Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data.

BACKGROUND: Reducing disease can maintain personal individual income and improve societal economic productivity. However, estimates of income loss for multiple diseases simultaneously with thorough adjustment for confounding are lacking, to our knowledge. We estimate individual-level income loss for 40 conditions simultaneously by phase of diagnosis, and the total income loss at the population level (a function of how common the disease is and the individual-level income loss if one has the disease). METHODS AND
FINDINGS: We used linked health tax data for New Zealand as a high-income country case study, from 2006 to 2007 to 2015 to 2016 for 25- to 64-year-olds (22.5 million person-years). Fixed effects regression was used to estimate within-individual income loss by disease, and cause-deletion methods to estimate economic productivity loss at the population level. Income loss in the year of diagnosis was highest for dementia for both men (US$8,882; 95% CI $6,709 to $11,056) and women ($7,103; $5,499 to $8,707). Mental illness also had high income losses in the year of diagnosis (average of about $5,300 per year for males and $4,100 per year for females, for 4 subcategories of: depression and anxiety; alcohol related; schizophrenia; and other). Similar patterns were evident for prevalent years of diagnosis. For the last year of life, cancers tended to have the highest income losses, (e.g., colorectal cancer males: $17,786, 95% CI $15,555 to $20,018; females: $14,192, $12,357 to $16,026). The combined annual income loss from all diseases among 25- to 64-year-olds was US$2.72 billion or 4.3% of total income. Diseases contributing more than 4% of total disease-related income loss were mental illness (30.0%), cardiovascular disease (15.6%), musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), neurological (6.5%), and cancer (4.5%). The limitations of this study include residual biases that may overestimate the effect of disease on income loss, such as unmeasured time-varying confounding (e.g., divorce leading to both depression and income loss) and reverse causation (e.g., income loss leading to depression). Conversely, there may also be offsetting underestimation biases, such as income loss in the prodromal phase before diagnosis that is misclassified to "healthy" person time.
CONCLUSIONS: In this longitudinal study, we found that income loss varies considerably by disease. Nevertheless, mental illness, cardiovascular, and musculoskeletal diseases stand out as likely major causes of economic productivity loss, suggesting that they should be prioritised in prevention programmes.

Introduction

Health shocks adversely affect labour force participation [1], incomes, and productivity [2,3]. Estimates of income loss following diagnosis of disease may be used as ancillary estimates of the individual burden from poor health, and the aggregate societal economic productivity loss [4-6]. These income loss estimates can also inform policy on sickness benefit and health insurance and can be used in prioritisation of preventive interventions. As longevity increases and populations have older age structures, there is an increasing need for health interventions to be assessed not only on health sector impacts (i.e., health gains and health system expenditure), but also on the impact of interventions on wider society—including workforce productivity given the need to support an increasingly aged population.

There are large variations in contexts, data, and methods used to estimate disease-related income loss. Studies often focus on a single disease, limiting the ability to compare across different diseases. In addition, confounding by comorbidities (or other diseases) is often ignored in studies that estimate income loss from one disease in isolation (e.g., ischaemic heart disease [7], rheumatoid arthritis [8], cancer [9]) leading to likely overestimation of income loss for a given disease. Put another way, if such studies were undertaken separately for all diseases, the sum of income loss across these studies would (likely greatly) exceed the actual income loss from all diseases considered together. Even studies from Scandinavia, with population-wide disease registers linked to taxation or employment data, have also focused on single diseases such as diabetes [10], breast cancer [11], and injury [12]. We are aware of only one study that has considered multiple diseases simultaneously in estimating productivity loss for a national cohort [4]. On the other hand, some studies only include income loss or productivity loss from deaths [13,14] tallying up all income loss had the person hypothetically lived to (say) 65 years of age (which also ignores competing morbidity and competing mortality risk).

To overcome the limitations in previous studies on disease-related income loss and provide income loss estimates that are comparable across diseases, we used population-wide panel data on disease and injury events linked to income data. We estimated income loss while the participant is alive or in the tax year of their death, for adults 25 to 64 years of age, adjusting for all diseases simultaneously to overcome confounding, which we believe is unique. There is also likely to be further confounding by socioeconomic position (SEP), in that SEP causes both variations in disease rates and income (loss); we aimed to estimate the income loss a person would have avoided had they counterfactually not developed the given disease. Our inference target was the average citizen or total population—not just those employed; accordingly, we estimated income loss estimates averaged across all citizens. Specifically, our research objectives were to determine (1) individual-level disease-specific income loss estimates by phase of diagnosis; and (2) population-level estimates of total income loss by disease and the ranking of disease contributions to income loss in the total population.

Methods

We created a cohort of the entire New Zealand usually resident population 25 to 64 years of age during the observation window of 2006 to 2007 to 2015 to 2016, using linked administrative health and income/tax data.

The study was approved by Statistics New Zealand (SNZ) for undertaking in the SNZ Integrated Data Infrastructure (IDI) and separately approved by the University of Otago Ethics Committee. This study is reported as per the RECORD guideline (S1 Checklist).

Population

New Zealand is a high-income country with a population of 5 million people with a median age of 38 years, 16% of the population 65 years of age and older, and a life expectancy of 81.4 years. Most New Zealanders are of European extraction with sizeable populations of Māori (Indigenous population; 16.5%), Asian (15.1%), and Pacific peoples (8.1%; 2018 Census data). The GDP per capita in 2018 was about US$42,000, ranked about 30th among all countries. Over 80% of total health expenditure is government funded (through tax revenue). Regarding income protection, a separate accident insurance corporation exists that will cover 80% of one’s wage or salary while incapacitated, but income protection in the event of sickness is patchy comprising: accrued sick leave from one’s employer; income protection insurance from private insurance companies; and a relatively low publicly funded sickness benefit safety net.

Datasets

The health data comprised the following national datasets, all linkable with the National Health Index unique identifier: the National Minimum Data Set (NMDS) for all inpatient events since 1988; cancer registrations since 1995; retail pharmaceuticals since 2005; mental health event data since 2000 (Programme for the Integration of Mental Health Data (PHRIMD) and Mental Health Information National Collection (MHINC)); virtual diabetes register; and mortality data during the 2006 to 2007 to 2015 to 2016 observation window.

The income data were sourced from Inland Revenue Department (IRD) data collated for all New Zealand residents, from 2 sources: automatic filings from employers to IRD for wages and salary under the “pay as you earn” system; and self-employed income from annual returns submitted by residents.

Multiple government datasets (including the above health and income data) are available to users of the SNZ IDI (available to New Zealand–resident researchers by application to SNZ). All datasets are prelinked (before researchers have access) using a resident population spine that is maintained by SNZ (a detailed data profile and methods regarding the SNZ IDI is published elsewhere [15]). Our study cohort was taken from a SNZ resident population [16] and includes anyone with activity in the IRD, health, education, and Accident Compensation Corporation datasets within 12 months prior to the reference date (31 March of each year) and excludes individuals classified as having moved overseas (i.e., if the total length of time spent overseas was at least 10 of the 12 months spanning the reference date (6 months either side of the reference date). The population was modified to include individuals who died within 1 year prior to the reference date.

