The decentralized wastewater case study is based on an operating treatment system in northern California.

This community-scale system (Figure 1) provides wastewater treatment for a 47-lot suburban subdivision of Stonehurst in Martinez.

The treatment system has operated since the 4 early 1990's and has been described as a successful and innovative decentralized wastewater treatment strategy for California (Crites, 1998).

The details of this wastewater treatment system have been outlined in previous publications (Crites, 1998; Tchobanoglous et al., 2003).

The system was designed to annually treat nearly 20 million liters of liquid wastewater and operates at capacity.

Each lot includes a 5.7 m 3 concrete septic tank.

Septic tanks are commonly used in rural areas and can be found near 25% of U.S. homes (USEPA, 2005).

Effluent from septic tanks is typically distributed to an adjacent drainfield for aerobic treatment, requiring a large amount of open space.

The footprint for the septic tanks in the decentralized wastewater case study is reduced through the use of a community wastewater collection system that transports the septic tank effluent for nearby treatment.

Each onsite septic tank is connected to a 5-cm (two- inch) diameter sewer main.

Thirty-two homes are located uphill of the sewer main and are connected through small diameter gravity-forced piping.

Each of the other 15 homes, located downhill, has a small (0.2 kW) septic tank effluent pump (STEP) to transport wastewater to the sewer main.


Approximately 5 km of sewer-main piping connects effluent from the homes to a single wet-pump station that uses two 1.5 kW pumps to transport the effluent to a community treatment plant.

The treatment plant consists of a recirculating sand filter, where the wastewater is first sent to a recirculating tank and then pumped through a 0.6 m gravel bed approximately five times before being sent across an open three channel ultra-violet (UV) supply sump system for disinfection.

An effluent pump station then transports the treated water to an 11.4 m 3 hilltop dosing tank where the water is distributed to a 10,000 m 2 community soil absorption field.

Treated water in the dosing tank is also available for irrigation use through a subsurface drip system for a small nearby park.

Septic tank cleanings are arranged by each individual 5 homeowner and assumed to occur every five years for this study.

The removed biosolds are assumed to be transported to landfill.


The decentralized wastewater system is compared to a previously published WWEST case study for a large centralized wastewater utility in California (Stokes and Horvath, 2010).

The centralized system serves approximately half a million residents over a 200 km 2 area and includes conventional liquid and sludge treatment process streams.

The treatment processes used in this centralized plant are outlined in Figure 2.

The liquid waste is treated with primary sedimentation, pure-oxygen activated sludge, secondary clarification, and disinfection.

Solids are anaerobically digested and dewatered before disposal.


Table 1 summarizes the major energy and equipment demands that are used in the WWEST analysis for both wastewater systems.

Information regarding the systems was obtained through publications and direct communication with utility employees.

Methane from the treatment process is assumed to be flared for safety reasons (Sahely et al., 2006).

While flaring will convert methane biogas to carbon dioxide, a conversion efficiency of 95% was assumed to account for any incomplete combustion and leaks that may occur (Monteith et al., 2005).

Results are provided for the centralized plant assuming flaring.

For comparison, Table 2 includes the estimated results if the methane was used to generate electricity.


These data assume that the methane produced when treating 3785 m 3 (one million gallons) per day with anaerobic digestion will produce the equivalent of 26 kW (USEPA, 2011).

The centralized plant would produce 14,500 MWh per year assuming all the methane captured (95%) is used for electricity production.

The U.S. Environmental Protection Agency reports that biogas is used to offset energy use at approximately 8% of wastewater plants where it is technically feasible and at less than half of the plants where this strategy would be considered economically feasible (i.e., 6 payback in less than 7 years).

These statistics indicate there is significant potential for future improvements in energy consumption and related emissions in the wastewater industry.


Figure 3 compares the life-cycle energy use, separated by life-cycle phase and treatment phase, for the decentralized treatment system evaluated in this study with the centralized plant case study from Stokes and Horvath (2010).

The results show that the decentralized system used about 37 GJ of primary energy for every million liters of wastewater treated, while the centralized system used about 6.8 GJ for the same functional unit.

The increased life-cycle energy associated with the decentralized system can be partially explained by the significantly greater direct operational electricity use for the decentralized system, as presented in Table 1.

These operational electricity values for both the centralized and decentralized systems are obtained from utility meters that represent the entire treatment plants.

No data were provided to effectively disaggregate by process.

After normalizing for differences in treated waste volumes, utility records indicate that the decentralized system requires seven times more electricity to operate than the centralized system.


Additionally, the construction phase contributes significantly to the life-cycle energy of the decentralized system while the centralized system is dominated solely by the operational phase.

Dixon et al. ( 2003) estimate life-cycle energy use for two small-scale wastewater treatment systems that are of equal magnitude to the life-cycle energy use of the centralized system presented in this study.

However, those two small-scale designs - a reed bed system and an 7 aerated filter treatment unit- require minimal pump power at the treatment stage and pumping for collection and discharge was beyond of the boundaries of that analysis.


While the decentralized system in this study is a fairly low-technology design, the pumps required for collection, discharge, and recirculation through the sand filter, as well as the UV lights, require significant electricity during operation relative to the amount of treated water.

An effluent pump station is required in the decentralized system to transport treated wastewater uphill to the holding tank, the effects of which can be seen in the discharge category of life-cycle energy distribution in Figure 3.

The centralized treatment plant is located adjacent and above the final discharge into the San Francisco Bay so no effluent pumping is required.

Table 2 presents life-cycle energy and emissions of greenhouse gas and criteria pollutants for both the centralized and decentralized wastewater treatment system separated by activity.


The influence of operational electricity demand on life-cycle impacts is highlighted by the Energy Consumption category in Table 2.

The relatively large contribution of Energy Consumption to the total life-cycle impacts for both treatment systems supports previous work by Gaterell and Lester (2000) that suggests the majority of the environmental burdens from wastewater treatment are associated with consumption of energy during the operational phase.

Table 2 shows that the life-cycle energy required for Material Production is comparable to Energy Consumption in the centralized plant, which is partly due to the high embodied energy of chemicals used in the centralized treatment.

Energy Consumption is responsible for most of the life-cycle energy needs in the decentralized system, with Material Production and Onsite Equipment/Vehicle Use also contributing significantly due to the relatively greater influence of the construction phase.


When comparing greenhouse gas emissions (expressed in units of carbon dioxide equivalent, CO 2 (e)), the difference between the two systems increases, primarily due to 8 direct methane losses from the decentralized septic tanks, represented in the Direct Emissions category of Table 2.

Even after adjusting for new findings that indicate septic tank emissions are below previously established IPCC estimates (Diaz-Valbuena et al. 2011), the methane released from the decentralized system is still significantly higher compared to the centralized plant, which is designed to effectively flare methane produced during treatment.

Furthermore, generating electricity from methane is becoming increasingly utilized in central treatment plants (Connelly 2011; USEPA, 2011).

Methane emissions from landfilled biosolids contribute only slightly to overall greenhouse-gas emissions.

Both treatment systems are assumed to have solid waste disposed at a nearby landfill with an 85% efficient gas recovery system.


Table 2 shows that the difference in criteria pollutant emissions between the two systems scales closely with the difference in operational electricity consumption shown in Table 1.

This scaling is partly because electricity use is a significant source of criteria pollutants and both treatment plants are assumed to use electricity generated from California's average primary energy mix.

The greater difference in particulate matter emissions between the two systems is due to increased influence of tailpipe emissions from off-road vehicle equipment needed for construction and maintenance of the decentralized system, as indicated in the Onsite Equipment/Vehicle Use category of Table 2.

Off-road vehicle use also increases NO x emissions associated with the decentralized plant, but a greater difference between the two systems is not observed here since the centralized plant's NO x emissions also increase due to Material Delivery needs.


A key advantage to decentralized wastewater treatment is the potential for reduced potable water demand due to onsite or near-site water reuse.

The environmental benefit of avoided water demand, and therefore the overall reduced impact of decentralized wastewater treatment, has 9 been shown to vary by water source.

Stokes and Horvath (2009) estimated that imported water in California, currently a major source of water for this region, requires about 18 to 27 GJ per million liters, while water demand met through desalination would increase the energy requirement to about 43 GJ per million liters.

These energy effects of water supply indicate that, even when taking avoided water demand into account, decentralized wastewater treatment may still require more energy than centralized treatment.

However, a decentralized wastewater treatment plant with high-yield water reuse may be the lower energy option if the plant serves a region where water is supplied through desalination.


While septic systems are the most common form of decentralization, new forms of decentralized treatment such as membrane bio-reactors are being developed.

High-technology decentralized systems may have different life-cycle impacts than the systems evaluated in this paper.

Additional decentralized systems should be evaluated as these emerging technologies become established and reliable data become available.


Multiple factors influence the choice of wastewater treatment strategies, and decentralized systems provide many benefits including lower initial investment costs, increased flexibility, the opportunity to reduce potable water needs through wastewater reuse, and the possibility of reduced ecological impacts outside the scope of this analysis.

LCA methods are inherently limited and previous evaluation has shown that life-cycle energy impacts do not necessarily align with other qualitative considerations such as public amenity, ecosystem conservation, or aesthetics (Brix, 1999).

The results presented in this study indicate that along with any social and environmental benefits from decentralized treatment comes the potential for an increase in life- cycle energy and air pollutants.

Decision makers should be aware of this possible increase and the life-cycle impacts of proposed decentralized designs should be evaluated to minimize environmental burdens of future wastewater treatment development.


In the article ‘Energy and air emission implications of a decentralized wastewater system’ published in Environmental Research Letters (2012 Environ.

Res.

Lett.

7 024007), Shehabi et al compared a decentralized and a centralized system on the basis of energy use, greenhouse gas emissions and air pollutants, and claimed that economies of scale lower the environmental impacts from a centralized system on a per-volume basis.

In this comment, we present literature and data from New York State, USA to argue that the authors’ comparison between a small decentralized system (0.015 MGD) and a large centralized system (66.5 MGD) is unconventional and inappropriate [1].

The recent article by Shehabi et al (2012) compared a decentralized and a centralized system on the basis of energy use, greenhouse gas emissions and air pollutants, and argued that economies of scale lower the environmental impacts from a centralized system on a per-volume basis.

