Features of Pre-Pregnancy Seizure Control Medication

- The group that received counseling had a significantly lower average frequency of seizures occurring in the month before pregnancy (0.67 seizures/month) than the group that did not receive counseling (1.48 seizures/month), with f=11.86 ANOVA test; p=0.001.

- The rate of patients without seizures for at least 1 year in group 1 (62.8%) was higher than in the group not counseled (20.37%) with RR = 2.62; CI 95% = 1.65-4.17; p = 0.001.

Table 3.4. Characteristics of seizure control medication use before pregnancy

Variable

Group 1 n=43	Group 2 n=54	Total n=97	P	OR (95% CI)	RR (95% CI)
Medication -Monotherapy - Multi-therapy -Are not	34 09 00	33 17 04	67 26 04	0.36	1.95 (0.76-4.9)	1.46 (0.82-2.6)
Use valproate - Are not - Have	36 7	26 24	62 31	0.001	4.75 (1.78-12.67)	2.57 (1.3-5.1)
Treatment compliance - Have - Are not	38 5	31 19	69 24	0.004	4.65 (1.56-13.89)	1.76 (1.26-2.46)
Valproate dose (mg/day) Dose ≤ 700 mg/day Over 700 mg/day	514.3 ±37.79 7 0	895.8 ±254.49 6 18	809, 7 ±276.11 13 18	P=0.001

