Control of excitatory synaptic transmission by capsaicin is unaltered in TRPV1 vanilloid receptor knockout mice Several studies have shown that capsaicin could effectively regulate excitatory synaptic transmission in the central nervous system , but the assumption that this effect is mediated by TRPV1 vanilloid receptors -LRB- TRPV1Rs -RRB- has not been tested directly . To provide direct evidence , we compared the effect of capsaicin on excitatory synapses in wild type mice and TRPV1R knockouts . Using whole-cell patch-clamp techniques , excitatory postsynaptic currents -LRB- EPSCs -RRB- were recorded in granule cells of the dentate gyrus . First , we investigated the effect of capsaicin on EPSCs evoked by focal stimulation of fibers in the stratum moleculare . Bath application of 10 μM capsaicin reduced the amplitude of evoked EPSCs both in wild type and TRPV1R knockout animals to a similar extent . Treatment of the slices with the TRPV1R antagonist capsazepine -LRB- 10 μM -RRB- alone , or together with the agonist capsaicin , also caused a decrease in the EPSC amplitude both in wild type and TRPV1R knockout animals . Both drugs appeared to affect the efficacy of excitatory synapses at presynaptic sites , since a significant increase was observed in paired-pulse ratio of EPSC amplitude after drug treatment . Next we examined the effect of capsaicin on spontaneously occurring EPSCs . This prototypic vanilloid ligand increased the frequency of events without changing their amplitude in wild type mice . Similar enhancement in the frequency without altering the amplitude of spontaneous EPSCs was observed in TRPV1R knockout mice . 1 Introduction Capsaicin , the pungent ingredient of red peppers , severely affects pain sensation , inflammation or hyperalgesia . Systematic studies aiming to elucidate the effects of capsaicin revealed that this alkaloid primarily targets sensory fibers of the C type , where it activates a member of the transient receptor potential -LRB- TRP -RRB- channels , TRPV1 vanilloid receptors -LRB- TRPV1Rs -RRB- -LRB- Szallasi and Blumberg , 1999 ; Szolcsanyi , 2004 -RRB- . These receptors , cloned by Caterina et al. -LRB- 1997 -RRB- , are non-selective cation channels gated by heat , low pH or endogenous ligands , such as anandamide -LRB- Tominaga et al. , 1998 ; Zygmunt et al. , 1999 ; Caterina et al. , 1999 ; Smart et al. , 2000 -RRB- . In addition , capsaicin could also affect the operation of both voltage-gated sodium and calcium channels -LRB- Balla et al. , 2001 ; Lundbaek et al. , 2005 ; Kofalvi et al. , 2006 -RRB- , indicating that some of the capsaicin effects might not be linked to TRPV1Rs . In contrast to the well-established function of TRPV1Rs in the periphery , its role is much less obvious in the central nervous system . Using autoradiography or immunohistochemistry , TRPV1Rs were shown to be present in several brain regions , including cortical structures -LRB- Acs et al. , 1996 ; Mezey et al. , 2000 ; Roberts et al. , 2004 ; Toth et al. , 2005 ; Cristino et al. , 2006 -RRB- . Importantly , the specificity of signals in two of these reports has been confirmed in TRPV1R knockout mice -LRB- Roberts et al. , 2004 ; Cristino et al. , 2006 -RRB- , strongly arguing for the existence of TRPV1Rs in the CNS , yet their subcellular -LRB- synaptic or extrasynaptic -RRB- localization remains to be determined by high resolution electron microscopy . The functional role of TRPV1Rs in distinct brain regions was addressed by electrophysiological experiments . In the hippocampus , for example , an increase in paired-pulse depression of field potentials after application of capsaicin or anandamide has been noticed , and this effect was found to be sensitive to TRPV1R antagonists -LRB- Al-Hayani et al. , 2001 ; Huang et al. , 2002 -RRB- . In addition to the investigation of capsaicin effects on field potentials , other studies have examined the activation of putative TRPV1Rs on synaptic transmission more directly . These studies have found that in distinct parts of the brain , glutamatergic , but not GABAegic synaptic communication could be controlled by capsaicin , an effect that was also antagonized by TRPV1R antagonists -LRB- Sasamura et al. , 1998 ; Hájos and Freund , 2002 ; Marinelli et al. , 2002 , 2003 ; Xing and Li , 2007 -RRB- . Interestingly , excitatory postsynaptic currents -LRB- EPSCs -RRB- evoked by electrical stimulation were found to be depressed after application of capsaicin , whereas the same treatment significantly increased the occurrence of spontaneous EPSCs without affecting their amplitude . To reveal whether the effect of capsaicin on synaptic glutamate release is indeed mediated by TRPV1Rs , we investigated the properties of EPSCs in dentate granule cells after bath application of this prototypic vanilloid ligand both in wild type and TRPV1R knockout mice . 2 Experimental procedures Experiments were carried out according to the guidelines of the institutional ethical code and the Hungarian Act of Animal Care and Experimentation -LRB- 1998 . XXVIII . section 243/1998 -RRB- . Wild type and TRPV1R knockout mice of both sexes -LRB- 20 -- 77 days old , C57BL/6J strain -RRB- -LRB- Davis et al. , 2000 -RRB- were used . Mouse genotyping was performed on tail DNA . Neo PCR primer sequences were the following : NeoF 5 ′ - CCGGCCGCTTGGGTGGAGAGG and NeoR 5 ′ - products on 300 bp -LRB- targeted allele -RRB- , and TRPV1Rs -LRB- VRF1 5 ′ - CATGGCCAGTGAGAACACCATGG and VRR2 5 ′ - AGCCTTTTGTTCTTGGCTTCTCCT -RRB- products on 150 bp -LRB- wild type allele -RRB- . Amplification reactions were carried out in 25 μl total volume with presence of 1 % dimethyl-formamide , 0.2 μM primer each , 0.2 μM dNTP , 1.5 mM MgCl2 -LRB- 93 °C for 15 s , 58 °C for 15 s , 72 °C for 45 s , for 30 cycles -RRB- . Amplification products were analyzed by agarose gel electrophoresis on 1.2 % agarose gels . An example for the results of genotyping of a litter is shown in Fig. 1 . The animals were deeply anaesthetized with isoflurane followed by decapitation . After opening the skull , the brain was quickly removed and immersed into ice-cold cutting solution containing -LRB- in mM : NaCl 126 , KCl 2.5 , NaHCO3 26 , CaCl2 0.5 , MgCl2 5 , NaH2PO4 1.25 , glucose 10 -RRB- bubbled with 95 % O2/5 % CO2 -LRB- carbogen gas -RRB- . Thick horizontal slices -LRB- 300 -- 350 μm from mice -RRB- were prepared using a Leica VT1000S Vibratome . The slices were stored in an interface type chamber containing ACSF -LRB- in