Health - Revision history

Wikiadmin at 22:24, 6 September 2018

2018-09-06T22:24:31Z

← Older revision		Revision as of 22:24, 6 September 2018
Line 1:		Line 1:
	Hughes, Barry B., Cecilia M. Peterson, Dale S. Rothman, and José R. Solörzano . 2017. ''"IFs Health Model Documentation." ''Working paper 2014.09.01. Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY ~~<~~https://pardee.du.edu/wiki/Health~~>~~		Please cite as: Hughes, Barry B., Cecilia M. Peterson, Dale S. Rothman, and José R. Solörzano . 2017. ''"IFs Health Model Documentation." ''Working paper 2014.09.01. Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY <[https://pardee.du.edu/wiki/Health https://pardee.du.edu/wiki/Health]>

	The IFs health model allows users to forecast age, sex, and country specific health outcomes related to 15 cause categories (see table) out to the year 2100.  Based on previous work done by the World Health Organization’s (WHO) Global Burden of Disease (GBD) project<ref>See Mathers and Loncar (2006) for details on GBD projections of cause-specific mortality out to 2030.</ref>, formulations based on three distal drivers – income, education, and technology – comprise the core of the IFs health model.  However, the IFs model goes beyond the distal drivers, including both richer structural formulations and proximate health drivers (e.g. nutrition and environmental variables).  Integration into the IFs system also allows us to incorporate forward linkages from health to other systems, such as the economic and population modules.  Importantly, IFs provides the user the ability to vary model assumptions and create customized scenarios; as such, IFs is a tool exploring how policy choices might result in alternative health futures.		The IFs health model allows users to forecast age, sex, and country specific health outcomes related to 15 cause categories (see table) out to the year 2100.  Based on previous work done by the World Health Organization’s (WHO) Global Burden of Disease (GBD) project<ref>See Mathers and Loncar (2006) for details on GBD projections of cause-specific mortality out to 2030.</ref>, formulations based on three distal drivers – income, education, and technology – comprise the core of the IFs health model.  However, the IFs model goes beyond the distal drivers, including both richer structural formulations and proximate health drivers (e.g. nutrition and environmental variables).  Integration into the IFs system also allows us to incorporate forward linkages from health to other systems, such as the economic and population modules.  Importantly, IFs provides the user the ability to vary model assumptions and create customized scenarios; as such, IFs is a tool exploring how policy choices might result in alternative health futures.

Wikiadmin at 22:22, 6 September 2018

2018-09-06T22:22:13Z

← Older revision		Revision as of 22:22, 6 September 2018
Line 1:		Line 1:
			Hughes, Barry B., Cecilia M. Peterson, Dale S. Rothman, and José R. Solörzano . 2017. ''"IFs Health Model Documentation." ''Working paper 2014.09.01. Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO. Accessed DD Month YYYY <https://pardee.du.edu/wiki/Health>

	The IFs health model allows users to forecast age, sex, and country specific health outcomes related to 15 cause categories (see table) out to the year 2100.  Based on previous work done by the World Health Organization’s (WHO) Global Burden of Disease (GBD) project<ref>See Mathers and Loncar (2006) for details on GBD projections of cause-specific mortality out to 2030.</ref>, formulations based on three distal drivers – income, education, and technology – comprise the core of the IFs health model.  However, the IFs model goes beyond the distal drivers, including both richer structural formulations and proximate health drivers (e.g. nutrition and environmental variables).  Integration into the IFs system also allows us to incorporate forward linkages from health to other systems, such as the economic and population modules.  Importantly, IFs provides the user the ability to vary model assumptions and create customized scenarios; as such, IFs is a tool exploring how policy choices might result in alternative health futures.		The IFs health model allows users to forecast age, sex, and country specific health outcomes related to 15 cause categories (see table) out to the year 2100.  Based on previous work done by the World Health Organization’s (WHO) Global Burden of Disease (GBD) project<ref>See Mathers and Loncar (2006) for details on GBD projections of cause-specific mortality out to 2030.</ref>, formulations based on three distal drivers – income, education, and technology – comprise the core of the IFs health model.  However, the IFs model goes beyond the distal drivers, including both richer structural formulations and proximate health drivers (e.g. nutrition and environmental variables).  Integration into the IFs system also allows us to incorporate forward linkages from health to other systems, such as the economic and population modules.  Importantly, IFs provides the user the ability to vary model assumptions and create customized scenarios; as such, IFs is a tool exploring how policy choices might result in alternative health futures.