Variables

We included the following covariates and interaction terms in the models (by sex):

Diseases

We used the NZ Burden of Disease Study (NZBDS) condition groupings [17,18] to select 14 aggregated and 40 disaggregate-level diseases or conditions (see S1 Table for coding, S2 Table for categories, NZBDS categories with insufficient data or no variation over time (dental and congenital) were excluded). To determine if any of these diseases were prevalent before or were incident during the 2006 to 2007 to 2015 to 2016 observation window, a thorough case finding algorithm was applied consistent with that used for the NZBDS (S1 Table). In general terms, International Classification of Disease (ICD) codes for events and disease-specific drug combinations were developed, disease by disease; primary and secondary hospitalisation diagnoses were used in the look-back period (i.e., pre-2006 to 2007) to determine presence or absence of disease, but only primary diagnoses were used to determine incident disease in the 2006 to 2007 to 2015 to 2016 observation window. Once a person was diagnosed with a disease, they were assumed to have that disease for the rest of their life—except skin disorders, infections, internal injuries, poison injuries, and other injuries who only had the disease for the year, and cancers where survival beyond 5 years for lung, 8 years for colorectal, 10 years for “other” cancers, and 20 years for breast and prostate resulted in that person being recoded as being free of that cancer (based on statistical cure times) [19]. Each disease was coded by phase as not present (reference category), diagnosed in that year, died in that year of that disease, and otherwise prevalent. Note, therefore, the costs for the first 2 categories are for people with an average of 6 months in that state (but for the diagnosis category still including the time and costs for events preceding the diagnosis date in the same tax year).

Income

Each eligible resident was assigned a total pretax income as collected by IRD for each tax year 1 April to 31 March, for the 10 years. In main analyses, self-employed income included sole trader income but excluded income from partnerships (if not paid as personal income), rental properties, company directorships, and shareholdings. It is important to note that if an individual received sick pay from their employer at the same level as their usual income, their income (apparent to us) did not change—meaning that we missed this component of income loss relevant to the underlying construct of productivity loss. All annual total income was inflation adjusted to the 2020 reference year using the consumer price index, then converted to US$2020 using the NZ-USA OECD purchasing power parity of 1.445.

Covariates

Age was treated as a 5-year categorical variable for main effects and grouped to 25- to 39-, 40- to 49-, 50- to 59-, and 60- to 64-year-old categories for interaction with diseases.

Each individual was assigned to one ethnic group in a prioritised manner (given individuals can nominate more than one self-identified ethnic group), in order of the following: Māori (indigenous population of New Zealand), Pacific peoples, Asian peoples, and the rest as Other (i.e., largely European).

To allow for time-varying confounding by changing SEP, and variations in income loss by SEP, we assigned each individual person-year to a quintile of deprivation using a validated small area deprivation measure called NZDep [20]. The NZDep measure is a principal components calculated index using 9 census variables at a small area (meshbock) level of about 100 people: proportion of income as benefit receipt; household income; housing tenure; sole parent family; unemployment; qualifications; household crowding; telephone access; and car access. (We did not use a comorbidity index per se, but rather as all models were adjusted for all other diseases (be that the 14 or 40 disease level) we consider this adjustment for confounding by other diseases as equivalent to adjusting for comorbidity).

Analyses

Our analytical plan for this study was to follow the analyses we previously conducted (and published in this journal) for disease-related health system expenditure [21]. We, however, deviated from this previous approach to use fixed effects (FE) regression analyses for the “main analyses,” reporting ordinary least squares (OLS) regression in sensitivity analyses due to concerns about residual confounding (below).

Fixed effects regression modelling

We used an “excess” or “net” cost approach [21-25], with total income as the dependent variable in the regression analyses (uses within individual variation that removes time-invariant confounding [26] with cluster-adjusted standard errors). Conceptually, this excess costing approach examines how individual’s incomes vary year by year, corresponding to their disease status in each year. If the average difference in income in people’s first year of diagnosis of stroke (compared to their own pre-stroke years) was $5,000 (adjusted for other changes over time such as other diseases and changing deprivation), then this is the income loss.

Observations were only excluded if they had missing geocode for assigning NZDep (0.96%). Observations were censored after the year of death and if not eligible to be in the usually resident population (e.g., it was possible for a person living overseas for a period to contribute person observations in early and late years but be censored for midyears). Due to extreme income outliers, we further excluded year observations with a total income that was negative and less than the 0.1th percentile or greater than the 99.9th percentile.

The FE regression model is where y is the dependent variable, income of the individual i (inflation adjusted) in time period or tax year t; x represents

the independent variables (i.e., main effects for time-varying covariates [disease-phase (nil, first year, prevalent, and last year of life if dying of the disease); tax year (categorical); 5-year age-group (categorical); deprivation quintile (categorical); and a dummy variable for dying in that year if dying of a disease not included in model;

interactions of the disease-phase variable (our “main exposure” or variable of interest) with a 4-level age category covariate; ethnicity (Māori, Pacific, Asian, and European Other; time invariant characteristics can be included in interactions with time-varying covariates, here because of likely varying income loss by ethnicity); and deprivation. (These interactions were included to allow for likely variations in income loss by disease with age and ethnicity; while not a research question per se in this study, allowing for such interactions is important in the cause-deleted analyses below that estimate total income loss from disease across the population.)

β1 represents the coefficient for the independent variables (with the standard error used to generate 95% CI); a is the unknown intercept for each individual; and u is the idiosyncratic error term.

Cause-deleted analyses

We used a cause-deleted approach to estimate population-level income loss from disease. First, we predicted back onto every individual their expected income loss, using their disease covariates and the matching regression coefficients (including for interactions). Then, disease by disease, we set everyone’s disease values to 0 (i.e., no disease); predicted each individual’s cause-deleted income loss; and the difference between the as-observed and cause-deleted models total predicted income was the cause-deleted income loss attributable to each disease.

Sensitivity analyses

While FE regression has the strong advantage of removing all time-invariant confounding, it also has a disadvantage of relying on within-individual differences in income before and after diagnosis. For some chronic diseases such as diabetes, mental illness, and respiratory disease, the year during which our case finding algorithms determine a person to be incident is somewhat arbitrary—as it assumes a discontinuity in health at that point, when indeed the person may have had slowly progressing impacts on their health leading up to (say) their first hospitalisation. This will mean a bias towards underestimating income loss. A between-person regression approach does not suffer from this bias but is prone to residual confounding by SEP. Therefore, the OLS regressions also restricted observations to only those people with no disease 2 years before the observation window and adjusted for prior average income (continuous variable) in the 2-year period 2003 to 2004 to 2004 to 2005.

The case finding algorithm for depression and anxiety used a reasonably stringent case definition—some contact with some publicly funded mental health services (e.g., hospitalisation, acute assessment). Much depression and anxiety is treated in primary care, often with antidepressants, and is not registered on the PRIMHD database. We, therefore, also used an extended case definition in sensitivity analyses that included receipt of mental health medication—although this will now overestimate income loss due to disease as, for example, antidepressants have treatment indications other than just depression. Conversely, due to variability in quality and duration of pharmaceutical records (especially in the look-back period), we ran analyses where pharmaceutical records were not used in case finding (for all diseases).

