Wenping Gong

Jian Shen
email: shen@vims.edu

Bo Hong

The Influence of Wind on the Water Age in the Tidal Rappahannock River

Keywords: Wind, water age, estuarine circulation, stratification, EFDC, Rappahannock River

Wind plays an important role in regulating mixing/stratification, estuarine circulation, and transport timescale in estuaries. A three-dimensional model was used to investigate the effect of wind on transport time by using the concept of water age (WA) in the tidal Rappahannock River, a western tributary of the Chesapeake Bay, USA. The model was calibrated for water level, current, and salinity. A series of experiments regarding the effects of wind on WA was conducted under various dynamic conditions. The effect of wind on transport timescale depends strongly on the competition between the wind and buoyancy forcings, and on the pre-status of the circulation. A down-estuary wind generally decreases WA along the estuary. An up-estuary wind increases WA substantially because it changes the vertical mixing and estuarine circulation more significantly. When the buoyancy forcing increases, the up-estuary wind effect decreases whereas the down-estuary wind effect increases. A 2-day period wind pulse with a maximum speed of 15 ms -1 can alter WA for 3 days; but the wind influence on WA lasts up to 40 days in the simulation. Both local and non-local wind forcings alter WA distribution. The local wind enhances vertical mixing and changes the gravitational circulation in the downstream portion of the estuary whereas it enhances transport in the freshwater portion of the estuary. Consequently, the local wind has a significant impact on WA distribution. In contrast, the non-local wind does not change the gravitational circulation significantly by imposing setup (setdown) of water level at the open boundary, resulting in a lesser impact on WA distribution.

Introduction

The amount of nutrients discharged into an estuary and the transport time required for these nutrients to be exported to the ocean, along with other biogeochemical processes, play important roles in the eutrophication processes of an estuary [START_REF] Nixon | The fate of nitrogen and phosphorus at the land-sea margin of the North Atlantic Ocean[END_REF][START_REF] Boynton | Inputs, transformations, and transport of nitrogen and phosphorus in Chesapeake Bay and selected tributaries[END_REF]. To quantify the transport timescale in lagoons, estuaries and oceans, the age concept of a water parcel has been introduced and utilized (e.g., [START_REF] Zimmerman | Mixing and flushing of tidal embayments in the Western Dutch Wadden Sea, Part I: distribution of salinity and calculation of mixing time scales[END_REF][START_REF] Takeoka | Fundamental concepts of exchange and transport time scales in a coastal sea[END_REF][START_REF] Deleersnijder | The concept of age in marine modeling. I. Theory and preliminary model results[END_REF]Beckers et al., 2001;[START_REF] Delhez | The concept of age in marine modeling, II. Concentration distribution function in the English Channel and the North Sea[END_REF][START_REF] Shen | Calculating age and residence time in the tidal York River using three-dimensional model experiments[END_REF][START_REF] Shen | Modeling study of the influences of tide and stratification on age of water in the tidal James River[END_REF][START_REF] Shen | Determining the age of water and long-term transport timescale of the Chesapeake Bay[END_REF]. Following [START_REF] Delhez | Toward a general theory of the age in ocean modeling[END_REF], the water age (WA) is defined in this study as the time elapsed since a dissolved substance is discharged into the estuary, and WA at any location is representative of the timescale for the dissolved substance to be transported from its source to that location. [START_REF] Delhez | Toward a general theory of the age in ocean modeling[END_REF] introduced the water age theory based on the advection-diffusion of a tracer and provided a general methodology to compute the age using an Eulerian-frame modeling approach. Delhez and Delleersnijder (2002) simulated the age of technetium-99 released from the Cap de La Hague nuclear fuel reprocessing plant in the English Channel successfully by using a 3-D model. The studies of WA in the tidal York River and James River indicate that WA depends highly on river discharge [START_REF] Shen | Calculating age and residence time in the tidal York River using three-dimensional model experiments[END_REF], and the transport timescale and vertical distribution relies on the strength of the gravitational circulation [START_REF] Shen | Modeling study of the influences of tide and stratification on age of water in the tidal James River[END_REF] The characteristics of the transport processes for dissolved substances depend primarily on the low frequency and mean motions of the water in an estuary [START_REF] Mccarthy | Residual currents in tidally dominated, well-mixed estuaries[END_REF]. For a given estuary, the variation of the low-frequency residual flow relies on the interactions of the density field, river flow, wind, and the nonlinear rectification of the periodic tides. The impact of river flow and tidal mixing on estuarine circulation evolves in different stages and depends on the competition between the buoyancy input and tidal amplitude [START_REF] Park | Effect of variation in vertical mixing on residual circulation in narrow, weakly nonlinear estuaries[END_REF][START_REF] Maccready | Estuarine adjustment to changes in river flow and tidal mixing[END_REF]. Many researches have demonstrated that wind plays a dominant role in affecting stratification and non-tidal circulation in estuaries [START_REF] Geyer | Influence of wind on dynamics and flushing of shallow estuaries[END_REF][START_REF] Sanay | Wind-induced exchange in semi-enclosed basins[END_REF][START_REF] North | The influence of wind and river pulses on an estuarine turbidity maximum: Numerical studies and field observations in Chesapeake Bay[END_REF][START_REF] Scully | Control of estuarine stratification and mixing by wind-induced straining of the estuarine density field[END_REF][START_REF] Li | Hurricane-induced destratification and restratification in a partially-mixed estuary[END_REF]. [START_REF] Scully | Control of estuarine stratification and mixing by wind-induced straining of the estuarine density field[END_REF] demonstrated that the influence of wind on stratification is the competition between wind straining and wind mixing. A down-estuary wind increases stratification, decreases the vertical eddy viscosity and enhances gravitational circulation.

An opposite situation occurs for an up-estuary wind. Under less stratified conditions, the wind is more effective in creating mixing while wind straining can dominate over wind stirring under more stratified situations. These results suggest that the effect of wind depends on the pre-status of mixing in the water column.

Besides investigating the vertical and longitudinal structure of estuarine circulation, recent studies also focused on their transverse structure (e.g., [START_REF] Guo | Wind effects on the lateral structure of density-driven circulation in Chesapeake Bay[END_REF]. For a cross-section with a channel and side shoals in the estuaries, the net flow is in the direction of the density gradient or of the local winds over the two shallow sides whereas a reversed net flow develops at the channel [START_REF] Wong | On the nature of transverse variability in a coastal plain estuary[END_REF][START_REF] Friedrichs | Effects of channel geometry on cross-sectional variation in along-channel velocity in partially stratified estuaries[END_REF]. For a down-estuary wind, the wind-driven and buoyancy-induced flows are additive, and the lateral structure of the estuarine flows is enhanced [START_REF] Wong | On the nature of transverse variability in a coastal plain estuary[END_REF][START_REF] Friedrichs | Effects of channel geometry on cross-sectional variation in along-channel velocity in partially stratified estuaries[END_REF]. With an up-estuary wind forcing, the effect of wind on the estuarine circulation is controlled by the relative importance of the wind and buoyancy forcings, which can be represented by a Wedderburn number [START_REF] Geyer | Influence of wind on dynamics and flushing of shallow estuaries[END_REF][START_REF] Sanay | Wind-induced exchange in semi-enclosed basins[END_REF]:

e W =    2 h g l s (1)
where s  is the wind stress, l is the salt intrusion length, g is the gravity acceleration of gravity, h is the mean water depth and l /   is the horizontal density gradient. A strong up-estuary wind will induce a transverse structure of flow (with upstream flows along the side shoals and downstream flows at the channel) while a weak up-estuary wind can not alter the gravitational circulation driven by the buoyancy force.

The study concerning the effect of wind on WA is limited. [START_REF] Shen | Determining the age of water and long-term transport timescale of the Chesapeake Bay[END_REF] examined the influence of wind on WA and transport timescale in the Chesapeake Bay. It was found that the influences of wind on the spatial and temporal distribution of WA depend on both the magnitude and direction of the wind. The up-estuary wind increases the transport time and the down-estuary wind decreases the transport time in the model simulations. [START_REF] Gustafsson | Elucidating the dynamics and mixing agents of a shallow fjord through age tracer modeling[END_REF] studied the effect of wind on WA in a shallow estuary in Denmark. The results indicated that surface salinity and WA decreased due to the reduced vertical mixing and stratification increased if the wind was turned off in the model, and the fresh water runoff was effectively confined in the surface layer.

Their results are relevant for shallow fjords where wind is the major vertical mixing agent.

Conversely, the variations of water level, current, and mass transport in a tributary are highly affected by the non-local wind forcing [START_REF] Elliott | Response of the Patuxent Estuary to a winter storm[END_REF]Wang and Elliot, 1978;[START_REF] Elliott | Observations of the meteorologically induced circulation in the Potomac estuary[END_REF][START_REF] Sanford | Wind-forced salt intrusion into a tributary estuary[END_REF]. It is reasonable to speculate that the non-local wind effect can additionally impact the transport timescale in a tributary. The above results indicate that the influence of wind on WA is complex and warrants further investigation.

A three-dimensional Environmental Fluid Dynamics Code (EFDC) developed by [START_REF] Hamrick | A Three-Dimensional Environmental Fluid Dynamics Computer Code: Theoretical and Computational Aspects[END_REF] was used in this study to investigate the effect of wind on WA in the Rappahannock River. The model was calibrated with the available water elevation, current and salinity data. A series of numerical simulations were conducted to investigate the effect of wind on transport timescales and their corresponding mechanisms under various dynamic conditions. The interactions among the density field, river flow, and wind, and their influences on WA were examined. The effects of local and non-local winds on WA were explored.

Study site

The Rappahannock River is one of the major tributaries of the Chesapeake Bay (Fig. 1).

The cross-sectional areas of the estuary increase exponentially as the shore widens from about 0.2 km at the headwater to 5 km at the mouth and the channel deepens from 5 to 24 m. The drainage area above the fall line is 4132 km 2 . The stream flow (based on the data from 2000 to 2007) at the USGS gauging station near Fredericksburg, Virginia (ID 01668000), shows that the low flow discharge (10 th percentile) is 13.56 m 3 s -1 , the mean flow rate (50 th percentile) is 37.66 m 3 s -1 , and the high flow (95 th percentile) is 182.36 m 3 s - 1 . The principal tidal component is the lunar semi-diurnal tide with a period of 12.42 hours. The mean tidal range is 37 cm near the mouth and the tidal wave takes about 9 hours to propagate from the river mouth to the fall line [START_REF] Park | A model study of hydrodynamic and water quality characteristics of the Rappahannock Estuary, Virginia[END_REF]. The lower portion of the tidal Rappahannock River is a partially mixed estuary. The salt water intrusion is generally located around 120 km from the mouth during low-flow and around 70 km during high-flow. The Rappahannock River is characterized by persistent hypoxic conditions in the bottom water from the mouth to 50 km upstream during the summer;

this is partly attributed to the weak gravitational circulation of the estuary [START_REF] Kuo | Hypoxia and salinity in Virginia estuaries[END_REF].