Line 246:		Line 248:
	<span>For help understanding the equations see</span> [[Understand_IFs#Equation_Notation\|Notation]] <span>.</span>		<span>For help understanding the equations see</span> [[Understand_IFs#Equation_Notation\|Notation]] <span>.</span>
	</div>		</div>

	== <span style="font-size:x-large;">Distal Driver Formulation</span> ==		== <span style="font-size:x-large;">Distal Driver Formulation</span> ==

Line 279:		Line 280:
	The equation allows scenario modification with multiplicative parameters that change mortality overall ('''''mortm'' ''') or by cause of death ('''''hlmortm'' ''').  Not shown in the equation,    '''''hlmortcdchldm'' ''' changes the rates of all communicable diseases for children aged 5 and younger, while '''''hlmortcdadltm'' ''' affects rates of death from communicable diseases for adults aged 15-49.		The equation allows scenario modification with multiplicative parameters that change mortality overall ('''''mortm'' ''') or by cause of death ('''''hlmortm'' ''').  Not shown in the equation,    '''''hlmortcdchldm'' ''' changes the rates of all communicable diseases for children aged 5 and younger, while '''''hlmortcdadltm'' ''' affects rates of death from communicable diseases for adults aged 15-49.
	</div>		</div>

	=== <span style="font-size:medium;">Forecasting Income and Education for the Distal Driver Formulation</span> ===		=== <span style="font-size:medium;">Forecasting Income and Education for the Distal Driver Formulation</span> ===

Line 350:		Line 350:

	:<math>HLDIABETESRR_{r,c,p}=HLDIABSHIFT_{r,c,p}\int{e^{((HLBMI_{r,p}-(\frac{HLBMI_{r,p}-avgBMI}{StdDev}+21))Log(RR_{c,p}))}}P(HLBMI)dBMI</math>		:<math>HLDIABETESRR_{r,c,p}=HLDIABSHIFT_{r,c,p}\int{e^{((HLBMI_{r,p}-(\frac{HLBMI_{r,p}-avgBMI}{StdDev}+21))Log(RR_{c,p}))}}P(HLBMI)dBMI</math>
	LogRR is the change in log of RR per 1 unit change in BMI.<ref>See the WHO Comparative Risk Assessment Methodology and Kelly, Christopher, Nora Pashayan, Sreetharan Munisamy, and Joshn W. Powles. 2009. “Mortality attributable to excess adiposity in England and Wales in 2003 and 2015: explorations with a spreadsheet implementation of the Comparative Risk Assessment mentodology.” Population Health Metrics 7(11): 1-7.</ref> These values are age category (c) and sex (p) specific; the absolute relative risk of diabetes-related mortality in relation to a unit increase in BMI varies from between 1.47 (females under 45) and 1.2 (females over 80).<ref>See associated data table and Kelly et al 2009 (full reference above).</ref~~><font color="#990000"><span style="font-size: 10.8333px;"></span></font~~>P(HLBMI) is a normal distribution function with mean of avgBMI; StdDev is a fixed 10% of avgBMI.<ref>avgBMI is our forecast of BMI, while BMI are the values from -3 standard deviations to +3 standard deviations away from that avgBMI. Cecilia Peterson determined the fixed 10 percent rate for StdDev from the literature.</ref>
			LogRR is the change in log of RR per 1 unit change in BMI.<ref>See the WHO Comparative Risk Assessment Methodology and Kelly, Christopher, Nora Pashayan, Sreetharan Munisamy, and Joshn W. Powles. 2009. “Mortality attributable to excess adiposity in England and Wales in 2003 and 2015: explorations with a spreadsheet implementation of the Comparative Risk Assessment mentodology.” Population Health Metrics 7(11): 1-7.</ref> These values are age category (c) and sex (p) specific; the absolute relative risk of diabetes-related mortality in relation to a unit increase in BMI varies from between 1.47 (females under 45) and 1.2 (females over 80).<ref>See associated data table and Kelly et al 2009 (full reference above).</ref>P(HLBMI) is a normal distribution function with mean of avgBMI; StdDev is a fixed 10% of avgBMI.<ref>avgBMI is our forecast of BMI, while BMI are the values from -3 standard deviations to +3 standard deviations away from that avgBMI. Cecilia Peterson determined the fixed 10 percent rate for StdDev from the literature.</ref>

	=== <span style="font-size:medium;">Equations: Chronic Respiratory Disease</span> ===		=== <span style="font-size:medium;">Equations: Chronic Respiratory Disease</span> ===
Line 382:		Line 383:
	The results for these three subtypes are then subtracted from the mortality for the total Group I category (except HIV/AIDS). The reason for this is to make sure that the sum of all them does not exceed the total of Group 1 (excluding HIV/AIDS), which is a result the equation could theoretically produce.  In fact, we want to be sure that there is room for the Other Group 1 cause of death, so IFs limits the sum for diarrhea, malaria, and <span lang="EN-GB">respiratory infection to 95 percent of the total of Group 1 (excluding HIV/AIDS).   If necessary, all three subcategories are reduced proportionally by a factor of 0.95/(SUM(3 subtypes)/Tot(big type)). Note that, if this restraint needs to be imposed, the denominator will always be higher than 0.95 and then the multiplicative adjustment factor will always be lower than 1.</span>		The results for these three subtypes are then subtracted from the mortality for the total Group I category (except HIV/AIDS). The reason for this is to make sure that the sum of all them does not exceed the total of Group 1 (excluding HIV/AIDS), which is a result the equation could theoretically produce.  In fact, we want to be sure that there is room for the Other Group 1 cause of death, so IFs limits the sum for diarrhea, malaria, and <span lang="EN-GB">respiratory infection to 95 percent of the total of Group 1 (excluding HIV/AIDS).   If necessary, all three subcategories are reduced proportionally by a factor of 0.95/(SUM(3 subtypes)/Tot(big type)). Note that, if this restraint needs to be imposed, the denominator will always be higher than 0.95 and then the multiplicative adjustment factor will always be lower than 1.</span>
	<div>		<div>