Results

There were 22.5 million person-year observations over the 10-year observation period, of which 49.5% of observations were for a person with at least one disease or condition (Table 1). The total income was $606 billion (all cost values in US$, 2020), of which 45.4% was generated by people with at least one diagnosis or condition in the year. Person-years for people with a gastrointestinal condition were most common at 15.1% of all person-years (3.4 million (S2 Table) out of 22.5 million total person-years), followed by musculoskeletal diseases 14.3%.

Table 1

Descriptive data within observational window 2006–2007 to 2015–2016, by sex.

	Females		Males
Variables	With disease or condition	Healthy	With disease or condition	Healthy
Total person-years	5,968,488	5,455,674	5,159,391	5,919,615
Total income in billion (2020 US$)	$120.57	$124.31	$154.39	$206.87
Person observations by tax year
2006–2007	509,163	588,984	427,068	639,996
2007–2008	529,917	582,372	448,047	631,662
2008–2009	550,947	574,137	468,900	621,816
2009–2010	574,017	563,448	492,240	609,288
2010–2011	594,021	550,023	510,279	594,681
2011–2012	609,084	536,013	525,387	578,931
2012–2013	623,547	524,478	541,434	567,684
2013–2014	641,466	516,993	561,093	563,154
2014–2015	659,502	514,992	582,528	562,458
2015–2016	676,821	504,234	602,412	549,942
Total income in billion (2020 US$; per person year in parentheses)
2006–2007	$9.23 ($18,100)	$12.68 ($21,500)	$12.01 ($28,100)	$21.67 ($33,900)
2007–2008	$10.08 ($19,000)	$12.98 ($22,300)	$13.00 ($29,000)	$21.92 ($34,700)
2008–2009	$10.72 ($19,500)	$12.96 ($22,600)	$13.68 ($29,200)	$21.60 ($34,700)
2009–2010	$11.25 ($19,600)	$12.66 ($22,500)	$13.98 ($28,400)	$20.62 ($33,800)
2010–2011	$11.63 ($19,600)	$12.26 ($22,300)	$14.54 ($28,500)	$20.07 ($33,700)
2011–2012	$12.03 ($19,700)	$11.94 ($22,300)	$15.21 ($29,000)	$19.69 ($34,000)
2012–2013	$12.69 ($20,300)	$11.94 ($22,800)	$16.26 ($30,000)	$19.83 ($34,900)
2013–2014	$13.38 ($20,900)	$11.99 ($23,200)	$17.26 ($30,800)	$20.05 ($35,600)
2014–2015	$14.25 ($21,600)	$12.23 ($23,800)	$18.56 ($31,900)	$20.48 ($36,400)
2015–2016	$15.32 ($22,600)	$12.67 ($25,100)	$19.89 ($33,000)	$20.94 ($38,100)
Person-year observations by:
Age-group
25–34	1,144,080	1,642,794	1,004,244	1,753,671
35–44	1,535,346	1,561,965	1,206,579	1,747,845
45–54	1,748,103	1,342,479	1,477,311	1,507,095
55–64	1,540,962	908,439	1,471,257	911,004
Ethnicity
Māori	1,018,983	621,849	877,980	698,352
Pacific peoples	339,252	324,831	314,880	355,107
Asian peoples	420,855	934,179	308,370	896,748
Other (mainly European)	4,189,398	3,574,815	3,658,158	3,969,405
Deprivation quintile (NZDep)
1 (least deprived)	1,211,781	1,225,521	1,017,936	1,267,881
2	1,183,701	1,175,664	1,008,984	1,240,317
3	1,173,612	1,106,049	1,010,547	1,198,434
4	1,185,894	1,029,006	1,039,056	1,154,370
5 (most deprived)	1,213,500	919,434	1,082,865	1,058,613

All numbers are random rounded to near multiple of 3 as per Statistics New Zealand requirements.

Age at beginning of tax year.

Data for healthy and disease combined, stratified by age-group, ethnicity, and deprivation are presented in S4–S6 Tables.

Objective 1: Individual-level disease-specific income loss estimates by phase of diagnosis

Figs 1 and 2 show the FE regression coefficients—inflation and purchase power parity adjusted to US$—by sex and two of the disease phases: first year of diagnosis and prevalent disease. These per capita income losses represent the reference case person (age 50 to 54, Other/European ethnic group, living in the middle quintile of neighbourhoods ranked by deprivation). Income losses tended to be greater for males. Income loss in the year of diagnosis was highest for dementia for both men ($8,882; 95% CI $6,709 to $11,056) and women ($7,103; $5,499 to $8,707). Traumatic brain injury, mental illness, and lung cancer cases had the next largest income losses for males and females. One disease had a significant increase in income postdiagnosis, namely migraine for females ($446, 95% CI $265 to $627). Similar patterns were evident for prevalent years of diagnosis.

Fig 1

Average per capita income loss (US$ 2020) per year by phase of disease or condition, from FE regression models, by sex (error bars are 95% confidence intervals): Females.

COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; FE, fixed effects; GI, gastrointestinal; GU, genitourinary; IHD, ischaemic heart disease; MSK, musculoskeletal; TBI, traumatic brain injury; T2DM, type 2 diabetes mellitus.

Fig 2

Average per capita income loss (US$ 2020) per year by phase of disease or condition, from FE regression models, by sex (error bars are 95% confidence intervals): Males.

COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; FE, fixed effects; GI, gastrointestinal; GU, genitourinary; IHD, ischaemic heart disease; MSK, musculoskeletal; TBI, traumatic brain injury; T2DM, type 2 diabetes mellitus.

Income loss in the last year of life was (unsurprisingly) high for all diseases, given on average 6 months of income was lost if the person was employed at the time of death and greater for males than females (Figs 1 and 2). Cancers tended to have the highest income losses in the last year of life, e.g., colorectal cancer for males ($17,786, 95% CI $15,555 to $20,018) and females ($14,192, $12,357 to $16,026).

Objective 2: Population-level estimates of total income loss by disease

Table 2 shows the cause-deleted income gains; cause-deleted results combined the prevalence of each condition with the per capita income loss (as above). The average annual income for all 25- to 64-year-olds was $60.6 billion; if all diseases were deleted, we estimated the total income would have been $63.3 billion—a 4.3% or $2.72 billion increase in income ($121 per adult). Of this increase in income by deleting all diseases, the largest contributor was clearly mental illness at 30.0%—with nearly half of this from the depression and anxiety category (13.1%). For aggregated disease categories contributing more than 5% of this total disease-related income loss, the rank order after mental illness was as follows: cardiovascular disease (15.6%); musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), and neurological (6.5%). Other notable findings included type 2 diabetes mellitus contributing to 6.5% of all disease-related income loss and, conversely, cancer only contributing 4.5% and injury only 3.5%.

Table 2

Population-level cause-deleted average annual income gain as estimated from FE models for 40 diseases (2020 US$ millions).