The wind data of 2006 at the NOAA station of Lewisetta (ID 8635750) show that the most frequent winds are southwesterly and northwesterly with a mean wind velocity of 4.2 and 5.0 ms -1 , respectively. The wind is relatively weak during the summer when the southwesterly wind prevails. The northeasterly and northwesterly winds usually dominate the winter season. As wind plays an important role for the hydrodynamics and water quality in the Rappahannock River, it is an ideal place for studying the wind effect on the transport timescale.

Methodology

Numerical model

The EFDC model was used to simulate water level, current, salinity, tracer concentration, and WA in this study. The model solves the three-dimensional continuity and free surface equations of motion (Hamrick, 1992a;[START_REF] Hamrick | Computational design and optimization of the EFDC/HEM3D surface water hydrodynamic and eutrophication models[END_REF]. The Mellor and Yamada level 2.5 turbulence closure scheme is implemented in the model [START_REF] Mellor | Development of a turbulence closure model for geophysical fluid problems[END_REF][START_REF] Galperin | A quasi-equilibrium turbulent energy model for geophysical flows[END_REF]. The model uses stretched (or sigma) vertical coordinates and curvilinear, orthogonal horizontal coordinates. It simulates density and topographically-induced circulation, tidal and wind-driven flows, and spatial and temporal distributions of salinity, temperature, and conservative/non-conservative tracers. The model has been successfully applied to a wide range of environmental studies (e.g. [START_REF] Shen | Calculating age and residence time in the tidal York River using three-dimensional model experiments[END_REF][START_REF] Shen | Modeling study of the influences of tide and stratification on age of water in the tidal James River[END_REF][START_REF] Gong | The hydrodynamic response of the York River estuary to Tropical Cyclone Isabel, 2003[END_REF][START_REF] Xia | Modeling of the Cape Fear River Estuary plume[END_REF].

Rectangular Cartesian grids were used in most portions of the Rappahannock River

Age calculation

Several methods have been introduced for computing WA (Bolin and Rodhe, 1972;[START_REF] Zimmerman | Mixing and flushing of tidal embayments in the Western Dutch Wadden Sea, Part I: distribution of salinity and calculation of mixing time scales[END_REF][START_REF] Takeoka | Fundamental concepts of exchange and transport time scales in a coastal sea[END_REF]. [START_REF] Delhez | Toward a general theory of the age in ocean modeling[END_REF] provided a general method using numerical models to compute spatially varying WA distributions in a real estuarine environment. WA can be computed based on a tracer and age concentrations. Assuming that there is only one tracer discharged into the system without other sources and sinks, the transport equations for calculating the tracer and age concentrations can be written as [START_REF] Deleersnijder | The concept of age in marine modeling. I. Theory and preliminary model results[END_REF]:

0 )) , ( ) , ( ( ) , (        x t c K x t c u t x t c     (2) ) , ( )) , ( ) , ( ( ) , ( x t c x t K x t u t x t                ( 3
)
where c is the tracer concentration,  is the age concentration, u  is the velocity field in space and time domains, K is the diffusivity tensor, t is time and x  is coordinate. The WA " a "can be calculated as:

) , ( ) , ( ) , ( x t c x t x t a      (4)
The vertically averaged WA can be computed by averaging WA at each layer. was continuously released at the headwater of the River.

Model setup

Five sets of numerical experiments (Table 1) were conducted for investigating the effects of wind on WA, which are grouped as: (1) real-time down-estuary and up-estuary wind effects (E01-E03); (2) local and non-local wind effects (E11 and E12); (3) wind pulse effect (E21 and E22); (4) constant wind effect (E31-E33); and (5) river discharge and wind interaction (E41-E43). As the alignment of the Rappahannock River is in a northwest direction, winds from the northwest and southeast directions were used for the down-estuary and up-estuary winds, respectively.

All the setup details of the experiments are described in Table 1. Experiment E00 is the simulation forced by real observations and is referred to as the Real Case hereafter. For experiment E02-03, the real-time wind vectors were projected on the northwest-southeast axis and only downstream or upstream components were used as wind forcings for E02 or E03, respectively. For experiments E11-E12, the non-local wind effect was examined by conducting E11 using the same observed wind forcing, river discharge, and salinity at the boundary as those in E00, except that the water level at the open boundary was replaced by astronomical tides. The difference between E11 and E00 represents the nonlocal wind effect. The local wind effect was studied by specifying the observed water level at the open boundary without wind forcing in the estuary (E12). The difference between E12 and E00 denotes the local wind effect. For the wind pulse study (E21 and E22), a half sinusoidal function for the wind velocity was specified as wind forcing.

Wind started on Day 200 and reached a maximum magnitude of 15 ms -1 on Day 201, and waned for another day. After Day 202, the wind was stopped. This wind pulse represents a typical strong wind event in the Chesapeake Bay region. For E31-E33, down-and upestuary winds were set as 5 ms -1 from the northwest and southeast directions, to represent the mean wind conditions in the Rappahannock River. For E41-E43, the 95 th percentile high flow was utilized as the upstream boundary condition.

Model calibration

Water elevation

The water elevation was calibrated by adjusting the bottom roughness height ( 0 z ) to make the modeled water elevations along the estuary agree with the predicted mean tide characteristics in the tidal table (National Ocean Survey, 1989). A mean freshwater inflow was specified at the upstream boundary as it does not affect the mean tidal range along the estuary [START_REF] Park | A model study of hydrodynamic and water quality characteristics of the Rappahannock Estuary, Virginia[END_REF]. A sinusoidal ( 2 M ) tide with a range of 0.37 cm was specified at the open boundary. The model result is shown in Fig. 2. It can be seen that the modeled mean tidal range compares favorably with the observations, which indicates that the characteristics of the tidal propagation from the mouth to the headwater is well represented by the model [START_REF] Park | A model study of hydrodynamic and water quality characteristics of the Rappahannock Estuary, Virginia[END_REF]. As the tide propagates upstream (with the decreasing of cross-section area), the tidal range increases from the mouth to 55 km upstream. With the superposition of a reflected wave, a nodal point is located approximately 100 km from the mouth. Beyond the nodal point, the tidal range continues to increase towards the headwater.

Current

For the current calibration, the available measurements in the summer of 1987 (lasted approximately one month from 8/4 to 9/3) was collected. The detailed description of the field observation can be found in [START_REF] Kuo | Hypoxia in the lower Rappahannock Estuary[END_REF]. The stations for current measurements are shown in Fig. 1. The open boundary conditions of the model for water level and salinity was set the same as [START_REF] Park | A model study of hydrodynamic and water quality characteristics of the Rappahannock Estuary, Virginia[END_REF]. The wind forcing data at Gloucester Point (Fig. 1) during the simulation period was applied to the model.

Salinity

The the measured data. The model was able to catch salinity variations and vertical stratification (Fig. 5). Overall, the model results are unbiased (Fig. 6) and are acceptable.

Model results

The effects of down-estuary and up-estuary winds

The temporal and spatial variations of WA depend on the freshwater discharges, wind, and tidal forcing conditions. WA under a realistic forcing condition was simulated by using real observation data (E00). An example of vertically averaged WA variation at Wind contributes greatly to the estuarine circulation in the Chesapeake Bay. Previous investigations suggest that wind energy peaks at 8 and 3.5 days in this area [START_REF] Wang | Subtidal sea level variations in the Chesapeake Bay and relations to atmospheric forcing[END_REF].

To for the tracer released at the headwater to be transported out of the estuary (Fig. 8a). The down-estuary wind generally reduced the transport time in the estuary (Fig. 8b). The tracer needed about 130 days to escape from the estuary. WA decreased by 2.48% on average over the whole estuary. In contrast, the up-estuary wind increased WA significantly (Fig. 8c). It took more than 150 days for the tracer to travel from the headwater to the river mouth. WA increased by 10.65 % on average in the estuary.

The distribution of WA depends highly on the gravitational circulation [START_REF] Shen | Modeling study of the influences of tide and stratification on age of water in the tidal James River[END_REF]. The vertical distributions of 25-hour tidally averaged salinity and WA along the longitudinal transect is shown in Fig. 9. It can be seen that the down-estuary wind generally increased the buoyancy-induced circulation (Fig. 9b). Salt intrusion moved farther upstream and stratification increased in the region from 60 to 90 km, which is consistent with the enhanced gravitational circulation. In contrast, under up-estuary wind forcing, WA increased about 30 days near the mouth of the estuary compared to the cases with down-estuary wind forcings. It can be seen that the up-estuary wind reduced salt intrusion and the 1 ppt salinity contour moved 15 km downstream (Fig. 9c). WA increased in the segment from 60 to 110 km, where gravitational circulation was reduced (Figs. 9b and9c). [START_REF] Sanay | Wind-induced exchange in semi-enclosed basins[END_REF] demonstrated that for a down-estuary wind forcing, buoyancy-induced circulation is enhanced. For a moderate up-estuary wind forcing, wind-driven circulation overwhelms buoyancy-induced circulation. This is consistent with the model results in this study. Our results also demonstrate that the influence of upestuary wind on the transport timescale of dissolved substances is stronger than the down-estuary wind. This complies with the pattern found in the Chesapeake Bay [START_REF] Shen | Determining the age of water and long-term transport timescale of the Chesapeake Bay[END_REF].

A noticeable feature of WA distribution is the lateral variation. Under the buoyancy force, WA at the western side of the River (i.e, the right hand side facing downstream)

was generally lower than that near the eastern side. This can be attributed to the bathymetry, Coriolis force, and tidal asymmetries [START_REF] Shen | Calculating age and residence time in the tidal York River using three-dimensional model experiments[END_REF]). The downestuary wind enhanced the buoyancy-induced lateral variations of non-tidal current and WA while the moderate up-estuary wind could change the lateral distribution of longitudinal non-tidal flow and thus alter the lateral variation of WA. An example of the lateral non-tidal current and WA distributions at a selected Section 1 from Day 238 to 240 are shown in Fig. 10. Without the wind forcing, the downstream current was biased at the surface of the western side while the upstream current was located at the bottom (Fig. 10d). WA at the western side of the river was lower than that at the eastern side (Fig. 10g). The down-estuary wind slightly enhanced the estuarine circulation. The isohalines were vertically distributed and the lateral gradient of salinity was increased whereas the stratification pattern was identical with the case without wind forcing. The lateral asymmetric distribution of WA with an overall reduction of WA was similar to the situation without wind. In contrast, the up-estuary wind changed the estuarine circulation more apparently. It generated a lateral pattern with upstream flows located in the side shoals and downstream flows located in the channel at Section 1(Fig. 10f). The lateral distribution of WA was altered correspondingly (Fig. 10i): the lower WA occurred in the channel while higher WA was present along the side shoals.