	== <span style="font-size:x-large;">HIV/AIDS</span> ==		== <span style="font-size:x-large;">HIV/AIDS</span> ==

Line 467:		Line 467:
	IFs also computes a ratio (in a variable internal to the model) of traffic accident mortality for males compared to females.   The model converges that ratio to 1.5 over 100 years by preserving the total mortality for each age category but adjusting the distribution between males and females.		IFs also computes a ratio (in a variable internal to the model) of traffic accident mortality for males compared to females.   The model converges that ratio to 1.5 over 100 years by preserving the total mortality for each age category but adjusting the distribution between males and females.
	</div>		</div>

	== <span style="font-size:x-large;">Mental Health</span> ==		== <span style="font-size:x-large;">Mental Health</span> ==

Line 1,487:		Line 1,486:

	United Nations Population Division. 2009. '' '' ''World Population Prospects: The 2008 Revision, Highlights.''  New York:  United Nations. Department of Economics and Social Affairs.		United Nations Population Division. 2009. '' '' ''World Population Prospects: The 2008 Revision, Highlights.''  New York:  United Nations. Department of Economics and Social Affairs.
			Wagstaff, Adam. 2002. “Inequalities in Health in Developing Countries: Swimming Against the Tide?” Unpublished Manuscript <references /></div>[[Category:Pages with broken file links]]
	Wagstaff, Adam. 2002. “Inequalities in Health in Developing Countries: Swimming Against the Tide?” Unpublished Manuscript <references />
	[[Category:Pages with broken file links]]

Wikiadmin at 20:55, 6 August 2018

2018-08-06T20:55:30Z

← Older revision		Revision as of 20:55, 6 August 2018
Line 1,409:		Line 1,409:

	:<math>YRSEDCOMP^t_{\gamma}=F(GDPPCP^t_{\gamma})</math>		:<math>YRSEDCOMP^t_{\gamma}=F(GDPPCP^t_{\gamma})</math>

	~~----~~

	~~<span style="color: #990000" lang="EN-GB">[1]</span> The lag is the difference from the midpoint of childhood (7.5) to the midpoint of adulthood (32.5).~~
	~~<div>~~
	<span style="color: #990000" lang="EN-GB">[2]</span> Global data on stunting among adults appear nearly nonexistent. UNICEF (2009:5) suggests that under-5 stunting exceeds that of malnutrition (200 versus 130 million) and that stunting is nearly irreversible with aging; these facts suggest very high percentages of stunting among global adults, concentrated in Africa and Asia.
	~~</div><div>~~
	<span style="color: #990000" lang="EN-GB">[3]</span> While IFs includes historic data series from WDI and WHO for child undernutrition, many countries do not have data for 1980 (25 years prior to 2005, our initial year).

	<span style="color: #990000" lang="EN-GB">[4]</span> As a quick reality check on those numbers, dividing disability years for the working population by population aged 15-65 generates numbers in 2005 that range from around 0.20-0.27 at the top end of the range (Timor-Leste, Afghanistan, Montenegro, Puerto Rico, Cambodia, and mostly other African countries) to 0.05-0.06 at the bottom end (Kuwait, UAE, Cape Verde, Algeria, Japan and mostly other rich countries).    <span style="color: #990000" lang="EN-GB">[5]</span> Because mental health rates do not change in base forecasts, we used the world average as an “expected” value—in 2005 that value, with mental health included, is .097 (mental health alone accounts for .025).  That is, we calculate about 0.1 year of disability per worker across a working life.

	<span style="color: #990000" lang="EN-GB">[5]</span> Because mental health rates do not change in base forecasts, we used the world average as an “expected” value—in 2005 that value, with mental health included, is .097 (mental health alone accounts for .025).  That is, we calculate about 0.1 year of disability per worker across a working life.
	~~</div>~~

	== <span style="font-size:x-large;">Additional Potential Forward Linkages</span> ==		== <span style="font-size:x-large;">Additional Potential Forward Linkages</span> ==

Wikiadmin at 20:54, 6 August 2018

2018-08-06T20:54:39Z

Wikiadmin at 20:51, 6 August 2018

2018-08-06T20:51:51Z

← Older revision		Revision as of 20:51, 6 August 2018
Line 1,338:		Line 1,338:
	The paths in IFs that link age structure most directly to domestic savings have two important elements.  The most fundamental one represents the understanding of life-cycle dynamics in income, consumption and savings.  The cycle for income is fairly clear-cut with a peak in the middle to latter periods of the working years.  Workers set aside some portion of income as savings and that portion, too, tends to peak in the middle and late period of working years.  Society-wide savings themselves become negative after retirement age (65 in the Base Case scenario) even though some portion of the population will continue to work. The second fundamental element is that both the horizon of life expectancy and the average income level of a society can have an impact on the portion set aside for savings and the degree to which it rises and then falls.  Thus, for example, the life-cycle “bulge” of savings may be earlier and flatter in developing countries.		The paths in IFs that link age structure most directly to domestic savings have two important elements.  The most fundamental one represents the understanding of life-cycle dynamics in income, consumption and savings.  The cycle for income is fairly clear-cut with a peak in the middle to latter periods of the working years.  Workers set aside some portion of income as savings and that portion, too, tends to peak in the middle and late period of working years.  Society-wide savings themselves become negative after retirement age (65 in the Base Case scenario) even though some portion of the population will continue to work. The second fundamental element is that both the horizon of life expectancy and the average income level of a society can have an impact on the portion set aside for savings and the degree to which it rises and then falls.  Thus, for example, the life-cycle “bulge” of savings may be earlier and flatter in developing countries.