mM : 126 NaCl , 2.5 KCl , 26 NaHCO3 , 2 CaCl2 , 2 MgCl2 , 1.25 NaH2PO4 , and 10 glucose -RRB- at room temperature for at least 1 h before recording . Whole-cell patch-clamp recordings were obtained at 34 -- 36 °C from granule cells in the dentate gyrus visualized by infrared videomicroscopy -LRB- Versascope , Marton Electronics , Canoga Park , CA -RRB- . Patch electrodes were pulled from borosilicate glass capillaries with an inner filament -LRB- 1.5 mm o.d. , 1.12 mm i.d. ; Hilgenberg , Germany -RRB- using a Sutter P-87 puller . Electrodes -LRB- ∼ 3 -- 6 MΩ -RRB- were filled with a solution containing -LRB- in mM -RRB- 80 CsCl , 60 Cs-gluconate , 3 NaCl , 1 MgCl2 , 10 HEPES , 2 Mg-ATP , and 5 QX-314 -LRB- pH 7.2 -- 7.3 adjusted with CsOH ; osmolarity 275 -- 290 mOsm -RRB- . Excitatory postsynaptic currents -LRB- EPSCs -RRB- were recorded at a holding potential of − 65 mV . Slices were perfused with ACSF containing 70 -- 100 μM picrotoxin to block inhibitory neurotransmission . The solution was bubbled with carbogen gas at room temperature and perfused at a flow rate of 2 -- 3 ml/min in a submerged type chamber . To evoke EPSCs , the stimulating electrode was placed in the stratum moleculare of the dentate gyrus . Pairs of electrical stimuli separated by 50 ms were delivered via a theta glass pipette -LRB- Sutter Instrument Company , Novato , CA -RRB- filled with ACSF at 0.1 Hz using a Supertech timer and isolator -LRB- Supertech Ltd. , Pécs , Hungary , http://www.superte.ch -RRB- . Access resistances -LRB- between 4 and 18 MΩ , compensated 65 -- 70 % -RRB- were frequently monitored and remained constant -LRB- ± 20 % -RRB- during the period of analysis . Signals were recorded with an Axopatch 200B -LRB- Molecular Devices , Sunnyvale , CA -RRB- , filtered at 2 kHz , digitized at 6 kHz -LRB- National Instruments PCI-6024E A/D board , Austin , TX -RRB- , and analyzed off-line with the EVAN program -LRB- courtesy of Prof. I. Mody , UCLA , CA -RRB- . The drugs were perfused until the maximal effect was reached -LRB- usually 3 -- 4 min -RRB- . The effect of drugs on evoked EPSCs was calculated as follows : control EPSC amplitudes in a 2 -- 3 min time window were compared to those measured after 5 -- 6 min drug application for the same period of time . Only those experiments were included that had stable amplitudes at least for 10 min before drug application . The paired-pulse ratio was calculated from the mean amplitude of the second EPSCs divided by the mean amplitude of the first EPSCs . For spontaneously occurring EPSCs , the amplitude and the inter-event interval for individual events were calculated and medians of their distributions were compared before and after 5 min of capsaicin application . After each experiment , the tubing made of Teflon was washed with ethanol for 10 min and with ACSF for 15 min . For comparison of data , Wilcoxon matched pairs test or Mann -- Whitney U-test were used in STATISTICA 6.1 -LRB- Statsoft , Inc. , Tulsa , OK -RRB- . Data are presented as mean ± S.E.M. Picrotoxin was purchased from Sigma -- Aldrich , while -LRB- E -RRB- - capsaicin and capsazepine were obtained from Tocris . Both drugs were dissolved in DMSO giving a 100 mM stock solution , which were stored at 4 °C . 3 Results The effects of the prototypic TRPV1R agonist capsaicin on EPSCs evoked by focal stimulation of fibers in the stratum moleculare were measured in dentate granule cells of wild type mice and TRPV1R knockouts . Similar to what we found earlier -LRB- Hájos and Freund , 2002 -RRB- , bath application of 10 μM capsaicin significantly reduced the amplitude of EPSCs -LRB- by 36.6 ± 6.1 % of control -RRB- in wild type mice -LRB- control : 143.8 ± 19.3 pA ; capsaicin : 88.6 ± 12.1 pA ; n = 6 ; p < 0.02 ; Fig. 2A and B -RRB- . In TRPV1R knockout mice , a similar significant reduction was observed after capsaicin application , the amplitude of EPSCs was suppressed by 31.6 ± 4.1 % of control -LRB- control : 146.9 ± 34.2 pA ; capsaicin : 103.8 ± 26.9 pA ; n = 6 ; p < 0.02 ; Fig. 2A and B -RRB- . The inhibitory effect of capsaicin on the amplitude of EPSCs was indistinguishable in wild type mice and TRPV1R knockouts -LRB- p > 0.1 -RRB- . Next , we tested the effect of 10 μM capsazepine , a TRPV1R antagonist , on excitatory synapses . We found that bath application of this drug also significantly reduced the EPSC amplitude -LRB- by 34.9 ± 5.4 % of control -RRB- in wild type animals -LRB- control : 165.1 ± 35.5 pA , capsazepine : 112.4 ± 28.2 pA ; n = 5 ; p < 0.05 ; Fig. 3A and B -RRB- . When we co-applied 10 μM capsaicin together with 10 μM capsazepine , the amplitude of evoked EPSCs was similarly decreased -LRB- by 33.3 ± 9.4 % of control ; control : 149.5 ± 21.2 pA ; capsaicin + capsazepine : 99.6 ± 19.6 pA ; n = 4 ; p < 0.05 -RRB- . In TRPV1R knockouts , suppression of the EPSC amplitude was comparable to that seen in wild type mice -LRB- i.e. , by 30.2 ± 1.5 % of control after capsazepine treatment ; control : 205.2 ± 7.3 pA ; capsazepine : 143.2 ± 6.4 pA ; n = 4 ; p < 0.05 ; Fig. 3A and B -RRB- . Similarly , the treatment of slices with a mixture of capsaicin and capsazepine reduced the EPSC amplitude by 38.1 ± 6.2 % of control -LRB- control : 211.8 ± 4.4 pA , capsaicin + capsazepine : 131.3 ± 13.5 pA ; n = 3 ; p < 0.05 -RRB- , just like in the wild types . These results suggest that capsazepine , as well as capsaicin alone can reduce the amplitude of EPSCs independent of TRPV1Rs , and their effects are not additive . By a comparison of the paired-pulse ratios of evoked EPSCs , we next examined whether capsaicin and capsazepine affect excitatory synapses presynaptically or postsynaptically . If glutamate release is altered , then the paired-pulse ratio should change . If the reduction in EPSC amplitude is not accompanied by changes in the paired-pulse ratio , then the conductivity of glutamate receptors should be modified by the drug treatment . Therefore , we first investigated the effect of capsaicin on paired-pulse ratio in wild type mice and TRPV1R knockouts . After drug application , the ratio significantly increased to 121.3 ± 3.2 % of control in wild types and to 132.5 ± 9.9 % of control in knockouts -LRB- n = 6 each , p < 0.02 -RRB- . Comparable to these findings , capsazepine treatment also caused a significant increase in the paired-pulse ratio both in wild types -LRB- 129.7 ± 10.1 % ; n = 5 ; p < 0.05 -RRB- and knockouts -LRB- 126.4 ± 8.8 ; n = 4 ; p < 0.05 -RRB- . Thus , the effects of both