The local and non-local wind effect

Water motions in a tributary can be induced by both the local and non-local winds through coupling with a neighboring large estuary. Non-local wind often causes sea level fluctuations in the Rappahannock River (Fig. 4). The influences of wind on the WA can be distinguished as the local and non-local wind effects. The former acts directly on the surface of the waterbody and the latter influences the estuary through wind-induced subtidal variations at the river mouth. Experiments E31-E33 were conducted to diagnose the local and non-local wind effects on WA.

Because WA varied during the model simulation, the results averaged over the last 2 days of the simulations were selected for analysis. For the Real Case simulation, it took approximately 130 days for the tracer released at the headwater to be transported to the river mouth (Fig. 11a). Because setup dominated the variation of subtidal water level during the simulation period (Fig. 4), excluding setup at the river mouth would enhance downstream transport, thus decreasing WA. Without non-local wind forcing, WA decreased but the root mean square (RMS) difference between the simulations with and without non-local wind is only 2.64 days in the estuary. The WA distribution shares a similar pattern with the results of the Real Case. However, without local wind forcing, WA in the estuary became much different from that of the Real Case (Fig. 11b). The

Wind pulse effect

The model results show that the wind effect on WA is accumulative within a certain period even when wind forcing is periodic. The duration of the wind pulse effect is crucial for the transport processes in the estuary. A strong wind pulse can generate a strong perturbation to the estuary and alter the mixing and stratification pattern in estuaries. The response of WA to the wind pulse was examined by conducting simulations E21 and E22. We separated a wind pulse into two stages: (1) accelerating phase (from Day 200 to 201) when the wind speed increased;

(2) decelerating period (from Day 201 to 202) when the wind speed decreased. During a storm event (such as a hurricane), the response of an estuary is basically barotropic [START_REF] Gong | The hydrodynamic response of the York River estuary to Tropical Cyclone Isabel, 2003[END_REF][START_REF] Li | Hurricane-induced destratification and restratification in a partially-mixed estuary[END_REF]. During the acceleration phase, the wind induces either a flow pattern with downwind current at the water surface and upwind current at the bottom or a unidirectional downwind flow depending on the mixing, and establishes a horizontal water level slope against the wind forcing. During the deceleration stage, the established barotropic gradient drives an upwind flow in the water column. After the passage of a storm event, the estuary readjusts itself, enduring oscillations for both elevation and current. For the wind pulse simulations, the maximum wind speed was 15 ms -1 and the maximum e W was calculated as 10.92, showing a dominant effect of wind forcing relative to the buoyancy effect.

The downstream wind, compared with the data without wind (Fig. 12a), generated a strong downstream current at Station 1 and enhanced estuarine circulation in the region downstream of Station 2 during the acceleration phase (Fig. 12b).The up-estuary wind induced an upstream current at the surface layer and a downstream current at the bottom layer in the region downstream of Station 2. The changes in current and mixing altered the tracer transport and WA. The strong mixing induced by the wind pulse destratified the vertical WA distribution. The down-estuary wind pushed the WA contour downstream, thus decreasing WA in the estuary, while the up-estuary wind induced an opposite effect.

During the deceleration phase, the surface gradient established during the acceleration phase generated a reverse flow in the estuary. Large upstream currents were developed at the subsurface and middle layers in the water column for the down-estuary wind while a strong downstream current was observed for the up-estuary wind. For the down-estuary wind, the vertical distribution WA became more stratified (Fig. 12e). The bottom WA increased while the surface WA decreased compared to that without wind in the region downstream of Station 2. For the up-estuary wind, WA contour was advected downstream and WA was generally higher than that without wind (Fig. 12f).

After the passage of the wind pulse, the vertical distribution of WA for both cases of down-estuary and up-estuary wind pulses became similar to that without wind. For the down-estuary wind pulse, WA upstream of Station 2 was higher than that without wind while WA was lower downstream of Station 2 (Fig. 12h). For the up-estuary wind pulse, WA adjusted quickly (Fig. 12i). A time series plot of the perturbation and recovery processes at Station 4 are shown in Fig. 13. It can be seen that WA changed in 3 days for a 2-day period wind pulse and WA returned to a without wind status at approximately Day 240, which was 40 days for the adjustment.

Overall, the influence of wind pulse on WA is more pronounced downstream in the estuary where stratification persists. A wind pulse for a 2-day period can impact WA and the influence lasts up to 40 days. As the wind pulse can alter the mixing, stratification and estuarine circulation in an estuary, the adjustment time is longer in the deeper area, such as Station 4 (Fig. 13), as demonstrated by [START_REF] Maccready | Estuarine adjustment to changes in river flow and tidal mixing[END_REF]. The results suggested that an accumulative effect can be expected for the tributaries of the Chesapeake Bay, which experiences consecutive wind events with a dominant period of 2-10 days.

Discussion

Model simulations under different wind forcing conditions suggested that wind has a significant influence on the estuary gravitational circulation, upon which WA highly depends. [START_REF] Hansen | Gravitational circulation in straits and estuaries[END_REF] obtained the estuarine circulation pattern by using a steady-state approach:

                                         2 0 3 2 3 1 2 3 8 9 1 48 1 ) ( h z u h z h z K h x g z u                    2 3 1 4 1 h z h z K h m s   (6)
where m K is the eddy viscosity, and 0 u is the mean outflow due to river discharge. The first two terms of Eq. ( 6) describes the estuarine circulation without wind forcing and the last term represents the circulation induced by the wind effect. It can be seen that the gravitational circulation will be strengthened with constant down-estuary wind. When wind becomes up-estuary, the wind-induced current can reduce the estuarine circulation.

It can cause inflow at the surface and outflow at the middle layer [START_REF] Geyer | Influence of wind on dynamics and flushing of shallow estuaries[END_REF]. Our model simulations (E01-E03) agree with the circulation pattern obtained by Eq. ( 6) under continuously varying wind conditions. The up-estuary wind forcing weakens the estuarine circulation and reduces outflow, resulting in an increase in transport time.

Under down-estuary wind forcing, the estuarine circulation undergoes minor changes and the decrease in transport time is not significant.

Many studies suggest that the influence of wind on stratification depends highly on the precondition of the estuary stratification. The competition between wind and buoyancy force can be represented by the Wedderburn number. When buoyancy forcing becomes stronger (smaller Wedderburn number situations), the wind effect on transport timescale is reduced given the same magnitude of wind forcing. To understand the competition between wind and buoyancy forces and their influence on transport timescale, we conducted model simulations under constant (with a velocity of 5 ms -1 ) down-estuary and up-estuary wind forcing (Experiments E31-E33 and E41-E43). Although wind forcing changes continuously in magnitude and direction in a real situation, the model results under constant wind forcing can be considered as an extreme case that provide measures for the maximum deviation of transport timescale that can be expected. These results can be further compared with estuary circulation derived from a steady-state approach.

For the experiments E31-E33, river discharge was specified as constant mean flow.

Under mean flow condition, e W = 0.77, representing a moderate wind effect in respect to the buoyancy effect (plus sign signifies the down-estuary wind situation). For the baseline condition (E31), it took about 160 days for the tracer released at the headwater to be transported out of the estuary (Fig. 14a). Under the down-estuary wind (Fig. 14b), the tracer took about 150 days to escape from the estuary. WA decreased 6.13% on average over the whole estuary. Conversely, the up-estuary wind increased WA significantly (Fig. 14c). It took about 210 days for the tracer to be transported from the headwater to the river mouth, a 50 day increase compared to the baseline condition. WA increased 19.42% on average in the estuary. These results agree with those of experiments E01-E03.

Under high flow conditions (Experiments E21-E23), e W is reduced to 0.59. With high river discharge, WA at the estuary decreased greatly compared to the baseline condition (Fig. 15a). It took less than 60 days for the tracer to be transported out of the estuary. The down-estuary wind reduced WA overall. It took less than 50 days for the tracer to be transported from the headwater to the mouth (Fig. 15b). The down-estuary wind reduced WA by 13.96% (note WA decreased by 6.13% under the mean flow condition) on average in the estuary. With an increase of buoyancy force under a high flow condition, the pre-status of stratification can be expected to be less altered by wind forcing. For the up-estuary wind, WA at the river mouth increased by 10 days (Fig. 15c). The increase of transport time was much less than that under the mean flow condition, which is 50 days.

The up-estuary wind increases WA by 13.97% (note WA increasd by 19.42% under the mean flow condition) on average in the estuary. This suggests that the impact of wind forcing on the transport timescale depends highly on the competition between the wind induced mixing and the stratification established under the buoyancy forcing of the freshwater discharge. As high flow inputs large buoyancy to the estuary and results in the establishment of strong estuarine circulation in the Mesohaline region, the wind forcing is not strong enough to change the estuarine stratification status significantly even though the up-estuary wind increases vertical mixing. This effect can also be observed from the model simulation using real-time data (simulations E00-E12). The local wind effect was reduced when the estuary endures high inflow after Day 200 (Fig. 11).

Since the wind pulses usually occur intermittently or continuously in a real situation, studying a single wind pulse has limitations. An alternative is to use multiple wind pulses in the study. But it would be difficult to isolate the effect of a specific wind pulse. In this study, we diagnosed the influence of an isolated wind event on the transport timescale and examined its recovery time. If a perturbation generated by an energetic wind event only lasts for a short period, its contribution to the long-term transport can be considered insignificant. On the other hand, if a wind generated perturbation persists in the system for a long period, the accumulative effect is important. Our model results forced by intermittent up-estuary or down-estuary winds (E02-E03) indicate that the up-estuary wind effect is accumulative, persistent and plays a dominant role for controlling estuarine transport timescale.

Our model results show that the down-estuary wind reduces WA in the whole estuary. This is different from [START_REF] Gustafsson | Elucidating the dynamics and mixing agents of a shallow fjord through age tracer modeling[END_REF] but is consistent with [START_REF] Shen | Determining the age of water and long-term transport timescale of the Chesapeake Bay[END_REF]. The increase of WA under down-estuary wind in [START_REF] Gustafsson | Elucidating the dynamics and mixing agents of a shallow fjord through age tracer modeling[END_REF] could be due to the large Wedderburn number and thus the decrease of estuarine circulation in their study.

Summary

WA provides a good measure for transport timescale of dissolved substances in estuaries. Besides the influence of tidal variation and river discharge, wind also affects WA significantly. Wind plays an important role in regulating mixing, stratification, salt transport, and estuarine circulation in estuaries. Therefore, it has a great impact on the transport timescale in estuaries.