Disease groupings	Annual income loss†			% of all disease related loss
	Total	Females	Males	Total	Females	Males
All disease#	$2,729.9	$1,110.3	$1,619.6
Cancer #	$122.2	$70.1	$52.1	4.48%	6.31%	3.22%
Cancer–Colorectal	$13.5	$5.8	$7.7	0.49%	0.52%	0.48%
Cancer–Lung	$7.8	$3.8	$4.0	0.29%	0.34%	0.25%
Cancer–Breast	$37.0	$37.0		1.36%	3.33%
Cancer–Prostate	$10.0		$10.0	0.36%		0.61%
Cancer–Other	$53.9	$23.5	$30.4	1.98%	2.12%	1.88%
CVD #	$426.6	$168.1	$258.6	15.63%	15.14%	15.97%
CVD–IHD	$94.0	$25.3	$68.8	3.44%	2.28%	4.25%
CVD–Stroke	$78.1	$31.9	$46.2	2.86%	2.87%	2.85%
CVD–Other CVD	$159.7	$55.0	$104.6	5.85%	4.96%	6.46%
CVD–Blood disorders	$94.9	$55.9	$39.0	3.48%	5.03%	2.41%
Endocrine #	$242.8	$123.3	$119.5	8.89%	11.10%	7.38%
Endocrine–T2DM	$176.7	$98.8	$77.9	6.47%	8.90%	4.81%
Endocrine–Other	$66.1	$24.4	$41.6	2.42%	2.20%	2.57%
GI #	$201.7	$97.9	$103.8	7.39%	8.82%	6.41%
GI–Chronic liver disease	$27.9	$12.6	$15.3	1.02%	1.13%	0.95%
GI–Upper disease	$41.7	$15.4	$26.3	1.53%	1.39%	1.62%
GI–Other	$132.1	$69.9	$62.2	4.84%	6.29%	3.84%
GU #	$36.0	$0.7	$35.3	1.32%	0.06%	2.18%
GU–Chronic kidney disease	$17.9	$10.5	$7.4	0.66%	0.94%	0.46%
GU–Other	$18.1	($9.8)	$27.9	0.66%	−0.88%	1.72%
Infection	$9.0	$3.5	$5.4	0.33%	0.32%	0.34%
Injury #	$94.9	$18.5	$76.4	3.47%	1.67%	4.71%
Injury–TBI	$47.8	$6.0	$41.8	1.75%	0.54%	2.58%
Injury–Internal	$1.0	$0.2	$0.8	0.04%	0.02%	0.05%
Injury–Poison	$4.3	$1.4	$2.8	0.16%	0.13%	0.18%
Injury–Other	$41.7	$10.9	$30.9	1.53%	0.98%	1.91%
Neurological #	$178.5	$36.3	$142.3	6.54%	3.27%	8.78%
Neurological–Migraine	−$58.8	−$60.1	$1.3	−2.15%	−5.41%	0.08%
Neurological–Dementia	$11.7	$4.5	$7.3	0.43%	0.40%	0.45%
Neurological–Sleep	$0.6	$0.1	$0.5	0.02%	0.01%	0.03%
Neurological–Other	$225.0	$91.7	$133.2	8.24%	8.26%	8.23%
Mental illness #	$817.9	$343.1	$474.8	29.96%	30.90%	29.32%
Mental illness–Depression and anxiety	$356.2	$201.5	$154.7	13.05%	18.15%	9.55%
Mental illness–Alcohol	$156.1	$37.1	$119.1	5.72%	3.34%	7.35%
Mental illness–Schizophrenia	$85.2	$23.9	$61.3	3.12%	2.15%	3.78%
Mental illness–Other	$220.5	$80.7	$139.8	8.08%	7.27%	8.63%
MSK #	$372.8	$112.2	$260.6	13.66%	10.10%	16.09%
MSK–Spine	$106.1	$36.5	$69.6	3.89%	3.29%	4.30%
MSK–Osteoarthritis	$41.6	$21.4	$20.1	1.52%	1.93%	1.24%
MSK–Chronic pain	$46.6	$15.2	$31.4	1.71%	1.37%	1.94%
MSK–Rheumatoid arthritis	$7.0	$5.4	$1.7	0.26%	0.48%	0.10%
MSK–Other	$171.5	$33.7	$137.8	6.28%	3.03%	8.51%
Skin	$0.7	$0.2	$0.5	0.03%	0.02%	0.03%
Sensory	$52.6	$14.9	$37.7	1.93%	1.34%	2.33%
Reproductive	$93.7	$93.7		3.43%	8.44%
Respiratory #	$80.4	$27.9	$52.6	2.95%	2.51%	3.25%
Respiratory–COPD	$25.0	$10.8	$14.2	0.92%	0.97%	0.88%
Respiratory–Asthma	$59.6	$28.8	$30.8	2.18%	2.60%	1.90%
Respiratory–Other	−$4.2	−$11.8	$7.6	−0.15%	−1.06%	0.47%

†The change in income for the total population, between their observed disease status and a counterfactual of the given diseases deleted (i.e., setting disease dummies all to no disease), using the FE regression coefficients to predict the difference in income between the observed and counterfactual states. Divided by 10 to give annual income loss. The total disease-related income loss ($2,729 million) was 4.31% of all income earnt if diseases were deleted (4.34% for females, 4.29% for males).

#Rather than just one disease’s dummy coefficients being set to 0, all diseases (or all diseases within the given disease grouping) were set to 0.

COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; FE, fixed effects; GI, gastrointestinal; GU, genitourinary; IHD, ischaemic heart disease; MSK, musculoskeletal; TBI, traumatic brain injury; T2DM, type 2 diabetes mellitus.

Fig 3 shows the cause-deleted income gains by age (sexes combined) by 14-level grouping. Unsurprisingly, the income gain increases with age. Mental illness stands out as the major cause of income loss at younger ages and continues to be a major contributor into older ages. Conversely, musculoskeletal and the vascular and blood category make increasingly large contributions at older ages.

Fig 3

Cumulative income gain at the population level, by age-group, from “deleting” diseases using FE regression coefficients.

FE, fixed effects.

Sensitivity analyses

S1 Fig shows the comparison of the FE regression results (above) with the between person OLS regression estimates of income loss per person, and OLS regression on only those with no disease prior to the observation window and additionally adjusted for average income before the observation window. First, the income loss estimates are greater using OLS estimates—more so in absolute terms for last year of life if dying of the disease, but more so relatively for diagnosis year and prevalent cases. Second, and as expected, the OLS results adjusted for prior income are lesser in absolute magnitude (due to likely confounding of the unadjusted OLS results by SEP).

For most diseases, there were income drops in the year prior to diagnosis (S2 Fig). These drops were 50% or more of the income drop observed in the first year of diagnosis, for both males and females, for mental health disorders, dementia, chronic obstructive pulmonary disease, type 2 diabetes, chronic liver disease and sensory disorders—consistent with all these diseases having substantial prodromal periods. However, the possibility of some reverse causation for mental diseases (i.e., drop in income causing mental disorder) exists. Therefore, we undertook a crude test by lagging income forward and back 1 year in FE analyses. Consistent with the assumption that mental illness is mostly causing income loss, not vice versa, the association of mental illness to income in the next year was stronger than to income in the previous year.