capsaicin and capsazepine appear to be presynaptic , reducing glutamate release from excitatory terminals both in wild type and TRPV1R knockout mice . In further experiments , we investigated how capsaicin alters the properties of spontaneous EPSCs -LRB- sEPSCs -RRB- . In wild type mice , bath application of capsaicin significantly increased the occurrence of spontaneous events -LRB- i.e. , reduced the inter-event interval by 32.5 ± 11.8 % of control , control : 0.43 ± 0.23 s ; capsaicin : 0.2 ± 0.05 s ; n = 5 ; p < 0.04 , Fig. 4A and B -RRB- without changing their amplitude -LRB- control : 16.6 ± 2.1 pA ; capsaicin : 16.1 ± 2.7 pA ; n = 5 ; p > 0.1 ; Fig. 4A and B -RRB- . Similarly , capsaicin also elevated the frequency of sEPSCs in TRPV1R knockouts , since the inter-event interval was reduced by 29.3 ± 7.4 % of control -LRB- control : 0.16 ± 0.04 s ; capsaicin : 0.12 ± 0.04 s ; n = 6 ; p < 0.02 ; Fig. 4A and B -RRB- . Similar to those observed in wild types , the amplitude of synaptic events did not change -LRB- control : 19.2 ± 3.6 pA ; capsaicin : 17.7 ± 3.3 pA ; n = 6 ; p > 0.1 ; Fig. 4A and B -RRB- . The comparison of the decrease in the inter-event interval of sEPSCs between wild type mice and TRPV1R knockouts showed no difference -LRB- p > 0.1 -RRB- . These results provided further evidence that capsaicin affected synaptic glutamate release in wild type and TRPV1R knockout mice to a similar degree , in a similar manner . 4 Discussion Electrophysiological data presented here strongly suggest that capsaicin actions on excitatory synaptic transmission are not mediated by TRPV1Rs , at least in the dentate gyrus . Our previous observations already raised this possibility -LRB- Hájos and Freund , 2002 -RRB- , as we have shown that the suppression of the amplitude of EPSCs after the second application of capsaicin was indistinguishable from that seen after the first application . This observation was not consistent with the known desensitization properties of TRPV1Rs upon repeated capsaicin application -LRB- Dray et al. , 1989 ; Docherty et al. , 1991 ; Caterina et al. , 1997 -RRB- . Our results seem to contradict those pharmacological data , where the effect of capsaicin on synaptic transmission has been found to be fully blocked by antagonists specific for TRPV1Rs -LRB- e.g. , capsazepine or iodo-resiniferatoxin -RRB- -LRB- Al-Hayani et al. , 2001 ; Marinelli et al. , 2003 -RRB- . Here we found that , in adult mice , capsazepine also effectively reduced the amplitude of EPSCs , similar to that seen after capsaicin application . In line with these data , a study by Kofalvi et al. -LRB- 2003 -RRB- has shown that glutamate release from synaptosomes prepared from the hippocampus of adult rats could be significantly suppressed by capsazepine . These findings are in contrast with our published results -LRB- Hájos and Freund , 2002 -RRB- , where capsazepine could antagonize the effect of capsaicin on EPSC amplitude recorded in slices from rats of P15-22 . We repeated the experiments with capsazepine in young rats and found that this drug on its own could substantially enhance the amplitude of EPSCs -LRB- unpublished observations -RRB- , which is in sharp contrast observed in adult animals -LRB- present study ; Kofalvi et al. , 2003 -RRB- . Thus , it seems likely that during development the molecular target of capsazepine changes its effect on synaptic transmission , or the binding site -LRB- s -RRB- of capsazepine might alter . We therefore propose that , in the hippocampus of young rats , the suppression of EPSC amplitude by capsaicin is counterbalanced by the enhancement caused by capsazepine , therefore no reduction in glutamate release can be observed . As to the presynaptic mechanism of capsaicin actions , a recent study showed that iodo-resiniferatoxin as well as capsaicin -LRB- both applied in μM concentrations -RRB- could markedly reduce the high K + - induced Ca2 + entry -LRB- Kofalvi et al. , 2006 -RRB- . Since transmitter release is highly sensitive to Ca2 + entry , one might assume that glutamate release from excitatory terminals in the dentate gyrus could be affected with a similar mechanism . In the present study we found that capsaicin , capsazepine , or co-application of the two , reduce the amplitude of EPSCs to a similar extent , suggesting that capsazepine might also decrease Ca2 + entry at the same site , where capsaicin acts . Data from other laboratories , as well as our own results , showed that capsaicin could reduce the amplitude of evoked EPSCs , while it increased the frequency of spontaneous EPSCs without changing their amplitude -LRB- Hájos and Freund , 2002 ; Marinelli et al. , 2002 , 2003 -RRB- . These unconventional effects seem to suggest that the target molecule of capsaicin regulating glutamatergic transmission could be located at the presynaptic axon terminals , an assumption that is also supported by the observed increase in the paired-pulse ratio -LRB- present study -RRB- . If capsaicin modulated postsynaptic glutamate receptors , the amplitude of sEPSCs should have also been altered , which was not the case -LRB- present study ; Marinelli et al. , 2003 -RRB- . The question arises how capsaicin could reduce the amplitude of evoked EPSCs , while in the same time increases the frequency of sEPSCs . One explanation might be that if capsaicin triggers release of retrograde messengers from postsynaptic neurons that directly promote fusion of vesicles irrespective of Ca2 + concentration within the terminals , this could enhance the action potential-independent glutamate release -LRB- i.e. , the majority of sEPSCs under our circumstances -RRB- . Although TRPV1Rs have been shown to be located in the dendrites and somata of hippocampal principal cells -LRB- Cristino et al. , 2006 -RRB- , these receptors are unlikely to be involved in the enhancement of sEPSC frequency , since the same effect was observed in the TRPV1R knockout mice . Further studies should clarify the possible mechanisms underlying the capscaicin-induced changes in excitatory neurotransmission . In summary , our data presented here suggest that TRPV1Rs may not be the target molecules for capsaicin regulating glutamatergic synapses in the hippocampal network , although they might have a function during pathological conditions like fever or osmotic changes in extracellular space -LRB- Caterina et al. , 1997 ; Gavva et al. , 2007 ; Liu et al. , 2007 -RRB- .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

