Health care during electricity failure: The hidden costs

Abstract

Background: Surgery risks increase when electricity is accessible but unreliable. During unreliable electricity events and without data on increased risk to patients, medical professionals base their decisions on anecdotal experience. Decisions should be made based on a cost-benefit analysis, but no methodology exists to quantify these risks, the associated hidden costs, nor risk charts to compare alternatives.

Methods: Two methodologies were created to quantify these hidden costs. In the first methodology through research literature and/or measurements, the authors obtained and analyzed a year's worth of hour-by-hour energy failures for four energy healthcare system (EHS) types in four regions (SolarPV in Iraq, Hydroelectric in Ghana, SolarPV+Wind in Bangladesh, and Grid+Diesel in Uganda). In the second methodology, additional patient risks were calculated according to time and duration of electricity failure and medical procedure impact type. Combining these methodologies, the cost from the Value of Statistical Lives lost divided by Energy shortage ($/kWh) is calculated for EHS type and region specifically. The authors define hidden costs due to electricity failure as VSL/E ($/kWh) and compare this to traditional electricity costs (always defined in $/kWh units), including Levelized Cost of Electricity (LCOE also in $/kWh). This is quantified into a fundamentally new energy healthcare system risk chart (EHS-Risk Chart) based on severity of event (probability of deaths) and likelihood of event (probability of electricity failure).

Results: VSL/E costs were found to be 10 to 10,000 times traditional electricity costs (electric utility or LCOE based). The single power source EHS types have higher risks than hybridized EHS types (especially as power loads increase over time), but all EHS types have additional risks to patients due to electricity failure (between 3 to 105 deaths per 1,000 patients).

Conclusions: These electricity failure risks and hidden healthcare costs can now be calculated and charted to make medical decisions based on a risk chart instead of anecdotal experience. This risk chart connects public health and electricity failure using this adaptable, scalable, and verifiable model.

Fig 1. Global surgeries per 100,000 population is not equitably distributed around the world, dramatically interfering with average life expectancy at birth (colored data) plotted by country (size of circle is a country’s population).

As global surgeries increase, average electrical power density consumed (2012 data on x-axis in units of Watts/Capita) must increase to ensure accessibility and reliability to health care facilities adding surgical wards.

Fig 2. lectricity failure events (red) for Iraq Rural health care facility published in Solar Energy, 2010 [16].

Hours of the day on the y-axis and days of the year on the x-axis with color showing state of electricity system: on (blue) or off (red).

Fig 3. The y-axis shows additional risk to patient as a result of starting a medical procedure which required an electrically powered medical device.

The duration of the electricity failure increases the patient risk, which is on the x-axis. Medical procedures can be placed into four categories of impact regions (high impact—top, medium impact—middle, and low impact—bottom) or no impact (not shown, but flat line on zero). These curves can also be easily adjusted with two parameters as data becomes available.

Fig 4. Top figures show four health care electricity failure events for a year with electricity ON in blue (0) and electricity OFF in red (1).

The bottom figures show corresponding calculated deaths per 1,000 patients for the entire year for high impact, medium impact, and low impact medical procedures for each system depending on time and duration of electricity failure. Even with grid and backup diesel generator system at a regional hospital, with only 4% energy and time capacity shortages, there were additional risks to patients.

Fig 5. Risk chart showing (a) number of days in a year when health care facilities experience additional risks to patients due to electricity failures (chance of death and likelihood of electricity failure), and (b) VSL/E defined as hidden costs associated with costs of statistical lives lost (VSL: $) divided by energy shortage (E: kWh).

This health care facility has 150 beds; 5% of medical procedures that are highly impacted by electricity failures, 10% medium impact, 20% low impacts, and 65% of medical procedures with no impact due to electricity failure; and uncertainty parameters (c,k) in additional patient risk function explained in S5 Fig in S1 File. This data from the publication in Solar Energy, 2010, sited in rural Iraq was scaled up to a hospital (see S1 File for other ESH types). Note: the LCOE range used by engineers is between $0.05/kWh and $11/kWh and yet these non-zero costs are between $412/kWh and $24,243/kWh.