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• Qos Assurance Methods for Multimedia Communications zt2i3t4l5ee zt2a3gs zt2a3ge zc2o3n4t5e6n7ts low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter. EF PHBs can be implemented if the output port's bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the router's service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port. This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation. PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values. Figure 2.10 Example of EF installation Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values ​​agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ. Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets. 2.2.4.2 Assured Forwarding (AF) PHB PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels. Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values ​​for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance. PHB Class PHB Subclass Package type DSCP AF4 AF41 Short 100010 AF42 Medium 100100 AF43 High 100110 AF3 AF31 Short 011010 AF32 Medium 011100 AF33 High 011110 AF2 AF21 Short 010010 AF22 Medium 010100 AF23 High 010110 AF1 AF11 Short 001010 AF12 Medium 001100 AF13 High 001110 Table 2.8 AF DSCPs The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped. When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels. In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards. 2.2.5.Example of Differentiated Services We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions: Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped. Figure 2.12 DiffServ Example Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a router's per-hop behavior is based only on the packet's marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability. Chapter Conclusion Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of ​​solving the disadvantages of the IntServ model. DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy. Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc. In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being "frozen", or many packets being dropped, seriously affecting service quality. Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion. 3.1. DropTail method DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped. Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages: − Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially. − Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended, Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed. − Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the router's buffer, this method is very expensive and ineffective. − No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy. The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers. In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management. 3.2. Random elimination method – RED 3.2.1 Overview RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet. The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router. Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways: − Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes. − Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet). Figure 3. 1 RED algorithm The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols. To achieve the above goals, RED gateways must do the following: − The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to. − The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network. − An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connection's shared bandwidth at the gateway. − Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows). 3.2.2 Algorithm This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5] For each packet arrival Caculate the average queue size avg If minth ≤ avg < maxth div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s12 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s13 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-d
• Pre-tax Profit of Bidv Tien Giang in the Period 2011-2015 zt2i3t4l5ee zt2a3gsnon-credit services, joint stock commercial bank zt2a3ge zc2o3n4t5e6n7ts At that time, the Branch had to set aside a provision for credit risks, which reduced the Branch's income. Chart 2.2. Pre-tax profit of BIDV Tien Giang in the period 2011-2015 Unit: Billion VND 140 120 100 80 60 40 20 0 63.3 80.34 89.29 110.08 131.99 2011 2012 2013 2014 2015 Profit before tax (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) However, through chart 2.2, it can be seen that BIDV Tien Giang's profit is still increasing continuously, and its operating efficiency is currently leaking. This is a contribution of non-credit services, and this service segment will be increasingly focused on growth by BIDV Tien Giang to ensure the highest profit safety because credit activities have many potential risks. At the same time, focusing on developing non-credit services is consistent with one of the contents of restructuring the financial activities of credit institutions in the project "Restructuring the system of credit institutions in the period 2011-2015" approved by the Prime Minister in Decision No. 254/QD-TTg dated March 1, 2012 [14]: "Gradually shifting the business model of commercial banks towards reducing dependence on credit activities and increasing income from non-credit services". 2.2. Current status of non-credit service development at BIDV Tien Giang. 2.2.1. BIDV Tien Giang has deployed the development of non-credit services in recent times. Along with the development of the Head Office, BIDV Tien Giang's products and services are constantly improved and deployed in a diverse manner to ensure provision for many different customer groups in the area: individual customers, corporate customers, and financial institutions. Typical services are as follows: Payment services, treasury services, guarantee services, card services, trade finance, other services: Western Union, insurance commissions, consulting services, foreign exchange derivatives trading, e-banking services,... 2.2.1.1. Payment services: In accordance with the Prime Minister's Project to promote non-cash payments in Vietnam [15], banks in Tien Giang province have continuously developed payment services to reduce customers' cash usage habits through card services and electronic banking services such as: salary payment through accounts, focusing on developing card acceptance points, developing multi-purpose cards, paying social insurance by transfer, paying bills through banks, etc. Chart 2.3. Net income from payment services in the period 2011-2015 Unit: Million VND 6000 5000 4000 3000 2000 1000 0 3922 4065 4720 5084 5324 2011 2012 2013 2014 2015 Net income from payment services (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) Along with the technological development of the entire system, BIDV Tien Giang has a payment system with a fairly stable transaction processing speed, bringing many conveniences to customers. The results of observing chart 2.3 show that the income from payment services that the Branch has achieved has grown over the years but the speed is not high and the products are not outstanding compared to other banks. Domestic payment products such as: Online bill payment, electricity bills, water bills, insurance premiums, cable TV bills, telecommunications fees, airline tickets, etc. bring many conveniences to customers. Regarding international payment, this is an indispensable activity for foreign economic activities, BIDV Tien Giang is providing international payment methods for small enterprises producing agriculture, aquatic food and seafood that have credit relationships with banks in industrial parks in Tien Giang province such as: money transfer, collection, L/C payment. 