The authors’ analysis builds on a similar effort by Muga and Mihelcic (2008) that used a combination of economic, environmental and societal factors to evaluate the effectiveness of various wastewater treatment systems [2].


We found the comparison between a small decentralized system (0.015 MGD serving 100 residents) and a large centralized system (66.5 MGD serving 500,000 residents) unconventional and inappropriate for at least a few reasons.

First, the centralized treatment plant chosen was too large not only in comparison to the decentralized plant, but also in comparison to other centralized plants.

Muga and Mihelcic (2008) reviewed 16,255 wastewater treatment plants in the US and observed that over 80% of them have a capacity less than 5 MGD [2].

In the state of New York, only 16 of the over 600 municipal treatment plants have capacities greater than 65 MGD.

While these 16 plants constitute 62% of the installed capacity and serve 64% of the residents in the state, a disproportionate majority of plants are much smaller in size.

87% of the plants are smaller than 5 MGD, 69% are smaller than 1 MGD and 28% are smaller than 0.1 MGD [1].

This trend may be evident in other states as well, in some cases more pronounced depending on proportion of rural/urban populations.


Second, it would take 4600 small decentralized systems, such as the one described in Shehabi et al (2012), to make up a 66.5 MGD centralized treatment plant—an idea no city would entertain in its planning formulation.

Similarly, a large centralized plant would be an inappropriate and highly expensive solution for a rural community.

Large treatment plants achieve economies of scale in operational parameters such as energy and emissions, and are able to leverage rapid changes in technology, which smaller decentralized systems are unable to do.

Indeed, size plays a role even within centralized treatment plants.

The effluent violation rates for some parameters at small plants (0.01 MGD) are 10 times that of plants that are sized 100 MGD or larger [3].

The energy use per volume is higher in centralized plants sized 0.1 MGD or smaller, as compared to those sized up to 1 MGD and 5 MGD [2].


Many communities are not large and dense enough to warrant large treatment plants.

A comparison between the two methods of treatment is only fair if similar-sized plants are considered.

The inclusion of alternative scenarios with a large plant for a rural area or many small plants for an urban area can greatly enhance the validity of the model presented by Shehabi et al (2012).

Based on the data from New York, there are 34 plants that have capacities of 0.015 MGD or smaller serving 102 residents on average, nearly the same as that in case of the decentralized system described in Shehabi et al. Such a comparison is more likely to yield meaningful results and policy-worthy conclusions.


While GHG emissions and particulate pollution are critical factors in evaluating the efficiency of wastewater treatment systems, very rarely do decisions about construction of new treatment plants get made on those factors.

The lack of federal and state funds and an ageing infrastructure have forced many small communities to look toward decentralized solutions for managing wastewater.

It is very likely that decentralized treatment systems will have a resurgence in small communities, this time in a planned and well-designed manner.

Therefore, it is imperative that a fair comparison between the centralized and decentralized approaches be conducted to allow decision-makers and stakeholders to make informed choices.


Complementing centralized water-related infrastructure with decentralized facilities is being considered in some communities and a life-cycle perspective is needed for informed decision making.

Our 2012 study presents a framework for analyzing the environmental effects of decentralized wastewater systems.


While the analysis framework could be applied to cases with a variety of sizes, we evaluated two currently operating systems in California, one decentralized and one centralized plant with a much larger capacity.

The disparate scales of the two plants represent an ‘off-the-grid’ suburban neighborhood-scale system compared with a similarly sized neighborhood connecting to an adjacent large centralized plant.

Deciding whether or not to connect expanding developments to nearby centralized plants is a realistic scenario for future growth, making the treatment plants evaluated in our study a realistic choice for comparison.


The comment submitted by Vedachalam and Riha (2013) argues that the case studies compared in our paper (Shehabi et al 2012) were ‘unconventional and inappropriate’ because of the disparate scales of the two wastewater treatment plants (WWTP).

We do not argue with the commenters’ assessment that most WWTPs are smaller than the centralized case study system in the paper.

They have clearly shown this to be true in New York and likely in other areas as well.

However, comparing a large centralized WWTP with a small decentralized WWTP is neither ‘unconventional’ nor ‘inappropriate’.

The two systems compete, but also complement each other, and life-cycle perspective is needed for informed decision making.


As we state in the introductory paragraph, ‘complementing centralized water-related infrastructure with decentralized facilities’ is an option being investigated in some communities, and that ‘decentralized wastewater systems can be located adjacent to areas with high demands for non-potable water’.

Both WWTPs in our study serve suburban areas of relatively similar population density, with the large centralized plant simply serving a much larger area.

When the neighborhood served by the evaluated decentralized WWTP was developed approximately 20 years ago, it could have been connected with a few miles of pipe to the nearest centralized WWTP, which serves a population of over 450,000 and processes an average of 45 MGD.

Though somewhat smaller than the large centralized plant described in the paper, this alternative centralized WWTP is characteristically similar.

As in the case study, this plant uses biosolids to produce energy to be used on-site.


The suburban setting of our evaluated decentralized systems—and the decision whether or not to connect to nearby larger centralized WWTPs—is a realistic scenario for future growth.

Based on our recent discussions with other engineering and planning academics, designing decentralized treatment strategies to serve new developments on the edge, or ‘at the margins’, of an urban or suburban region served by a centralized WWTP, regardless of size, may be a realistic strategy for meeting any of several different goals, such as: providing a local, non-potable water source; preserving strained infrastructure within a city’s core; and allowing for use of natural treatment processes, which may be more energy efficient.


In contrast to the one example of an application of decentralized systems (rural settings) described in the comment, we expect that the example of comparing an ‘off-the-grid’ neighborhood-scale system to a connection to a large centralized plant will be increasingly common.


Vedachalam and Riha (2013) conclude that we should have chosen a centralized plant of a similar size (approximately 0.015 MGD) to provide a fair comparison, essentially arguing that we should be attempting to compare two different configurations of similarly sized plants.

The paper they are suggesting has an entirely different goal.


As the abstract of our letter states, ‘this paper presents a framework for analyzing the environmental effects of decentralized wastewater systems and an evaluation of the environmental impacts associated with two currently operating systems in California, one centralized and one decentralized’.

In this case, the centralized plant has a larger capacity, but the analysis framework could be applied to cases with a variety of sizes.


The questions surrounding decentralized wastewater treatment contain many unresolved issues and more research topics remain to be evaluated.

Additional comparisons of decentralized WWTPs with smaller centralized plants would be interesting and valuable assessments.


We do not consider our letter to be the final word on the issue of decentralized treatment.

Instead, we intend our letter to initiate what we hope to be an extended discussion in the academic literature about the tradeoffs between centralized and decentralized systems.

We intend to do additional analyses in this area and welcome other perspectives and input on the issue.


Consider a set S of n points in three-dimensional Euclidean space, ~3, and a collection of t triangles with vertices in S. Clearly, if t is not too large it could be that any two triangles intersect only in common vertices or edges, if at all.

Indeed, this is possible for t almost as large as n 2.

Once t exceeds n 2, some of the triangles start having common interior points, no matter how the points of S are placed in space.

If t > ~n 2 we start to see crossin9 pairs, that is, pairs of vertex disjoint triangles with common interior points.

This basic fact can be used to show that for t > 2n 2 the number of crossing pairs is at least some positive constant times t4/n 6.

This extends a result of I-1] that for n points and t > 4n edges in R 2 there are at least some positive constant times t3/n 2 crossing edge pairs.


Why is this interesting?

A consequence of the lower bound for crossing triangle pairs is that n points in R 3 have at most some positive constant times n 8/3 k-sets, for any fixed k, 0 < k < n. A k-set of S is a subset S' = SnH, where H is a half-space and IS'l = k. For three-dimensional k-sets the first nontrivial bound of 282 T.K. Dey and H. Edelsbrunner O(n 3- x/343) was reported in [4].

This was improved to O(n a/3 log 5/3 n) and later to O(n 8/3 log 2/3 n) in [3] and [6].

This paper removes the remaining poly- logarithmic factor from the upper bound.

While the improvement is not dramatic, we feel the main contribution is the introduction of a new and more direct approach to proving bounds on k-sets.


A set V of three noncollinear points in R a defines a triangle crv = conv(V).

We say that two triangles have a nontrivial intersection if their intersection is neither empty nor a vertex or edge of both.

Two triangles, av and a v, can form three different types of nontrivial intersections, as illustrated in Fig. 2.1.

If crv c~ av r and U n V = ~, then we say that a v and av cross.


We also say that an edge crosses a triangle if it intersects the triangle without sharing a vertex with it.

A finite point set is in oeneral position if any k of the points, 1 < k < 4, are affinely independent.

A set S of n points in general position in ~3 defines a collection of (~) triangles, denoted (~).

For any we write x(S, T) for the number of crossing triangle pairs in T. Furthermore, x(n, t) = rain x(S, T).

The results of this section are summarized in the following theorem.


We begin with a trivial but most useful observation.


Notice that because of (2.2) the lower bound for x(n, t) implies the lower bound for y(n, t).

To see this, count a crossing between two triangles once for each edge crossing the other triangle.

Give one unit of credit to each such edge.

There are two credits per crossing and at most 3t edges.

Assume the lower bound for x(n, t) and use the pigeonhole principle to get an edge with more than c3(t3/n 6) credits, where c3 = 2cx/3.


Consider a plane that contains no point of S, and let I 0 and I t be two parallel lines in this plane so that It n conv(S) = ~. For each 0 < z < 1 define I, = zl o + (1 - z)ll.

As z increases from 0 to 1, l, translates from I t to lo.

The 288 T.K. Dey and H. Edelsbrunner number of halving triangles crossed by l~ changes only when l~ translates through an edge,


A simple rotation argument shows that the number increases or decreases by at most one.

Given lo we can choose 11 so that l~ translates through fewer than ~n 2 edges as follows.

Choose the plane through lo so that there are (n- 1)/2 and (n + 1)/2 points on the two sides.

There are (n 2 - 1)/4 edges ab that cross the plane and thus there is one side of l 0 with fewer than n2/8 edges.

It follows that lo crosses at most n2/8 halving triangles.

We prove an upper bound on the number of halving planes by selecting a line that crosses many triangles.

Suppose there are t halving triangles.