	We implemented the representation of savings and investment in accord with that understanding.  Relying upon analyses of selected countries that Fernández-Villaverde and Kruegger (2004 and 2005) and Deaton  and Paxson (2000) undertook, we extracted general stylized patterns of the savings life cycle to represent more and less developed (and lower life expectancy) countries. In forecasting we use the pattern for less developed countries when life expectancy falls below 40 years, use that for more developed countries when life expectancy exceeds 80 years, and interpolate in between for all other countries.  The result of this largely algorithmic approach<~~sup~~><~~span style="color: #990000"~~><span style="color: #990000" ~~lang="EN-GB">[1</span~~> ] </span> </sup> is an adjustment factor (SavingsAgeAdj) that augments or reduces investment.		We implemented the representation of savings and investment in accord with that understanding.  Relying upon analyses of selected countries that Fernández-Villaverde and Kruegger (2004 and 2005) and Deaton  and Paxson (2000) undertook, we extracted general stylized patterns of the savings life cycle to represent more and less developed (and lower life expectancy) countries. In forecasting we use the pattern for less developed countries when life expectancy falls below 40 years, use that for more developed countries when life expectancy exceeds 80 years, and interpolate in between for all other countries.  The result of this largely algorithmic approach<ref>See the subroutine SavingsDemogAdj in routine Populat.bas, which draws upon table IncConSav in IFs.mdb with different patterns of income, consumption, and savings for more developed countries (MDCs) and less developed countries (LDCs) across age categories; in general, peaks of income, consumption, savings occur the in late 40s and savings turn negative at 65.</ref><sup><span style="color: #990000"> </span></sup>is an adjustment factor (SavingsAgeAdj) that augments or reduces investment.

	In addition, investment is somewhat augmented or reduced as a direct result of changing life expectancy.  Life expectancy is compared over time with an expected value (tied to cross-sectional estimation with income).  That difference is compared to the difference in the initial year and, if it rises, augments investment.		In addition, investment is somewhat augmented or reduced as a direct result of changing life expectancy.  Life expectancy is compared over time with an expected value (tied to cross-sectional estimation with income).  That difference is compared to the difference in the initial year and, if it rises, augments investment.
Line 1,347:		Line 1,347:

	The path linking health to foreign direct investment is potentially quite important.  Alsan, Bloom and Canning (2006: 613) report that one additional year of life expectancy boosts FDI inflows by 9 percent, controlling for other variables.  We have implemented that relationship in IFs.  The representation of FDI in IFs captures the accumulation over time of FDI inflows in stocks of FDI, as well as the accumulation of FDI outflows in stocks.  In addition, the stocks set up their own dynamics, including the tendency for stocks to reinforce flows.  For that reason, we have set the base case parameter for the impact of each year of life expectancy on FDI flows to 0.05 (5 percent), lower than the estimate of Alsan, Bloom and Canning (2006).		The path linking health to foreign direct investment is potentially quite important.  Alsan, Bloom and Canning (2006: 613) report that one additional year of life expectancy boosts FDI inflows by 9 percent, controlling for other variables.  We have implemented that relationship in IFs.  The representation of FDI in IFs captures the accumulation over time of FDI inflows in stocks of FDI, as well as the accumulation of FDI outflows in stocks.  In addition, the stocks set up their own dynamics, including the tendency for stocks to reinforce flows.  For that reason, we have set the base case parameter for the impact of each year of life expectancy on FDI flows to 0.05 (5 percent), lower than the estimate of Alsan, Bloom and Canning (2006).
	~~<div>~~
	~~----~~
	<span style="color: #990000"><span style="color: #990000" lang="EN-GB">[1]</span> </span> See the subroutine SavingsDemogAdj in routine Populat.bas, which draws upon table IncConSav in IFs.mdb  with different patterns of income, consumption, and savings for more developed countries (MDCs) and  less developed countries (LDCs) across age categories; in general, peaks of income, consumption, savings occur the in late 40s and savings turn negative at 65.</div>
	== <span style="font-size:x-large;">Productivity</span> ==		== <span style="font-size:x-large;">Productivity</span> ==

Wikiadmin at 20:50, 6 August 2018

2018-08-06T20:50:33Z

← Older revision		Revision as of 20:50, 6 August 2018
Line 1,318:		Line 1,318:
	== <span style="font-size:x-large;">Population and Labor Supply</span> ==		== <span style="font-size:x-large;">Population and Labor Supply</span> ==

	The IFs demographic model captures the mechanical or accounting effects of mortality on population.  A key pathway passes from mortality through adult age population to labor supply (including aging-related lags).<~~sup~~>~~<span style="color: #990000"><span style="color: #990000" lang="EN~~-~~GB">[1]</span> </span>~~ </~~sup~~>  Similarly, IFs captures the mechanical effect of mortality on fertility through the death of women of childbearing age.		The IFs demographic model captures the mechanical or accounting effects of mortality on population.  A key pathway passes from mortality through adult age population to labor supply (including aging-related lags).<ref>IFs also includes income-based formulations for changing the female participation rate.</ref> Similarly, IFs captures the mechanical effect of mortality on fertility through the death of women of childbearing age.