glutamate
transmitter release
dentate gyrus
excitatory synapses


Human Immunodeficiency Virus-1 and Preconception Care Human immunodeficiency virus -LRB- HIV -RRB- is a condition that increasingly affects women , especially women of childbearing age . Early in the HIV epidemic , HIV infection and AIDS were diagnosed in relatively few women in the United States . Today , the HIV/AIDS epidemic represents a growing and persistent health threat to women , especially young women and women of color . In 2001 and 2002 , HIV infection was the leading cause of death for African American women aged 25 -- 34 years and was among the four leading causes of death for African American women aged 20 -- 24 and 35 -- 44 years , as well as Hispanic women aged 35 -- 44 years -LSB- 1 , 2 -RSB- . Although HIV is increasing among women , the incidence in children from perinatal transmission is decreasing . With recommendations for routine screening for HIV during pregnancy and the availability of effective interventions to dramatically reduce the risk of perinatal transmission , the number of annual perinatally acquired AIDS cases from 1985 -- 1994 has declined approximately 95 % -LSB- 3 -RSB- , representing one of the most striking success stories in the 25-year effort to reduce the impact of HIV/AIDS in the United States . HIV infection can be transmitted from mother to child in utero , during labor and delivery and through breastfeeding . In the absence of intervention and without breastfeeding , the risk of perinatal HIV transmission is approximately 25 % -LRB- range 13 -- 39 % -RRB- -LSB- 4 -RSB- . Evidence suggests that in the absence of breastfeeding , 30 % of transmission occurs before birth and 70 % around the time of delivery -LSB- 4 -RSB- . In February 1994 , the results of the Pediatric AIDS Clinical Trials Group -LRB- PACTG -RRB- Protocol 076 documented that zidovudine -LRB- ZDV -RRB- chemoprophylaxis , initiated at 14 -- 34 weeks gestation and continued through pregnancy , followed by intravenous ZDV during labor and oral ZDV to the infant for the first 6 weeks of life , could reduce perinatal HIV transmission by nearly 70 % -LSB- 5 -RSB- . A U. S. Public Health Service -LRB- USPHS -RRB- task force subsequently issued recommendations for the use of ZDV for reduction of perinatal HIV transmission -LSB- 6 -RSB- and universal prenatal HIV counseling and HIV testing with consent for all pregnant women in the United States -LSB- 7 -RSB- . Epidemiologic data have further confirmed the efficacy of ZDV for reduction of perinatal transmission , even among women with advanced disease , low CD4 + T-lymphocyte counts , and prior ZDV therapy -LSB- 8 -- 13 -RSB- . In addition , substantial advances have taken place in the treatment and monitoring of persons with HIV disease , which have resulted in changes in standard antiretroviral therapy for HIV infected adults . More aggressive combination drug regimens are now recommended to maximally suppress viral replication . For pregnant women , however , the benefits of antiretroviral therapy must be weighed against the risk of adverse events to the woman , fetus , and newborn ; however pregnancy is generally not a reason to defer standard therapy . It should be offered with the addition of ZDV for prevention of perinatal HIV transmission to HIV infected pregnant women -LSB- 4 , 14 -RSB- . For women with HIV infection , preconception care must focus on recommendations for safe sexual practices that prevent HIV transmission to sexual partners and protect women from acquiring potentially harmful sexually transmitted diseases or even additional strains of HIV that may be more virulent or resistant to therapy . Women also need education about perinatal transmission risks and prevention strategies , expectations for the child 's future , and where desired , effective contraception until the optimal maternal health status for pregnancy is achieved , including appropriate therapy to maximally reduce viral load and optimize immune function . For HIV-infected women desiring pregnancy , specific counseling regarding available reproductive options that both prevent HIV exposure to uninfected partners , as well as preventing superinfection with resistant or more virulent virus should be discussed . The USPHS recommends that the following be included in preconception counseling for HIV-infected women : 1 -RRB- selection of effective and appropriate contraceptive methods to reduce the likelihood of unintended pregnancy ; 2 -RRB- education and counseling about perinatal transmission risks , strategies to reduce those risks , and potential effects of HIV or treatment on pregnancy course and outcomes ; 3 -RRB- initiation or modification of antiretroviral therapy avoiding agents with potential toxicity for the developing fetus -LRB- e.g. , efavirenz , hydroxyurea -RRB- , choosing agents effective in reducing perinatal HIV transmission , attaining a stable , maximally suppressed maternal viral load , evaluating and controlling for therapy associated side effects which may adversely impact maternal-fetal health outcomes -LRB- e.g. , hyperglycemia , anemia , hepatic toxicity -RRB- ; 4 -RRB- evaluation and appropriate prophylaxis for opportunistic infections and administration of immunizations -LRB- e.g. , influenza , pneumococcal , or hepatitis B vaccines -RRB- as indicated ; 5 -RRB- optimization of maternal nutritional status ; 6 -RRB- institution of the standard measures for preconception evaluation and management -LRB- e.g. , assessment of reproductive and familial genetic history , screening for infectious diseases/sexually transmitted diseases , and initiation of folic acid supplementation -RRB- ; 7 -RRB- screening for maternal psychological and substance abuse disorders , and 8 -RRB- planning for perinatal consultation if desired or indicated -LSB- 4 -RSB- . The USPHS further notes that HIV infected women of childbearing potential receive primary healthcare services in various clinical settings , e.g. , family planning , family medicine , internal medicine , obstetrics/gynecology , and stress that primary health-care providers should consider the fundamental principles of preconception counseling an integral component of comprehensive primary health care for improving maternal and child health outcomes -LSB- 4 -RSB- . The Infectious Disease Society of America -LRB- IDSA -RRB- recommends in-depth discussions about childbearing early in the course of HIV care for women who express a desire for a future pregnancy , who are not trying to conceive but are not using appropriate contraception consistently or who express uncertainty about reproductive plans . The goal of these discussions is to ensure informed decisions about contraception and to offer preconception counseling if pregnancy is desired . IDSA recommends that providers regularly assess women 's reproductive plans and their desire to have questions answered and , that providers question patients at each visit about interval menstrual history and sexual and contraceptive practices , offering pregnancy testing as indicated -LSB- 15 -RSB- . The American College of Obstetricians and Gynecologists -LRB- ACOG -RRB- and the American Academy of Pediatrics -LRB- AAP -RRB- recommend that screening for HIV infection be strongly recommended for women with unknown HIV status and who are considering pregnancy -LSB- 16 , 17 -RSB- . The Centers for Disease Control and Prevention -LRB- CDC -RRB- is considering this recommendation for its revised recommendations for HIV testing in healthcare settings planned for 2006 -LSB- 3 -RSB- . In addition , these organizations as well as the Institute of Medicine and the U. S. Preventive Services Task Force recommend universal prenatal screening for HIV infection -LSB- 3 , 17 -- 21 -RSB- , so that HIV-infected pregnant women can take full advantage of the available interventions to reduce perinatal transmission as well as to receive care for themselves . These organizations support an opt-out approach to HIV screening , -LRB- i.e. , patients are notified that HIV testing will be done and consent is inferred unless testing is declined -RRB- -LSB- 3 -RSB- to normalize HIV testing in health care settings and to reduce the barriers clinicians experience with specific HIV test counseling and written informed consent . Pre-test discussions regarding risk reduction for HIV infection are not a requirement for HIV testing and can be provided in the context of discussions of reproductive health and STD prevention . Routine HIV testing during preconception care can be integrated with other aspects of preconception care that require a blood sample and should not require substantial additional time commitments by providers . Improved implementation of preconception care will further extend the success in reducing perinatal HIV transmission in the U. S. as well as improve access to HIV testing and care to women . Because the issues accompanying HIV treatment during pregnancy and perinatal prophylaxis are rather complex , the case for preconception counseling and care for known HIV-infected women is clear .

O O O O B I O O O O B O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O B O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B B O O B O O O O O O O O O O O O B O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O B B O O O O O O O O O O O B O O O O O O O O O O B O O B O B O O O O O O O O O O O O O O O O O O O