Some disease classifications likely detected more severe cases, e.g., mental illness that relied upon a presentation to and diagnosis at a publicly funded inpatient or outpatient service. Using mental illness as an example, if we added community dispensed pharmaceuticals (e.g., antidepressants) to the case classification algorithm, prevalent person years of depression and anxiety increased from 3.6% to 14.4%, but commensurately the per person income loss more than halved (as less severe disease was being included, and conditions other than anxiety and depression that share the same pharmaceutical treatments). At the aggregate level of all mental illness, in terms of cause-deleted analyses, the wider case definition using pharmaceuticals increased the percentage of all income loss from mental illness from 30.0% to 39.9%.

Conversely, excluding pharmaceuticals from all our base–case finding algorithms made little difference to FE regression results.

Discussion

Diseases cause substantial income loss. For a counterfactual scenario of no disease and using income loss from diagnosis to the year of death (but not after the year of death), our results suggest that the 25- to 64-year-old population’s income would be 4.3% greater. For aggregated disease categories contributing more than 4% of this total disease-related income loss, the rank order was as follows: mental illness (30.0%); cardiovascular disease (15.6%); musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), neurological (6.5%), and cancer (4.5%). Migraine was the only disease that resulted in a (modest) increase in income, for females only, but with 95% confidence intervals excluding the null. While it could be a chance finding, it is also not implausible: A diagnosis of migraine may lead to better treatment and more productivity; conversely, increased stress in the workplace due to longer hours or more responsibilities with career advancement (and, therefore, income) may trigger migraines. The finding of high per capita income loss for people with dementia in their first year of diagnosis and if prevalent was unexpected but also based on relatively few people (Figs 1 and 2); it may be that dementias before the age of 65 selectively impact the ability to work of high-income occupations. Unsurprisingly, there is a strong correlation between aggregate disease-related income loss and health loss from the same conditions measured in disability-adjusted life years and years of life lived with disability—due to both being largely driven by the prevalence of the condition (S4 Fig).

The pattern of income loss in the current study is similar to that observed by Kinge and colleagues in Norway [4] (see S2 Fig for a comparative breakdown), albeit the Kinge and colleagues study used more approximation methods than our actual linked data. Generalizability to other high-income countries, the duration, and generosity of employer sick leave may vary with other countries; in our analysis, employer-funded sick leave was not able to be “seen” as it is simply part of salary or income. Therefore, the reader will need to be aware of this if their conceptualization of income loss (or productivity loss) includes employer sick pay. Second, the extent of assistance provided to people with health conditions to return to (paid) work varies by country. Third, the extent of disease-related income loss is likely to vary with the unemployment rate. If unemployment is high, then disease-related income loss is likely to be higher due to a greater pool of competitors for the same job (but, conversely and rather brutally, the productivity loss to society will be less as the sick person is more easily “replaced” in the workforce). Nevertheless, the general patterns we observe likely hold in other countries—and our study is a valuable template for future comparison studies.

There are 2 main approaches to estimate economic productivity losses: (a) human capital approach (HCA); and (b) friction cost approach (FCA) [5,6]. In the HCA, productivity losses are equated to losses of income. In the FCA, the illness and reduced work capacity of an individual is assumed to be replaced by another citizen after a certain time if the economy has structural unemployment or other means to replace workers (e.g., immigration). Our study uses an HCA among the living (i.e., loss of income compared to the participant’s “healthy self”) and a hybrid approach among decedents whereby income loss in the tax year of death is included, which on average includes the 6 months predeath and (complete income loss) 6 months postdeath. We do not include income loss in years after death, equivalent to an FCA of people being “replaceable” in the workforce.

We believe that our study is a substantial advance in methods and data over previous studies. First, by adjusting for many diseases in one model, we provide estimates of how diseases compare against one another and prevent overestimation of income loss due to the presence of comorbidities. Second, we use population-wide linked health and tax data, over 10 years, offering high power and avoiding selection biases that may arise with panel studies and attrition. Third, we have data on health conditions prior to the 10-year observation window to ascertain preexisting health status, which allows us to accurately estimate income loss by stage of disease.

There are limitations of our study. First, while the income/tax data is an objective income assessment, it is not perfect for the measure of productivity; employers will continue to pay employees who are sick during their sickness leave entitlement period, and we had no data on this. Accordingly, we underestimate “productivity loss” to some extent. It would be a useful extension to our study to impute this employer-funded sick pay using external survey data—however, we doubt that it will change the relative income loss and rankings by disease. Second, our FE regression models remove all time-invariant confounding by design and measured time-varying confounders, but they are likely to underestimate income loss due to failure to “capture” any deterioration in income prior to diagnosis using our case finding criteria. Hence, we ran between-person OLS regression models, for a healthy cohort at the outset and adjusting for income before the observation window, as a sensitivity analysis. The truth probably lies somewhere between the FE estimates and the OLS estimates in the model adjusted for prior income. We favour the FE analyses because it removes time-invariant confounding by design. Also, in the between-person analyses, we noted patterns consistent with some residual confounding, for example, in people contracting breast cancer and prostate cancer (which tend to be higher SEP, the latter due to unequal uptake by SEP in prostate-specific antigen testing) have higher incomes after diagnosis—likely due to residual confounding by SEP. Conversely, there are likely competing residual biases in the FE regression towards overestimation. Residual time-varying confounding likely remains, for example, divorce as a cause of both income loss and depression. Also, reverse causation may inflate some estimates, for example, again the mental health estimates whereby low income causes poor mental health (although in sensitivity analyses, we found that the association of mental illness with income loss lagged 1 year was stronger than the association of income with mental illness lagged 1 year—suggesting that any reverse causation is “less” than the direction of causation we assumed).

We find that absolute income losses by disease are generally higher for males than females, but relative income losses are similar—reflecting known inequities in pay between males and females. However, it must be noted that we used a population-wide approach, not an analysis restricted to only those employed. Given that females have lower workforce participation, our estimated income loss among all females getting a disease will for this reason alone be less than among all males (with higher employment rates) getting the same disease. If results such as ours are to be used in prioritising health interventions based on their ability to improve individual incomes and aggregate productivity, we recommend careful attention to, and correction for, structural societal inequalities. A similar argument applies to diseases with varying rates by ethnicity and SEP; income loss by disease will vary in at least one of absolute or relative terms by SEP. Also, there may be subadditive or superadditive impacts on income loss from having 2, 3, or more conditions compared to the independent and separate (unconfounded) income loss for each condition as reported in this paper. These questions were beyond the scope of this study and will be pursued as additional follow-up publications.