The effects of wind on transport timescale depend strongly on the competition between the wind forcing and buoyancy forcing. The pre-status of the estuarine circulation can also alter the effect of wind forcing. The down-estuary wind generally decreases WA along the estuary. Because the stratification persists in the Rappahannock River, a decrease in WA due to down-estuary wind is marginal. The up-estuary wind produces the opposite impact on WA and has a more important impact on age distribution as it changes estuarine circulation more significantly. For a situation in which wind forcing dominates over buoyancy forcing, the up-estuary wind causes greater changes in WA than that of the down-estuary wind. As buoyancy forcing increases under high river discharge, the effect of up-estuary wind decreases.

In general, the lateral age distribution is more biased towards the right side of the estuary when looking downstream. Such an asymmetric pattern is due to the asymmetric lateral variation of net circulation. However, this pattern can be changed by the upestuary wind forcing. The interaction of up-estuary winds and geometry can alter the lateral circulation, resulting in a more symmetric pattern in the lower estuary.

Wind pulse can impose great disturbances on the non-tidal circulation and stratification in the estuary, thus affecting WA in a certain period of time. Our model results show that a 2-day period wind pulse with a peak velocity of 15 ms -1 can change WA by approximately 3 days and its influence can last for up to 40 days. The simulation, forced only by real up-estuary wind components, indicates that the influence of up-estuary wind persists in the system and represents a significant contribution to the transport timescale.

The model experiments suggest that the setup and setdown of water level imposed at the river mouth due to remote wind effect has a weak impact on WA under normal wind conditions. The local wind has a significant impact on WA distribution as it directly changes the vertical mixing and subsequent estuarine circulation.

  with a cell size of 200 m in both x and y directions. Because the upper portion of the estuary becomes narrow and meandering, curvilinear grids were used to better fit the shoreline in this upstream portion. The grid resolution ranged from 200 m to 1400 m. Six layers were used in the vertical direction. The open boundary was extended a short distance out of the estuary mouth to avoid any influence of the boundary condition on the circulation near the mouth. The model domain is shown in Fig.1. The bathymetry data from the NGDC (National Geophysical Data Center ) were interpolated into the model grids (http://www.ngdc.noaa.gov/mgg/coastal/coastal.html).

A

  radiation boundary condition was used for the water level at the open boundary to allow the outgoing wave to propagate out of the model domain freely. For the model calibration, the observed water level data at the NOAA Windmill Point gauge station (ID 8636580) were used as an incoming wave. The salinity data from the LE3.6 station, measured monthly by the Chesapeake Bay Program (http://www.chesapeakebay.net), were linearly interpolated both spatially and temporally during the simulation period and used as the incoming salinity at the open boundary. The salinity at ebb tide at the open boundary was calculated using upwind concentrations immediately inside the open boundary. When the flow at the open boundary changed from outflow to inflow, the model provided a linear interpolation based on the last outflowing salinity and the specified incoming salinity within a specified time interval (Yang and Hamrick, 2005). At the estuary headwater, daily freshwater discharge measured at the USGS gauge station near Fredericksburg, Virginia was utilized. The wind data at the NOAA station of Lewisetta (ID 8635750) were applied to force the model at the water surface. The model's initial condition was obtained by running the model iteratively until the modeled salinity distribution reached the quasi-equilibrium state. The open boundary condition of WA was treated the same as the salinity except that the incoming tracer and age concentrations were set to zero. The tracer with a concentration of 1 (arbitrary units)

  salinity calibration period was from 5/1/2006 to 12/31/2006. The distributions of wind, non-tidal water level at the river mouth, and river discharge during the simulation period are shown in Fig. 4. The background diffusivity was calibrated to obtain better results. It was shown that a background diffusivity of 4.010 -5 m 2 s -2 resulted in a reasonable agreement. A comparison between the model results and measurements is shown in Fig. 5 The salinity shows large variations in responding to the variations of water level and river discharge. A water level rise at Day 38.7 resulted in a salinity increase in the whole estuary. A large river discharge event occurred around Day 60 and diluted the estuary significantly. Another tremendous salinity increase happened before the tropical storm Ernesto on September 1, 2006 (Day 123), which was mostly induced by a month long period of extremely low river inflow. The salinity decreased quickly as freshwater discharge increased after Day 122. Due to the lack of daily observed salinity data at the open boundary, the modeled results could not be expected to represent the complete history of salinity variations in the estuary. Generally, the modeled salinities agreed with

  Station 3 from Day 120 to 240 is shown in Fig.7 (see Fig. 1 for station location). WA varied between 70 to 125 days during the simulation period. WA increased from Day 120 to Day 190 and it decreased significantly from Day 200 to 210. A large fluctuation occurred around Day 205. WA oscillated continuously from Day 210 to 290.

  diagnose the effect of wind on WA, experiments E01 (without wind), E02 (with intermittent down-estuary wind components), and E03 (intermittent up-estuary wind components) were conducted. The time series of WA variations at Station 3 for the experiments are shown in Fig.7. The results show that WA fluctuated in a similar pattern for all the simulations. This suggests that river flow contributed to WA variation to a large extent. In general up-estuary winds increased WA while down-estuary winds decreased WA. The results are consistent with the findings of[START_REF] Shen | Determining the age of water and long-term transport timescale of the Chesapeake Bay[END_REF].Before Day 178, WA of the Real Case (E00) was similar to that with up-estuary wind components, indicating the importance of up-estuary winds in affecting transport timescale. Although the intermittent up-estuary wind components were forced in the experiment, an accumulative effect was evident, which will be further discussed. After Day 205, the down-estuary wind played a more important role in altering WA as river discharge increased. An increased WA was evident from Days 190 to 200 under upestuary wind forcing. The spatial distributions of vertically-averaged (averaged from Day 192 to 193) WA in the estuary are shown in Fig. 8. For the case without wind forcing, it took about 140 days

  RMS difference of WA increased to 28.32 days between the simulations with and without local wind forcing. Without local wind forcing, the lateral differentiation increased, WA decreased near the western shoreline but increased near the eastern shoreline. This clearly indicates the effect of local wind on the spatial distribution of WA.To further examine the local and non-local wind effects on WA, the time series of WA at two stations (Stations 1 and 3) are shown in Figs.11c and 11d, respectively. The change of WA in response to the freshwater discharge was evident, especially at Station 1, which lies in the freshwater portion of the estuary. The influence of freshwater discharge on WA decreased downstream. WA increased up to 20 days with the local wind forcing in the estuary. It can be clearly seen that WA almost follows that of the Real Case without non-local wind while WA deviated from that of the Real Case more dramatically without the local wind. The increase of WA was less than 5 days during strong setup events(i.e., around Day 125, 160, 206, 227). Because the local wind has a much stronger impact on estuarine mixing, it alters WA more notably. The results indicate that the local wind effect is more important for long-term transport in the Chesapeake Bay tributaries under normal wind conditions.

Figure 2 .

 2 Figure 2. Results of mean tidal range calibration along the estuary (Square denotes tidal table data; solid line is the model result).

Figure 3 .

 3 Figure 3. Calibration results of water currents (cross represents measurements, solid line signifies model results).

Figure 4 .

 4 Figure 4. Time series of wind forcing, non-tidal water level at the river mouth and river discharge at the headwater of the estuary from 5/1/2006 to 12/31/2006.

Figure 5 .Figure 6 .Figure 7 .

 567 Figure 5. Salinity calibration at stations surveyed by the Chesapeake Bay Program (solid line signifies the modeled surface salinity, dashed line signifies the modeled bottom salinity, rectangular and circle are observations at surface and bottom);

Figure 8 .

 8 Figure 8. Distributions of vertically averaged WA for (a) without wind; (b) down-estuary wind component; and (c) up-estuary wind component.

Figure 9 .

 9 Figure9. Longitudinal distributions of salinity, residual current, and WA along the main channel of the estuary. Let panel is salinity, middle panel is current, and right panel is WA(a, d, g: without wind; b,e,h: down-estuary wind, c,f,i: up-estuary wind)

Figure 11 .

 11 Figure 11. The distribution of vertically-averaged WA (a: with real wind (black line) vs. without non-local wind (red line); b: with real wind (black line) vs. without local wind effect (red line)) and time series of WA at selected stations with, and without local and non-local wind (c: at Station 1; d: at Station 3).

Figure 12 .

 12 Figure 12. The distribution of non-tidal longitudinal current along the channel of the estuary and wind pulse induced change of WA. Left column (a, d, g) is the baseline condition, middle column (b, e, h) is the case with down-estuary wind pulse and right

Figure 13 .

 13 Figure 13. Time series of subtidal longitudinal current and wind pulse induced change of WA at Station 4.

Figure 14 .

 14 Figure 14. The distribution of vertically-averaged WA under mean flow condition (a: without wind; b: with constant down-estuary wind; c: with constant up-estuary wind).

Figure 15 .

 15 Figure 15. The distribution of vertically-averaged WA under high flow condition (a: without wind; b: with constant down-estuary wind; c: with constant up-estuary wind).

  Fig.1

  The river discharge data at the USGS gauge station near Fredericksburg, Virginia was utilized for the upstream boundary condition. The initial salinity field was interpolated from the data measured on 8/4/1987. A comparison between the model results and observations are shown in Fig.3. The modeled velocities almost agree with the observations. The Mean

	Error, Mean Absolute Error, and Mean Absolute Relative Error between the model
	results and observations were as follows: 0.04 ms -1 , 0.08 ms -1 , and 42%, respectively, at
	the surface layer of Station C1; 0.03 ms -1 , 0.08 ms -1 , and 33%, respectively, at the bottom
	layer of Station C1; 0.01ms -1 , 0.10 ms -1 and 94%, respectively, at the surface layer of
	Station C2; 0.03, 0.07 ms

-1 , and 52%, respectively, at the bottom layer of Station C2. The main discrepancies occurred during the slack period. Overall, the model satisfactorily reproduced the variations of water currents during the summer of 1987.

Table 1 :

 1 Model simulation experimentsFigure1. Map of the Rappahannock River and the locations of measurement stations of the Chesapeake Bay Program (Circles are salinity stations, rectangles are water current stations, triangles are locations selected for analysis, and longitudinal and lateral crosssections for analysis).