hiv
pregnancy
preconception care
perinatal


Variance Estimation , Design Effects , and Sample Size Calculations for Respondent-Driven Sampling Hidden populations , such as injection drug users and sex workers , are central to a number of public health problems . However , because of the nature of these groups , it is difficult to collect accurate information about them , and this difficulty complicates disease prevention efforts . A recently developed statistical approach called respondent-driven sampling improves our ability to study hidden populations by allowing researchers to make unbiased estimates of the prevalence of certain traits in these populations . Yet , not enough is known about the sample-to-sample variability of these prevalence estimates . In this paper , we present a bootstrap method for constructing confidence intervals around respondent-driven sampling estimates and demonstrate in simulations that it outperforms the naive method currently in use . We also use simulations and real data to estimate the design effects for respondent-driven sampling in a number of situations . We conclude with practical advice about the power calculations that are needed to determine the appropriate sample size for a study using respondent-driven sampling . In general , we recommend a sample size twice as large as would be needed under simple random sampling . Introduction To understand and control the spread of HIV , it is important to have accurate information about hidden populations such as injection drug users and sex workers .1 However , these populations are difficult to study with standard sampling methods because sampling frames do not exist . The need to gather information about such hidden populations is not limited to public health . Social scientists and policy-makers are interested in many other hidden populations such as undocumented immigrants , artists , and members of some social movements . In response to the problem of studying hidden populations , a new statistical approach called respondent-driven sampling has been developed .2 -- 4 Respondent-driven sampling data are collected via a link-tracing -LRB- snowball -RRB- design , where current sample members recruit future sample members . For many years , researchers thought it was impossible to make unbiased estimates from this type of sample . However , it was recently shown that if certain conditions are met and if the appropriate procedures are used , then the prevalence estimates from respondent-driven sampling are asymptotically unbiased .4 For example , respondent-driven sampling can be used to estimate the prevalence of HIV among drug injectors in New York City . Despite the progress that has been made in making prevalence estimates , less is known about the sample-to-sample variability of these estimates . This gap in knowledge can lead researchers to construct inaccurate confidence intervals around estimates and to undertake studies with sample sizes that are too small to meet study goals . Filling this important gap in the respondent-driven sampling literature , this paper explores issues related to the sample-to-sample variability of estimates . The paper consists of four main parts . First , we briefly review the existing respondent-driven sampling methodology . Next , we develop and evaluate a bootstrap procedure for constructing confidence intervals around respondent-driven sampling estimates . Then , we estimate the design effect of the prevalence estimates in a number of simulated and real populations . The paper concludes with advice about the sample sizes needed for studies using respondent-driven sampling . In general , we recommend a sample size twice as large as would be needed under simple random sampling . Review of Respondent-driven Sampling A respondent-driven sample is collected with a link-tracing design , similar to a snowball sample .5 -- 7 The sampling process begins with the selection of a set people in the target population who serve as seeds . After participating in the study , these seeds are each provided with a fixed number of unique recruitment coupons , which they use to recruit other people they know in the target population . After participating in the study , these new sample members are also provided with recruitment coupons , which they then use to recruit others . The sampling continues in this way , with subjects recruiting more subjects , until the desired sample size is reached .2 -- 4 Experience has shown that this sample selection method is practical and it has already been used to study a number of different hidden populations , including jazz musicians ,8 drug injectors ,2 Latino gay men ,9 and MDMA/Ecstasy users .10 In addition to collecting information that addresses the substantive focus of the study , a researcher using respondent-driven sampling must also collect two other pieces of information : the recruiter of each sample member and the number of relationships that each sample member has in the population of interest -LRB- sometime called degree of each person -RRB- . This additional information is necessary because the estimation procedure is a variation of the traditional sampling and estimation process . Ordinarily , researchers take a sample and then use that sample to make inferences about a population -LRB- Figure 1a -RRB- . However , when the data are collected with respondent-driven sampling , it is very difficult -LRB- and maybe even impossible -RRB- to make inferences from the sample directly to the population . Fortunately , such data can be used to make inferences about the social network connecting the hidden population , and this information about the social network can be used to estimate the prevalence a specific trait -LRB- Figure 1b -RRB- .3 Within this new framework , unbiased estimation becomes possible .1 A more detail description of the estimation procedure and the conditions under which it is unbiased is available in the literature .4 Figure 1Schematic of both traditional sampling and estimation and respondent-driven sampling . By not attempting to estimate directly from the sample to the population , respondent-driven sampling avoids many of the well-known problems with estimation from snowball samples . While the ability to make unbiased prevalence estimates represented a step forward for the study of hidden populations , it was an incomplete one . In order for respondent-driven sampling to be practical as a methodology , a procedure is needed in order to put confidence intervals around these prevalence estimates . Confidence Intervals Before introducing the confidence interval procedure , we first need to introduce some language with which to describe the hidden population . In this paper we will consider the situation of a hidden population that is made up of two mutually exclusive and collectively exhaustive groups that , for the sake of generality , we will call group A and group B . The groups could be , for example , people with and without HIV . The proportion of the population in group A will be called PA. . A point estimate of this prevalence is useful , but it is difficult to interpret without some measure of the precision of the estimate . One common way of describing this precision is with a confidence interval that provides a range within which the researcher expects to find the true population value with some level of certainty . Procedures to generate confidence intervals are well developed in the case of simple random sampling ,12,13 but researchers using a complex sample design , where not all units have the same probability of selection , are often left without guidance . Despite numerous warnings ,7,14 researchers often ignore the fact that their data were collected with a complex sample design and construct confidence intervals as if they had a random sample . This approach of ignoring the sampling design , which we will call the naive method , will generally cause researchers using respondent-driven sampling to produce confidence intervals that are too small . These incorrect confidence intervals are not just a technical concern ; incorrect confidence intervals can lead to incorrect substantive conclusions . In order to produce better confidence intervals , we will develop and evaluate a bootstrap method specifically designed for respondent-driven sampling .2 Although an analytic approach would be preferable ,3 bootstrap methods are commonly used for variance estimation from complex sample designs because analytic solutions are often not possible .16,17 In the next sections , we will describe our proposed bootstrap procedure and then evaluate its performance using computer simulations . Proposed Bootstrap Procedure The general idea of the bootstrap procedure is to use the observed sample to generate a set of replicate samples . Then this set of replicate samples is used to produce a set of replicate estimates . By examining the variation in these replicate estimates , one can construct a confidence interval around the original point estimate . This three-step process can be seen in Figure 2.17 Figure 2Schematic of the procedure for producing confidence intervals . The first step in our procedure is the resampling step . In traditional bootstrapping , this resampling is done by randomly sampling with replacement from the original sample until the replicate sample is the same size as the original sample . This resampling procedure is well grounded theoretically for the case where the original sample is collected via simple random sampling .17 However , as described previously , in respondent-driven sampling there are dependencies in the sample selection process , and so we must use a modified resampling procedure which mimics these features . The modification of the resampling step is the main way that this approach deviates from traditional bootstrapping techniques . Under our proposed procedure we divide the sample members into two sets based on how they were recruited : people recruited by someone in group A -LRB- which we will call Arec -RRB- and people recruited by someone in group B -LRB- which we will call Brec -RRB- . For example , Arec could be the set of all sample members