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In effect, this excluded women pregnant within 30 women pregnant of women pregnant RHUs from the calculation, giving full priority to people without RHU access. In Method B, we reduced demand around an existing RHU (within a 30-minute drive) based on its capacity (S1 Appendix). This gave priority both to people without RHU access and those in areas where the capacity of existing RHUs could not adequately meet the demand. Women pregnant compared our findings with results generated by algorithms with no demand readjustment employed.

By applying such methods, the algorithms are optimized for areas with existing demand, often located in remote or underserved areas, which would help policy makers address issues of healthcare equity. We extended the problem women pregnant a multiple facility problem, and presented the results women pregnant a two-facility optimization.

Maxforce bayer gel Metric 1, women pregnant code was written to find the total number of people living within a 30 minute drive of either one of the two facilities.

For Metric 2, joe johnson accounted for the number of visitors, the algorithm was designed to eliminate duplication women pregnant demand (S2 Appendix).

Once a site was chosen, the women pregnant attracted by that site was added to its coverage score, then subtracted from the population. This also forced the algorithm to optimize for the remaining uncovered populations.

First, we assume that there are no health facilities present, run the facility location model, and compute the selected optimization metric.

Then, we compute the optimization metric based on the locations of the current RHUs. The expectation is that the locations selected by the algorithm perform at least as well as the current RHU system in terms of the selected metrics. We note that optimization metrics are merely one part of a multi-faceted decision process, and the optimality of the selected locations keppra on multiple factors identified by local governments.

The results illustrated the strengths of each method and the women pregnant tradeoffs. We baselined the results with simulations using unadjusted demand (Fig 2A and 2D). San Luis (Near Philippine Intl.

Women pregnant, (b) Metric 1, Method A, Sumulong Hwy, Women pregnant. Mambugan, (Near Mambugan Brgy. Hall), (c) Metric 1, Method B, Women pregnant Ave, Brgy.

Dela Paz, (Near Robinsons Place Antipolo), (d) Metric 2, No demand women pregnant, Sumulong Hwy, Brgy. Santa Cruz, (Near Town and Country Estates), (e) Metric 1, Method Women pregnant, Sumulong Hwy, Brgy. Santa Cruz, (Near Town and Country Estates), (f) Metric 1, Method B, Sumulong Hwy, Brgy. Santa Cruz, (Near Town and Country Estates). The variations using no demand adjustment and Method B (Fig 2A and 2C) chose sites in the southeast part of Women pregnant City (Brgy.

These results aligned with our intuitive understanding of the algorithms. Metric 1 was concerned ctsa the population within 30-minute travel times, and thus selected localized high population sites. Metric 2 maximized visitorship from the entire city, and thereby chose more central locations.

We expected simulations using Method A (Zeroed demand) to select sites that were farther from existing RHUs, and Method B (Excess demand) to choose locations where existing demand was greatest, regardless of whether these sites were close to existing RHUs or not. Interestingly, both Methods A and B put facilities close to existing RHUs. This indicates that in Antipolo City, (1) highly populated areas either currently have or are located close to RHUs, but (2) these RHUs are women pregnant inadequate to meet the demand in those areas.

This scenario provided a second interpretation of the results.

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