	Model simulation	Description	Simulation period	Wind forcing	River flow	Tidal condition	Salinities at the open boundary	Wedderburn number
	E00	Real Case	5/1/2006 to	Observed wind
			12/31/2006
			(240 days)
	E01	Diagnose		No wind
	E02	down-	5/1/2006 to	Down-estuary
		estuary and	12/31/2006	wind	Observed	Observed	Observed
		up-estuary	(240 days)	component	river flow	water level	salinity
		wind forcing		only			data
	E03	using		Up-estuary
		observation		wind
		data		component
				only
	E11	Without		Observed wind Observed	Astronomic	Observed
		non-local	5/1/2006 to		river flow	tide only	salinity
		wind effect	12/31/2006
	E12	Without	(240 days)	No wind		Observed	Observed
		local wind				water level	salinity
		effect
	E21	Up-estuary	300 days	Southeasterly	Mean	Mean tide	14 and 16	-10.92
		wind pulse		wind pulse	flow		psu were
				from Day 200			specified at
				to 202			the surface
							and bottom
	E22	Down-		Northwesterly	Mean	Mean tide	same as	10.92
		estuary wind		wind pulse	flow		above
		pulse		from Day 200
				to 202
	E31	Baseline		No wind	Mean	Mean tide	same as
		condition			flow	(tidal range	above
						of 0.37m)
	E32	Constant		Northwesterly	Mean	Mean tide	same as	0.77
		down-		wind of 5m/s	flow		above
		estuary wind
	E33	Constant up-		Southeasterly	Mean	Mean tide	same as	-0.77
		estuary wind	300 days	wind of 5m/s	flow		above
	E41			No wind	High	Mean tide	The same
					flow		as above
	E42	High flow	280 days	Northwesterly wind of 5 ms -1	flow High	Mean tide	above same as	0.59
	E43			Southeasterly wind of 5 ms -1

Acknowledgement

The authors would like to thank for Dr. Albert Y. Kuo for his constructive suggestions. This is contribution number  from the Virginia Institute of Marine Science, School of Marine Science, College of William and Mary, Virginia. The first author was supported by the "one-hundred-scholar" program of Sun Yat-sen University, China.


MD, PhD Wolfgang Gaebel
email: wolfgang.gaebel@lvr.de

MD Andreas Schreiner

MSc Paul Bergmans

PhD Rosario De Arce

MD Frédéric Rouillon

MD Joachim Cordes

MD Lars Eriksson

MD, PhD Enrico Smeraldi

Klinik Und

Relapse Prevention in Schizophrenia and Schizoaffective Disorder with Risperidone Long-Acting Injectable Versus Quetiapine: Results of a Long-Term, Open-Label, Randomized Clinical Trial

Keywords: risperidone, quetiapine, relapse prevention, schizophrenia, long-acting injectable, schizoaffective disorder ClinicalTrials

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Medication adherence is a major challenge in the clinical care of patients with schizophrenia.

During the first month of therapy, 60-85% of patients with schizophrenia are adherent, with only 50% adherent by treatment month 6 [START_REF] Llorca | Partial compliance in schizophrenia and the impact on patient outcomes[END_REF]. Poor treatment tolerability is a main contributor to antipsychotic nonadherence or partial adherence [START_REF] Yamada | Prediction of medication noncompliance in outpatients with schizophrenia: 2-year follow-up study[END_REF], which may be addressed by choosing efficacious medications with favorable tolerability profiles, such as atypical antipsychotics. Other factors contributing to non-and partial-adherence include lack of insight, health beliefs, problems with treatment access, embarrassment/stigma over illness, patient or family opposed to medications, no perceived daily benefit, medication interferes with life goals, poor therapeutic alliance, complicated treatment regimen, cognitive dysfunction, and lack of social support [START_REF] Dolder | Antipsychotic medication adherence: is there a difference between typical and atypical agents?[END_REF][START_REF] Löffler | Schizophrenic patients' subjective reasons for compliance and noncompliance with neuroleptic treatment[END_REF][START_REF] Kane | Treatment adherence and long-term outcomes[END_REF][START_REF] Linden | The differentiation between "lack of insight" and "dysfunctional health beliefs" in schizophrenia[END_REF]. Poorer insight and increased conceptual disorganization have been independently associated with nonadherence [START_REF] Acosta | Evaluation of noncompliance in schizophrenia patients using electronic monitoring (MEMS(R)) and its relationship to sociodemographic, clinical and psychopathological variables[END_REF]. Medication formulation can also affect adherence, with more reliable administration assured using depot formulations, which may result in improved long-term symptomatic control maintenance [START_REF] Kane | Utilization of long-acting antipsychotic medication in patient care[END_REF][START_REF] Leucht | Epidemiology, clinical consequences, and psychosocial treatment of nonadherence in schizophrenia[END_REF].

Non-or partial treatment adherence is a major risk factor for relapse and rehospitalization of patients with schizophrenia [START_REF] Leucht | Epidemiology, clinical consequences, and psychosocial treatment of nonadherence in schizophrenia[END_REF]. A review of studies comparing 1-year relapse with oral versus depot antipsychotics reported substantially more relapse with oral therapy (42% vs 27%) [START_REF] Schooler | Relapse and rehospitalization: comparing oral and depot antipsychotics[END_REF]. To date, two long-acting injectable atypical antipsychotics have been developed and undergone randomized controlled clinical trials for the treatment of schizophrenia: risperidone long-acting injectable (RLAI) and olanzapine pamoate.

RLAI is approved and available in Europe and the United States. Approval of olanzapine pamoate in the United States has been delayed, due to safety concerns; however, the European Commission approved it in November 2008, with labeling requiring 3 h of observation in a healthcare facility by appropriately qualified personnel after each administration for the occurrence of postinjection delirium sedation syndrome [severe sedation occurring after about 1% of olanzapine pamoate injections [START_REF] Citrome | Olanzapine pamoate: a stick in time? A review of the efficacy and safety profile of a new depot formulation of a second-generation antipsychotic[END_REF]].

Long-term relapse data with long-acting injectable atypical antipsychotics are available for RLAI. Comparison of long-term benefit with RLAI and oral risperidone has been evaluated in several studies. In a 2-year naturalistic study, 55 consecutive patients were prospectively treated with open-label RLAI or oral risperidone (Kim, et al 2008). Compliance was significantly better with RLAI versus oral risperidone at both 1 year (86% vs 54%, p < 0.01) and 2 years (81% vs 55%, p < 0.01). Relapse was significantly less frequent with RLAI at 1 year (18% vs 50%, p = 0.03) and 2 years (23% vs 75%, p < 0.01). A recent post hoc analysis of studies treating adults with schizophrenia and lifetime treatment with antipsychotic for ≤12 weeks compared 2-year efficacy with open-label RLAI (n = 50) with double-blind treatment with oral risperidone or haloperidol (n = 47) [START_REF] Emsley | Oral versus injectable antipsychotic treatment in early psychosis: post hoc comparison of two studies[END_REF]. Among treatment responders, relapse occurred in significantly fewer patients treated with RLAI versus oral antipsychotics (9.3% vs 42.1%, p = 0.001). Relapse was also indirectly evaluated using hospitalization as a marker in the observational electronic Schizophrenia Treatment Adherence Registry (e-STAR) (Olivares, et al 2009b). Patients initiated on RLAI (n = 1345) or a new oral antipsychotic (n = 277) were monitored for 2 years. The most common oral atypicals initiated were risperidone (35.7%) and olanzapine (36.5%). Treatment retention at 2 years was 82% with RLAI and 63% with oral antipsychotics (p < 0.0001). Hospitalization had occurred during the 12 months prior to RLAI in 35% of patients, decreasing to 14% during the first year of RLAI and 8% during the second year.

Hospitalization had occurred during the 12 months prior to oral antipsychotic for 27% versus 10% during the first year after initiating oral antipsychotic and 9% during the second year.

Benefits from RLAI may be due to better achievement of steady-state drug levels with longacting injectable therapy, better treatment adherence, increased contact with healthcare providers to receive injections, and other factors.

The current study was designed to expand on data from earlier studies, by investigating whether RLAI would provide better effectiveness maintenance over 2 years compared with oral quetiapine, when used in routine care settings of general psychiatric services. The primary effectiveness parameter was time-to-relapse. To our knowledge, this is the first published report directly comparing relapse prevention in a randomized, controlled study treating patients with a long-acting injectable vs. oral second generation antipsychotic. This study was conducted in an open-label fashion because of ethical concerns involved with blinding already approved oral and injectable therapies for long-term treatment. This study was not designed to determine factors that might influence differences in relapse between a long-acting injectable versus daily oral therapy. The use of non-blinded treatment in the current study allows a more real-world evaluation of treatment effectiveness, which will be influenced by adherence, rather than a direct efficacy analysis of differences in the pharmacological agents risperidone and quetiapine.

MATERIALS

This multicenter, open-label, randomized, active-control, long-term treatment with RLAI versus oral quetiapine was conducted from October 2004 to November 2007 at 124 sites (Appendix 1) in 25 countries (ClinicalTrials.gov identifier: NCT00216476). Results of a small descriptive arm with patients also randomized to aripiprazole will be described separately. This additional analysis with aripiprazole was included in the trial because aripiprazole was a relatively new drug at the time the study was designed and aripiprazole has been shown to have a favorable safety and tolerability profile compared with other atypical antipsychotics, including relatively low risks for weight gain and metabolic dysfunction [START_REF] Pae | A review of the safety and tolerability of aripiprazole[END_REF] This trial was conducted in accordance with guidelines of the International Conference on Harmonization for Good Clinical Practice. The study protocol and consent were approved by Ethic Committees/Institutional Review Boards. Informed consent was obtained on all patient candidates prior to enrollment.

Patients

Symptomatically stable adults aged ≥18 years were eligible if they met DSM-IV criteria (American Psychiatric Association, 1994) for schizophrenia or schizoaffective disorder, and were candidates for switching therapy due to insufficient symptomatic control, side effects, or patient request. Candidates were considered symptomatically stable when using a stable dose of antipsychotic for ≥4 weeks (including monotherapy with oral risperidone ≤6 mg daily, olanzapine ≤20 mg daily, or a conventional neuroleptic ≤10 mg haloperidol or its equivalent) and were living in the same residence for ≥30 days. Women were surgically sterile or practising effective contraception with a baseline negative pregnancy test. Patients were excluded if they were previously determined to be non-responders to risperidone, quetiapine, or ≥2 antipsychotics despite adequate drug plasma levels (previous non-responders due to nonadherence were not excluded); had a DSM-IV axis I diagnosis other than schizophrenia or schizoaffective disorder; were treated with antipsychotics other than oral risperidone, olanzapine, or conventional oral neuroleptics or with mood stabilizers or antidepressants and had not received a stable dose for ≥3 months prior to study entry; had phenylketonuria or hypersensitivity to risperidone or quetiapine; had drug or alcohol dependence during the preceding 1 month; or were at acute risk or had a history of suicide attempt(s).