Condition (ii) of Theorem (2.1) implies that there is a line that crosses more than c3(t3/n 6) of them, with c 3 = r~. Because of (3.1), s 6) < n2/8 which implies the main result of this section.


H. The number of halving planes is closely related to the number of k-sets of S. Indeed, the number of k-sets, for k = (n - 1)/2, does not exceed three times the number of halving planes, see [5].

For other values of k consider the planes spanned by three points of S so that k - 1 points lie on one and n - k - 2 points lie on the other side.

Again, the number of such planes is proportional to the number of k-sets.

Furthermore, the same arguments imply the same upper bound as in Theorem (3.2) for any fixed k.


The bound in Theorem (3.2) slightly improves the ones in 1-3] and [6].

Besides this improvement, the main contribution of the new proof is the uncovering of the close relationship between k-sets and the combinatorics of crossings between edges and triangles in R 3.


To prove (ii) assume that no two triangles in T cross.

An edge that crosses a triangle a can only belong to triangles that share a vertex with a. There are at most three such triangles incident to that edge.

Remove this edge together with the at most three triangles.

Let k be the number of such edges that need to be removed until no nontrivial intersection remains.

We lose at most 3k triangles and decrease the total vertex degree to ~p, Es 61 - 2k or less.

It follows that pjeS p, eS is an upper bound on the number of crossing-free triangles.

The rest of the argument for the lower bound on x(n, r) is inductive and is inspired by the proof of a similar result for line segments in [1].

Specifically, we prove the following inequality.


The upper bound for y(n, t) in (ii) of Theorem (2.1) implies the upper bound for x(n, t) in (i).

To see this assume no edge crosses c4(t2/n 3) or more triangles.

Since there are at most 3t edges the total credit accumulated is less than 3c4(ta/n3).

Each crossing pair of triangles generates two credits which implies that there are fewer than c2(t3/n 3) such pairs, where c2 = 3c4/2.


To prove the upper bound on y(n, t) we choose the points of S on the moment curve M(z)= (z, T 2, z3) defined for all re R. For two points a = M(zl) and 286 T.K. Dey and H. Edelsbrunner b = M('c2) we write a ~( b if z 1 < z 2.

It is easy to establish that M intersects any plane in at most three points.


Let S be a set of n points in •3.

Assume that n is odd and that no four points are coplanar.

For three points a, b, c e S let a = abe be the corresponding triangle and let h be the plane that contains a, b, c. We call h a halving plane, and a a halving triangle, if there are (n - 3)/2 points on each side of h. The following extension of the two-dimensional Lovfisz lemma [7] has been used in [3] and [4].

We say a line crosses a triangle if it intersects the triangle but not any of its edges.


(3.1) A line crosses less than n2/8 halving triangles.


Maybe the most interesting extension of the methods in this paper is to four and higher dimensions.

It is possible to generalize all results of Section 2 to crossing pairs of (d - 1)-simplices in R d. Specifically, we can show that t > cn d- 1 (d - 1)- simplices defined by n points in R d form at least c'(d§ dtd-l~) crossing pairs.

However, to prove a nontrivial upper bound on the number of halving hyper- 289 planes, we need a nontrivial lower bound on the number of (d- 1)-tuples of (d - 1)-simplices that have a common point in their interiors.

While the main part of the induction in (2.4) generalizes to this situation, we have no good extensions of the base case in (2.3).


We should be more accurate.

Recent results obtained with methods from algebraic topology I-9] do imply a nontrivial base case, and induction can be used to get nontrivial bounds for (d - 1)-tuples of (d - 1)-simplices and for halving hyperplanes.

These bounds come out very similar to those derived in [2] and are therefore omitted.

An interesting question is whether the elementary methods of this paper suffice to derive such nontrivial bounds.


We consider the problem of bounding the complexity of the kth level in an arrangement of n curves or surfaces, a problem dual to, and an extension of, the well-known k-set problem.

Among other results, we prove a new bound, O(nk^{5/3}), on the complexity of the kth level in an arrangement of n planes in R3, or on the number of k-sets in a set of n points in three dimensions, and we show that the complexity of the kth level in an arrangement of n line segments in the plane is O(n\sqrt{k} \alpha(n/k)), and that the complexity of the kth level in an arrangement of n triangles in 3-space is O(n^2k^{5/6}\alpha(n/k)) [1].


The k-set problem is one of the most challenging open problems in combinatorial geometry.

The simplest variant of the problem is: Given a set S of n points in the plane in general position, and a parameter 0 ≤ k ≤ n - 2, what is the maximum possible number of lines that pass through a pair of points of S and have exactly k points of S in one of the open half-planes that they define [2].

In a dual setting, we are given a set L of n lines in the plane in general position, and want to bound the maximum possible number of vertices v of the arrangement A(L), so that exactly k lines pass below v. We denote this set of vertices by Vk.

The kth level of the arrangement is defined to be the closure of the set of points that lie on the lines and have exactly k lines below them.

The set of vertices of this level is Vk ∪ Vk-1 [3].


The k-set problem was first studied about 1970 by Erdős et al. [ 14] and Lovász [22].

These papers have established an upper bound O(n\sqrt{k}) and a lower bound Ω(n log k) on the desired quantity, for k > 0, leaving a fairly big gap.

The upper bound was slightly improved to O(n\sqrt{k/log* k}) by Pach et al. [ 25].

After the original submission of this paper, significant progress has been made by Dey [8], who improved the upper bound to O(nk^{1/3}), for k > 0.

Dey’s proof is based on some of the ideas presented in this paper [4].


In the dual setting, the problem can be generalized in an obvious manner: In the plane, we are given a collection 6 of n x-monotone curves, each being the graph of a continuous totally or partially defined function, and a parameter 0 ≤ k < n, and wish to bound the complexity (i.e., the number of vertices) of the kth level in the arrangement A(6), defined exactly as in the case of lines [5].

In this more general setting only two results are known: A recent seemingly weak, but elegant analysis by Tamaki and Tokuyama [28] yields the bound O(n^{23/12}) on the complexity of a level in an arrangement of n pseudoparabolas, which are graphs of total functions, each pair of which intersect at most twice [6].

We also mention the case of pseudolines, which in this context are graphs of continuous totally defined functions, each pair of which intersect exactly once, where a slightly larger lower bound of Ω(n log n) for the complexity of the median level is established in [21].

Our proof techniques and upper bounds apply equally well to the case of pseudolines.

Recently, Dey’s techniques have also been extended to the case of pseudolines [9], [29].


Similar extensions apply in higher dimensions.

In the primal setting, we are given a set S of n points in Rd in general position, and wish to bound the number of hyperplanes passing through d of the points such that one of the half-spaces bounded by the hyperplane contains exactly k points of S [7].

For d = 3, the best-known upper and lower bounds are, respectively, O(n^{8/3}) and Ω(n^2 log n) [4], [10].

For d > 3, the best-known upper bound is O(n^{d-cd}), for some exponentially small but positive constant cd [30].

Note that, in contrast to the planar case, these bounds depend only on n and not on k [8].

We can formulate the problem in an arbitrary dimension, in a dual setting: We consider an arrangement of hyperplanes, or, more generally, of surfaces that are graphs of continuous total or partial functions, and define the kth level of the arrangement exactly as in the planar case.

We now seek bounds on the maximum possible number of vertices (or of faces of all dimensions) of the level.

Except for the case of hyperplanes, which is equivalent to (a variant of) the k-set problem mentioned in the preceding paragraph, no nontrivial bounds for the entire range of values of k are known [9].


In spite of the sorry state of the problem, even after Dey’s improvements, we can obtain improved nontrivial bounds when k is small.

The probabilistic analysis of Clarkson and Shor [7] (see also [26]) yields fairly sharp bounds on the combined complexity of the first k levels in arrangements.

For the case of hyperplanes, for example, the bound is 2(n^{bd/2c}k^{dd/2e}).

For sufficiently small k, this gives a better upper bound on the complexity of a single level than the general bound stated above [10].


In this paper we make several contributions to these problems.

In the preliminary version of this paper [1], we briefly reviewed and simplified some old proofs of the upper bound O(n\sqrt{k}) for the original planar k-set problem (or, dually, for the case of the kth level in an arrangement of n straight lines in the plane).

These proofs are related to the proof technique of Gusfield [16], [17].

We also gave a simple proof of the dual version of what is known as the “Lovász lemma” that is used to prove the bound.

These techniques apply equally well to arrangements of pseudolines; see, for example, [15].

For the sake of brevity, this part is not included in this version of the paper [11].


We adapt our proof techniques to obtain the bound O(n^{3/2}) on the complexity of the kth level in an arrangement of n line segments (or “pseudosegments,” to be defined below).

The same bound also follows from a result by Katoh et al. [ 20].

We then describe two simple approaches that reduce the bound to O(n\sqrt{k}\alpha(n/k)), where \alpha(n) is the inverse Ackermann function [12].


We then proceed to study the k-set problem for higher-dimensional point sets (or, dually, though not quite equivalently, to bound the complexity of the kth level in a hyperplane arrangement).

In the preliminary version of this paper [1], we have observed that the O(n^{8/3}) bound in R3 can be immediately brought down to O(n^2k^{2/3}), if we exploit a simple improved version of the Lovász lemma in an arbitrary dimension derived in this paper.

However, using Clarkson and Shor’s technique, we improve this bound further to O(nk^{5/3}), in a manner that makes no use of the improved Lovász lemma [13].

Although the improved lemma has so far no significant applications, we include it here because we believe it to be of independent interest and an extension of it is needed for the case of triangles in R3.

Over the whole range of k > 0, the O(nk^{5/3}) bound is stronger than all previous bounds, including the aforementioned O(nk^2) bound on the overall complexity of the first k levels [14].

A similar improved bound, of the form O(n^{bd/2c}k^{dd/2e-cd}), can be obtained in any dimension d > 3, for the same constant cd > 0 obtained by Živaljević and Vrećica [30].

Again, this bound is the best-known upper bound, for all values of k > 0 [15].


Finally, we consider the problem of bounding the complexity of the kth level in an arrangement of n triangles in 3-space.

We first obtain a nontrivial bound of O(n^{17/6}) and then improve it to O(n^2k^{5/6}\alpha(n/k)).

Our bound strongly depends on an upper bound on the complexity of a single level in an arrangement of line segments in the plane [16].