There are important implications of this study. First, disease advocacy groups and researchers often invoke large estimates of the economic impact of their disease of choice, using studies with highly variable methods and not allowing for comorbidity. Our analysis provides a realistic and “confined within the total income envelope” estimate of income loss, conducted comparatively across diseases. Second, a major implication of our study is that preventing diseases that cause substantial income loss to individuals, and economic productivity loss to society at the aggregate level, justify greater weighting in prioritising intervention programmes. For example, our findings suggest that preventing mental illness, musculoskeletal diseases, and cardiovascular disease might justify some more weighting, if we also value the contribution of health interventions to economic outcomes. While the exact absolute value of income loss in our study is subject to our assumptions, the relative comparisons are robust between diseases—all diseases were analysed together with similar assumptions. We argue that such comparability of estimates by disease, especially by phase of disease, at least within one country, opens a useful policy door to estimating the impact of interventions (e.g., salt reduction in bread that lowers stroke and ischaemic heart disease rates, e.g., obesity reduction programmes; e.g., treatments) on income loss in addition to the usual health gain and health expenditure impacts. Such additional analyses should be a useful adjunct to prioritise health interventions, if a desirable additional impact of interventions in the health sector is improvements in economic productivity. However, such analyses must be handled carefully, given the potential equity implications (e.g., sex as above).

In summary, we used a unique data base of repeated disease and tax income measures on an entire population. FE regression estimated the within-individual change in income when developing disease among 25- to 64-year-olds. Income loss among individuals developing disease was highest for dementia (noting this was dementia onset before the age of 65 years), followed by mental illness. From a total population perspective, combining the prevalence of disease with the income loss estimates per individual, the 3 largest causes of income loss were mental illness, cardiovascular disease, and musculoskeletal diseases. Our study is a major advance, including all diseases simultaneously and quantifying within-individual income loss. We encourage other countries to also conduct such comparable analyses and then further to trial including such income loss estimates as additional considerations in intervention prioritisation with policymakers.

Disease and condition case definitions.

(DOCX)

Click here for additional data file.

Observation person-years and total income (2020 US$, millions) by diseases by phase.

(DOCX)

Click here for additional data file.

Annual income loss (US$ 2020) for 14 and 40 diseases and conditions models predicted by fixed effects regression for 50- to 54-year-olds (95% confidence intervals in parentheses).

(DOCX)

Click here for additional data file.

Descriptive data (healthy and diseased combined) within observational window 2006–2007 to 2015–2016 by sex and age.

(DOCX)

Click here for additional data file.

Descriptive data (healthy and diseased combined) within observational window 2006–2007 to 2015–2016 by sex and ethnicity.

(DOCX)

Click here for additional data file.

Descriptive data (healthy and combined) within observational window 2006–2007 to 2015–2016 by sex and deprivation.

(DOCX)

Click here for additional data file.

Annual income loss (US$ 2020) for 14 and 40 disease models predicted by OLS regression for 50- to 54-year-olds (95% confidence intervals in parentheses).

(DOCX)

Click here for additional data file.

Annual income loss by disease phase for 3 model specifications: FE (main analysis); OLS unadjusted for prior income; OLS adjusted for prior income and restricted to health people at beginning of observation window.

COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; FE, fixed effects; GI, gastrointestinal; GU, genitourinary; MSK, musculoskeletal; OLS, ordinary least squares; TBI, traumatic brain injury; T2DM, type 2 diabetes mellitus.

(DOCX)

Click here for additional data file.

Comparison of income within-individual income change for year before diagnosis and first year of diagnosis, FE model.

COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; FE, fixed effects; GI, gastrointestinal; GU, genitourinary; MSK, musculoskeletal; TBI, traumatic brain injury; T2DM, type 2 diabetes mellitus.

(DOCX)

Click here for additional data file.

Proportionate contribution of diseases to HCA productivity loss in Norway in 2013 [4]. HCA, human capital approach.

(DOCX)

Click here for additional data file.

Cause-deleted income gain, plotted against YLDs and DALYs in 2011 from the GBD for 25- to 64-year-olds in NZ.

CVD, cardiovascular disease; DALY, disability-adjusted life year; IHD, ischaemic heart disease; YLD, years lost to disability.

(DOCX)

Click here for additional data file.

The RECORD statement—Checklist of items, extended from the STROBE statement, which should be reported in observational studies using routinely collected health data.

(DOCX)

Click here for additional data file.

20 Jul 2020

Dear Dr Blakely,

Thank you for submitting your manuscript entitled "Disease-related income and economic productivity loss: longitudinal analysis of individual-level data for an entire country" for consideration by PLOS Medicine.

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22 Sep 2020

Dear Prof. Blakely,

Thank you very much for submitting your manuscript "Disease-related income and economic productivity loss: longitudinal analysis of individual-level data for an entire country" (PMEDICINE-D-20-03416R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also evaluated by three independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Requests from the editors:

*At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

*Please clarify whether the analytical approach reported here corresponded to one laid out in a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

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Comments from the reviewers:

Reviewer #1: I confine my remarks to statistical aspects of this paper. I have some concerns that need to be addressed before I can recommend publication

The biggest one is whether the fixed effect regression is the appropriate method. More detail is needed about the method in the 1st para of p. 6. My preference would be for a multilevel model, but the methods used may be OK --it depends on details.

Another general concern is what population you are inferrring to. You have the whole population of New Zealand. Are you inferring to the rest of the world? That seems likely to be very wrong. Or perhaps to other high-income countries? Or what? Some statisticians justify this by inferrring to some "super-population" -- I am not a big fan of this, but it's not outright wrong. But you need to specify what the population or super population is.

The authors also categorized many continuous variables. This is nearly always a mistake. In *Regression Modeling Strategies* Frank Harrell lists 11 problems with this and sums up "nothing could be more disastrous". I wrote a blog post showing some of the problems, graphically https://medium.com/@peterflom/what-happens-when-we-categorize-an-independent-variable-in-regression-77d4c5862b6c Instead, leave the variables continuous and use splines to investigate nonlinearity.

Finally, the figures need work Figure 1 is a dynamite plot. These are not recommended. See e,g, https://simplystatistics.org/2019/02/21/dynamite-plots-must-die/

Figure 2 is a stacked bar chart. These also cannot be recommended, see the work of William S. Cleveland, who showed that people cannot estimate the size of the bars. A Cleveland dot plot might be better, or perhaps a mosaic plot.

Lastly, fig. 3 is a pie chart, also not a good graphic. See my article: Graphics for univariate data: Pie is Delicious but not Nutritious http://statisticalanalysisconsulting.com/graphics-for-univariate-data-pie-is-delicious-but-not-nutritious/

Peter Flom

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Reviewer #2: The paper is an important contribution to the field of disease-related economic impacts and priority setting. It reads well.

Below are a number of specific comments.

1. Title/Abstract: New Zealand should be mentioned in both the title and the abstract.

2. Introduction:

More general background information should be given on New Zealand, including: summary economic indicators (e.g. GDP per capita, income distribution, level of inequality); population numbers (e.g. total, proportion of old vs. young, distribution by ethnic group); health indicators (e.g. life expectancy at birth, composition of burden of disease); and health system characteristics (e.g. level of public vs. private financing, type of health system, what kind of insurance schemes?).

3. Methods:

* Datasets: The authors should provide more details (and possibly references) on how the linkage between health and income data was conducted.