Treatment

Treatment recommendations followed approved dosing guidelines for both drugs. Stratified randomization according to previous treatment used three strata: oral risperidone (40%), olanzapine (30%), and conventional oral neuroleptics (30%). Patients were randomly allocated 1:1 RLAI or quetiapine, or alternatively in countries where aripiprazole was available 2:2:1 to RLAI, quetiapine or aripiprazole, respectively. As noted above, results for those patients randomized to aripiprazole will be described in a separate publication. Eligible patients were randomly assigned to open-label RLAI or oral quetiapine for up to 24 months. During titration and throughout the study, nursing assessments occurred every 2 weeks (at the time of RLAI injection or over the phone for patients randomized to quetiapine). If a patient's psychotic condition had a negative change, an unscheduled additional visit was made. RLAI was initiated with 25 mg injections every 2 weeks, with patients continuing current oral medication (risperidone, olanzapine, or neuroleptic) for the first 3 weeks of RLAI treatment before tapering baseline oral psychotic off over 1-2 weeks. Risperidone-naïve patients randomized to RLAI received 2 mg oral risperidone daily for 2 days before the first RLAI injection to ensure tolerability. RLAI dosage could be increased by 12.5 mg for worsening of psychotic symptoms or insufficient effectiveness to the maximum approved dose of 50 mg every 2 weeks. Increases were only permitted during scheduled visits and at ≥4 weeks after a previous dose change. RLAI dosage could be decreased as needed due to adverse events (AEs).

Quetiapine was initiated at 25 mg twice daily, with patients given a titration schedule for increasing quetiapine by 25-50 mg two-to three-times daily on the second and third day, as tolerated, to achieve a target dosage by day 4 of 300-400 mg daily in divided doses, two-to three-times daily. If needed, additional dosage adjustments of 25-50 mg/day were permitted at ≥2 days to the maximum approved daily dose of 750 mg. Antipsychotics used prior to randomization were tapered off over 2 weeks, starting after the first administration of quetiapine.

Mood stabilizers or antidepressants used in stable doses for ≥3 months prior to enrollment were continued after enrollment. Changes in dosage or initiation of a mood stabilizer or antidepressant were permitted during this study, if clinically necessary. Permitted concomitant medications included anticholinergics and benzotropine mesylate for extrapyramidal symptoms, benzodiazepines for sleep, and beta-blockers for hypertension or treatment-emergent akathisia.

Assessments

Patient demographics, disease characteristics, a physical examination, and serum prolactin levels were obtained at an initial screening visit. Weight, height, and questionnaires assessing symptom severity were obtained at a subsequent baseline evaluation 2 weeks later. Assessments occurred every 2 weeks (with injection for patients treated with RLAI and by phone for quetiapine) for changes in patient status necessitating an unscheduled visit. Follow-up appointments were conducted every 3 months. Symptom severity measures were obtained at each visit, including the Positive and Negative Syndrome Scale (PANSS) and change in Clinical Global Impression (CGI)-Severity. Vital signs were recorded at treatment months 3 and 6, and then every 6 months.

Weight was evaluated every 6 months, with body-mass index (BMI) calculated using baseline height. Urinalysis and serum tests of hematology, chemistries, and prolactin were obtained annually. Extrapyramidal symptoms were evaluated using the Extrapyramidal Symptom Rating Scale (ESRS) [START_REF] Chouinard | Extrapyramidal Symptom Rating Scale[END_REF], a valid measure of drug-induced movement disorders that effectively discriminates drug-induced disorders from psychiatric symptoms [START_REF] Chouinard | Manual for the Extrapyramidal Symptom Rating Scale (ESRS)[END_REF]. ESRS measures 4 types of drug-induced movement disorders (Parkinsonism, akathisia, dystonia, and tardive dyskinesia) and was obtained at baseline and treatment months 3, 6, 12, 18, and 24.

The primary effectiveness assessment was the time from randomization to documentation of relapse, defined using criteria from a previous risperidone comparative study [START_REF] Csernansky | A comparison of risperidone and haloperidol for the prevention of relapse in patients with schizophrenia[END_REF]: psychiatric hospitalization increase in level of care necessary AND ≥25% increase in PANSS total score from baseline or increase of 10 points when baseline score was ≤40 deliberate self-injury emergence of clinically significant suicidal or homicidal ideation violent behavior resulting in significant injury to another person or property significant clinical deterioration defined as a CGI of Change score of 6 (much worse) exceeding registered drug dose (50 mg/2 weeks for RLAI and 750 mg daily for quetiapine).

Relapsed patients were required to meet any of the criteria above on 2 consecutive evaluations, 3-5 days apart, with the first visit considered the time of relapse. Effectiveness was also evaluated by mean changes in total PANSS. Safety and tolerability were assessed by recording treatment-emergent AEs at each visit. AEs were considered to be serious if they resulted in: hospitalization or prolongation of existing hospitalization, significant or persistent disability, life-threatening symptoms or death, or a condition judged to be clinically significant by the investigator. Clinically significant changes in clinical laboratory tests, ESRS score, weight, and BMI were also evaluated.

Data Analysis

Using data from studies comparing 1-year relapse with depot versus oral antipsychotics (27% vs 42% relapse over 1 year) [START_REF] Schooler | Relapse and rehospitalization: comparing oral and depot antipsychotics[END_REF], the current study assumed a relapse rate of 0.30 with RLAI and 0.42 with quetiapine. A sample size of 251 patients per treatment was calculated as needed to detect, with 80% power, a 0.05 level two-sided difference in equality of survival curves, by means of log-rank test. To adjust for an estimated 20% patient discontinuation for reasons other than relapse, a minimum of 628 patients were determined to be necessary.

Based on protocol design, an analysis of effectiveness was performed after the last patient had completed 1 treatment year. The protocol allowed early trial termination if the last patient completed 1 year of follow-up and a difference in effectiveness at the 0.1% significance level (two-tailed) was observed.

All patients treated with at least one dose of study drug were eligible for effectiveness and tolerability analyses (intent-to-treat). Relapse rate was analyzed using the Kaplan-Meier method.

The primary comparison of time-to-relapse between RLAI and quetiapine was performed using the log-rank test with alpha, adjusted for the planned analysis after the last patient had completed 1 year of treatment, to ensure an overall alpha level of 5%. A hazard ratio was calculated to estimate the difference in relapse risk between RLAI and quetiapine. Mean time-to-relapse was estimated with the Kaplan-Meier product limit method, in which non-relapsed drop outs are accounted for by decreasing the number at-risk at the relapse event following the drop out, and not by their actual observation time. Therefore, the Kaplan-Meier estimate of time-to-relapse differs from the mean observed time of patients and, if there are many drop outs, can be higher than the mean treatment duration. The significance level for endpoint testing of the primary parameter, time-to-relapse, was set to 3% (α = 3% and confidence interval [CI] = 97%) to ensure an overall significance level of 5% when tested initially in an interim analysis and subsequently for the final analysis that is reported in the current paper.

Demographics, disease characteristics, and AEs were assessed using descriptive analyses.

Percentage change in PANSS was determined as: (follow-up PANSSbaseline PANSS)/baseline PANSS. For secondary parameters, observed case analysis was applied. The endpoint, i.e. the last observation, was created using the last observation carried forward (LOCF) method. Within-group differences for ordinal/continuous data were assessed using the Wilcoxon 2-sample test. Nominal data were tested using the Fisher exact test. All statistical tests were interpreted at the 5% significance level (2-tailed). Safety differences between treatments were not statistically tested because the study was not powered to show differences or equivalence in these parameters.

RESULTS

The results of the pre-specified analysis after the last patient had completed 1 year of treatment led to the recommendation by independent experts to terminate the trial early due to achieving the pre-determined difference in effectiveness.

Patients

A total of 710 patients were enrolled and randomized to RLAI (n = 355) or quetiapine (n = 355) (Figure 1). Data collected on 25 patients from 1 site were excluded from effectiveness and safety analyses because the study at that site was not conducted in a manner consistent with Good Clinical Practice Guidelines. An additional 19 patients (14 RLAI, 5 quetiapine) did not receive trial medication, leaving an evaluable data set of 666 patients. As intended per stratification, 49% of the patients (N = 328) included in the study used oral risperidone as antipsychotic therapy prior to the study, 22% (N = 144) were on olanzapine, and 29% (N = 194) were on conventional oral neuroleptic monotherapy. A total of 19 patients treated with RLAI and 8 with quetiapine were ongoing at the time the study was stopped per the pre-specified analysis by the sponsor.

Baseline demographics were similar between treatment groups (Table 1). Mean baseline PANSS score of 73 in both groups corresponded to being moderately ill [START_REF] Leucht | What does the PANSS mean?[END_REF]. Patients were permitted to endorse >1 reason for discontinuing switching treatment, with similar reasons endorsed by those patients randomized to RLAI or quetiapine. Reasons for switching included insufficient effectiveness with persistent negative symptoms in 30.5% of patients, positive symptoms in 13.7%, general symptoms in 21.8%, and AEs in 17.1%. Among patients randomized to RLAI, 49.8% had previously used oral risperidone; among patients randomized to quetiapine, 48.7% had previously used oral risperidone. As noted previously, patients who had been identified as non-responders to either risperidone or quetiapine were excluded from this study; therefore, although about half of the patients in each group had previously been treated with oral risperidone or quetiapine, none of the included patients was considered to be a nonresponder.

Two-year treatment was completed by 151 (45.9%) patients randomized to RLAI and 120 (35.6%) to quetiapine (p = 0.0074). Excluding patients who discontinued due to relapse, no differences were observed in other reasons for discontinuation between the treatment groups.

The most common reasons for discontinuation included withdrawal of consent (33.4%), AEs (4.6%), lost to follow-up (4.8%), and refused injection (2.8%). The most common reasons for discontinuation in non-relapsed patients included withdrawal of consent (61.8%), AEs (7.5%), and lost to follow-up (9.0%). Mean duration of treatment was 483.8±277.8 days with RLAI and 400.7±290.6 days with quetiapine. The mode drug doses were 33.6±10.1 mg every 2 weeks with RLAI and 413.4±159.2 mg daily with quetiapine.

Concomitant medications were used by 82.7% with RLAI (N = 272) and 75.1% with quetiapine (N = 253). Concomitant medications most commonly used by patients treated with RLAI and quetiapine, respectively, were: loraepam (14.6% and 10.1%), diazepam (12.8% and 14.2%), and biperiden (12.5% and 11.3%). Concomitant medications most frequently started during the course of treatment with RLAI and quetiapine, respectively, were diazepam (8.5% and 9.8%), lorazepam (8.5% and 5.9%), and paracetamol (6.4% and 4.5%). Antidepressants were used by 22.8% treated with RLAI and 22.3% treated with quetiapine.

Effectiveness

Effectiveness data were available for 327 patients treated with RLAI and 326 with quetiapine. A Kaplan-Meier plot was generated for time to confirmed relapse, the primary endpoint (Figure 2).