As discussed above, Dey’s results supersede some of the bounds obtained in this paper, but they do rely on some of the machinery developed here.

Some other, more recent bounds derived here can be further improved by exploiting Dey’s results.

We have written this revised version of our paper in a manner that is mostly independent of Dey’s progress.

Nevertheless, for completeness, we conclude the paper with a brief overview of the connections between Dey’s results and ours [17].


We prove an O.n.k C 1/1=3/ upper bound for planar k-sets.

This is the first considerable improvement on this bound after its early solution approximately 27 years ago.

Our proof technique also applies to improve the current bounds on the combinatorial complexities of k-levels in the arrangement of line segments, k convex polygons in the union of n lines, parametric minimum spanning trees, and parametric matroids in general.


The problem of determining the optimum asymptotic bound on the number of k-sets is one of the most tantalizing open problems in combinatorial geometry.

Due to its importance in analyzing geometric algorithms [8], [9], [19], the problem has also caught the attention of computational geometers [5], [14], [18], [27], [29].

Given a set P of n points in <d , a k-set is a subset P 0 (cid:181) P such that P 0 D P \ H for a half-space H , and jP 0j D k where 0 • k • n. A close to optimal solution for the problem remains elusive even in <2.

In spite of several attempts, no considerable improvement could be made from its early bound of O.n.k C1/1=2/ given by [20] and [24].

Several proofs exist for this well-known upper bound [3], [5], [17], [29] which is quite distant from the best-known lower bound of ˜.n log.k C 1// [17].


Pach et al. made the first dent in this upper bound improving it to O.n.k C 1/1=2=log⁄.k C 1// [25].

Even such a small improvement in <2 was a distinguished result [25].

Recently Agarwal et al. [ 3] attacked the problem in the dual setting with a fresh look.

Although they could not improve the worst-case upper bound, several new approaches were put forward to estimate the complexity of k-levels in the arrangement of n lines.

By a well-known duality these results also apply to k-sets.

One of the approaches of [3], based on “convex chains,” inspired our proof for the new upper bound of O.n.k C 1/1=3/ in <2.


Our proof technique is surprisingly simple.

It uses the concept of crossings in geometric graphs [1] which was first used by us to prove an O.n8=3/ bound on three-dimensional k-sets [14].

Crossings in geometric graphs have been successfully used for many problems in combinatorial geometry.

See, for example, [12], [15], [16], [28].

It is expected that our approach would open up new avenues to solve the d-dimensional k-set problem, which remains largely unsolved for d > 3.

The only nontrivial bound known for d > 3 is insignificantly better than the trivial bound [4], [9], [30].

In spite of the miserable state of the problem, an exact asymptotic bound is known for the number of i-sets summed over all i • k. Alon and Gy˝ori [2] showed that this number is 2.nk/ in <2.

Clarkson and Shor [11] generalized the bound to 2.nbd=2ckdd=2e/ for <d .


Our proof technique also applies to establish a new O.nk1=3 C n2=3k2=3/ complexity bound for k convex polygons whose edges are nonoverlapping and lie in the union of n lines.

A number of other results follow from this bound.

An optimal 2.nk1=3/ bound on the complexity of n-element parametric matroids with rank k follows due to a result by Eppstein [21].

As an immediate consequence, we obtain an O.E V 1=3/ bound on the number of parametric minimum spanning trees of a graph with E edges and V vertices whose edge weights vary linearly with time.

A new O.n4=3/ bound for k-levels in arrangement with n line segments can also be derived from the aforesaid bound.


The paper is organized as follows.

We develop major tools for our proof in Section 2.

A new bound for k-sets in <2 is proved in Section 3.

Section 4 describes the application of our proof technique to other related problems.

Finally, we conclude in Section 5.


Chronic kidney disease is becoming a major public health problem worldwide [1].

While the number of patients requiring kidney replacement therapy in the USA was 340,000 in 1999, it increased to 651,000 in 2010 [2].

Chronic kidney disease, which progresses cres- cively and irreversibly, is associated with high cardiovas- cular risk and has a prevalence of 10-13% in the general population [3].

Glomerulosclerosis and kidney interstitial fibrosis, which cause the progression of chronic kidney disease, are targeted during treatment.

Chronic tubulointerstitial disease, which leads to chronic kidney disease, causes glomerular, vascular, and tubulointerstitial damage, eventually leads to interstitial scarring, fibrosis, and tubular atrophy.


It is critical to understand the link between the 'supersonic shear imaging' (SSI) technique, a novel and promising approach for assessing the elasticity of numerous organs, including the liver, and pathologically identifi- able interstitial fibrosis and overall histological score in chronic kidney disease.

Histopathological diagnosis with kidney biopsy gives important information for the prognosis and management of chronic kidney disease, while kidney biopsy has complications such as bleed- ing, fistula, and rarely death [4,5].

Proteinuria and eGFR (estimated glomerular filtration rate) are important and noninvasive biomarkers of chronic kidney disease, how- ever, studies for developing early and more sensitive biomarkers are ongoing [6].


In addition to biomarkers, observation of the course of the disease with advanced ultrasound or magnetic resonance-based or molecular imaging techniques is also promising [7].

In elastogra- phy, which is a novel ultrasound-based method, tissue RENAL FAILURE 2022, VOL. 44, NO. 1, 314-319 https://doi.org/10.1080/0886022X.2022.2035763 stiffness or elasticity is measured using ultrasound [8].

Tissue stiffness is measured using Young's modulus and expressed in kilopascals or pascals [9].

Young's elastic modulus, as evaluated by noninvasive elastography, correlates with the degree of interstitial fibrosis [10].

Among various elastography methods, the SSI method is the most appropriate method to evaluate the stiff- ness of natural kidneys [8].

This technique is a simultan- eous and quantitative method that has been successfully applied to curved surfaces for mapping tis- sue elasticity [9].


In our study, we investigated the relationship between the interstitial fibrosis detected in biopsy and the total histological score obtained by the scoring of the histopathological findings and the kidney tissue stiffness determined by the SSI method.


Fifty-seven native kidney patients with proteinuria who were hospitalized or examined at the outpatient clinic of a tertiary care university hospital with a kidney biopsy indication were included in the study.

The inclu- sion criteria were: patients who were over 18 years of age, in whom kidney biopsy was indicated, and those who showed cooperation to have elastography examin- ation.

Patients who did not give informed consent to participate in the study, underwent kidney transplantation, had severe heart failure and urinary obstruction, were on dialysis, had suffered from neuro- logical illnesses such as delirium dementia, and patients with data loss were excluded.


The patients' demographic information, diagnoses, kidney biopsy indications, laboratory results (urea, cre- atinine, glomerular filtration rate [GFR], proteinuria in 24-h urine sample), and pathology information were obtained from the patient files.

Ethical approval for the study was obtained from the Ethics Committee of Cerrahpaşa Faculty of Medicine at Istanbul University (No: 12452).


Supersonic shear imaging elastography was per- formed using a sonoelastography device (Aixplorer; SuperSonic Imagine, Aix-en-Provence, France) and 4 MHz transducers before biopsy in all patients.

The patients were placed on a stretcher in the supine pos- ition.

The left native kidney was visualized using B- mode ultrasound imaging.

The color scale formed as a result of the signals obtained from the inferior pole and the cortex of the kidney using SSI elastography was evaluated in kilopascals (kPa) using the Tsukuba scoring system.

The elastography measurements were made both in the sagittal and transverse sections.

Sagittal and transverse elastography images are shown in Biopsies were obtained from the same left kidney as the ultrasound measurement has taken place.

Biopsies of the patients were evaluated at the Department of Pathology.

The routine hematoxylin & eosin, Masson's trichrome, and PAS stainings were used to detect inter- stitial fibrosis.


The glomerular score, tubulointerstitial score, and vascular score were also calculated to evalu- ate the presence of interstitial fibrosis.

The kidney histo- logical total score was calculated by recording the glomerular score (3-12 points), tubulointerstitial score (3-9 points), and vascular score (3-6 points) according to the percentage of involvement, as suggested by Hu et al. [ 11].

Segmental and global glomerulosclerosis per- centages were evaluated with the glomerular score, tubular atrophy, tubular cell infiltration, and interstitial fibrosis percentages with the tubulointerstitial score, and the arterial hyalinosis percentage with the vascular score.

A total score of 9 was considered as a 'mild' impairment, a score of 10-18 as a 'moderate' impair- ment, and a score of 19 and above as a 'severe' impair- ment.

The correlation between the total histological score and elastography measurements was also eval- uated.


Apart from the total histological score, the patients were also evaluated based on the presence of interstitial fibrosis.

The patients were separated into two groups: those with interstitial fibrosis (n ¼ 6) and those without it (n ¼ 51).

Patients with interstitial fibro- sis in more than 25% of the pathological sample were assigned to the fibrosis group, whereas patients with interstitial fibrosis in 25% or lesser section of the sample or no fibrosis on pathological examination were assigned to the non-fibrosis group.

The presence of interstitial fibrosis in the biopsies was compared to the elastography measurements.


The SPSS for Windows v.17.0 software was used in all statistical analyses.

The data are expressed as mean ± SD.

The distribution of the variables and the relationship between them were evaluated with the independent samples t-test.

The intergroup elastogra- phy measurements, age, creatinine, and global sclerosis were analyzed with one-way analysis of variance (ANOVA).

The chi-square test was used for the analysis of the nominal data, while Pearson's correlation test was used to evaluate the correlation between the numerical variables.

The statistical significance level was set at p < .05.


The demographic characteristics, clinical, and laboratory findings of the patients are given in Table 1.

The patients were middle-aged with an average age of 41.4 years and with a slight predominance of the male gender.

In the evaluation of the creatinine and GFR lev- els, we observed that the patients did not develop end- stage renal disease but had overt proteinuria.


Table 2 shows the comparison of the patients based on the presence of fibrosis.

The elastography measurements in the transverse sections revealed a kidney stiffness of 12.83 ± 9.80 kPa in the fibrosis group and 7.76 ± 5.16 kPa in the non-fibrosis group (n ¼ 51), exhibiting a signifi- cant difference between the groups (p ¼ .046).