2.2.1.2. Treasury services: BIDV Tien Giang always focuses on ensuring treasury safety and currency security, always complies with legal regulations, and minimizes risks in operations such as: counting and collecting money from customers, receiving and delivering internal transactions, collecting from the State Bank (SBV) or other credit institutions, receiving ATM funds, bundling money, etc. BIDV Tien Giang's treasury service management department is always fully equipped with modern machinery and equipment such as: money transport vehicles, fire prevention tools, money counters, money detectors, magnifying glasses, etc. to ensure absolute safety in treasury operations, immediately identifying real and fake money and other risks that may affect people and assets of the bank and customers. In addition, implementing regulation 2480/QC dated October 28, 2008 between the State Bank of Tien Giang province and the Provincial Police on coordination in the fight against counterfeit money, in the 3-year review of implementation, BIDV Tien Giang discovered, seized and submitted to the State Bank of Tien Giang province 475 banknotes of various denominations and was commended by the Provincial Police and the State Bank of Tien Giang province [17]. Chart 2.4. Net income from treasury services in the period 2011-2015 Unit: Million VND 350 300 250 200 150 100 50 0 105 122 309 289 279 2011 2012 2013 2014 2015 Net income from treasury services (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) However, as shown in Figure 2.4, income from treasury operations is not high and fluctuates. Specifically, in the period 2011-2013, net income increased and increased most sharply in 2013, then in the period 2013-2015, there was a downward trend. This fluctuation is due to the fact that fees collected from treasury services are often very low and can even be waived to attract customers to use other services. 2.2.1.3. Guarantee and trade finance services: BIDV Tien Giang, thanks to the advantages of the province and the favorable location of the Branch, has continuously focused on developing income from guarantee services and trade finance. Chart 2.5. Net income from guarantee and trade finance services in the period 2011-2015 Unit: Million VND 14000 12000 10000 8000 6000 4000 2000 0 5193 5695 2742 3420 8889 3992 11604 12206 5143 5312 2011 2012 2013 2014 2015 Net income from guarantee services Net income from Trade Finance (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) Through chart 2.5, we can see that BIDV Tien Giang's income from guarantee services and trade finance has grown over the years. The reason is: Among BIDV Tien Giang's corporate customers, the construction industry is the industry with the highest proportion of customers after the trading industry, this is a group of customers with potential to develop guarantee services. The second group of customers is corporate customers in the fields of agricultural production, livestock and seafood processing with high import and export turnover in the area. are the target of trade finance development. In addition, BIDV Tien Giang also focuses on continuously developing these customer groups to increase revenue for many other products and services in the future. 2.2.1.4. Card and POS services: As a service that BIDV Tien Giang has recently developed strongly, it can be said that this is a very potential market and has the ability to develop even more strongly in the future. Card services with outstanding advantages such as fast payment time, wide payment range, quite safe, effective and suitable for the integration trend and the Project to promote non-cash payments in Vietnam. Cards have become a modern and popular payment tool. BIDV Tien Giang early identified that developing card services is to expand the market to people in society, create capital mobilized from card-opened accounts, contribute to diversifying banking activities, enhance the image of the bank, bring the BIDV Tien Giang brand to people as quickly and easily as possible. BIDV Tien Giang is currently providing card types such as: credit cards (BIDV MasterCard Platinum, BIDV Visa Gold Precious, BIDV Visa Manchester United, BIDV Visa Classic), international debit cards (BIDV Ready Card, BIDV Manu Debit Card), domestic debit cards (BIDV Harmony Card, BIDV eTrans Card, BIDV Moving Card, BIDV-Lingo Co-branded Card, BIDV-Co.opmart Co-branded Card). These cards can be paid via POS/EDC or on the ATM system. In addition, with debit cards, customers can not only withdraw money via ATMs but also perform utilities such as mobile top-up, online payment, money transfer,... through electronic banking services. In order to attract customers with card services, BIDV Tien Giang has continuously increased the installation of ATMs. As of December 31, 2015, BIDV Tien Giang has 23 ATMs combined with 7 ATMs in the same system of BIDV My Tho, so the number of ATMs is quite large, especially in the center of My Tho City, but is not yet fully present in the districts. Basic services on ATMs such as withdrawing money, checking balances, printing short statements,... BIDV ATMs accept cards from banks in the system. Banknetvn and Smartlink, cards branded by international card organizations Union Pay (CUP), VISA, MasterCard and cards of banks in the Asian Payment Network. From here, cardholders can make bill payments for themselves or others at ATMs, by simply entering the subscriber number or customer code, booking code that service providers notify and make bill payments. Chart 2.6. Net income from card services in the period 2011-2015 Unit: Million VND 3500 3000 2500 2000 1500 1000 500 0 687 1023 1547 2267 3104 2011 2012 2013 2014 2015 Net income from card services (Source: Report on the implementation of the annual business plan of the General Planning Department of BIDV Tien Giang [24]) Through chart 2.6, it can be seen that BIDV Tien Giang's card service income is constantly growing because the Branch focuses on developing businesses operating in industrial parks, which are the source of customers for salary payment products, ATMs, BSMS. Specifically, there are companies such as Freeview, Quang Viet, Dai Thanh, which are businesses with a large number of card openings at the Branch, contributing to the increase in card service fees [25]. Table 2.6. Number of ATMs and POS machines in 2015 of some banks in Tien Giang area. Unit: Machine STT Bank name Number of ATMs Cumulative number of ATM cards POS machine 1 BIDV Tien Giang 23 97,095 22 2 BIDV My Tho 7 21,325 0 3 Agribank Tien Giang 29 115,743 77 4 Vietinbank Tien Giang 16 100,052 54 5 Dong A Tien Giang 26 97,536 11 6 Sacombank Tien Giang 24 88,513 27 7 Vietcombank Tien Giang 15 61,607 96 8 Vietinbank - Tay Tien Giang Branch 6 46,042 38 (Source: 2015 Banking Activity Data Report of the General and Internal Control Department of the Provincial State Bank [21]) Through table 2.6, the author finds that the number of ATMs of BIDV Tien Giang is not much, ranking fourth after Agribank Tien Giang, Dong A Tien Giang, Sacombank Tien Giang. The number of POS machines of BIDV Tien Giang is very small, only higher than Dong A Tien Giang and BIDV My Tho in the initial stages of merging the BIDV system. Besides, BIDV Tien Giang has a high number of cards increasing over the years (table 2.7) but the cumulative number of cards issued up to December 31, 2015 is still relatively low compared to Agribank, Vietcombank, Dong A (table 2.6). div.maincontent .content_head3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s1 { color: black; font-family:"Courier New", monospace; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; 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• Internal control of revenue and expenditure activities at the National Children's Hospital - 2