* Analyses: This part is critical to the understanding of the methods and is currently too succinct and vague. More details should be provided with respect to the two steps behind the analysis:

(i) regression modeling and (ii) cause-deleted analyses. For instance: for (i) model equations should be given and described; and for (ii) the estimation of 95% uncertainty ranges based on (i) (I presume based on the regression coefficients?) should be explained.

4. Results:

* Could the authors report also per capita numbers for the estimates of disease-related costs?

* It would be interesting to see how the results (per disease) are correlated (or not) to the burden of disease by cause (e.g. using disease prevalence estimates).

* Could the authors pursue an analysis by socioeconomic status? That would be useful, in particular, given that, besides variations in disease prevalence, people may have different insurance coverage and schemes varying with income and socioeconomic status.

5. Discussion:

* It would be good to have more explanations for the findings that are highlighted in the paper. The comparison with Norway is interesting but insufficient.

The authors should balance the epidemiological/burden of disease factors that may explain their results, with also some more fundamental health system issues (e.g. coverage of insurance and copayment, which may vary by disease condition). In other words, I believe the findings are highly contextual and depend on the local health system and levels of social protection: what is covered (e.g. specific disease treatments) by specific insurance programs and sick leaves? This should be better acknowledged in the context of what is known of copayments, sick leave policies, per disease/condition and income group/profession in New Zealand.

* Based on the previous point, it would be good to reflect on how the same analysis would likely differ in other countries with very different health systems than New Zealand.

* A better explanation could be provided with respect to why this approach can be used for priority setting: Is this something that can be ethically justified or not? And how such analyses could complement (or not) burden of disease arguments for example?

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Reviewer #3: Dear Authors,

Thanks for the opportunity to review your manuscript entitled, Disease-related income and productivity loss: longitudinal analysis of individual-level data for an entire country". Estimating income loss directly attributed to chronic disease is an important research area, especially from a population-level perspective. A highlight of the paper was the availability of linked health and income data which the research team uses to estimate income loss. Overall, the paper was interesting. However, there are several areas of improvement which I suggest in the following comments.

Background

* The authors note that their study is novel because it is one of the first to estimate the impact of comorbidity on income loss. It is important to acknowledge the large body of research which has estimated the impact of specific chronic diseases on income and employment. Additionally, there are also studies that have examined the relationship between comorbidity and employment at the population level (e.g., US National Comorbidity Survey, US National Health Interview Survey, Canadian longitudinal National Populational Health Survey). It would have been helpful for the authors to dig deeper into the existing research and describe the specific gaps their research is filling. Also, through a more comprehensive synthesis of existing research, the authors could highlight the uniqueness of their research approach and methodology.

* In the background it would also be important for the authors to provide support for their study objective. Why is phase of diagnosis important when it comes to estimating income loss? I was expecting the authors to include specific study objectives or hypotheses comparing different health conditions. I was also expecting objectives or hypotheses related to having a single chronic disease versus one or more chronic diseases.

Methods

* The analytical process that the authors take is unique. However, the authors assume that the reader will completely understand the statistical methods and approaches that they used. For greater clarity, I would suggest that the authors consider adding some more information to this section. I note several suggestions in the sections below.

* First, the author draws on multiple registries and health-related datasets. Given that these appear to be different sources that authors should elaborate on how these datasets were harmonized in order to estimate the number of people with a chronic disease. Additional information would also be helpful to provide details on how the different registries/data sources were used to estimate comorbidity.

* The authors describe including 14 aggregate disease. How did they decide on which diseases they would include and which they might exclude? Did the authors chose to focus on commonly reported chronic diseases?

* In their analytical models, were the authors able to control for disease management? Certain chronic diseases can have a significant impact on work if they are not treated with pharmacologic or non-pharmacologic approaches. These same diseases could have no impact on income generation if appropriate management is accessed.

* Also, the authors examine the time varying nature of chronic disease. Were the authors able to capture the episodic nature of chronic disease? Certain conditions like depression, arthritis, multiple sclerosis can fluctuate on a weekly, monthly or yearly bases. Indeed, a period of disease flare will have a more significant impact on income generation. Could the authors account for this level of variability?

* How did the authors account for comorbidity? If a person was living with more than one chronic condition, was there a method to determine which chronic condition had the biggest impact on income loss?

* I was curious to learn more about the ethnic group covariate. I found it troubling that members of the research team prioritized an ethnic group, when a participant noted being part of more than one ethnic group. Perhaps I misunderstood. Maybe the authors could clarify.

* Similarly, more details on the material deprivation covariate would have been helpful.

* Can the authors provide a few more details on the net cost approach?

* Also, the authors include interaction effects into the models. Perhaps they can elaborate on the interaction effects they included and how they align with study objectives they describe in the background sections.

* I found the cause-deleted analyses to be an interesting approach. Did they authors develop this approach themselves? Is it commonly used in economic estimates of a similar nature.

Results

* I found the results section a bit hard to follow. I would suggest making the findings clearly align with the study objectives.

Discussion section

* A comprehensive discussion of the implications of the study findings was missing. I was curious to know the author's interpretation of the findings. Why was income loss greater for males? For example, why would dementia be attributed to the greatest income loss post diagnosis? Is it a clinical impact? Age-related impact? Or perhaps dementia is more likely to affect people earning a greater amount of income to begin with? Also, I am also curious to know why migraines may have been associated with an income gain. I would also suggest the authors provide more insight into the implications of the findings.

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Any attachments provided with reviews can be seen via the following link:

[LINK]

29 Oct 2020

Submitted filename: Plos Med Income Loss Revision Letter.docx

Click here for additional data file.

15 Jan 2021

Dear Dr. Blakely,

Thank you very much for re-submitting your manuscript "Disease-related income and economic productivity loss: longitudinal analysis of linked individual-level data for all New Zealanders" (PMEDICINE-D-20-03416R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

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If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jan 22 2021 11:59PM.

Sincerely,

Artur Arikainen,

Associate Editor

PLOS Medicine

plosmedicine.org

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Requests from Editors:

1. Please respond to and, where possible, address the reviewer’s final comments. Additional comments from the Academic Editor: “I would emphasize that the authors should clearly explain the pros and cons of converting the continuous predictors into categorical variables. Despite their reasonable response to reviewer #1 on this issue, the authors do need to convince the more statistically oriented reader why it is that categorical variables are preferred in this situation. Ideally, they should show that a sensitivity analysis with continuous variables produces qualitatively similar results.”

2. Financial Disclosure: Please add “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”; or explain otherwise.

3. Data Availability Statement: Correct to “…data…are available…”

4. Title: Please amend to: “Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data”

5. Short title: Please amend to: “Disease-related income loss in New Zealand”

6. Please add line numbers in the margin throughout.

7. Abstract:

a. Reword to “…adjustment for confounding are lacking, to our knowledge.”

b. Please briefly quantify these results: “Mental illness also had high income

c. losses in the year of diagnosis. Similar patterns were evident for prevalent years of diagnosis.”

d. Please make more explicit that all $ values shown are USD.

e. At the end of the ‘Methods and findings’ subsection, please reword the limitations to be more explicit: “The limitations of this study were…” (or similar). You can also be more concise in describing the limitations by removing the examples after each “e.g.”. However, you can also add in other limitations such as omitting sick pay or migrant workers.

f. Conclusions: Please begin with “In this longitudinal study, we found that…”

8. Page 3: Please rename to simply “Author Summary”.

9. Author Summary: Please clarify that $ is USD.

10. Please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references. Citations should be in square brackets, before punctuation, and not superscript, eg. “…[1,2].”