The whole cohort had a mean total histological score of 4.49 ± 3.3, a glomerular score of 2.64 ± 1.85, a tubular score of 1.45 ± 1.48, and a vascular score of 0.22 ± 0.25.


The total histological score was found to be positively correlated with age (r ¼ 0.428, p ¼ .001), creatinine level (r ¼ 0.759, p < .001), global sclerosis percentage (r ¼ 0.874, p < .001), and proteinuria (r ¼ 0.316, p ¼ .017), whereas it showed a negative correlation with GFR (r ¼ -0.537, p < .001).

No correlation was observed between the transverse elastography measurements and the total histological score (r ¼ 0.006, p ¼ .967) (Table 3).


We also performed a ROC curve analysis and calcu- lated the threshold value for the diagnosis of fibrosis as 6.50 kPa.

Accordingly, we calculated the relevant values for the sensitivity (50%), specificity (62%), positive pre- dictive value (13%), and negative predictive value (91%).


The prevalence of end-stage chronic kidney disease is increasing all around the world [3].

Although kidney length is a simple parameter that shows kidney func- tions, it has been recently discovered that cortical thick- ness measurement has a stronger relationship with kidney functions [12,13].

Hoi et al. detected a strong correlation between cortical thickness and kidney func- tion [14].


The fibrotic process, which progresses due to the formation of interstitial fibrosis or glomerulosclero- sis, accelerates the progression to chronic kidney dis- ease.

Despite its invasive nature and complications, kidney biopsy is still the gold standard in detecting fibrosis, since recently studied ideal noninvasive markers, such as elastography, is limited [15].


In their study, Moon et al. [ 16] evaluated the tissue stiffness by elastography, and the kidney dimensions, resistive index, and echogenicity by ultrasonography.

The authors divided the 38 kidneys of 19 rabbits into three subgroups and found a correlation between the degree of fibrosis and renal tissue stiffness, however, they observed no difference among the groups in terms of resistive index and other sonographic parameters.

Unlike this study, Dalkiran et al. recommended the use of the resistive index in the differentiation of atrophy and hypoplasia in their study of 63 patients [17].

In another study of 202 patients with chronic renal failure, Hanamura et al. showed that the resistive index increased in cases of glomerulosclerosis, arteriolosclero- sis, and tubulointerstitial damage [18].


Although the SSI elastography method is the most ideal elastographic method, different results have been reported in differ- ent studies.

Derieppe et al. investigated the relationship between the cortical tissue elasticity determined by the SSI elastography method and the histopathological development of fibrosis in the kidney in their experi- mental study on a mouse model and detected a rela- tionship between impaired kidney function and increased tissue elasticity [19].

Contrary to this finding, Desvignes et al. found no relationship between elasti- city and fibrosis in their study of 95 children and ado- lescents, 31 of whom were transplant recipients [20].


In their study of 177 patients, Jenkins et al. found a significant relationship between the global and seg- mental sclerosis percentages and interstitial fibrosis, and between the global and segmental sclerosis per- centages and serum creatinine level [21].

Turgutalp et al. found a correlation between interstitial fibrosis and elastography in a study involving 30 patients with IgA nephropathy and 32 healthy controls [10].

Jenkins et al. 's findings are similar to those of our study [21].

In our study, we observed that the elastography trans- verse measurement value was significantly different in the group with fibrosis than the group without fibrosis.


In a study of 76 patients with chronic kidney disease, Guanghe et al. measured tissue elasticity quantitatively with the ARFI (acoustic radiation force impulse) imaging technique and found a significant difference between the groups with and without fibrosis [22].

Our study comprised of patients with proteinuria and a kidney biopsy justification with a mean GFR greater than 60, which differs from previous studies owing to the lack of patients with end-stage chronic kidney disease and extensive fibrosis.


In another study by Iyama et al., the authors found no correlation between kidney elasticity and interstitial fibrosis and between kidney elasticity and glomerular sclerosis [23].

Similar to our study, Hu et al. found a correlation between elastography meas- urements and histological score, and between elastog- raphy measurements and creatinine level in their study on 162 chronic kidney disease patients and 32 healthy patients [11].

In another study on 75 patients with chronic kidney disease, Leong et al. found that a kidney tissue stiffness above 5.81% kPa was associated with glomerular sclerosis, tubular atrophy, and interstitial fibrosis [24].


Because of the high negative predictive value, we suggest that elastography should mainly be used as an exclusion test for the presence of fibrosis.

However, we want to point out that because of the low number of patients with fibrosis our results should be confirmed in larger groups.

Indeed, the most important limitation of our study was the small number of patients in the fibrosis group, since patients with proteinuria with new onset of chronic kidney disease or developing acute kidney disease rather than the end-stage chronic kidney disease group were included in the study.

Despite these limitations, a significant relationship was observed between elastography measurements and fibrosis.

If we included the patients with end-stage chronic kidney disease in whom fibrosis is more prevalent, the signifi- cant relationship could have been established more strongly.

Further studies with more chronic kidney dis- ease patients are needed.


In conclusion, we found a significant relationship between the kidney shear wave elastography measure- ments and the fibrosis detected in kidney biopsy.

The prevalence of chronic kidney disease and consequent end-stage chronic kidney disease is increasing gradually and has become an important public health problem due to its increasing prevalence and morbidity.

Since fibrosis is an important risk factor for the progression of chronic kidney disease, we recommend kidney elastog- raphy as a key noninvasive method to evaluate fibrosis.


Dear Editor,


We read with great interest the recently published article by Islamoglu MS et al. [ 1] entitled ‘Kidney tissue elastography and interstitial fibrosis observed in kidney biopsy’.

The study aimed to investigate the relationship between tissue stiffness detected in shear wave elastography (SWE) and interstitial fibrosis observed in kidney biopsy.

The authors propose a significant relationship between the SWE measurements and interstitial fibrosis.

Although we appreciate the authors for their hard work on the construction of a study to enhance our understanding of the possible association of renal fibrosis with renal stiffness derived from SWE, some methodological notes should be pointed out to make this study more reasonable and practical, at least theoretically.


First, the authors pointed out that the SWE procedure was performed using a device (Aixplorer; SuperSonic Imagine, Aix-en-Provence, France).

However, to our knowledge, the images (Figures 1 and 2) [1] presented in this study were not derived from Aix-en-Provence Ultrasound system [2].

The authors need to clarify the elastography system used as the accuracy of the results could be affected by different elastic imaging systems [3].


Second, the SWE values were obtained from the inferior pole of the kidney in this study.

However, due to the nature of renal anisotropy, the difference of elastic measurements between renal poles and mid-region was significant, which was related to the angle between the ultrasound beam and the cortex [4].

The success rate of measurements in the renal poles was low compared with measurements in the mid-portion of the renal parenchyma and suggested that the renal poles should be avoided during measurements to ensure good reproducibility [5].


Third, in this study, no effective measures were taken to control confounding factors during SWE procedure.

Patients need to be instructed to hold their breath in order to minimize motion artifacts when taking elastic measurements.

Additionally, patients were requested to empty their bladder prior to SWE examination to avoid the influence induced from the filling renal pelvis.


Due to the lack of rigorous methodology, the conclusion of this study may not be reliable in fact.

Recently, a study conducted by Chen Z et al. had demonstrated that the SWE derived elastic values reduced as pathology grade of renal fibrosis or grade of vessel wall thickening progresses in patients with CKD, which may be attributed to renal hypo-perfusion rather than tubulo-interstitial fibrosis progression [2].

Another study performed by Guven AT et al. also shown that magnetic resonance elastography-derived stiffness values were lower in patients with chronic injury [6].

The authors need to further explain why their findings were inconsistent with the above studies.


It’s because of the nature of renal anisotropy which may affect the application of SWE in the evaluation of renal fibrosis, a more scientific and rigorous experimental protocol needs to be conducted to unveil the true relationship between SWE-derived stiffness and renal fibrosis.


We read the letter of Chen et al. with interest and we thank the authors for pointing out an inadvertent error in our manuscript [1].

We noticed that we have sent the wrong figures which belong to our another study on elastography.

Therefore, we are sending you the figures that we have taken with the Aixplorer (SuperSonic Imagin, Aix-en-Provence, France) brand device which we used in our study [2].

No changes are needed in the figure legends.


Chen et al. made comments about the methodology of our manuscript and pointed out different results from the literature.

Elastography is a new tool and standardization of methodology will add to its clinical applications.

Our study and this letter at least raised attention to this issue.

We think that constructive criticism raised by the authors will be a guide for further studies on this subject.


Nicotine replacement therapy (NRT) helps reduce the nicotine withdrawal symptoms experienced by people when they quit smoking.

However, no NRT product currently available matches the nicotine concentrations and rapid delivery achieved with smoking.

In contrast, contemporary nicotine e-cigarettes enable users to obtain nicotine as rapidly, and potentially in as high concen- trations, as that from cigarettes.

1 E-cigarettes, mostly with nicotine, are used by smokers in many countries as an aid to quit smoking.

2 To date, seven clinical trials 3-9 (total N=8222; range n=68 to 6006) have investigated the efficacy and effectiveness of e-cigarettes for smoking cessation and overall suggest a net benefit.

However, only four trials (N=7849; n=300 to 6006) had 6-month abstinence as a primary outcome 3,4,8,9 and only two trials (total N=6892) 8,9 tested a second-generation e-cigarette (capable of delivering nicotine more effectively than first-generation devices).


56 www.thelancet.com/respiratory Vol 8 January 2020 lished protocol describes procedures in detail.

11 In brief, people were eligible if they were living in New Zealand, were aged 18 years or older, smoked tobacco (amount not specified), were motivated to quit in the next 2 weeks, were able to provide verbal consent, were prepared to use any of the trial treatments, and had access to a telephone.

We excluded pregnant or breastfeeding women, people who had used an e-cigarette for smoking cessation for more than 1 week anytime in the past year, people currently using smoking cessation medication, people enrolled in another cessation programme or study, or who self-reported a history of severe allergies, poorly controlled asthma, or a cardiovascular event in the 2 weeks before enrolment.

Only one participant per household was permitted.


We recruited participants using national media advertising and contacted them via text message or phone after registration of interest online.

Potential participants were called by a researcher to obtain verbal consent and undertake screening.

The Northern A Health and Disability Ethics Committee approved the study (15/NTA/123) and the Standing Committee on Therapeutic Trials approved the use of nicotine e-cigarettes.