	The most important non-mechanical linkage is almost certainly the relationship between child mortality and fertility. IFs forecasts fertility as a relationship with infant mortality, the log of educational level of those aged 15 and older (neither the education of women alone nor the education of those 15-24 work as well), and the percentage use of modern contraception.  Adding the rate of infant mortality boosted the overall adjusted R-squared to 0.84.		The most important non-mechanical linkage is almost certainly the relationship between child mortality and fertility. IFs forecasts fertility as a relationship with infant mortality, the log of educational level of those aged 15 and older (neither the education of women alone nor the education of those 15-24 work as well), and the percentage use of modern contraception.  Adding the rate of infant mortality boosted the overall adjusted R-squared to 0.84.
Line 1,325:		Line 1,325:

	R-squared = 0.8345; other terms and algorithmic specifications modify the relationship, most importantly a term that slowly shifts TFR over time and the specification of a minimum level toward which the function slowly converges if it overshoots on the downward side.		R-squared = 0.8345; other terms and algorithmic specifications modify the relationship, most importantly a term that slowly shifts TFR over time and the specification of a minimum level toward which the function slowly converges if it overshoots on the downward side.
	~~<div>~~
	~~----~~
	~~<div><span style="color: #990000"><span style="color: #990000" lang="EN-GB">[1]</span> </span> IFs also includes income-based formulations for changing the female participation rate.</div></div>~~
	== <span style="font-size:x-large;">Capital Stocks</span> ==		== <span style="font-size:x-large;">Capital Stocks</span> ==

Wikiadmin at 20:49, 6 August 2018

2018-08-06T20:49:42Z

← Older revision		Revision as of 20:49, 6 August 2018
Line 1,138:		Line 1,138:
	=== <span style="font-size:medium;">Overview</span> ===		=== <span style="font-size:medium;">Overview</span> ===

	There are two main mortality-related outcomes from the Health Module: deaths by cause-category (DEATHCAT) and life expectancy (LIFEXPHLM).<~~span style="color: #990000"><sup><span lang="EN-GB"~~>~~<span style="color: #990000" lang="EN-GB">[1~~</~~span> ] </span> </sup> </span~~>  From these, IFs calculates other relevant outcome variables including: years of life lost (YLL; HLYLL in IFs); years lived with disability (YLD; HLYLD); disability adjusted life years (DALY; HLDALY); YLLs and YLDs among the working-age population (HLYLLWORK and HLYLDWORK,<span style="color: #990000"><sup><span lang="EN-GB">~~<span style="color: #990000" lang="EN-GB">[2</span> ]~~ </span> </sup> </span> respectively, the sum of which would be DALYs for the working population); morbidity and the probability of mortality during a user-defined age-range.<~~span style="color: #990000"><sup><span lang="EN-GB"><span style="color: #990000" lang="EN-GB">[3</span> ]</span~~></~~sup></span~~>		There are two main mortality-related outcomes from the Health Module: deaths by cause-category (DEATHCAT) and life expectancy (LIFEXPHLM).<ref>Note that LIFEEXPHLM replaces the old IFs variable LIFEEXP which was forecasted in the population module.</ref> From these, IFs calculates other relevant outcome variables including: years of life lost (YLL; HLYLL in IFs); years lived with disability (YLD; HLYLD); disability adjusted life years (DALY; HLDALY); YLLs and YLDs among the working-age population (HLYLLWORK and HLYLDWORK,<ref>As of October 2013 we were using the ages 20-64 as the range for computation of these two variables. We may want to make that dynamic now that we have dynamic working life years.</ref><span style="color: #990000"><sup><span lang="EN-GB"> </span></sup></span>respectively, the sum of which would be DALYs for the working population); morbidity and the probability of mortality during a user-defined age-range.<ref>The default is child mortality (0 to 5) and adult mortality (15 to 60).</ref>

	The computation of life expectancy in the health module is a replica of the one in the population module, with the only difference being the mortality distribution.  In the initial year, these match because of the normalization process but they grow apart as the model advances.  DEATHCAT is computed by multiplying the mortality distribution from the Health Module by population age categories.<~~span style="color: #990000"><sup><span lang="EN-GB"><span lang="EN-GB"><span style="color: #990000">[4</span> ]</span></span></sup~~></~~span~~>		The computation of life expectancy in the health module is a replica of the one in the population module, with the only difference being the mortality distribution.  In the initial year, these match because of the normalization process but they grow apart as the model advances.  DEATHCAT is computed by multiplying the mortality distribution from the Health Module by population age categories.<ref>If the user turns off the health model switch (hlmodelsw), IFs stops computing all of the HL variables and DEATHCAT (also morbidity and the probabilities of mortality); they have values with the switch set to off because they take those of the base case, but they are not used to compute LIFEXP, which would then come from the older or legacy population model. It is important to note also that if the health model switch is off, it is impossible to expand the age categories beyond 100+.</ref>

