 to 23,623 on 2 April 2020. At the peak of the wave, the number of available beds increased by 160 in 48 h, with an occupancy of 90.3%. Within that fifteen-day period, the number of COVID-19 inpatients increased by 200% in non-intensive care unit (non-ICU) wards and by 155% in intensive care unit (ICU) wards. The estimated ARRUP for non-ICU beds in the CoM hospital network during the first pandemic wave was 305.56 beds/day, the RDUP was 15 days, and the RGUP was 4598 beds. For the ICU beds, the ARRUP was 36.73 beds/day, the RDUP was 20 days, and the RGUP was 735 beds. This paper includes a further analysis of the response estimated for each hospital. Conclusions:This research provides insights not only for academia, but also for hospital management and practitioners. The results show that not all of the hospitals dealt with the sudden increase in bed demand in the same way, nor did they provide the same flexibility in order to increase their bed capabilities. The bed margin and the proposed indicators of flexibility summarize the dynamic response and can be included as part of a hospital’s management dashboard for monitoring its behavior during pandemic waves or other health crises as a complement to other, more steady-state indicators.