We did a parallel group, community-based, pragmatic, three-arm, randomised trial in New Zealand.

The pub- Research in context


The Cochrane review of e-cigarettes for smoking cessation sought studies published between 2004 and January, 2016.

Two trials (N=953) were identified that measured smoking abstinence rates after 6 months or more, and both tested ad libitum use of first-generation e-cigarettes.

Combined data from the trials showed that using a nicotine e-cigarette significantly increased smoking abstinence at 6 months compared with using a nicotine-free e-cigarette, although absolute quit rates were low (9% vs 4%).

No serious adverse events were reported.

Confidence in the findings was low given the "small number of trials, low event rates and wide confidence intervals around the estimates".


We sought to identify e-cigarette trials that measured smoking abstinence rates at 6 months or more, published between Jan 1, 2016, and June 30, 2019.

We searched MEDLINE, Embase, and PsycINFO using the same search terms as the Cochrane Review (e-cig$ OR electr$ cigar$ OR electronic nicotine OR [vape or vaper or vapers or vaping]) plus "Randomized Controlled Trial" (Publication Type).

We identified 48 articles, of which two were trials.


The first trial (N=54 US companies, 6006 employees and their spouses) tested the effectiveness of four smoking cessation interventions (involving cessation medications, nicotine e-cigarettes, and financial rewards) against usual care (behavioural support).

The verified continuous smoking abstinence rate at 6 months in the usual care plus e-cigarette group was higher, but not significantly different, to that observed in the usual care plus cessation medication group (1·0% vs 0·5%) or usual care alone (1·0% vs 0·1%).


The second trial, done within the UK national stop smoking services, tested the effectiveness of second-generation e-cigarettes plus moderate level face-to-face behavioural support for smoking cessation (N=886).

Self-reported continuous smoking abstinence rates at 6 months were higher in the 18 mg nicotine e-cigarette group (35·4%) than in those allocated 3 months of nicotine replacement therapy (NRT) (25·1%).


Our study adds to the scarce trial evidence base on e-cigarettes as a smoking cessation tool and, to our knowledge, is the first to investigate the effectiveness and safety of combining nicotine patches and second-generation e-cigarettes (with and without nicotine) on smoking abstinence.

We did the trial in a country with strong tobacco control measures, where advertising is restricted, use of e-cigarettes is low, and there was a ban on the sale of nicotine e-cigarettes.

The findings may therefore be generalisable to countries with similar policy backgrounds.

For example, Canada, Costa Rica, Ecuador, Japan, and Mexico had similar advertising restrictions on e-cigarettes at the time of the study and Australia, Canada, Costa Rica, Jamaica, Japan, Malaysia, Mexico, and Switzerland prohibited the sale of nicotine e-cigarettes.

The research questions addressed in our trial have informed tobacco control policy being developed by the New Zealand Ministry of Health.


All five published trials on e-cigarettes for smoking cessation are pragmatic in design, suggesting that under real-world conditions, e-cigarettes (with and without nicotine) could help some people quit smoking but the tobacco control policy environment within which they are available might affect cessation rates.

E-cigarettes should be offered as one of the many smoking cessation aids available to people wanting to quit, but do not appear to be a solution for all.

People using e-cigarettes should be encouraged to fully switch away from tobacco to e-cigarettes, with the aim of eventually also stopping vaping (if possible) given the lack of any long-term safety data for these devices.

Future e-cigarette trials should consider carefully which comparator intervention(s) to use given the high likelihood of differential loss to follow-up (or withdrawal) when only usual care or NRT is offered.


The study statistician (VP) prepared the computer- generated randomisation sequence.

Eligible participants were randomly allocated by computer to patches, patches plus a nicotine e-cigarette, or patches plus a nicotine-free e-cigarette in a 1:4:4 ratio using stratified block ran- domisation (block size of nine).

An unequal allocation ratio was used in an effort to minimise loss of power from withdrawal or cross-over.

Randomisation was stratified by ethnicity: Māori (who comprise 15% of the NZ popu lation 17 and among whom 37% were current smokers in 2015 compared with 16% of the general population) 12 versus non-Māori.

Participants and researchers collecting outcome data were masked to the nicotine content of the e-liquid.


Moderate-intensity behavioural support was available for all participants immediately after randomisation, then once a week for 6 weeks.

This support con sisted of 10-15 min of withdrawal-oriented behavioural support 18 and advice on using their allocated treatment, delivered proactively over the phone by researchers who had received standardised training in delivery of such support, including motivational interviewing principles.


Participants received a 14-week supply of their allo cated treatment (provided at no cost and couriered in one shipment).

A 21 mg, 24 h nicotine patch (Habitrol) was selected because it is the standard patch strength used in New Zealand.

The choice of e-cigarette type and brand (plus e-liquid, nicotine strength, and flavours) was informed by our consultation with New Zealand e-cigarette retailers.


We selected a second-generation eVOD (Kangertech, Shenzhen GuangDong, China) starter kit, containing two batteries, two refillable tanks, one charging kit, one carry case, and five (2·2 mL, 1·8 OHM) atomisers.

Participants had a choice of one of two tobacco e-liquid flavours-one often preferred by those who usually smoke roll-your-own tobacco and one usually preferred by those who smoke factory-made cigarettes.

Irrespective of the type of tobacco used by the participant at baseline, they could choose the e-liquid flavour they wanted.


The flavours selected for the trial were those recommended by the vaping retailer NZVAPOR (www.nzvapor.com) based on their experience with new e-cigarette users and unpublished sales data.

The e-liquid had a 60:40 PG to VG ratio, a masked nicotine content of 0 mg/mL or 18 mg/mL, and was provided in childproof 30 mL brown bottles (four bottles per participant).

Independent testing of each batch was done by NicoTar (Roswell Park Cancer Institute, Buffalo, NY, USA).

We considered variability of plus or minus 10% nicotine concentration as acceptable.

Participants were free to seek out new e-cigarettes and e-liquids if they wished, reflecting the real-world nature of the trial.


We advised participants to start using one patch per day, 2 weeks before their quit-date; although pre-cessation use of NRT is not mentioned as a treatment option in the New Zealand smoking cessation guidelines (unless as part of a cut-down to quit strategy), 19 trial evidence indicates this approach significantly increases long-term quitting success.

20 During this period, participants randomly assigned to an e-cigarette were advised to use the device as and when necessary or desired, and in accordance with the manufacturer's written instructions, to become familiar with its use.

3,21,22 In this way, the time-period of pre-cessation use of treatment products was matched.

Written and online video instructions on how to assemble and use an e-cigarette were also provided via a weblink hosted by NZVAPOR, plus participants were advised that they could call NZVAPOR for advice on how to assemble the device, add the e-liquid, and use and maintain the device (reflecting the support offered by the New Zealand vaping community for new e-cigarette users).

The above material is available on request from the authors.


Participants were instructed to stop smoking from their quit date and continue with their allocated treat- ment for 12 weeks (one patch per day, ad libitum use of the e-cigarette), irrespective of any lapses to smoking.

8-12 weeks of NRT treatment is standard care in New Zealand.

19 Participants who had not quit at the end of their time in the trial were referred to publicly funded smoking cessation services.

No participant payments were made to encourage retention.


All data (except verification of quit status) were collected by telephone, in calls separate to the behavioural support calls.

The published trial protocol 11 presents a tabulated list of outcomes and definitions.

In summary, baseline data included date of birth, gender, self-reported ethnicity, years Articles www.thelancet.com/respiratory Vol 8 January 2020 57 of schooling, self-reported height and weight, smoking history (daily or non-daily smoker, cigarettes smoked per day by daily smokers, age started smoking, years smoked, type of tobacco smoked, quit attempts in the past 12 months and method[s] used), cigarette dependence (measured using the Fagerström Test of Cigarette Dependence, which is on a scale of 1-10, with scores >5 indicating high cigarette dependence and ≤5 indicating low cigarette dependence), 23 concomitant medi cation, motivation to quit (measured on a 5-point Likert scale, for which 1=very low motivation and 5=very high motivation), tobacco withdrawal symptoms and urge to smoke, 24 urge to vape, household smoking and e-cigarette use, exposure to other e-cigarette users, smoke-free home and car policies, self- reported frequency of shortness of breath and cough (measured on a 5-point Likert scale, for which 1=not at all and 5=all the time), self-reported asthma, chronic obstructive pulmonary disease (COPD), and current and history of mental health problems.


The primary outcome was continuous smoking absti- nence 6 months after the agreed quit date (self-reported abstinence since quit date, allowing five or fewer cigarettes in total).

25 Abstinence was verified by a researcher or community-based cessation provider using standardised exhaled carbon monoxide (CO) measurement with a Bedfont Smokerlyzer (Bedfont Scientific Ltd, Kent, UK), with a reading of 9 ppm or lower signifying abstinence.

25 Secondary outcomes assessed at quit date, 1, 3, 6, and 12 months after the agreed quit date were continuous abstinence (CO-verified at 12 months); 7-day point prevalence abstinence (no cigarettes, not a single puff, in the previous 7 days); 25 relapse (time to returning to regular smoking of cigarettes: five cigarettes in the past 7 days); self-reported treatment adherence; tobacco withdrawal symptoms and urge to smoke; 24 urge to vape; self-reported weight; concomitant medication; treatment cross-over; use of other smoking cessation support or medication; continued use of allocated treatment past 14 weeks; changes in shortness of breath, cough, asthma, COPD, and mental health problems; belief in ability to quit and remain tobacco-free; smoking identity (whether those allocated e-cigarettes considered themselves a smoker; a smoker still trying to quit; an ex-smoker; an ex-vapor; a vapor trying to quit smoking; a vapor trying to quit vaping; a vapor, other; or none of the above); and views on their allocated treat- ment for smoking cessation and whether they would recommend it to other smokers who want to quit.

In people still smoking at each follow-up call, outcomes were date returned to daily smoking (for daily smokers); number of cigarettes smoked per day (or when smoking for non-daily smokers); reduction in cigarettes smoked per day; and proportion who reduced the number of cigarettes smoked per day (or when smoking for non-daily smokers) by at least 50%.