who were recruited by someone with HIV . Note that this set could include both people with and without HIV . In order to mimic the actual sampling process , the resampling begins when a seed is chosen with uniform probability from the entire sample . Then , based on the group membership of the seed , we draw with replacement from either Arec or Brec . For example , if the seed chosen for the replicate sample was a sample member with HIV , we draw from the set of sample members who were recruited by someone with HIV . Next , we examine the group membership of this newly chosen person and then draw again with replacement from either Arec or Brec .4 This process continues until the bootstrap sample is the same size as the original sample . Overall , this resampling scheme preserves some , but not all , of the dependencies that exist in the respondent-driven sampling data collection .5 Once the bootstrap samples are selected , we move to step 2 in Figure 2 : the estimation step . Here we use the normal respondent-driven sampling estimation procedure on each of the R replicate samples to produce a set of R replicate estimates . Finally , in step 3 of the bootstrap procedure , the R replicate estimates are converted into a confidence interval . One way to do this would be to construct a 90 % confidence interval based on the normal approximation , where the estimated standard error , , is the standard deviation of the replicate estimates . While this approach is reasonable , it has two main disadvantages . First , it forces the confidence intervals to be symmetric , which can reduce accuracy , and second , it can produce intervals with endpoints outside of the range -LSB- 0 , 1 -RSB- . Fortunately , there are several improvements over this standard error method , and in this paper we will use the percentile method .6 When using the percentile method , we define the endpoints of the 90 % confidence interval to be the two replicate estimates , such that 5 % of the replicate estimates fall below the interval , and 5 % of the replicate estimates fall above the interval . For example , if a researcher generated 2,000 bootstrap replicates , a 90 % confidence interval would be defined by the 100 and 1,900 ordered replicate estimates . As we shall see in the next section , the proposed resampling scheme combined with the percentile method produces confidence intervals that are generally good in an absolute sense and better than the naive method .7 Comparing the Naive and Bootstrap Methods The quality of a confidence interval procedure can be measured by calculating φ , the percentage of proposed intervals that contain the true population value . For example , if we took 1,000 samples from the population and produced a 90 % confidence interval from each of these samples , then 900 out of 1,000 of these confidence intervals should include the true population prevalence .8 Unfortunately , due to resource constraints , we can not repeatedly sample from real hidden populations . However , using computer simulations , we can construct hypothetical hidden populations and then repeatedly sample from them to evaluate the coverage properties of the different confidence interval procedures . Further , in these computer simulations we can systematically vary the characteristics of the hidden population in order to understand the effects of population and network characteristics on the quality of the proposed confidence intervals . For example , to explore how network structure affects the quality of the confidence intervals , we constructed a series of hypothetical populations that were identical except for the amount of interconnectedness between the two groups . More specifically , we varied the ratio of the actual number of cross-group relationships to the number of possible cross-group relationship , and thus , our measure of interconnectedness , I , can vary from 0 -LRB- no connections between the groups -RRB- to 1 -LRB- maximal interconnection -RRB- . All populations were constructed with 10,000 people , 30 % of which were assigned a specific trait , for example HIV . Next , we began to construct the social network in the population by giving each person a number of relationships with other people in the population . The number of relationships that an individual has is called her degree . When assigning an individual 's degree we wanted to roughly match data collected in studies of drug injectors in Connecticut ,2 so each person with HIV was assigned a degree drawn randomly from an exponential distribution with mean 20 , and those without HIV were assigned a degree drawn from an exponential distribution with mean 10 ; later in this paper we will explore other degree distributions . Once the degrees were assigned , we insured that the population had the appropriate amount of interconnection between the groups .9 After each population was constructed , we took 1,000 samples of size 500 , and for each of these 1,000 samples we constructed a confidence interval using both the naive method -LRB- i.e. , ignoring the complex sample design and pretending to have a simple random sample -RRB- and the proposed bootstrap method . By seeing if each of these confidence intervals included the true population prevalence , we calculated and . The results of these simulations are presented in Figure 3 and reveal two important features . First , the figure shows that , for the populations used in these simulations , the proposed bootstrap procedure outperforms the naive procedure . Second , it shows that the bootstrap procedure also performs well in an absolute sense , meaning . Figure 3Coverage probabilities of the naive and bootstrap procedure . Results indicate that the proposed bootstrap procedure outperforms the naive procedure and performs well in an absolute sense . To test the robustness of these findings , we explored the coverage properties in a larger portion of the possible parameter space by varying the sample size , the proportion of the population in the groups , and the average degree of the groups -LRB- results not shown -RRB- . To summarize these findings , in a few unusual portions of the parameter space , the proposed bootstrap procedure did not perform well in an absolute sense , but in most portions of the parameter space , the proposed procedure performed well .10 Additionally , in all cases the proposed bootstrap procedure outperformed the naive procedure . To conclude , in the situations that we have examined , the proposed bootstrap procedure works well in an absolute sense and better than the naive procedure . Further , these results seem robust . Therefore , until some superior procedure is developed , we recommend this bootstrap procedure for future researchers who wish to construct confidence intervals around prevalence estimates from respondent-driven sampling . Design Effects Even though respondent-driven sampling produces unbiased prevalence estimates and allows researchers to produce approximate confidence intervals , respondent-driven sampling may not be appropriate for all situations . The unbiasedness of the estimates does not ensure that any particular estimate will be equal to the true population value . Rather , just like all other unbiased estimators , sometimes the estimate will be too low or too high ; only on average will the estimate equal the true population value . If the respondent-driven sampling estimates are too variable , then , even if they are unbiased , they might not be useful in practice . For example , we constructed a population with 30 % prevalence of a specific trait , for example HIV , and repeatedly sampled from that population using respondent-driven sampling . To provide a benchmark for interpreting the sample-to-sample variability , we also repeatedly sampled from the population using simple random sampling -LRB- even though this would not be possible in a real hidden population -RRB- . Figure 4 shows the distribution of prevalence estimates from these two methods . Both distributions center around 0.30 , the true value , so both sampling methods produced unbiased estimates . But , the distribution of respondent-driven sampling estimates was more variable than the distribution of estimates under simple random sampling . A common way to quantify this difference is the design effect ,12 which measures the increased variation of the estimates under respondent-driven sampling .11 That is , where is the variance of under respondent-driven sampling and is the variance of under simple random sampling . Because respondent-driven sampling generally provides less information , we expect that the design effect will generally , but not always , be greater than 1 . For example , the results in Figure 4 show a design effect of 2.8 . In the next section we will explore the design effects for a range of different network structures to better understand the situations in which respondent-driven sampling is likely to be precise enough to be useful . Figure 4Distribution of estimates from respondent-driven sampling and simple random sampling . The respondent-driven sampling estimates center around the true population value , 0.30 , but they are more variable than the estimates from simple random sampling . This extra variation corresponds to a design effect of 2.8 . Simulation Results on Design Effects Estimating the design effect requires comparing the variance of the prevalence estimate under different sampling methods . While the variability of the prevalence estimate under simple random sampling can be derived from basic probability theory ,12 we must use simulations to discover the variability under respondent-driven sampling . Thus , as when we evaluated the confidence interval procedure , we constructed a range of populations and simulated repeated sampling from them . We observed several general patterns that seem to occur in all portions of the parameter space . First , generally , but not always , the design effects from respondent-driven sampling were greater than 1 , which indicates that respondent-driven sampling estimates were less precise than estimates from simple random sampling . This finding is consistent with the literature