A log-rank test for equality of survival distributions between treatments showed a significant difference in time-to-relapse for patients treated with RLAI versus quetiapine (p < 0.0001). The 25 th quartile of time-to-relapse for quetiapine was 248.0 days (97% CI = 205.0-397.0 days). This quartile could not be defined for RLAI as the percentage of RLAI-treated patients experiencing relapse was <25%. The relative risk for relapse was less than half with RLAI compared with quetiapine (hazard ratio = 0.46, 97% CI = 0.32-0.67). At the excluded site, 13 patients were treated with quetiapine and 12 with RLAI. There were two relapses, one in each treatment arm.

Analysis on the primary parameter, with and without these patients, did not reveal any statistical difference. Relapse occurred in 54 of 327 patients (16.5%) treated with RLAI and 102 of 326 patients (31.3%) with quetiapine. Reported reasons for relapse are in Table 2. More than 1 reason was permitted, with only 32 patients (20.5%) reporting a single reason.

A subanalysis was performed among patients treated with RLAI to determine if pre-trial use of oral risperidone affected effectiveness outcome. Prior to enrollment, 163 patients were treated with oral risperidone and 164 were treated with other antipsychotics. During RLAI treatment, 27 patients in each group relapsed. There was no difference in time-to-relapse between the two previous-medication groups (255.3±231.12 days in patients previously using oral risperidone versus 234.4±206.4 days in patients using other antipsychotics). The Kaplan-Meier estimate of the mean relapse-free period was 607.9±16.3 days for patients using oral risperidone immediately before switching to RLAI versus 598.2±16.0 days for those using other antipsychotics.

Improvements in mean PANSS total scores are shown in Table 3. Baseline and endpoint data were available for 326 patients treated with RLAI and 325 with quetiapine. Total PANSS improved significantly compared to baseline for both groups at each post-treatment assessment (p < 0.001). Numerical improvements at endpoint reached statistical significance for RLAI (p < 0.001), but not for quetiapine (p = 0.10). A significant difference was seen between RLAI and quetiapine at treatment months 15, 21, and 24 (p < 0.05), and at endpoint (p < 0.001). At endpoint, PANSS improvement was ≥20% for 42% of RLAI and 30% of quetiapine patients, ≥30% for 30% of RLAI and 17% of quetiapine patients, ≥40% for 15% of RLAI and 7% of quetiapine patients, and ≥50% for 7% of RLAI and 3% of quetiapine patients (p < 0.05).

Safety and Tolerability

Changes in vital signs, urinalysis, and serum hematology and chemistry tests with both groups were small. The incidence of treatment-emergent AEs was similar between groups (Table 4).

The most common serious AEs were psychiatric symptoms (15% with RLAI and 18% with quetiapine). Death occurred in 3 patients treated with RLAI (1 deep vein thrombosis with peptic ulcer perforation and 2 suicides) and 2 with quetiapine (myocardial infarction and suicide). None of the deaths was considered to be related to the study drug. Somnolence was reported in 2% of RLAI-treated and 11% of quetiapine-treated patients. Extrapyramidal symptom AEs occurred more often with RLAI versus quetiapine (10.3% vs 5.6%), with Parkinsonism the most commonly reported individual extrapyramidal symptom AE (4.6% vs 1.8%). Treatmentemergent potentially prolactin-related AEs were reported in 15 patients with RLAI (4.6%) and 5 with quetiapine (1.5%). The percentage of patients discontinuing the study due to prolactinrelated AEs was low for both drugs; 0.3% for quetiapine and 1.8% for RLAI. Hyperprolactinemia, i.e. elevated prolactin plasma levels based on laboratory testing, occurred in 43 patients with RLAI (13.1%) and 5 with quetiapine (1.5%). The percentage of female patients reporting potentially prolactin-related AEs was 7.5% with RLAI and 3.4% with quetiapine.

Hyperprolactinemia in females occurred in 20.1% with RLAI and 2.7% with quetiapine. In male patients, potentially prolactin-related AEs were only reported with RLAI (2.6%) and not with quetiapine. The percentage of male patients reporting hyperprolactinemia was 8.2% with RLAI and 0.5% with quetiapine. Similarly, the mean prolactin levels at endpoint were lower for quetiapine compared with RLAI for both genders (Table 5).

Weight gain was reported in 23 patients (7.0%) treated with RLAI and 21 (6.2%) with quetiapine. Mean weight change at endpoint was 1.25±6.61 kg with RLAI and 0±6.55 kg with quetiapine. There were no significant between-group differences. Among those patients who completed 2 years of treatment, mean increase in weight from baseline to 24 months was 1.16±7.11 kg with RLAI and 0.84±7.51 kg with quetiapine. Mean BMI increases from baseline to endpoint were small and not significantly different between treatment groups (0.3±2.38 kg/m 2 with RLAI vs 0.3±2.59 kg/m 2 with quetiapine).

Decreases in ESRS compared with baseline were significant at each assessment and endpoint for both RLAI and quetiapine (p < 0.001). There were no significant between-group differences.

Mean endpoint total ESRS scores were 1.90±4.31 with RLAI and 2.07±4.51 for quetiapine, resulting in endpoint changes vs. baseline of -2.32±4.56 with RLAI and -2.07±4.67 with quetiapine.

DISCUSSION

Time-to-relapse in patients with clinically stable schizophrenia or schizoaffective disorder treated with oral risperidone, olanzapine, or a typical antipsychotic was significantly longer when switched to treatment with RLAI compared with oral quetiapine (p < 0.0001). Patients switched to RLAI were also less likely to experience symptom relapse. Over 2 years of possible treatment in the present study, twice as many patients treated with quetiapine relapsed compared with RLAI (31% with quetiapine vs 17% with RLAI). The benefit for RLAI treatment over quetiapine emerged early and was sustained up to 2 years.

Relapse in the current study with RLAI compared favorably with relapse rates reported in earlier studies. Data from the current study expand on data available in previously published long-term studies by utilizing a larger sample size than some of the previously published comparative studies (Kim, et al 2008;[START_REF] Emsley | Oral versus injectable antipsychotic treatment in early psychosis: post hoc comparison of two studies[END_REF] and providing a direct comparison against quetiapine. Due to non-blinded treatment with oral versus injectable treatment used in this study and the ability to more completely ensure compliance with injectable therapy, this study provides more insight into the differences between two treatment approaches rather than a direct comparison of two pharmacological agents. These data are important because they reflect more real-world use of therapy, where adherence is better ensured with injectable therapy. This study, however, did not include a detailed analysis of medication adherence, which might have added additional useful information.

Endpoint improvements in PANSS similarly favored RLAI. Interestingly, between-group differences seemed to become apparent after 6 months of treatment, when treatment adherence has been shown for only half of patients prescribed antipsychotics [START_REF] Llorca | Partial compliance in schizophrenia and the impact on patient outcomes[END_REF]. The current study did not evaluate adherence directly, but this may be interesting to evaluate in future studies to identify the role of medication adherence versus benefits related to the drug itself. Applying criteria from a recent comparison of PANSS and CGI scores [START_REF] Leucht | What does the PANSS mean?[END_REF] to the results of the present study showed illness severity at baseline suggested moderate illness for both treatment arms, while endpoint improvement in PANSS corresponded to a reduction to nearlymild illness severity with RLAI only.

Approved doses based on the Summary of Product Characteristics were selected for both drugs and mean doses of both drugs were similar to effective doses reported in other controlled clinical trials. The dose of quetiapine used in this study may have been lower than doses used for some patients in clinical practice. A recent review of the dose-response relationship of quetiapine in schizophrenia concluded that optimal dosage of quetiapine is 150-800 mg/day, with most data supporting an optimal dosage of about 300-400 mg/day; some data, however, do support that higher doses of 600-800 mg/day may be more advantageous [START_REF] Sparshatt | Quetiapine. Dose-response relationship in schizophrenia[END_REF]. Doses utilized in the current study, however, were comparable to other published data. For example, a double-blind comparison of oral risperidone, quetiapine, or placebo in adults with schizophrenia W Gaebel et al. Relapse prevention: risperidone vs quetiapine 20 with an acute exacerbation requiring hospitalization used a target quetiapine dose of 400-600 mg/day, with a maximum of 600-800 mg/day [START_REF] Potkin | A double-blind comparison of risperidone, quetiapine and placebo in patients with W Gaebel et al. Relapse prevention: risperidone vs quetiapine 29 schizophrenia experiencing an acute exacerbation requiring hospitalization[END_REF]. The mean modal dose at treatment day 42 was 556.4±141.9 mg/day. Additionally, recent review of healthcare claims database dosing information reported an average overall daily initiation dose with quetiapine of 358.83 mg and an end dose of 382.56 mg for patients with schizophrenia (Citrome, et al 2009).

Overall tolerability was generally similar with both treatments and comparable to previously published studies [START_REF] Schooler | Relapse and rehospitalization: comparing oral and depot antipsychotics[END_REF]Gharabawi, et al 2007). Most treatment-emergent AEs were transient and did not result in any change in therapy. Serious AEs were reported for 19% of patients treated with RLAI, compared with 27% from an earlier trial switching stable patients to 1-year, open-label treatment with RLAI (Gharabawi, et al 2007). Weight gain affected 7% of patients treated with RLAI and 6% with quetiapine, with a mean increase at 24 months of 1.16±7.11 kg with RLAI and 0.84±7.51 kg with quetiapine. At endpoint, mean weight increase was 1.25±6.61 kg with RLAI and 0±6.55 kg with quetiapine. Extrapyramidal AEs occurred for 10% with RLAI and 6% with quetiapine. ESRS total scores decreased similarly after switching to either RLAI or quetiapine. Potentially prolactin-related AEs occurred more frequently with RLAI, and hyperprolactinemia occurred in 43 patients with RLAI (13%) and 5 with quetiapine (2%). Previous studies have reported a lower risk of hyperprolactinemia for patients treated with quetiapine [START_REF] Emsley | Oral versus injectable antipsychotic treatment in early psychosis: post hoc comparison of two studies[END_REF][START_REF] Taylor | Psychopharmcology and adverse effects of antipsychotic long-acting injections: a review[END_REF][START_REF] Madhusoodanan | Hyperprolactinemia associated with psychotropicsa review[END_REF]. In the present study, potentially prolactin-related AEs and incidence of hyperprolactinemia were lower with quetiapine. These prolactin-related findings may pose a limitation for RLAI in both male and female patients, although discontinuation rates due to potentially prolactin-related adverse events were low for both drugs (1.8% with RLAI and 0.3% with quetiapine). Somnolence occurred more frequently with quetiapine (11% vs 2%) similar to previous observations [START_REF] Harvey | Cognitive functioning and acute sedative effects of risperidone and quetiapine in patients with stable bipolar I disorder: a randomized, double-blind, crossover study[END_REF][START_REF] Said | Somnolence effects of antipsychotic medications and the risk of unintentional injury[END_REF].