Comment:

- There were 04 patients who did not use anti-epileptic drugs before pregnancy, all in group 2.

- Compared with the group of patients who did not receive counseling, the group that received counseling had a 1.76-fold higher rate of treatment adherence (RR=1.76; CI 95%=1.26-2.46; p=0.0004), a 2.57-fold higher rate of not using valproate (RR=2.57; CI 95%=1.3-5.1; p=0.001), and a lower valproate dose of 514.3 mg/day (f= 15.27; ANOVA test; p=0.001).

3.1.1.3. Clinical characteristics of epileptic patients during pregnancy

A. Characteristics of seizure activity during pregnancy

Table 3.5. Clinical characteristics of seizures in pregnancy

Type of seizure

Group 1 n=43	Group 2 n=54	Total n=97	p
Generalized epilepsy	29(67.4%)	27(50%)	56(57.7%)	0.137
Partial onset seizures:  There is totalization  Have cognitive impairment  No disturbance of consciousness	14(32.6%) 8 2 4	27(50%) 12 8 7	41(42.3%) 20 10 11

Comment:

The rate of patients with generalized seizures was 57.7%, partial seizures accounted for 42.3%, of which the rate of severe tonic-clonic seizures was 78.35%. There was no statistically significant difference between the two groups in the rate of seizure type (p=0.137).

Table 3.6. Characteristics of the frequency of seizures during pregnancy

Frequency of seizures during pregnancy

Group 1 n=43	Group 2 n=54	Total(%) n=97
No attack	24	10	34(35.05)
Less than 2 attacks/month	4	14	18(18.55)
2 attacks/month	6	12	18(18.55)
3-4 attacks/month	8	13	21(21.65)

More than 1 attack/week

1

5

6(6.19)

Comment:

- No seizures during pregnancy 35.05%, 1-4 seizures/month 58.76%, more than 1 seizure/week 6.19%.

Table 3.7. Characteristics of seizure activity in pregnancy

Research variables

Group 1 n=43	Group 2 n=54	Total n=97
No seizures during pregnancy	23(53.5%)	10(18.5%)	33(34.02%)
Average number of seizures/month during pregnancy	1.05±1.31	2.09±1.68	1.63±1.32
Seizure frequency/month during pregnancy Less than 2 seizures Minimum 2 attacks	28 15	24 30	52 45
Seizure frequency - Increase - No change - Reduce	9(20.9%) 29(67.4%) 5 (11.6%)	28(51.9%) 19(35.2%) 7(12.9%)	37(38.1%) 48(49.5%) 12(11.4%)
Increased seizures during pregnancy: - Recurrent seizures - Increased frequency of seizures	9/43 5 4	28/54 6 22	37/97 11 26

Comment:

- The rate of patients with increased seizures during pregnancy was 38.1%, including recurrent seizures (11.34%) and seizures with increased frequency of activity (28.8%).