Participants allocated e-cigarettes were asked about their urge to vape; whether they changed devices or e-liquid, or both; whether they accessed any e-cigarette support or met anyone at least once a week that currently used an e-cigarette; and dual use (daily use of both their allocated e-cigarette and cigarettes).


We recorded prespecified self-reported adverse effects from product use and serious adverse events.

Serious adverse events were allocated International Classification of Diseases 10th edition Australian Modification codes and were classified by a physician (CB) as non-serious or serious (death, life-threatening, led to hospitalisation, otherwise medically important [ie, that might not be immediately life threatening or result in death or hospitalisation but might jeopardise the participant or might require medical or surgical intervention to prevent one of the other outcomes]).


A sample size of 1809 (804 in both e-cigarette groups and 201 in the patches only group) conferred 90% power, with a two-sided p=0·05 to detect an absolute difference of 8% in 6-month quit rates between the patches plus nicotine e-cigarette group and the patches plus nicotine-free e-cigarette group (1:1 ratio), and a 15% difference between the patches plus nicotine e-cigarette group and the patches only group (4:1 ratio).

11 A 20% loss to follow-up was assumed.

3 The assumed 6-month quit rate of 16% in the patch group was the average observed in the 2012 Cochrane review 16 for nicotine patches versus placebo or no NRT control.

The assumed 6-month quit rate of 23% for the patches plus nicotine-free e-cigarette group was based on our trial of NRT combined with very low nicotine cigarettes.

26 The estimated quit rate of 31% for the patches plus nicotine e-cigarette group was observed in a trial comparing patches plus nicotine spray to patches plus placebo spray.

27 For primary analyses, we used the intention-to-treat (ITT) approach whereby participants with unknown smoking status were deemed to be smoking.


The vali- dated primary outcome relates to people who self-reported quitting, could be located, and for whom verification of smoke-free status was confirmed.

However, locating people to verify quit status proved difficult given the geographically-dispersed trial population.

Therefore, we calculated a post-hoc, extrapolated, validated abstinence rate in which the proportion of people who were located and confirmed as smoke-free was applied to all people who self-reported being smoke-free.

We calculated quit rates, relative risks (RR), risk differences (RD), number needed to treat (NNT), and 95% CIs for both compari- sons.

We compared treatment groups using χ² tests.

We did adjusted analyses using log-binomial regression and adjusted for the stratification factor (ethnicity).


In prespecified subgroup analyses, we assessed the consistency of effects for the primary outcome for ethnicity, age, sex, education, level of cigarette depen dence, motivation to quit, and e-liquid batch using tests for heterogeneity.

We did post-hoc subgroup analyses for Māori and non-Māori women.

Per-protocol analysis excluded participants with missing 6-month data and major protocol Articles www.thelancet.com/respiratory Vol 8 January 2020 violations.

Additional per-protocol analysis also excluded non-adherent participants, with adherence defined as use of both patches and the e-cigarettes (if allocated) at both one and three months.

We also did complete case analysis.


Sensitivity analyses addressed the effect of varying cutoffs for CO mea surements (ie, at ≤3 ppm, ≤5 ppm, and ≤8 ppm).

27 We assessed change from baseline in the number of cigarettes per day (for non-abstainers) and weight and body-mass index over time using repeated measures mixed models with a compound symmetry covariance structure adjusted for baseline value.

We used a Mann-Whitney U test to compare data that were non- normally distributed.

We used Kaplan-Meier curves, the log-rank test, and Cox proportional hazards regression analysis to measure time-to-first-lapse from quit date.

We analysed serious adverse events by treatment group, and calculated an incidence rate ratio (IRR) for each comparison.

We used SAS (version 9.3) and R for analysis, guided by a prespecified plan.

This trial is registered with ClinicalTrials.gov, NCT02521662.


The sponsor of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.

The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.


Between March 17, 2016, and Nov 30, 2017, 1623 people were screened, of whom 1124 (69%) were eligible and randomly assigned to the patches only group (n=125), the patches plus nicotine e-cigarette group (n=500), or the patches plus nicotine-free e-cigarette group (n=499; figure).

This sample size was less than planned, as the trial ran out of time and funding to continue recruitment due to delays in sourcing nicotine e-liquid and slower than anticipated recruitment.


Last follow-up occurred on Aug 31, 2018.

In the patches only group, 42 (34%) of 125 participants were lost to follow-up at 6 months (those with a low level of education and high cigarette dependence were more likely to be lost to follow-up; appendix p 22).

In comparison, approximately 30% (152 of 500 and 155 of 499) of participants in each of the two e-cigarette groups were lost to follow-up at 6 months.

Participants in the patches plus nicotine e-cigarette group were more likely to be lost to follow-up if they were Māori, were younger than 40 years, or had a low level of education (appendix p 22).

Participants in the patches plus nicotine-free e-cigarette group were more likely to be lost to follow-up if they were Māori (appendix p 22).


In the patches only group, 20 (16%) participants with- drew, seven immediately post-randomisation (six citing they did not want to be in the patches only group, and one gave no reason).

In comparison, nine (2%) partici- pants withdrew in the patches plus nicotine e-cigarette group, two immediately post-randomisation (one citing they didn't like the products and one providing no reason), and seven (1%) participants withdrew in the patches plus nicotine-free e-cigarette group, one immediately post- randomisation (citing they had changed their mind).


In March 2018, 4 months after the last person was randomly assigned, and 5 months before the last follow- up call, the New Zealand Ministry of Health allowed nicotine e-cigarettes to be advertised and sold to adult New Zealanders.

Before this date, participants could only legally access nicotine e-liquid via the trial or by Baseline characteristics were balanced between the three treatment groups (table 1).

Participants were pre- dominantly daily smokers (99%) with a high level of cigarette dependence and motivation to quit.

A high pro- portion of participants were women (768 [68%]; table 1), Māori (451 [40%]), and Māori women (342 [30%]).

Overall, 18% of participants reported at least one current mental health problem, 10% reported asthma, and 3% reported COPD (table 1).

Additional baseline variables are presented in table 1.


Verified continuous abstinence at 6 months after the agreed quit date was significantly higher in the patches plus nicotine e-cigarette group (35 [7%] of 500) than in the patches plus nicotine-free e-cigarette group (20 [4%] of 499; RR 1·75 [95% CI 1·02-2·98]; p=0·038; RD 2·99 [95% CI 0·17-5·81]; NNT 33·4 [95% CI 17·2-596·1; table 2).

Verified, extrapolated, continuous abstinence rates at 6-months post-quit date were similar to self-reported 6-month continuous abstinence rates.

Complete case and per-protocol analyses, and sensitivity analyses around varying CO concentrations, concurred with ITT analysis (table 2).

In participants who meet the study definition for treatment adherence, quit rates remained in favour of the patches plus nicotine e-cigarette com bination, but did not reach statistical significance (table 2).


In prespecified subgroup analyses we found no significant differences in quit rates nor for post-hoc subgroup analyses comparing quit rates in Māori and non-Māori women (appendix p 9).

However, quit rates were significantly higher in the patches plus nicotine e-cigarette group for non-Māori, participants younger than 40 years, non-Māori women, and in those with a low level of cigarette dependence.


Secondary self-reported cessation outcomes at 1, 3, and 6 months after the agreed quit date were consistent with the primary outcome (table 2).

In those still smoking, no difference between groups, at any timepoint, was noted for the change from baseline in the mean number of cigarettes smoked per day (appendix p 23).

However, a higher proportion in the patches plus nicotine e-cigarette group We found no significant difference in 6-month verified continuous abstinence rates between the patches plus nico- tine e-cigarette group (35 [7%] of 500) and the patches only group (3 [2%] of 125; RR 2·92 [95% CI 0·91-9·33]; p=0·05; RD 4·60 [1·11-8·09]; NNT 21·7 [12·4-90·3]; table 3).

Veri- fied, extrapolated, continuous smoking abstinence at 6 months post-quit date was statistically in favour of the patches plus nicotine e-cigarette group (table 3).

Complete case and per-protocol analyses found similar findings to the ITT analysis (table 3).

However, self-reported secondary cessation out comes were significantly higher at 1, 3, and 6 months after the agreed quit date in favour of the patches plus nicotine e-cigarette group (table 3).

We did not do prespecified subgroup analyses for this comparison due to a small number of participants who quit.

In those still smoking, no difference was noted between the two groups at any timepoint for the change from baseline in the mean number of cigarettes smoked per day (appendix p 26).

However, the proportion of people reducing the number of cigarettes per day by more than 50% was signifi cantly higher (at all timepoints) in the patches plus nicotine e-ciga - rette group than in the patches only group (appendix p 26).


Use of the allocated treatments was high at quit date (90% of participants in all three groups reported patch use, 94% reported e-cigarette use, and 85% reported using both products), but decreased over time (table 4).

By 3 months, only 50% of participants in all three groups were still using patches, while 67% were still using their nicotine e-cigarette and 56% were still using their nicotine-free e-cigarette (table 4).

In the e-cigarette groups, only 33% of participants were still using both of their allocated products at three months.

The propor tion of participants reporting that they were using their allocated treatment at both 1 month and 3 month follow-up calls was low (23 [56%] of 41 participants in the patches only group, 76 [29%] of 262 participants in the patches plus nicotine e-cigarette group, and 67 [29%] of 229 participants in the patches plus nicotine-free e-cigarette group; table 4), so we did not adjust per-protocol analyses for both primary com parisons for adherence.

In those that did use their allocated products in the past 7 days, use was predomi nantly on a daily basis (appendix pp 10-11).

At 6 months after the agreed quit date, 21 (40%) of 52 participants contacted in the patches only group were still using patches (table 4).

In comparison, 70 [22%] of 317 participants contacted in the patches plus nicotine e-cigarette group, and 88 [29%] of 308 participants contacted in the patches plus nicotine-free e-cigarette group were still using patches.

Furthermore, 143 [45%] of 317 participants contacted in the patches plus nicotine e-cigarette group,


The median time to relapse back to smoking was 5-6 months, with no statistical difference noted for the two comparisons (appendix p 13).

Almost 70% of participants allocated to e-cigarettes correctly identified the nicotine content of their e-liquid (appendix p 27).

Most participants with asthma, COPD, or mental health problems at baseline reported that their condition re- mained the same or improved over time (appendix p 5).

The prevalence of self-reported side-effects of treatment was low (appendix p 28).