The study additionally highlights the difficulty of achieving long-term treatment persistence in patients with schizophrenia, even when involved in a controlled study. Over half of all patients in the current study withdrew before completing the full 2-year treatment; however, rates and reasons for withdrawal were similar between assigned treatments. Rates and reasons for withdrawal were also comparable to an earlier, analogous study of stable patients with schizophrenia or schizoaffective disorder randomized to oral risperidone or haloperidol, with 18% of patients given either risperidone or haloperidol withdrawing due to patient choice and 12% with risperidone and 15% with haloperidol withdrawing due to side effects; withdrawal for reasons other than relapse occurred in 14% with risperidone and 20% with haloperidol [START_REF] Csernansky | A comparison of risperidone and haloperidol for the prevention of relapse in patients with schizophrenia[END_REF]. Likewise, only 12 of the initial 29 patients in a trial randomizing patients to quetiapine or haloperidol decanoate for 48 weeks completed treatment [START_REF] Glick | Long-term maintenance therapy with quetiapine versus haloperidol decanoate in patients with schizophrenia or schizoaffective disorder[END_REF].

To our knowledge, this is the first published report comparing relapse prevention with a longacting injectable vs. oral atypical antipsychotic, using relapse prevention as the primary efficacy assessment. Additional strengths of this study are the inclusion of a substantial female cohort and the long duration of follow-up. As determined through a pre-specified data analysis after the last patient completed 1 year of follow-up, this trial was terminated early because a difference in effectiveness at the 0.1% significance level had already been achieved. Because of this early study termination, treatment was discontinued in 19 patients treated with RLAI and 8 with quetiapine prior to completing the full 2-year treatment.

Interpretation of data from the current study is limited by those factors inherent to open-label treatment studies. A double-blind design requiring patients to accept placebo injections over a 2year period for treatment with drugs in an approved indication, however, would have been unethical. Similar to previously published studies evaluating long-term outcome with RLAI versus oral therapy, this study does not differentiate benefit due to risperidone versus benefit due to the injectable formulation. Previous studies comparing effectiveness with RLAI and oral antipsychotics similarly showed reduced relapse with RLAI; however, treatment adherence is generally better with RLAI, which likely confers a substantial impact to better effectiveness maintenance (Kim, et al 2008;[START_REF] Emsley | Oral versus injectable antipsychotic treatment in early psychosis: post hoc comparison of two studies[END_REF]Olivares, et al 2009a). These data suggest that adherence and effectiveness maintenance are better with RLAI, similar to the report of the current study. Furthermore, the current study excluded previously determined non-responders to risperidone, quetiapine, or ≥2 antipsychotics; therefore, results achieved in the current study may be better than those found when treating general clinical or treatment-naïve patients. Finally, healthcare provider contact was greater with RLAI, due to the need for repeated injections; in an effort to equalize healthcare provider contact, patients randomized to quetiapine received an equivalent number of follow-up contacts, although these occurred via telephone. Although the impact of contacts is likely greater with face-to-face interactions for the RLAI group, this difference does more closely simulate what would occur in routine clinical practice follow-up for patients receiving RLAI versus an oral antipsychotic therapy. It is important to recognize, however, that some of the benefit from RLAI in the current study may have been related to the increased healthcare provider contact time necessary for patients receiving an injectable therapy.

This study was not designed to determine factors that might influence differences in relapse between a long-acting injectable versus daily oral therapy. Future studies may wish to evaluate possible predictive factors. In addition, while medication adherence was generally ensured in the current study, a detailed evaluation of treatment adherence was not conducted and such information might be useful to include in future research.

In the current study, as patients were clinically stable but requiring/desiring a treatment change at study entry, additional analysis on extent of improvement would supplement data on evaluation of symptom worsening or relapse after switching therapies. Future analyses may include an evaluation of factors that may have predicted relapse in each patient group, including treatment non-compliance, baseline remission, and baseline symptom severity. Additionally, patients taking oral risperidone as maintenance therapy immediately prior to switching to RLAI may have responded differently than those using a different antipsychotic, due to pre-RLAI initiation receptor binding from oral risperidone and possibly previously demonstrated effectiveness and tolerability to a risperidone formulation. Subsequent analyses of benefits with RLAI may wish to include a comparison based on pre-treatment antipsychotic use. Finally, the effect of diagnosis was not evaluated in this study, although the distribution of diagnoses was similar in both treatment arms. Future analyses of these data may wish to include a comparison of outcome based on a diagnosis of schizophrenia versus schizoaffective disorder.

In summary, data from the current study support earlier studies demonstrating good relapse prevention in stable patients with schizophrenia or schizoaffective disorder switched to treatment with RLAI. Relapse was significantly lower among patients using RLAI compared with oral quetiapine. Both RLAI and quetiapine were safe and well-tolerated.

DISCLOSURES

W. Gaebel has received research grants, and served as consultant, advisor and speaker for AstraZeneca, Janssen-Cilag, Eli-Lilly, Servier, Lundbeck and Wyeth Pharma.

A. Schreiner is a full employee of Janssen-Cilag, Medical Affairs EMEA, Germany. P. Bergmans is a full employee of Janssen-Cilag, Medical Affairs, Clinical Research Information Group, The Netherlands. R. de Arce has received research grants, and served as consultant, advisor or speaker for the following companies: AstraZeneca, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Janssen-Cilag, Novartis, Bristol, Pfizer Inc, Sanofi-Aventis and UCB Pharma.

F. Rouillon has received grants, and served as consultant, advisor and speaker for BMS, Sanofi-Aventis, Servier, Lundbeck, Eli-Lilly, Wyeth and Biocodex.

J. Cordes has received grants, and served as a consultant, advisor and speaker for Pfizer, Lundbeck, AstraZeneca, Eli-Lilly, Janssen-Cilag, BMS, Alpine Biomed and Servier. L. Eriksson served as consultant, advisor and speaker for Janssen-Cialag, AstraZeneca, Eli Lilly and BMS. E. Smeraldi served as consultant, advisor and speaker for Janssen-Cilag, Innova-Pharma and Wyeth.

ACKNOWLEDGMENTS

This study was sponsored by Janssen-Cilag Medical Affairs EMEA. The authors would like to acknowledge Rossella Medori for trial design and medical conduct supervision. Dr. Medori was European Medical Director at Janssen-Cilag during study conduct and analysis. Robert Wapenaar from Janssen-Cilag, The Netherlands is also acknowledged for providing statistical analyses. Editorial support was provided by Remon van den Broek and team from Excerpta Medica.

TITLES AND LEGENDS TO FIGURES

Patient disposition. RLAI, risperidone long-acting injectable *Treatment discontinued due to the study being stopped early by the sponsor because the prespecified analysis showed the pre-determined difference in effectiveness had been achieved.

Figure 2

Kaplan-Meier estimate of relapse-free time. Among 327 subjects treated with risperidone long-acting injectable (RLAI), relapse occurred in 54; among the 326 subjects treated with quetiapine, relapse occurred in 102. Censored subjects are those not relapsing by the time of assessment.

Table 1 .

 1 Baseline Demographics

	Characteristic	RLAI (n = 329)	Quetiapine (n = 337)
	Mean±SD age, years	40.6±12.5	42.6±13.1
	Gender, n (%)
	Male	195 (59.3)	191 (56.7)
	Female	134 (40.7)	146 (43.3)
	Diagnosis, n (%)
	Schizophrenia	273 (83.0)	275 (81.6)
	Schizoaffective disorder	56 (17.0)	62 (18.4)
	Mean±SD time since symptom diagnosis, years	9.9±9.9	10.0±10.1
	Mean±SD number of psychiatric hospitalizations	5.0±6.5	5.5±7.3
	Symptom severity, mean±SD score
	PANSS	72.7±21.0	73.2±22.2
	CGI-S	2.8±1.0	2.7±1.0
	ESRS	4.2±6.7	4.2±7.0
	CGI-S, Clinical Global Impression-Severity; ESRS, Extrapyramidal Symptom Rating Scale;
	PANSS, Positive and Negative Syndrome Scale; RLAI, risperidone long-acting injectable; SD,
	standard deviation.

Table 2 .

 2 Reason for Relapse, n (%)

	Reason

PANSS, Positive and Negative Syndrome Scale; RLAI, risperidone long-acting injectable.

Table 3 .

 3 Mean Positive and Negative Syndrome Scale (PANSS) total scores (n)

	Treatment					Treatment month
	group	Baseline	1	3	6	9	12	15	18	21	24	Endpoint
	RLAI	72.7	68.7	65.8	61.7	58.6	56.8	54.9	53.8	53.1	50.9	63.4
	n	(326)	(325)	(298)	(268)	(240)	(220)	(198)	(189)	(180)	(173)	(326)
	Quetiapine	73.2	70.4	66.0	65.9	64.5	62.2	60.3	58.8	58.2	56.9	72.1
	n	(325)	(319)	(284)	(249)	(210)	(181)	(160)	(146)	(139)	(131)	(325)

Table 4 .

 4 Treatment-emergent AEs, n (%)

	AE	RLAI (n = 329)	Quetiapine (n = 337)
	Any AE	225 (68.4)	235 (69.7)
	Serious AE	63 (19.1)	77 (22.8)
	Common AEs
	Psychiatric symptoms	142 (43.2)	145 (43.0)
	Prolactin-related	45 (13.7)	11 (3.3)
	Weight gain	23 (7.0)	21 (6.2)
	Headache	20 (6.1)	17 (5.0)
	Somnolence	6 (1.8)	38 (11.3)
	Relationship to study drug
	None/doubtful	222 (67.5)	186 (55.2)
	Possible/probable/very likely	107 (32.5)	151 (44.8)
	Extrapyramidal symptom AEs
	Any	34 (10.3)	19 (5.6)
	Tremor	10 (3.0)	2 (0.6)
	Dystonia	1 (0.3)	2 (0.6)
	Hyperkinesia	13 (4.0)	8 (2.4)
	Parkinsonism	15 (4.6)	6 (1.8)
	Dyskinesia	1 (0.3)	3 (0.9)
	AE, adverse event; RLAI, risperidone long-acting injectable.

Table 5 .

 5 Patients with Reported Prolactin-Related Adverse Events at Endpoint According to Mean±SD prolactin levels, mIU/l 1590.5±924.57 767.4±540.68 855.1±1066.92 367.0±352.51 RLAI, risperidone long-acting injectable.

	Gender
		RLAI (n = 329)	Quetiapine (n = 337)
		Female	Male	Female	Male
		(n = 134)	(n = 195)	(n = 146)	(n = 191)
	Potentially prolactin-related, n (%)	10 (7.5)	5 (2.6)	5 (3.4)	0
	Hyperprolactinemia, n (%)	27 (20.1)	16 (8.2)	4 (2.7)	1 (0.5)
		(n = 107)	(n = 145)	(n = 104)	(n = 133)