- The rate of constant seizures during pregnancy was 49.5%, no seizures during pregnancy was 34.02% and only 11.4% had reduced seizure activity during pregnancy.

- The average seizure frequency of patients in the study was 1.63 ± 1,601 seizures/month (min=0 seizures, max=6 seizures/month).

Table 3.8. Comparison of epileptic activity during pregnancy

Research variables

Group 1 n=43	Group 2 n=54	OR (95% CI)	RR (95% CI)	p
Partial epilepsy	Are not	27	29	2.07 (0.9-4.6)	1.5 (0.92-2.5)	0.1
	Have	14	23
Tonic-clonic seizures	Have	37	39	2.4 (0.8-6.8)	1.4 (0.98-1.9)	0.137
	Are not	6	15
Seizures in pregnancy	Are not	23	10	5.1 (2.03-12.6 )	2.2 (1.2-4.3)	0.004
	Have	20	44
Increased seizures	Are not	34	26	4.1 (1.6-10.1)	2.3 (1.5-3.4)	0.003
	Have	9	28
Frequency of attacks/month during pregnancy	Less than 2 attacks	28	24	2.3 (1.02-5.3)	1.6 (0.99-2.6)	0.065
	Minimum 2 attacks	15	30
Average number of attacks/month		1.05±1.31	2.09±1.68	P= 0.001

Comment:

Compared with the uncounseled group, the counseled group had a 2.2-fold higher rate of patients with no seizures during pregnancy (RR=2.2, CI 95%=1.2-4.3, p=0.004); no increased seizures (RR=2.3, CI 95%=1.5-3.4, p=0.003); and a 1.6-fold higher rate of patients with a frequency of less than 2 seizures/month (RR=1.6; CI 95%=0.99-2.6;

p=0.065) as well as the mean number of seizures was 1.04 lower/month (f=12.43 ANOVA test, p=0.001).

Table 3.9. Seizure activity levels during pregnancy

Type of seizure

Increase	No increase		Total
	Unchanged	Reduce
Generalized seizures	15	33	8	56
Partial seizures with generalization	11	9	0	20
Partial seizures with cognitive impairment	5	3	2	10
Partial seizure without impaired consciousness	6	2	3	11
Total	37	47	13	97

Comment:

- The rate of patients with increased seizure activity during pregnancy of partial epilepsy is more than 50% (55% with partial seizures with generalization, 50% with partial seizures with cognitive impairment and 54.5% with partial seizures without impaired consciousness).

- The rate of patients with increased seizure activity during pregnancy in generalized epilepsy is only 26.79%.

Table 3.10. Seizure activity according to clinical seizure classification

Variable

General condition n=56	Local Condition n=41	p	RR(95%CI)	OR (95% CI)
Seizure 1 year	Have	31	28	0.197	1	1
	Are not	25	13		1.25 (0.9-1.7)	1.74(0.75-4.03)
Pregnancy seizures	Have	33	31	0.087	1	1
	Are not	23	10		1.4 (0.98-1.9)	2.2(0.89-5.3)
Intensification	Have	15	22	0.007	1	1
	Are not	41	19		1.7(1.1-2.6)	3.2(1.4-7.4)

Comment

- The rate of patients without increased seizures during pregnancy in generalized epilepsy was 1.7 times higher than in partial epilepsy (RR=1.68; CI 95%=1.1-2.7; p=0.007), and the rate of patients without seizures during pregnancy was 1.4 times higher (RR=1.4; CI 95%=0.98-1.89, p=0.087).