	== <span style="font-size:x-large;">Years of Life Lost (YLL)</span> ==		== <span style="font-size:x-large;">Years of Life Lost (YLL)</span> ==

Wikiadmin at 20:44, 6 August 2018

2018-08-06T20:44:25Z

← Older revision		Revision as of 20:44, 6 August 2018
Line 1,144:		Line 1,144:
	== <span style="font-size:x-large;">Years of Life Lost (YLL)</span> ==		== <span style="font-size:x-large;">Years of Life Lost (YLL)</span> ==

	Years of Life Lost (YLLs) are computed using the number of deaths in each age category multiplied by the number of years they died prematurely (the potential life expectancy at the age category as determined by a stored vector of standard life expenctancy):<~~sup><span style="color: #990000"><span style="color: #990000">[1</span> ]</span~~></~~sup~~>		Years of Life Lost (YLLs) are computed using the number of deaths in each age category multiplied by the number of years they died prematurely (the potential life expectancy at the age category as determined by a stored vector of standard life expenctancy):<ref>Randall Kuhn provide the hard-coded vector for standard life expectancy.</ref>

	:'''Years of Life Lost = Deaths in age category * StdLifeExp for age'''		:'''Years of Life Lost = Deaths in age category * StdLifeExp for age'''
Line 1,157:		Line 1,157:

	YLLWORK uses the same formulations, but only includes the population of 20 to 64 year-olds.		YLLWORK uses the same formulations, but only includes the population of 20 to 64 year-olds.
	~~<div>~~
	~~----~~
	~~<span style="color: #990000">[1]</span> Randall Kuhn provide the hard-coded vector for standard life expectancy.</div>~~
	== <span style="font-size:x-large;">Years of Life Lost to Disability (YLD)</span> ==		== <span style="font-size:x-large;">Years of Life Lost to Disability (YLD)</span> ==

Wikiadmin at 20:43, 6 August 2018

2018-08-06T20:43:37Z

← Older revision		Revision as of 20:43, 6 August 2018
Line 1,065:		Line 1,065:
	==== <span style="font-size:medium;">Revised Approach: Forecasting Smoking Rates using a Stages Model</span> ====		==== <span style="font-size:medium;">Revised Approach: Forecasting Smoking Rates using a Stages Model</span> ====

	Smoking rate itself is computed in two different ways.  The basic formulation uses only the initial condition, historical rates of growth in smoking, and a cross-sectionally estimated function linked to the simple and squared values of GDP per capita at PPP.  The more extended formulation (still in development and testing and therefore not turned on as the default approach) is an algorithmic one based on the same general concept of a pattern that initially rises with GDP per capita, peaks, and then falls, but with a series of parameters that allow much more control over the stages.<~~sup><span style="color: #990000"><span style="color: #990000">[1</span> ] </span~~> </~~sup~~>  This staged algorithmic approach (see Lopez et al. 1994; Shibuya et al. 2005; Ploeg et al. 2009) is turned on with a switch ('''''hlsmokingstsw'' '''=1; the default =0).  Based on country- and sex-specific trends, plus initial prevalence, in the first year each country is placed into one of four sequential stages: rising, peak, falling, late.  During the forecast horizon they can move through other stages.  The approach is heavily algorithmic and the logic is explained below.		Smoking rate itself is computed in two different ways.  The basic formulation uses only the initial condition, historical rates of growth in smoking, and a cross-sectionally estimated function linked to the simple and squared values of GDP per capita at PPP.  The more extended formulation (still in development and testing and therefore not turned on as the default approach) is an algorithmic one based on the same general concept of a pattern that initially rises with GDP per capita, peaks, and then falls, but with a series of parameters that allow much more control over the stages.<ref>Cecilia Peterson developed this approach for IFs.</ref> This staged algorithmic approach (see Lopez et al. 1994; Shibuya et al. 2005; Ploeg et al. 2009) is turned on with a switch ('''''hlsmokingstsw'' '''=1; the default =0).  Based on country- and sex-specific trends, plus initial prevalence, in the first year each country is placed into one of four sequential stages: rising, peak, falling, late.  During the forecast horizon they can move through other stages.  The approach is heavily algorithmic and the logic is explained below.

	===== <span style="font-size:medium;">Early to Rising (Stage 1)</span> =====		===== <span style="font-size:medium;">Early to Rising (Stage 1)</span> =====
Line 1,133:		Line 1,133:

	For small values of SI (lower than 1) we restrict the annual change rate to be between -50% and 100%. If SI is 0 in the base year then it stays at 0. If the estimated value of SI reaches 0, and the next year is positive, then a 100% growth rate is used, but if the next year is negative then a -50% growth rate is used.		For small values of SI (lower than 1) we restrict the annual change rate to be between -50% and 100%. If SI is 0 in the base year then it stays at 0. If the estimated value of SI reaches 0, and the next year is positive, then a 100% growth rate is used, but if the next year is negative then a -50% growth rate is used.
	~~<div>~~
	~~----~~
	~~<span style="color: #990000">[1]</span> Cecilia Peterson developed this approach for IFs.</div>~~
	= <span style="font-size:xx-large;">Outcomes</span> =		= <span style="font-size:xx-large;">Outcomes</span> =