on complex sampling designs , which generally finds that departures from simple random sampling lead to increased variability of estimates . Second , as the interconnectedness , I , increased , that is , as the two groups became more closely connected , the design effect decreased -LRB- see Figure 5 -RRB- . Third , the minimum design effect for a given interconnectedness occurred not when the two groups had the same average degree -LRB- DA = DB -RRB- , but when the two groups had the same total degree , -LRB- PADA = PBDB -RRB- -LRB- see Figure 6 -RRB- . Fourth , the design effects were sensitive to the degree distribution assumed in the simulations . Previously in this paper we assumed an exponential degree distribution , but for specific subpopulations , such as drug injectors , the true functional form of the degree distribution is unknown . When we assigned a Poisson degree distribution for both groups , we observed much lower design effects , including some design effects below 1 -LRB- Figure 7 -RRB- ; the reason for this change is currently unknown .12 Overall , these observations should be viewed with some caution because they have not been verified analytically due to the previously mentioned inability to develop closed-form expressions for the variance of the prevalence estimate under respondent-driven sampling . Figure 5Design effect as a function of interconnectedness I . In general , as the interconnectedness increases the design effect decreases . Results are based on 10,000 replicate samples.Figure 6Design effect as a function of DA for different DB . In general , the minimum design effect , for a given interconnectedness , occurs when the two groups have the same total degree -LRB- PADA = PBDB -RRB- . So if PA = 0.3 and PB = 0.7 , then when DB = 10 the minimum design effect occurs when DA ≈ 23 and when DB = 20 the minimum occurs when DA ≈ 46 . Results are based on 10,000 replicate samples.Figure 7Design effect as a function of interconnectedness for different degree distributions . In general , the design effects are smaller when the degree distribution of the groups is Poisson rather than exponential . Results are based on 10,000 replicate samples . Taken together , these simulation results suggest that the design effect is a complex function of the network structure in the population .13 The simulation results also suggest that in some cases respondent-driven sampling can be quite blunt , with design effects as large as 10 , but that in other cases it can be extremely precise , sometimes even more precise than simple random sampling . Estimated Design Effects in Real Studies The simulation results indicate that a range of design effects are possible . Therefore , an important question becomes : What are the design effects in populations that people actually study ? Our best attempt to answer that question is presented in Table 1 , where we report the estimated design effects from all studies that are currently available .14 To produce the estimated design effects we took the published estimates of PA and used them to estimate the variability of the prevalence estimates -LRB- -RRB- . This variability is then compared to the published estimates of the variability under respondent-driven sampling -LRB- -RRB- .15 We report only one design effect because , due to the symmetry of the two-group system , . Table 1Estimated design effects from real respondent-driven sampling data .8 -- 10 Sample sizes vary within the same study due to missing dataStudy descriptionStudy resultsPopulationLocationnTraitLatino gay menChicago69HIV +0.170.00240.00211.1 Latino gay menSan Francisco72HIV +0.490.00410.00351.2 MDMA/Ecstasy usersOhio374Male0 .580.00120.00071.7 Jazz musiciansNew York City263Male0 .760.00160.00072.3 Jazz musiciansNew York City261Union member0 .250.00100.00071.4 Jazz musiciansNew York City253Received airplay0 .750.00170.00072.4 Overall , Table 1 shows that the prevalence estimates from existing studies had design effects around 2 , suggesting that respondent-driven sampling is reasonably precise in the situations in which it has been used so far .16 Based on this crude analysis of existing respondent-driven sampling data , we recommend that when planning a study using respondent-driven sampling researchers should assume a design effect of 2 . This guideline should only be considered a preliminary rule-of-thumb and should be adjusted , if necessary , depending on pre-existing knowledge of the study population . Sample Size Calculation Information on design effects should be used when planning the sample size of a study using respondent-driven sampling , or else the sample size will not meet the goals of the study . Fortunately , once the researcher has an estimated design effect , it is rather straightforward to adjust the required sample size ; the researcher need only to multiply the sample size needed under simple random sampling by the assumed design effect . Thus , for studies using respondent-driven sampling we recommend a sample size twice as large as would be needed under simple random sampling . However , calculating the appropriate sample size under simple random sampling is often difficult due to the overly general nature of the power analysis literature .20,21 Therefore , we will review the sample size calculations for two specific cases of most interest to researchers using respondent-driven sampling : estimating the prevalence of a trait with a given precision and detecting a change in prevalence over time .17 One common goal of studies is to estimate the prevalence of a characteristic with some pre-specified precision , for example , to estimate the proportion of sex workers in New York City that are HIV-positive with a standard error of no greater than 0.03 . Since it is the case that , we can solve for the required sample size , n , in terms of the desired standard error , which yields , Therefore , if based on pre-existing knowledge we suspect that 20 % of the sex workers have HIV and that the design effect is 2 , we would need a sample size of at least 356 sex workers to estimate the HIV prevalence with a standard error no greater than 0.03 . Notice that this calculation depends on our initial guess of the prevalence . If researchers do not have enough information to make such a guess , they should assume a value of 0.5 which is maximally conservative . A second problem of interest to many researchers is comparing the prevalence of some behavior at two time points . For example , a researcher might want to test whether an outreach program was successful at getting drug injectors to stop sharing injection equipment . Assume that the researcher suspects that before the intervention 40 % of drug injectors share injection equipment and that the researcher would like to choose the appropriate sample size to be able to detect a drop to 25 % such that there is an 80 % probability that a 95 % confidence interval for the estimated difference will not include 0 . Further , assume that the researcher suspects that each prevalence estimate will have a design effect of 2 . Based on a derivation available in the literature ,23 we can calculate that the required sample size is , More generally , the required sample size for comparing prevalence in two populations is , where Z1 - and Z1 − β are the appropriate values from the standard normal distribution and deff is the design effect .18 These sample size calculations are based on assumptions about the prevalence of the characteristics and the design effect . Therefore , the sample sizes produced by Eqs . 4 and 6 should be considered approximate . Conclusions This paper makes two main contributions to the literature on respondent-driven sampling . First , we introduce a bootstrap confidence interval procedure that in simulations outperforms the naive method currently in practice . Therefore , we recommend this bootstrap procedure be used in future analysis of respondent-driven sampling data . The procedure requires some custom computer programming to implement , but , fortunately , it is already included in RDSAT , a software package for organizing and analyzing respondent-driven sampling data .19 The second major contribution of this paper is the information on design effects . The simulation results suggest that the design effects can range from as high as 10 to less than 1 . These findings imply that , because of the possibility of high design effects , respondent-driven sampling is not appropriate in all cases . In some extreme network structures , the prevalence estimates could be so variable that , even though they are unbiased , they might not be very useful . Fortunately , data from existing studies suggest that , so far , respondent-driven sampling has been used in situations where it is reasonably precise , yielding estimated design effects around 2 -LRB- see Table 1 -RRB- . Based on these data , we suggest that when using respondent-driven sampling , researchers collect a sample twice as large as would be needed under simple random sampling . The sensitivity of the design effect to the functional form of the degree distribution further emphasizes the need for more research on methods to accurately measure the degree of each respondent . Currently , the estimated average degree depends on subjects ' self-reported degree , and these reports may be inaccurate .26,27 In almost all cases , inaccuracy in the self-reported degree will introduce bias into the prevalence estimates .4 As far as we know , the best methods for estimating an individual 's degree are scale-up method and summation method .28 However , it is not clear that either of these approaches , which were designed for the general population , is appropriate for studying hidden populations . Taken together , the results about the sample-to-sample variability presented in this paper add to the growing literature on respondent-driven sampling . By allowing researchers to obtain better information about key hidden populations , this research should allow public health professionals to monitor population dynamics more accurately , target resources more carefully , and intervene to slow the spread of disease more effectively .