B. Characteristics of drug use in epilepsy patients during pregnancy Table 3.11. Characteristics of drug use in epilepsy patients during pregnancy

Variable

Group 1 n=43	Group 2 n=54	Total n=97
Monotherapy: - Levetiracetam - Lamotrigine - Valproate - Carbamazepine - Gardenal, phenytoin Multi-therapy: - Contains levetiracetam - Contains valproate - Contains topiramate - Contains carbamazepines - Contains gardenal, phenytoin	32(74.1%) 26 2 2 2 0 11 (25.9%) 10 5 1 2 4	23(42.6%) 14 0 4 2 3 31 (57.4%) 23 16 0 8 12	55(56.7%) 40 2 6 4 3 42(43.3%) 33 21 1 10 16
Use valproate	7(16.3%)	20(37%)	27(27.8%)
Can I use levetiracetam?	36(83.7%)	37(68.5%)	73(75.3%)

Comment:

- The proportion of patients using monotherapy was 56.7%. The proportion of patients using levetiracetam was 75.3%, and the proportion using valproate during pregnancy was 27.8%.

Table 3.12. Comparison of antiepileptic drug use during pregnancy between two groups of patients

Variable

Group 1 n=43	Group 2 n=54	OR (95% CI)	RR (95% CI)	p
Monotherapy	Have	32	23	3.9 (1.6-9.4)	2.2 (1.3-3.9)	0.002
	Are not	11	31
Use valproate	Are not	36	34	3.02 (1.2-8.1)	1.98 (1.01-3.9)	0.039
	Have	7	20
Use levetiracetam	Have	36	37	2.4 (0.88-6.4)	1.7 (0.87-3.2)	0.101
	Are not	7	17
Adjust medication	Are not	31	16	6.1 (2.5-14.8)	2.7 (1.6-4.7)	0.008
	Have	12	38
Treatment compliance	Have	39	33	6.2 (1.9-19.9)	3.4 (1.3-8.5)	0.001
	Are not	4	21
Additional folic acid	Have	39	46	2.6 (0.48-5.1)	1.2 (0.79-1.9)	0.54
	Are not	4	8

Comment:

The counseled group had a 2.2-fold higher rate of monotherapy use (RR=2.2; CI 95%=1.3-3.9; p=0.002), a 1.98-fold higher rate of not using valproate (RR=1.98; CI 95%=1.01-3.9; p=0.039), a 2.7-fold higher rate of not needing medication adjustment (RR=2.7; CI 95%=1.6-4.7; p=0.008), and a 3.4-fold higher rate of treatment adherence (RR=3.4; CI 95%=1.8-8.5; p=0.001) compared with the uncounseled group.

C. Characteristics of pregnancy outcomes in epileptic patients

Table 3.13. Clinical characteristics of mother and child during labor

Variable

Group 1 n=43	Group 2 n=54	Total n=97	p
Average birth weight (kg)	62.5±4.39	61.9±4.03	62.2±4.19	0.51
Average BMI of patients at birth	25.5±1.58	25.4±1.56	25.44±1.56	0.74
Average weight gain during pregnancy	11.7±1.81	11.04±1.86	11.4±1.86	0.084
Average gestational age at birth (weeks)	38.6±1.67	37.9±1.56	38.3±1.63	0.07
Birth weight (kg)	3.11±0.33	2.96±0.38	3.03±0.36	0.047

Comment: The average birth weight of children in the group of patients who received counseling was 0.16 kg higher than that of the group whose mothers did not receive counseling (t=2.005, T-test; p=0.047)

Table 3.14. Maternal and fetal events during pregnancy

Event

Group 1 (n=43)	Group 2 (n=54)	Total (n=97)
Active abortion	0	6	6
Miscarriage	0	1	1
Premature birth	1	5	6
labor spasms	1	3	4
Children with congenital abnormalities	0	1	1
Total maternal and child events	2	16	18

Comments: The number of events occurred mostly in the group that did not receive counseling (16/18 cases, accounting for 88.9%), the most common events were premature birth (6/18 cases, accounting for 33.3%) and abortion (6/18 cases, accounting for 33.3%).