Wikiadmin at 20:42, 6 August 2018

2018-08-06T20:42:16Z

← Older revision		Revision as of 20:42, 6 August 2018
Line 1,017:		Line 1,017:
	It is important to recognize not just the initial empirical (or estimated) value of smoking rate in our base year for each country, but the trajectory of country-specific change in smoking rates. Our approach to capturing the trajectory is a variation on a moving-average approach.  Every year our first step is to compute a compound growth rate of the smoking rate over the last 10 years.  In the second step we also compute the rate of change that one would expect based solely on applying the cross-sectional formulation in two consecutive years.  The third step is to compute a slowly-changing moving average by combining those two growth rate computations, weighting the compound historical growth rate 90 percent and the expectation of growth from the cross-sectional formulation 10 percent.		It is important to recognize not just the initial empirical (or estimated) value of smoking rate in our base year for each country, but the trajectory of country-specific change in smoking rates. Our approach to capturing the trajectory is a variation on a moving-average approach.  Every year our first step is to compute a compound growth rate of the smoking rate over the last 10 years.  In the second step we also compute the rate of change that one would expect based solely on applying the cross-sectional formulation in two consecutive years.  The third step is to compute a slowly-changing moving average by combining those two growth rate computations, weighting the compound historical growth rate 90 percent and the expectation of growth from the cross-sectional formulation 10 percent.

	In the first step described above we obtain every year the compounded growth rate (Comp_Gr_Rt) over the previous 10 years. That growth rate is based on historical (or constructed) smoking rate data (Smok_Rate).<~~sup><span style="color: #990000"><span style="color: #990000">[1</span> ]</span~~></~~sup~~>		In the first step described above we obtain every year the compounded growth rate (Comp_Gr_Rt) over the previous 10 years. That growth rate is based on historical (or constructed) smoking rate data (Smok_Rate).<ref>The constructed historical smoking data contributes to this computation of the compound growth rate for the first 10 years of its computation; thereafter, the computation will rely only on values generated by our forecasting.</ref>

	:<math>CompGrRt_{r,p,t=1}=(\frac{SmokRate_{r,p,t=1}}{SmokRate_{r,p,t=1-10}})^{1/10}-1</math>		:<math>CompGrRt_{r,p,t=1}=(\frac{SmokRate_{r,p,t=1}}{SmokRate_{r,p,t=1-10}})^{1/10}-1</math>
Line 1,050:		Line 1,050:

	Finally, a multiplier ('''''hlsmokingm'' ''') by country and sex is available and applied to the smoking rate to compute the final value for the country-sex-year; the default value of the multiplier is 1 and alternative values introduce scenarios. Note that having a multiplier for a specific sex (e.g. 0.9 for males) and another for “total” or both sexes (e.g. 0.8) will produce a multiplicative effect on the forecast of 0.72.  This ability to stack multipliers for individual sexes with total or both is not the standard practice in IFs; this may be the only example of it.		Finally, a multiplier ('''''hlsmokingm'' ''') by country and sex is available and applied to the smoking rate to compute the final value for the country-sex-year; the default value of the multiplier is 1 and alternative values introduce scenarios. Note that having a multiplier for a specific sex (e.g. 0.9 for males) and another for “total” or both sexes (e.g. 0.8) will produce a multiplicative effect on the forecast of 0.72.  This ability to stack multipliers for individual sexes with total or both is not the standard practice in IFs; this may be the only example of it.
	~~<div>~~
	~~----~~
	<div><span style="color: #990000">[1]</span> The constructed historical smoking data contributes to this computation of the compound growth rate for the first 10 years of its computation; thereafter, the computation will rely only on values generated by our forecasting. </div></div>
	==== <span style="font-size:medium;">Computing Future Smoking Impact from Future Smoking Rates</span> ====		==== <span style="font-size:medium;">Computing Future Smoking Impact from Future Smoking Rates</span> ====

← Older revision		Revision as of 20:54, 6 August 2018
Line 1,364:		Line 1,364:
	:<math>STUNTINGCOMP^t_{\gamma}=F(GDPPCP^t_{\gamma})</math>		:<math>STUNTINGCOMP^t_{\gamma}=F(GDPPCP^t_{\gamma})</math>

	We compute HLSTUNT in the health model itself.  We initialize adult stunting in a long-term lagged relationship (using a moving average of 25 years)<~~sup~~><~~span style="color: #990000"~~><span style="color: #990000~~" lang="EN-GB~~">~~[1</span> ]~~ </span> </sup> with child malnutrition and forecast it as a function of both malnutrition and child mortality as a proxy for morbidity.  Initial values in 2005 range up to about 55 percent for India and Bangladesh and even over 80 percent for Somalia; in the base case these generally but not universally decrease.<~~sup~~>~~<span style="color~~: ~~#990000"><span style="color: #990000" lang="EN~~-~~GB">[2</span> ] </span>~~ </~~sup~~>~~ ~~		We compute HLSTUNT in the health model itself.  We initialize adult stunting in a long-term lagged relationship (using a moving average of 25 years)<ref>The lag is the difference from the midpoint of childhood (7.5) to the midpoint of adulthood (32.5).</ref><sup><span style="color: #990000"> </span></sup>with child malnutrition and forecast it as a function of both malnutrition and child mortality as a proxy for morbidity.  Initial values in 2005 range up to about 55 percent for India and Bangladesh and even over 80 percent for Somalia; in the base case these generally but not universally decrease.<ref>Global data on stunting among adults appear nearly nonexistent. UNICEF (2009:5) suggests that under-5 stunting exceeds that of malnutrition (200 versus 130 million) and that stunting is nearly irreversible with aging; these facts suggest very high percentages of stunting among global adults, concentrated in Africa and Asia. </ref>