Data for other secondary and post-hoc outcomes can be found in the appendix (pp 1-32).


18 serious adverse events occurred in 16 people in the patches plus nicotine e-cigarette group compared with 27 events in 22 people in the patches plus nicotine-free e-cigarette group (IRR 0·66 [95% CI 0·36-1·20]; p=0·18), and four events in three people in the patches only group (0·86 [0·29-2·53]; p=0·78; table 5; appendix p 24-25).

No serious adverse events were treatment related.


This effectiveness trial found that a combination of nicotine patches plus a nicotine e-cigarette was superior to patches plus a nicotine-free e-cigarette for 6 month smoking abstinence among dependent smokers moti vated to quit.


For the patches plus nicotine e-cigarette versus patches plus nicotine-free e-cigarette comparison, as well as for the patches plus nicotine e-cigarette versus patches only comparison, self-reported, self-reported quit rates at all timepoints were in favour of patches plus a nicotine e-cigarette, so the fact that the comparison of patches plus a nicotine e-cigarette versus patches alone did not reach significance is likely to be a result of the smaller than anticipated sample size.


Overall, the observed absolute 6-month quit rates in the e-cigarette groups were low and similar to rates reported in the first trials 28 of e-cigarettes for smoking cessation that used first-generation devices with minimal behavioural support.

3,4 However, quit rates at 1 months and 3 months in the e-cigarette groups in the current study were almost twice those reported in two early trials.


The safety findings are consistent with previous e-cigarette trials and cohort study findings.

9,29 Despite a higher proportion of par ticipants in the patches only group using the patch (compared with patch use in the other treatment groups), 6-month self-reported and verified absolute quit rates for this group were low, but similar to those observed in trials of over-the-counter NRT.


Our study is the second largest e-cigarette trial to date with a primary outcome of at least 6-month smoking abstinence.


To our knowledge, it is also the first large trial testing the effectiveness of a second-generation e-cigarette for smoking cessation within the general population and with the selection of e-cigarette and e-liquid based Sixth, the use of patches differed to what participants might have been used to in the past, in that we started product use 2 weeks before the target quit date.


This change did not affect adherence (because product use at quit date was very high), but might limit the generalisable of the findings to policy environments where precessation use of NRT is not supported.


Finally, serious adverse events were self-reported and similar to those reported in previous e-cigarette 3-7,9 and NRT trials.

16 However, the sample size was not sufficient to assess uncommon serious adverse events, nor was follow- up long enough to identify serious adverse events with a long lead-time.


In summary, when looking at continuous abstinence from smoking, provision of patches plus a nicotine e-cigarette resulted in three to seven more smokers per 100 quitting long-term (depending on the analyses done) than with patches plus a nicotine-free e-cigarette.

The smaller than anticipated sample size meant the study was not sensitive enough to pick up a definitive finding for the second comparison, although analyses suggest combination nicotine therapy-ie, use of a slow release nicotine patch, together with a faster-acting oral nicotine product (in this case a nicotine e-cigarette)-could result in five to ten more smokers per 100 quitting long-term than with mono therapy (ie, nicotine patches alone).

Our findings are consistent with the current findings of the Cochrane review of e-cigarettes for smoking cessation 29 and contribute to the growing body of evidence from randomised trials on the efficacy, effectiveness and safety of e-cigarettes for smoking cessation.


NW, VP, GL, ML, and CB conceived the original idea for the trial, and sought and obtained funding.

All authors contributed to the writing Articles www.thelancet.com/respiratory Vol 8 January 2020 of the study protocol.

VP did all data analyses.

The paper was written by NW, with input from all co-authors.

NW is guarantor for this paper.

All authors read and approved the final manuscript.


NW, CB, MV, GL, ML, and VP report grants from the Health Research Council of New Zealand, during the conduct of the study.

NW, CB, MV, and VP report grants from Pfizer, outside of the submitted work.

GL chairs the organisation End Smoking New Zealand, which advocates for harm reduction approaches to tobacco control.

E-cigarettes were purchased from a New Zealand e-cigarette online retailer (NZVAPOR, https://www.nzvapor.com/), e-liquid was purchased from Nicopharm, Australia (https://www.nicopharm.com.au/), and nicotine patches were supplied by the New Zealand Government via their contract with Novartis (Sydney, Australia).

NZVAPOR also provided, at no cost to participants, on-line and phone support regarding use of the e-cigarettes.

Neither NZVAPOR nor Nicopharm have links with the tobacco industry.

None of the above parties had any role in the design, conduct, analysis, or interpretation of the trial findings, or writing of this publication.


Requests for de-identified individual participant data or study documents will be considered where the proposed use aligns with public good purposes, does not conflict with other requests or planned use by the trial steering committee, and the requestor is willing to sign a data access agreement.

Contact is through the corresponding author.


On Dec 12, 2019, our Twitter-based University of Toronto Respirology and Sleep Journal Club discussed Walker and colleagues’ pragmatic, randomised, parallel-arm trial designed to look at the effectiveness of combining nicotine patches with an e-cigarette (with and without nicotine) on 6-month smoking abstinence [1].

Our discussions highlighted the challenges of studying populations of smokers in general, given the habitual nature of the exposure, challenges related to follow-up, and a need for reliable and valid objective measures of smoking abstinence.


With regard to the primary outcome of smoking abstinence at 6 months, the risk difference observed was only 2.99 (95% CI 0.17–5.81; p=0.038) between the patches with nicotine e-cigarette group compared with patches with nicotine-free e-cigarette group, and 4.6 (1.11–8.09; p=0.05) when compared with patch monotherapy.

Besides reaffirming the superiority of combination smoking cessation therapy over monotherapy, which has already been shown [2][3], this study establishes the importance of the use of nicotine in e-cigarettes to optimise smoking cessation success.


Although the results presented are significant, the effect size in terms of abstinence is quite modest.

It might have been underestimated owing to substantial crossover among participants who used e-cigarettes outside of the study protocol, but if so, the true magnitude of effect is unclear.

Furthermore, the findings are not readily generalisable given the multitude of e-cigarette brands and delivery systems available worldwide with probably different amounts of nicotine content from that used in this trial.

Other study limitations we discussed included the sizable loss of follow-up, which is unsurprising in this context.


However, 70% of those allocated to the e-cigarette groups were able to correctly determine the nicotine content of their e-liquid, raising the question of whether true masking can be achieved in e-cigarette trials.

Notably, exhaled carbon monoxide measurement was used to verify smoking cessation only at 6 months, but not at earlier timepoints, calling into question whether continuous abstinence was truly identified.

Objective confirmation is crucial in determining smoking cessation efficacy owing to the well-known bias associated with self-reported abstinence.


Although evidence exists that nicotine-free e-cigarettes help in reducing cravings and increase quit rates in smokers [4], it would seem preferable to establish the efficacy of nicotine e-cigarettes for smoking cessation before doing a pragmatic trial, as was done for other smoking cessation interventions [2][5].

This pragmatic study reaffirms that e-cigarettes can function as nicotine replacement therapy, but did not evaluate whether e-cigarettes are superior to established forms of nicotine replacement that do not expose the respiratory tract to potential toxins with uncertain long-term health consequences.


A key question for clinicians is whether the putative advantages of e-cigarettes, including higher nicotine content, faster nicotine delivery to the brain and similarity to the act of smoking a cigarette, actually make a difference to smoking cessation rates.

Efficacy trials comparing e-cigarettes with other on-demand nicotine replacement modalities (eg, gum) are therefore needed to answer this.


The Lancet Respiratory Medicine published a letter by Nermin Diab and colleagues (2020) [1] about our three-arm trial investigating the effectiveness and safety of nicotine patches combined with e-cigarettes (with and without nicotine) for smoking cessation [2].

We wish to address two points made by the authors of the letter.


First, they say, '...it would seem preferable to establish the efficacy of nicotine e-cigarettes before doing a pragmatic trial' [1].

We would suggest that both efficacy and effectiveness trials are important, but they serve very different purposes and provide very different information.

Both designs have pros and cons—the choice of design relates to the research question of interest, is context specific, and one does not need to occur before the other.

Efficacy trials are highly controlled (both in patient population and conduct) and their findings are therefore not translatable to the real world.

In comparison, trials on the effectiveness of e-cigarettes have the most relevance for decision making by health-care policy makers and health-care providers.

Such trials tend to have a usual care comparator, but can be expensive to do as they often require a large sample size and long-term follow-up.

It is important when reading effectiveness trials to consider the context of the trial (ie, the country in which it was done, and the country’s policy environment).

In New Zealand, a country with a smoke-free 2025 goal and the urgent need for policy relevant information about the effect of e-cigarettes on quitting, our effectiveness trial design was the most appropriate design to answer the policy-based research question.


When our trial findings are considered in their entirety, one can see that they are consistent across different time points, measures of cessation, and with most of the sensitivity analyses supporting the conclusions.

We acknowledge the authors’ comments about the desirability for more frequent verification of smoking status.

However, New Zealand is a small country (of approximately 5 million people) with little public research funding available.

It was not feasible or affordable to do more extensive follow-up of our trial participants, as they were geographically dispersed throughout the entire country and we had only around US$400,000 to run a three-year trial.

Our chosen measurements of abstinence are those recommended by the Society for Research on Nicotine and Tobacco for pragmatic trials [3].

In New Zealand, we do not have access to large pools of research funding, so must maximise our use of every dollar.

Efficacy trials of little relevance to the population using e-cigarettes are a poor use of our time and money.


Second, they say, '...the findings are not readily generalisable given the multitude of e-cigarette brands and delivery systems available worldwide and probably different amounts of nicotine content from that used in the trial' [1].

We agree, but no efficacy or effectiveness trial of e-cigarettes will ever be fully generalisable.

Nor is it possible to design a trial where every type of e-cigarette ever designed (and nicotine concentration) can be evaluated.

However, there are other pathways to inference than those of drug evaluation.

In this case we have created generalisable evidence as proof of principle that nicotine-containing e-cigarettes combined with nicotine patches will increase quit rates.

We tested the type of e-cigarette (and nicotine concentration) that was the most popular amongst naive e-cigarette users in New Zealand at the time the trial was done.

Newer devices are known to deliver nicotine more efficiently and effectively, so one can assume quit rates would be higher.