11. The terms gender and sex are not interchangeable (as discussed in http://www.who.int/gender/whatisgender/en/ ); please use the appropriate term.

12. Methods:

a. Please provide a copy of your analytical plan as Supporting Information, and cite the file under ‘Analyses’.

b. Please mention that separate ethical approval was not required for your study.

c. Please cite your RECORD checklist: "This study is reported as per the RECORD guideline (S1 Checklist)."

13. Page 10: Please make the Results subheading more distinct.

14. Discussion:

a. Please fix broken citation “(Error! Reference source not found.)”

b. Please rephrase to avoid nesting brackets: “…(but conversely, and rather brutally, the productivity loss to society will be less as the sick person is more easily ‘replaced’ in the workforce).”

c. Please add a one-paragraph conclusion to the end, summarising your study.

15. Please remove funding information from the Acknowledgements.

16. References: Please remove bold and italic formatting. First 6 authors should be shown, followed by “et al.”

17. Please complete all sections of the RECORD checklist; state ‘n/a’ for items not applicable (though this should be rare).

18. Please provide more access details (eg. volume/issue/pages, DOI or URL) for references 15, 16, 17, 18.

Comments from Reviewers:

Reviewer #1: NOTE: Much of what I have below is a matter of informed opinion rather than absolute fact. I think my opinions are justified, but other statisticians might disagree. The authors clearly know a lot about statistics. But the editors asked for my opinions again, rather than those of some other statistician, so, here they are.

The authors have responded to my comments, but there are some remaining issues:

I wrote

Another general concern is what population you are inferrring to. You have the whole population of New Zealand. Are you inferring to the rest of the world? That seems likely to be very wrong. Or perhaps to other high-income countries? Or what?

Some statisticians justify this by inferrring to some "superpopulation" -- I am not a big fan of this, but it's not outright wrong. But you need to specify what the population or super population is.

Author response:

We are inferring to the total NZ population, then talking about generalizability in the Discussion (see below).

My response: You cannot infer to the sample that you have. If you try, then all p values would be exactly 0, all CIs would be point estimates. The p values and CIs have to refer to some larger group. Generalizability is a different issue - that's one of how well analysis done in New Zealand would apply to other places. So, the authors could do a purely descriptive study of NZ -- I think that would be fine.

-------------------------

I objected to categorizing continuous variables. The authors responded, first, with regard to age:

We tried extensive alternative specification of variables in analyses prior to submission. Continuous variants for age (say) were problematic due to non-linearities by age, requiring separate specification by each disease in interaction terms and such like. We had problems with (say) quadratics for age generating predictions that were far from the observed data at the younger and older age groups. For model parsimony reasons we used categorical variables. We note that with the entire population of NZ included, that study power is good (i.e. we were not sacrificing many degrees of freedom). Second, we note that with covariate adjustment in epidemiology and biostatistics, it is common practice to use categorical variables - with a common rule of thumb being that more than 4 or 5 categories captures the majority of confounding.[9] Third, we note that with cause deleted analyses categorical variables are perhaps a bit easier conceptually.

My response:

The authors point about this being a common method is entirely correct. But I'm not sure that's an argument that it is a good method.

I agree that quadratics are problematic; I would suggest a spline. It's true that splines can be a bit hard to interpret (although graphs can help). The problem with categorizing the variable at arbitrary points is that there is no way to know if those are the right points, And, since the authors note that the data are nonlinear, using categories imposes linearity within each category. Regarding parsimony - well, you certainly have ample degrees of freedom to play with, but parsimony is, indeed, a good aim. But, from what I can tell, the parsimony gain is from assuming linearity between categories. I'm not sure that is worth it. Perhaps a compromise would be for the authors to do an analysis with splines and see if it is much different than the one with categories, then proceed accordingly.

More generally (and this is more for the PLOS editors than the authors), I think it is good for authors who know what they are doing (as these authors clearly do) to use advanced methods where appropriate. PLOS Medicine wants to advance medical techniques by publishing articles by authors who are experts in new methods. So, I think, they should also want to advance statistical methods.

For the other categorical variables I think the authors' response makes sense.

------------------

Finally, we disagree about two of the graphics. I still think I am right, but this is (partly) a matter of style and I leave it to the editors to decide between the different graphs. Certainly the authors graphs are common choices.

Peter Flom

Reviewer #2: The authors have addressed my comments.

Reviewer #3: Dear Author,

Thank you for your revisions. You addressed all of my comments.

I would suggest that you carefully review grammar and formatting of the paper to improve flow and readability. Also, there were a few issues with the referencing that should be fixed.

Best.

Any attachments provided with reviews can be seen via the following link:

[LINK]

18 Feb 2021

Submitted filename: Plos Med Income Loss 2nd Rev_Response to Reviewers.docx

Click here for additional data file.

30 Aug 2021

Dear Dr. Blakely,

Thank you very much for re-submitting your manuscript "Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data" (PMEDICINE-D-20-03416R3) for consideration at PLOS Medicine. We do apologize for the delay in sending you a response.

I have discussed the paper with our academic editor, and we will need to ask you to address some additional points before we are in a position to proceed further.

The issues that need to be addressed are listed at the end of this email. Please take these into account before resubmitting your manuscript.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

Please let me know if you have any questions, and we look forward to receiving the revised manuscript.

Sincerely,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

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Requests from Editors:

Please make that "these data" in the data statement. We note that you specify that "some restrictions will apply" to data access, and we ask that you briefly state the nature of these restrictions, e.g., that access might be restricted to researchers with suitable ethics approval for their planned study.

At line 23, for example, please adapt the phrasing to: "... cardiovascular ... endocrine ... and neurological diseases, ... and cancer".

Please restructure the early part of the Discussion section. The first paragraph should summarize the study's findings, so we suggest introducing a paragraph break where the findings are compared to those of a previous study.

Please ensure that reference call-outs precede punctuation (e.g., "... injury [12].") and do not contain spaces, e.g., at line 91 ("... loss from deaths [13,14] ").

Please substitute "sex" for "gender" where appropriate, e.g., at line 431; in the RECORD checklist.

Please use the style "age X years" consistently, e.g., at line 435.

***

12 Oct 2021

Submitted filename: Disease-related income loss, Revision 3 letter.docx

Click here for additional data file.

13 Oct 2021

Dear Dr Blakely,

On behalf of my colleagues and the Academic Editor, Dr Song, I am pleased to inform you that we have agreed to publish your manuscript "Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data" (PMEDICINE-D-20-03416R4) in PLOS Medicine.

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Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org