B I O B I O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

design effects
hidden populations
power analysis
sample size
variance estimation

respondent-driven sampling
snowball sampling


Treatment

of

Tobacco

Use

in

Preconception

Care

The

preconceptional

period

provides

an

important

opportunity

to

actively

assess

and

treat

tobacco

use

and

to

assist

parents-to-be

in

quitting

permanently

to

avoid

smoking-related

pregnancy

and

long-term

health

consequences

The

use

of

tobacco

prior

to

conception

is

associated

with

male

impotence

conception

delay

and

primary

as

well

as

secondary

infertility

During

pregnancy

tobacco

use

increases

the

risk

for

spontaneous

abortion

ectopic

pregnancy

low

birth

weight

SIDS

premature

birth

and

other

fetal

and

maternal

complications

-LSB-

-RSB-

Each

year

almost

one-half

million

babies

in

the

United

States

are

born

to

mothers

who

report

smoking

during

pregnancy

-LSB-

-RSB-

For

smokers

tobacco

use

continues

to

be

the

leading

cause

of

morbidity

and

mortality

in

the

United

States

resulting

in

approximately

deaths

per

year

-LSB-

-RSB-

Women

smokers

are

at

increased

risk

for

cancer

cardiovascular

disease

and

pulmonary

disease

Since

lung

cancer

has

outpaced

breast

cancer

as

the

leading

cause

of

cancer

death

among

women

and

exposure

to

environmental

tobacco

smoke

-LRB-

ETS

-RRB-

is

a

cause

of

lung

cancer

and

coronary

heart

disease

among

lifetime

nonsmokers

Moreover

infants

born

to

women

who

are

exposed

to

ETS

during

pregnancy

may

also

have

increased

health

risks

including

small

decrements

in

birth

weight

and

slightly

increased

risk

for

intrauterine

growth

retardation

compared

with

infants

born

to

women

who

were

not

exposed

-LSB-

-RSB-

Despite

all

that

is

known

about

the

devastating

health

consequences

of

smoking

of

U.S.

women

smoked

cigarettes

in

-LSB-

-RSB-

and

of

women

giving

birth

reported

smoking

during

their

pregnancy

-LSB-

-RSB-

It

is

encouraging

that

of

smokers

want

to

quit

and

about

two-fifths

quit

for

at

least

a

day

every

year

-LSB-

-RSB-

To

avoid

early

pregnancy

complications

women

who

smoke

should

be

counseled

to

quit

before

they

become

pregnant

In

addition

smokers

who

are

partners

of

mothers-to-be

should

also

be

included

in

any

treatment

plans

given

the

strong

association

with

partner

smoking

and

relapse

-LSB-

-RSB-

as

well

as

concerns

about

ETS

exposure

Treatment

for

tobacco

dependence

is

safe

effective

available

and

affordable

-LSB-

-RSB-

The

Public

Health

Service

document

Treating

Tobacco

Use

and

Dependence

A

Clinical

Practice

Guideline

-LSB-

-RSB-

as

well

as

the

Centers

for

Disease

Control

and

Prevention

publication

Guide

to

Community

Preventive

Services

-LSB-

-RSB-

both

provide

strong

evidence-based

guidelines

for

treating

smokers

using

a

comprehensive

system

that

includes

the

's

-LRB-

a

brief

counseling

approach

recommended

by

the

PHS

clinical

practice

guideline

for

all

smokers

-RRB-

These

documents

also

provide

details

of

how

reminder

systems

telephone

quitlines

decreased

co-pays

and

costs

of

treatment

mass

media

campaigns

smoke

free

environments

and

increased

prices

for

tobacco

products

support

the

cessation

process

-LSB-

-RSB-

At

the

clinical

level

every

clinician

providing

preconception

care

should

implement

a

tobacco

treatment

system

based

on

the

A

's

and

beginning

with

a

systematic

way

to

assess

tobacco

use

This

evidence-based

counseling

approach

includes

asking

every

patient

about

tobacco

use

and

if

they

smoke

advising

them

to

quit

assessing

willingness

to

quit

assisting

them

in

quitting

and

arranging

follow-up

Assisting

may

include

the

offer

of

pharmacotherapies

or

additional

counseling

each

of

which

doubles

the

quit

rates

in

non-pregnant

adult

populations

-LSB-

-RSB-

The

effectiveness

of

counseling

increases

as

the

intensity

-LRB-

number

and

length

of

sessions

-RRB-

increases

At

present

seven

first-line

FDA-approved

medications

are

available

bupropion

varenicline

and

five

nicotine

replacement

therapies

-LRB-

gum

patch

lozenge

inhaler

and

nasal

spray

-RRB-

-LSB-

-RSB-

Because

the

safety

and

efficacy

of

pharmacotherapy

during

pregnancy

has

yet

to

be

established

the

preconception

period

provides

an

excellent

interval

before

pregnancy

to

offer

these

effective

adjuncts

to

quitting

Once

abstinent

patients

should

be

followed

to

help

avoid

relapse

For

those

smokers

who

are

not

willing

to

quit

in

the

near

future

the

PHS

clinical

practice

guidelines

recommend

the

R

's

-LRB-

relevance

risks

rewards

roadblocks

repetition

-RRB-

to

enhance

patients

motivation

to

quit

smoking

-LSB-

-RSB-

Clinical

systems

that

provide

tobacco-dependence

treatment

should

also

include

a

reminder

system

to

alert

providers

to

advise

smokers

in

their

practice

-LSB-

-RSB-

This

system

might

be

an

electronic

medical

record

alert

or

simply

a

stamp

or

sticker

on

the

patient

's

chart

to

indicate

tobacco

status

current

former

or

never

The

PHS

guideline

also

recommends

using

the

code

for

tobacco

dependence

for

billing

purposes

-LSB-

-RSB-

This

code

also

may

be

used

by

health-care

systems

to

monitor

and

improve

the

provision

of

effective

treatment

State

quitlines

provide

an

effective

vehicle

for

telephone

counseling

and

can

be

accessed

by

calling

NOW

a

national

portal

number

to

refer

tobacco

users

directly

to

their

state

quit

line

based

on

their

area

code

A

small

number

of

states

also

provide

over-the-counter

medications

to

eligible

populations

in

conjunction

with

telephone

counseling

Another

group

of

states

-LRB-

e.g.

Maine

Massachusetts

Oklahoma

Oregon

and

Wisconsin

-RRB-

encourages

providers

to

fax

referrals

-LRB-

with

patient

consent

-RRB-

to

the

state

quitline

which

in

turn

proactively

calls

the

smoker

to

begin

counseling

Most

quitlines

send

a

quit

kit

to

each

caller

With

effective

treatment

quit

rates

can

be

as

high

as

in

the

general

population

-LSB-

-RSB-

Because

many

women

spontaneously

quit

upon

learning

about

their

pregnancy

ranging

from

to

in

publicly

insured

pregnant

smokers

to

to

in

privately

insured

pregnant

smokers

-LSB-

-RSB-

it

is

reasonable

to

expect

that

effective

preconception

tobacco-dependence

treatment

could

exceed

these

rates

particularly

if

partners

who

smoke

are

treated

as

well

Placing

these

cost-effective

treatment

systems

into

preconception

practice

should

not

be

insurmountable

given

the

evidence

the

affordability

the

lessons

learned

from

other

clinical

tobacco-dependence

systems

and

the

availability

of

resources

Available

Resources

for

learning

more

about

tobacco

dependence

treatment

Treating

Tobacco

Use

and

Dependence

JAMA

June

-LRB-

Summary

article

-RRB-

Treating

Tobacco

Use

and

Dependence

-LRB-

full

document

-RRB-

Available

at

for

Health

Care

Research

and

Quality

patient

and

provider

materials

Available

at

Guide

to

Community

Preventive

Services

Available

at

Telephone

Quitlines

A

Resource

for

Development

Implementation

and

Evaluation

Available

at

Families

provider

patient

and

system

materials

Available

at

Protection

Agency

smokefree

home

ban

materials

Available

at

March

of

Dimes

at

and

Tobacco

Research

Journal

supplement

Helping

Pregnant

Women

Quit

Smoking

Progress

and

Future

Directions

Available

at

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O