	To initialize HLSTUNT in the preprocessor, IFs must first estimate historic levels of childhood undernutrition (MALNCHP):<~~sup><span style="color: #990000"><span style="color: #990000">[3</span> ]</span~~></~~sup~~>		To initialize HLSTUNT in the preprocessor, IFs must first estimate historic levels of childhood undernutrition (MALNCHP):<ref>While IFs includes historic data series from WDI and WHO for child undernutrition, many countries do not have data for 1980 (25 years prior to 2005, our initial year). </ref>

	:<math>MN=23.853*GDPPCP^{-0.6721}</math>		:<math>MN=23.853*GDPPCP^{-0.6721}</math>
Line 1,376:		Line 1,376:
	:<math>HLSTUNT(t)=(HLSTUNT(t-1)*24+MALCHP(t))/25</math>		:<math>HLSTUNT(t)=(HLSTUNT(t-1)*24+MALCHP(t))/25</math>

	Childhood malnutrition and morbidity do not give rise to all disability in working years; much also comes from disabilities arising during the working years. IFs therefore also calculates millions of years of living with disability related to mortality rates specific to the working aged-population.<~~span style="color: #990000"~~>[4]</~~span~~>		Childhood malnutrition and morbidity do not give rise to all disability in working years; much also comes from disabilities arising during the working years. IFs therefore also calculates millions of years of living with disability related to mortality rates specific to the working aged-population.<ref>As a quick reality check on those numbers, dividing disability years for the working population by population aged 15-65 generates numbers in 2005 that range from around 0.20-0.27 at the top end of the range (Timor-Leste, Afghanistan, Montenegro, Puerto Rico, Cambodia, and mostly other African countries) to 0.05-0.06 at the bottom end (Kuwait, UAE, Cape Verde, Algeria, Japan and mostly other rich countries). [5] Because mental health rates do not change in base forecasts, we used the world average as an “expected” value—in 2005 that value, with mental health included, is .097 (mental health alone accounts for .025). That is, we calculate about 0.1 year of disability per worker across a working life.</ref>

	=== <span style="font-size:medium;">Disability Contribution</span> ===		=== <span style="font-size:medium;">Disability Contribution</span> ===

	Turning to the forecasting relationship between disability and productivity, the IFs approach drives changes in the growth of productivity from the changing difference between computed and expected values of disability.<~~span style="color: #990000"~~>~~[5]~~</~~span~~> Because we have replicated the practice of the GBD project and kept mental health disability rates constant over time, and because mental health generally dominates disability, forecasts of this disability term are relatively stable over time. Thus analysis with respect to this variable will depend on scenarios that increase or decrease those disability rates.  In IFs, changes in disability levels result in a -0.5 change in productivity (mfphlyld = 0.5 in the Base Case).		Turning to the forecasting relationship between disability and productivity, the IFs approach drives changes in the growth of productivity from the changing difference between computed and expected values of disability.<ref>Because mental health rates do not change in base forecasts, we used the world average as an “expected” value—in 2005 that value, with mental health included, is .097 (mental health alone accounts for .025). That is, we calculate about 0.1 year of disability per worker across a working life.</ref> Because we have replicated the practice of the GBD project and kept mental health disability rates constant over time, and because mental health generally dominates disability, forecasts of this disability term are relatively stable over time. Thus analysis with respect to this variable will depend on scenarios that increase or decrease those disability rates.  In IFs, changes in disability levels result in a -0.5 change in productivity (mfphlyld = 0.5 in the Base Case).

	''<span lang="EN-GB"><span lang="EN-GB">where</span></span>''		''<span lang="EN-GB"><span lang="EN-GB">where</span></span>''
Line 1,422:		Line 1,422:
	<span style="color: #990000" lang="EN-GB">[5]</span> Because mental health rates do not change in base forecasts, we used the world average as an “expected” value—in 2005 that value, with mental health included, is .097 (mental health alone accounts for .025).  That is, we calculate about 0.1 year of disability per worker across a working life.		<span style="color: #990000" lang="EN-GB">[5]</span> Because mental health rates do not change in base forecasts, we used the world average as an “expected” value—in 2005 that value, with mental health included, is .097 (mental health alone accounts for .025).  That is, we calculate about 0.1 year of disability per worker across a working life.
	</div>		</div>

	== <span style="font-size:x-large;">Additional Potential Forward Linkages</span> ==		== <span style="font-size:x-large;">Additional Potential Forward Linkages</span> ==